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title: 'E-DES-PROT: A novel computational model to describe the effects of amino acids
and protein on postprandial glucose and insulin dynamics in humans'
authors:
- Bart van Sloun
- Gijs H. Goossens
- Balázs Erdõs
- Shauna D. O’Donovan
- Cécile M. Singh-Povel
- Jan M.W. Geurts
- Natal A.W. van Riel
- Ilja C.W. Arts
journal: iScience
year: 2023
pmcid: PMC9989689
doi: 10.1016/j.isci.2023.106218
license: CC BY 4.0
---
# E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans
## Summary
Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual’s metabolic health.
## Graphical abstract
## Highlights
•We developed a computational model that incorporates postprandial effects of AAs•Introducing AAs is an important move toward describing complex meals•E-DES-PROT is able to accurately describe postprandial glucose and insulin dynamics•The model allows insight into physiological processes underlying meal responses
## Abstract
Biomolecules; Human metabolism; In silico biology
## Introduction
Glucose homeostasis is primarily regulated by the hormones insulin and glucagon, which act in antagonistic fashion to maintain circulating glucose concentrations within a healthy range.1,2 When glucose concentrations are elevated (i.e. following meal intake), pancreatic β-cells secrete insulin to suppress hepatic glucose output and promote glucose uptake in peripheral organs, predominantly in the skeletal muscle.3 In contrast, when glucose concentrations drop (i.e. during fasting or physical exercise), pancreatic α-cells secrete glucagon to stimulate glycogen breakdown and gluconeogenesis (formation of glucose from non-carbohydrate precursors), allowing glucose release from the liver into the circulation, thereby preventing hypoglycemia.4 As such, glucagon and insulin exert opposing actions on glucose metabolism and are part of a tightly regulated feedback system to maintain glucose homeostasis.
Computational models of whole-body glucose homeostasis describe and incorporate the current mechanistic understanding of insulin-mediated regulation of circulating glucose concentrations.5,6,7 These processes are represented by model parameters, which can be estimated from postprandial time-series data without requiring direct invasive measurements. One of the earliest computational glucose models, the Bergman minimal model,5 was able to determine insulin sensitivity (i.e. the capability of insulin to suppress hepatic glucose output and increase glucose disposal in insulin-sensitive tissues) and glucose effectiveness (i.e. the ability of glucose to enhance its own disposal at basal insulin levels) in response to an intravenous glucose tolerance test. The Bergman minimal model formed the basis of the Food and Drug Administration–approved glucose-insulin model by Dalla Man and colleagues,6,8 which is used for in silico simulation and testing of insulin pump systems. The Dalla Man model has been parameterized using triple tracer glucose data to allow quantification of glucose fluxes between tissues.
The Eindhoven-Diabetes Education Simulator (E-DES), a multi-compartmental ordinary differential equation model, has been used to describe glucose dynamics following a glucose challenge in healthy individuals as well as patients with type 1 and type 2 diabetes.7,9,10 We have previously individualized the E-DES model to allow accurate description of individual postprandial responses compared to population-based models, demonstrating it is capable of providing mechanistic insight into glucose homeostasis of individuals.11 While the E-DES model performs very well in response to an oral glucose challenge, modeling the response to more complex meals is still challenging because these contain fat and protein, which also influence glucose homeostasis.
Dietary protein consists of amino acids (AAs) which are used for synthesis of body protein and of nitrogen-containing compounds, such as creatine, peptide hormones, and several neurotransmitters.12 AAs have been shown to influence glucose metabolism by inducing insulin secretion to facilitate AA uptake and incorporation into protein in muscle tissue, and secreting glucagon to enhance hepatic AA uptake, production of ketone bodies from AAs, and formation of glucose from AAs (i.e. gluconeogenesis).2,4 *In a* systematic review, we have recently summarized available studies describing postprandial glucose and insulin responses to AAs.13 In the present study, we aimed to extend an existing computational model of the glucose-insulin regulatory system to account for the postprandial effects of AAs. To parameterize the model, we used time-series data of postprandial AA, glucose and insulin concentrations following AA challenges (with and without glucose), dried milk protein ingredients, and dairy products, derived both from a previously performed randomized, single-blind crossover trial14 as well as data extracted from available literature.13 Here, we show that this novel model, which we termed E-DES-PROT, accurately describes postprandial glucose and insulin dynamics, outperforms the original E-DES model, and allows insight into the physiological processes underlying meal responses.
## Postprandial simulation of AA, glucose, and insulin dynamics following AA challenges and intake of protein ingredients
We investigated whether our newly developed model was able to capture AA and protein challenges, estimating only the model parameters accounting for AAs (k11-k13). The parameters pertaining to the original E-DES model were kept to their healthy average population value and the measured plasma AA concentration (pertaining to the challenge) was interpolated and provided to the model as an input.9 The simulated glucose and insulin responses, parameterized on the AA challenges (1 mmol/kg body weight), are shown in Figure 1. The simulated glucose and insulin responses, parameterized on the milk protein ingredients (i.e. WPC and MCI) containing 25 g of protein in a 700 mL solution, are shown in Figure 2. Here, the leftmost column pertains to the average population responses, whereas the other columns show selected individual responses highlighting striking model behavior. The complete overview of all the individual glucose and insulin responses is shown in Data S2.Figure 1Simulated postprandial responses in the literature study following ingestion of amino acidsThe model parameters pertaining to amino acids (AAs, k11–k13) were estimated, whereas the other parameters were kept to their original population value. The tAA input is shown in black (data and polynomial interpolation). The simulated glucose and insulin concentrations are shown in red and blue, respectively. The measured concentrations, obtained from van Sloun et al. ,13 are shown as black asterisk with corresponding standard errors of the means. Figure 2Plasma glucose and insulin simulation following intake of whey protein concentrate (WPC) and micellar casein isolate (MCI) in the average healthy study population and selected individualsThe model parameters pertaining to amino acids (AAs, k11–k13) were estimated, whereas the other model parameters were kept to their original population value. The tAA input is shown in black (data and polynomial interpolation). The simulated glucose and insulin concentrations are shown in red and blue, respectively. The measured concentrations, obtained from Horstman et al. ,14 are shown as black asterisks with corresponding standard errors of the means. The leftmost column in panel (A and B) pertains to average study population, whereas the other columns represent selected individuals.
Visual inspection of the plasma glucose and insulin simulations following the AA challenges and protein ingredients displays good agreement with the measured data. *In* general, our new model is able to capture the postprandial glucose and insulin following AA challenges, as well as protein ingredients. In addition, our model is also able to capture individual glucose and insulin concentrations following the intake of protein ingredients, being able to capture more pronounced glucose and insulin responses (Figure 2A, subject 9), but also less prominent responses (Figure 2A, subject 3).
## E-DES-PROT improves upon the original E-DES model in capturing glucose dynamics following the intake of AA + glucose and dairy products
We investigated whether our newly developed model was able to capture meals that in addition to AAs and protein also contained glucose and carbohydrates. The E-DES-PROT model was compared to the original E-DES model using the AIC and BIC, with the lowest AIC and BIC value pertaining to the preferred model.
## Amino acids + glucose challenge
The simulated glucose and insulin responses using the original E-DES and the newly developed E-DES-PROT model, parameterized on the AA + glucose challenges (1 mmol/kg body weight +25 g glucose), are shown in Figure 3. For the original E-DES model, parameters (k1, k5, k6, and k8) were estimated. For the E-DES-PROT model, these model parameters were estimated in conjunction with the model parameters accounting for AAs (k11–k13). The measured plasma AA concentration (pertaining to the challenge) was interpolated and provided to the model as an input.9Figure 3Plasma glucose and insulin simulation following intake of different amino acids (AAs) together with glucose in healthy individuals, using the original E-DES and E-DES-PROT modelThe AA input is shown in black (data and polynomial interpolation). The simulated glucose and insulin concentrations following parameter estimation (k1, k5, k6, and k8) using the original E-DES model, are shown in dashed red and blue, respectively. The simulated glucose and insulin concentrations following parameter estimation (k1, k5, k6, k8, and k11–k13) using the E-DES-PROT model, are shown in red and blue, respectively. The other model parameters were kept to their original population value. The measured concentrations, obtained from Sloun et al. ,13 are shown as black asterisks with corresponding standard errors of the means.
Visual inspection of the plasma glucose and insulin simulations following the AA + glucose challenges displays good agreement with the measured data using the E-DES and E-DES-PROT model. The E-DES-PROT model is able to capture AA + glucose challenges and improves in capturing the measured postprandial glucose data (Figure 3, AIC, 1.05 and −1.45; BIC, 3.31 and 2.50 for E-DES, and E-DES-PROT, respectively, across all challenges). For glycine + glucose (Figure 3A), the improvement pertained to the period from 60 min after intake onward, whereas the E-DES-PROT model improved the overall postprandial glucose response for isoleucine + glucose (Figure 3B). The postprandial insulin data were nicely captured using both models. Thus, both the E-DES and E-DES-PROT model are able to describe postprandial responses to simple meal challenges consisting of single AAs co-ingested with glucose. The complete overview of the AIC and BIC for the AA + glucose challenges using the E-DES and E-DES-PROT model is shown in Table S1.
## Dairy products
The simulated glucose and insulin responses using the original E-DES and the newly developed E-DES-PROT model, parameterized on responses to selected dairy food products (i.e. low-fat untreated milk (LF-UHT) and yoghurt) containing 25 g of protein and a variable amount of carbohydrates in a 700 mL solution, are shown in Figure 4. Here, the leftmost column pertains to the average population responses, whereas the other columns show selected individual responses highlighting striking model behavior. The complete overview of the individual glucose and insulin responses for the dairy products (i.e. LF-UHT, LF-PAS, FF-UHT, FF-PAS, and yoghurt) is shown in Data S3. For the original E-DES model, parameters (k1, k5, k6, and k8) were estimated. For the E-DES-PROT model, these parameters were estimated in conjunction with the model parameters accounting for AAs (k11–k13). The measured plasma AA concentration (pertaining to the challenge) was interpolated and provided to the model as an input.9Figure 4Plasma glucose and insulin simulation following intake of low-fat untreated treated milk (LF-UHT) and yoghurt in the average healthy study population and selected individuals using the original E-DES and E-DES-PROT modelThe tAA input is shown in black (data and polynomial interpolation). The simulated glucose and insulin concentrations following parameter estimation (k1, k5, k6, and k8) using the original E-DES model, are shown in dashed red and blue, respectively. The simulated glucose and insulin concentrations following parameter estimation (k1, k5, k6, k8, and k11–k13) using the E-DES-PROT model, are shown in red and blue, respectively. The other model parameters were kept to their original population value. The measured concentrations, obtained from Horstman et al. ,14 are shown as black asterisks with corresponding standard errors of the means. The leftmost column in panel A & B pertains to average study population, whereas the other columns represent selected individuals.
The plasma glucose and insulin simulations following LF-UHT and yoghurt ingestion are in good agreement with the measured data using the E-DES-PROT model. In particular, the original E-DES model was less able to capture the measured postprandial glucose data compared to the E-DES-PROT model (Figure 4, AIC, 16.01 and −5.44; BIC, 17.21 and −3.32 for E-DES and E-DES-PROT, respectively, across all challenges). Whereas the first glucose data point after intake ($t = 15$ min) is accurately captured with the original E-DES model, the remainder of the response is not, and appears to overshoot the measured concentration. The postprandial insulin data were captured well using both models. Looking at the individual level, the E-DES-PROT model was able to capture a wide variety of measured postprandial glucose and insulin responses. Here, the E-DES-PROT model was better able to capture the measured data, for instance for subject 3, 10 (Figure 4A) and subject 3, 5 (Figure 4B). The E-DES-PROT model thus allows capture of more complex meals containing protein as well as carbohydrates, which the original E-DES model was unable to do. The complete overview of the AIC and BIC for the dairy products using the E-DES and E-DES-PROT model is shown in Table S2.
Model fluxes were compared between E-DES-PROT and the original E-DES model following LF-UHT intake in the average healthy population (Figure S1). The fluxes for endogenous glucose production and insulin-dependent glucose uptake increased more in the E-DES-PROT model compared to the original E-DES model. Despite the small increase in the insulin-dependent glucose uptake flux, a minor change greatly affects the postprandial glucose and insulin concentrations (Figure S2). In addition, model fluxes were compared for different types of meals, ranging from simple AA challenges to more complex dairy products in the average healthy study populations, using the E-DES-PROT model (Figure 5).Figure 5Model fluxes following intake of various meal challenges in the average healthy study populations using the E-DES-PROT modelThe corresponding model fluxes pertaining to the E-DES-PROT model simulations for leucine (green), micellar casein isolate (blue), leucine + glucose (brown), and LF-UHT (red) intake are shown in panels (A–D).
The glucose appearance in the gut appears to be more spread out following LF-UHT intake, as compared to leucine + glucose co-ingestion, which has an earlier peak. Insulin secretion and insulin-dependent glucose uptake are substantially lower for leucine and micellar casein isolate ingestion compared to co-ingestion of leucine with glucose and LF-UHT intake, with the largest peak in insulin secretion in the latter. Furthermore, a clear increase from baseline in endogenous glucose production is observed for micellar casein isolate intake, in contrast to LF-UHT and in particular leucine + glucose, which shows the largest decrease from baseline. Leucine ingestion alone only slightly increased endogenous glucose production.
## Discussion
Dietary protein and AAs play an important role in glucose metabolism through stimulating both insulin and glucagon secretion.13,15,16 *In this* study, we developed a novel computational model of the glucose-insulin regulatory system, taking the effects of AAs into account, and used this novel model to describe postprandial glucose and insulin dynamics following a variety of simple to complex meals containing AAs and protein. Here, we show that our E-DES-PROT model accurately describes the measured glucose and insulin concentrations, allows insight into the underlying model fluxes, and outperforms the original E-DES model that only takes the postprandial effects of glucose ingestion into account.
The E-DES model by Maas et al.7 was selected as a basis for model extension due to its relatively simple description of glucose metabolism. Other models that have previously been reported such as the model of Dalla Man et al.6 require data derived from complex, costly measurements (i.e. stable isotope studies) to allow estimation of its model parameters, making it challenging for models to parameterize. In contrast, the E-DES model is less complex in terms of the number of parameters included in the model, and has so far been shown to describe glucose homeostasis in different populations as well as individuals, while including the most important metabolic fluxes.7,9,11 The present E-DES-PROT model introduces several novel terms accounting for the postprandial effects of both individual and total AAs on glucose and insulin regulation. More specifically, the equation regulating liver glucose production was extended to increase glucose output with increasing plasma AA levels, representing the physiological effects of AAs on glucagon secretion, and consequently hepatic glucose output.17 Secondly, the equation regulating pancreatic insulin secretion was extended to increase insulin secretion with increasing plasma AA levels, representing the physiological effects of AAs on β-cells, causing a rise in the ATP/ADP ratio, ultimately leading to the stimulation of insulin granule exocytosis.18 These extensions were necessary to capture the characteristics of the postprandial data, while adhering to established human physiology.17,19 To prevent the development of an overly complex model, we modeled these processes using simple linear and derivative terms; in this way, the model can still be readily individualized using standard plasma glucose, insulin, and AA measurements. With the addition of only three parameters, the E-DES-PROT model was able to accurately capture postprandial glucose and insulin data following various challenge tests containing AAs and protein ingredients. The E-DES-PROT model outperforms the original E-DES model in capturing postprandial glucose data, particularly in the case of the dairy challenges, where both AIC and BIC showed a preference for the E-DES-PROT model. For the AA + glucose challenges, both E-DES and E-DES-PROT were able to accurately capture the insulin response, explaining why the AIC and BIC preferred the E-DES model. However, in contrast to the insulin response, the E-DES model was not able to accurately capture the glucose responses. These results confirm the necessity of including the effects of AAs and protein in the models to be able to capture glycemic responses to foods such as to yoghurt. A model based on E-DES that incorporates dietary fat has been developed in parallel and was recently published.20 A next step would be to merge these two models into a model able to fully capture the effects of a complex meal, taking into account all three major macronutrient classes (i.e. carbohydrate, protein, and fat).21 Despite only slightly improving in capturing the glucose response following AA + glucose challenges, the E-DES-PROT model is physiologically more accurate and provides more detailed insight into the underlying physiological processes (i.e. insulin secretion and endogenous glucose production). Besides being able to describe average postprandial responses to the various challenges, the E-DES-PROT showed the ability to reproduce a wide variety of individual postprandial glucose and insulin responses as well. However, there were some exceptions in which the model did not perfectly capture certain individual postprandial responses. This was observed for responses in which the data points following meal ingestion ($t = 0$) were below basal glucose concentration (e.g. participant 5, Figure 4B). Furthermore, the model struggled accurately predicting an intermediate dip in the glucose response (e.g. participant 5, Figure 4A).
The mechanistic nature of the model also allows the investigation of non-measured variables such as metabolite fluxes between tissues. Inspecting the metabolite fluxes, we found that there was an increase in insulin-dependent glucose uptake using the E-DES-PROT model compared to the original E-DES model, resulting in accurate description of the postprandial glucose data. The model fluxes calculated for various meals included in this study provide information on physiological processes underlying the dynamic responses. For example, glucose appearance in the gut seems to be more spread out for the dairy product (i.e. LF-UHT) compared to the simpler AA + glucose co-ingestion test (i.e. leucine + glucose). Furthermore, endogenous glucose production was increased for protein-only meals (i.e. micellar casein isolate), corresponding with findings from literature.15,22 While beyond the scope of the present study, investigating model parameters and corresponding fluxes at the individual level with the new E-DES-PROT model might provide further insight into the glucometabolic status of individuals.
In conclusion, we present a new physiology-based computational model of the glucose homeostasis that extends the E-DES model with the postprandial effects of AAs and protein. The E-DES-PROT model allows, for the first time, to accurately describe postprandial responses following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy challenges, and is able to provide information on physiological processes underlying the meal responses. Introducing AAs in these models is important to move toward describing physiologically relevant complex meals. In addition, our model outperforms the original E-DES model in terms of describing postprandial glucose responses following dairy products. As the model covers two out of three macronutrient classes (carbohydrates and protein), future studies should explore the possibility to further extend the E-DES-PROT model with fat to allow model-based prediction of glucose responses to complex meals varying in macronutrient composition and content.
## Limitations of the study
The increased liver glucose output was modeled to be dependent on the AA concentration in the plasma. However, AAs are known to stimulate glucagon secretion, which in turn increases liver glucose output.17 As glucagon is not explicitly accounted for in the E-DES model, future work should consider incorporating glucagon in the E-DES model, as has been implemented before in the Dalla Man model.23 Secondly, as the objective of our research was to quantify the effect of AAs on postprandial glucose-insulin dynamics, a forcing function is used to describe the rate of appearance of AAs in E-DES-PROT. In future research, the addition of a function to explicitly describe the rate of appearance could increase the functionality of our model. This rate of appearance function would allow simulation of plasma AAs, without the need for measured plasma AAs to be provided as input. Furthermore, this would allow refinement of the glucose rate of appearance, as protein (and fat) has been known to delay gastric emptying.24 Individual AAs have been shown to have distinct effects on the glucose and insulin response,13,19 but also interact with each other when provided together.25 *In this* study, we added up the AA profiles (tAA) for the protein ingredients and dairy products, and did not include possible interactions between individual AAs in the E-DES-PROT model. Furthermore, not only AAs but also fat influences the blood glucose response in response to complex meals.26,27 However, incorporating the postprandial effects of fat on glucose metabolism was beyond the scope of this present study. Identifiability analysis showed that the parameters related to AAs (k11–k13) were identifiable for AA challenges and milk protein ingredients (examples are shown in Figure S3). However, for the AA + glucose challenges as well as for the dairy products, only the parameters k1, k5, k6, k8, and k13 were consistently deemed identifiable. The unidentifiability of the k11 and k12 parameter in several of these challenges might have resulted from functional relationships between parameters.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERSoftware and algorithmsMATLAB 2018bThe MathWorks Inc.https://nl.mathworks.com/products/matlab.htmlOtherOriginal E-DES-PROT Model codeThis paperData S1, https://github.com/BartvSloun/E-DES-PROT
## Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead contact Bart van Sloun ([email protected]).
## Material availability
No new materials or reagents were generated during this study.
## Study workflow
The study workflow is illustrated in Figure S4. Briefly, the existing E-DES model was extended to a model that accounts for the postprandial effects of AAs and protein on glucose and insulin dynamics. Model equations were adjusted and additional parameters were introduced to take the effects of AAs on insulin secretion and liver glucose production, as observed from literature, into account. Subsequently, postprandial time-series data, extracted from the literature,13 and obtained from a previously performed randomized, single-blind crossover trial in healthy elderly males and females (RCT; NCT02546141)14 were used to estimate the model parameters. The ability of the model to describe the measured data was evaluated using the sum of squared residuals (SSR), the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Model fluxes were compared between the E-DES and the newly developed E-DES-PROT model, as well as for various meal challenges.
## Collection of data
Publicly available datasets, containing postprandial time-series data of AAs, glucose, and insulin following various AA challenge tests (leucine, isoleucine, lysine, glycine, proline, and phenylalanine; with or without glucose) in healthy individuals were included in the present study (summarized in13). In all experiments, plasma samples were taken from the antecubital vein in the fasting state ($t = 0$) and 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, and 150 minutes after ingestion of 1 mmol AA per kg of lean body weight (with or without 25g glucose). In addition, we used data on postprandial AAs (arginine, glutamine, serine, asparagine, glycine, threonine, alanine, methionine, proline, lysine, aspartic acid, histidine, valine, glutamic acid, tryptophan, leucine, phenylalanine, isoleucine, cysteine and tyrosine), glucose, and insulin time-series from a randomized single-blind crossover trial (RCT; NCT02546141), in which ten participants (five male) received two spray dried milk protein ingredients (whey protein concentrate, WPC; micellar casein isolate, MCI) and six dairy products (low-fat untreated milk (LF-UHT); low-fat pasteurized milk (LF-PAS); full-fat untreated milk (FF-UHT); full-fat pasteurized milk (FF-PAS); low-fat yoghurt; full-fat cheese) in random order, as previously described.14 The dairy products and protein ingredients were supplied on eight separate test days, with a one-week washout period in between. For each meal, an appropriate amount of the product to ensure 25g of protein intake was consumed. For the milk protein ingredients (i.e. WPC and MCI), this was achieved by dissolving an appropriate amount of powder in water to attain a solution of 700mL containing 25g of protein. To standardize the volume for all products, water was added to a total of 700mL of volume ingested. Plasma samples were taken from the antecubital vein in the fasting state ($t = 0$) and 15, 30, 45, 60, 75, 90, 105, 120, 150, 180, 210, 240, and 300 minutes after ingesting the protein ingredients and dairy products. An overview of the datasets included in the present study is given in Table S3.
## Development of a novel physiology-based computational model of glucose homeostasis
The Eindhoven Diabetes Education Simulator (E-DES, version 1.1) published by Maas et al.7,9 formed the basis for the model extension with AAs in the present study. The E-DES model is a physiology-based computational model of the glucose regulatory system in healthy individuals and patients with type 1 and type 2 diabetes.10 It consists of a system of coupled differential equations, which describe the change of the mass or concentration of either glucose or insulin over time. Each of these equations consists of a positive inflow and negative outflow term and can be summarized as follows: (i) glucose balance in the gut is determined through the inflow of glucose mass from the stomach and glucose leaving the gut through uptake by the plasma (ii) glucose balance in the plasma is determined by glucose inflow from the gut in conjunction with glucose output from the liver and glucose uptake by insulin-(in)dependent tissues (iii) insulin balance in the plasma is determined by inflow of endogenously produced insulin from the pancreas and uptake of insulin by the interstitial fluid (iv) insulin balance in the interstitial fluid is determined by insulin inflow from the plasma and removal of insulin from the interstitial fluid proportional to the interstitial insulin fluid concentration. The rates through which these processes occur are controlled by parameters (denoted with k), which have been estimated and validated on multiple oral glucose tolerance tests (OGTTs) in healthy populations.10 The model parameters are described in Table S4. The model inputs, equations, fluxes, constants are described in detail in Data S4.
## Model development
In this study, we extended the previously developed E-DES model to also account for the postprandial effects of AAs on glucose and insulin dynamics (illustrated in Figure S5). Firstly, the equation regulating glucose production from the liver (Equation 1) was extended with a proportional (k11) term to accommodate an increase in liver glucose production proportional to the AA concentration present in the plasma (AApl(t)) relative to the basal concentration (AAbpl).(Equation 1)gliv(t)=gbliv−k3(Gpl(t)−Gbpl)−k4β(Iif(t))+k11(AApl(t)−AAbpl) Secondly, the equation regulating insulin secretion from the pancreas (Equation 2) was extended with a derivative (k12) and proportional (k13) term to accommodate an increase in insulin secretion (i) based on the rate of change of plasma AAs (dAApldt), and (ii) proportional to the AA concentration present in the plasma (AApl(t)) relative to the basal concentration (AAbpl).(Equation 2)ipnc(t)=β−1(k6(Gpl(t)−Gbpl)+(k7τi)∫(Gpl(t)−Gbpl)dt+(k7τi)Gbpl+(k8τd)dGpldt+k12dAApldt+k13(AApl(t)−AAbpl)) The extended Equations 1 and 2 described above require plasma AA concentrations as model input. Therefore, measured AA concentrations following the challenge tests were interpolated via a fitted piecewise cubic Hermite interpolating polynomial (pchip), and provided to the model as AApl(t). For the RCT (NCT02546141), the following AA measurements were added up, interpolated, and denoted as total AA (tAA): arginine, glutamine, serine, asparagine, glycine, threonine, alanine, methionine, proline, lysine, aspartic acid, histidine, valine, glutamic acid, tryptophan, leucine, phenylalanine, isoleucine, cysteine, and tyrosine.
## Model calibration
Model calibration was performed by generating parameter values that resulted in an optimal description of measured data. This was done through minimizing a cost function, representing the sum of squared residual (SSR) in the model prediction for glucose and insulin (Equation 3). The SSR is minimized using lsqnonlin, a local, gradient-based least squares solver in MATLAB (Version R2018b). Optimal parameter sets were obtained using twenty-five initializations of the optimization algorithm with $25\%$ random noise starting from the original parameter value for the average healthy population.9(Equation 3)SSR=∑$j = 1$m∑$i = 1$N(γ((yi,j|θ→)−di,j))2Where m, and N represent the number of metabolites and the number of time-points, respectively. The measured data is denoted by d, while y is the corresponding model prediction given the parameter vector θ→. A weight factor γ = 0.1 was used in the case of insulin (γ = 1 in case of glucose) to account for the unit difference (mmol/L, mU/L for glucose and insulin, respectively) between the molecules. As the lsqnonlin function, that minimizes the sum of squared error, does this simultaneously for glucose and insulin, the γ factor aims to bring the units for glucose and insulin closer together to avoid prioritizing one or the other in the optimization process.
## Model selection and analysis
Visual inspection was performed to evaluate the goodness-of-fit of the simulated glucose and insulin responses to the measured data. In order to compare the E-DES and the E-DES-PROT model, we selected the parameters identified from our previous work.11 In that work, a systematic model selection pipeline was implemented to allow personalization of the E-DES model through reducing the number of parameters to be estimated, resulting in a model containing parameters k1, k5, k6, and k8 (sensitivity is shown in Figure S6). In the current work, we estimated those parameters, both for the systematic review datasets and the randomized single-blind crossover trial. The selected parameters represent distinct physiological processes involved in glucose and insulin regulation, described in Table S4. For the E-DES-PROT model simulation, the AA parameters (k11-k13) were also estimated. Parameters Gbpl and Ibpl (sensitivity is shown in Figure S7) were set to be equal to the first data-point ($t = 0$ min) of the measured responses, whereas the other parameters were set to the average healthy population values from the original publication.9 Model performance was evaluated using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), in which model complexity (i.e. number of estimated parameters) was penalized (Equations 4 and 5 respectively).(Equation 4)AIC=N∗ln(SSRN)+2∗K(Equation 5)BIC=N∗ln(SSRN)+ln(N)∗K N represents the number of observations, and K the number of parameters. Given a set of candidate models that describe the postprandial time-series data, the preferred model is the one with the lowest AIC and BIC value, indicating the better-fit model whilst taking the number of parameters into account. In addition, model fluxes were calculated and compared between the E-DES and E-DES-PROT model, as well as for the various meal challenges. Parameter identifiability was assessed using Profile Likelihood Analysis (PLA). In PLA, the value of one parameter is changed iteratively from its optimal value and the remaining parameters are re-estimated. An increase in the cost function for the model fit indicates that a reliable parameter estimate has been obtained and the parameter is identifiable given the model structure and data.
## Computer software
The model was implemented and analyzed in MATLAB (MATLAB, Version R2018b, The Mathworks, Inc., Natick, Massachusetts, United States). The ordinary differential equation model was simulated using the variable step solver ode15s.
## Supporting citations
The following references are included in the supplemental information (i.e. Table S3): 28, 29, 30, 31, 32, 33.
## Supplemental information
Document S1. Figures S1–S7, Data S2–S4, and Tables S1–S8 Data S1. MATLAB implementation of the E-DES-PROT model used in the manuscript, related to STAR Methods
## Data and code availability
•*The data* of the randomized single-blind crossover trial study (NCT02546141) are available to eligible researchers from Thom Huppertz (Thom. [email protected]).•All original code is provided in the supplementary materials (Data S1) and has also been deposited in GitHub (https://github.com/BartvSloun/E-DES-PROT).•All additional information required to re-analyze the data reported in this paper is available from the lead contact upon request
## Author contributions
Conceptualization: B.v. S., G.H.G., N.A.W.v. R., I.C.W.A. Data curation: B.v. S. Funding acquisition: N.A.W.v. R., I.C.W.A. Investigation: B.v. S., G.H.G., I.C.W.A. Methodology: B.v. S., G.H.G., B.E., S.D.O., N.A.W.v. R., I.C.W.A. Project administration: B.v. S., I.C.W.A. Resources: C.M.S.-P., I.C.W.A. Software: N.A.W.v. R. Supervision: G.H.G., N.A.W.v. R., I.C.W.A. Validation: B.v. S. Visualization: B.v. S. Writing – original draft: B.v. S. Writing – review & editing: B.v. S., G.H.G., B.E., S.D.O., C.M.S.-P., J.M.W.G., N.A.W.v. R., I.C.W.A.
## Declaration of interests
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. C.M.S.-P. and J.M.W.G. are both employed by FrieslandCampina, Amersfoort, The Netherlands.
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|
---
title: Ready meals, especially those that are animal-based and cooked in an oven,
have lower nutritional quality and higher greenhouse gas emissions and are more
expensive than equivalent home-cooked meals
authors:
- Magaly Aceves-Martins
- Philippa Denton
- Baukje de Roos
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989702
doi: 10.1017/S1368980023000034
license: CC BY 4.0
---
# Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality and higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals
## Body
Ultra-processed foods and formulations of ingredients, primarily of exclusive industrial use and typically created by a series of industrial techniques and processes, are increasingly dominating our food supply chains. Ultra-processed foods are mostly ready-to-consume, hyper-palatable and profitable branded products designed to displace other food groups[1]. Consumption of ultra-processed foods has been associated with a range of detrimental health outcomes in epidemiological studies, including an increased risk of all-cause mortality, CVD, hypertension, metabolic syndrome, overweight and obesity[2]. Whilst there is increasing evidence that consumption of ultra-processed foods may be damaging to human health, its environmental impacts are poorly quantified. Current evidence only considers the effects of primary commodities used for their production rather than capturing the overall impact of ultra-processed foods from farm to fork, including processing, packaging and distribution[3].
Many ready meals, defined as pre-prepared main courses sold in a pre-cooked form that only requires pre-heating prior to consumption, can be classified as ultra-processed foods. The UK has one of the largest ready meal markets globally, with a market value of over £3·9 billion[4]. It is estimated that 88 % of the UK adult population eat ready meals, with two out of five people eating them every week[5]. Chilled ready meals make up 70 % of the UK ready meals market share, with frozen meals occupying the remaining 30 %[6]. Some of the main drivers for the steady rise in the purchase and consumption of ready meals include time scarcity in modern life, more women in the workplace, varying eating times, lack of cooking skills or dislike of cooking, and a growing number of single households[7,8].
Like most ultra-processed foods, ready meals are generally energy-dense and contain higher levels of low-cost ingredients such as saturated and trans-fats, refined starches, free sugars and salts, whilst being low in fibre and micronutrients[1]. Besides their poor nutritional profile, another main concern is that greenhouse gas emissions (GHGE) from the consumption of ready meals in the UK currently contribute 15·7 % of the total annual GHGE from the UK food and drink sector[6]. Also, it is estimated that ready meals represent 8 % of the total per capita carbon budget related to food production for the climate target of limiting warming to 2°C[6]. A few studies have compared the environmental effects of consuming ready meals v. home-cooked meals, with one study finding that the environmental impact of homemade meals was lower because of avoidance of meal manufacturing, reduced refrigeration and a lower amount of waste in the life cycle of the homemade meal[9]. However, another study noted that the differences in environmental impact between both ready and homemade meal options were small and highlighted that homemade meals had a higher environmental impact than semi-prepared or ready-to-eat meals[10].
This study aimed to assess how ready meals compared with equivalent home-cooked meals in terms of nutritional quality indicators and GHGE, but also in terms of cost, in main meals consumed in the UK. Indeed, affordability is an essential determinant of food choice by consumers in the UK and a pivotal contributor to socio-economic inequalities when considering the healthiness of food and drink choices(11–14).
## Abstract
### Objective:
To examine whether ready meals and equivalent home-cooked meals differ in nutritional quality indicators, greenhouse gas emissions (GHGE) and cost.
### Design:
We performed a cross-sectional analysis of meal data from the National Diet and Nutrition Survey (NDNS) nutrient databank ($\frac{2018}{19}$). Additional data on nutrient composition, cost and cooking-related GHGE were calculated and compared between fifty-four ready meals and equivalent home-cooked meals.
### Setting:
The UK.
### Participants:
Not applicable.
### Results:
Ready meals, overall and those that were animal-based, had significantly higher levels of free sugar compared with equivalent home-cooked meals ($P \leq 0$·0001 and $P \leq 0$·0004, respectively). Animal-based ready meals had significantly higher levels of GHGE ($P \leq 0$·001), whereas the cost of ready meals, overall, was significantly higher ($P \leq 0$·001), compared with equivalent home-cooked meals. Animal-based meals, whether ready meals or equivalent homemade meals, had significantly higher levels of protein ($P \leq 0$·0001), contained significantly more kilocalories ($$P \leq 0$$·001), had significantly higher levels of GHGE ($P \leq 0$·0001) and were significantly more expensive ($P \leq 0$·0001), compared with plant-based meals. Overall, plant-based meals home-cooked on the gas or electric stove had the lowest GHGE and cost, whereas animal-based oven-cooked ready meals had the highest levels of GHGE and were most expensive.
### Conclusions:
Ready meals have lower nutritional quality and higher GHGE and are more expensive than equivalent home-cooked meals, especially those meals that are animal-based and prepared in an oven.
## Data
We performed a secondary data analysis using the National Diet and Nutrition Survey (NDNS) nutrient database year 11 ($\frac{2018}{19}$)(15–17). The NDNS nutrient databank contains compositional data from the nearly 6000 foods, drinks and prepared dishes available in the UK, including home-cooked and ready meals. Of these, we selected all main course meals, chilled or frozen, that needed to be heated prior to consumption, sold within a container, and had an equivalent home-cooked version in the NDNS nutrient database. As a result, we included fifty-four main courses with data on nutrient profile, and on frequency of consumption over 4 d, in 444 participants (Table 1).
Table 1Ready meal dishes, and home-cooked equivalent dishes, including frequency of consumption, GHGE and cost per dishReady mealsHome-cooked equivalent mealsFood nameFrequency* GHGE† Cost‡ Food nameFrequency* GHGE† Cost‡ Animal-basedBeef and potato pie215430·51Beef and potato pie 2 crusts04330·96Beef stew and dumplings frozen or chilled ready meal114000·87Beef stew and dumplings03900·56Beef stir fry with green peppers and black bean sauce012801·36Beef stir fry011500·55Minced beef pie purchased412000·62Minced beef pie top pastry07060·96Steak pie, short crust, purchased312000·84Steak pie pastry top only07970·19Cornish pasty purchased1012000·44Cornish pasty homemade05080·14Corned beef pasty purchased012000·66Corned beef pasty05080·14Lamb bhuna purchased011801·00Lamb curry (no potatoes) with onions and curry pas010191·20Tagliatelle carbonara ready meal111100·37Spaghetti carbonara04310·74Chilli con carne no rice ready meal010700·37Chilli con carne minced beef kid beans and tin tom24330·16Cottage pie, frozen/chilled beef410400·64Cottage pie02850·22Cottage pie, reduced fat, ready meal010400·28Cottage pie with lean minced beef, potatoes and butter02850·22Lasagne beef, ready meal510000·66Lasagne homemade14970·24Lasagne, reduced fat, ready meal110000·66Lasagne made with extra lean mince04970·24Beef curry frozen/chilled ready meal no rice19000·25Beef curry with cream or coconut sauce02420·32Shepherd’s pie, lamb, ready meal08800·65Shepherd’s pie homemade with minced lamb04850·48Beef hot pot with pots ready meal18100·56Beef hot pot made with stewing steak carrots cab04980·56Moussaka ready meal chill/frozen/long life16700·87Moussaka with aubergines homemade06780·36Chicken curry frozen chilled no rice36700·2Chicken curry homemade65530·62Lamb hot pot with potatoes ready meal06700·87Lamb hot pot05900·48Lemon chicken05301·00Lemon chicken – chicken breasts in sauce14631·25Chicken chow mein ready meal35300·70Chicken Chow Mein34010·87Quiche, meat-based, Quiche Lorraine not low fat124910·58Quiche Lorraine not wholemeal06110·52Fishcakes, salmon, retail, coated in breadcrumbs, baked/grilled54600·74Salmon fishcakes grilled02091·20Smoked haddock chowder, for example M&S04600·44Fish and seafood chowder22560·58Tuna and pasta bake ready meal04600·68Tuna and pasta bake04980·39Sweet and sour pork frozen ready meal no rice04601·10Sweet and sour pork03600·58Chicken and sweetcorn soup14100·21Chicken and veg soup with carrot potato and onion01470·19Chicken pie frozen/chilled individual two crusts74000·37Chicken pie 2 crusts05250·62Chicken in white sauce ham mushroom and rice04000·85Chicken and mushrooms in white wine sauce15070·53Chicken and pasta bake with broccoli, low fat04000·68Chicken and broccoli pasta bake06500·65Chicken casserole chicken in tomato/gravy/sauce and vegetables04000·62Chicken and vegetable casserole with olive oil03450·23Fisherman’s pie (white fish) retail24000·5Fisherman’s pie (potato based) with cod and prawns02650·96Fisherman’s pie reduced calorie and fat retail04000·5Fisherman’s pie with prawns and smoked haddock02650·96Tuna and red pepper fish cakes03291·0Tuna and potato fish cakes02150·21Plant-basedMacaroni cheese ready meal low fat411100·20Macaroni cheese semi skim milk and reduced fat spread14070·25Macaroni cheese purchased311100·22Macaroni cheese with butter and semi-skimmed milk04070·25Broccoli and stilton soup, premium, chilled carton011100·11Broccoli and cheese soup homemade02030·18Quiche, cheese and onion, purchased134910·58Cheese and onion quiche homemade04790·38Cheese and vegetable quiche purchased14910·57Cheese and tomato quiche14760·38Quiche, vegetable only, no cheese, purchased14910·58Cauliflower and broccoli quiche06580·38Mushroom soup, premium, chilled, carton14800·18Homemade mushroom soup01070·18Vegetable curry, ready meal, no rice02800·66Vegetable curry31230·29Ross veg chow mein stir-fried in olive oil02700·54Vegetable Chow Mein03160·25Vegetable lasagne purchased02600·87Vegetable lasagne homemade12410·32Vegetable bake purchased ready meal02600·37Vegetable bake with carrots, broccoli, potatoes and cheese sauce01200·21Cauliflower cheese: ready meal purchased standard22200·67Cauliflower cheese (whole milk)02790·20Cauliflower cheese: healthy range ready meal purchased02200·67Cauliflower cheese with butter and semi-skimmed milk02790·20Vegetable shepherd’s pie – purchased ready meal02200·27Vegetable shepherd’s pie02650·21Spinach and potato curry purchased or takeaway11800·66Spinach and potato curry with tomatoes and onion01230·29Vegetable soup carton61500·18Soup vegetable0560·21Carrot and coriander soup, purchased21500·12Carrot and onion soup homemade0580·04Cream of tomato soup, carton11500·25Tomato soup with cream, homemade01100·19Ratatouille frozen purchased01200·66Ratatouille homemade11550·25*Frequency of consumption across all participant’s (n 444, NDNS $\frac{2018}{2019}$) 4-d dietary recalls.†GHGE per 100 g of product up to supermarket shelf.‡Cost per 100 g of product.
## Nutritional quality indicators
Relevant indicators of nutritional quality, including total kilocalories, carbohydrates (including free sugars), protein, fat (including trans-fats), fibre and salt, were selected based on previous publications reporting on differences in nutritional quality between ready-made and home-cooked meals[18,19]. These indicators were estimated per 100 g of a meal.
## Greenhouse gas emissions
GHGE values for individual foods and ready meals expressed as gCO2 equivalents (gCO2e) were obtained from a range of open-access sources, including academic studies, retailers and producers published between 2008 and 2016[20,21], added to the NDNS nutrient databank[21,22]. GHGE values were based on the emissions of six greenhouse gases which were converted into an equivalent amount of carbon dioxide (CO2 equivalent or CO2e), based on the relative global warming impact of each gas, and the final carbon footprint was expressed as the weight of carbon dioxide[20]. The climate metric used to aggregate the GHGE measurements into CO2e were those reported by Department for Environment Food and Rural Affairs, UK[23]. GHGE values from studies using complete cradle-to-grave life cycle analysis (LCA)[20], obtained following the international PAS 2050 standard[24], were selected where possible. We identified CO2e for 153 food and drink items in the NDNS nutrient databank, and where a GHGE value for a specific item was not available, reasonable substitute data were discussed and imputed by a team of three nutrition scientists, based on the food type, food group and compositional similarity of the products.
To estimate the GHGE for home-cooked meals, we estimated GHGE of the raw ingredients, establishing the weight of each ingredient and the weight of the whole cooked meal using Nutritics, which is nutrition management software for recipe and menu management, food labels, diet and activity analysis, and meal planning (Nutritics Ltd). Based on BBC Good Food[25] and Sainsbury’s recipes[26], we established cooking methods and times. For home-cooked meals requiring more than one cooking method, GHGE data for each cooking method were added together. In addition, we recorded the longest cooking time suggested for the frozen versions of ready meals. If there was more than one suggested cooking method (e.g. oven and microwave), data for both methods were recorded separately.
To estimate the full GHGE until serving the meal, we combined the GHGE from the recipes’ ingredients or ready meals (value up to the supermarket shelf), which include emissions due to land use change, farm-related emissions, animal feed, processing, transport, retail and packaging) with GHGE produced by the different cooking methods. For the latter, GHGE of cooking appliances were based on manufacturer information[27] and adjusted to the conversion factors provided by the UK government in 2021[28] and cooking time (Equation 1): [1] where a is the cooking time, b is the GHGE of cooking appliances based on manufacturer information and adjusted to the conversion factors given by the UK government 2021, and c is the weight of the recipe or ready meal product.
## Cost
For ready meals, we used the retail prices from the supermarket/products webpages (last accessed in January 2022) to estimate the total cost per 100 g. We used the price per serving of home-cooked meals published on either the BBC Good Food[25] or Sainsbury’s[26] recipes website (last accessed on November 2021). If prices were not available, we estimated the cost of the raw ingredients established the weight of each ingredient and the weight of the whole cooked meal using Nutritics (Nutritics Ltd). We added the costs from each ingredient to get the total cost for the meals and then estimated the total cost per 100 g. Our analysis did not include the costs for reheating or cooking the meals.
## Analysis
We analysed nutritional quality and estimated total GHGE (values up to supermarket shelf plus GHGE after cooking) and cost for each of the fifty-four ready meals and fifty-four equivalent home-cooked meals. Distributions of data were analysed visually, and Shapiro–Wilk tests were performed to test normality for each outcome (online Supplementary Table 2). These tests suggested significant non-normality; hence, non-parametric tests (median differences) were selected for analysis. Mann–Whitney tests were used to compare nutritional values, GHGE and cost between ready meals and their equivalent home-cooked meals. The percentage change was estimated from GHGE values up to supermarket shelves and after cooking, and statistical significance was assessed through paired t test analysis. We expressed data in medians and interquartile ranges (IQR). Statistical significance was estimated at $P \leq 0$·05, but a Bonferroni correction was included in the analysis to control the family-wise error. We performed a sub-analysis of plant- v. animal-derived meals because of the published evidence on differences in nutritional quality and GHGE between these meals[29].
Data were visualised with Tableau software, and statistical analysis was performed in R software using the libraries ‘ggthemes’, ‘tidiverse’ ‘for data visualisation and graphs), ‘dplyr’ (for testing normality), ‘psych’ and ‘pastecs’ (for descriptive statistics).
## Results
Of the fifty-four ready meal and home-cooked meal main courses we identified in the NDNS nutrient database (Table 1), 65 % were animal-based and 35 % were plant-based. Ready meals, overall and those that were animal-based had significantly higher levels of free sugar per 100 g of product, compared with equivalent home-cooked meals ($P \leq 0$·0001 and $P \leq 0$·0004, respectively). Animal-based ready meals had significantly higher levels of GHGE (up to supermarket shelf) per 100 g of product ($P \leq 0$·001), whereas the cost of ready meals, overall, was significantly higher per 100 g of product ($P \leq 0$·001), compared with equivalent home-cooked meals (Table 2). Across ready meals and equivalent home-cooked meals, animal-based meals had significantly higher levels of protein ($P \leq 0$·0001), contained significantly more kilocalories per 100 g of product ($$P \leq 0$$·001), had significantly higher levels of GHGE (up to supermarket shelf) per 100 g of product ($P \leq 0$·0001), and were significantly more expensive ($P \leq 0$·0001), compared with plant-based meals (Table 2).
Table 2Differences in nutritional quality, greenhouse gas emissions between ready meals and equivalent home-cooked meals, and between animal-based meals and plant-based meal variantsMeal originReady mealsHome-cooked mealsPrm-hc Pab-pb Interquartile rangeIQRInterquartile rangeIQRNutritional quality indicatorsTotal carbohydrates (g/100 g)All meals (n 54)11·310·310·310·70·27–Animal-based meals (n 35)11·79·2510·810·90·330·24Plant-based meals(n 19)9·610·77·39·250·64Free sugars (g/100 g)All meals (n 54)0·51·10·10·40·0001* –Animal-based meals (n 35)0·50·900·30·0004* 0·87Plant-based meals(n 19)0·81·100·50·08Total protein (g/100g)All meals (n 54)6·74·78·95·30·01–Animal-based meals (n 35)8·13·710·52·70·002< 0·0001* Plant-based meals(n 19)3·53·34·16·050·39Total fat (g/100g)All meals (n 54)4·77·56·45·60·10–Animal-based meals (n 35)4·87·16·45·50·150·22Plant-based meals(n 19)3·65·36·33·20·52 Trans-fat (g/100 g)All meals (n 54)0·10·10·10·20·30–Animal-based meals (n 35)0·10·10·10·20·160·42Plant-based meals(n 19)0·120·90·10·20·92Fibre (g/100 g)All meals (n 54)1·50·71·20·60·06–Animal-based meals (n 35)1·40·71·10·70·090·09Plant-based meals(n 19)1·60·51·30·50·38Salt (mg/100 g)All meals (n 54)600303·7445482·50·006–Animal-based meals (n 35)665276·2482·55590·020·02Plant-based meals(n 19)500213·7415216·20·17Energy (kcal/100 g)†All meals (n 54)116·578·8128·589·20·29–Animal-based meals (n 35)1248514079·70·210·001* Plant-based meals(n 19)8678·596·5650·63GHGETotal GHGE up to shelf (gCO2e/100 g)All meals (n 54)491640404259·20·002–Animal-based meals (n 35)6706304871970·001* < 0·0001* Plant-based meals(n 19)260291263·2169·60·15CostTotal cost (GBP/100 g)All meals (n 54)0·620·310·320·360·001* –Animal-based meals (n 35)0·620·350·540·470·13< 0·0001* Plant-based meals(n 19)0·580·430·230·090·006GHGE, greenhouse gas emissions up to supermarket shelf; gCO2e, gCO2 equivalents; GBP, Great British pound £; Prm-hc, P-value of difference between ready meals and equivalent home-cooked meals; Pab-pb, P-value of difference between animal-based and plant-based meals, across ready and home-cooked meals. Data represent medians and interquartile range (IQR).*Statistical significance, adjusted using Bonferroni correction, estimated at a P-value < 0·0016.
Stove and microwave cooking of ready meals and equivalent home-cooked meals generally resulted in a small increase in GHGE, adding on average 1–4 % to ‘up to supermarket shelf’ GHGE. Oven cooking of ready meals and equivalent home-cooked meals resulted in much higher increases in GHGE, adding on average 19 and 8 %, respectively, to ‘up to supermarket shelf’ GHGE. Ready meals, overall and those that were animal-based, had significantly higher levels of GHGE, after cooking, compared with equivalent home-cooked meals ($P \leq 0$·0005). Levels of GHGE, after cooking, were significantly higher for animal-based meals than for plant-based meals, ($P \leq 0$·0027), across meals and cooking methods (Fig. 1, Table 3).
Fig. 1Distribution of GHGE per cooking method, type and origin of the meals. gCO2e,gCO2 equivalents. Boxplots presenting median values. Whisker’s extension to data within 1·5 times the interquartile ranges. Darker grey within and bar shows the lower whisker, and the lighter grey, the higher whisker. No homemade meal recipe required the use of microwave, and hence no comparison among ready meals and homemade meals was feasible for this cooking method. Oven-cooked ready meals had significantly higher levels of GHGE compared with equivalent home-cooked meals ($P \leq 0$·05). *Cooking* generally resulted in a significant increase in GHGE across all meals and cooking methods ($P \leq 0$·05). Across meals and cooking methods, GHGE values after cooking meals that were animal-based were significantly higher than GHGE values for plant-based meals ($P \leq 0$·005) (Table 3). Gas and electric stove-cooked meals: n 62 (twelve ready meals of which five animal-based and seven plant-based; and fifty home-cooked meals of which thirty-three animal-based and seventeen plant-based). Microwave-cooked meals: thirty-nine ready meals of which twenty-five animal-based and fourteen plant-based. Oven-cooked meals: n 77 (forty-six ready meals of which fifty-two animal-based and twenty-five plant-based; and thirty-one home-cooked meals of which twenty animal-based and eleven plant-based) Table 3Differences in greenhouse gas emissions between ready meals and equivalent home-cooked meals, and between animal-based meals and plant-based meal variants, by cooking methodCooking methodOrigin of mealsGHGE valueReady mealsHomemade mealsPrm-hc Pab-pb MedianIQRMedianIQRGas stove cooked(Total gCO2e/100 g)All meals (n 62)Ready meals (n 12)Home-cooked meals (n 50)Up to supermarket shelf435472·5395·5252·20·72–After cooking448·0474·4404·9237·30·85GHGE % increase due to cooking2·9 %2·3 %–Animal-based meals (n 38)Ready meals (n 5)Home-cooked meals (n 33)Up to supermarket shelf530440474232·20·17< 0·0001* After cooking541·1430·9491·8229·50·22GHGE % increase due to cooking2·1 %3·7 %–Plant-based meals (n 24)Ready meals (n 7)Home-cooked meals (n 17)Up to supermarket shelf150225253185·20·83After cooking159·3227·2265·7199·30·92GHGE % increase due to cooking6·1 %4·0 %–Electric stove cooked(Total gCO2e/100 g)All meals (n 62)Ready meals (n 12)Homemade meals (n 50)Up to supermarket shelf435472·5395·5252·20·72–After cooking451474·9409·7238·50·89GHGE % increase due to cooking3·7 %3·5 %–Animal-based meals (n 38)Ready meals (n 5)Homemade meals (n 33)Up to supermarket shelf530440474232·20·17< 0·0001* After cooking543·8428·7495·8226·30·22GHGE % increase due to cooking2·6 %4·6 %–Plant-based meals (n 24)Ready meals (n 7)Home-cooked meals (n 17)Up to supermarket shelf150225253185·20·83After cooking161·5227·79265·75199·30·97GHGE % increase due to cooking7·6 %5·1 %–Microwave cooked (Total gCO2e/100 g)All meals (n 39)Ready meals (n 39)Up to supermarket shelf480700–––After cooking483·1700·2––GHGE % increase due to cooking0·6 %––Animal-based meals (n 25)Ready meals (n 25)Up to supermarket shelf810580––0·002* After cooking817·8588·3––GHGE % increase due to cooking0·9 %––Plant-based meals (n 14)Ready meals (n 14)Up to supermarket shelf240272·5––After cooking246·1271·5––GHGE % increase due to cooking2·5 %––Oven cooked(Total CO2e/100 g)All meals (n 77)Ready meals (n 46)Home-cooked meals (n 31)Up to supermarket shelf510·5640479234·50·04–After cooking607·3686·5518·9280·10·0002* GHGE % increase due to cooking18·9 %8·3 %–Animal-based meals (n 52)Ready Meals (n 32)Home-cooked meals (n 20)Up to supermarket shelf740667·5498141·50·01< 0·0001* After cooking917·6670·3534·7134·70·0004* GHGE % increase due to cooking24·0 %7·3 %–Plant-based meals (n 25)Ready meals (n 14)Homemade meals (n 11)Up to supermarket shelf265271282165·20·95After cooking401·4254·1300·7176·80·07GHGE % increase due to cooking51·4 %6·6 %–GHGE, greenhouse gas emissions; gCO2e, gCO2 equivalents; N/A, not applicable; Prm-hc, P-value of difference between ready meals and equivalent home-cooked meals; Pab-pb, P-value of difference between animal-based and plant-based meals after cooking. Data represent medians and interquartile ranges (IQR). None of the retrieved recipes for the homemade version of the meals reported the use of microwave. *Cooking* generally resulted in a significant increase in GHGE across all meals and cooking methods ($P \leq 0$·05).*Statistical significance, adjusted using Bonferroni correction, was estimated at a P-value < 0·0027. No homemade meal recipe required the use of microwave, and hence no comparison among ready meals and homemade meals was feasible.
Overall, the most environmentally friendly and affordable (i.e. cheaper) products were plant-based home-prepared meals cooked on the gas or electric stove. Some examples include vegetable chow mein, ratatouille, spinach and potato curry with tomatoes and onion, vegetable curry or carrot and onion soup. In contrast, animal-based ready meals, either cooked in the oven or the microwave, produce the highest levels of GHGE and were the most expensive (online Supplementary Fig. 1).
## Discussion
This study explored how ready meals compared to equivalent home-cooked meals in terms of nutritional quality, GHGE and cost in dishes relevant to the UK market. All ready meals, but especially animal-based ready meals, had significantly higher levels of free sugars compared with equivalent home-cooked meals. In addition, ready meals had significantly higher GHGE than home-cooked meals up to the supermarket shelf, with cooking adding further GHGE, depending on the cooking method. Generally, ready meals costed significantly more (£0·$\frac{30}{100}$ g more) than their equivalent home-cooked meals. Animal-based oven-cooked ready meals had the highest levels of GHGE and were most expensive, whereas plant-based home-prepared meals cooked on the gas or electric stove had the lowest GHGE and costed least.
Diet-based studies have already shown that reductions in animal-based foods reduce GHGE, increase the nutritional quality and reduce the costs of total diets[30] but thus far food-based studies are mostly lacking. No previous papers have compared ready meals to equivalent home-cooked meals in terms of nutritional quality, GHGE and/or cost; when such indicators were used, the research focused either on ready meals or home-cooked meals[31,32]. From a nutritional perspective, we did not find that ready meals were higher in salt, in line with one study[33] whilst other studies did observe higher salt levels in ready meals[18,34,35]. However, it should be noted that the amount of salt added during cooking can vary, and furthermore, the salt content for home-cooked meals may be underestimated as many people cook with ‘salt to taste’ and may add more to the meal they consume. We did not find significant differences in the content of trans-fat, fibre and energy between ready meals and equivalent home-cooked meals, as has been observed in other studies[18,19,33]. This is relevant because previous studies have argued that increased consumption of ready meals was associated with a higher energy intake, poor compliance with national nutritional recommendations and abdominal obesity[36].
Our data on GHGE is in line with those presented by Reynolds [2020][37], who found cooking to contribute between 8 and 84 % to total GHGE, with the environmental impact of cooking meats being higher than cooking vegetables. In our data, the main differences in GHGE align with the well-documented differences between plant- and animal-based meals, and differences due to the cooking method and cooking time, which are usually shorter for plant-based meals than animal-based meals. However, our data also showed that different cooking methods differentially contributed to GHGE, with oven cooking producing most GHGE, but with other cooking methods like gas and electric stove, and microwave cooking, contributing less than 10 % of total GHGE. In addition, recipes for the same type of meal can vary considerably, thereby affecting the environmental impact[6]. For example, replacing meat with soya and seitan could reduce the environmental impact by up to 27 %[6]. In our study, homemade meals were cheaper, had lower GHGE and had a better nutritional quality up to a supermarket shelf, which may be partly due to differences in nutrient composition.
Large food producers and supermarkets can influence the way we eat by offering healthier, more environmentally sustainable and affordable choices. The sector of plant-based meals is currently the fastest-growing food category in the UK, with a growth of 92 % since 2018[38]. Furthermore, a recent study highlighted that adequate labelling of ready meals could help improve food consumption-related climate change and health issues[39]. Therefore, cooking instructions on the back of the packaging may encourage consumers to choose the cooking method causing the least GHGE. Using cooking methods such as slow cookers, pressure cookers and microwaves, all of which have a lower energy use, would significantly lower GHGE from home-cooking[27].
The strengths of this study include considering three dimensions (nutritional quality indicators, GHGE and cost) that are important for healthy, environmentally sustainable and affordable food choices. Previous studies have considered the estimation of GHGE of recipes or meals[37,40,41]; however, our current analysis also includes the cost of the meals. We also studied a larger number of ready meals and equivalent home-cooked meals than many previous studies, particularly concerning their GHGE[33]. We also considered the differences in GHGE and cost across animal- and plant-based meals, which is relevant considering the importance of moving towards less meat-intensive diets in order to reduce GHGE[39]. Lastly, this study used more up-to-date data than previous studies, which is essential as ready meals are constantly being reformulated based on salt and sugar reduction targets.
An important limitation of this study is that we did not include the cost of reheating or cooking in our analysis. This is important considering that home-cooked meals can be up to six times more expensive after cooking than ready meals[18,33], and consumers may purchase ready meals because these are quicker and cheaper to (re)heat in the microwave. Thus, whilst we found that ready meals, overall and those that are plant-based, cost significantly more than equivalent home-cooked meals, based on a large selection of meals, a previous study found that ready meals were no more expensive than buying the ingredients for home-cooked meals when considering the ten most frequently purchased ready meals in a sample of Scottish households[33]. Furthermore, we did not consider the cost of household labour, which might be typically valued at the wage level of the household meal preparer, and this could be significant[42]. These issues will need to be interpreted in the context of additional cooking costs. Another limitation of our study is that we did not examine artificial preservatives, stabilisers, colourings or flavours as part of the nutritional quality of the meals[43]. Also, no side dishes were considered, and all our measurements were expressed per 100 g of the meal. Thus, we did not consider the possible differences in portion sizes between home-cooked meals and ready meals. For example, ready meals are typically bought to provide for one or sometimes two portions, whilst home-cooked meals are often prepared as multiple portions. As in our study, values of GHGE were expressed per 100 g of product, and our calculations may have led to relatively higher GHGE (per 100 g of product) for ready meals, compared with equivalent home-cooked meals.
In conclusion, whilst the purchase and consumption of ready meals in the UK has increased in the past years, homemade meals have better nutritional characteristics, are cheaper and have lower GHGE, especially those that are plant-based. However, cooking can add to GHGE and the cost of preparing a ready or home-cooked meal, and better dissemination of this information to the consumer could potentially lead to more healthy, sustainable and affordable meal choices.
## Conflict of interest:
The authors have no conflict of interest.
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|
---
title: 'Dietary diversity and depression: cross-sectional and longitudinal analyses
in Spanish adult population with metabolic syndrome. Findings from PREDIMED-Plus
trial'
authors:
- Naomi Cano-Ibáñez
- Lluis Serra-Majem
- Sandra Martín-Peláez
- Miguel Ángel Martínez-González
- Jordi Salas-Salvadó
- Dolores Corella
- Camille Lassale
- Jose Alfredo Martínez
- Ángel M Alonso-Gómez
- Julia Wärnberg
- Jesús Vioque
- Dora Romaguera
- José López-Miranda
- Ramon Estruch
- Ana María Gómez-Pérez
- José Lapetra
- Fernando Fernández-Aranda
- Aurora Bueno-Cavanillas
- Josep A Tur
- Naiara Cubelos
- Xavier Pintó
- José Juan Gaforio
- Pilar Matía-Martín
- Josep Vidal
- Cristina Calderón
- Lidia Daimiel
- Emilio Ros
- Alfredo Gea
- Nancy Babio
- Ignacio Manuel Gimenez-Alba
- María Dolores Zomeño-Fajardo
- Itziar Abete
- Lucas Tojal Sierra
- Rita P Romero-Galisteo
- Manoli García de la Hera
- Marian Martín-Padillo
- Antonio García-Ríos
- Rosa M Casas
- JC Fernández-García
- José Manuel Santos-Lozano
- Estefanía Toledo
- Nerea Becerra-Tomas
- Jose V Sorli
- Helmut Schröder
- María A Zulet
- Carolina Sorto-Sánchez
- Javier Diez-Espino
- Carlos Gómez-Martínez
- Montse Fitó
- Almudena Sánchez-Villegas
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989703
doi: 10.1017/S1368980022001525
license: CC BY 4.0
---
# Dietary diversity and depression: cross-sectional and longitudinal analyses in Spanish adult population with metabolic syndrome. Findings from PREDIMED-Plus trial
## Body
The metabolic syndrome (MetS) is defined as a group of metabolic abnormalities that include central obesity, insulin resistance, dyslipidaemia and hypertension, which are risk factors for the development of CVD[1]. In addition, this metabolic alteration has been associated with an increased risk of developing other chronic diseases as cancer[2], neurodegenerative diseases[3] and mental disorders, such as depression[4]. Depression is a common mental disorder, particularly in older adults[5], being the third largest cause of years lived with disability in developed countries.
Some authors have pointed out that the modification of lifestyle factors, including inactivity and unhealthy dietary intake, could prevent and manage the progression of depression[6]. However, the most common treatments for depressive symptoms in late life is the use of antidepressive medications and psychotherapy, which are not effective in some patients and are a burden on health care utilisation and costs[7].
Regarding the relationship between diet and depression, several studies point out towards a bidirectional association, with the possibility of a reverse causality between them. On the one hand, subjects with depression have worse dietary habits[8] and on the other hand, healthy dietary patterns have been shown to be beneficial reducing the risk of depressive outcomes[9]. Hence, healthy dietary patterns have been shown to be beneficial reducing the risk of depressive outcomes. One possible explanation is that dietary quality might modulate several brain pathways including low-grade inflammation and oxidative stress, which intervene in the aetiology of depression[10]. Among the different dietary patterns, the strongest evidence for a reduced risk of depression have been found for Mediterranean diet. This fact could be explained by the high diversity of healthy food groups that characterises this dietary pattern, increasing the likelihood to meet nutritional requirements[11]. Despite of this, a recent meta-analysis have analysed a subset of studies that controlled for baseline symptoms of depression, reporting no association between diet quality and depression risk[12]. So, clear inconsistencies in establishing the diet–depression link still exist.
Dietary diversity (DD) has been universally identified as a key element of high-quality diets. The dietary diversity score (DDS) is a simple count of food groups consumed, in conformity with advices provided by dietary guidelines as indicators of nutritional adequacy worldwide. In patients with mood disorders, particularly prenatal and postpartum women[13], and in younger adult population[14] deficiencies have been found, for nutrients including Ca, vitamins B9, B12 and n-3 fatty acids.
DDS, an useful indicator of nutrient adequacy, has been found to be inversely associated with anxiety after adjusting for socio-economic and lifestyle factors[15]. International dietary recommendations in general, and the Spanish dietary guidelines in particular, promote a healthy diet to reduce the incidence of diet-related chronic diseases. The healthy message that the Spanish Society of Community Nutrition (SENC) conveys to the population is that ‘Diet should be balanced, moderate and varied’[16]. Meanwhile, the role of a varied diet over chronic diseases as obesity[17], cancer[18] or CVD[19] has been adressed, specifically the potential prevention of depression is yet to be determined. Understanding and addressing the possible role of DD in depressive symptoms can be of great public health importance.
To our knowledge, no previous study has focused on the relationship between the DD and mental health among older Spanish population with MetS. Hence, our research was designed to examine the cross-sectional and longitudinal (2-year follow-up) associations between DD and depressive symptoms in a cohort of Spanish older adults with MetS.
## Abstract
### Objective:
To examine the cross-sectional and longitudinal (2-year follow-up) associations between dietary diversity (DD) and depressive symptoms.
### Design:
An energy-adjusted dietary diversity score (DDS) was assessed using a validated FFQ and was categorised into quartiles (Q). The variety in each food group was classified into four categories of diversity (C). Depressive symptoms were assessed with Beck Depression Inventory-II (Beck II) questionnaire and depression cases defined as physician-diagnosed or Beck II >= 18. Linear and logistic regression models were used.
### Setting:
Spanish older adults with metabolic syndrome (MetS).
### Participants:
A total of 6625 adults aged 55–75 years from the PREDIMED-Plus study with overweight or obesity and MetS.
### Results:
Total DDS was inversely and statistically significantly associated with depression in the cross-sectional analysis conducted; OR Q4 v. Q1 = 0·76 (95 % CI (0·64, 0·90)). This was driven by high diversity compared to low diversity (C3 v. C1) of vegetables (OR = 0·75, 95 % CI (0·57, 0·93)), cereals (OR = 0·72 (95 % CI (0·56, 0·94)) and proteins (OR = 0·27, 95 % CI (0·11, 0·62)). In the longitudinal analysis, there was no significant association between the baseline DDS and changes in depressive symptoms after 2 years of follow-up, except for DD in vegetables C4 v. C1 = (β = 0·70, 95 % CI (0·05, 1·35)).
### Conclusions:
According to our results, DD is inversely associated with depressive symptoms, but eating more diverse does not seem to reduce the risk of future depression. Additional longitudinal studies (with longer follow-up) are needed to confirm these findings.
## Design of the study
The PREDIMED-Plus study is a randomised primary prevention trial involving twenty-two centres throughout Spain with a planned follow-up of 6 years. Participants were randomly assigned to two groups: intervention group and control group. The main objective of the clinical trial is to determine the effect on cardiovascular mortality of an intensive dietary advice for weight loss based on a traditional hypocaloric Mediterranean dietary pattern promoting physical activity and behavioural therapy (intervention group) v. Mediterranean-type dietary advice for CVD prevention in the context of usual health care (control group). More detailed information on the study protocol can be found in the publication by Martínez-González et al. [ 20]. The database used was updated on 26 June 2020.
## Ethics approval
The trial was registered at the International Standard Randomized Controlled Trial (ISRCTN: http://www.isrctn.com/ISRCTN89898870) with number 89898870 and registration date of 24 July 2014. All participants gave written informed consent, and the study was approved by the Research Ethics Committees from all recruitment centres, according to the ethical standards of the Declaration of Helsinki.
## Participants and data collection procedures
Eligible participants were men (aged 55–75 years) and women (aged 60–75 years), with overweight or obesity (BMI ≥ 27 and <40 kg/m2), who at baseline met at least three components of the MetS: TAG level ≥150 mg/dl, blood glucose ≥ 100 mg/dl or use of oral antidiabetic drugs, systolic blood pressure ≥130 mmHg and diastolic blood pressure ≥85 mmHg or use of antihypertensive drugs and/or HDL-cholesterol level <40 mg/dl for men and <50 mg/dl for women according to the harmonised criteria of the International Diabetes Federation and the American Heart Association and National Heart, Lung and Blood Institute[21] and without other neurological or endocrine disease active.
Of the 6874 participants enrolled in the PREDIMED-Plus study, only participants who completed a semi-quantitative FFQ and a depressive symptoms questionnaire (Beck Depression Inventory-II, Beck II) at baseline were included in the current analysis. Those participants with missing dietary data and with extreme energy intakes (<500 or >3500 kcal/d for women and <800 or >4000 kcal/d for men)[22] (n 227) at baseline were excluded. Among the available participants, we also excluded those who failed to complete the Beck II questionnaire at baseline (n 22). The final sample for the cross-sectional analysis was 6625 participants. For the longitudinal analysis, out of the eligible individuals, we excluded those with prevalent depression at baseline, those who had a Beck II score ≥18 points at baseline (n 1772), and those who did not complete the Beck II questionnaire after 2 years of follow-up (n 993). Finally, for the longitudinal analysis, 3860 participants were included (Fig. 1).
Fig. 1Flow chart of the study participants
## Dietary intake assessment
At baseline, trained dieticians filled out a validated 143-item semi-quantitative FFQ[23] in a face-to-face interview. The FFQ provides a list of foods commonly used by the Spanish population and asks about the consumption of these foods during the previous year. From this questionnaire, total energy and nutrient intake were calculated based on Spanish food composition tables[24,25].
## Dietary diversity score construction
The 143-item FFQ was also used to calculate an energy-adjusted DD score (DDS). This DDS was calculated by the method originally developed by Kant et al. [ 26] and recently reported by Farhangi et al. [ 27] and Cano-Ibáñez et al. [ 11,18,28]. DDS was calculated based on the method using the food groups recommended by the Spanish guidelines’ pyramid[16]. Table 1 shows a detailed description of food groups and subgroups considered in the DDS and their recommended consumption measured as servings/d.
Table 1Food groups and the recommended servings/d/week used in the dietary diversity score (DDS) according to the Spanish guidelinesFood groupsFood subgroupsRecommended servingsVegetables[1] Green vegetables: spinach, cruciferous, lettuce, green beans, eggplant, peppers and asparagus[2] Tomatoes[3] Yellow vegetables: carrots and pumpkin[4] Mushrooms2 servings/dFruits[1] Citrus fruits: orange[2] Tropical Fruits: banana, kiwi and grapes[3] Other seasonal fruits: Apple, peach, strawberries, watermelon and melon3 servings/dDairy products[1] Milk: low fat and high fat[2] Yogurt: low fat and high fat[3] Cheese: low fat and high fat2 servings/dCereals[1] Potatoes[2] Grain: bread, pasta, rice, and whole breakfast cereals4 servings/dProtein food groups[1] Legumes: peas, beans, lentils and chickpeas[2] White meats: poultry and rabbit[3] Fish: oily fish, white fish and other shellfish/seafood[4] Eggs[5] Nuts: almonds, pistachios and walnuts3 servings/week The non-recommended food groups (which should be consumed only exceptionally)[29] have not been included in the calculation of the DD. These are products with low nutritional content and unhealthy and, therefore, their variety is not desirable. These food categories include those foods containing refined sugars and alcohol (bakery products, ice cream, pastries, sweetened beverages, chocolate, fruit-flavoured drinks and alcohol beverages) and food groups high in salt, cholesterol and/or trans-fat and saturated fat (butter, cream, fried foods, unhealthy vegetable fats, processed meats, sauces, ready meals, condiments and snacks). Therefore, we only analysed diversity of recommended food groups[30]. To be counted as a consumer for any of the food group categories reported previously, the participant should consume at least one-half of the recommended serving per d for each of the items included in the food group, scoring with 2 points for each item. A maximum score of 2 was awarded to each of the five groups and so that each participant received a score ranging from 0 (minimum) to 10 (maximum). To calculate the score of each group, the number of subgroups consumed was divided by the total number of subgroups in each main group, and then it was multiplied by 2. The sum of the scores of the five main groups is reported as the total score. The score was adjusted for total energy intake according to the residual method proposed by Willett et al. [ 22], due to the general concern that high food variety might be a consequence of overconsumption of energy[31]. For example, if the Spanish nutritional recommendation advises a usual vegetable intake of two servings per d, for each vegetable item, participants should consume at least one serving/d). Thus, if the consumption per d for a vegetable item is lower than one serving, the value for this item will be 0; conversely, if the consumption is higher than one serving, the value will be 2. For the five considered groups, the procedure is similar. Finally, DDS was categorised in quartiles (Q) and the cut-off points were 3·9, 4·6, 5·4 and 8·0. The variety in each food group was classified into four categories (C) (C1 = 0 points), (C2 => 0–≤0·5 points), (C3 => 0·5–<1 points) and (C4 ≥ 1 point).
## Outcome assessment
Depressive symptoms were collected at baseline and at 1 and 2 years of follow-up visits by trained PREDIMED-Plus staff through a validated questionnaire, the Beck-II. The Beck II includes twenty-one multiple-choice questions, rating on a scale of 0 to 3 according to symptom severity. Total score of the Beck-II questionnaire ranges from 0 to 63 points[32]. Prevalent depression was defined as the presence of depressive symptoms at baseline (Beck-II ≥ 18 points) or a current depression diagnosis. The depression diagnosis was collected at baseline, and it was defined as a self-reported lifetime medical diagnosis of depression. Changes in depressive symptomatology were calculated as the difference in Beck-II questionnaire score between the baseline and the 2-year score.
## Covariate assessment
At baseline and 1-year of follow-up visits, participants filled out a general questionnaire to provide data on lifestyle habits and socio-economic factors. Sociodemographic and lifestyle variables were categorised as follows: educational level (three categories: primary level, secondary level and tertiary level which includes university studies), civil status (two categories: married or not, which includes widowed, divorced/singled or others) and whether participants lived alone or not. Other lifestyle variables such as smoking habits (three categories: smoker, never smoker and current smoker), leisure-physical activity status (three categories: active, moderately active and less active) and sleep duration (h/d) were also recorded. Regarding the hours of sleep, participants reported both the average amount on weekdays and weekends. The non-validated and open question used was: ‘How many hours do you sleep on average per d on weekdays and weekends?’ Leisure-time physical activity was measured by the short form of the Minnesota Leisure Time Physical Activity Questionnaire validated for the Spanish population[33,34]. Leisure-time activities were computed by assigning a metabolic equivalent score to each activity, multiplied by the time spent for each activity and summing up all activities. Time spent and intensity in leisure-physical activity was calculated as a product of the frequency and duration of six types of activities categorised into three intensities: light PA (< 4 Metabolic Equivalent Tasks (MET)) – walking at a slow/normal pace; moderate PA (4–5·5 MET) – brisk walking and gardening; and vigorous PA (≥ 6·0 MET) – walking in the countryside, climbing stairs, exercise or playing sports.[35]. Anthropometric parameters were measured in every follow-up visit according to the PREDIMED-Plus protocol. The measures collected were height (using a wall-mounted stadiometer, in m2) and weight (using high-quality electronic calibrated scales, in kg). BMI was calculated as weight in kilograms by the square of height in metres (kg/m2). Finally, personal history of baseline chronic diseases (hypertension, dyslipidemia and type 2 diabetes) was collected from the patients’ medical records.
## Statistical analysis
Statistical analyses were performed using STATA software (version 15.0, StataCorp., LP). For the current study, we used the PREDIMED-Plus longitudinal database generated on 26 June 2020 (202006290731_PREDIMEDplus). Data are presented as mean and standard deviations for continuous variables or number and percentages for categorical variables. Cut of points for DDS were defined by quartiles (Q1, low diversity intake and Q4, high diversity intake). Cut of points for each food groups were defined by categories (C1, low diversity intake and C4, high diversity intake).
## Performance of cross-sectional analysis
Logistic regression models were fitted to assess the relationship between the energy-adjusted total DDS and each of the food groups and the prevalence of depression at baseline (cross-sectional analysis). OR and their 95 % CI were calculated considering the lowest quartile as the reference category. All cross-sectional analyses were adjusted for potential confounders based on prior knowledge: sex, age, smoking habits, physical activity, educational level, BMI, living alone, civil status, sleep duration, presence of chronic diseases, allocation group and recruitment centre. Moreover, in order to assess the effect of diet quality over depressive symptomatology at baseline, we performed an ancillary analyses, excluding all depression cases in which age of depression diagnosis was not available or in which the diagnosis date was very remote (more than 10 years before enrolment) (n 1378). These data were obtained through medical records.
## Performance of longitudinal analysis
The association between the baseline and their changes was evaluated through multivariable regression models adjusted for the same potential confounders mentioned above plus depressive symptomatology at baseline. We also analysed the possible interaction between DDS and allocation group (intervention and control group). Regression coefficients (β) and their 95 % CI were calculated. Finally, the exclusion of individuals with high baseline depressive symptomology could limit the possibility of finding longitudinal associations. For this reason, we performed an ancillary analysis not excluding those subjects with a Beck-II score higher than 18 points at baseline or with prevalent depression diagnosis at baseline. Statistical significance was set at $P \leq 0$·05.
## Baseline characteristics of the study participants according to dietary diversity score quartiles
This study analysed a sample of 6625 participants from the PREDIMED-Plus cohort. Table 2 provides an overview of the sample characteristics according to baseline DDS quartiles. There were statistically significant differences in the distribution of sociodemographic and lifestyles characteristics across DDS quartiles. Compared to those in the higher quartile of diversity, participants in the lowest quartile were more likely to be younger, male, current smokers and with higher educational level (tertiary school).
Table 2Baseline characteristics of PREDIMED-Plus participants according to quartiles of DDS (total population = 6625)Q1 (n 1657)Q2 (n 1656)Q3 (n 1656)Q4 (n 1656) P-value n % n % n % n %Age (years) Mean64·164·865·265·8<0·001 sd 5·14·94·84·7Sex Male112267·791755·477746·960236·4<0·001Smoking habits Current smoker29717·919812·017310·51539·2<0·001 Former smoker80248·474444·972543·859736·1 Never smoker54933·170842·875345·589854·2 Without information90·560·450·380·5Physical activity Less active102361·999260·098960·094957·60·296 Moderately active29517·930618·532219·532619·8 Active33920·335821·534520·638122·6Educational level Tertiary42325·536422·034220·732519·6<0·001 Secondary53732·448529·345327·443626·3 Primary69742·180748·786152·089554·1Civil status Married126676·7126076·4125275·8128677·80·028 Living alone (yes)19411·718911·421913·221412·90·307BMI (kg/m2) Mean32·632·532·532·50·727 sd 3·43·43·53·5Presence of diseases Hypercholesterolemia113768·6115769·9114469·1115069·40·412 Type 2 diabetes44026·644526·946828·347028·40·598 Hypertension138283·4137583·0140684·9136282·30·229 *Prevalence of depressive symptoms42425·643726·446227·944927·10·479Baseline score of Beck Mean8·28·28·59·00·005 sd 7·47·47·57·52-year score of Beck Mean6·56·56·87·20·020 sd 7·06·87·17·0*Prevalence of depressive symptoms: prevalence of depressive symptoms was defined as the presence of depressive symptoms at baseline (Beck≥18 points) or a current depression diagnosis. DDS cut-off points for each quartile: (Q1 = 0.8–3.9, Q2 = 4.0–4.6, Q3 = 4.7–5.4 and Q4 = 5.5–8.0).Values are presented as means ± sd for continuous variables and n (%) for categorical variables. Pearson’s chi-square test was performed for categorical variables and ANOVA test for continuous variables (Q1, less diversity; Q4, more diversity).
## Cross-sectional associations between dietary diversity score and variety in food intake and depressive symptomatology (assessed by Beck-II score at baseline point)
As seen in Table 3, total DDS was not associated with depressive symptomology (assessed by Beck-II score) at baseline. Considering each of the components of the total DDS separately, we found significant associations between the consumption of high diversity of groups of vegetables and depressive symptoms compared to the lowest diversity category: β-coefficients (95 % CI) for successive categories (C2–C4 v. C1) were −0·86 (−1·58, −0·15); −0·81 (−1·47, −0·14) and −0·69 (−1·37, −0·01), respectively.
Table 3Multivariable linear regression models for the association between total DDS and variety in food intake and Beck Depression Inventory-II score at baseline in the PREDIMED-Plus study participants. β-Coefficients (95 % confidence intervals) (total population = 6625)Total DDSQ1 (n 1657)Q2 (n 1656)Q3 (n 1656)Q4 (n 1656) P for trend β 95 % CI β 95 % CI β 95 % CI β 95 % CIModel 10Ref.−0·51−1·00, −0·01−0·50−1·00, 0·02−0·33−0·84, 0·180·231Model 20Ref.−0·45−0·95, 0·04−0·46−0·96, 0·04−0·24−0·75, 0·260·375Vegetable groupC1 (n 551)C2 (n 1319)C3 (n 2492)C4 (n 2263)Model 10Ref.−0·89−1·61, −0·17−0·85−1·52, −0·18−0·63−1·31, 0·050·506Model 20Ref.−0·86−1·58, −0·15−0·81−1·47, −0·14−0·69−1·37, −0·010·335Fruit groupC1 (n 848)C2 (n 4529)C3 (n 779)C4 (n 469)Model 10Ref.−0·27−0·80, 0·26−0·26−0·97, 0·45−0·48−1·30, 0·350·312Model 20Ref.−0·25−0·78, 0·29−0·32−1·03, 0·40−0·72−1·55, 0·110·104Cereal groupC1 (n 353)C2 (n 4791)C3 (n 1396)C4 (n 85)Model 10Ref.−0·04−0·82, 0·750·32−0·52, 1·170·52−1·20, 2·240·123Model 20Ref.−0·41−1·21, 0·38−0·16−1·04, 0·730·30−1·46, 2·070·464Proteins groupC1 (n 25)C2 (n 1258)C3 (n 2787)C4 (n 2555)Model 10Ref.−1·79−4·67, 1·08−2·03−4·89, 0·83−2·03−4·89, 0·820·282Model 20Ref.−1·83−4·67, 1·00−2·15−4·98, 0·67−2·24−5·07, 0·590·095Dairy groupC1 (n 690)C2 (n 2454)C3 (n 2622)C4 (n 859)Model 10Ref.0·49−0·12, 1·100·40−0·21, 1·010·55−0·18, 1·280·327Model 20Ref.0·36−0·25, 0·970·32−0·30, 0·930·29−0·45, 1·030·638C, category; DDS, dietary diversity score; Q, quartile (Q1, less diversity; Q4, more diversity).Values are presented as β-coefficients and 95 % CI for Beck Depression Inventory-II score at baseline as continuous variable according to total DDS and variety in food intake. Model 1: Adjusted for sex and age. Model 2: Additionally adjusted for energy intake, smoking habits, physical activity, educational level, BMI, living alone, civil status, sleep duration and presence of chronic diseases. Values presented in bald showed a statistically significant association ($P \leq 0$·05).DDS cut-off points for each quartile: (Q1 = 0·8–3·9, Q2 = 4·0–4·6, Q3 = 4·7–5·4 and Q4 = 5·5–8·0).The variety in each food group was classified into four categories (C): (C1 = 0 points), (C2 => 0–≤0·5 points), (C3 => 0·5–<1 points) and (C4 ≥ 1 point).
## Cross-sectional associations between dietary diversity score and variety in food intake and prevalence of depression
Total DDS was inversely and significantly associated with prevalence of depression in logistic analysis (Table 4). Participants in the highest quartile of total DDS showed lower odds of depression as compared to those participants in the lowest quartile (OR = 0·76, 95 % CI (0·64, 0·90)). Regarding the specific components of the total DDS, high (C3) or very high (C4) diversity of groups of vegetables reduced the odds of depression (OR = 78, 95 % CI (0·63, 0·97)) and (OR = 0·75, 95 % CI (0·60, 0·94)), respectively. In the case of proteins, the OR (95 % CI) were 0·26 (0·11, 0·61) (C3) and 0·24 (0·10, 0·56) (C4) as compared to the reference category (C1). For cereals, only moderate diversity in intake was associated with lower probability of depression. The OR (95 % CI) for C2 and C3 were 0·69 (0·54, 0·89) and 0·71 (0·54, 0·94), respectively.
Table 4Multivariable logistic regression models for the association between total DDS and variety in food intake and prevalence of depression in the PREDIMED-Plus study participants. Odds ratios (95 % confidence intervals) (total population = 6625)Total DDSQ1 (n 1657)Q2 (n 1656)Q3 (n 1656)Q4 (n 1656) P for trend * OR95 % CIOR95 % CIOR95 % CIOR95 % CIModel 11Ref.0·890·75, 1·040·870·74,1·020·730·62, 0·87<0·001Model 21Ref.0·920·78, 1·080·880·75, 1·040·760·64, 0·900·001Vegetable groupC1 (n 551)C2 (n 1319)C3 (n 2492)C4 (n 2263)Model 11Ref.0·820·65, 1·040·760·61, 0·940·720·58, 0·900·004Model 21Ref.0·830·65, 1·050·780·63, 0·970·750·60, 0·940·017Fruit groupC1 (n 848)C2 (n 4529)C3 (n 779)C4 (n 469)Model 11Ref.0·890·75, 1·060·800·63, 1·000·810·62, 1·060·051Model 21Ref.0·910·76, 1·080·810·64, 1·030·790·60, 1·040·043Cereal groupC1 (n 353)C2 (n 4791)C3 (n 1396)C4 (n 85)Model 11Ref.0·690·54, 0·870·710·55, 0·920·720·41, 1·250·197Model 21Ref.0·690·54, 0·890·710·54, 0·940·810·46, 1·450·320Proteins groupC1 (n 25)C2 (n 1258)C3 (n 2787)C4 (n 2555)Model 11Ref.0·310·13, 0·710·260·11, 0·590·230·10, 0·52<0·001Model 21Ref.0·310·13, 0·720·260·11, 0·610·240·10, 0·56<0·001Dairy groupC1 (n 690)C2 (n 2454)C3 (n 2622)C4 (n 859)Model 11Ref.0·970·79, 1·190·890·73, 1·080·880·69, 1·110·105Model 21Ref.0·990·81, 1·210·930·76, 1·150·910·72, 1·170·292C, category; DDS, dietary diversity score; Q, quartile (Q1, less diversity; Q4, more diversity).*DDS/food group measure as continuous variables in order to estimate P for trend. Values are presented as OR and 95 % CI for prevalence of depression (≥18 p at Beck Depression Inventory II and/or a current depression diagnosis) as categorical variable according to total DDS and variety in food intake. Model 1: Adjusted for sex and age. Model 2: Additionally adjusted for energy intake, smoking habits, physical activity, educational level, BMI, living alone, civil status, sleep duration and presence of chronic diseases. Values presented in bald showed a statistically significant association ($P \leq 0$·05).DDS cut-off points for each quartile: (Q1 = 0·8–3·9, Q2 = 4·0–4·6, Q3 = 4·7–5·4, and Q4 = 5·5–8·0).The variety in each food group was classified into four categories (C): (C1 = 0 points), (C2 => 0–≤0·5 points), (C3 => 0·5–<1 points) and (C4 ≥ 1 point).
In ancillary analyses performed, we excluded all depression cases in which age of depression diagnosis was not available or in which the diagnosis date was very remote (more than 10 years before enrolment) (n 1378). In this subsample (n 5247, cases = 394), the results were no longer significant although the magnitude of effect was quite similar to that observed in the overall sample. OR and 95 % CI for successive quartiles of DDS were 1 (ref.), 0·92 (0·68, 1·24), 0·87 (0·64, 1·17) and 0·81 (0·60, 1·10).
## Longitudinal associations between total dietary diversity score and variety in food intake and changes in depressive symptomatology after 2 years of follow-up
The association between total DDS and variety in food intake and changes in depressive symptomatology after 2 years of follow-up is presented in Table 5. We did not find any significant association between total DDS or each of the food groups considered and changes in depressive symptomatology after 2 years of follow-up even after adjustment for potentially confounding factors, except for the vegetable group (β-coefficient for C4 = 0·70, 95 % CI (0·05, 1·35)), which, unexpectedly, showed a positive association with an increase of depressive symptomatology over time.
Table 5Change in Beck Depression Inventory-II score across quartiles of DDS and variety in food intake after 2 year of follow-up in the PREDIMED-Plus trial. β-Coefficients and 95 % confidence intervals (total population = 3860)Total DDSQ1 (n 908)Q2 (n 947)Q3 (n 984)Q4 (n 1021) β 95 % CI β 95 % CI β 95 % CI β 95 % CIModel 10Ref.−0·04−0·47, 0·380·03−0·40, 0·450·12−0·30, 0·55Model 20Ref.0·02−0·41, 0·440·08−0·35, 0·500·22−0·21, 0·65Vegetable groupC1 (n 308)C2 (n 713)C3 (n 1430)C4 (n 1409)Model 10Ref.0·59−0·10, 1·280·71−0·07, 1·350·57−0·07, 1·21Model 20Ref.0·62−0·07, 1·320·720·07, 1·370·700·05, 1·35Fruit groupC1 (n 475)C2 (n 2611)C3 (n 482)C4 (n 292)Model 10Ref.0·18−0·32, 0·69−0·08−0·73, 0·58−0·38−1·13, 0·37Model 20Ref.0·12−0·39, 0·64−0·11−0·78, 0·56−0·35−1·12, 0·42Cereal groupC1 (n 173)C2 (n 2774)C3 (n 860)C4 (n 53)Model 10Ref.−0·55−1·34, 0·24−0·38−1·22, 0·46−0·63−2·21, 0·96Model 20Ref.−0·52−1·33, 0·29−0·31−1·19, 0·57−0·61−2·26, 1·04Proteins groupC1 (n 8)C2 (n 638)C3 (n 1614)C4 (n 1600)Model 10Ref.−2·13−5·72, 1·45−2·09−5·67, 1·48−2·52−6·10, 1·05Model 20Ref.−2·30−5·88, 1·28−2·26−5·83, 1·30−2·63−6·20, 0·94Dairy groupC1 (n 397)C2 (n 1442)C3 (n 1510)C4 (n 511)Model 10Ref.−0·43−1·00, 0·14−0·27−0·85, 0·30−0·52−1·20, 0·16Model 20Ref.−0·39−0·97, 0·19−0·21−0·80, 0·38−0·45−1·15, 0·25DDS, dietary diversity score; Q, quartile (Q1, less diversity; Q4, more diversity).Values are presented as β-coefficients and 95 % CI for changes in depressive symptomatology after 2 years of follow-up as continuous variable according to total DDS.Model 1: Adjusted for sex and age. Model 2: Additionally adjusted for depressive symptomatology at baseline, smoking habits, physical activity, educational level, BMI, living alone, civil status, sleep duration, presence of chronic diseases, allocation group and recruitment centre. Values presented in bald showed a statistically significant association ($P \leq 0$·05).DDS cut-off points for each quartile: (Q1 = 0·8–3·9, Q2 = 4·0–4·6, Q3 = 4·7–5·4 and Q4 = 5·5–8·0).The variety in each food group was classified into four categories (C): (C1 = 0 points), (C2 => 0–≤0·5 points), (C3 => 0·5–<1 points) and (C4 ≥ 1 point).
Considering that the allocation group could exert an interaction with DDS and/or variety in food in depression, we explored this fact in the multivariate analysis. This variable was not an ‘effect modifier’ in the association between the changes in depressive symptomatology and DDS/food groups (P for interaction >0·05) (data not shown). In order to avoid that the exclusion of individuals with high baseline depressive symptomology or with prevalent depression at baseline limits the possibility of finding longitudinal associations, we performed an ancillary analyses, not excluding those subjects with a Beck-II punctuation higher than 18 points at baseline or with prevalent depression at baseline. In the subsample analysed, the results obtained were not significant; however, the magnitude of the effect observed was quite similar to that found in the overall sample.
## Discussion
The present analysis was conducted as an observational prospective cohort study within the PREDIMED-Plus trial. In the cross-sectional analysis, total DDS was inversely associated with prevalent depression. Thus, study participants with higher DD (Q4) showed a significant decrease in the odds of depression compared to participants with lower DD (Q1). Taking into account each of the components of the total DDS, the consumption of a high diversity of vegetables, cereals and proteins also showed an inverse association with prevalence of depression in cross-sectional analyses. Nevertheless, in the longitudinal analysis, after 2 years of follow-up we did not find any significant association, except for the vegetable group, which, unexpectedly showed a positive association with an increasing risk of depressive symptomatology over time.
Some authors have pointed out that monotonous and unhealthy dietary patterns are directly associated with a higher risk of depression in community-dwelling adults[36]. According to our cross-sectional results, this study primarily showed that the variety of some food’s groups is related to lower prevalence of depression, particularly for vegetables, cereals and proteins diversity. A possible explanation for this finding could be that these food groups have a specific role against oxidative stress and brain signalling which could contribute to reduce depression in adults[36]. Particularly, the beneficial role of dietary fibre (main component of some food groups as vegetables, fruits and whole cereals) in the prevention of depressive disorders maybe related with gut microbiota composition and activity, including some mechanisms linked with the gut–bran axis, immune, neural and metabolic pathways involved in depression[37,38]. For instance, whole grains and vegetables are rich sources of fibre, antioxidant vitamins and flavonoids; meanwhile, protein food (fish and seafood, white meat, legumes, nuts and eggs) contains folate and B-vitamins. Furthermore, these food groups are important components of the Mediterranean diet, which has been extensively reported with lower likelihood of depressive symptoms in older adults[39,40].
In nutritional epidemiology, dietary pattern analysis has emerged as an alternative and complementary approach to examining the relationship between diet and the risk of chronic diseases. Instead of looking at individual nutrients or foods, pattern analysis examines the effects of overall diet[41]. This approach is able to assess the overall food pattern because it goes beyond nutrients or foods and examines the effects of the overall diet, capturing a wide range of potential interactions between different nutrients and foods[41]. According to this concept, we constructed a DDS originally developed by Kant et al. [ 26] that reflects the diversity of food and provides greater knowledge about the dietary pattern in an objective way.
Our cross-sectional results showed that total DDS had an inverse association with depression at baseline. Participants in the highest DDS quartile showed a significantly lower depression prevalence compared to those participants in the lowest quartile. The results of the present study are in line with previous studies which employed self-reported questionnaire to evaluate depressive symptomatology that have shown the same trend in a cohort of Chinese pregnant women[15] in a cohort of a Japanese community-dwelling aged 65 years or older[42] and also, in the PREDIMED-Plus cohort[43]. This association could be related to the fact that a dietary pattern which contains more healthy food sources of major nutrients, such as vitamins and minerals, would decrease the risk of depression given that nutrients may affect brain development and functioning as we mentioned previously[44,45].
However, we have to highlight the fact that the reported analyses are cross-sectional. In this sense, a cross-sectional study does not provide the temporal relationship between food intake and depression. That is, nutrition could play an important role in the development, course and treatment of depression, but at the same time depressive symptoms might also predict the adoption of poor diet (‘reverse causality’)[46]. In fact, some authors have pointed out that depressed individuals tend to have unhealthy behaviours such as engaging in less physical activity and poor dietary habits[47]. Either way, recent meta-analyses have indicated that dietary interventions based on adherence to healthy dietary patterns produce not only a reduction in depressive symptoms but also a lower risk of developing depressive symptoms in non-clinical populations[48].
Although an inverse association was observed in cross-sectional analyses, we did not find any statistically significant association between total DDS (or the variety of food groups) and depressive symptomatology after 2 years of follow-up, except for the variety of vegetable food group. Although some prospective studies have pointed out that the intakes of some food groups, fundamentally fruits and vegetables and protein food groups (meat and fish), are protective against (incident) depression and depressive symptoms in non-European elderly populations[49], several methodological aspects such as the use of different questionnaires, the measure of total intake instead of DD, the disease induction time or the brevity in the follow-up period could explain the differences observed between our study and other published analyses. In line, with our longitudinal findings, the MooDFOOD randomised clinical trial reported that among overweight or obese adults with subsyndromal depressive symptoms and multinutrient supplementation compared with placebo did not reduce episodes of major depressive disorder during 1 year[50].
The current study has some limitations that should be noted. First, the results cannot be extrapolated to other populations, as the PREDIMED-Plus study population (participants with overweight or obesity and MetS) is not representative of the general population; however, our study population represents a significant proportion of current Western societies. Second, although the FFQ is a nutritional validated tool[23], self-reporting questionnaires, in combination with memory loss of older participants, might lead a no differential misclassification bias. Nevertheless, this bias would tend to the null value, so the association would be greater than observed. Moreover, we excluded participants with energy intakes outside of predefined limits proposed by Willet et al. [ 22] using in addition the residual method in order to adjust for energy intake. Third, the DDS is a simple count of food groups consumed developed as indicator of nutritional adequacy that excludes non-recommended food products that are high in sugar, saturated fatty acids and meats owing to the high-energy density of these foods, as well as their low-nutrient density with high levels of Na, sugar and saturated fat. Thus, we considered that any intake of these not recommended food products would not increase DD. Despite this, we have not distinguished the subgroups foods following the original categorisation proposed by other authors[17,26]. We have previously shown that this score which evaluates DD is correlated to better micronutrient intake and overall dietary quality in the Spanish older adult population[11,18].
Fourth, a selection bias may be present, since after 2 years of follow-up, only the healthiest participants would remain in the longitudinal study, producing an attenuation of the association found. Furthermore, significant associations were found only in cross-sectional analysis, but not in longitudinal, so we cannot elucidate a possible reverse causality. Finally, the follow-up time considered (only 2 years) is probably too short to assess changes in the primary outcome.
However, our study presents several strengths that enhance our findings. We used a repeated and validated measurement of the outcome over 2 years. Another strength is that, besides the use of a DDS that provides a more intuitive view of the whole dietary pattern, we also examined the variety of each food group, which allowed us to identify some of them as important components linked to depression. Another strength is the large sample size with a multicentre design and a longitudinal approach. Finally, the considerable amount of participant information collected using a standardised protocol that reduces information bias regarding reported food intakes, sociodemographic characteristics and lifestyle variables are other strengths that should be taken into account.
Our results suggest that recommending diets with high diversity of vegetables, grains and protein food groups (fish/seafood, white meat, nuts, eggs and legumes) may represent an effective approach to improve depression outcomes in community-dwelling population with overweight/obesity and MetS. That is, in people with depressive symptoms fostering dietary patterns such as the MedDiet would presumably result in a far greater impact over prevalence and symptomatology on depression. Nevertheless, these associations were only found in cross-sectional analysis. It is necessary to assess the entire cohort for longer in order to establish significant associations between DD and depression status.
In summary, our study found that higher DDS, and in particular, a high diversity intake of vegetables, cereals and proteins (fish/seafood, legumes, nuts, eggs and white meat) was inversely associated with depression status at baseline in community-dwelling older Spanish people. However, these result did not replicate in the longitudinal analysis. For that reason, further longitudinal studies with longer follow-up are needed to confirm our findings and deepen the understanding about the relationship between DD and depression status.
## Conflicts of interest:
Jordi Salas-Salvadó reports serving on the board of and receiving grant support through his institution from International Nut and Dried Fruit Council; receiving consulting personal fees from Danone, Font Vella Lanjarón, Nuts for Life and Eroski; and receiving grant support through his institution from Nut and Dried Fruit Foundation and Eroski. Emilio Ros reports grants, non-financial support and other fees from California Walnut Commission and Alexion; personal fees from California Walnut Commission and Alexion; personal fees, non-financial support and other fees from Aegerion and Ferrer International; grants and personal fees from Sanofi Aventis; grants from Amgen and Pfizer and personal fees from Akcea and Amarin, outside of the submitted work. Xavier Pintó reports serving on the board and receiving consulting personal fees from Sanofi Aventis, Amgen and Abbott laboratories; receiving lecture personal fees from Esteve, Lacer and Rubio laboratories. Lidia Daimiel reports grants from Fundación Cerveza y Salud. All other authors declare no conflicts of interests.
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|
---
title: Content of lunchboxes of Dutch primary school children and their perceptions
of alternative healthy school lunch concepts
authors:
- Frédérique C Rongen
- Ellen van Kleef
- Monique H Vingerhoeds
- Jacob C Seidell
- Sanne Coosje Dijkstra
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989704
doi: 10.1017/S1368980022002282
license: CC BY 4.0
---
# Content of lunchboxes of Dutch primary school children and their perceptions of alternative healthy school lunch concepts
## Body
Children in most Western countries often do not meet the recommended dietary guidelines that increases their risk of diet-related chronic diseases as obesity[1]. This is also the case in the Netherlands where most children eat and drink too many snacks and drinks high in sugar and consume too much fat and salt[2]. Taking into account that the eating habits of children established in childhood track into later life, it is important to teach children healthy eating habits early on[3,4].
A healthy diet for children can be stimulated in various settings, for example in the home, neighbourhood and school environment. From the age of 4, Dutch children spend a large amount of their time at school[5]. Therefore, public health interventions organised in school settings may contribute to healthy dietary habits. In addition, school interventions have the potential to reach children from all socio-economic backgrounds and therefore reduce the observed socio-economic inequalities in dietary intake[6,7]. Effective methods for schools to promote a healthier diet for children are nutrition education, rules for healthier foods and drinks or offering a healthy school-provided meals[8]. A study performed in the Netherlands on differences between the content of home packed lunches brought to school compared with consuming lunch at home showed that children who brought a home-packed lunch to school had a higher intake of sugar-sweetened beverages (defined as carbonated soft drinks, other non-carbonated sugar-sweetened drinks (water-based beverages that contain sugar) and sport drinks[9]) during lunch than those who ate their lunch at home[10]. However, both lunches consumed at school and at home contained a very low quantity of fruit and vegetables[10]. Other studies also showed that lunchboxes contained large amounts of energy-dense foods and drinks high in sugar[11,12]. However, most information about the content of the lunch at schools is via self-reported data of the parents or children and an objective measurement is lacking.
School-provided lunches have proven to be effective in improving children’s diets and often have a better nutritional quality than packed lunches brought from home(3,13–19). A study among Danish children showed improvement in the overall dietary intake when their packed lunches were replaced by a school-provided meal for a period of 3 months. Children participating in this school lunch program consumed half the amount of sugar, Na and saturated fat during lunch compared with those taking a packed lunch to school. Furthermore, most children participating in the school lunch program ate vegetables while this was only 8 % of the children who brought a packed lunch[8]. A study in Norway showed that serving a free school meal increased children’s intake of healthy foods for a period of a year[20], and a study in Sweden showed that healthy school lunches have a positive contribution to intake of children[21].
In contrast to many other countries, primary schools in the Netherlands do not serve school-provided lunches. Not so long ago most Dutch children either eat their lunch at home and had a more traditional timetable in which there was a morning and an afternoon schedule in school, separated by a long lunch break of 60 to 90 min during which children could go home to eat lunch. However, over the past few years, an increasing number of schools has shifted towards a continuous timetable, which means that all school days will last from morning to halfway the afternoon with a short 15 min lunch break where all children eat their packed lunch from home at school. This transition, which makes that more Dutch children eat their lunch at school, creates an opportunity for the introduction of a healthy school-provided lunch.
When developing a healthy school-provided lunch program from scratch, understanding children’s preferences and support for alternative school lunch concepts is crucial for the acceptance of such a considerable change. Research has shown that children are able to express their views when it comes to their food choice[22]. Our recent qualitative study showed that Dutch children, parents and school staff are open towards the idea of a school-provided healthy lunch. To smooth a potential transition to a new and unfamiliar lunch situation, it is important to have a better insight in the content of children’s current lunch and their support for a set of alternative school lunch concepts. This support can be critical for implementation and can increase the acceptance of such changes. To our knowledge, no research has previously been done on children’s specific preferences and support regarding a healthy school lunch and alternative school lunch concepts in the Netherlands. Also outside the Dutch setting, understanding and involving children in such major potential changes is relevant in order to increase the chance of success of school meal programs and adapt school meal programs to the needs and preferences of children. Therefore, the aim of this study is twofold. First, we investigated the content of the school lunchboxes of Dutch primary school children. Second, we examined children’s support and preferences for six healthy school lunch concepts varying in type of food served and presentation mode. Besides, we examined whether the lunch content and the support and preferences varied depending on sex, educational group and migration background of the children.
## Abstract
### Objective:
To investigate the content of lunchboxes of primary school children and to examine children’s support and preferences for alternative healthy school lunch concepts.
### Design:
A cross-sectional study among Dutch children from seven primary schools. The content of the lunchboxes was assessed by photographs. Support and preferences for alternative lunch concepts were examined via a self-reported questionnaire. Linear regression analyses were used to investigate the associations between children’s support and preferences and sex, educational group and migration background.
### Setting:
The Netherlands.
### Participants:
Primary school children.
### Results:
A total of 660 children were included (average 9·9 years old). Most lunchboxes contained sandwiches and a drink. Few lunchboxes contained fruit or vegetables. The alternative school lunch concepts elicited mixed support among children. The lunch concepts ‘Sandwiches prepared by the children themselves’ and a ‘hot lunch buffet’ had the highest mean support, while the concept ‘a healthy lunch brought from home’ was the most preferred concept. Small significant differences were observed depending on sex, educational group and migration background.
### Conclusion:
Lunchboxes of Dutch children contained sandwiches and a drink but rarely fruit and vegetables. Among different alternatives, children reported the highest support for the preparation of their own sandwiches in class or a hot lunch buffet. Future studies should investigate if these alternative lunch concepts improve the dietary intake of children.
## Design and procedure
This study is part of the Healthy School Lunch project in the Netherlands[23]. The overall aim of this project is to encourage healthy eating behaviour of children at primary schools by offering a healthy school lunch, based on the Dutch guidelines for a healthy diet[24]. In this part a cross-sectional study design was used to examine the content of children’s lunchboxes and their support and preferences for different healthy school lunch concepts in primary schools in two cities in the Netherlands including Amsterdam with approximately 854 000 habitants and Ede, with approximately 115 000 habitants. Data were collected between September and November 2017. The study was approved by the Social Ethical Commission of Wageningen University Research, the Netherlands. Most schools in Amsterdam (206 of 221) as well as schools in Ede with a known interest in nutrition were approached by email to inform them about the study. Follow-up phone calls were made until a sample of different schools (e.g. size and neighbourhood) participated. The following selection criteria were used for the inclusion of the schools: [1] schools in primary education; [2] schools in different neighbourhoods and [3] schools with a lower or higher social economic position population. The level of social economic position was based on the social economic position of the neighbourhood where the school was located and is derived from a number of characteristics such as the people who live there, their educational level and income, which is defined by the local central office for statistics[25]. Seven schools (three in Amsterdam and four in Ede) agreed to participate, and these schools distributed a letter among parents, with information about the study. Parents had the opportunity to refuse participation of their child via passive informed consent. Children also had the opportunity to refuse participation before or during the data collection. Data collection took approximately 30 min, and all children received a small present for their participation. The sample consisted of 720 children, of which twenty-two parents refused participation of their child and thirty-eight children were absent during the day of measurements (e.g. sickness). In total, 660 children participated in the study.
## School lunchbox content assessment
The content of the lunchbox was assessed via a photo of each lunchbox and by the use of code cards. All children consumed their home-packed lunch in their own classroom. At the beginning of the lunch break, children were asked to take their lunchboxes and to present the content of their lunchbox (foods and drinks) that they were planning to consume during lunch on their table. Each lunchbox was provided with a code card that contained an ID number of the child and questions about the number of slices of bread, the type of bread and toppings, the type and quantity of drinks and other products. Children were asked to postpone eating until the researcher filled out the code cards and took a picture of the content of the lunchbox. There were two or three researchers per class room, one was filling out the code cards and one took pictures of the lunchboxes.
Each photo and corresponding code card was entered in a data entry file that was made using the Qualtrics survey tool[26]. All foods and drinks and their quantity on the code cards were entered. In case it was not observable what the specific product was (e.g. full-fat or low-fat milk), the most commonly brought product version in the Netherlands was entered in the database. The content of the lunchboxes was classified into food groups that correspond to the most commonly brought lunch products. The following food groups were created: bread; white, brown/whole grain and other (e.g. cornbread, croissant, currant bun, unclear), sandwich toppings; processed meats, peanut butter/nut paste, cheese and cheese products, sweet bread toppings (e.g. chocolate sprinkles or jam), hummus/sandwich spread/sandwich salad toppings, no topping, vegetables and other (e.g. fish and eggs), drinks; water/tea, sweetened drinks and unsweetened drinks (including milk, butter milk (a common unsweetened fermented low fat (0·9 g/100 ml) dairy drink in the Netherlands), fruit, vegetables and other foods (e.g. nuts, wraps, pancakes, cookies, chocolate, slices of sausage, donuts and chips).
Parents were not informed on which day photos would be taken of the lunch boxes of their children in order to avoid that parents would pack a healthier lunch on the day that the researchers were visiting the school. Due to a miscommunication between the researchers and one of the schools in Ede, one data collection day occurred when children had a short school schedule (till 12:00 p.m.) and did not bring lunch to school. Therefore no photos were taken of their lunchboxes (n 25). Two schools had a traditional time schedule where children have the choice to stay over at school or go home for lunch. Children who went home had no lunchboxes and therefore no photos were taken (n 137). Eleven children refused a photo of their lunch box. These children did filled out the questionnaire about the perceptions of the healthy school lunch concepts. In total, 487 photos of lunchboxes were made.
## Evaluation of the alternative healthy school lunch concepts
The alternative healthy school lunch concepts were evaluated by the use of two questions. The first question examined the support for each concept individual, and the second question examined the preference for the best concept. The evaluation of the six alternative healthy school lunch concepts was measured with a digital questionnaire that was filled out on a tablet with a Qualtrics app, which took children on average 15 min. This approach was pre-tested in a small group of children. Based on the interviews and the preferences of children and parents in our previous study (Frédérique C Rongen, S Coosje Dijkstra, Tobie H Hupkens, Monique H Vingerhoeds, Jaap C Seidell and Ellen van Kleef, unpublished results), six alternative school lunch concepts were developed. School lunch concepts were described in terms of the food and drinks offered in general, the way it was served and whether children had a free choice (for description and pictures of the six lunch concepts see online supplemental 1). All the concepts were based on the Dutch dietary guidelines and in accordance with the Dutch Nutrition Centre[24]. The six developed school lunch concepts were [1] a healthy lunch brought from home; [2] packed sandwiches provided at school; [3] sandwiches prepared by children themselves at school; [4] soup or salad with bread provided at school; [5] a hot lunch on plates provided at school and [6] a hot lunch buffet provided at school. Before introducing the concepts, it was explained that the following aspects were the same for each lunch concept: every child gets the same food, the provided beverages will be water, milk or buttermilk and that allergies and special diets (e.g. halal and/or vegetarian) were taken into account. Furthermore, it was stated that the children consumed their lunch into their own classroom with their classmates, that they were given enough time to eat (approximately around 30 min) and that the food provided in each lunch concept would be slightly different every day of the week. Concepts were randomly displayed for each child and after each concept children could state their support by answering the question ‘how much do you like this concept if this was your lunch at school every day?’. Children could select their support by choosing one of the five smileys. The smileys ranged from a red smiley, orange smiley, yellow smiley, light green smiley and a dark green smiley, faces ranging from sad to happy. Literature showed that especially the use of smiley rating questions is child-friendly and makes children feel at ease when filling out questionnaires (W Yahaya and S Salam, unpublished results). Preference for the alternative school lunch concepts was assessed with a final question ‘which concept do you prefer the most to have at your school?’. Children could select their preference by selecting one concept.
## Demographic variables
Information on age, sex and education group of the children was asked at the end of the questionnaire. Migration background was obtained with three open-ended questions in which was asked in which country they, their mother and their father were born. Children were categorised as having a migration background when at least one of their parents was not born in the Netherlands[27]. Migration background is further categorised into no migration background, Western or Non-Western. Children were categorised as having a no migration background or a Western migration background if they had no migration background or if they were born in a country in Europe, North-America or Oceania. Non-Western migration background included countries of origin as Africa, Latin-America or Asia.
## Place of consumption
Schools with a continuous schedule or a traditional schedule were included in this study. To define the place of consumption, all children with a continuous schedule were categorised as consuming their lunch at school. Children with a traditional schedule had the opportunity to go home or to stay at school for lunch received an extra question ‘where do you consume your lunch today?’.
## Analysis
Descriptive statistics were used to summarise the characteristics of the study sample. The content of the lunchboxes per food group was presented in means, standard deviations (sd) and percentage of users. Logistic regression models were used to investigate the association between the presence of brown/whole grain bread, white bread, water/tea and sweetened drinks and sex, migration background and educational group. Analyses were (if possible) adjusted for sex, educational group and migration background. OR and their 95 % CI were presented.
To analyse the difference in children’s support for the different alternative healthy school lunch concepts by sex, migration background and educational group, a linear regression is performed. Analyses were adjusted for sex, educational group and migration background. Regression coefficients (β) and their 95 % CI were presented. In the supplements, an overview of the percentages per smiley for each school lunch concept divided by sex, educational group and migration background was added. This was done to check if the results of the regression analyses were confirmed since there is discussion about the use of Likert scales as a continuous variable[28,29]. To analyse the difference in children’s preference for one of the alternative healthy school lunch concept by sex, migration background and educational group logistic regression models were used. R and their 95 % CI were presented. Data were analysed with IBM SPSS Statistics 24[30].
## Characteristics
The sample included 660 children, of which 296 boys and 343 girls (Table 1). They were on average 9·9 years (sd = 1·2) old. More than half of the children were in educational groups 7 and 8 (58·6 %) (comparable with US elementary school grades 5 and 6) and had no or a Western migration background (67·0 %).
Table 1Characteristics of the sampleTotal (n 660) n %SexMale29652·0Female34344·8Missing213·2Educational group5–625238·27–838758·6Missing213·2Migration backgroundNo migration background40361·1Western migration background395·9Non-Western migration background18227·6Unknown/missing365·5Place of lunch consumptionHome15723·8School 30 min break15924·1School 15 min break34452·1Photograph of the lunch boxYes48773·8No17326·2
## Lunch box content assessment
In total, 487 lunchboxes were photographed (Table 2). The majority of the children brought brown or whole grain bread (70·2 %) for lunch with an average of 2·3 (sd = 0·7) slices of bread per lunchbox, followed by white bread (18·9 %) with an average of 2·6 (sd = 1·0) slices of bread per lunchbox. Most children had processed meats (44·4 %), peanut butter (38·8 %) or cheese products (29·2 %) as a sandwich topping. Almost half of the children drink water or tea (42·9 %) during their lunch with an average estimated amount of 400 ml (sd = 100) per drink. The other half of the children brought sweetened drinks (42·9 %) during lunch with an estimated average amount of 270 ml (sd = 100) per drink. Only 5 % of the lunchboxes contained fruit and 6 % of the lunchboxes contained vegetables.
Table 2Content of the lunchboxes per food group (n total lunchboxes = 487) n %Mean sd Bread (slices) Brown/whole grain only34270·22·30·7 White only9218·92·61·0 Other only173·52·01·0 Combination white and brown91·92·80·7 Combination white or brown with other10·24·0Sandwich spreads or toppings (unit) Processed meats21644·41·60·7 Peanut butter/nut paste18938·81·61·0 Cheese and cheese products14229·21·50·6 Sweet bread toppings12826·31·40·6 Other214·31·50·5 Hummus/sandwich spread/sandwich salad toppings183·71·50·5 No topping142·91·10·4 Vegetables102·11·50·5Drinks (ml) Water/tea only20942·9400100 Sweetened drinks only20942·9270100 Other unsweetened drinks only204·123050 Combination71·4600150Fruit (piece)255·110·3Vegetables (piece)296·01·10·4Other foods (piece)438·81·10·3ml, millilitre.
Table 3 shows the adjusted associations between sex, educational groups and migration background and the presence of brown/whole grain bread, white bread, water/tea and sweetened drinks in the lunchbox. The results showed that boys were more likely to bring sweetened drinks (OR 1·42, 95 % CI (1·00, 2·00)), but were less likely to bring white bread (OR 0·58, 95 % CI (0·35, 0·95)) for lunch than girls. Children from educational groups 5 and 6 were less likely to bring white bread (OR 0·57, 95 % CI (0·35, 0·92)) than children from educational groups 7 and 8. Children with a non-Western migration background were less likely to bring brown/whole grain bread (OR 0·57, 95 % CI (0·40, 0·81)) and sweetened drinks (OR 0·55, 95 % CI (0·36, 0·82)) for lunch, but were more likely to bring white bread (OR 2·97, 95 % CI (1·84, 4·81)) for lunch than children with no or a Western migration background.
Table 3Results of the adjusted logistic regression analyses for the associations between sex, educational groups, migration background and the content of children’s lunchboxes per food itemBrown breadWhite breadWater or teaSweetened drinksOR95 % CIOR95 % CIOR95 % CIOR95 % CISex† Girls (REF)1·001·001·001·00 Boys1·090·79, 1·500·580·35, 0·95* 0·730·05, 1·021·421·00, 2·00* Educational group‡ Groups 5–6 (REF)1·001·001·001·00 Groups 7–80·870·63, 1·210·570·35, 0·92* 0·890·63, 1·250·530·53, 1·07Migration background§ No or a Western migration background (REF)1·001·001·001·00 Non-Western migration background0·570·40, 0·81* 2·971·84, 4·81* 1·410·98, 2·030·550·36, 0·82* Ref, reference group.* $P \leq 0$·05.†Adjusted for educational group and migration background.‡Adjusted for sex and migration background.§Adjusted for sex and educational group.
## Evaluation school lunch concepts
The mean support for the alternative healthy school lunch concepts by the children is shown in Table 4 (for frequency results of every category of the five point scale, see online supplemental 2). Generally, all the alternative concepts were positively evaluated by the children and had a mean score around or above the midpoint of the scale (score ranges from −2 to +2). The lunch concepts ‘Sandwiches prepared by the children themselves at school’ (mean = 0·54, sd = 1·21) and a ‘hot lunch buffet provided at school’ (mean = 0·49, sd = 1·39) had the highest mean support.
Table 4Children’s support for the alternative healthy school lunch concepts measured on a five-point scale ranging from −2 to +2Mean sd Sandwiches prepared by the children themselves at school0·541·21A hot lunch buffet provided at school0·491·39A healthy lunch brought from home0·441·21A hot lunch on plates provided at school0·341·36Soup or salad with bread provided at school0·251·34Packed sandwiches provided at the school0·081·30Scale −2 to +2 with red (−2), orange (−1) yellow [0], light green [1], and dark green [2] smileys.
Table 5 shows the results of the adjusted association between children’s support for the healthy school lunch concepts and sex, educational group and migration background. Girls reported a higher support for the concepts ‘sandwiches prepared by the children themselves at school’ (β = -0·71, 95 % CI (−0·49, −0·12)) and ‘soup or salad with bread provided at school’ (β = -0·28, 95 % CI (−0·49, −0·07)) than boys. We observed no other statistically significant differences across sex.
Table 5Results of the linear regression analyses for the associations between mean support of the alternative school lunch concepts and sex, educational group and migration backgroundA healthy lunch brought from homePacked sandwiches provided at the schoolSandwiches prepared by the children themselves at schoolSoup or salad with bread provided at schoolA hot lunch on plates provided at schoolA hot lunch buffet provided at school β 95 % CI β 95 % CI β 95 % CI β 95 % CI β 95 % CI β 95 % CISex† Girls0·600·220·710·250·260·50 Boys−0·02−0·21, 0·17−0·18−0·38, 0·02−0·30−0·49, −0·12* −0·28−0·49, −0·07* −0·4−0·35, 0·07−0·13−0·34, 0·09Educational group‡ Groups 5–60·600·220·710·250·260·50 Groups 7–8−0·23−0·42, −0·04* −0·23−0·44, −0·03* −0·22−0·41, −0·03* 0·04−0·18, 0·250·01−0·21, 0·22−0·1−0·32, 0·12Migration background§ No or a Western migration background0·600·220·710·250·260·50 Non-Western migration background0·01−0·20, 0·220·320·10, 0·55* 0·410·21, 0·62* 0·380·15, 0·61* 0·520·29, 0·75* 0·410·17, 0·64* β, beta; Ref, reference group.* $P \leq 0$·05.†Adjusted for educational group and migration background.‡Adjusted for sex and migration background.§Adjusted for sex and educational group.
Children from educational group 7 and 8 reported a lower support for the concepts ‘a healthy lunch brought from home’ (β = -0·60, 95 % CI (−0·42, −0·04)), ‘packed sandwiches provided at the school’ (β = -0·22, 95 % CI (−0·44, −0·03)) and sandwiches prepared by the children themselves at school (β = -0·71, 95 % CI (−0·41, −0·03)) than children from lower educational groups. We observed no other statistically significant differences across the educational groups.
Children with a Western migration background reported a higher support for the concepts ‘packed sandwiches provided at the school’ (β = 0·32, 95 % CI (0·10, 0·55)), ‘sandwiches prepared by the children themselves at school’ (β = 0·41, 95 % CI (0·21, 0·62)), ‘soup or salad with bread provided at school’ (β = 0·38, 95 % CI (0·15, 0·61)), ‘a hot lunch on plates provided at school’ (β = 0·52, 95 % CI (0·29, 0·75)) and ‘a hot lunch buffet provided at school’ (β = 0·41, 95 % CI (0·17, 0·64)) than children with a non-Western migration background.
The preferences for the most favourable concept are shown in Table 6. The concept ‘a healthy lunch from home had the highest preference (30·2 %), followed by ‘a hot lunch buffet provided at the school’ (26·8 %). The concept ‘packed sandwiches provided at the school’ had the lowest preference among the children (8·8 %).
Table 6Children’s preference for the best healthy school lunch concept%A healthy lunch brought from home30·2A hot lunch buffet provided at school26·8Sandwiches prepared by the children themselves at school13·8Soup or salad with bread provided at school10·8A hot lunch on plates provided at school9·7Packed sandwiches provided at the school8·8 Table 7 shows the associations between sex, educational groups and migration background and the preference for the most favourable lunch concepts. The results showed that children from educational groups 7 and 8 had a lower preference for the concepts ‘a healthy lunch brought from home (OR 0·70, 95 % CI (0·49, 0·99)) than children from educational groups 5 and 6. Children with a non-Western migration background had a lower preference for the concept ‘a healthy lunch from home (OR 0·53, 95 % CI (0·35, 0·80)) and a higher preference for the concept ‘A hot lunch on plates provided at school’ (OR 2·15, 95 % CI (1·26, 3·67)) than children with no or a Western migration background. There were no significant differences between sex and the preference for each lunch concept. Results of the ordinal logistic regression analysis confirmed all the results, see online supplemental 3.
Table 7Results of the adjusted logistic regression analyses for the associations between sex, educational groups, migration background and preferences for the most favourable lunch conceptA healthy lunch brought from homePacked sandwiches provided at the schoolSandwiches prepared by the children themselves at schoolSoup or salad with bread provided at schoolA hot lunch on plates provided at schoolA hot lunch buffet provided at schoolOR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CISex† Girls (REF)1·001·001·001·001·001·00 Boys1·090·77, 1·540·900·90, 2·740·720·45, 1·150·810·48, 1·351·560·92, 2·660·830·58, 1·19Educational group‡ Groups 5–6 (REF)1·001·001·001·001·001·00 Groups 7–80·700·49, 0·99* 0·590·34, 1·031·070·67, 1·721·580·91, 2·741·250·72, 2·181·300·90, 1·89Migration background§ No or a Western migration background (REF)1·001·001·001·001·001·00 Non-Western migration background0·530·35, 0·80* 1·110·61, 2·040·670·39, 1·161·390·81, 2·362·151·26, 3·67* 1·290·88, 1·89Ref, reference group.* $P \leq 0$·05.†Adjusted for educational group and migration background.‡Adjusted for sex and migration background.§Adjusted for sex and educational group.
## Discussion
The first objective of this study was to identify the content of the lunchboxes of Dutch primary school children and investigate potential differences between sex, migration background and educational group. The results showed that children’s lunchboxes contain a traditional Dutch lunch with mostly brown, whole grain or multigrain bread with either cheese, processed meat or sweet bread toppings, with low quantities of fruits and vegetables. The majority of the drinks children brought from home were water or sweetened beverages. These results are consistent with the results from the Dutch National Food Consumption Surveys, which also showed that lunches of primary school children mostly contain bread[31]. Other studies confirmed that the consumption of sweetened beverages among children is high[32].
Only a few of children’s lunchboxes in this study contained fruit or vegetables, which is worrisome given the low intake of children’s fruit and vegetables worldwide[33]. This result may be explained by the fact that children may already have eaten fruit during the mid-morning school break. The Dutch National Food Consumption Surveys showed that children mostly ate fruit in between meals and not during lunch. A possible explanation for the low consumption of vegetables in this study might be that in the Dutch National Food Consumption Surveys, it was found that usually children only eat vegetables during dinner and not during lunch[2,31]. In a qualitative study with Dutch and Flemish children, children indicated that they rarely brought vegetables to school for lunch, and it was perceived as weird to eat vegetables at school[34]. Considering the low amount of fruit and vegetables consumption in primary schoolchildren, it is important to find ways to increase their intake during lunch.
The second objective was to examine the support and preferences of children for alternative school lunch concepts and whether this differed across sex, migration background and educational group. Most children reported a neutral or positive support towards the alternative school lunch concepts, but they reported the most support for the healthy school lunch concept ‘Prepare your own sandwiches’ and ‘a hot lunch buffet provided at school’ and had the highest preference for the lunch concept ‘a healthy lunch brought from home’. This finding can be explained by the fact that it is possible that many children like to keep their lunch familiar, but that they also have the possibility to choose what they consume for lunch[22]. Besides, the fact that children choose the option that is most familiar can also be explained by the fact that it could be difficult for children to evaluate lunch concepts they have never experienced before. It takes time to appreciate and learn about new ways of consuming lunch at school. From our results, there are some small differences between sex and educational group and migration background.
## Strengths and limitations
A strength of this study was the relatively large sample size of 639 children who filled out the questionnaire and 487 photographs of children’s lunchboxes. Moreover, the schools participating in this study are from two different cities, which represents a large city (around 854 000 inhabitants) and a small city (around 115 000 inhabitants). Instead of using self-reporting questionnaires to determine the content of children’s lunches, we used photographs, which gives a more objective view and is not prone to possible socially desirable answers or recall bias. Besides, for young children, it is not possible to describe the content of their lunchboxes in detail which makes photographs a better suited method. Furthermore, to our knowledge, this is the first study that investigated the support and preferences of alternative school lunch concepts.
Several limitations of this study should also be considered. First, it should be noted that only primary schools in the cities Ede and Amsterdam were included. The content of children’s lunchboxes at schools in villages or rural areas could differ from what is observed in this population. Furthermore, the photographs of the lunchboxes used in this study were only taken during one particular school day. Therefore, it is possible that children could have had a healthier/unhealthier lunch on the other days of the week. However, due to time and cost constraints, it was not possible to examine the content of the lunchbox on multiple days. Furthermore, the food or drinks displayed in the photographs could not be categorised by type or brand. Besides, there were no photos taken after the lunch was consumed, which gives only an indications of what they brought to school and not what was actually consumed. Finally, the statistical testing of differences between school lunch concepts evaluations was performed with a linear regression what may lead to an underestimation of the results. However, the ordinal logistic regression analysis showed similar results.
Based on the results of this study, we have several recommendations for research and practice. This study showed that the current lunch of primary school children leaves substantial room for nutritional improvement. This can be done through several actions including for example school food policies (e.g. no sugar-sweetened beverages and more fruit and vegetables) or by providing a healthy school lunch. School food policies regarding a healthy lunch have been shown to only moderately impact the nutritional quality of lunchboxes[35]. Providing a national school meal program showed more positive results(3,13–17). For countries such as the Netherlands, where there is currently no national school meal program in place, more research about the possibilities of implementation is needed. Our results showed that children reported the highest support for a concept most familiar to their current lunch situation. However, before implementing a particular school lunch concept, it is necessary to investigate the support of other stakeholders including the parents and schools since it is important for successful implementation to have support from all the stakeholders involved (e.g. children, parents and schools).
## Conclusion
The purpose of this study was to gain insight into the current content of lunchboxes that contained mostly a traditional Dutch lunch with bread, a drink and little fruit or vegetables. This leaves room for nutritional improvement. If a healthy school lunch provision will be implemented, children have the highest support and preferences for a concept that is the most familiar with the current situation. Children in this study had the highest support for the preparation of their own sandwiches in class and the highest preference for a healthy lunch from home. More work integrating insights from this study into the development of a school lunch program and studies towards the effectiveness of a school lunch provision is needed.
## Conflict of interests:
The authors declare that they have no conflict of interests.
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|
---
title: Food industry donations to patient advocacy organisations focussed on non-communicable
diseases
authors:
- Inés M Del Giudice
- Krystle A Tsai
- Josh Arshonsky
- Sara Bond
- Marie A Bragg
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989705
doi: 10.1017/S1368980022001859
license: CC BY 4.0
---
# Food industry donations to patient advocacy organisations focussed on non-communicable diseases
## Body
Patient advocacy organisations (PAO) are non-profits dedicated to helping patients affected by certain medical conditions[1]. Beyond raising public awareness about those diseases, PAO provide patient education and services and influence health policy through their lobbying activities(1–4). Historically, these organisations have been praised for their support of patients. More recently, though PAO have been scrutinised due to their financial ties to different industries[5,6]. These financial relationships can compromise the integrity of the organisation, leading to potential conflicts of interest.
A conflict of interest occurs when ‘an institution’s own financial interests or the interests of its senior officials pose risks to the integrity of the institution’s primary interests and missions’[7]. Conflicts of interest can emerge when advocacy organisations receive funding from companies that promote products or services that may be at odds with the mission of the advocacy organisation. In 2015, for example, the New York Times revealed that in the previous year, Coca-Cola had donated $1·5 million to start the Global Energy Balance Network—a non-profit that minimised the role of diet as a driver of obesity and instead overemphasised the role of physical inactivity[8].
Past scholarship has documented extensive conflicts of interest within the tobacco and pharmaceutical industries,(9–11) but few studies investigate food industry donations to PAO, specifically. Research sponsored by the food industry has been shown to support industry aims(12–20). And relationships between the food industry and academia have been shown to influence medical journalism[20] and public policy[21]. In 2011, for example, the American Beverage Association donated over $10 million for ‘childhood obesity prevention initiatives’ to the Children’s Hospital of Philadelphia[22]. The association happened to donate this generous amount just as the City Council was deliberating over a soda tax proposal. In the end, the Council rejected the tax, showing the extent to which industry can influence academic programmes and public health policies. Public health experts question the motivations behind food industry donations such as the one in Philadelphia, and worry that food corporations presenting themselves as part of the solution to obesity and other diet-related health problems may actually undermine efforts to enact meaningful public health policy. Corporations are, by definition, obligated to sell products—even when such products are at odds with promoting good health. They must be able to make a profit, and as history shows, they are willing to interfere with public health policies that may jeopardise those profits.
External financial support is valuable for PAO, especially because they tend to rely on industry donations to fund their work[1,23]. A 2013 and 2014 survey conducted by researchers at Case Western Reserve University of PAO leaders in the USA revealed that 67 % of PAO reported receiving private industry funding at a median amount of $50 000 in their prior fiscal year[6]. Recent studies from the Perelman School of Medicine in Philadelphia, Pennsylvania also document financial support from the pharmaceutical, device and/or biotechnology industries to PAO[2,6,24,25]. In a study by McCoy et al., authors found that 83 % of PAO received financial support from partners in the pharmaceutical industry. The majority of those PAO (88 %; n 104) published a list of donors, but only 57 % published the amount of donations they received[2]. To increase transparency of financial relationships between PAO and the pharmaceutical industry, Kaiser Health News developed the PreScription for Power database. Researchers from PreScription for Power tracked $162·6 million donated to 650 PAO by twenty-six pharmaceutical companies in 2015[24]. To our knowledge, however, no studies have examined the extent to which food and drink companies fund PAO dedicated to fighting non-communicable diseases (i.e. CVD, cancer and diabetes). Given the links between sugar-sweetened beverage and ultra-processed food intake and non-communicable diseases (e.g. diabetes)(26–29), documenting food industry funding to PAO focussed on non-communicable diseases is a critical first step in increasing transparency addressing potential conflicts of interest[30].
To address this gap in research, the present study, conducted in New York City, aimed to quantify the frequency and types of national and international food industry donations to PAO focussed on non-communicable diseases in the USA over an 18-year period. We aimed to: [1] document the frequency and monetary value of donations; [2] identify any stated reasons for donations; [3] quantify the percentage of funding distributed among chronic health conditions and [4] quantify the percentage of money per health condition per food company.
## Abstract
### Objective:
This study used publicly available Form 990 tax documents to quantify food industry donations to patient advocacy organisations (PAO) dedicated to supporting patients with non-communicable diseases.
### Design:
Observational, cross-sectional assessment of significant national and international food industry donations to US-based non-communicable disease-focussed PAO between 2000 and 2018. Researchers recorded and categorised the: [1] frequency and value of donations; [2] reason for donation; [3] name and type of PAO recipient and [4] non-communicable disease focus of the PAO.
### Setting:
Form 990 tax documents.
### Participants:
Nine food and beverage companies that donated to non-communicable disease-focussed PAO.
### Results:
Adjusting for inflation, nine food and beverage companies collectively donated $10 672 093 (n 2709) to the PAO between 2001 and 2018. The largest category of donations was ‘matching gifts’ (67·9 %, median amount = $115·16), followed by ‘general operations support’ (25·8 %, median amount = $107·79). Organisations focussing on cancer received the largest number and amount of donations ($6 265 861, n 1968). Eight of the nine companies made their largest monetary value of donation to PAO focussed on cancer.
### Conclusions:
Publicly available tax data provide robust information on food industry donation practices. Our findings document the food industry’s role in supporting patient advocacy organisations and raise questions regarding conflicts of interest. Increased awareness of food industry donation practices involving PAO may generate pressure for policies mandating transparency or encourage donors and recipients to voluntarily disclose donations. If public disclosure becomes widespread, constituents, advocates, researchers and policymakers can better supervise and address potential conflicts of interest.
## Methods
We conducted an observational, cross-sectional assessment of significant national and international food industry donations to US-based non-communicable disease-focussed PAO between 2000 and 2018. The Institutional Review Board at New York University School of Medicine exempted this study from review.
## Sample
We defined non-communicable disease-focussed PAO as non-profit groups whose primary mission is to combat a non-communicable disease or improve the health and well-being of a patient population[2]. In our study, we focussed on PAO tackling diet-related non-communicable diseases, including the most prevalent ones: CVD (heart disease and stroke), cancer, chronic respiratory diseases, diabetes and obesity[31].
We defined the food industry as any company whose primary objective is to sell food or beverage products[30]. To identify food and beverage companies, we used the Food Advertising to Children and Teens Scores (FACTS) reports(32–36) published by the Rudd Center for Food Policy and Obesity. These reports rank companies based on their marketing budgets in six categories: fast food, sugary drink, children’s drinks, baby food, snack food and cereals. Research assistants identified 101 companies within the categories most relevant to adults: fast food, sugary drinks, cereals and snacks. We excluded companies in the categories of ‘children’s drinks’ and ‘baby food’ in order to conduct a future study on a number of comprehensive issues relevant to younger age groups, including physical health, but also other factors related to child development.
In 2020, we randomly assigned nine or ten of the 101 identified food and beverage companies to eleven research assistants, assigning no single company to more than one assistant. We trained the research assistants to search for donations from their assigned companies between 2000 and 2018 using the procedures described in Fig. 1. Researchers used independent investigative journalism site www.propublica.org and non-profit search database www.guidestar.org to identify donations using every combination of the following keywords: name of the assigned food or beverage company plus the words: ‘foundation’, ‘contribution’, ‘donation’, ‘gift’, ‘funding’, ‘grant’ or ‘financial support’. These searches yielded results that included food or beverage company websites, media press releases and US Form 990 filings. Form 990 is a US Internal Revenue Service document that all tax-exempt organisations (e.g. The Coca-Cola Foundation, Inc.) are required to file annually. This document provides information about the organisation to the Internal Revenue Service, promotes tax compliance and assists the government with charitable and regulatory oversight. Form 990 is also open to public inspection and allows organisations to share information about their programmes with the public. We asked research assistants to download and save only the tax forms covering the 18-year period. These documents include information on the corporate sponsor, donor recipients, the nature of the support, the year the donation was distributed and the monetary value of the donation.
Fig. 1Flow chart of the online search processes and data collection, coding and cleaning Based on our previously described methodology, we instructed researchers to limit their donation searches to 5 h/company, unless they continued to find additional donations[30]. After excluding sixty-eight companies for which they could not find any donations, the research assistants collected data for thirty-three companies. After collecting the data, thirteen companies were excluded due to a lack of donations to non-communicable disease-focussed PAO. Eleven companies were excluded due to lack of complete data on donations from 2000 to 2018; those included Burger King, Essentia Water, General Mills Foundation, Jack in the Box, Mars, McDonald’s, PepsiCo, Quaker, Spindrift, Kellogg and Yum! Brands. Our final sample included the following nine food and beverage companies: Clif Bar & Company, The Coca-Cola Company, Newman’s Own, Mondelez International, Wendy’s, The Kraft-Heinz, Ferrero USA Inc, Campbell’s Soup and Chick-fil-A. We excluded the year 2000, as tax documentation from our selected food companies could not be found.
## Data collection and analysis
A separate team of fifteen research assistants then examined the 2001–2018 tax documents collected by the previous set of researchers and collectively spent 46 h recording and organising the data into: [1] the frequency and value of donations; [2] reason for the donation; [3] the name and type of PAO recipient and [4] the non-communicable disease focus, if any, of the PAO. Research assistants categorised PAO using keywords that appeared in their mission statements online. These included a combination of the following nine non-communicable disease keywords: ‘obesity’; ‘heart’; ‘CVD’; ‘cancer’; ‘tumor’; ‘lung disease’; ‘asthma’; ‘chronic disease’; or ‘diet-related’, and 12 advocacy keywords: ‘prevent’; ‘cure’; ‘fight’; ‘advocacy’; ‘education’; ‘research’; ‘raise money’; ‘fund’; ‘awareness’; ‘improve lives’; ‘save lives’ or ‘support’.
Using the descriptions of each donation in the companies’ tax forms, we categorised the reasons for donating into eight categories: [1] research; [2] educational initiative; [3] miscellaneous programme (i.e. family support programme or building stronger communities); [4] matching gift (i.e. when a company matches the amount its employees donate to a non-profit organisation); [5] general operations support; [6] scholarship and fellowship; [7] health and human services and [8] environmental initiative. Using mission statements, we organised health conditions into the following categories: [1] CVD; [2] cancer; [3] respiratory disease; [4] diabetes; [5] obesity and [6] multiple diseases and/or singular diet-related/chronic diseases that are not specified in one of the previous categories (e.g. chronic kidney disease).
After research assistants finished recording and coding data, the lead author searched for and removed duplicate donations (n 2) and cleaned the final dataset of donations (n 2709). To verify reliability in the coding process, a separate team of three research assistants re-coded 1500 donations from the dataset. The lead author then calculated the percentage of agreement, ensuring that agreement for all variables was above 90 %. We adjusted the donation amounts for inflation by calibrating them to the year 2018—the final year of our data collection period—using the World Bank’s US historical inflation rates[37]. We then quantified the frequency and monetary value of donations for each company and for the entire sample. We also calculated the frequencies of donation reasons listed, as well as health conditions targeted. Finally, we calculated the total number, monetary value and percentage of donations per health condition per company.
## Number and monetary value of donations over time
The food and beverage companies in our sample collectively made 2709 times donations to 146 PAO in our sample between 2001 and 2018 (Fig. 2). Between 2001 and 2009, the total annual monetary value of donations increased from $38 800 (n 4) to $2·2 million (n 307). From 2009 to 2010, the total monetary value of donations declined from $2·2 million to $335 000, but the number of donations stayed almost the same (n 309). From 2010 to 2018, the number and monetary value fluctuated based on the data provided on the tax forms, with the largest monetary value occurring in 2012 ($1·3 million, n 18). The highest number of donations occurred in 2013 (n 555).
Fig. 2Trends in food and beverage company donations made to non-communicable disease-focussed PAO from 2001 to 2018
## Monetary value of the donations and years the donations were distributed
Table 1 lists the donors, amounts and number of donations, and the number of years for which we found donations for a given company. In total, the nine companies in our sample donated $10·7 million, adjusted for inflation. Clif Bar & Company was the largest donor; its seventy donations totalled nearly $4 million and accounted for 36·9 % of the total monetary value of donations we studied.
Table 1Summary of public information on nine food company donations to non-communicable disease-focussed PAO between 2001 and 2018Name of donor companyTotal donations amount, adjusted for inflationMedian donation value/year, adjusted for inflationTotal number of donationsNumber of years actively donatingMedian number of donations/yearClif Bar & Company (e.g. snack bars)$3 976 003$337 59670125·5The Coca-Cola Company (e.g. sugary drinks)$3 521 460$453 9171462·5Newman’s Own (e.g. salad dressings)$1 131 954$94 28190128Mondelez International Inc (e.g. chocolate bars)$857 327$63 07017989204Wendy’s (e.g. fast food)$560 902$67 32233103·5The Kraft-Heinz Company (e.g. condiments)$237 591$77 8456553219Ferrero USA, Inc. (e.g. chocolate-hazelnut spreads)$233 527$12 69629123Campbell’s Soup Company (e.g. canned soups)$114 320$28 2651441Chick-fil-A (e.g. fast food)$39 008$7952641Total$10 672 093$77 8452709x˜: 9x˜: 3·5 Table 2 lists the ten largest individual donations in our sample. In 2009, the Coca-Cola Company made the largest individual donation ($1 million) to support operations of the British Nutrition Foundation, whose mission is to translate ‘evidence-based nutrition science in engaging and actionable ways’[38]. The Coca-Cola Company gave the second largest individual donation ($436 000 made in 2013 to EPODE International Network, an organisation that aims to prevent childhood obesity[39].
Table 2Ten largest donations, adjusted for inflation, to non-communicable disease-focussed PAO by food companies between 2001 and 2018, ranked by total monetary amountDonor companyName of recipientYearDonation amountReason for donationLanguage from data sourceHealth conditionThe Coca-Cola CompanyBritish Nutrition Foundation2009$1 041 709General operations support“Foundation Contribution”Diet-Related Diseases and/or Chronic DiseasesThe Coca-Cola CompanyEPODE International Network2013$436 554General operations support“EPODE France”ObesityThe Coca-Cola CompanyMagyar Dietetikusok Orszagos Szovetsege (Hungarian Dietetic Association)2012$366 390Scholarship and fellowship“Dietitian Support Program for Hungarian University Students”Diet-Related Diseases and/or Chronic DiseasesThe Coca-Cola CompanyFrench Diabetics’ Association2012$328 110Health and human services“Balanced Diet and Physical Activity for Diabetic Peer Support Groups”DiabetesClif Bar & CompanyBreast Cancer Fund2014$307 604Environmental initiative“Reducing environmental health hazards”CancerClif Bar & CompanyBreast Cancer Fund2008$334 034General operations support“Charitable”CancerClif Bar & CompanyBreast Cancer Fund2007$345 156General operations support“Charitable”CancerClif Bar & CompanyBreast Cancer Fund2006$356 995General operations support“Charitable”CancerThe Coca-Cola CompanyAmerican Council for Fitness and Nutrition Foundation2009$321 876General operations support“Foundation Contribution”ObesityThe Coca-Cola CompanyFondazione Diabete Ricerca Onlus – Diabetes Research Foundation2015$217 187Research“Epode Umbria Region Obesity Intervention Study”Diabetes
## Purpose of donations as categorised based on information presented in company tax reports
Research assistants identified a reason for 94 % of the donations (n 2557). The identified reasons represented eight broad categories that were not mutually exclusive (i.e. some donations listed more than one reason) (Table 3). The largest categories included ‘matching gifts’ (67·9 %, n 1738, median amount = $115·16); ‘general operations support’ (25·8 %, n 661, median amount = $107·79); ‘miscellaneous programs’ (3·2 %, n 81, median amount = $8164·08) and ‘research’ (1·8 %, n 46, median amount = $4754·74).
Table 3Purpose of donations as categorised based on information presented in food and drink company tax reports from 2001 to 2018Reason for donationNumber (%) of donations with that reason listed (n 2557) n %Matching gifts173867·9General operations support66125·8Miscellaneous programmes813·2Research461·8Health and human services100·4Environmental initiative100·4Educational initiatives80·3Scholarship and fellowship50·2Total number of reasons listed* 2559*There were 2557 donations with one or two specific reasons for the gift. As some donations had more than one reason listed, the total is 2559.
## Number and monetary value of the donations to each health condition
Compared to other non-communicable diseases, cancer received the largest number and amount of donations from the food industry ($6·26 million, n 1968) (Table 4). CVD received the second largest number of donations (n 364) but ranked fifth in total monetary value ($357 000), followed by respiratory disease ($82 000). Finally, diabetes ranked third in both number and amount of donations ($1·37 million, n 315).
Table 4List of health conditions that received donations from nine food companies from 2001 to 2018, ranked by total number of donationsHealth conditionNumber (%) of donations to that health condition listedTotal donation amount (%) to that health condition, adjusted for inflation n % n %Cancer196872·6$6 265 86158·7CVD36413·4$356 7333·3Diabetes31511·6$1 371 63812·9Respiratory disease542$82 2470·8Diet-related diseases and/or chronic diseases40·1$1 636 06315·3Obesity40·1$959 5529Total2709$10 672 093
## Number and monetary value of donations from nine food companies to each health condition
Table 5 lists food company name, number, monetary value and percentage of donations to each health condition. Eight of the nine companies included in the analysis made their largest monetary value of donation to PAO focussed on cancer. The only exception was Coca-Cola, which made the largest donation to PAO focussed on ‘diet-related diseases’ and/or ‘chronic diseases’.
Table 5Food company donations to health conditions from 2001 to 2018Company nameHealth conditionNumber of donationsTotal donation amount (%), adjusted for inflation n %Clif Bar & CompanyCancer51$3 841 84296·6Diabetes19$134 1613·4The Coca-Cola CompanyCancer2$93 4222·7Diabetes4$832 42423·6Obesity4$959 55227·2Diet-related diseases and/or chronic diseases4$1 636 06346·5Newman’s OwnCancer62$847 13474·8Diabetes11$96 5878·5CVD16$175 77715·5Respiratory disease1$12 4561·1Mondelez International, IncCancer1335$635 49074·1Diabetes206$147 47517·2CVD216$70 3928·2Respiratory disease41$39700·5Wendy’sCancer14Diabetes6$130 44723·3CVD7$78 33014Respiratory disease6$64 97411·6The Kraft Heinz CompanyCancer459$177 99374·9Diabetes68$28 21211·9CVD122$30 53912·9Respiratory disease6$8460·4Ferrero USACancer26$229 59098·3Diabetes1$23331CVD2$16040·7Chick-fil-ACancer6$39 008100Campbell Soup CompanyCancer13$114 23099·9CVD1$900·1
## Discussion
This investigation generated the largest database to date of food industry donations to non-communicable disease-focussed PAO. The data show the extent of food industry donations to organisations and raise questions about potential conflicts of interest that may arise. The three reasons provided most frequently for food company’s donations included matching gifts, general operations support and miscellaneous programmes. Although food and beverage company support may enable PAO to engage in valuable research, education and advocacy activities, it is also possible that these relationships may result in conflicts of interest. Case in point: many academic institutions and universities have received gift donations from opioid companies, often using these large gifts to establish research centres and degree programmes. Thousands of documents made public in 2019 revealed how Purdue Pharma’s relationships with academic institutions provided them with opportunities to influence research, curricula, speaker series and other events[10]. Companies in the pharmaceutical industry—as well as those in other industries—understand how non-profit organisations depend on and are profoundly influenced by their gifts and relationships. And they regularly exploit this relationship with organisations to promote policies that protect their interests. In a working paper from 2018, the National Bureau of Economic Research concluded that ‘corporations strategically deploy charitable grants to induce non-profit grantees to make comments that favor their benefactors, and that this translates into regulatory discussion that is closer to the [corporation’s] own comments’[40].
Previous research showed that Coca-Cola and PepsiCo sponsored a total of ninety-six national health organisations between 2011 and 2015, including the Academy of Nutrition and Dietetics[41]. Six of these ninety-six organisations are non-communicable disease-focussed PAO organisations that received donations from Coca-Cola in our sample. Nearly three-quarters of the total number of donations, and more than half of total monetary value, was made to cancer-focussed PAO, supporting previous research in funding distributions that showed the relatively greater investment on cancer research compared to other non-communicable diseases[42]. These donations reinforce the need for more transparency and policies to reduce potential conflicts of interest.
Few donations in our sample (n 79; 3·1 %) were earmarked for research, health and human services, environmental initiatives, scholarships and fellowships and educational initiatives. Despite the small number of donations in these categories, some of these donations were among the ten largest in monetary value in the sample (e.g. Coca-Cola’s $366 000 donations to the Hungarian Dietetic Association was classified as scholarship and fellowship). Donations that provide scholarships and other forms of financial support reflect donors’ corporate social responsibility, defined as ‘context-specific organisational actions and policies that take into account stakeholders’ expectations and the triple bottom line of economic, social and environmental performance’[43]. Research has shown that corporate social responsibility increases employee’s work motivation and performance[44,45] and increases consumers’ loyalty and trust in the company or brand[46,47]. More research is needed to understand how PAO daily operations may—overtly or inadvertently—be shaped by loyalty toward their food industry donors.
The strengths of our study include the large number of donations included in the sample and our extensive data collection method. These factors allowed us to [1] examine the types of PAO that receive donations from the food industry and [2] create a comprehensive list of reasons for donations. Most previous studies on PAO focussed on donations from the pharmaceutical, device and/or biotechnology industry[2,6,24,25].
Our study has several limitations. One limitation is that we did not score companies according to the percentage of their products that are unprocessed or minimally processed. It is possible that food companies that produce unprocessed foods may also engage in practices that generate conflicts of interest. Our search focussed only on tax documents, excluding other sources of donation information like PAO or food company web pages and news from reputable sources. Another limitation is the exclusion of eleven food companies due to incomplete donation data from 2000 to 2018. Finally, our study only included publicly disclosed data from 990 forms available on www.propublica.org and www.guidestar.org. It is possible, however, that these websites might not have every 990 form for each company and year in our sample. Propublica provides Internal Revenue *Service data* from 2013 onwards but relies on company self-reporting or investigative journalism for 2001–2012. While most years are complete, some are missing or illegible — potentially interfering with data collection. Future studies could include a complete analysis of omitted food companies to generate a more comprehensive database and could prospectively track new tax documents to reduce the probability of missing donation information that might be deleted or replaced. Lastly, future research could include other countries and international brands in order to highlight similarities and discrepancies in donation practices across different markets. Different data sources may be needed to complete an international analysis.
## Conclusions
Our study demonstrates the need for PAO to publicly disclose the receipt of donations from the food industry, as these relationships have a great impact on public health policy. Increased awareness of food industry donation practices involving PAO may generate pressure for policies mandating transparency or encourage both actors to voluntarily disclose donations. This study also provides the foundation for a comprehensive understanding of food companies’ donation practices over time. If public disclosure becomes widespread, constituents, advocates, researchers and policymakers can better supervise and address potential conflicts of interest that may arise from food and beverage company donations to non-communicable disease-focussed PAO. Ultimately, this may also allow policymakers and public health experts to enact public health policies without interference from the food industry and other corporate actors.
## Conflicts of interest:
The authors have no conflicts of interest to disclose.
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|
---
title: Red meat consumption, incident CVD and the influence of dietary quality in
the Jackson Heart Study
authors:
- Sherman J Bigornia
- Sabrina E Noel
- Caitlin Porter
- Xiyuan Zhang
- Sameera A Talegawker
- Teresa Carithers
- Adolfo Correa
- Katherine L Tucker
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989707
doi: 10.1017/S1368980022001434
license: CC BY 4.0
---
# Red meat consumption, incident CVD and the influence of dietary quality in the Jackson Heart Study
## Body
The consumption of unprocessed red meat and processed meat is widely considered to adversely affect risk for cardiometabolic diseases. Federal and professional dietary recommendations support moderation of meat intake as part of a healthy diet[1,2]. Considering the long latencies of cardiometabolic diseases, prospective observational cohort studies have been a critical tool in identifying the potential impact of diet on long-term health[3]. Meta-analyses of prospective cohort studies provide support that greater intake of unprocessed red and processed meat may increase the risk for type 2 diabetes[4,5], as well as for certain cancers[6].
The impacts of unprocessed red and processed meat consumption on heart disease and stroke, the first and fifth leading causes of US mortality[7], are also of great public health interest. Several recent meta-analyses of prospective cohort studies have examined unprocessed or processed meat consumption with incident CHD[8,9], stroke(8–11) and heart failure[8,12]. Greater intakes of both unprocessed red and processed meat have been consistently associated with an elevated risk of stroke(8–11). Lower unprocessed red and processed meat intakes were associated with lower CHD risk in one meta-analysis[9], but not in another[8]. Heart failure was reported to be associated with processed meat intake in two meta-analyses[8,12], but conflicting results were observed for unprocessed red meat[12]. The low availability of large prospective studies on meat intake in relation to incident CHD(13–15) and heart failure(16–19) may contribute to the inconsistent evidence. Furthermore, evidence from randomised controlled trials support that unprocessed red meat consumption does not adversely affect CVD risk factors, specifically blood lipids and blood pressure[20]. This may be because red meat is a source of nutrients that are associated with better cardiometabolic risk(21–24) including vitamin B6, vitamin B12, Zn and Mg[25].
Further, few studies have been conducted among non-European ancestry cohorts and, specifically, African American (AA) adults. This reduces the generalisability of this growing body of research. AA adults experience higher rates of CVD, compared to Hispanic and non-Hispanic White adults; a disparity expected to be sustained for the next 20 years[26]. The prevalence of CVD also varies geographically, with the highest CVD mortality observed in Southern states[27]. Some data suggest that, compared to non-Hispanic White adults, AA consume more processed meat, less unprocessed red meat[28,29] and less lean beef[30]. AA adults living in the South also tend to have higher total red meat intake than AA adults in other geographic regions[31]. A Southern dietary pattern has been characterised by relatively high intake of processed meats, organ meats, fried foods and sugar-sweetened beverages, and low intake of fruit, vegetables and fibre[32]. In the REGARDS study, greater adherence to a Southern dietary pattern was associated with CHD[32] and stroke risk[33]. Despite the interest in the impact of meat consumption on cardiovascular health and the elevated risk of CVD among AA adults, few studies have examined these diet–disease associations. An analysis from the Black Women’s Health Study reported that total, processed and unprocessed red meat consumption was associated with greater CVD mortality [31]. Further research is necessary to confirm these findings.
Another important question is the potential modifying effect of overall dietary quality on unprocessed red and processed meat associations with CVD outcomes. Greater unprocessed red and processed meat intakes have been correlated with lower overall dietary quality[34,35], suggesting that observed adverse associations with cardiovascular health may be partly due to the lower dietary quality of individuals who have greater meat intake[35,36]. We know of only one study to examine this, where greater adherence to the Danish Dietary Guidelines did not modify the adverse associations observed between red meat and processed meat consumption and IHD among Danish adults [37]. In that study, stroke and congestive heart failure were not investigated.
To address limitations of the available evidence, we evaluated prospective associations of total meat, unprocessed red meat and processed meat intakes with CVD (stroke, myocardial infarction or congestive heart failure) in a Southern cohort of AA adults residing in the Jackson, Mississippi area, using data from the Jackson Heart Study (JHS) [38]. We also assessed the potential modifying effect of overall dietary quality on these associations. We hypothesised that adverse associations between meat intake categories and incident CVD, CHD, stroke, and heart failure would be observed, and that they would be stronger with lower overall dietary quality.
## Abstract
### Objectives:
We investigated the prospective associations between meat consumption and CVD and whether these relationships differ by dietary quality among African American (AA) adults.
### Design:
Baseline diet was assessed with a regionally specific FFQ. Unprocessed red meat included beef and pork (120 g/serving); processed meat included sausage, luncheon meats and cured meat products (50 g/serving). Incident total CVD, CHD, stroke and heart failure were assessed annually over 9·8 years of follow-up. We characterised dietary quality using a modified Healthy Eating Index-2010 score (m-HEI), excluding meat contributions.
### Setting:
Jackson, MS, USA.
### Participants:
AA adults (n 3242, aged 55 y, 66 % female).
### Results:
Mean total, unprocessed red and processed meat intakes were 5·7 ± 3·5, 2·3 ± 1·8 and 3·3 ± 2·7 servings/week, respectively. Mostly, null associations were observed between meat categories and CVD or subtypes. However, greater intake of unprocessed red meat (three servings/week) was associated with significantly elevated risk of stroke (hazard ratio = 1·43 (CI: 1·07,1·90)). With the exception of a more positive association between unprocessed meat consumption and stroke among individuals in m-HEI Tertile 2, the strength of associations between meat consumption categories and CVD outcomes did not differ by m-HEI tertile. In formal tests, m-HEI did not significantly modify meat–CVD associations.
### Conclusions:
In this cohort of AA adults, total and processed meat were not associated with CVD outcomes, with the exception that unprocessed red meat was related to greater stroke risk. Dietary quality did not modfiy these associations. Research is needed in similar cohorts with longer follow-up and greater meat consumption to replicate these findings.
## Study population
Data are from participants of the JHS, a population-based longitudinal cohort of 5306 non-institutionalised AA adults living in the Jackson, Mississippi area, aged ≥ 21 years. Baseline recruitment occurred between late 2000 and early 2004 from the Jackson site of the Atherosclerosis Risk in Communities study[39] and from resident volunteers, randomly contacted individuals, and secondary family members living in the Jackson Mississippi metropolitan area[38,40,41]. For the current analysis, we excluded individuals with baseline CVD, and those missing CVD outcome (CHD, stroke and heart failure), food-frequency data, or any data on control variables, as discussed below. In addition, participants with estimated total energy intake of less than 600 kcal/d or greater than 5000 kcal/d were not included. The final analytical sample size was 3242 men and women. All participants provided written informed consent.
## Unprocessed red and processed meat intake ascertainment
Dietary intake data were collected using the Delta NIRI (Nutrition Intervention Research Initiative) JHS FFQ. This FFQ contains 158 items, was administered by JHS clinic staff and has been previously validated against multiple 24-h recalls and biomarkers for use in this population[42,43]. Total meat intake was further categorised into unprocessed red meat (beef and pork) and processed meat (online Supplementary Table 1). Nutrition Data Systems for Research (NDSR, Minneapolis, MN) was used to estimate food and nutrient intakes from FFQ responses. This software also allowed for the weight estimation of meat found in mixed component foods/dishes, such as hamburgers. A weighted value was used to separate the contribution of mixed-meat dishes (i.e. pasta and rice dishes and pizza) to unprocessed red meat and processed meat food categories. Due to the format of the FFQ, processed meats could not be separated into beef and pork. Total meat was calculated as the sum of unprocessed red meat and processed meat intakes. A serving of unprocessed red meat was defined as 120 g, and of processed meat, 50 g[9]. Meat intakes were adjusted for total energy using the nutrient density approach and expressed per 2000 kcal. The primary exposures were total meat, unprocessed red meat and processed meat. Unprocessed beef and pork were examined as secondary exposures.
## Dietary quality
Overall dietary quality was measured using the Healthy Eating Index (HEI)-2010 score, which was informed by recommendations from the 2010 Dietary Guidelines[44]. Higher HEI-2010 score has been related to lower CVD mortality among AA adults living in the South[45]. Further, in relation to CVD mortality, the HEI score (hazard ratio (HR) = 0·74 (CI: 0·69, 0·81) and 0·77 (CI: 0·71, 0·84)), for men and women, respectively, top v. bottom quintile) performed similarly to the alternative HEI-2010 (HR = 0·79 (CI: 0·73, 0·86) and 0·76 (CI: 0·69, 0·83)), alternative Mediterranean diet (HR = 0·79 (CI: 0·72, 0·86) and 0·81 (CI: 0·74, 0·89)) and DASH dietary indices (HR = 0·83 (0·76, 0·91) and 0·78 (CI: 0·71, 0·85)) in a large multiethnic cohort study[46]. HEI components include total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, refined grains, Na, and empty calories. Refined grains, Na and empty calories are moderation components (limit consumption) and were reversed scored with lower intake relating to higher component score. Components were scored on a 0- to 5-point scale or 0- to 10-point scale, where intermediate values received a proportional score. The overall HEI score was derived by summing the component scores. In our study, total meat intake was inversely associated with dietary quality (rho = –25, $P \leq 0$·0001). Because multiple components of the HEI score are influenced by meat intake, the main explanatory variable in the present study, we created a modified HEI-2010 score (m-HEI) excluding contributions from processed and unprocessed meat.
Unprocessed red meat and processed meat contribute to multiple components of the HEI score, including total protein, unsaturated fatty acid-to-SFA ratio, Na and empty calories. To derive the m-HEI, processed meat and unprocessed red meat were removed from self-reported intakes of foods. The modified estimated weights of consumed foods were then used as inputs into NDSR to calculate nutrient and food group intakes. Some studies have used HEI cut-offs to indicate a good diet (HEI > 80), a diet that needs improvement (HEI 51 to 80), and a poor diet (HEI < 51)[47]. In the present study, 81 individuals were categorised as having a good diet, and 533 as a poor diet, whereas the majority were in the needs improvement category (n 2628). Due to the small number of individuals in the good diet category, we chose to categorise JHS participants by lower, medium and higher diet quality using m-HEI tertiles.
## CVD ascertainment
Primary outcomes were total CVD, CHD, stroke and congestive heart failure[48]. Annual phone calls to living participants or their proxies were conducted to assess CVD event status. Medical records were reviewed to verify diagnoses. For each hospitalisation or death due to CVD, medical records were obtained. Trained clinicians adjudicated CVD events following published guidelines[49]. Ascertainment of heart failure outcomes began on 1 January 2005. Events were available through 2010. Censoring occurred at death, loss-to-follow-up or at the end of the follow-up period.
## Assessment of covariates
Data were obtained through in-home interview and clinic examination. Sociodemographics variables, lifestyle behaviours and medical history were captured by interviewer-administered questionnaires during the in-home visit. Anthropometry, blood sampling and medication use were obtained at the clinic examination. The JHS Physical Activity Survey quantified duration, frequency and intensity of physical activity across four domains: active living, work, home life and sports/exercise activities[50]. Minutes per week (min/week) of reported moderate or vigorous physical activity were used to categorise participants according to the American Heart Association’s Life’s Simple 7 metric: poor (0 min/week), intermediate (> 0 to < 150 min/week) or ideal (≥ 150 min/week)[51,52]. Smoking status was determined by affirmative responses to the questions: ‘Have you smoked more than 4000 cigarettes in your lifetime?’ and ‘Do you smoke cigarettes?’ Waist circumference, in cm, was the average of two measurements obtained at the umbilicus in the standing position[53].
## Statistical analyses
SAS version 9.4 was used for all analyses. Age- and sex-adjusted demographics and dietary intakes were reported by m-HEI tertile using ANCOVA (proc GLM). P for trend was determined by treating m-HEI tertile category as a ordinal variable. Cox proportional hazards (proc PHREG) was used to quantify associations between meat exposures and each CVD outcome. The proportional hazards assumption for Cox regression was examined by inspection of Kaplan–Meier curves and Schoenfeld residual plots for categorical and continuous covariates, respectively. Non-proportional hazards were observed for high school education level and diabetes status. Interaction terms with time to event for these covariates were included in all models. Compared to models without covariates, model fit statistics (–2 log-likelihood, Akaike information criterion and Schwarz’s Bayesian criterion) improved with the addition of selected confounders. Standard errors are presented for mean values and 95 % CI for estimated HR.
In primary analyses, we quantified the associations of total meat (sum of unprocessed red and processed meat), unprocessed red meat and processed meat with CVD, CHD, stroke and heart failure. Models were adjusted for age, sex, high school education attainment, medical insurance, waist circumference, physical activity level, current smoking status, diabetes history, total energy intake and overall dietary quality (m-HEI score). Interaction terms for [1] time to event and high school education attainment and [2] time to event and diabetes history were also added to models. Based on our a priori hypothesis, we also stratified our analyses by lower, medium and higher dietary quality accordingly, by m-HEI tertile. Formal tests of effect modification were conducted; interaction terms (e.g. unprocessed red meat × m-HEI) were added to models including variables for meat intake (e.g. unprocessed red meat), dietary quality (m-HEI) and other model covariates. Significant evidence of effect modification was considered at $P \leq 0$·1. Informed by the results of our primary analyses, we secondarily investigated the individual associations of unprocessed beef and pork with incident stroke in both unstratified and m-HEI-stratified analyses.
## Sample characteristics
Our cohort sample (n 3242) was 66·3 % female with mean age 54·6 ± 0·2 years. Participants with greater unprocessed or processed meat intake, tended to be younger, male, and have higher waist circumference, and were less likely to have an ideal physical activity level (Table 1). The HEI score tended to be lower with greater meat consumption, as were the subcategories of total fruit, whole fruit, whole grains, dairy, seafood and plant protein, and unsaturated-to-saturated fat ratio. Further, both unprocessed red and processed meat intakes were associated with greater Na and empty calorie intakes. Mean total meat, unprocessed red meat and processed meat intakes were 5·7 ± 0·06, 2·3 ± 0·03, and 3·3 ± 0·05 servings/week, respectively. Mean unprocessed red meat intakes ranged from 0·8 ± 0·03 to 4·2 ± 0·03 servings/week across consumption tertiles, whereas processed meat intake ranged from 1·2 ± 0·05 to 6·1 ± 0·05 servings/week. ( Table 2).
Table 1Baseline sample characteristics by unprocessed red meat and processed meat consumption* Sample characteristicsUnprocessd red meat (tertiles)Processed meat (tertiles)123 P for trend 123 P for trend n 1080 n 1081 n 1081 n 1080 n 1081 n $1081\%$Mean se %Mean se %Mean se %Mean se %Mean se %Mean se Age, years57·60·3655·20·3650·90·36<0·000156·70·3754·50·3752·50·37<0·0001Male26·932·142·2<0·000130·435·135·60·01High school education or equivalent84·283·482·50·2986·282·881·10·0008Medical insurance8887·187·30·6186·488·987·10·62Waist circumference, cm98·60·4999·70·481020·49<0·000197·90·481010·481020·48<0·0001Physical activity level† Poor41·147·450·9<0·000142·247·449·90·0003 Intermediate34·63332·50·303434320·31 Ideal24·319·616·6<0·000123·818·618·10·001Current smoker8·8410·612·20·019·4610·411·70·10Diabetes15·616·618·10·1312·217·320·7<0·0001Hypertension51·351·754·40·145153·4530·32Dietary variables‡ Total energy, kcal196026205026221026<0·0001197026209026217026<0·0001HEI-2010 score60·90·2958·80·29580·29<0·000161·00·2959·10·2957·70·29<0·0001 Total fruit, cup1·50·021·30·021·10·02<0·00011·50·021·20·021·10·02<0·0001 Whole fruit, cup0·70·020·60·010·60·01<0·00010·70·010·60·010·60·01<0·0001 Total vegetables, cups1·10·011·20·011·20·01<0·00011·20·021·20·011·20·010·53 Green vegetables and beans, cups0·30·010·30·010·40·010·010·30·010·30·010·30·010·81 Whole grains, oz1·50·031·00·030·90·03<0·00011·30·031·10·031·00·03<0·0001 Dairy, cups1·10·021·00·020·90·02<0·00011·10·021·00·020·90·02<0·0001 Total protein, oz5·00·065·70·067·00·06<0·00015·20·065·80·066·70·06<0·0001 Seafood and plant protein, oz2·20·042·00·041·80·04<0·00012·30·042·00·041·70·04<0·0001 Unsaturated-to-saturated fat ratio2·10·012·00·011·90·01<0·00012·10·012·00·011·90·01<0·0001 Refined grains, oz4·50·044·60·044·50·040·264·40·044·60·044·60·040·003 Na, grams3·70·033·90·034·10·03<0·00013·60·033·90·034·30·03<0·0001 Empty calories (solid fats, added sugar, alcohol), % kcal27·70·2627·70·2526·40·200·000627·90·2627·60·2526·30·25<0·0001*Means ± se or proportions, adjusted for sex and age (as appropriate) and stratified by unprocessed red and processed meat intake tertiles.†Physical activity level was defined according to American Heart Association criteria.‡Daily nutrient and food intakes are expressed per 2000 kcal unless otherwise noted. Diet data were collected using a validated semi-quantitative FFQ.
Table 2Meat intake by unprocessed red meat and processed meat intake* Meat, servings/week† Unprocessed red meat (tertiles)Processed meat (tertiles)123123 n 1080 n 1081 n 1081 n 1080 n 1081 n 1081Mean se Mean se Mean se Mean se Mean se Mean se Total meat3·60·095·20·098·20·093·00·075·20·078·90·07Unprocessed red meat0·80·031·90·034·20·031·80·052·40·052·80·05 Beef0·60·031·40·033·140·031·40·041·80·041·90·04 Pork0·20·020·50·021·050·020·40·020·60·020·80·02Processed meat2·70·083·30·084·00·081·20·052·70·056·10·05*Mean ± se adjusted for age, sex and total energy intake using ANCOVA.†A serving was defined as 4·2 oz (120 g) for unprocessed red meat and 1·8 oz (50 g) for processed meat. Diet data were collected using a validated semi-quantitative FFQ.
Participants with greater m-HEI score tended to be older, more physically active, female, to have attained at least a high school education and to have health insurance. They were also more likely to have diabetes or hypertension (online Supplemental Table 2). As expected, m-HEI score was positively associated with HEI adequacy scores for component foods and nutrients (fruit, vegetables, whole grains, dairy, total and seafood and plant protein, unsaturated-to-saturated fat ratio) and with lower intake of moderation components (refined grains, Na and empty calories) (Table 1). Total meat, unprocessed red meat and processed meat consumption were significantly higher among those in the middle m-HEI tertile, relative to the bottom or top tertiles ($P \leq 0$·05) (online Supplemental Table 3).
## Meat food sources
The top five contributors to total meat consumption were ground beef (15·8 %), luncheon meats (14·3 %); chicken-fried steak (11·2 %), pork main dishes (11·1 %), and hot dogs and sausages (10·5 %) (online Supplementary Table 1). These foods accounted for 62·9 % of total meat intake.
## Total, unprocessed red and processed meat consumption and incident CVD
CVD incidence rates/1000 person-years (P-Y) were 10·2 (CVD), 3·8 (CHD), 2·6 (stroke) and 5·9 (heart failure). Total, unprocessed red and processed meat intakes were not significantly associated with all CVD, CHD or heart failure (Table 3). However, each three serving/week intake of unprocessed red meat consumption was associated with 42 % higher risk of stroke (HR = 1·43, CI: 1·07, 1·90). Conversely, null associations were observed between total and processed meat consumption and stroke. In analyses stratified by m-HEI score tertile, total and processed meat intakes were not significantly associated with CVD or CVD type (CHD, stroke and HF) ($P \leq 0$·05, for all) (Table 4). Unprocessed red meat was also not associated with most CVD outcomes, with the exception of of stroke among those in the m-HEI Tertile 2 (HR = 2·45, CI: 1·32, 4·55). Formal tests of effect modification did not support that associations of total meat, unprocessed red meat and processed red meat with CVD, CHD, stroke and heart failure were modified by m-HEI score (P for interaction ≥ 0·1 for all, data not shown).
Table 3Associations between meat consumption and incidence of CVD† Meat, three servings/week‡ Full sample n 3242HR95 % CIAll CVD (cases/1000 P-Y)10·2Total meat1·000·91, 1·11Unprocessed red meat1·060·86, 1·29Processed meat0·980·86, 1·12CHD (cases/1000 P-Y)3·8Total meat1·020·87, 1·19Unprocessed red meat1·110·82, 1·50Processed meat0·950·77, 1·18Stroke (cases/1000 P-Y)2·6Total meat0·940·77, 1·16Unprocessed red meat1·431·07, 1·90* Processed meat0·710·50, 1·00Heart failure (cases/1000 P-Y)5·9Total meat1·040·92, 1·17Unprocessed red meat0·990·76, 1·3Processed meat1·050·90, 1·21HR, hazard ratio; P-Y, person-years.* $P \leq 0$·05.†Covariates include baseline sex and baseline values for age, high school attainment, medical insurance, smoker, waist circumference, diabetes status, physical activity level, as well as, total energy and modified HEI-2010 score. Values are HR (95 % CI) and can be interpreted as the increase in risk associated with each three serving/week increase in the meat exposure of interest.‡A serving was defined as 4·2 oz (120 g) for unprocessed red meat and 1·8 oz (50 g) for processed meat. Diet data were collected using a validated semi-quantitative FFQ.
Table 4Associations between meat consumption and incidence of CVD stratified by modified HEI score† Meat, 3 servings/2000 kcal/week‡ Modified HEI-2010 score, tertile123 n 1080 n 1081 n 1081HR95 % CIHR95 % CIHR95 % CIAll CVD (cases/1000 P-Y)10·49·310·8Total meat0·970·81, 1·161·040·87, 1·241·030·87, 1·22Unprocessed red meat0·990·67, 1·471·180·81, 1·731·060·77, 1·45Processed meat0·940·75, 1·191·000·79, 1·261·010·82, 1·26CHD (cases/1000 P-Y)3·14·24·2Total meat0·940·65, 1·350·980·75, 1·281·110·87, 1·41Unprocessed red meat0·930·45, 1·911·150·65, 2·041·190·78, 1·81Processed meat0·920·55, 1·480·910·62, 1·311·030·74, 1·43Stroke (cases/1000 P-Y)3·21·92·8Total meat0·770·51, 1·161·200·84, 1·730·940·67, 1·32Unprocessed red meat1·070·53, 2·172·451·32, 4·55* 1·400·94, 2·08Processed meat0·640·35, 1·190·820·44, 1·520·660·37, 1·17Heart failure (cases/1000 P-Y)5·605·806·50Total meat1·040·85, 1·281·020·82, 1·281·050·85, 1·30Unprocessed red meat1·100·66, 1·850·910·55, 1·521·000·65, 1·56Processed meat1·020·79, 1·311·080·82, 1·411·060·81, 1·38HR, hazard ratio; P-Y, person-years.* $P \leq 0$·05.†Covariates include baseline sex and baseline values for age, high school attainment, medical insurance, current smoker, waist circumference, physical activity level, diabetes status and total energy. Values are HR (95 % CI) and can be interpreted as the increase in risk associated with each three serving/week increase in the meat exposure of interest.‡A serving was defined as 4·2 oz (120 g) for unprocessed red meat and 1·8 oz (50 g) for processed meat. Diet data were collected using a validated semi-quantitative FFQ.
In secondary analyses, we explored whether the association of unprocessed red meat with stroke was driven by beef or pork consumption. We observed that beef (HR = 1·45 (1·07, 1·96)), but not pork (HR = 1·26 (0·44, 3·61)) was significantly associated with stroke overall, and among those in m-HEI Tertile 2 (HR = 3·00 (1·38, 6·52)) (online Supplementary Table 4).
## Discussion
Contrary to our hypothesis, after 9·8 years of follow-up, meat consumption (total meat, unprocessed red meat and processed meat) was not significantly associated with incident CVD or CVD types (CHD, stroke and heart failure), with the exception of unprocessed red meat, particularly beef, on stroke. Further, our results do not support that overall dietary quality, as measured by the m-HEI, differentially impacts associations of these meat categories with incident CVD or examined CVD subtypes.
To our knowledge. only one other study has investigated the prospective associations between red and processed meat consumption and CVD outcomes, specifically among AA adults. Using 22 years of follow-up data from the Black Women’s Health Study, a large cohort of AA women living across the USA, Sheehy et al[31] found that each serving per d increase in unprocessed red and processed red meat intakes was associated with 9 % (HR = 1·09 (CI: 1·00, 1·18)) and 14 % (1·09 (CI: 1·07, 1·21)) greater risk of CVD mortality, respectively. Although CVD mortality was not examined in the present study, we observed that neither CVD (non-fatal and fatal) nor CHD was associated with unprocessed red meat or processed meat intake. For comparisons purposes, our observed HR in servings/d of unprocessed red meat with CVD and CHD were 1·13 (0·71,1·83) and 1·11 (0·82,1·50), respectively. For processed meat, these values were 0·96 (0·71,1·29) and 0·95 (0·77,1·18), respectively. Our study builds upon prior evidence in AA adults[31] by examining individual CVD events, including CHD, stroke and heart failure, as well as total CVD.
Few prospective cohort studies have investigated unprocessed red and processed meat intakes with CHD(13–15) or heart failure(16–19). We observed that neither unprocessed red nor processed meat intakes were related to CHD. This is consistent with evidence from a multiethnic US cohort study (about 25 % Black)[14] and a recent meta-analysis of prospective cohorts[8]. In contrast, processed meat was associated with increased risk of CHD among US female nurses[15] and Danish men[13]. Our findings that unprocessed red meat was not associated with heart failure is in line with results from two Swedish cohort studies[17,18] and a recent meta-analysis[12]. Contradicting our null observations between processed meat and heart failure, these same studies reported an adverse association[12,17,18].
We may have observed null associations in our study, in part, because unprocessed red and processed meat consumption in the JHS did not reach threshold levels required to adversely affect CVD risk. Trends in US meat consumption have been estimated using data from NHANES cycles 1999–2000 to 2015–2016.[54] *During this* time period, mean intakes for adult groups 20 years and older ranged from 11·7 to 12 oz/week (284 to 340 g/week) for unprocessed red meat and 6·4 to 6·9 oz/week (182 to 196 g/week) for processed meat[54]. In the JHS, reported mean intakes per 2000 kcal were lower, at 9·7 oz/week for unprocessed red meat and 6·0 oz/week for processed meat. Low variability in meat intake in JHS may have also contributed to the observed null associations. Another potential reason for null results could have been the relatively short duration of follow-up (9·8 years). In a meta-analysis of prospective cohort studies, greater risk of CVD mortality from unprocessed red meat intake was observed among studies with 15 years or more follow-up, but not in those followed for less than 15 years[55].
The use of a single FFQ collected at baseline prevented us from examining changes in dietary intake during follow-up that may have an impact on CVD risk. This may introduce misclassification errors related to meat intake. The FFQ used in the JHS did not allow for the separation of processed meat by type (red meat v. poultry). In another study, red processed meat, specifically, was adversely associated with CHD risk[13]. Additionally, the JHS FFQ could not discern between most dishes prepared at home and pre-prepared frozen dishes. The latter may contain additives that may influence CVD risk, including preservatives. As reviewed elsewhere, there is great heterogeneity in how meat is defined across research studies[56]. Given the paucity of studies examining unprocessed red and processed meat consumption among AA adults and of studies examining CHD and heart failure, further studies examining CVD subtypes are needed with longer duration of follow-up, repeated measures of dietary intake and conducted in communities with greater variation in meat intake.
A critical barrier to improving dietary recommendations for unprocessed red meat and processed meat consumption for CVD risk reduction is the need for a clearer understanding of the impact of overall dietary quality on these relationships. Although we observed that unprocessed red meat intake was associated with greater risk of stroke among participants categorised as having medium dietary quality (m-HEI Tertile 2), tests of effect modification were not significant. Thus, the present study does not support that overall dietary quality influences the associations of unprocessed red and processed meat intake with CVD, CHD, stroke and heart failure. It is possible that our observation of a lack of effect modification by HEI score was due to a low variability in HEI scores. Our results are, however, in support of those from a recent study using data from the Danish National Survey on Diet and Physical Activity[37]. Dietary quality measured using a Danish Dietary Guidelines score did not significantly modify the associations of unprocessed red and processed meat intakes and IHD. In contrast to the present study, they did not exclude contributions from meat intake in their dietary quality score. Other studies have examined the potential modifying effect of fruit and vegetable intake on unprocessed red and processed meat and CVD associations[36,57]. In a cohort of Swedish adults, the harmful associations between red meat consumption and CVD mortality were not modified by fruit and vegetable consumption[36].
In the present study, unprocessed meat consumption was associated with a significantly greater risk of stroke. This result, however, should be interpreted with caution as it may be a chance finding due to multiple testing. Our findings are inconsistent with results from randomised controlled trials showing that red meat consumption does not adversely affect blood pressure[20,58], a leading risk factor for stroke[59]. Regardless, unprocessed red meat consumption could specifically elevate stroke risk through several biological mechanisms. Some epidemiological evidence supports that greater Fe status[60] and hereditary hemochromatosis[61] are risk factors for stroke. It has been proposed that Fe-catalysed reactions may result in thrombus formation[62,63], which can contribute to ischemic stroke. An estimated 87 % of strokes are ischemic[64]. Beef is the third major source of dietary Fe in the USA[65], and our results suggest that unprocessed beef was the main driver of the unprocessed meat–stroke association. In addition, unprocessed meats, particularly those that are cooked through direct heat (e.g. frying), are a major source of dietary advanced glycation end products[66]. In the present study, chicken-fried steak and ground beef, the latter typically consumed as hamburgers, were the top two contributors to unprocessed beef consumption. Dietary advanced glycation end products are generated through the Maillard reaction and have proinflammatory properties, including activation of the NF-κβ pathway[67]. Advanced glycation end product biomarkers are associated with an elevated risk of stroke[68], as well as CHD[68] and peripheral arterial disease[69]. In addition, meals containing fresh red meat may be a source of Na. Na in the form of salt is commonly added to red meat as well as to foods often consumed with red meat, such as French fries. Salt reduction reduces blood pressure[70] and elevated salt consumption increases the risk for stroke[59].
Our study has several strengths. We carefully considered the effect of overall dietary quality on associations between meat consumption and CVD risk. We conducted this study in an AA adult cohort, a group that experiences disproportionate CVD burden. We also considered a number of potential confounders. A limitation of the present study is the relatively low number of observed events, which resulted in wide confidence limits, lowering our ability to detect associations. In the ARIC study, CHD incidence rates/1000 P-Y were reported to be 5·1 and 10·6 among Black women and men, respectively[71], whereas it was 3·8 in the present study. Repeating analyses after a longer duration of follow-up will help to address this issue. Other limitations include the lack of objective biomarkers of meat consumption and the potential for residual confounding. The present study examined data from individuals living in Jackson, Mississippi. The average overall dietary quality (HEI-2010) in the present study (60·3 ± 11 for women and 57·4 ± 10 for men) was comparable to that reported in another study of AA adults living in the South (60·0 ± 12 for women and 55·3 ± 11)[45]. However, the limited geographic representation of the present study reduces the generalisability of our findings. Although a semi-quantitative FFQ validated for use among AA adults was used to measure long-term dietary intakes, the self-reported nature of the assessment method increases the chance for non-differential misclassification. This issue may have contributed to the null findings.
Among AA adults followed for 9·8 years, we observed that total meat, unprocessed red meat and processed meat intakes were generally not associated with elevated risk of CVD, CHD, stroke or heart failure. Unprocessed red meat, in particular beef, was associated with increased risk of stroke. There was little evidence to support that consuming a healthier overall diet impacted the strength of these associations. Additional studies in AA adult cohorts of longer duration follow-up and with greater meat intake are needed to replicate these findings.
## Conflict of interest:
There are no conflicts of interest.
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|
---
title: 'Community perceptions on the factors in the social food environment that influence
dietary behaviour in cities of Kenya and Ghana: a Photovoice study'
authors:
- Milkah N Wanjohi
- Rebecca Pradeilles
- Gershim Asiki
- Michelle Holdsworth
- Elizabeth W Kimani-Murage
- Stella K Muthuri
- Ana Irache
- Amos Laar
- Francis Zotor
- Akua Tandoh
- Senam Klomegah
- Fiona Graham
- Hibbah Araba Osei-Kwasi
- Mark A Green
- Nathaniel Coleman
- Kobby Mensah
- Robert Akparibo
- Richmond Aryeteey
- Emily K Rousham
- Nicolas Bricas
- Marco Bohr
- Paula Griffiths
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989710
doi: 10.1017/S1368980022002270
license: CC BY 4.0
---
# Community perceptions on the factors in the social food environment that influence dietary behaviour in cities of Kenya and Ghana: a Photovoice study
## Body
Globally, *Africa is* among the regions with the highest rate of urban population increase; about 60 % of the population is projected to live in urban areas by 2050[1]. Kenya and Ghana exemplify these trends. By 2020, about a third (28 %) and slightly more than half (57 %) of the Kenya and Ghana’s population respectively was urban[2]. Rapid and unplanned urbanisation in low- and middle-income countries is associated with urban poverty and various emerging environmental and health hazards[3]. It is also linked to changes in social economic and physical food environments and a subsequent nutrition transition[4] characterised by shifts in people’s food habits such as an increase in the consumption of unhealthy foods that are high in calories, fat, salt and sugar[4]. Unhealthy diets are estimated to make a greater contribution to the non-communicable disease burden than alcohol, smoking and physical inactivity, combined[5].
Both Kenya and Ghana are experiencing a nutrition transition, and an increasing trend in overweight and obesity[6]. Between 2000 and 2016, the prevalence of overweight and obesity combined increased, from 28 % to 45 % and 12 % to 20 % among women and men respectively in Kenya[7]. In Ghana, overweight and obesity combined increased, from 38 % to 58 % and 16 % to 27 % among women and men respectively in the same period[8]. Studies from these countries indicate a higher prevalence of overweight and obesity in women and urban residents[9]. Further evidence indicates a higher rate of increase in overweight and obesity among the poorest population segments in urban Africa[10]. A systematic review of dietary behaviours in both countries also revealed relatively low consumption of healthy foods, such as fruit and vegetables (52 %) and widespread consumption of unhealthy foods such as sugar sweetened beverages (40 %)[11].
The social food environment, defined as the food-related interactions between friends, family and peers,[12] has a major influence on individuals’ intent and actual food behaviour[13]. The social food environment, including social norms, networks and contexts that promote the adoption of unhealthy dietary behaviour, is a potential underlying factor for the development of obesity[14]. Understanding the role of the social food environment in influencing dietary behaviour is important to identify effective interventions for the promotion of healthier diets and prevention of diet-related non-communicable diseases, especially in urban contexts[15]. Currently, there is demand for nutrition policies and interventions to be more evidence-based, context and culturally specific, with recommendations for qualitative research to enhance the understanding of social processes that drive dietary behaviours in urban Africa’s social environment[15]. However, the specific social influences of dietary behaviours in urban contexts are not well documented in Africa[16], despite the increasing urbanisation and growing trends in overweight/obesity.
Hence, this study aimed to explore the perspectives of communities living in urban cities in Kenya and Ghana on the factors in the social food environment that influence their dietary behaviours. The ‘Photovoice’ methodology that uses the support of photography taken by local people to talk about their environment was used to provide the evidence for this aim[17]. Photovoice allows for an emic approach in investigating how local people identify their own food environment and how they perceive it. It also allows the researcher to see issues ‘through the eyes’ of study participants and communities. This methodology has been used in other high- and low-income countries to understand social food environments as perceived by adults[18], as well as adolescents and youth[19].
## Abstract
### Objective:
To explore communities’ perspectives on the factors in the social food environment that influence dietary behaviours in African cities.
### Design:
A qualitative study using participatory photography (Photovoice). Participants took and discussed photographs representing factors in the social food environment that influence their dietary behaviours. Follow-up in-depth interviews allowed participants to tell the ‘stories’ of their photographs. Thematic analysis was conducted, using data-driven and theory-driven (based on the socio-ecological model) approaches.
### Setting:
Three low-income areas of Nairobi (n 48) in Kenya and Accra (n 62) and Ho (n 32) in Ghana.
### Participants:
Adolescents and adults, male and female aged ≥13 years.
### Results:
The ‘people’ who were most commonly reported as influencers of dietary behaviours within the social food environment included family members, friends, health workers and food vendors. They mainly influenced food purchase, preparation and consumption, through [1] considerations for family members’ food preferences, [2] considerations for family members’ health and nutrition needs, [3] social support by family and friends, [4] provision of nutritional advice and modelling food behaviour by parents and health professionals, [5] food vendors’ services and social qualities.
### Conclusions:
The family presents an opportunity for promoting healthy dietary behaviours among family members. Peer groups could be harnessed to promote healthy dietary behaviours among adolescents and youth. Empowering food vendors to provide healthier and safer food options could enhance healthier food sourcing, purchasing and consumption in African low-income urban communities.
## Study setting
This study was part of a wider project[20] conducted in three rapidly growing urban African cities; Accra and Ho (Ghana) and Nairobi (Kenya). The study in Ho was part of a larger project (drivers of food choices) that targeted women of reproductive age only, whereas the studies in Accra and Nairobi were part of the TACLED project, which included both men and women aged 13 to 49 years[20]. The different cities represented different contexts in East and West Africa, and different levels of urbanisation and nutrition transition, including major cities (Accra and Nairobi) and a secondary city (Ho).
## Study design
This was a cross-sectional, qualitative study that employed Photovoice methodology. Photovoice is a community-based participatory and visual research methodology in which participants are given a camera to capture conditions and issues in their environment, through photographs[21]. The photographs taken then act as a visual prompt for participants, providing them with an opportunity to describe realities, communicate perspectives and raise awareness of complex public health issues in their environments[17,22].
## Sampling and data collection
As this study focused on lower income groups, a list of all deprived neighbourhoods in the selected cities (excluding slums) was compiled. This list was further restricted by retaining neighbourhoods that were deemed to be safe to work in by the research team. One neighbourhood in each city was then randomly selected using a manual lottery method: James Town (Accra), Dome (Ho) and Makadara (Nairobi).
Within the selected neighbourhoods, participants were purposefully recruited using quota sampling based on key characteristics (i.e. age, gender, BMI, socio-economic level, and education level and occupation status) (Supplementary file 1). This was to ensure breadth in the range of views, perspectives and environments that participants were exposed to. The Photovoice study was carried out on a random sub-sample (i.e. a third) of the overall study population of the wider project (target sample: n 64 in Accra, n 32 in Ho and n 48 in Nairobi; total n 144). Recruitment took place through the communities, schools and health services. Additional information on the sampling and recruitment strategy can be found elsewhere[23].
## Data collection
The Photovoice activity was conducted between September 2017 and June 2018. The format of the Photovoice prompt and interview guide used for this study was adapted from the conventional format proposed by Wang [1999], to suit the research context. The main adaptation that was made for this project was to conduct one-to-one interviews instead of the more collective workshop or focus group discussion approach that is normally used in Photovoice. The individual approach was used because our initial community engagement activities suggested that most of the targeted participants in urban areas were busy with work or school, and it would be hard to bring them together at the same time. In addition, the safety of group gatherings was considered a problem since Kenya was experiencing political instability at the time of data collection.
Prior to the start of data collection, the Photovoice open-ended interview guide was piloted (Accra (n 3), Ho (n 3) and Nairobi (n 4) and subsequently amended. The amendments mainly included simplifying the guiding questions (Photovoice prompts) and rephrasing of sentences to suit the local contexts. During the period of taking photographs, participants were visited four times in their homes by trained research assistants on a day that was most convenient to them. In the first visit, participants were taken through (i) the consent process, (ii) the Photovoice methodology, (iii) the use of a camera to take different types of photographs and (iv) photo ethics including the no face or identification details’ protocol to ensure anonymity of people or places. Participants were then requested to take five photographs during one week that best represented (i) a place where you eat food and/or beverage from; (ii) something that makes healthy eating difficult for you; (iii) something that makes healthy eating easy for you; (iv) something that influences what you eat in your (local) area; and (v) a person that influences what you eat in your (local) area. Two follow-up visits were made by research assistants during the week, to check on the progress and address any issues arising with the photography activity.
After one week, follow-up in-depth interviews to discuss the photographs were conducted, in which the participants told the ‘stories’ of the five photographs they had selected and provided a short caption to describe their favourite photograph. The in-depth interviews were conducted by research assistants, using the Photovoice open-ended interview guides (Supplementary file 2). The interviews were conducted mainly in local languages (Swahili for Kenya, & Twi, Ga and Ewe for Ghana) or (less often) in English. The prompts and interviews guides used were translated into local languages by accredited translators, and then back translated into English, to ensure that meaning was not lost. Interviews were digitally recorded and lasted between 45 and 60 min.
## Data analysis and synthesis
Interviews were transcribed verbatim, reviewed for accuracy and coded in NVivo 11 by at least two members of the research team in each study site (MNW/RP/AT/SK/FG/AI). All coders were extensively trained, and double coding of 25 % of the transcripts (n 36) was performed to ensure consistency when applying the codebook. Any discrepancies identified during the double coding process were discussed and resolved. External opinion was also sought from another member of our research team (PG) to discuss any unresolved coding approaches.
The approach taken for the development of the codebook and subsequent thematic analysis was both theory-driven, using a priori themes compiled using existing socio-ecological models of dietary behaviours[12,24] and data-driven (grounded), to allow for themes emerging from the data.
The socio-ecological model highlights factors influencing dietary behaviours across four levels: individual (preferences, knowledge, socio-demographic characteristics); social (family, friends, and peers); physical (the home, workplace, schools, restaurants, supermarkets) and macro (food marketing, food production, distribution systems)[12]. All the interviews were coded for individual level factors, social environment factors, physical food environment factors and macro-level factors. This manuscript reports a synthesis of the themes and subthemes on factors in the social food environment that are perceived to influence dietary behaviours in the three cities (Accra, Ho and Nairobi). The findings on the role of the individual- and physical-level food environments on dietary behaviours have been published elsewhere[23].
## Results
A total of 142 participants from Nairobi (n 48), Accra (n 62) and Ho (n 32) participated in this study, slightly lower than the targeted sample of 144 participants. Overall, 68·3 % of participants were female and nearly half were 19–49 years old. With regard to participants’ occupation, 35·2 % were in work, 13·4 % in education and 51·4 % not in work nor education. The proportion of participants with a BMI ≥ 25 kg/m2 was higher in Kenya (60·4 %) than in Ghana (Accra: 48·4 % and Ho: 46·9 %) (Table 1).
Table 1Socio-demographic characteristics of the participantsAccra (n 62)Ho (n 32)Nairobi (n 48) n % n % n %Gender Females4064·532100·02552·1 Males2235·500·02347·9Age 13–18 years2032·31237·51531·3 19–49 years2743·52062·51735·4 ≥50 years1524·2001633·3Socio-economic status Lowest3251·61650·02960·4 Low to middle3048·41650·01939·6Occupation In work2235·51237·51633·3 In education812·9412·5714·6 Not in work or education3251·61650·02552·1BMI <25 kg/m2 3251·61753·11939·6 ≥25 kg/m2 3048·41546·92960·4 The themes emerging on the influence of social environment on the participant’s dietary behaviour included [1] family members’ food preferences, [2] family members’ health and nutrition needs, [3] social support by family and friends, [4] provision of nutritional advice and modelling food behaviour by parents and health professionals, and [5] food vendors’ services and social qualities.
## Family members’ food preferences
Food preferences for different household members were central to the decisions that participants made on foods purchased, eaten or prepared for the entire household. For instance, in all the three cities, participants acknowledged buying or preparing foods that their children liked or could easily eat or take to school. Among married couples, the food preferences of one spouse influenced their partner’s food choices. Women across all cities reported considering their husbands’ preferences while making decisions on the foods prepared for the family. In Nairobi, some male participants indicated that their food consumption was solely dependent on the foods that their wives cooked for them. Further, some participants from households that had vegetarians or young children reported to mainly cook vegetarian meals or foods that young children could easily eat. Preparing common meals that everyone in the household could eat was seen as convenient, saving time and resources (Table 2).
Table 2Narratives and photographs on the theme ‘household members’ food preferences’Accra‘It’s because of my children that I mostly cook at home… At times I ask them what they want to eat and I prepare it and I also eat some of the food. The other day, they wanted to take in ‘Gari soakings’ (Cassava flour meal) and I did that for them and I also ate some.’ ( Female, 19–49 years, lowest SES, A35). Ho‘These are my kids. They are the ones that sometimes tell me what we should eat, then I cook it. You can’t just cook whatever you want. Because it is not everyone who will eat what you want to eat. So you will ask, ‘what should we eat?’ and then the kids can say mama, let us eat this or let us cook that’ (Female, 19–49 years, lowest SES, H6). Nairobi‘These are (my) children eating ‘Githeri’ (cooked mixture of maize and beans), it is their favourite, and they make me cook it, every time… In fact, we eat it around 3–4 times a week. This is important… because I value and love them (my children)’(Female, 19–49 years, low to middle SES, N11). Accra‘My husband likes fufu (yam or cassava flour meal) and soup, banku (fermented corn and cassava flour meal) and pepper and fish are his favourite. He loves soupy foods and because of that, I always cook at home, he does not encourage that we buy food from food vendors outside so he always encourage cooking at home’ (Female, 19–49 years, low-middle SES, A34). Ho‘I don’t really like yam, but as for them (husband and children), they like yam, they enjoy every day, so I have to prepare it for them. When I prepare it, he tells me to eat, so I take some and eat. They are the ones who make me eat’ (Female, 19–49 years, lowest SES, H13). Nairobi‘Most of the time, I am tired and I don’t have appetite but, my wife makes my eating easier, she influences me to eat because she knows the food that I like and the food that I like is traditional vegetables’ (Male, 19–49 years, low-middle SES, N30).‘For ‘mrenda’ (jute mallow) most of the time I eat because I find that is what my wife has prepared in the house so I cannot leave it since I also like it’ (Male, 50 years and above, lowest SES, N38).
## Health and nutritional needs of family members
Considerations for the health and nutrition of various family members influenced the foods purchased, prepared or eaten at home by the rest of the family. In Ghana (Accra and Ho), female participants, mainly in their role as mothers reported preference for foods perceived to promote their children’s health over those that were considered unhealthy. In Accra (but not Ho and Nairobi), there was a preference for preparing meals at home, citing the reasons that home prepared food is healthier, prevents children from falling sick and is cheaper so children can have enough food to satisfy them (Table 3).
Table 3Narratives and photographs on the theme ‘ health and nutritional needs of the family members’Accra‘The banku sold outside will not give you the needed strength and it’s not healthy that is why I prefer to cook at home so my family and children can eat it at home and not fall sick’ (Female, 19–49 years, low to middle SES, A22) Ho‘*It is* because of them (my children) that I eat a lot of healthy foods, so they can get the breast milk to feed on. If I don’t eat properly or healthy, they won’t get enough to feed on. It is because of them that I eat a lot or eat a healthy food to get more breast milk for them to feed on. Because if I don’t eat a lot or eat a healthy food, they will not get the breast milk to feed on and they need to grow well. And if they don’t eat well, me too, I will not get rest’ (Female, 19–49 years, low to middle SES, H57)
## Social support
Social support, by way of eating together, providing pleasant company during mealtimes and providing support with food provision and preparation emerged as a key influence on participant’s dietary behaviours.
## Eating together and company during mealtimes
Eating together habitually as a family was regarded as a moment of joy, bonding, fun, an easy and interesting time or a healthy practice. Some participants also reported that eating with family members facilitates ‘eating well’. Further, eating together was seen as an opportunity where family members connect. In Nairobi, breakfast or dinner was the meals that most families reported eating together, while in Accra, eating from the same bowl was a common family practice, but was not commonly mentioned in Ho or Nairobi.
Some of the participants acknowledged that they enjoyed eating in the company of their family as it improved their ‘appetite’ and fostered love and unity in the family Older participants in all the three cities particularly reported that their children influenced their dietary behaviour by encouraging them to eat and providing pleasant company during mealtimes. Among the younger participants, in Nairobi and Accra, but not Ho, friends were also reported to provide pleasant and enjoyable company during mealtimes (Table 4).
Table 4Narratives and photographs on the sub-theme ‘eating together and company during mealtimes’Accra‘We eat together as a family but everyone eats from their own bowl unless we are eating a meal like fufu where we eat from the same bowl. If we are eating a meal like rice and stew, we eat in different bowls. My two sisters normally eat from the same plate but I eat alone and my father and my mother eat in the same bowl’ (Female, 13–18 years, low to middle SES, A45). Ho‘*This is* my room, I eat from here, but sometimes we eat together, when we eat together I enjoy, than eating alone. I am from an extended family so when they want to give us food, they give it and we all sit together and eat. That is how I grew up, so everyone putting their hands in the food and then we all eat together’ (Female, 19–49 years, low-middles SES, H32). Nairobi‘We eat together as a family we sit together at the table we eat when we finish we start talking as a family to discuss issues just to enhance the togetherness’ (Male, 13–18 years, lowest SES, N36). Accra‘She (my daughter) will say ‘Dad since morning have you taken in food’? Then I will say ‘no’. She will say ‘Dad, don’t do that because you know you have stomach problem’ So she will force me and influence me then I will take the food’ (Male, 50 years and above, low-middle SES, A25). Ho‘Eating with my kids makes it easy for me to eat. And when we eat together, I enjoy it. When I am eating alone, I am unable to eat well; when we eat together, I am able to eat, they make jokes and make the eating fun, and then we all eat. It is important to eat with your kids and eating together fosters togetherness and friendship and love’ (Female, 19–49 years, low to middles SES, H27). Nairobi‘Yes, I feel happy because when I eat with her (child) I get the appetite to eat. Because when I eat alone I find it hard to cook because I am alone’ (Female, 50 years and above, lowest SES, N16). Accra‘Gbense Friday! A time where me and my friends come together and contribute money to prepare food and eat together… The reason is if we are all eating together we feel happy as compared to if you are eating alone every day. The way the food is, requires that boys come together to eat it’ (Male 19–49 years, low-middle SES, A23) Nairobi‘I pick this place because most of my friends are there, so if I go there I cannot miss my friends’ (Female 13–18 years, lowest SES, N14).
## Support with food provision and preparation
Husbands were identified as influencers of dietary behaviours, since they provided money for food purchase and, on some occasions, assisted with food preparation. In both Accra and Ho, female participants reported receiving support from their spouses, with food preparation, but this was not reflected in the narratives from Nairobi.
Younger participants in all three cities also highlighted that their friends influenced their dietary behaviours by buying food for them or by lending them money to buy food (Table 5).
Table 5Narratives and photographs on the sub-theme ‘support with food provision and preparation’Accra‘My husband is someone who provides the money that I use to buy food stuff and cook for the family to eat. At times I will be preparing banku and we will volunteer to help by frying the fish while I concentrate on preparing the banku… At times he goes to the kitchen to cook for the family whilst I rest. He could prepare the soup and also pound the fufu for us to eat’ (Female, 19–49 years, low-middle SES, A34). Ho‘Me, even my husband cooks for me. He can really cook. I don’t have time at all, So the males here also cook. So when you have a wife and you cook for her, it doesn’t mean, she has overcome you’ (Female, 19–49 years, low-middle SES, H15). Accra‘Sometimes he (my friend) has the money so he will be the one to choose the food. Sometimes I also buy and the two of us eat but mostly he is the one having money so most of the times he does and we go and buy food’ (Male 19–49 years, low-middle SES, A23). Ho‘There are times too, when I go and ask my friend to lend me some, and if she also doesn’t have the money, then that days food will be difficult for me and I won’t be able to eat. And I just can’t get up and ask anybody for money, I have to ask from the person I am close with. They are the only ones I can I ask money from, and if they also do not have it, well, then I can’t do anything about it’ (Female, 19–49 years, lowest SES, H13). Nairobi‘This picture is for my friends, you will find that there are times you find yourself you do not have enough money or you do not have money, you will find from one of your friends volunteers to buy you food, if another day also he does not have you are able to sort him out’ (Male, 19 to 49 years, lowest SES, N44).
## Nutrition advice and modelling foods behaviours
Parents were commonly mentioned as influencing their children’s food behaviour through providing advice on optimal amounts of food to consume, healthy places to buy food and types of foods to eat and those to avoid, particularly during sickness, pregnancy or lactation. This was more common in Accra and Ho, but less in Nairobi. In addition, parents were reported to model food practices, in Accra and Nairobi (but not Ho), with some participants acknowledging that their preferences of certain foods were based on the foods that they observe their parents eating, preparing, liking or disliking. Some of the younger participants in all the cities further reported that their dietary behaviours were exclusively based on the foods prepared for them by their parents and particularly their mothers, a few reported going along with their parents’ decisions and preferences for family meals even when it was against their own preferences.
Health professionals (nurses, nutritionists and doctors) appeared to influence mainly pregnant women and those with young children, in Nairobi and Accra. This was not reflected in Ho. When visiting health facilities, health professionals advised and encouraged participants to eat certain foods in order to be healthy or promote optimal development for their children. However, a participant in Accra also reported that community nurses advised on food and nutrition targeting children, but not enough for the adult population.
Friends were also mentioned as influencers of younger participant’s dietary behaviour and choices, by advising or encouraging them to eat certain foods (Table 6).
Table 6Narratives and photographs on the theme ‘support with nutrition advice and modelling food behaviours’Accra‘She (mother) advises me not to eat too many oily foods and also not to eat a lot of sugar and to eat foods that are hot. She said too much oily food will give me malaria, too much sugar I will get diabetes and it is also not good for my health. She said I should eat heavy food so that I don’t feel dizzy when walking and also to take in palmnut soup so that I will get more blood’ (Female, 19–49 years, lowest SES, A 53). Ho‘What he (father) does is that, some foods that I want to eat, cause some diseases, since he is older and he has passed through a lot of things in life, he will advise and tell me that it is not good. If I am going to cook such things, he tells me not to cook them. Or he will show me the way I can use them, then he will teach me before I will cook it and eat. And it will not affect my body’ (Female, 19–49 years, low-middle SES, H23). Accra‘My father was a cook so we all learnt how to cook, I can prepare any food. I like Banku with Okra stew because it used to be the favourite food of my dad’ (Male, 50 years and above, lowest SES, A24). Nairobi‘I love melon because it is my mum who influences me to eat melon because she loves melon a lot, most of the times you find that she buys melon a lot, like daily melons are in the house’ (Female, 19–49 years, lowest SES, N20). Accra‘Since my childhood, she (mother) is the only woman who has been cooking for me to be well satisfied and she is the only woman who in this area cooks and makes me healthy… She influences me with the foods that she cooks, sometimes rice, egg stew, sometimes too rice and okra stew which makes me healthy’ (Male, 13–18 years, low to middle SES, A13). Ho‘*My mum* and my dad, they take green pepper and lettuce. But I don’t like it. When she cooks it, I will not be able to remove them from the stew, so I must eat it like that. Also, sometimes, when she cooks jollof (spicy west African rice dish), she doesn’t always use meat, sometimes she uses fish. So, when you are eating the jollof, you are eating fish as well. I don’t like fish, if I was the one cooking, I would use meat’ (Female, 13–18 years, low-middle SES, H30). Nairobi‘*This is* the food that our parents cook but I don’t like it so much I prefer other foods. It is the food that my mother makes me eat, I don’t like to eat it, this one for avocado, banana and ugali (corn flour meal)’ (Male, 13–18 years, lowest SES, N47). Accra‘The nurse’s advice that we should be eating more frequently. We should not wait when we are hungry before we decide to eat. The eating interval should be short because the child in the womb eats from the mother so you have to eat frequently’ (Female, 19–49 years, low to middle SES, A50). ‘ These health workers (community health nurses) come around to advise us on how children should be fed and what to give them daily. But they don’t talk to us, the adults. It’s rather when you are sick and goes to the hospital that you are told what to eat and what not to eat’ (Male, 50 years and above, lowest SES, A24). Nairobi‘The doctor taught me if it is Ugali, the vegetables should be many and I should not exceed two pints of milk in the tea. That is the doctor’s advice so as to avoid cholesterol since cholesterol is what spoils your body’ (Female, 50 years and above, lowest SES, N12).
## Food vendors’ services and social qualities
In all three cities, the social qualities of local, mainly informal food vendors, largely influenced participants’ decisions on the places where they purchased food. Most participants preferred purchasing from food vendors who are friendly and hospitable, and avoided those who were impolite. Cleanliness of food vendors (referring to their appearance or how they dressed or smelt) and their food handling practices were key considerations by participants when making decisions on where to buy food. Participants in the three cities preferred food from vendors with whom they had established a good relationship, as it led them to trust the quality of foods sold. Food vendors providing credit services were also preferred by most participants, as they would always have access to food even when they did not have enough money. In Accra and Nairobi, provision of additional food services such as cutting /chopping vegetables and packaging were a consideration during food purchasing (Table 7).
Table 7Narratives and photographs on the theme ‘food vendors’ services and social qualities’Accra‘Sometimes you get to the food seller and the food will be cold and when you ask her to heat it for you, she will insult on you. This doesn’t show respect and you leave and not go there again’ (Female, 13–18 years, lowest SES, A41). Ho‘The way they talk to you, ‘Please, what are you buying?’ or ‘Please, thank you.’ You will be very happy then you will leave there glad. Even with how they will show their appreciation, you will want to go back and buy from there’ (Female, 13–18 years, lowest SES, H14). Nairobi‘*There is* one that I buy from, the first day he/she talked to me nicely and that is the reason why I buy from there’ (Female, 19–49 years, low-middle SES, N4). Accra‘There was a time a woman was roasting plantain that was nice but the woman selling the food, she was dirty. When I looked at the fingers, it didn’t attract me… So I didn’t buy it anymore’ (Male, 50 years and above, low to middle SES, A25). Ho‘(I consider) how they take care of the surroundings and how they take care of the food, when you go and eat, because you will enjoy the food and also feel the sweetness in the food, you will go there again. This will even pull customers to them’ (Female, 19–49 years, low-middle SES, H23). Nairobi‘She makes sure that she has washed those (vegetables) for you in clean water, for me it’s about cleanliness if you see me going somewhere it’s because of cleanliness’ (Male, 50 years and above, lowest SES, N39). Accra‘*She is* good to me because at times when I don’t have money, I go there and explain to her and she gives me food stuff to go and cook and when I get money I go back to pay her. This helps me a lot and makes my eating easy for me’ (Male, 50 years and above, lowest SES, A28). Ho‘I know she does it for me, I don’t have any issue with her, and so I am able to get things on credit’ (Female, 19–49 years, low to middle SES, H27). Nairobi‘This shop belongs to my neighbour we have known each other for many years and we have brought our children up together you can see that girl I send her there I like there because if I do not have she can lend me’ (Male, 50 years and above, lowest SES, N12). Accra‘They do the grinding nicely for you and the millers always have a smile on their faces when I go there. On some occasions I may not have the amount they charge for grinding but they do not turn me away. They take the things I have and grind it for me’ (Female, 19–49 years, lowest SES, A38). Nairobi‘It influences my eating because the man selling there is, he knows how to cut, when you go to purchase cabbage, you can tell him the size you want, and he can cut big ones or small ones’ (Male, 19–49 years, low to middle SES, N28).
## Discussion
This study set out to explore the factors in the social food environment that influence dietary behaviour in urban cities, in Kenya and Ghana. The findings highlight the important role played by the social environment in shaping individual’s dietary behaviours.
Family plays a critical role throughout Africa and is seen as a source of identity and support[25]. The impact of family relations including parents, spouses, children, grandparents and siblings on individuals’ health behaviour and wellbeing is documented in other settings[26]. Family socialisation and habits were strong influencers of individual members’ food habits in disadvantaged communities in the United Kingdom[27], and food selection for the family in urban Ethiopia[28]. Family members and household structure have also been shown to previously influence food choices and sourcing among Ghana’s urban poor communities[29].
In this study, considerations for specific member’s food preferences were a major influencer on the household’s food purchase, preparation and consumption. For instance, women in their role as ‘mothers’ or ‘wives’ were seen as key influencers of family members’ dietary behaviour by incorporating the nutritional needs and preferences of their family members in food preparation and purchase decisions or by providing nutrition advice to their children. Similarly, women in Singapore reported to consider the health and nutrition needs of their children, and also the food preferences of various family members, especially their children and spouses, while making decisions on food purchase and preparation[30]. In Ethiopia, women acknowledged that they give in to their children’s food preferences as a way of encouraging them to eat[28]. Results from this study (published elsewhere) further indicated that family food preferences were also shaped by the family’s food access and availability[23], which explains the preference for foods that most family members liked or could eat, which was seen as convenient, time and resource saving.
Younger participants acknowledged to have their dietary behaviour shaped by their parents’ choices on foods purchased, prepared and consumed by the family. Other studies indicate that parents influence their children’s behaviour through providing food, modelling dietary behaviour or encouraging specific dietary patterns(31–33). This influence may also be through exerting authority in food provision in the household or negotiating with children on the foods prepared or available at home[31], providing information on food selection and guiding them on good nutrition or preventing them from consuming hazardous food[32,34]. In Accra, it has been reported that mothers influence their children’s food behaviour by teaching them how to cook and choose the foods to eat[29]. Health professionals to a lesser extent also influenced individual health and dietary practices, through provision of information and advice, mainly to pregnant and lactating women. Our findings are similar to another study among young adults in Ghana’s Accra city which found that health professionals were among the key sources of nutrition information and were also perceived as the most credible sources of information[35]. In the smaller city of Ho in our study, this influence was found to be less common. This finding suggests that there is more opportunity for health professionals to influence healthy nutrition in urban populations, especially in smaller city contexts and to broaden nutrition information offered beyond pregnancy and the early years of life[36].
Social relations and support plays an important role in health and diet behaviour. In this study, children and friends appeared to be an important factor among the older and younger participants respectively through provision of food and pleasant company during meals. Eating together as a family was also seen a time for family bonding and interactions. Similarly, in the USA, social support mediated the effect of nutrition interventions in improving dietary behaviour in middle aged and older adults[37] while families that reported to eat their meals together exhibited a higher healthy index score[38]. A study in South Africa further concluded that individuals who experienced support from their friends and family in adopting a healthy diet were more likely to be motivated to identify healthy eating as their autonomous goals[39].
The younger participants in our study reported enjoying the company of their friends, highlighting that it ‘felt good’ eating together and ‘sharing’ some foods, than eating alone. In the same vein, a qualitative study in Lima Peru identified peers as among the key influencers of adolescents dietary behaviour, through sharing of foods such as energy dense snacks and sweetened beverages[34]. In Indonesia, eating together at school was considered an important social activity in forming and maintaining friendships and peer groups[40]. A review by Stok et al.[41] revealed that peer social norms influence food behaviour and that manipulation of these norms to promote certain food behaviours could yield significant beneficial results. Integrating social support in interventions targeting individual’s nutrition is therefore prudent. Interventions targeting adolescents and youth could also incorporate peer groups as avenues for delivering interventions to promote healthy dietary behaviour in these population groups.
Men in their role as ‘husbands’ or ‘fathers’ were sometimes referred to as providing supportive roles in food provision, purchase and preparation. This indicates that men may have ultimate influence on the food purchased and consumed in the household. A major difference, however, between the Kenya and Ghana cities was the influence that husband or male involvement had on family members’ dietary behaviour, through support with food preparation as reported in the data from Accra and Ho but not in Nairobi. The Nairobi findings are similar to those in previous studies in Uganda, Malawi and Ethiopia, where the role of men is depicted mainly as providing finances for food purchase[28,42,43], with little or no involvement in decision making on foods prepared or consumed in the household. This could be because food preparation and purchase are traditionally viewed as the primary role of women in the African culture. A study in Malawi highlights the challenge of men feeling stigmatised if they are seen to be taking up ‘women’s’ traditional roles, such as food preparation[43]. The Malawi study however revealed a progressive shift in gender roles from the perceived traditional role of men as merely ‘financial’ food provision to actively supporting women in food preparation, sourcing and purchase, which aligns with situation observed in Ghana in the current study. Development and enforcement of government policies to encourage greater male involvement in food and nutrition issues as well as gender equality advocacy programmes were some of the strategies employed in central Malawi, to enhance men involvement in food and nutrition issues[43], which could also be applied in other settings.
It was apparent in this study that food vendors have influence on individuals’ choices on food sourcing and provisioning. Food vendors play an important role in urban food systems, since a significant proportion of the food consumed by the urban poor is retailed by street vendors[44]. In a review by Pawel et al. [ 2012], characteristics of food sellers, including their friendliness and courteous service, were identified as determinants of the place of food purchase by consumers[45]. In our study, food safety, especially hygiene and sanitation in the food outlets, was raised as a concern by the study participants as discussed in a separate publication[23] which may explain the preference for food vendors who appeared clean and prepared their foods hygienically. A Photovoice study with adolescents in urban Ethiopia also highlighted food vendors hygiene as a major consideration and influencer of their dietary behaviour[19]. Other studies document that consumers’ relationship with food suppliers influences their choice of food purchase; long-term relationships and food supplier’s reputation are among the key considerations of clients[46]. In Accra, consumers have been reported to be motivated by relationships with food vendors and good customer care practices when making food purchase decisions[47]. Urban low-income settings in Ghana and Kenya are prone to food insecurity[44,48], and buying food through credit is a common coping strategy for food insecure households in urban settings[49,50]. In addition, results from this study also highlighted economic barriers to food access and overpricing of food products by some of the local food vendors as a potential hindrance to healthy eating[23]. This may explain the preference for food vendors who offer goods and services on credit in this study. In Ethiopia, buying food from food vendors who provide services such as packaging food in small and cheaper portions, and also credit services were reported as coping strategies for families experiencing food insecurity[28]. Developing, implementing and enforcing hygiene and safety regulations for food outlet owners were recommended by the participants in this study to ensure that food vendors maintained cleanliness as highlighted in a previous publication[23]. In addition, inclusion of food vendors in interventions aimed at improving populations’ dietary behaviour through provision of and increased access to healthier, hygienic and safer foods, will be prudent, given their influence on food purchase and dietary behaviour.
## Strengths and limitations
It is recommended that repeated group discussions are conducted throughout a Photovoice project to facilitate the community’s full engagement in the research. In our project however, only individual in-depth interviews were conducted due to logistic constraints of it being too difficult to find convenient times when a group of the urban low-income participants could come together frequently given other constraints on their time. We acknowledge this as a limitation, but we strived to ensure community engagement via separate community events. For example, a photography exhibition was held in each city to raise awareness of the drivers of unhealthy food consumption in the targeted low-income communities. The photography exhibitions served as a platform for community dialogue between study participants, the media and local government officers, during which, issues in their food environment and policy implications were discussed[23]. The qualitative study we conducted focused on understanding individual, social, physical and macro-level drivers of dietary behaviours. One paper combining all levels and describing the interactions between these four levels would have provided valuable information. However, we conducted 142 in-depth interviews across the three cities and gathered a large volume of data and photographs which provided a detailed, rich and comprehensive account of the drivers of dietary behaviours in the targeted African cities. As such, we wanted to describe in an in-depth manner those influences and pathways through which these factors may influence dietary behaviour and hence the choice to split the different components of the study into different papers[23].
A key strength of this study is that it provides empirical findings from three cities in two African countries. Using the Photovoice methodology allowed participants to visually present social issues that influence their dietary behaviour. It also allowed active participation of respondents in the data collection, not only as mere respondents but playing an active role in identifying, capturing and describing the social influencers of their dietary behaviour that they perceive as important. The photographs taken by the participants allowed the research team to better appreciate the issues presented and hence facilitate richer discussions on these issues. This approach was enriching to the quality of the data collected and presented in this paper.
## Policy implications
In cognisance of the role that the family plays in influencing individual members’ dietary behaviours, interventions focusing on enhancing dietary behaviour at the individual level should consider and leverage the existing household and family structure, and their interconnectedness to be successful. In addition, friends and peers were common influencers of food consumption behaviour among younger participants. As such, peer groups may be considered as effective avenues for delivering interventions targeting adolescents. Food vendors influence food purchase behaviour. Empowering them to provide healthier and safer food options could also enhance healthier food sourcing, purchasing and consumption in African low-income urban communities. Healthcare workers influence nutrition knowledge, through provision of nutrition advice predominantly to pregnant and lactating mothers and mothers with young children, revealing a gap in the interaction with healthcare workers regarding diet for those not falling into these categories. The aspects of the social food environment highlighted in this study, how they influence dietary behaviour, and the population groups that they are most relevant to, should be considered when developing context and population-specific interventions to enhance healthier dietary behaviour.
## Conflict of interest:
The authors declare that they have no competing interests in the manuscript.
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|
---
title: Dose–response relationship between dietary inflammatory index and diabetic
kidney disease in US adults
authors:
- Yong-Jun Wang
- Yang Du
- Guo-Qiang Chen
- Zhen-Qian Cheng
- Xue-Mei Liu
- Ying Lian
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989711
doi: 10.1017/S1368980022001653
license: CC BY 4.0
---
# Dose–response relationship between dietary inflammatory index and diabetic kidney disease in US adults
## Body
Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease, accounting for an estimated 50 % of all end-stage renal disease cases globally[1,2]. It has been confirmed that DKD, as one of the most common microvascular complications of diabetes, is associated with considerable morbidity and mortality[3]. In addition, the progression of DKD to end-stage renal failure frequently requires renal replacement therapy, which carries with substantial health care costs[4]. With the rising incidence of diabetes mellitus, threat from DKD will possibly be exacerbated[5]. Therefore, it is imperative to uncover additional modifiable factors in addition to the classic factors for DKD, which would be helpful in developing targeted prevention strategies.
The pathophysiological changes of DKD are likely attributable to the metabolic and haemodynamic abnormalities[6]; however, the exact underlying mechanisms are complex and may involve multiple pathways. DKD is regarded as a metabolic-driven immunological disease[7]. Existing evidence has suggested associating DKD risk with inflammation, indicating that both systemic and local renal inflammation play crucial roles in the development of DKD[8]. Diet, as one of modifiable lifestyles, should not be neglected as a potential source of inflammation because of the property of specific nutrient. Existing studies indicated that some anti-inflammatory nutrients, for example, fibre, vitamin D and n-3 PUFA, are associated with lower serum levels of inflammatory biomarkers and lower risk of albuminuria and then slower kidney function decline(9–11). Of note, current clinical guidelines recommend a comprehensive approach examining the effects of overall diet rather than solely looking at individual nutrient, considering that it allows for easier translation into practical dietary advice[12]. Therefore, assessing the overall inflammatory potential of diet and understanding the relation of diet-induced inflammation with DKD risk are important, as it may offer a unique perspective to develop strategy to alter dietary habits and harness the onset and progression of DKD.
The dietary inflammatory index (DII) represents a new tool to measure the dietary inflammatory potential[13], which has been proved to be associated with inflammation[14,15]. Accumulating evidence has identified DII to be a potential risk factor for various diseases, such as cancers[16] and diabetes[17], which are risk factors for DKD. However, fewer studied have investigated the association between dietary inflammatory potential and kidney health, and some limitations have been proposed. For example, previous studies only conducted in specific population (e.g. specific age groups)[18]. Yet, no study has investigated the effect of the dietary inflammatory potential on DKD in a nationally representative sample, and the pattern of dose–response associations remains to be explored.
Because of lack of exhaustive estimate on the relationship, it is imperative to undertake an updated, comprehensive research to bridge the knowledge gap. Accordingly, the present study using data from the National Health and Nutrition Examination Survey (NHANES) aimed to assess if the DII is associated with DKD and to determine if the association presents in a dose–response manner.
## Abstract
### Objective:
The impact of the dietary potential inflammatory effect on diabetic kidney disease (DKD) has not been adequately investigated. The present study aimed to explore the association between dietary inflammatory index (DII) and DKD in US adults.
### Design:
This is a cross-sectional study.
### Setting:
Data from the National Health and Nutrition Examination Survey (2007–2016) were used. DII was calculated from 24-h dietary recall interviews. DKD was defined as diabetes with albuminuria, impaired glomerular filtration rate or both. Logistic regression and restricted cubic spline models were adopted to evaluate the associations.
### Participants:
Data from the National Health and Nutrition Examination Survey (2007–2016) were used, which can provide the information of participants.
### Results:
Four thousand two-hundred and sixty-four participants were included in this study. The adjusted OR of DKD was 1·04 (95 % CI 0·81, 1·36) for quartile 2, 1·24 (95 % CI 0·97, 1·59) for quartile 3 and 1·64 (95 % CI 1·24, 2·17) for quartile 4, respectively, compared with the quartile 1 of DII. A linear dose–response pattern was observed between DII and DKD (P nonlinearity = 0·73). In the stratified analyses, the OR for quartile 4 of DII were significant among adults with higher educational level (OR 1·83, 95 % CI 1·26, 2·66) and overweight or obese participants (OR 1·67, 95 % CI 1·23, 2·28), but not among the corresponding another subgroup. The interaction effects between DII and stratified factors on DKD were not statistically significant (all P values for interactions were >0·05).
### Conclusions:
Our findings suggest that a pro-inflammatory diet, shown by a higher DII score, is associated with increased odd of DKD.
## Study sample
The NHANES is a nationally representative survey conducted in the US population, aimed to assess health and nutrition status[19]. The periodic surveys, in which a stratified multistage probability sampling method was used, included data regarding demographics, socio-economics, lifestyle habits and laboratory tests. The details of NHANES are available elsewhere[20]. The written informed consent was obtained from all participants[21].
Data from five NHANES cycles (2007–2016) were incorporated in the present study. Participants aged 20 and under were excluded (n 21 387). We excluded participants who were pregnant or lactating (n 403). According to previous studies, participants with unusual energy intakes of less than 2092 kJ/d (500 kcal/d) or above 20 920 kJ/d (5000 kcal/d) in females and less than 2092 kJ/d (500 kcal/d) or above 33 472 kJ/d (8000 kcal/d) in males (n 3277) were excluded[22]. Other participants were excluded for missing values of DKD (n 1121). Remaining 4264 participants with diabetes were included in the analyses. Figure 1 presents the flow chart of study sample.
Fig. 1Flow chart of study sample. NHANES, National Health and Nutrition Examination Survey; DKD, diabetic kidney disease
## Definition of diabetic kidney disease
DKD was defined as diabetes with the presence of albuminuria, impaired glomerular filtration rate (GFR) or both[23]. Diabetes was defined as [1] a self-reported previous diagnosis by health care professionals, [2] fasting plasma glucose level of 7·0 mmol/l or higher, [3] HbA1c concentration of 6·5 % or higher or [4] taking glucose-lowering medications. Albuminuria was defined as the ratio of urine albumin to creatinine (ACR) of 30 mg/g or higher. Chronic Kidney Disease Epidemiology Collaboration equation was used to estimate the GFR. GFR less than 60 ml/min per 1·73 m2 was defined as impaired GFR.
## Assessment of dietary inflammatory index
The DII was computed based on the dietary intake data gathered by day 24-h dietary recalls. We calculated the DII score for twenty-seven food parameters available. Table 1 presents the mean intakes of the included food parameters. The calculation of DII was as follows: first a Z-score was calculated by subtracting the ‘standard global mean’ from the reported amount for each food parameter, which were then divided by the sd. The Z value was converted to a percentile score and transformed to a centred score. The corresponding inflammatory effect score multiplied by the above derived values to produce the DII score. A lower DII score represents a more anti-inflammatory diet, whereas a higher DII score represents a more pro-inflammatory diet. In our analyses, DII score varied between −4·73 and 4·55 and categorised into quartiles: quartile 1 (Q1: −4·73, −0·52), quartile 2 (Q2: −0·51, 1·01), quartile 3 (Q3: 1·02, 2·30) and quartile 4 (Q4: 2·31, 4·55).
Table 1Food parameters included in the dietary inflammatory indexFood parameterMean intakeCarbohydrate (g)218·70Energy (kcal)1832·32Protein (g)75·97Fat (g)71·56Fibre (g)16·25Cholesterol (mg)291·17SFA (g)22·92MUFA (g)25·81PUFA (g)16·38 β-carotene (μg)2150·31Vitamins A (RE)607·49Vitamins B1 (mg)1·48Vitamins B2 (mg)1·91Vitamins B6 (mg)1·88Vitamins B12 (μg)4·91Vitamins C (mg)77·07Vitamins D (μg)4·50Vitamins E (mg)7·32Folic acid (μg)363·36Fe (mg)13·86Mg (mg)271·58Zn (mg)10·43Se (μg)106·15 n-3 PUFA (g)1·65 n-6 PUFA (g)14·55Alcohol (g)4·63Caffeine (g)0·14
## Covariates
The following potential covariates included in the analyses were selected based on literature review and availability in our data set: age, sex, race (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic and other race), educational level (below high school, high school and above), marriage status (married/living with partner, widowed/divorced/separated/never married), family poverty income ratio, smoking status (never, current and former), drinking status (no, yes), physical activity level (low, moderate and high), BMI and hypertension. The physical activity was assessed using the Global Physical Activity Questionnaire. BMI was calculated as weight in kilograms divided by height in meters squared. Participants rested quietly in a sitting position for 5 min, and three consecutive blood pressure readings were obtained and averaged. Hypertension was defined as the average systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or use of anti-hypertensive medication. All above related information was gathered through standardised questionnaire, physical examination and laboratory tests.
## Statistical analysis
Sample characteristics were compared using the ANOVA for continuous variables, and the χ 2 tests for categorical variables. Binary logistic regression models were conducted to examine the association between DII and DKD with OR and 95 % CI, in which the Q1 of DII was the reference category. We firstly adjusted for age, and sex, then for race, educational level, marriage status, family poverty income ratio, smoking status, drinking status, physical activity level, hypertension and BMI in the multivariable-adjusted model. Furthermore, restricted cubic spline models with knots at the 5th, 35th, 65th and 95th percentiles were performed to explore the shape of dose–response relationship between DII and DKD adjusted for all above covariates[24]. In addition, stratified analyses were carried out by socio-demographic and clinical characteristics including sex, age (middle-aged adults: < 60 years old, older adults: ≥60 years old), educational level, BMI (underweight/normal: ≤ 24·9 kg/m2, overweight/obese: >25 kg/m2) and status of hypertension. Stata 15.0 software (StataCorp., LP) was used in all analyses.
## Results
Among 4264 participants, the weighted proportion of DKD was 36·2 %. The characteristics of our sample across quartiles of DII score are shown in Table 2. Participants in the Q4 of DII score were older, more likely to be female, single, have lower educational level, less physical activity, less household income, higher BMI, current smoking and drinking. The prevalence of hypertension, DKD, impaired GFR and albuminuria was higher among participants with the most pro-inflammatory diet.
Table 2Characteristics of study sample according to DII quartilesCharacteristicsQ1 of DII (−4·73, −0·52)Q 2 of DII (−0·51, 1·01)Q3 of DII (1·02, 2·30)Q4 of DII (2·31, 4·55)Mean sd Mean sd Mean sd Mean sd Age (years)59·9913·1360·7913·5360·5413·4261·8613·62 n % n % n % n %Sex Male71166·7061457·6049146·0642539·87 Female35533·3045242·4057553·9464160·13Race Non-Hispanic White39737·2439937·4334832·6536233·96 Non-Hispanic Black22421·0125724·1130128·2431129·17 Mexican American22220·8320419·1418617·4518817·64 Other Hispanic1049·7612011·2614713·7913012·20 Other race11911·16868·07847·88757·04Educational level Below high school27425·7034432·2741939·3049045·97 High school and above79274·3072267·7364760·7057654·03Marriage status Married/living with partner70566·1364660·6060156·3858254·60 Widowed/divorced/separated/never married36133·8742039·4046543·6248445·40Family poverty income ratio ≤120719·4121920·5029727·8434131·99 1–1·8424522·9729027·2228726·9032630·54 ≥1·8561457·6255752·2848245·2639937·47Smoking status Never54951·5051147·9453049·7252749·44 Current12711·9116315·2919318·1022220·83 Former39036·5939236·7734332·1831729·74Drinking status No76071·2570866·4067663·3760756·93 Yes30628·7535833·6039036·6345943·07Physical activity level Low62858·8967563·3271867·3574870·16 Moderate28226·4325724·1123421·9521920·59 High15614·6813412·5711410·70999·25 Hypertension31529·5534031·9135433·2437635·26Mean sd Mean sd Mean sd Mean sd BMI (kg/m2)32·307·4332·547·3232·637·4932·647·31 n % n % n % n %DKD38035·6540437·9044942·1250447·28Impaired GFR16515·4619418·2423321·9025924·30Albuminuria28226·4830028·1534532·3938435·98DII, dietary inflammatory index; DKD, diabetic kidney disease; GFR, glomerular filtration rate.
Table 3 shows the weighted associations between DII with DKD, impaired GFR and albuminuria. In the multivariable-adjusted model, the OR of DKD was 1·04 (95 % CI 0·81, 1·36) for the Q2, 1·24 (95 % CI 0·97, 1·59) for the Q3 and 1·64 (95 % CI 1·24, 2·17) for the Q4 compared with Q1 of DII score. Similar figures were 1·16 (95 % CI 0·81, 1·68), 1·35 (95 % CI 0·97, 1·88) and 1·57 (95 % CI 1·10, 2·26) for impaired GFR, and 1·00 (95 % CI 0·75, 1·33), 1·28 (95 % CI 0·94, 1·72) and 1·56 (95 % CI 1·14, 2·12) for albuminuria.
Table 3Weighted OR (95 % CI) of the association between DII and DKDCharacteristicsQ1 of DIIQ2 of DIIQ3 of DIIQ4 of DIIOR95 % CIOR95 % CIOR95 % CIDKD Model 1* 11·130·91, 1·401·401·14, 1·721·911·57, 2·32 Model 2† 11·040·81, 1·361·240·97, 1·591·641·24, 2·17Impaired GFR Model 1* 11·290·96, 1·731·571·22, 2·021·981·52, 2·57 Model 2† 11·160·81, 1·681·350·97, 1·881·571·10, 2·26Albuminuria Model 1* 11·070·85, 1·361·411·10, 1·811·881·48, 2·39 Model 2† 11·000·75, 1·331·280·94, 1·721·561·14, 2·12DII, dietary inflammatory index; DKD, diabetic kidney disease; GFR, glomerular filtration rate.*Model 1 adjusted for age and sex.†Model 2 adjusted for age, sex, race, educational level, marriage status, family poverty income ratio, smoking status, drinking status, physical activity level, hypertension and BMI.
In the cubic spline model, a linear dose–response relationship was found between DII and DKD (P nonlinearity = 0·73). The adjusted OR for per unit increasing of DII was 1·13 (95 % CI 1·07, 1·19) for DKD. The dose–response relationship between DII and DKD is presented in Fig. 2.
Fig. 2The dose–response relationship between dietary inflammatory index (DII) and diabetic kidney disease (DKD) In the stratified analyses, the association of DKD for the Q4 of DII was statistically significant among adults with higher educational level, with OR 1·83 (95 % CI 1·26, 2·66), but not among adults with lower educational level. The association for the Q4 was statistically significant among overweight or obese participants, with OR 1·67 (95 % CI 1·23, 2·28), but not among underweight/normal participants. The interaction effects between DII and stratified factors on DKD were not statistically significant (all P values for interactions were >0·05). The associations of DII with DKD in stratified analyses are shown in Table 4.
Table 4The weighted OR (95 % CI) of the association between DII and DKD in stratified analysesCharacteristicsQ1 of DIIQ2 of DIIQ3 of DIIQ4 of DII P for interaction OR95 % CIOR95 % CIOR95 % CISex0·73 Female10·890·62, 1·311·200·82, 1·771·721·13, 2·62 Male11·491·07, 2·091·461·01, 2·131·711·20, 2·43Age0·12 Middle-aged adults10·970·67, 1·421·210·81,1·801·591·01, 2·51 Older adults11·230·89, 1·691·431·04,1·971·891·40, 2·56Educational level0·23 Below high school11·020·62, 1·701·150·76, 1·751·430·91, 2·23 High school and above10·960·69, 1·331·220·90, 1·641·831·26, 2·66BMI0·83 Underweight/normal10·390·16, 1·001·040·44, 2·471·390·67, 2·90 Overweight/obese11·150·89, 1·491·260·98, 1·621·671·23, 2·28Status of hypertension0·36 Hypertension10·980·63, 1·531·180·77, 1·821·971·29, 3·02 Non-hypertension11·210·91, 1·941·431·07, 1·901·651·19, 2·29DII, dietary inflammatory index; DKD, diabetic kidney disease. Models adjusted for age, sex, race, educational level, marriage status, family poverty income ratio, smoking status, drinking status, physical activity level, hypertension and BMI.
## Discussion
In the present study, we explored the association between DII and DKD using a nationally representative sample of US adults. A more pro-inflammatory diet, as estimated by a higher DII score, is associated with increased odd of DKD. Particularly important, the association presents in a linear dose–response manner.
The findings were consistent with previous studies on the association between dietary patterns and kidney function(25–27). For example, a prospective analysis in the Nurses’ Health study found that adherence to the pro-inflammatory Western-style diet, compared with a healthier diet, was associated with an increased risk of GFR decline, with the associations no variation by diabetes status[28]. Conversely, the higher alternative healthy eating index, a measure of diet quality negatively correlated with DII[29], was associated with a lower odd of albuminuria among population with diabetes[30]. Similarly, a multi-ethnic study also showed that a diet rich in fruit and wholegrains, with presumed anti-inflammatory properties, was associated with lower odds of micro-albuminuria in individuals with diabetes[31]. Although each dietary index represents a unique combination of dietary nutrients, to some extent, they share considerable similarities and have been significantly associated with inflammatory markers[32]. Notably, DII is designed to assess the dietary inflammatory potential, which represents the inflammatory mechanism underlying the diet–health link. Importantly, it is worth emphasising that our findings make a significant addition to literature by demonstrating association between DII and DKD among a nationally representative sample of population. The observed associations emphasise the potential of avoiding pro-inflammatory diet in DKD prevention.
In our study, subgroup analyses stratified by potential factors were established, followed by interaction terms to test the heterogenicity among different subgroups. The interaction between DII and stratified factors on DKD was not statistically significant, which ensures the reliability of the conclusion. This is in line with previous studies that identified similar associations between the Dietary Approaches to Stop Hypertension diet and kidney disease by sex, race and education level[33]. In contract, a study identified the statistically significant interaction between BMI status and alternative healthy eating index on end-stage kidney disease risk[25]. And, another study showed that the Dietary Approaches to Stop Hypertension diet was associated with rapid GFR decline among participants with hypertension but not among those without hypertension[34]. Further research is necessary to replicate these findings from stratified analyses.
The cubic spline analysis further visualised the association between dietary inflammatory potential and DKD development. A cohort study of women aged 70 years indicated that there was a linear association between DII and the baseline renal function, renal function trajectory, suggesting that one-unit higher DII score was associated with a 0·55 ml/min per 1·73 m2 lower GFR at baseline and a 0·06 ml/min per 1·73 m2 greater annual decline in GFR over 10 years[35]. Another study conducted among older adults indicated that an increment of 1 sd in DII was associated with lower GFR, with a β of −1·8 % (95 % CI −2·7 %, −0·9 %)[18]. Similarly, a linear dose–response pattern was found for the association between DII and DKD in the present study, in which 1-unit increasing of DII was associated with a 13 % higher odd of DKD. Previous studies have provided some supporting evidence for the benefits of maintaining an anti-inflammatory diet(36–38). It is encouraging of the observed association that avoiding a pro-inflammatory diet could serve to be an additional, non-pharmacologic means for prevention of DKD. In future, more research is required to develop evidence-based preventive models, whether lowering intake of inflammation-promoting diet can translate to reducing the development of DKD.
Several possible mechanisms may explain the link between DII and DKD. First, a pro-inflammatory diet is positively associated with elevated inflammatory levels such as leucocyte counts[39]. Although the metabolic disorder is historically considered as the pathogenesis of DKD, recent studies have established that the inflammatory responses also play a central role in progression of DKD. It has been shown that inflammation together with neutrophil–endothelium interactions could contribute to the pathogenesis of kidney injury, potentially leading to chronically impaired kidney function. Second, the dietary inflammatory potential is well recognised to regulate oxidative processes[40]. It is reported that the oxidant–antioxidant imbalance plays an important pathogenic role in the development of diabetic complications, including diabetic nephropathy[41,42]. Furthermore, the gut–kidney axis may represent a potential pathway underlying the inflammatory diet–DKD link[7]. Clinical trials have further shown the mechanistic involvement of gut microbiota in the pathophysiology of DKD by proving that gut microbiota can potentially trigger immune, metabolic and fibrotic pathways, which perpetuate the progression of renal pathology[43,44]. As has been reported that specific pro-inflammatory nutrients included in the calculation of DII serve to be key determinants of the modulating gut microbiota composition and activity[45,46].
One of the strengths is that this is the first study to explore the associations between the dietary inflammatory potential and DKD in a nationally representative sample of population. Second, data of NHANES are from a nationally representative sample of the USA, which enables the observed associations to be generalised. Third, the cubic spline analysis in our study, which characterises a dose–response association between a continuous exposure and an outcome, can help to clarify how the odd of DKD changes along with dietary inflammatory potential increasing. There are also some limitations in our study. First, our study is the cross-sectional design, which does not allow for inferences about causality. Second, as shown in online supplementary material, Supplemental Table 1, the proportion of excluded participants was relatively high, because of extreme energy intake, which might bring about bias in estimating the associations. Finally, the dietary data were from 24 h dietary recall interviews, and there might be ineluctable recall bias. Thus, well-designed longitudinal studies incorporating accurate assessment of measures are needed to fully clarify the causal relationship.
Our findings have public health and clinical significance, which is potentially important for not only the prevention but also the management of DKD. From the public health perspective, although this association was not causally shown, it may be legitimate to advise individuals to adhere to an anti-inflammatory diet to reduce their risk for kidney disease complications. Evidence-based public health education and publicity should be strengthened in an even broader segment of US population to raise awareness of altering dietary habits and promote an anti-inflammatory diet for DKD prevention. In addition, from the clinical perspective, future research should aim to evaluate the dietary inflammatory potential, develop nutritional protocol and consider incorporating it in dietary guidelines of managing DKD.
## Conclusion
In conclusion, a more pro-inflammatory diet, as estimated by the higher DII score, was significantly associated with higher odd of DKD. Our findings emphasised the importance of developing novel nutritional approaches to prevent and manage DKD. Further clinical trials are required to strengthen the evidence of the associations between DII and DKD.
## Conflicts of interest:
The authors declare no conflict of interest.
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|
---
title: 'Inequalities in energy drink consumption among UK adolescents: a mixed-methods
study'
authors:
- Christina Vogel
- Sarah Shaw
- Sofia Strömmer
- Sarah Crozier
- Sarah Jenner
- Cyrus Cooper
- Janis Baird
- Hazel Inskip
- Mary Barker
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989712
doi: 10.1017/S1368980022002592
license: CC BY 4.0
---
# Inequalities in energy drink consumption among UK adolescents: a mixed-methods study
## Body
Poor diet is a major contributor to the burden of non-communicable diseases and, in the UK, costs the National Health Service £6 billion annually[1,2]. Evidence from the annual rolling National Diet and Nutrition Survey (NDNS) shows that UK adolescents aged 11–18 years have poorer diets than other age groups[3]. Additionally, the Health Survey for England indicates that 23 % of adolescents aged 11–15 years are already obese; a figure that has gradually increased from 14 % in 1995[4]. Implementing strategies that improve adolescents’ dietary behaviours are crucial because a sub-optimal diet in adolescence affects immediate health as well as raising the risk of obesity and non-communicable diseases later in life and in the next generation[5,6].
Non-alcoholic beverages are the primary source of free sugars (sugar added to food/drink or found in honey, syrup or juice) in adolescents’ diets, most of which are sugary soft drinks and energy drinks[3]. A survey of energy drinks for sale in the UK indicates that their average sugar content was 9·7 g/100 ml, with some drinks containing up to 16 g/100 ml[7]. Approximately half of the energy drinks available also have a serving size of 500 ml meaning a single bottle markedly exceeds the current dietary recommendation for free sugars which is 30 g/d for individuals aged over 11 years[8].
Energy drinks are distinguishable from other soft drinks because they contain large amounts of caffeine and potentially other stimulants such as guarana, taurine and ginseng[7]. The energy drink survey described above identified that the average caffeine content of energy drinks is also high at 31·6 mg/100 ml (±0·8), equating to 158 mg of caffeine in a 500 ml bottle[8]. The current European caffeine recommendations specify a daily allowance of 3 mg of caffeine/kg body weight[9]. A single serving of these drinks therefore surpasses this recommendation for adolescents with a body weight below 53 kg. Among adolescents energy drink consumption has been linked to several physical symptoms including headaches, stomach aches, hyperactivity and insomnia; these symptoms largely relate to the high caffeine and sugar content of energy drinks[10].
Energy drink sales have grown substantially over the past decade with current UK sales estimated at 680 million l/year[11]. Alarmingly, a European Food Safety Authority report indicated that young people aged 10–17 years are the greatest energy drink consumers[12]. The report’s statistics indicate that British adolescents consumed the greatest quantity of energy drinks of all participating European countries, consuming over a litre a month more than the European average of 2 l/month. The report also showed that more older adolescents (73 %) and boys (74 %) reported consuming energy drinks than younger adolescents (55 %) and girls (63 %).
Increased awareness of the potential dangers that energy drinks pose to young people’s health has led several major retailers to impose voluntary bans on the sale of energy drinks to minors under 16 years[13]; many UK schools have also introduced voluntary bans to prevent students drinking them on school premises[14]. In 2018, the UK Government undertook a consultation on their proposal to introduce legislation to ban the sale of energy drinks to minors; they proposed this would create consistency across retailers and protect young people’s health[15]. The House of Commons Science and Technology Committee released an advisory report at the end of 2018 outlining their interpretation of the evidence and recommendations to government. They concluded that there was insufficient evidence to warrant introducing a ban on selling energy drinks to children[16]. The Committee’s report acknowledged that energy drinks were consumed disproportionately by disadvantaged groups but noted that evidence of this trend worsening over time or undermining educational or health outcomes was needed for action to be taken. Additionally, insufficient evidence about the impact of voluntary bans was highlighted, with recognition that qualitative evidence from teachers and parents could indicate societal concerns that would provide legitimacy for a statutory ban. Contrary to the Committee’s recommendations, the ‘Advancing our Health: prevention in the 2020’s’ green paper, released in 2019, announced that the UK Government intended to introduce a ban on the sale of energy drinks to individuals aged under 16 years[17]. The basis for this ban was largely founded on the rationale that reductions in energy drink consumption would decrease calorie intake and improve diet, thereby helping to lower obesity rates. Providing scientific evidence of these associations would further support government intervention and is necessary because the exact details of this policy are yet to be published.
To address existing evidence gaps and provide robust scientific evidence to inform policy change, we conducted a mixed-methods study combining data from a national dietary dataset with qualitative data from interviews with adolescents, parents and teachers. The specific aims were:To determine the prevalence of energy drink consumption among adolescents in the UK and assess how consumption varies by gender and age group. To examine associations between energy drink consumption among adolescents in the UK and deprivation and dietary inequalities. To explore teachers’, parents’ and adolescents’ perceptions of adolescent energy drink consumption and the effectiveness of current voluntary energy drink restrictions in schools and supermarkets.
## Abstract
### Objective:
To examine energy drink consumption among adolescents in the UK and associations with deprivation and dietary inequalities.
### Design:
Quantitative dietary and demographic data from the National Diet and Nutrition Survey (NDNS) repeated cross-sectional survey were analysed using logistic regression models. Qualitative data from semi-structured interviews were analysed using inductive thematic analysis.
### Setting:
UK.
### Participants:
Quantitative data: nationally representative sample of 2587 adolescents aged 11–18 years. Qualitative data: 20 parents, 9 teachers and 28 adolescents from Hampshire, UK.
### Results:
NDNS data showed adolescents’ consumption of energy drinks was associated with poorer dietary quality (OR 0·46 per sd; 95 % CI (0·37, 0·58); $P \leq 0$·001). Adolescents from more deprived areas and lower income households were more likely to consume energy drinks than those in more affluent areas and households (OR 1·40; 95 % CI (1·16, 1·69); $P \leq 0$·001; OR 0·98 per £1000; 95 % CI (0·96, 0·99); $P \leq 0$·001, respectively). Between 2008 and 2016, energy drink consumption among adolescents living in the most deprived areas increased, but decreased among those living in the most affluent neighbourhoods ($$P \leq 0$$·04). Qualitative data identified three themes. First, many adolescents drink energy drinks because of their friends and because the unbranded drinks are cheap. Second, energy drink consumption clusters with other unhealthy eating behaviours and adolescents do not know why energy drinks are unhealthy. Third, adolescents believe voluntary bans in retail outlets and schools do not work.
### Conclusions:
This study supports the introduction of age-dependent legal restrictions on the sale of energy drinks which may help curb existing socio-economic disparities in adolescents’ energy drink intake.
## Study design and setting
This study adopted a mixed-methods study design. Quantitative data were used to address the first two research aims and qualitative data used to address the third aim. Qualitative and quantitative datasets were then combined to corroborate findings and expand the breadth and depth of interpretation. Quantitative data were taken from the NDNS rolling programme, a repeated cross-sectional survey conducted with a representative sample of the national population. Each year the NDNS programme recruits approximately 500 adults and 500 children aged over 18 months from randomly selected households across the UK. Participants (or their parents) are asked to complete a face-to-face questionnaire about household and individual demographics as well as an estimated food diary (food/drink is not weighed to reduce participant burden).
Qualitative data were collected as part of the development work for the Engaging Adolescents in CHanging Behaviour (EACH-B) study, a multi-component intervention to support adolescent diet and physical activity[18]. This formative work was conducted in community settings in Hampshire, UK. All elements of this study were conducted according to the Declaration of Helsinki and data protection regulations and were approved by the University of Southampton, Faculty of Medicine Ethics Committee (ethics approval number 30054).
## Quantitative data: National Diet and Nutrition Survey
Dietary intake data were derived from food diaries. Participants recorded details of all foods and drinks consumed on up to four consecutive days, with estimated portion sizes, brand names or ingredients for homemade meals. Trained NDNS coders classified the items in the diaries into 154 food groups and assigned energy values (kcals). Detailed descriptions of the design and methodology can be found elsewhere[19]. Our analyses were performed on 2587 adolescents aged 11–18 years from the combined survey waves 1–8, from 2008 to 2016.
Frequency of energy drink consumption was calculated for each participant, adjusting for the number of diary days completed. As energy drinks are not categorised into their own NDNS food group, the names of all items categorised in the ‘Soft drink, not diet’ and ‘Soft drinks, diet’ categories were extracted and reviewed. Energy drinks were defined as drinks (excluding tea and coffee) containing over 150 mg of caffeine (but those with low caffeine (> 100 and < 150 mg/l) were excluded) in accordance with European Union labelling regulations for high-caffeine products requiring warning labels for children[16]. Energy drinks with low or no sugar were included.
Total daily energy intake was calculated for each participant by summing the energy for all the food and drink items consumed and averaging over the number of diary days. A diet quality score was derived for each participant using NDNS data using a published methodology[20]. Diet quality scores were generated using principal component analysis on 139 food groups; vitamins, minerals and artificial sweetener groups were removed. principal component analysis is a commonly used method for generating dietary patterns[21]. The first component of the principal component analysis explained the greatest variance in the dietary data and represented a diet consistent with UK dietary recommendation: higher consumption of fruit, vegetables, wholegrains and lower intake of sugar-sweetened beverages, chips and processed meats. The principal component analysis allocated coefficients to each food group to quantify their contribution to the overall component. The coefficients and reported frequencies of consumption were used to calculate a dietary quality score for each participant. To facilitate interpretation of the results, dietary quality scores were standardised to a mean of zero and a SD of one with higher scores representing better quality diets; dietary scores have been validated against fourteen nutritional biomarkers, including serum folate, homocysteine, total carotenoids and vitamins B12, C and D[20].
An equivalised household income variable was developed using total household income reported by the main food provider, adjusted for household size and demands. Index of multiple deprivation (IMD), the official measure of relative deprivation for small areas in England, was calculated for each participant based on the household postcode[22]. IMD scores were divided into quintiles and used to determine the neighbourhood deprivation for each household. IMD was not recorded in the NDNS for waves 5 and 6 due to changes in the study protocol. BMI Z-scores were created to adjust for age and sex[23] and categorised according to cut-offs defined from nationally representative surveys with adolescents.
## Qualitative data: interviews with adolescents, parent and teachers
Semi-structured interviews were conducted with parents, teachers and adolescents to learn about adolescents’ daily food and physical activity habits and what could support healthier choices. Interviews were conducted with an additional sample of adolescents to explore energy drink consumption in more depth. All interviews were conducted using semi-structured topic guides distinct for each participant group (available on request). Participants were not shown the questions in advance.
Participants were recruited in 2018 from a secondary school, a community youth club and at a hospital open day. Adolescents were interviewed at their school or youth club. The school was a non-selective mixed secondary school where above-average numbers of students were eligible for free school meals (36·7 %), compared to the national average (28·6 %). The youth club targeted adolescents from disadvantaged backgrounds with low school attendance. Teachers at the school were interviewed during working hours. Parents were interviewed at their workplace or home by telephone or in person at a hospital evening event for parents. Adolescents and teachers were interviewed in either pairs or groups of three to six participants; parents were interviewed individually or in groups containing three participants. All face-to-face interviews were conducted by one researcher (S.St., S.Sh. or S.J.) with an observer present who took notes (S.St., S.Sh., S.J., D.W., D.P.N. or T.M.); telephone interviews were conducted by one researcher (S.St., S.Sh. or S.J.) and with a single participant. Interviews were transcribed verbatim and pooled together into a single NVivo project (QSR International Pty Ltd., 2018: version 12). Participants did not comment on their transcripts.
## Quantitative data analyses
Summary statistics were used to describe NDNS sample characteristics: mean (sd) for normally distributed continuous variables and median (IQR) for non-normally distributed continuous variables. Frequencies are quoted for binary variables. Frequency of energy drink consumption was calculated per participant, adjusting for the fact that 1·8 % of diaries were completed for only three of the 4 d (98·2 % of participants completed 4 d diaries). Energy drink consumption was highly skewed, with 93·0 % of children reporting consuming no energy drinks in their food diaries; these data were therefore analysed as a dichotomous variable (consumer v. non-consumer). To assess differences in energy drink consumption according to age, gender and neighbourhood deprivation, the proportions of energy drink consumers were calculated across categories of demographic variables. Logistic regression models were fitted with energy drink consumption as the outcome to describe the effects of demographic variables on energy drink consumption. A logistic regression model was fitted with IMD, year of study and the interaction between the two as predictor variables; the interaction term describes whether the effects of IMD differ as the year of study increases. To assess whether energy drink intake related to dietary quality, diet scores were divided into tertiles describing poorer, medium and higher quality diets. Age was divided into two groups, 11–15 and 16–18 years because most adolescents commence secondary school at age 11 and college at age 16. Household income was divided into two categories (<£27 000 and ≥£27 000) which is reflective of the UK median household income in 2012, the midpoint for the data time period[24]. Daily energy intake was divided into four groups (<1400 kcals, 1400 kcal–<1700 kcal, 1700–< 2000 kcal, ≥2000 kcal). Changes in diet quality score are interpreted in terms of the original foods consumed by calculating the equivalent change on the original scale to the change from the median on the Fisher–Yates transformed scale.
Weights were provided in the NDNS dataset to adjust for the under-representation of children in households with more than one child (only one adult and up to one child were recruited per household) and for the cluster identifier (small geographic postcode sectors randomly selected from across the UK from which addresses were randomly selected). The weights were rescaled to reflect different sample sizes in different waves so all data could be combined. Weighted analyses are presented throughout.
## Qualitative data analysis
NVivo queries were used to extract the broad context of any references to ‘energy drinks’, and popular brands in the UK e.g. ‘Red Bull’, ‘Monster’ and ‘Rockstar’. ‘ Lucozade’ was also included because brands with lower caffeine levels are colloquially called energy drinks[16]. Quotes were analysed using conventional content analysis following established guidelines[25]. Initial codes were developed by C.V. and S.J. by creating ‘nodes’ in NVivo as new topics arose. After all transcripts had been coded, ‘nodes’ were refined with input from S.Sh., S.St. and M.B. and organised into themes and sub-themes. This approach is aligned with a relativist ontological and subjective epistemic position, which purports that reality is a matter of individual perspective and based on personal experience and insight[26]. To ensure the interpretation was an accurate representation of interviewees’ views and data analysis decisions were transparent, a rigorous process was adopted in which data were double-coded by pairs of the researchers, and disagreements were resolved in team discussions throughout the coding process. The five researchers involved in the qualitative analysis were all women, their expertise were in nutrition and/or psychology and their ages varied from young adult to middle age.
## Participant characteristics: quantitative data
The quantitative analysis sample comprised all 2587 adolescents, 1305 girls and 1282 boys (aged 11–18 years) in waves 1–8 (2008–2016) of the NDNS dataset (Table 1). The majority were aged 11–15 years (61 %), of white ethnicity (89 %) and lived in households with <£27 000/year (66 %). The distribution of BMI Z-score was similar by age categories, such that 31 % of those aged 11–15 were classified as overweight or obese, compared to 27 % of those aged 16–18.
Table 1Characteristics of NDNS sample (n 2587)CharacteristicsSummary statistics n %Gender Girls130550·4 Boys128249·6Age (years) 11–15 years157260·8 16–18 years101539·2Ethnicity White230989·3 Other27610·7Equivalised household income (£) <27 000152465·7 ≥27 00079734·3IMD Two least deprived quintiles66640·0 Three most deprived quintiles99960·0BMI (kg/m2) Thin1496·0 Healthy weight161364·7 Overweigh50620·3 Obese2259·0Completed four diary days Yes254198·2 No461·8Energy drink consumer Yes1827·0 No240593·0MedianIQREnergy intake (kcal/d)17111401, 2052IMD, index of multiple deprivation; NDNS, National Diet and Nutrition Survey.
## Participant characteristics: qualitative data
Of the fifty-seven interviews conducted, twenty-eight were with adolescents (n 74), twenty with parents (n 24) and nine with teachers (n 15) (Table 2). Demographic data were not collected from two adolescent group interviews (∼25 %) due to time restrictions. Of the adolescent participants who provided demographic data (n 55), most were aged 13–14 years (95 %) and of white ethnicity (98 %); fewer than half were girls (42 %). The majority of parents and teachers were women (100 % and 80 %, respectively) and of white ethnicity (96 % and 87 %, respectively); most parents were aged 40–49 years (71 %), while almost three-quarters of teachers were aged 20–39 years (73 %).
Table 2Characteristics of qualitative sampleCharacteristicSummary statisticAdolescents n 74Parents n 24Teachers n 15 n % n % n %Age 13 years2635·1–––– 14 years2635·1–––– 15 years11·4–––– 16 years11·4–––– 17 years11·4–––– 20–29––––640 30–39––28533·3 40–49––1771426·7 50+––52100 Missing1925·7––––Gender Girl/women2331·1241001280·0 Boy/man3141·9––320·0 Other11·4–––– Missing1925·7––––Ethnicity White5473·023961386·7 Other11·414213 Missing1925·7–––
## Aim 1: prevalence of energy drink consumption among adolescents in the UK
The NDNS data showed that 7·0 % of adolescents consumed at least one energy drink in a 4 d period. Older adolescents were more likely to consume energy drinks than younger adolescents (Table 3). A 1-year increase in age was associated with a 21 % increase in the likelihood of energy drink consumption (OR 1·21; 95 % CI (1·12, 1·31); $P \leq 0$·001). This trend of increased energy drink consumption through adolescence did not decline over time, despite the known increase in public awareness of safety concerns regarding energy drinks ($$P \leq 0$$·50). No difference was observed between the proportion of girls and boys consuming energy drinks ($$P \leq 0$$·81).
Table 3Associations of energy drink consumption status with participant demographics for 11–18 year oldsConsuming energy drinks (n)Total (n)Weighted proportion consuming energy drinksOR95 % CI P-valueAge (years) 11–159315724·9––– 16–188910158·3–––Trend per year–––1·211·12, 1·31 * < 0·001Gender Boys10012826·1Baseline–– Girls8213056·41·050·69, 1·610·81IMD 1 (least deprived)173473·5––– 2123193·0––– 3163184·6––– 4203426·8––– 5 (most deprived)3233910·4–––Trend–––1·401·16, 1·69 † < 0·001Equivalised household income < £27 00012815247·6––– ≥ £27 000367973·7–––Trend per £1000–––0·980·96, 0·99 ‡ < 0·001Diet quality score Low10286310·7––– Medium598626·6––– High218622·3–––Trend–––0·460·37, 0·58 § < 0·001Energy intake (kcal) < 1400326455·6––– ≥ 1400 and < 1700426354·3––– ≥ 1700 and < 2000415946·6––– ≥ 2000677138·2––Trend per 100 kcal–––1·041·01, 1·08 ‖ 0·02BMI (kg/m2) Thin151499·4––– Healthy weight11616135·6––– Overweight265065·3––– Obese2022510·1–––Trend per sd BMI Z-score–––1·090·90, 1·31 ¶ 0·40*Age modelled as a continuous variable in years.†Index of multiple deprivation (IMD) quintile modelled as a continuous variable.‡Income modelled as a continuous variable per £1000.§Diet quality score modelled as a continuous variable (sd units).‖Energy intake modelled as continuous variable per 100 kcal.¶BMI modelled as a continuous variable.
## Aim 2: associations between energy drink consumption and deprivation and dietary inequalities
Adolescents in more deprived areas consumed energy drinks more frequently than those in more affluent areas (Table 3). A one quintile increase in IMD was associated with a 40 % increased likelihood of consuming energy drinks (OR 1·40; 95 % CI (1·16, 1·69); $P \leq 0$·001). Similarly, adolescents from lower annual income households were more likely to consume energy drinks compared to those from higher annual income households (Table 3). A £1000 increase in household income was associated with being 2 % less likely to consume energy drinks (OR 0·98; 95 % CI (0·96, 0·99); $P \leq 0$·001). Between 2008 and 2016, energy drink consumption among adolescents living in the most deprived areas increased, whilst consumption among those living in the most affluent neighbourhoods decreased (P for interaction of year and IMD = 0·04) (Fig. 1).
Fig. 1Association between IMD and energy drink consumption over time among 11–18 year olds. Note: Index of multiple deprivation (IMD) was not collected in waves 5 (2012–2013) and 6 (2013–2014) Adolescents’ consumption of energy drinks was also associated with poorer dietary quality (Table 3). A 1 sd increase in dietary quality score was associated with being 54 % less likely to consume energy drinks (OR 0·46; 95 % CI (0·37, 0·58); $P \leq 0$·001). Changes in diet quality scores can be achieved in many ways; an illustration of 1 sd higher dietary quality score is consuming seven additional portions of nuts and seeds and six additional portions of salad and other raw vegetables/week, plus six fewer portions of chips and six fewer portions of sugar-sweetened carbonated-drinks/week. A 100 kcal increase in daily energy intake was also associated with 4 % increased likelihood of consuming energy drinks (OR 1·04; 95 % CI (1·01, 1·08)). Additionally, a 1 SD increase in BMI score was associated with 9 % increased likelihood of consuming energy drinks (OR 1·09; 95 % CI (0·90, 1·31); $$P \leq 0$$·40).
## Aim 3: to explore teachers’, parents’ and adolescents’ perceptions of adolescent energy drink consumption and the effectiveness of current voluntary energy drink restrictions in schools and supermarkets
Three dominant themes were identified from the qualitative interviews, which are summarised below along with illustrative quotes.
## Theme 1: a lot of young people drink energy drinks – friends and price are key reasons why
Many of the adolescents interviewed mentioned consuming energy drinks weekly or monthly, and a number reported more frequent consumption. For several adolescents, energy drinks were part of their daily routine:‘I like Rockstar. It’s the only thing I drink’ (Adolescent interview, school 13) ‘I buys one [energy drink] every time I gets chip shop though, and I gets chip shop like- every day.’ ( Adolescent interview, youth club 2) Adolescents sometimes struggled to say ‘no’ to energy drinks when they were offered them by their peers, but some had made a conscious decision to reduce their consumption after learning what the drinks contained. These adolescents expressed confusion when they saw their parents or other adults drinking them:‘If someone buys me one, I have a sip of it, but then give it away… I know what’s in them now’ (Adolescent interview, school 13) ‘My dad drinks like a big can of energy drink, like Rockstar, he drinks that…My mum’s cousin was drinking energy drinks literally all the time.’ ( Adolescent interview, youth club 7) Some adolescents described the high price and taste of energy drinks as a deterrent; however, many reported enjoying the taste and reported consuming the unbranded energy drinks because of their low cost:‘I’d rather just go buy a can of coke for that [price]. And it’s tastier.’ ( Adolescent interview, youth club 3) The cheap 30p ones [energy drinks]……better than paying like one pound forty for Red Bull.” ( Adolescent interview, youth club 1) Parents and teachers were also aware that many adolescents consume energy drinks regularly. Teachers raised concerns about the social desirability of energy drinks among adolescents and most acknowledged that energy drink consumption had become an accepted norm among their students and conformed to the social pressures:‘You can see what they’ve been buying on this App. And these fruit drinks, which are energy drinks, he bought four in 1 day’ (Parent interview 1) ‘There’s definitely a social pressure. If their friends [say], ‘oh I have five energy drinks a day’, there is just a constant pressure. I think they’re at that point where they’re thinking about who they are and who they want to be’ (Teacher interview 3)
## Theme 2: energy drinks are not good for health and cluster with unhealthy diets
Adolescents largely recognised that energy drinks are not good for their health and some had experienced negative physical side effects from drinking them. Many, however, were confused over exactly why energy drinks are deemed so unhealthy:‘I can’t drink them much ‘cause it makes me have really bad belly ache, and makes my chest feel even worse.’ ( Adolescent interview, youth club 6) I: ‘You said they did something at school about it [energy drinks]?’ P: ‘Yeah, I dunno they just said sugar.’ ( Adolescent interview, youth club 1) Teachers described having witnessed the harmful physical and behavioural effects energy drinks have had on their students. They also said that energy drink consumption was often accompanied by eating unhealthy foods or under eating; some teachers expressed alarm at how this affected students’ school attendance or ability to learn:‘I had a GCSE student [who] was making himself ill by drinking these energy drinks, he’d have one every morning but not eat anything. About mid-day he’d feel really ill, and he’d go home. He’d have stomach cramps, a headache.’ ( Teacher interview 2) ‘Their breakfast is a packet of crisps and an energy drink’ (Teacher interview 4)
## Theme 3: voluntary bans do not work
Adolescents described how the voluntary bans in place in food retail outlets did not prevent them from being sold energy drinks, particularly in smaller, convenience stores. Some adolescents did not think this was right but were also pleased they could buy energy drinks if they wanted them, indicating a conflict between societal and individual needs:‘They [brought] out that law like a couple of years ago didn’t they, that [shops] weren’t allowed to sell energy drinks and they still do. They still sell them to me, and I’m only fourteen’ (Adolescent interview, youth club 3) ‘Even my little sister drinks them – she’s eleven … my eleven year old sister would be able to go in there [local shop], get a can of energy drink.’ ( Girl, ED interview 2) Adolescents also felt that profits were more important to some businesses than following the voluntary bans to sell energy drinks to minors. These sorts of statements were said with disdain towards the shop owners indicating disapproval of this approach:‘Shops just don’t care, as long as they’re making their money.’ ( Adolescent interview, youth club 7) Teachers also reported that voluntary bans in schools had limited effectiveness on reducing energy drink intake. Teachers were aware that students smuggled energy drinks into school and acknowledged that enforcing the school bans were not always easy:‘I know quite a few of them still have Lucozade though and Red Bull…. They do, they hide it in their bags very well.’ [ Teacher interview 13] Some adolescents felt that if energy drinks are detrimental to their health, access to them in stores should be restricted so they were more difficult to buy. Other adolescents, however, were adamant that they would find ways around stricter sales restrictions:‘But then energy drinks, if they’re that bad and they’ve gotta have ID then surely they shouldn’t be in the fridge, they should be behind the tills with all the alcohol.’ ( Adolescent interview, youth club 7) ‘We’d get people to get them for us … and I’d get it from the shop myself ‘cose I know all the shops round here and they all love me.’ ( Adolescent interview, youth club 6)
## Main findings
This mixed-methods study used a nationally representative dietary dataset (n 2587) to characterise inequalities in energy drink consumption and semi-structured interviews with a large sample of adolescents, parents and teachers (n 113) to provide deeper insight into adolescent energy drink intake and effectiveness of its current regulation. The findings showed that the overall prevalence of adolescents’ energy drink consumption over a 4-d period was 7·0 %, and that consumption rates were higher among older adolescents, regardless of gender (Aim 1). Additionally, this trend of greater energy drink consumption with age did not decline over time. This study demonstrated clear associations between adolescent energy drink intake and markers of socio-economic deprivation and dietary inequalities (Aim 2). Adolescents living in more deprived areas and from lower income households were considerably more likely to consume energy drinks than those from more affluent areas and households, and higher energy drink consumption was associated with poorer dietary quality, higher energy intake and greater body mass. Worryingly, inequalities in energy drink consumption by area deprivation increased over the 8 year timeframe of the quantitative dataset, with rates increasing among those from the most deprived areas and decreasing among those most affluent.
Three themes were identified from the interviews with adolescents, parents and teachers (Aim 3). First, many adolescents who drink energy drinks do so because of their friends and because the unbranded drinks are cheap. Second, energy drink consumption clusters with other unhealthy eating behaviours and the harmful physical effects of energy drinks have been witnessed by teachers and some parents; yet many adolescents do not know exactly why energy drinks are unhealthy (Aim 3). Third, participants generally felt that voluntary bans in retail outlets, particularly smaller stores, and in schools do not work; many favoured the introduction of legal restrictions on selling energy drinks to minors but some felt they could find ways to circumvent tougher restrictions.
## Comparison with previous research
The prevalence estimate of adolescent energy drink consumption from this study seems lower than previous research from a similar point in time, including findings from the World Health Organisations’ European Health Behaviour in School-aged Children study which indicates that energy drink consumption rates across countries range from 9 to 24 %[27-30]. Such differences in prevalence rates may be due to variations in data collection methods. These previous studies asked about energy drink intake within the past week and reported prevalence rates of 15, 21 and 24 % for adolescent consumption at least once a week, and 9 % prevalence for consumption 2–4 times a week. The current study used food diaries of up to 4 d. It is therefore likely that due to this short time frame, our findings offer a more conservative estimate of energy drink consumption compared with other studies and indicate the prevalence of very frequent, or daily, energy drink intake among adolescents in the UK.
Teachers and parents perceived that energy drinks were associated with a specific social identity which fuelled their popularity among adolescents. Energy drink consumption has previously been linked to group membership and social identity among young people[31]. In this study, social status acquired from energy drink consumption was not expressed explicitly by adolescent participants and it is unclear how aware they were of their behaviour being influenced by cultural norms. Some adolescents, however, did mention feeling pressured to partake when energy drinks were being circulated by their peers. Adolescents are known to value social acceptance and group membership, but simultaneously strive for autonomy[32]. These somewhat conflicting determinants of behaviour may help to explain a reluctance in revealing or understanding the true motives for their energy drink consumption.
Internationally, research has shown that energy drinks are consumed more frequently by older adolescents and by those from more disadvantaged backgrounds[28,30,33,34]. Our findings align with this previous work, showing that each additional year of age increased the likelihood of consuming energy drinks by 21 %, with highest rates among 17 and 18 year olds. This pattern likely reflects the growing levels of independence over food choices that adolescents acquire with age, and challenges the UK Government’s proposal to prohibit the sale of energy drinks to those under 16 years of age. Applying the cut-point at 18 years of age would be more consistent with the evidence on energy drink intake and could help protect older adolescents from more disadvantaged backgrounds who appear to be particularly vulnerable to the regular intake of energy drinks.
A disturbing pattern of increasing inequalities in energy drink consumption was revealed in our study, whereby intakes among adolescents from the most deprived communities increased over an 8-year period while intakes among those from affluent communities decreased. Clustering of unhealthy behaviours among energy drink consumers was also apparent in both our quantitative and qualitative data results, showing that energy drink consumers had poorer quality diets, higher daily energy intake and larger BMI. Previous research has shown that multiple unhealthy behaviours cluster among young people from disadvantaged backgrounds, particularly low fruit and vegetable intake and high tobacco and alcohol use, as well as low fruit and vegetable intake and low physical activity levels coupled with high sedentary behaviour and high sugary drinks intake[35,36]. In our study, each additional sd increase in BMI was associated with 9 % greater likelihood of adolescents’ consuming energy drinks. Although not statistically significant, this may suggest the simultaneous occurrence of health-compromising behaviours that could accentuate the risk of non-communicable diseases among these young people. This higher risk has implications for themselves, their future off-spring and society. Interventions to reverse entrenched inequalities are likely to be most successful if they target multiple risk behaviours and address social and environmental drivers[37].
## Implications for policy
The findings from this study support the UK Government’s plans to introduce legislation to end the sale of energy drinks to minors[17]; it suggests that voluntary bans in large supermarket chains and schools are not implemented effectively and are undermined by smaller convenience stores who continue to sell these products to adolescents. More deprived neighbourhoods have higher concentrations of convenience stores and poorer in-store environments[38,39]. Such unhealthy environmental exposures have been shown to exacerbate existing dietary inequalities[40-42] and may be contributing to the increasing disparity in energy drink consumption between adolescents from more deprived and more affluent areas; legislation may therefore help to address inequalities.
Importantly, this study highlights that the proposed legislation would miss the opportunity to reduce consumption among the highest energy drink-consuming adolescents; those aged 16–18 years. The limit of 16 years may be challenging to implement and easier for younger adolescents to work around. Well-established age restrictions on the sale of tobacco and alcohol to those aged under 18 years already exist in the UK. Aligning the limits on the sale of energy drinks with these established legislations would provide a clear message to the public that these drinks are not suitable for adolescents, as well as facilitating consistent enforcement across all retail premises.
For the proposed legislation to be maximally effective additional actions could be considered by policymakers, including minimum pricing of energy drinks and positioning them in restricted areas of retail outlets. The cheap price of own-brand energy drinks was identified as a key determinant of their consumption by adolescents, particularly those from disadvantaged backgrounds, in this and previous research[16]. Introducing a minimum pricing of energy drinks could successfully limit intake in a similar way that the introduction of minimum alcohol pricing in Scotland showed immediate impact, reducing alcohol purchases among lower income and higher alcohol-purchasing households[43]. Additionally, adolescents interviewed in this study suggested that placing energy drinks behind counters with tobacco and some alcohol products would clearly indicate a health warning and make them less accessible to young people. There is increasing evidence that product placement influences purchasing patterns and could be used to support health behaviours, including among adolescents[44,45].
A communications campaigns about the harmful effects of energy drinks may also be warranted. While there is good evidence illustrating the harmful and unpleasant physiological effects of energy drinks[46], adolescents taking part in this study did not truly understand what made energy drinks so dangerous. Recent evidence indicates the harmful physiological effects of energy drinks on the cardiovascular system occur independently from caffeine, possibly caused by the additional energy-boosting substances such as taurine, guarana and sugar[47]. Overuse of energy drinks has caused sudden cardiac death, poor mental health and hinders academic performance[48]; these risks need to be appropriately communicated to young people and their families. Future research could: (i) test how labelling strategies, such as warning labels, may help to inform adolescents about the dangers of energy drinks[49] and (ii) co-create the design of communication strategies that align with adolescents’ values of autonomy and fun while informing them of healthier alternatives to energy drinks[50].
## Strengths and limitations
The use of mixed-methods is a strength of this study because it enables a more nuanced understanding of how energy drinks fit into the lives of young people in the UK. The quantitative analyses used a nationally representative dataset that is representative of, and generalisable to, the UK adolescent population. Self-report dietary assessment methods have been shown to be prone to under-reporting and thus reporting bias may have been possible, particularly among adolescents from more advantaged backgrounds[51]. The qualitative data included views from a range of population groups – adolescents, parents and teachers – different genders and individuals living in more disadvantaged areas. The interviews were conducted in pairs or groups which may have affected the responses received due to the dynamics between participants. For example, very close friends being interviewed as a pair may have given more detailed responses than a larger group interview with members from different friendship groups or different genders. Offering only large or smaller groups may have limited the scope of information received from participants. A methodological consideration is that the qualitative data were collected from a single southern county in the UK and that most participants were white. Unlike the quantitative data, the qualitative sample is therefore not representative of adolescents across England; however, recruitment strategies targeting lower income youth clubs and schools aimed to improve representation across the socio-economic spectrum. Interviews with more diverse groups of adolescents from a different area may have produced different information.
## Conclusions
This study supports the introduction of legal restrictions on the sale of energy drinks to minors but indicates that prohibiting energy drink sales to those under the age of 16 years would miss the opportunity to reduce consumption among the highest consumers, those aged 16–18 years from disadvantaged backgrounds. Such restrictions would level the playing field between retailers and may be maximumly effective if coupled with minimum-pricing strategies, placement restrictions and a communications campaign detailing their harmful effects.
## Conflicts of interest:
S.Sh., S.C., S.J. and H.I. have no conflicts of interests to declare. C.V. and J.B. have a non-financial research collaboration with a UK supermarket chain. M.B., J.B. and S.St. have received grant research support from Danone Nutricia Early Life Nutrition. C.C. has received consultancy, lecture fees and honoraria from AMGEN, GSK, Alliance for Better Bone Health, MSD, Eli Lilly, Pfizer, Novartis, Servier, Medtronic and Roche. The study described in this manuscript is not related to any of these relationships.
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|
---
title: Factors associated with the nutritional status of children under 5 years of
age in Guinea between 2005 and 2018
authors:
- Salifou Talassone Bangoura
- Muriel Rabilloud
- Alioune Camara
- Séphora Campoy
- Mamoudou Condé
- Philippe Vanhems
- Kadio Jean-Jacques Olivier Kadio
- Abdoulaye Touré
- Nagham Khanafer
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989713
doi: 10.1017/S1368980022002622
license: CC BY 4.0
---
# Factors associated with the nutritional status of children under 5 years of age in Guinea between 2005 and 2018
## Body
The early years of life, from birth to 5 years of age, are critical for the development of physical, mental and emotional characteristics into adulthood[1,2]. Malnutrition during this period is one of the most important global public health problems[3]; it is detrimental to proper brain development and linear growth of children and has both short- and long-term consequences for their health but also the economic productivity of countries[4]. About 45 % of deaths before the age of 5 years are related to malnutrition, and the majority of these occur mainly in low- and middle-income countries, particularly in Africa and South Asia[5]. International efforts such as the UN sustainable development goals are focusing attention and resources on this important issue[6]; the sustainable development goals 2·2 focuses on improving nutrition and eliminating all forms of malnutrition, including achieving by 2025 internationally agreed targets on stunting and wasting in children under 5 years of age and meeting the nutritional needs of adolescent girls, pregnant and lactating women, and older people[7]. However, progress is insufficient to meet global targets; for instance, the latest Global Nutrition Report indicated that 21·3 % of children under 5 years of age were stunted and 6·9 % wasted; in sub-Saharan Africa, it is estimated that 31·1 % of children under 5 years of age were stunted and 6·3 % were wasted[8]. In Guinea, the various forms of malnutrition affect every year thousands of children under the age of 5 years with more than 204 000 children affected by severe acute malnutrition and about 750 000 children by chronic malnutrition and are associated with more than half of all child deaths in the country[9]. Despite this alarming situation, the coverage of essential curative, preventive and promotional nutrition interventions remains low[9]. This was compounded by the *Ebola virus* disease epidemic that, between March 2014 and April 2016, contributed to the deterioration of the nutritional status of children and the weakening of the health system. Faced with the nutritional variations to which children have been exposed, no study has analysed the factors associated with the nutritional status of children under the age of 5 years post-Ebola and the temporal variations of this effect on these factors. The objective of this study was to determine the factors associated with stunting, underweight and wasting according to household, child and mother characteristics.
## Abstract
### Objective:
To determine the factors associated with the nutritional status of children under 5 years of age in Guinea between 2005 and 2018.
### Design:
Data from the 2005, 2012 and 2018 Guinea Demographic and Health Surveys (DHS) were used for this study. Three anthropometric indicators (stunting, underweight and wasting) were assessed according to the 2006 WHO Child Growth Standards and analysed according to the year, the characteristics of the household, the child and the mother using multivariate logistic regression.
### Setting:
Data were collected in the capital Conakry and in the seven administrative regions of Guinea.
### Participants:
The study included children under 5 years of age for whom height and weight were available: 2765 (DHS-2005), 3220 (DHS-2012) and 3551 (DHS-2018).
### Results:
Analysis of the data from the three surveys showed that children living in rural areas were more likely to be stunted than children living in urban areas (OR = 1·32, 95 % CI (1·08, 1·62)). Similarly, the children from middle, poor and the poorest households were more likely to be stunted and underweight than children from richest households. The chance to stunting increased with age in the first 3 years. However, the chance to wasting decreased with age. Children in all age groups were more likely of being underweight. Children of thin mothers were more likely to be both wasted (OR = 2·0, 95 % CI (1·5, 2·6)) and underweight (OR = 1·9, 95 % CI (1·5, 2·3)).
### Conclusion:
The implementation of targeted interventions adapted to the observed disparities could considerably improve the nutritional status of children and mothers.
## Study design and population
This study used data from the last three Demographic and Health Surveys (DHS) conducted in Guinea in 2005, 2012 and 2018 by the National Statistics Institute of Guinea (Institut National de la Statistique, INS) with technical assistance from the DHS programme (Program DHS, ICF/USAID). These surveys used two-stage stratified cluster sampling methods (region and place of residence). The weight and height of the children were measured and collected during these repeated cross-sectional studies. Weight was measured using electronic scales (Seca, Hamburg, Germany), while height measurements were taken using graduated height scales. Children under 2 years of age were measured lying down, while older children were measured standing up(10–12). Data of children under 5 years of age who had a weight and height measurement available were analysed.
## Study sampling
A two-stage stratified random sampling design was used for the 2005, 2012 and 2018 DHS-Guinea. At the first stage, clusters or areas of enumeration were drawn in each stratum from the list established during the mapping work for the second and third *General census* of population and habitation in Guinea (Récensement Général de la Population et de l’Habitation) in 2004 and 2014, respectively. At the second stage, households were drawn in clusters in each stratum. Within the selected households, women aged 15–49 years were interviewed (7954, 9142 and 10 874 in 2005, 2012 and 2018, respectively) and children under 5 years were registered (6364, 7039 and 7951 in 2005, 2012 and 2018, respectively). However, measurements of weight (in kilograms) and height (in centimetres) were taken for 2765 children under 5 years of age (DHS-2005), 3220 (DHS-2012) and 3551 (DHS-2018)(10–12).
## Outcome criteria
The nutritional status of the children was assessed, as recommended by the WHO, by three anthropometric indices: height-for-age, weight-for-height and weight-for-age. A child was considered stunted, wasted or underweight when, respectively, the value of the height-for-age-score, the weight-for-height-score or the weight-for-age-score was less than −2 sd below the median of the reference population as defined by the WHO in 2006[13].
## The year of the anthropometric measurements
2005, 2012 and 2018.
## The household characteristics
The region (Boké, Conakry, Faranah, Kankan, Kindia, Labé, Mamou and N’Zérékoré), the place of residence and the household wealth grouped into five categories according to the quintiles of the wealth score used by the DHS wealth index: poorest, poor, middle, rich and richest[14]. The latter was built using the following information: household ownership of certain goods (television, radio, car, etc.) and certain housing characteristics (availability of electricity, type of drinking water supply, type of toilet, flooring material, number of rooms used for sleeping, type of fuel for cooking, etc.). A score was assigned to each characteristic, and the overall score for each household was obtained by adding the scores[14].
## Characteristics of children
Age was categorised as follows: <6 months, 6–11 months, 12–23 months, 24–35 months, 36–47 months and 48–59 months; sex; clinical signs such as diarrhoea, fever and cough in last 2 weeks.
## Characteristics of the mother
Education level was categorised into three levels: unschooled, primary level (women who had stopped schooling between first and sixth grade) and secondary level or above, and BMI was calculated by dividing the weight in kilograms (kg) by the square of the height in metres (m) and categorised according to the WHO classification standards[15]: underweight (BMI < 18·5 kg/m2), normal weight (BMI between 18·5 and 24·9 kg/m²) and overweight (BMI ≥ 25 kg/m²).
## Statistical analysis
The data were analysed using the statistical software R version 1.4.1106R: A language and environment for statistical computing, and R Foundation for Statistical Computing (URL https://www.R-project.org/). Descriptive analyses of household, child and maternal characteristics were performed separately for each survey. Height-for-age, weight-for-height and weight-for-age Z-scores were calculated for children who had available height and weight measurements, and the height-for-age < -6 or > 6, weight-for-height < -5 or > 5 and weight-for-age < -6 or > 5 were excluded according to the WHO standards[13]. The exclusion was done by Z-score and not by child. Univariate and multivariate logistic regression models were used to identify factors associated with stunting, wasting and underweight. The variables with a P-value ≤ 20 % in the univariate analysis or considered clinically important were retained for the multivariate analysis by stepwise ascending method. The interactions between the variables retained in the multivariate analysis were tested. The different models were then compared using the likelihood ratio test to select the goodness of fit of the model. The effects of the studied factors were quantified by OR and their 95 % CI. The sample design of the three DHS was taken into account in the logistic regression models to allow the extrapolation of the results to the whole Guinean population.
## Ethical considerations
The protocol and questionnaires of the DHS-2005, 2012 and 2018 have been approved by the National Ethics Committee and endorsed by the ICF Ethics Committee (International Review Board), and the informed and voluntary consent of the parents was obtained for all children. The data were obtained, following a request on the DHS website (URL: https://www.dhsprogram.com/data/available-datasets.cfm). However, no formal ethical approval was obtained as the study involved secondary analysis of publicly available data.
## Results
A total of 21 354 children under 5 years of age were included in the three DHS. Height and weight measurements were available for 2765 children in 2005, 3220 in 2012 and 3551 in 2018. Valid height-for-age Z-scores were obtained for 2694 (97·4 %) children in 2005, 3174 (98·6 %) in 2012 and 3484 (98·1 %) in 2018. Similarly, valid weight-for-height Z-scores were obtained for 2686 (97·1 %) children in 2005, 3129 (97·2 %) in 2012 and 3430 (96·6 %) in 2018. Valid weight-for-age Z-scores were obtained for 2743 (99·2 %) children in 2005, 3208 (99·6 %) in 2012 and 3539 (99·7 %) in 2018 (Fig. 1).
Fig. 1Data selection flowchart for children under 5 years of age included in the DHS of 2005, 2012 and 2018, Guinea. * HAZ (Z-score height-for-age); WHZ (Z-score weight-for-height); WAZ (Z-score weight-for-age) During the three DHS, more than 70 % of children under 5 years old were included in rural areas. About 17 % of the children lived in the Kankan region and 24 % of the children were from poorest households. The majority of the children were boys (51·4 %) and between 12 and 23 months of age (20·7 %). Over 80 % of the children’s mothers were unschooled, and only 6·4 % of mothers were thin (Table 1).
Table 1Description of household, child and maternal characteristics according to the DHS 2005, 2012 and 2018 GuineaCharacteristics2005 (n 2765)2012 (n 3220)2018 (n 3551)Overall (n 9536) n % n % n % n %Household characteristics Place of residence Urban61022·190228·0100528·3251726·4 Rural215577·9231872·0254671·7701973·6 Region Boké33812·233910·550814·3118512·4 Conakry2117·63069·53048·68218·6 Faranah38313·947114·649513·9134914·1 Kankan49217·857818·054215·3161216·9 Kindia39814·439112·141811·8120712·7 Labé2619·436911·546113·0109111·4 Mamou2759·934710·839711·2101910·7 N’zérékoré40714·741913·042612·0125213·1 Household wealth Richest37813·741112·852314·7131213·8 Rich51718·773322·866118·6191120·0 Middle60321·862719·568419·3191420·1 Poor59821·667621·080322·6207721·8 Poorest66924·277324·088024·8232224·3Child characteristics Age (months) <639714·341913·045212·7126713·3 6–1130411·035110·93279·298210·3 12–2357220·767320·973320·6197820·7 24–3553319·358918·361417·3173618·2 36–4746116·759418·474621·0180118·9 48–5949818·059418·467919·1177118·6 Sex Female136949·5153147·5173548·9463548·6 Male139650·5168952·5181651·1490151·4 Diarrhoea in last 2 weeks No233184·3265782·5313088·1811885·1 Yes41815·154516·941711·7138014·5 Missing160·6180·640·1380·4 Fever in last 2 weeks No183266·3215667·0297783·8696573·0 Yes91833·2104732·557116·1253626·6 Missing150·5170·530·1350·4 Cough in last 2 weeks No212376·8254479·0314388·5781081·9 Yes62922·765520·340611·4169017·7 Missing130·5210·720·1360·4Mother characteristics Education Secondary or above1204·33079·542411·98518·9 Primary2107·636511·339011·096510·1 Unschooled243588·1254879·1273777·1772081·0BMI (kg/m²) Thin (<18·5)2037·32236·91895·36156·4 Normal (18·5–24·9)212877·0231071·7234366·0678171·1 Overweight (≥25)28310·254216·888925·0171418·0 Missing1515·51454·51303·74264·5 Compared with the 2005 and 2012 surveys, children from middle (OR = 1·8, 95 % CI (1·1, 2·8)), poor (OR = 1·7, 95 % CI (1·1, 2·6)) and poorest (OR = 2·0, 95 % CI (1·3, 3·2)) households were more likely to be stunted in 2018 than children from richest households. In all surveys, the children’s likelihood of stunting increased significantly with age in the first 36 months, with higher likelihood in 2005 and 2012. Boys were more likely than girls to be stunted in the 2005 (OR = 1·3, 95 % CI (1·1, 1·5)) and 2018 (OR = 1·4, 95 % CI (1·2, 1·7)) surveys. Children of thin mothers were more likely to be stunted in 2012 (OR = 1·6, 95 % CI (1·1, 2·4)) (Table 2).
Table 2Multivariate logistic regression of factors associated with stunting, in children under 5 years of age included in DHS of 2005, 2012 and 2018, GuineaStunting (height-for-age <-2 sd)Characteristics200520122018Adjusted OR95 % CIAdjusted OR95 % CIAdjusted OR95 % CIHousehold characteristics Place of residence Urban1·01·01·0 Rural1·30·9, 1·91·41·0, 2·01·10·8, 1·6 Region Conakry1·01·01·0 Boké0·60·3, 1·11·00·5, 1·91·20·7, 2·0 Faranah0·70·4, 1·41·20·6, 2·30·80·5, 1·2 Kankan1·30·7, 2·61·30·7, 2·50·70·5, 1·2 Kindia0·90·5, 1·71·20·6, 2·20·70·5, 1·2 Labé0·90·5, 1·81·30·6, 2·60·80·5, 1·2 Mamou0·80·4, 1·61·91·0, 3·70·70·4, 1·2 N’zérékoré1·30·7, 2·41·81·0, 3·31·00·6, 1·7 Household wealth Richest1·01·01·0 Rich1·20·7, 2·11·10·6, 2·11·30·9, 1·9 Middle1·60·9, 2·71·20·6, 2·41·81·1, 2·8 Poor1·81·0, 3·21·80·9, 3·71·71·1, 2·6 Poorest1·91·0, 3·41·50·8, 3·02·01·3, 3·2Child characteristics Age (months) <61·01·01·0 6–111·61·1, 2·41·60·9, 2·71·20·8, 1·8 12–235·83·9, 8·83·22·1, 4·92·31·7, 3·3 24–359·26·1, 13·76·04·0, 9·12·41·7, 3·4 36–476·84·5, 10·26·04·1, 9·02·21·5, 3·1 48–596·84·6, 10·14·83·2, 7·22·21·5, 3·1 Sex Female1·01·01·0 Male1·31·1, 1·51·21·0, 1·51·41·2, 1·7 Diarrhoea in last 2 weeks No1·01·01·0 Yes1·10·9, 1·41·10·8, 1·51·31·0, 1·7 Fever in last 2 weeks No1·01·01·0 Yes1·10·9, 1·41·00·8, 1·31·20·9, 1·5 Cough in last 2 weeks No1·01·01·0 Yes0·90·7, 1·21·00·7, 1·31·00·7, 1·4Mother characteristics Education Secondary or above1·01·01·0 Primary1·20·6, 2·21·20·7, 1·91·30·9, 1·9 Unschooled1·30·8, 2·31·30·8, 1·91·31·0, 1·8 BMI (kg/m²) Normal (18·5–24·9)1·01·01·0 Thin (<18·5)1·41·0, 2·11·61·1, 2·40·90·6, 1·2 Overweight (≥25)0·80·6, 1·00·90·7, 1·20·90·7, 1·1 Compared with children aged less than 6 months, children aged 6–11 months had a higher chance of being wasted in 2005 (OR = 1·7, 95 % CI (1·1, 2·6)), but this changed in 2018 (OR = 0·6, 95 % CI (0·4, 1·0)). In all surveys, children aged between 36 and 59 months were significantly less likely to be wasted. However, the children who had had fever in the last 2 weeks preceding the surveys had more chance of being wasted than those did not have fever, and this was significant in 2018. Children of thin mothers were more likely to be wasted than children of normal-weight mothers; this difference was significant in 2005 (OR = 2·3, 95 % CI (1·4, 3·6)) and 2018 (OR = 2·7, 95 % CI (1·7, 4·1)) surveys (Table 3).
Table 3Multivariate logistic regression of factors associated with wasting in children under 5 years of age included in DHS of 2005, 2012 and 2018, GuineaWasting (weight-for-height <-2 sd)Characteristics200520122018Adjusted OR95 % CIAdjusted OR95 % CIAdjusted OR95 % CIHousehold characteristics Place of residence Urban1·01·01·0 Rural1·00·6, 1·61·30·8, 2·10·80·5, 1·4 Region Conakry1·01·01·0 Boké0·40·1, 1·00·90·4, 1·90·50·2, 1·1 Faranah1·00·4, 2·41·00·4, 2·20·40·2, 0·9 Kankan0·90·3, 2·42·21·0, 4·70·80·4, 1·7 Kindia0·60·2, 1·30·70·3, 1·50·50·3, 1·1 Labé0·70·3, 1·81·10·5, 2·50·40·2, 0·9 Mamou0·20·1, 0·71·00·4, 2·20·50·2, 1·0 N’zérékoré0·60·3, 1·60·80·3, 1·70·60·3, 1·3 Household wealth Richest1·01·01·0 Rich1·30·5, 3·21·10·5, 2·21·40·8, 2·5 Middle1·20·5, 3·11·00·5, 2·11·40·7, 2·8 Poor1·20·5, 3·01·10·5, 2·41·60·8, 3·3 Poorest1·50·6, 3·91·00·5, 2·31·30·6, 2·8Child characteristics Age (months) <61·01·01·0 6–111·71·1, 2·61·20·7, 1·90·60·4, 1·0 12–231·00·6, 1·51·51·0, 2·40·60·4, 0·8 24–350·70·4, 1·10·70·4, 1·20·60·4, 0·9 36–470·50·3, 0·80·30·2, 0·60·40·2, 0·5 48–590·20·1, 0·40·30·2, 0·60·40·3, 0·7 Sex Female1·01·01·0 Male1·31·0, 1·61·20·9, 1·51·10·8, 1·4 Diarrhoea in last 2 weeks No1·01·01·0 Yes0·80·5, 1·11·20·8, 1·81·10·8, 1·5 Fever in last 2 weeks No1·01·01·0 Yes1·10·7, 1·50·90·7, 1·31·61·2, 2·1 Cough in last 2 weeks No1·01·01·0 Yes1·40·9, 2·01·10·8, 1·41·00·7, 1·4Mother characteristics Education Secondary or above1·01·01·0 Primary1·00·4, 2·51·20·7, 2·10·90·5, 1·6 Unschooled0·90·4, 1·91·00·5, 1·71·41·0, 2·1 BMI (kg/m²) Normal (18·5–24·9)1·01·01·0 Thin (<18·5)2·31·4, 3·61·40·9, 2·22·71·7, 4·1 Overweight (≥25)0·90·6, 1·50·70·5, 1·10·90·6, 1·3 Children from non-wealthy households (middle, poor and poorest) were more likely to be underweight than children from richest households; this difference was significant in 2012 and 2018. Compared with children under 6 months of age, all children aged 6 months and over were likely to be underweight; this difference was NS in 2012 for children aged 6–11 months and in 2018 for those aged 36–47 months. The likelihood of being underweight was significantly higher for children who had had diarrhoea in the last 2 weeks preceding the surveys compared with those who did not have diarrhoea in 2012 (OR = 1·4, 95 % CI (1·1, 1·9)) and in 2018 (OR = 1·4, 95 % CI (1·1, 1·9)). Similarly, children who had had fever in the last 2 weeks preceding the surveys had more chance of being underweight than those who did not have fever; this difference was significant in 2018. In all three surveys, children of thin mothers were more likely to be underweight than those of normal-weight mothers (Table 4).
Table 4Multivariate logistic regression of factors associated with underweight in children under 5 years of age included in DHS of 2005, 2012 and 2018, GuineaUnderweight (weight-for-age <-2 sd)Characteristics200520122018Adjusted OR95 % CIAdjusted OR95 % CIAdjusted OR95 % CIHousehold characteristics Place of residence Urban1·01·01·0 Rural1·51·0, 2·41·20·7, 2·00·90·6, 1·3 Region Conakry1·01·01·0 Boké0·40·2, 0·80·40·2, 0·91·00·6, 1·7 Faranah0·80·4, 1·60·50·2, 1·10·70·4, 1·3 Kankan0·90·5, 1·91·00·5, 2·01·00·6, 1·7 Kindia0·60·3, 1·20·50·2, 1·20·60·3, 1·1 Labé0·60·3, 1·20·60·3, 1·30·70·4, 1·3 Mamou0·40·2, 0·90·60·3, 1·40·80·5, 1·4 N’zérékoré0·90·4, 1·70·60·3, 1·30·50·3, 0·8 Household wealth Richest1·01·01·0 Rich1·00·5, 1·93·21·5, 6·61·61·0, 2·6 Middle1·00·6, 2·03·11·4, 7·22·11·2, 3·8 Poor1·30·7, 2·64·72·0, 10·82·01·1, 3·7 Poorest1·40·7, 2·73·71·6, 8·52·11·1, 3·8Child characteristics Age (months) <61·01·01·0 6–111·91·1, 3·11·50·8, 2·61·71·1, 2·7 12–232·41·6, 3·63·01·9, 4·52·01·4, 3·0 24–353·12·0, 4·72·61·7, 3·92·11·4, 3·1 36–472·41·6, 3·62·51·6, 3·91·40·9, 2·0 48–592·51·6, 3·82·71·7, 4·31·71·2, 2·5 Sex Female1·01·01·0 Male1·31·0, 1·61·00·8, 1·31·21·0, 1·5 Diarrhoea in last 2 weeks No1·01·01·0 Yes1·20·9, 1·61·41·1, 1·91·41·1, 1·9 Fever in last 2 weeks No1·01·01·0 Yes1·31·0, 1·71·00·8, 1·21·41·1, 1·8 Cough in last 2 weeks No1·01·01·0 Yes1·20·8, 1·51·10·9, 1·50·90·7, 1·3Mother characteristics Education Secondary or above1·01·01·0 Primary1·00·5, 2·31·30·7, 2·51·30·8, 2·1 Unschooled1·20·6, 2·41·30·7, 2·51·51·0, 2·3 BMI (kg/m²) Normal (18·5–24·9)1·01·01·0 Thin (<18·5)2·31·6, 3·41·71·2, 2·41·71·1, 2·5 Overweight (≥25)0·60·4, 1·00·70·5, 1·00·60·5, 0·8 *Multivariate analysis* of all data from the three surveys showed that children were significantly less likely to be stunted or underweight in 2012 and 2018 than in 2005. Children living in rural areas were more likely to be stunted than children living in urban areas (OR = 1·3, 95 % CI (1·1, 1·6)). Children from middle (OR = 1·5, 95 % CI (1·1, 2·0)), poor (OR = 1·7, 95 % CI (1·3, 2·4)) and the poorest (OR = 1·7, 95 % CI (1·3, 2·4)) households were more likely to be stunted than children from richest households. Similarly, the children from rich (OR = 1·6, 95 % CI (1·2, 2·3)) middle (OR = 1·7, 95 % CI (1·2, 2·5)), poor (OR = 2·1, 95 % CI (1·4, 3·0)) and poorest (OR = 2·0, 95 % CI (1·4, 3·0)) households were more likely to be underweight than children from the richest households. The likelihood of being stunted increased with age in the first 3 years of life. This difference changed very little for the older age groups. However, the likelihood of being wasted decreased with age. Children in all age groups were likely to be underweight. This likelihood was significantly higher for children aged 12–23 and 24–35 months. Boys were more likely than girls to be stunted (OR = 1·3, 95 % CI (1·2, 1·4)). Compared with their reference categories, children who had had diarrhoea and fever in the last 2 weeks preceding the surveys had more chance of being underweight. Similarly, children of thin mothers were more likely to be both wasted and underweight compared with children of normal-weight mothers (Table 5).
Table 5Multivariate logistic regression of factors associated with stunting, wasting and underweight in children under 5 years of age included in DHS of 2005, 2012 and 2018, Guinea: grouped dataCharacteristicsStunting (height-for-age <-2 sd)Wasting (weight-for-height <-2 sd)Underweight (weight-for-age <-2 sd)Adjusted OR95 % CIAdjusted OR95 % CIAdjusted OR95 % CIYear of survey 20051·01·01·0 20120·60·6, 0·70·90·8, 1·20·70·6, 0·9 20180·70·6, 0·81·00·8, 1·20·70·6, 0·9Household characteristics Place of residence Urban1·01·01·0 Rural1·31·1, 1·61·00·8, 1·31·20·9, 1·5 Region Conakry1·01·01·0 Boké0·90·7, 1·30·50·3, 0·90·60·4, 0·9 Faranah0·90·6, 1·20·70·4, 1·10·70·5, 1·0 Kankan1·10·8, 1·51·20·7, 1·81·00·7, 1·5 Kindia0·90·7, 1·30·60·4, 0·90·60·4, 0·9 Labé0·90·7, 1·30·60·4, 1·00·70·4, 1·0 Mamou1·00·7, 1·50·50·3, 0·80·70·4, 1·0 N’zérékoré1·30·9, 1·80·60·4, 1·00·70·5, 1·0 Household wealth Richest1·01·01·0 Rich1·20·9, 1·61·30·9, 2·01·61·2, 2·3 Middle1·51·1, 2·01·30·8, 2·01·71·2, 2·5 Poor1·71·3, 2·41·30·8, 2·12·11·4, 3·0 Poorest1·71·3, 2·41·40·9, 2·22·01·4, 3·0Child characteristics Age (months) <61·01·01·0 6–111·41·1, 1·81·10·8, 1·41·61·2, 2·2 12–233·42·7, 4·30·90·7, 1·22·41·9, 3·0 24–354·83·8, 6·00·70·5, 0·92·41·9, 3·1 36–474·23·3, 5·20·40·3, 0·51·91·5, 2·4 48–593·93·2, 4·90·40·3, 0·52·21·7, 2·8 Sex Female1·01·01·0 Male1·31·2, 1·41·21·0, 1·31·21·0, 1·3 Diarrhoea in last 2 weeks No1·01·01·0 Yes1·21·0, 1·41·00·8, 1·31·31·1, 1·6Child characteristics Fever in last 2 weeks No1·01·01·0 Yes1·10·9, 1·21·21·0, 1·41·21·1, 1·4 Cough in last 2 weeks No1·01·01·0 Yes0·90·8, 1·11·10·9, 1·41·10·9, 1·3Mother characteristics Education Secondary or above1·01·01·0 Primary1·20·9, 1·61·10·8, 1·61·30·9, 1·8 Unschooled1·31·0, 1·61·20·9, 1·61·41·0, 1·9 BMI (kg/m²) Normal (18·5–24·9)1·01·01·0 Thin (<18·5)1·31·0, 1·62·01·5, 2·61·91·5, 2·3 Overweight (≥25)0·80·7, 1·00·90·7, 1·10·70·5, 0·8
## Discussion
This study analysed the factors associated with stunting, wasting and underweight in children under 5 years of age in Guinea between 2005 and 2018 to identify areas for improvement to enable children to grow up in good health. During the period 2005–2018, Guinea has experienced several events that have negatively affected the nutritional status of households and therefore of children. First, the country has experienced a destabilisation of the political and socio-economic situation since 2008, which has led to a sharp deterioration in the food situation of households[16]. Second, the *Ebola virus* disease epidemic has severely affected the nutritional status of children in two ways. Firstly, many have lost one or both parents and have been orphaned. Second, the epidemic has had a catastrophic impact on already fragile health systems. In addition, the decline in income, the disruption of trade, commercial flights and harvesting activities and also the quarantine measures have increased the food insecurity of the majority of families[17]. In response to these events, several interventions have been implemented by the government, institutions and non-governmental organisations operating in the nutrition and food security sector. For example, the strategic collaboration between the World Food Programme and UNICEF was very dynamic in the period before the Ebola crisis. In addition, representatives of both agencies have been very active and have helped to focus attention on nutrition at the highest level of government. Guinea’s membership of Scaling Up Nutrition and Renewed Efforts Against Child Hunger and undernutrition is testimony to their efforts[18].
Analysis of the dataset showed that rural residence, low household socio-economic status, age and sex were the factors associated with stunting in children. Low maternal weight was the only factor associated with wasting. Household wealth quintile, age, occurrence of diarrhoea and fever in the last 2 weeks preceding the surveys, and low maternal weight were significantly associated with underweight children.
This study showed a higher likelihood to stunting and underweight in children from the poorest and poor categories. The reasons for poor nutritional status among poor communities could be their standard of living, unfavourable environmental conditions, low purchasing power and low use of health care services[5,19]. In addition, these families are often not in a position to spend more money to provide their children with a healthy and adequate diet, which could make them vulnerable to all these forms of malnutrition.
This study showed an age-dependent divergent trend between stunting and wasting. The likelihood of children to stunting increased with age, while the likelihood to wasting decreased. A study has reported that periods of wasting and weight fluctuations in childhood increase the risk of stunting in later life[20,21]. Other studies also indicate that when a child is treated for severe wasting, growth in height slows down until weight is restored, indicating that the body adapts itself to poor weight gain by slowing growth[22,23]. This information highlights the role that prevention and treatment of wasting can play in promoting the physical growth of children[24].
Boys were more likely to be stunted than girls. This finding is confirmed by a systematic review of stunting, wasting and underweight in sub-Saharan Africa[3] and a meta-analysis of sixteen DHS in sub-Saharan Africa[25] but contradicted by a study in Kenya which reported that female were more likely to be stunted[26]. Boys’ vulnerability to stunting may be explained by less attention than girls[27], despite the fact that boys need more calories for growth and development[28].
Similarly, low-weight maternal increased the risk of stunting, wasting and underweight among children. Poor maternal nutrition leads to poor intra-uterine growth and low birth weight. Undernourished mothers cannot breastfeed their children properly, which exposes their children to poor nutrition[29]. Therefore, improving maternal health is a prerequisite for reducing malnutrition in children[29]. The association between stunting, wasting, underweight and maternal characteristics (education and maternal BMI) has also been observed in other studies(30–32).
In view of these results, there is still much to be done to improve the nutritional status of children. For this to happen, policymakers must work more to fight poverty in all its forms. Awareness-raising activities on the nutritional practices of parents and children must be intensified. In addition, actions on the promotion of exclusive breast-feeding and the initiation and support of mothers in the diversification of their children’s diet must continue to take place.
The study has some limitations, mainly the lack of the weight and height data for most of the children surveys, which is due to DHS being household surveys; data are collected for all children under 5 years of age living in the selected households, but weight and height measurements are taken for children present on the day of the survey. In addition, the proportion of missing data was similar in all three surveys. This must be taken into account in future surveys in order to reduce bias in the estimation of prevalence. Another point is that it was not possible to establish a causal link as the data used herein were from a prevalence survey; cohort studies would be more adapted for this; however, the present study could enable policymakers to take effective and sustainable action to reduce the occurrence and consequences of malnutrition in children. It also raises the issue of inequality allowing authorities to set priorities and target interventions. Furthermore, the data analysed are from national surveys, and therefore, the results can be extrapolated to the entire Guinean population.
## Conclusion
The nutritional status of children under 5 years of age remains a concern in Guinea. Significant associations were observed between stunting, wasting and underweight and certain characteristics of the child, the mother and the household that can be targeted by governmental policies to improve the nutritional status of children and mothers.
## Conflicts of interest:
There are no conflicts of interest.
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|
---
title: Healthful and unhealthful provegetarian food patterns and micronutrient intake
adequacy in the SUN cohort
authors:
- Daniela Asfura-Carrasco
- Susana Santiago
- Itziar Zazpe
- Clara Gómez-Donoso
- Maira Bes-Rastrollo
- Miguel Ángel Martínez-González
journal: Public Health Nutrition
year: 2023
pmcid: PMC9989716
doi: 10.1017/S136898002200204X
license: CC BY 4.0
---
# Healthful and unhealthful provegetarian food patterns and micronutrient intake adequacy in the SUN cohort
## Body
The concept of plant-based diets (PBD) is used differently by researchers, and it has no specific definition[1]. *In* general, PBD provide the majority of energy from plant-based foods, including vegetables, wholegrains, legumes, nuts, seeds and fruits, with few or no animal products[2]. People choose PBD for a variety of reasons including concern about animal welfare, as well as health or environmental concerns[3].
There are different types of PBD according to the degree of exclusion of animal products, ranging from strict veganism to several types of vegetarianism[4]. In recent years, PBD have become more popular worldwide. While vegan or highly restrictive vegetarian diets are not likely to be easily adopted by the general population(5–8), provegetarian or flexitarian food patterns (FP) may be a better alternative to reduce the consumption of animal foods and achieve long-term sustainable adherence to PBD.
The provegetarian or flexitarian diet is a flexible dietary pattern style that prioritises the consumption of plant or plant-based foods and beverages and incorporates animal foods (dairy products, eggs, meats and fish) less frequently and/or in smaller portions. The overall provegetarian FP was calculated according to the score proposed by Martínez-González et al. [ 9], which quantifies the habit of preferentially consuming plant-derived foods instead of animal-derived foods without the need to follow a strict vegetarian diet.
The PBD, if well planned, can support healthy nutrition at every age and life stage in healthy subjects and contribute to preserving the environment[10]. In fact, the 2020 Dietary Guidelines Advisory Committee Scientific Report recommends the promotion of PBD for a better health, among other dietary patterns[11]. However, previous studies have indicated that PDB do not necessarily imply a high nutritional quality and could be associated with higher cardiometabolic risk, especially if the plant foods included are highly processed and not very healthy or if the excluded food groups are of high nutritional density[12]. It is also worth mentioning that very restrictive PBD might present an increased risk of nutritional deficiencies and could seriously affect health[6,13]. Additionally, supplementation of specific nutrients such as I, Ca and vitamins B12 and D should be considered in several risk groups that follow a PBD[10,14,15].
In nutritional epidemiology, there is a great interest to know how certain PBD could potentially reduce diet-related chronic disease morbidity and mortality[13]. Previous cohort studies have consistently used different versions of a provegetarian FP, including healthful and unhealthful ones[9,14], to explore their relationship with chronic conditions. These healthy and unhealthy versions follow the scoring criteria suggested by Satija et al. for type 2 diabetes[16]. These patterns were based on the classification of plant-derived foods in two groups: healthy (fruits, vegetables, wholegrains, nuts, legumes, olive oil and coffee) and less-healthy (fruit juices, potatoes, refined grains, pastries and sugary beverages). Evidence to date has shown that the overall and healthful provegetarian FP reduce the risk of all-cause mortality[9,14] and might decrease the risk of breast cancer[15], overweight/obesity[17] and cardiometabolic diseases[13,16]. Nevertheless, there is no evidence on the association between the provegetarian FP indices and micronutrient adequacy. Our hypothesis was that higher adherence to both overall and healthful provegetarian FP would be associated with higher micronutrient adequacy, whereas a higher adherence to an unhealthful provegetarian FP would be associated with lower nutritional adequacy. Thus, the aim of this study was to investigate the association between different versions of a provegetarian FP and nutritional adequacy considering nineteen micronutrients in the ‘Seguimiento Universidad de Navarra’ [University of Navarra Follow-up] (SUN) cohort study.
## Abstract
### Objective:
To investigate the association between different versions of a provegetarian food pattern (FP) and micronutrient inadequacy.
### Design:
Cross-sectional analysis. Dietary intake was assessed at baseline through a validated 136-item FFQ. Participants were classified according to groups of different versions of a provegetarian FP: overall, healthful and unhealthful. The prevalence of inadequate intake of vitamins B1, B2, B3, B6, B12, C, A, D, E, folic acid, Zn, I, Se, Fe, Ca, K, P, Mg and Cr was evaluated using the estimated average requirement (EAR) cut-point method and the probabilistic approach. Logistic regression analyses were conducted to estimate the probability of failing to meet EAR for either ≥ 3 or ≥ 6 micronutrients.
### Setting:
Seguimiento Universidad de Navarra (SUN) cohort.
### Participants:
17 825 Spanish adults.
### Results:
Overall, subjects in the highest group of the unhealthful provegetarian FP had the highest prevalence of inadequate dietary intake for every vitamin and mineral, compared to those in the lowest group. The adjusted OR of failing to meet ≥ 3 EAR (highest v. lowest group) was 0·65 (0·54, 0·69) for the overall, 0·27 (0·24, 0·31) for the healthful and 9·04 (7·57, 10·4) for the unhealthful provegetarian FP.
### Conclusion:
A higher adherence to an overall and healthful provegetarian FP was inversely associated with the risk of failing to meet EAR values, whereas the unhealthful version was directly associated with micronutrient inadequacy. Provegetarian FP should be well planned, prioritising nutrient-dense plant foods and minimising ultra-processed and unhealthy ones.
## Design
The SUN Project (http://medpreventiva.es/MvbqgK) is a dynamic prospective cohort study of university graduates conducted in Spain since December 1999. Baseline assessment and follow-up information is gathered biennially by mail or web-based questionnaires. Self-administered questionnaires include information on sociodemographic, medical, lifestyle and dietary variables. The overall retention in the cohort exceeds 90 %. Additional details on its objectives, design and methods can be found elsewhere[18].
## Subjects
Up to December 2019, 22 894 subjects had completed the baseline questionnaire of the SUN Project. Participants who were outside the predefined limits for energy intake, as proposed by Willett (< 3347·2 kJ/d or > 16 736 kJ/d for men and < 2092 kJ/d or > 14 644 kJ/d for women)[19], were excluded (n 2169). Subjects whose intakes were outside the predefined intake values of any micronutrient (≥ 3 sd from both sides of the mean) were also excluded (n 2900). Finally, 17 825 participants were included in the analyses for the present study.
## Dietary assessment
Dietary intake was assessed at baseline using a 136-item semi-quantitative FFQ repeatedly validated in Spain(20–22). The FFQ collected typical food intake over the previous year. A typical portion size was specified for each item, and consumption frequencies were registered in nine categories that ranged from ‘never or almost never’ to ‘≥ 6 times/day’. Daily intake (g/d) was calculated by multiplying the specified portion size of each food item by the frequency of consumption. A trained dietitian updated the nutrient database using the latest available information in the Spanish food composition tables.
## Exposure assessment – provegetarian food patterns
Provegetarian FP are based on gradual dietary changes, progressively increasing the consumption of plant-based foods and simultaneously reducing animal foods[9,16]. Specifically, the overall score quantifies the consumption (g/d) of seven plant food groups (fruits, vegetables, potatoes, nuts, legumes, cereal grains and olive oil) and five animal food groups (dairy products, eggs, meat, fish and seafood and animal fat). Quintile values of plant foods and reverse quintiles values of animal foods were summed; therefore, final scores can range from 12 (lowest adherence) to 60 points (highest adherence) (Table 1).
Table 1Scoring criteria for the provegetarian food patternsOverall provegetarian FP (potential range 12–60)Healthful/unhealthful provegetarian FP (potential range of 18–90)ComponentCriteriaComponentCriteriaHealthfulUnhealthful Plant food groups Plant food groups Healthful 1. VegetablesPositive1. VegetablesPositiveReverse2. FruitsPositive2. FruitsPositiveReverse3. LegumesPositive3. LegumesPositiveReverse4. Cereal grainsPositive4. WholegrainsPositiveReverse5. PotatoesPositive5. NutsPositiveReverse6. NutsPositive6. Olive oilPositiveReverse7. Olive oilPositive7. CoffeePositiveReverse Less-healthful Animal Food Groups 8. Fruit juicesReversePositive8. Dairy productsReverse9. PotatoesPositive9. EggsReverse10. Refined grainsReversePositive10. MeatReverse11. Sugary beveragesReversePositive11. Fish and seafoodReverse12. PastriesReversePositive12. Animal fatReverse Animal Food Groups 13. DairyReverseReverse14. EggsReverseReverse15. MeatReverseReverse16. Fish and seafoodReverseReverse17. Miscellaneous foodReverseReverse18. Animal fatReverseReverseMiscellaneous food includes pizza, instant soups and mayonnaise.
As shown in Table 1, healthy (fruits, vegetables, wholegrains, nuts, legumes, olive oil and coffee) and less-healthy plant foods (fruit juices, potatoes, refined grains, pastries and sugary beverages) were distinguished for the other versions. For the healthful provegetarian FP, positive scores were assigned to healthy plant foods and reverse scores to less-healthy plant foods as well as to animal foods. In contrast, for the unhealthful provegetarian FP, positive scores were assigned to less-healthy plant foods and reverse scores to healthy plant foods and animal foods. Quintiles and reverse quintiles were summed to obtain scores of the healthful and unhealthful versions of a provegetarian FP. Thus, final scores could range from 18 (lowest adherence) to 90 (highest adherence).
## Outcome assessment – micronutrient adequacy
The total micronutrient intake was calculated by adding the average micronutrient intake from foods, beverages and dietary supplements. We assessed micronutrient intake adequacy taking into account the following nineteen micronutrients with known public health relevance: vitamins B1, B2, B3, B6, B12, C, A, D, E, folic acid, Zn, I, Se, Fe, Ca, K, P, Mg and Cr. When the specific estimated average requirement (EAR) value for a nutrient could not be determined, the adequate intake was used as the reference. Inadequate intake was defined as any micronutrient intake below the EAR if available or the adequate intake if EAR values were not available. Both dietary reference intakes have been proposed by the Institute of Medicine[23]. Nutrient intake adequacy for sixteen micronutrients (all except K and Cr because they have no EAR values, and Fe because of its skewed distribution) was also evaluated using the probabilistic approach, which calculated the probability of adequacy for a nutrient’s usual intake as follows: Z score = (estimated nutrient intake – EAR)/SD of the EAR. The Z scores correspond to an estimated probability of inadequacy according to normal distribution. Because of the skewed distribution of Fe intake, its value was log-transformed for the present study.
## Assessment of other variables
Information on non-dietary variables was also collected at baseline (e.g. medical history, sociodemographic characteristics, lifestyle and health-related habits). Self-reported data, such as physical activity[24], BMI[25] or hypertension[26], have been previously validated in a subsample of the cohort. Three previously defined scores were also used to describe the baseline characteristics of participants: Carbohydrate Quality Index (range, 4–20)[27,28], Fat Quality Index (range, 0·62–5·92)[27,28] and the Mediterranean diet score (range, 0–9) developed by Trichopoulou et al. [ 29].
## Statistical analyses
Participants were categorised into the following three groups to create three reasonably equal groups in each provegetarian FP: [lowest adherence (overall provegetarian FP from 12 to 35, healthful from 30 to 52 and unhealthful from 31 to 55), medium adherence (overall provegetarian FP from 36 to 39, healthful from 53 to 58 and unhealthful from 56 to 60) and highest adherence (overall provegetarian FP from 40 to 57, healthful from 59 to 82 and unhealthful from 61 to 83)] according to their adherence to each of the provegetarian FP indices described above.
Baseline characteristics of participants as well as their baseline food consumption and energy and nutrient intakes were reported according to extreme groups of adherence to each provegetarian FP. The descriptive results are presented as mean and standard deviation or percentages (%) for quantitative variables and categorical variables, respectively[30]. The baseline prevalence of inadequate intake of each micronutrient (i.e. intake below EAR) according to groups of each provegetarian FP was also estimated.
Non-conditional logistic regression models were used to evaluate the relationship of each provegetarian FP and the risk of micronutrient inadequacy using the EAR cut-point method and the probabilistic approach. In all analyses, the lowest group was used as the reference category. Crude and multivariable-adjusted OR and its 95 % CI were estimated for two different outcomes: failing to meet EAR for either ≥ 3 or ≥ 6 micronutrients.
One multivariable-adjusted model was fitted for each provegetarian FP controlling for the following potential confounding factors: age (continuous), sex, supplement consumption (yes/no) and total energy intake (continuous). We did not control for education level, smoking, physical activity or previous weight change, because there is no convincing association with micronutrient adequacy (see online supplementary material, Supplemental Figure 1). Linear trend tests were performed through groups of each provegetarian FP by assigning the median score values of each group to participants and treating the variables as continuous. In addition, ANCOVA tests were performed to estimate the average number of micronutrients with intakes below the EAR across groups adjusting for sex and age.
Finally, sensitivity analyses were carried out to assess the robustness of the findings, excluding participants outside the 1st and 99th percentile of energy intake in one case and outside the 5th and 95th percentile in another. Additionally, analyses were performed excluding those with no answer in ≥ 30 items in the 136-item baseline FFQ.
Statistical analyses were carried out using STATA version 14 (STATA Corporation). All P values are two-tailed, and statistical significance was established in the conventional cut-off of $P \leq 0$·05.
## Results
The baseline characteristics of the 17 825 participants included in the study are summarised according to groups of the overall provegetarian FP in Table 2. Subjects with greater adherence to the overall provegetarian FP (third group, G3) were more likely to be more physically active and have a history of hypertension, CVD, diabetes, dyslipidaemia, cancer and hypercholesterolaemia. In addition, participants in the highest group of the overall provegetarian FP tended to have a higher consumption of dietary supplements, were more likely to follow special diets, and had a higher Mediterranean diet score[29] and a higher Carbohydrate Quality Index and Fat Quality Index (reflecting higher dietary quality of carbohydrates and fat). On the other hand, participants with lower adherence to the overall provegetarian FP (first group, G1) were more likely to be active smokers (≥ 15 cigarettes/d), to snack between meals and to gain weight (≥ 3 kg) over the last 5 years.
Table 2Baseline characteristics of participants according to adherence to the overall provegetarian FP: the Seguimiento Universidad de Navarra (SUN) cohort: 1999–2019Overall provegetarian FPG1G2G3 P value%Mean sd %Mean sd %Mean sd Range12–3536–3940–57 n 703254525341< 0·001Age36·211·737·812·040·012·9< 0·001Women (%)57·361·461·5< 0·001BMI (kg/m2)23·63·523·53·523·43·50·019Physical activity (MET-h/week)20·121·721·121·723·223·3< 0·001Sitting time (h/d)5·42·15·32·15·22·0< 0·001Sedentary activities (h/d)1·61·21·61·21·61·10·041Smoke (%)< 0·001 Never48·447·947·5 Former smokers25·728·330·2 Active smokers < 15 cigarettes/d12·312·712·1 Active smokers ≥ 15 cigarettes/d11·08·87·4Prevalent hypertension (%)10·210·012·4< 0·001Prevalent CVD (%)1·31·32·1< 0·001Prevalent diabetes (%)1·51·72·10·039Prevalent dyslipidaemia (%)6·16·87·70·002Prevalent cancer (%)1·92·72·80·002Prevalent hypercholesterolaemia (%)14·317·719·5< 0·001Family history of obesity (%)19·220·021·60·005Family history of CVD (%)12·913·714·40·044Marital status (%)< 0·000 Single48·644·641·9 Married46·749·851·9 Other4·75·66·2Supplements consumption (%)14·617·217·5< 0·001Weight gain of ≥ 3 kg in the last 5 years (%)32·830·226·9< 0·001Follow-up of special diet (%)7·27·28·60·007Snacking between meals (%)35·633·031·2< 0·001Trichopoulou MedDiet score (range, 0–9)3·21·54·31·55·51·5< 0·001Carbohydrate Quality Index (range, 4–20)10·02·811·43·012·83·0< 0·001Fat Quality Index (range, 0·62–5·92)1·50·31·70·42·00·5< 0·001MET, metabolic equivalents: FP, food pattern; G, groups. Mean ± (sd) or %.
Food consumption according to extreme groups of adherence to each provegetarian FP is shown in Table 3. As expected, the consumption of fruits, vegetables, legumes, cereal grains, potatoes, fruit juices, olive oil and nuts increased across categories of the overall provegetarian FP, whereas the consumption of all animal food groups, coffee, sugar-sweetened beverages, sweets and desserts and miscellaneous food decreased. Moreover, participants with greater adherence to the healthful provegetarian FP had a higher consumption of fruits, vegetables, legumes, wholegrains, olive oil, nuts and coffee and a lower consumption of less healthy plant foods as well as all animal origin foods. By contrast, participants with greater adherence to the unhealthful provegetarian FP had a higher consumption of refined grains, potatoes, fruit juices, sugar-sweetened beverages and pastries, whereas they had a lower consumption of fruits, vegetables, legumes, wholegrains, olive oil, nuts and animal foods.
Table 3Food consumption according to extreme groups of adherence to the overall, healthful and unhealthful provegetarian FP (Mean and sd)Overall provegetarian FPHealthful provegetarian FPUnhealthful provegetarian FPG1G3G1G3G1G3Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Range12–3540–5730–5259–8231–5561–83 n 703253416295586067455770Fruits (g/d)243·9193·2470·4291·9269·3197·0437·4300·0439·0279·3252·3210·8Vegetables (g/d)393·7230·1635·5283·7404·2225·5604·5294·9633·8280·2354·4210·1Legumes (g/d)18·615·527·318·920·915·324·520·326·520·318·013·8Cereal grains (g/d)76·457·5123·968·5107·567·490·663·896·661·3101·572·4 Wholegrains (g/d)7·018·718·535·04·114·422·836·920·534·24·116·1 Refined grains (g/d)69·456·3105·469·1103·466·467·857·776·157·397·471·3Potatoes (g/d)42·838·866·247·769·147·637·635·548·341·659·246·3Fruit juices (g/d)55·395·471·899·170·295·053·589·859·385·966·2106·5Olive oil (g/d)13·411·823·816·015·012·621·315·922·215·513·512·4Nuts (g/d)3·85·711·613·35·27·19·512·79·912·24·46·9Dairy products (g/d)429·2210·3327·1187·5432·7203·7332·0200·0430·3210·2331·7191·3Meat (g/d)189·376·5155·072·1206·173·8142·268·5192·479·4154·468·5Eggs (g/d)25·114·620·111·126·613·918·811·825·013·720·112·2Fish and seafood (g/d)95·351·893·752·694·550·994·253·8115·652·971·642·4Animal fat (g/d)1·63·00·51·71·83·20·51·81·42·90·72·1Coffee (g/d)62·063·459·660·251·657·470·264·673·664·346·155·2Sugar sweetened beverages (g/d)69·6111·254·196·397·8126·530·572·345·285·984·9123·0Pastries (g/d)50·244·747·842·564·244·934·637·542·637·656·849·2Miscellaneous food (g/d)23·936·620·635·934·442·611·625·125·539·618·931·7Alcohol (g/d)7·310·77·910·47·510·47·610·87·910·67·310·9FP, food pattern; G, groups. Miscellaneous food includes: pizza, instant soups and mayonnaise.
Energy and nutrients intake according to extreme groups of each provegetarian FP are shown in Table 4. The intake of total energy, carbohydrates, fibre, PUFA and n-3 fatty acids and all micronutrients except vitamin B12, vitamin D, Ca and I were significantly greater in the group with the highest adherence to the overall provegetarian FP (G3 compared to G1). Conversely, the intake of protein, total fat, MUFA, SFA, TFA and cholesterol were significantly lower in G3 compared to G1.
Table 4Energy and nutrient intakes according to extreme groups of adherence to the overall, healthful and unhealthful provegetarian FP (Mean and sd)Overall provegetarian FPHealthful provegetarian FPUnhealthful provegetarian FPG1G3G1G3G1G3Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Range12–3540–5730–5259–8231–5561–83 n 703253416295586067455770Energy (kJ/d)89832473·210 2762353·1* 10 6572350·285862312·5* 10 2552341·888782492·0Carbohydrates intake (% of total energy [E])40·37·046·16·7* 42·86·443·57·9* 41·46·945·07·4Fibre (g/d)20·77·934·210·3* 23·38·630·811·6* 32·210·520·98·4* Protein intake (% total E)19·23·316·82·6* 17·72·718·43·4* 18·93·017·13·1* Fat intake (% total E)38·26·135·06·2* 37·55·535·77·0* 37·56·235·66·3* PUFA intake (% total E)5·11·45·21·4* 5·41·44·91·4* 5·11·25·31·6* n-3 Fatty acids (g/d)2·51·22·62·81·22·31·0* 2·91·02·21·2* n-6 Fatty acids (g/d)17·412·117·510·921·813·013·49·3* 17·510·217·413·0* MUFA intake (% total E)15·93·415·73·7* 15·53·016·04·2* 16·43·614·93·4* SFA intake (% total E)13·93·110·82·6* 13·62·811·23·0* 12·43·012·53·2* TFA intake (% total E)0·40·20·30·1* 0·40·20·30·2* 0·30·20·40·2* Cholesterol (mg/d)437·9145·2367·4127·5* 478·4134·4332·5115·7* 443·9143·8362·3121·9* Vitamin A (μg/d)1482·8919·52366·31225·9* 1561·2929·72213·61257·9* 2352·91197·01342·0875·6* Vitamin E (mg/d)5·82·67·73·1* 6·72·86·63·1* 7·62·95·52·6* Vitamin C (mg/d)213·7107·0335·3133·3* 233·9111·0308·0139·9* 325·3131·9208·3108·6* Vitamin B1 (mg/d)1·70·62·00·7* 1·90·61·80·6* 2·10·61·60·6* Vitamin B2 (mg/d)2·10·62·20·6* 2·30·62·00·7* 2·40·61·90·6* Vitamin B3 (mg/d)40·711·342·610·9* 44·610·838·911·1* 47·210·435·69·8* Vitamin B6 (mg/d)2·50·83·10·9* 2·70·82·80·9* 3·20·82·30·8* Vitamin B12 (mg/d)9·74·38·64·1* 10·04·28·34·0* 10·74·37·53·6* Vitamin D (μg/d)6·13·96·13·9* 6·23·85·94·1* 7·44·24·83·2* Folic acid (μg/d)332·7129·6475·4150·7* 358·8132·4440·4161·5* 474·2148·1312·5124·1* Fe (mg/d)15·14·919·25·2* 17·25·117·05·8* 19·25·214·64·8* Ca (mg/d)1145·5390·21162·5372·1* 1207·2374·21099·7387·7* 1312·9376·0977·4337·6* K (mg/d)4002·91173·65277·81336·3* 4500·21228·44709·11453·4* 5302·01283·03801·51119·7* Mg (mg/d)354·995·0459·2107·2* 401·699·1408·5118·3* 460·4102·3339·892·1* P (mg/d)1818·1465·41872·6447·6* 1937·5441·91758·4465·6* 2109·3419·31552·1377·2* Cr (μg/d)74·830·794·631·8* 89·432·679·631·4* 90·931·076·133·1* Se (μg/d)89·229·995·429·7* 99·229·685·429·3* 103·028·380·329·0* I (μg/d)330·5165·5273·5148·0* 330·9159·3275·7156·0* 349·6163·3256·4147·7* Zn (mg/d)15·67·117·17·1* 16·05·916·78·2* 18·87·513·66·0* FP, food pattern; G, groups; TFA, trans fatty acids.* $P \leq 0$·001 obtained through ANOVA test.
Participants with higher adherence to the healthful provegetarian FP had a higher intake of carbohydrates, fibre, protein and MUFA, and lower intake of energy, total fat, PUFA, n-3 and n-6 fatty acids, SFA, TFA, cholesterol, vitamins E, B1, B2, B3, B12 and D, Fe, Ca, P, Cr, Se and I compared to those with lower adherence (all these differences were statistically significant).
Finally, the intake of energy, fibre, protein, fat, MUFA and n-3 fatty acid and all micronutrients assessed in this study were significantly lower among participants with the highest adherence to the unhealthful provegetarian FP (G3) compared to participants in G1.
Prevalence of inadequate intake, below the EAR, for each micronutrient and the average number of micronutrients with intakes below the EAR adjusted for sex and age, according to groups of each score is summarised in Table 5. *In* general, there was a lower prevalence of micronutrient inadequacy (except for vitamin B12 and I) in the highest group of the overall provegetarian FP. Among participants with the highest adherence to the healthful provegetarian FP, a higher prevalence of inadequacy was found for vitamins B1, B2, B3, B12, Ca, P, Se, I and Zn. Conversely, participants in the highest group of the unhealthful provegetarian FP had the highest prevalence of inadequate dietary intake for every vitamin and mineral. Overall, the lowest prevalence of micronutrient inadequacy was for vitamins B3, B12 and P, and the highest for folic acid and vitamins D and E.
Table 5Prevalence (%) of failing to meet EAR for each micronutrient and the average number of micronutrients failing to meet EAR according to groups of adherence to the overall, healthful and unhealthful provegetarian FPOverall provegetarian FPHealthful provegetarian FPUnhealthful provegetarian FPG1G2G3G1G2G3G1G2G3Range12–3536–3940–5730–5253–5859–8231–5556–6061–83 n 703254525341629556705860674553105770Number of nutrients below the EAR3·42·82·32·73·03·01·92·84·1Prevalence (%) of failing to meet EARVitamin A9·35·01·88·25·83·10·93·913·2Vitamin E97·194·788·994·494·792·790·494·997·1Vitamin C3·30·90·21·92·11·00·21·13·8Vitamin B1 6·32·70·91·74·44·80·42·68·2Vitamin B2 2·32·11·30·62·23·00·21·14·7Vitamin B3 0·20·10·10·030·10·30·00·10·4Vitamin B6 2·40·90·30·71·71·60·030·53·6Vitamin B12 0·50·91·30·20·61·80·20·61·9Vitamin D73·674·973·575·373·972·560·677·086·7Folic acid52·230·913·943·135·223·81333·659·7Fe2·40·60·10·61·51·40·10·72·9Ca19·920·118·213·520·125·38·718·632·9K19·59·13·411·213·310·12·39·124·4Mg28·916·47·417·420·817·95·217·435·4P0·20·20·10·030·20·30·00·040·5Cr2·71·10·20·91·91·60·11·03·4Se5·44·43·61·94·87·11·13·69·5I6·59·410·75·58·512·14·17·714·7Zn8·25·83·93·47·18·21·54·912·7EAR, estimated average requirement; FP, food pattern; G, groups.
On average, the highest group of the overall provegetarian FP showed the lowest average number (2·3) of micronutrients failing to meet EAR, while the highest group of the unhealthful provegetarian FP exhibited the highest average number (4·1) of micronutrients failing to meet EAR (Figs 1(a) and (c) respectively).
Fig. 1(a) Average number and 95 % CI of micronutrients with intakes below the EAR according to groups of the overall provegetarian FP. Adjusted for sex and age. ( b) Average number and 95 % CI of micronutrients with intakes below the EAR according to groups of the healthful provegetarian FP. Adjusted for sex and age. ( c) Average number and 95 % CI of micronutrients with intakes below the EAR according to groups of the unhealthful provegetarian FP. Adjusted for sex and age. EAR, estimated average requirement; FP, food pattern Tables 6 and 7 present the OR of failing to meet EAR for either ≥ 3 or ≥ 6 micronutrients, respectively, according to groups of the different provegetarian FPs. As shown in Table 6, greater adherence to the overall and the healthful provegetarian FP showed an inverse association with the risk of failing to meet ≥ 3 EAR values. The adjusted OR (95 % CI) for failing to meet ≥ 3 EAR (third v. first group) was 0·61 (0·54, 0·69), for the overall, 0·27 (0·24, 0·31) for the healthful and 9·04 (7·57, 10·4) for the unhealthful provegetarian FP. Only the healthful and unhealthful provegetarian FPs showed a statistically significant association with the risk of failing to meet ≥ 6 EAR values (Table 7). The adjusted OR (95 % CI) for failing to meet ≥ 6 EAR (third v. first group) was 0·31 (0·24, 0·42) for the healthful and 15·00 (8·95, 25·13) for the unhealthful provegetarian FP. Moreover, the analysis of the association between the healthful and unhealthful provegetarian FP and failing to meet EAR values showed a noticeable variation of the estimates after adjusting for age, sex, supplement consumption and total energy intake, which was not observed for the overall provegetarian FP. These results remained substantially unchanged after performing the above-described sensitivity analyses to verify their robustness (data not shown).
Table 6OR (95 % CI) of failing to meet the EAR for ≥ 3 micronutrients according to groups of adherence to the overall, healthful and unhealthful provegetarian FPG1G2G3 P for trend OR95 % CIOR95 % CI Overall provegetarian FP n 703254525341Prevalence of ≥ 3 inadequate micronutrient intake27·418·810·5Crude1 (Ref.)0·610·56, 0·670·310·28, 0·35< 0·001Multivariable 11 (Ref.)0·860·77, 0·950·610·54, 0·69< 0·001 Healthful provegetarian FP G1G2G3 n 629556705860Prevalence of ≥ 3 inadequate micronutrient intake17·821·420·1Crude1 (Ref.)1·261·15, 1·381·171·07, 1·28< 0·001Multivariable 11 (Ref.)0·540·48, 0·600·270·24, 0·31< 0·001 Unhealthful provegetarian FP G1G2G3 n 674553105770Prevalence of ≥ 3 inadequate micronutrient intake4·8917·2939·2Crude1 (Ref.)4·063·56, 4·6312·611·1, 14·2< 0·001Multivariable 11 (Ref.)2·852·46, 3·309·047·57, 10·4< 0·001EAR, estimated average requirement; FP, food pattern; G, groups. Multivariable 1: adjusted for age, sex, supplement consumption (yes/no) and total energy intake (continuous).
Table 7OR (95 % CI) of failing to meet the EAR for ≥ 6 micronutrients according to groups of adherence to the overall, healthful and unhealthful provegetarian FPG1G2G3 P for trend OR95 % CIOR95 % CI Overall provegetarian FP n 703254525341Prevalence of ≥ 6 inadequate micronutrient intake5·723·341·54Crude1 (Ref.)0·570·48, 0·680·260·20, 0·33< 0·001Multivariable 11 (Ref.)0·980·79, 1·220·850·64, 1·130·305 Healthful provegetarian FP G1G2G3 n 629556705860Prevalence of ≥ 6 inadequate micronutrient intake2·084·744·54Crude1 (Ref.)2·341·89, 2·892·241·81, 2·77< 0·001Multivariable 11 (Ref.)0·760·58, 0·980·310·24, 0·42< 0·001 Unhealthful provegetarian FP G1G2G3 n 674553105770Prevalence of ≥ 6 inadequate micronutrient intake0·242·009·43Crude1 (Ref.)8·65·06, 14·5143·8026·6, 72·1< 0·001Multivariable 11 (Ref.)3·482·00, 6·0015·08·95, 25·13< 0·001EAR, estimated average requirement; FP, food pattern; G, groups. Multivariable 1: adjusted for age, sex, supplement consumption (yes/no) and total energy intake (continuous).
## Discussion
Our findings showed that, as hypothesised, both higher overall and healthful provegetarian FP scores were inversely associated with the risk of micronutrient inadequacy (failing to meet ≥ 3 EAR values). On the contrary, a direct association was found between the unhealthful provegetarian FP and the risk of failing to meet micronutrient requirements. These results could play a useful role in contributing to the development of dietary guidelines based on the importance of nutritional content (i.e. micronutrient adequacy) besides the plant or animal origin of foods.
To the best of our knowledge, this is the first study to evaluate the association between different provegetarian FP with varying degrees of healthiness and the risk of micronutrient inadequacy in an adult population. Other prospective studies have previously examined the relationship between these provegetarian FP indices and several chronic disease[13,17] as well as mortality[9,31].
Diet quality indices, dietary patterns and food-based scores are all valid tools to determine the adequacy of micronutrient intake[32]. This focus is relevant due to the increasing prevalence of micronutrient inadequacy across the *European* general population. A study showed that the prevalence of inadequate intakes was particularly high for vitamins D, C, folic acid, Ca, Se and I in adults[33], which is consistent with our findings. Moreover, the ANIBES study found that there were inadequate intakes for ≥ 3 micronutrients across all age groups in a representative sample of the Spanish population[34]. Our results suggest that the adoption of an overall and healthful provegetarian FP could reduce the prevalence of micronutrient inadequacies.
When categorising participants into groups, the highest prevalence of inadequacy was found in the highest group of the unhealthful provegetarian FP compared to lowest group. However, a direct association was found between the adherence to overall provegetarian FP and the risk of micronutrient inadequacy of vitamin B12 and I. It has been previously reported that the requirements of these two micronutrients cannot be met in some vegetarian diets[10,35,36]. These results are in line with other studies that evaluated the degree of micronutrient adequacy when switching from omnivorous to PBD(37–42). It is also worth noting that in this cohort, participants who had the highest overall provegetarian FP scores were more likely to have prevalent hypertension, CVD, diabetes, dyslipidaemia, cancer and hypercholesterolaemia, which could explain why they followed a special, and generally healthier, diet. Interestingly, the analysis of the association between the healthful and unhealthful provegetarian FP and failing to meet ≥ 6 micronutrients shows a noticeable change of the OR estimate (i.e. attenuated effect) after adjusting for age, sex, supplement consumption and total energy that is not shown for the overall provegetarian FP. These changes could be explained because participants with greater adherence to the healthful and unhealthful provegetarian FP showed lower energy intake and as expected, with higher food consumption, there is a lower risk of failing to meet ≥ 6 EAR values.
Although PBD are widely recognised as a healthy dietary pattern, not every plant food is equally healthy and may exert different health effects due to their nutrient compositions[43,44]. These results highlight the importance of taking into account the nutrient density of different kinds of PBD and promoting a healthful provegetarian FP with a high consumption of fruits, vegetables, legumes, nuts and seeds, wholegrain products and olive oil[13,17,45]. On the other hand, an unhealthful provegetarian FP includes lower-quality foods such as ultra-processed foods, sweets and desserts, miscellaneous ready-to-eat meals, sugary drinks, fruit juices, refined cereals and red and processed meat[13].
There is currently sufficient evidence that a well-planned healthful provegetarian FP[10,35,36] have many health benefits(9,13,16,46–48), and this could be partly due to its ability to adequately meet the intake of essential nutrients.
We acknowledge that our study has some limitations. First, we used a self-reported FFQ, which can lead to measurement errors and may not be the best method to evaluate the intake of Se, Fe and folic acid[49]. However, FFQ is the most practical and feasible tool to evaluate diet in large epidemiological studies[20,21]. Second, as in any observational study, some residual confounding might be present. However, we carried out the analyses adjusting for the main known confounders of nutritional adequacy, and we do not consider it as a likely important bias impacting our results. Third, the micronutrient intake may have been underestimated, as we did not calculate the average intake from all food sources. We included the intake from food and dietary supplements, without considering the intake of fortified foods or medication that the participants might be consuming. Fourth, the results based on the EAR cut-point method only estimate the probability of adequacy but does not indicate nutrient deficiencies or whether the diet of this population is actually adequate. Nutritional deficiency should be confirmed by biological markers of nutrient intake. Finally, participants of the SUN cohort cannot be considered representative of the general population, and this could have reduced the variability between subjects in dietary exposures as they belong to a single (high) educational and socio-economic stratum. In this sense, the fact that all participants are university graduates can also be a strength since this allows obtaining a better quality of self-reported information, improving the retention rate and minimising confusion by educational level and, therefore, by socio-economic status[50].
On the other hand, the strengths of the present study are based on the fact that we used data from a well-known Mediterranean cohort with a large sample size and high response rate. Moreover, we adjusted for numerous potential confounders and we used the probabilistic approach and the EAR cut-off approach[23], and in both cases, the results were very similar. Finally, we used a FFQ repeatedly validated in Spain[20].
In conclusion, our findings showed that a greater adherence to an unhealthful provegetarian FP was directly associated with the risk of micronutrient inadequacy. Therefore, PBD do not always lead to a favourable nutritional quality, and it would be advisable that when PBD are followed, they should mostly include nutrient-dense plant foods and minimise ultra-processed and less-healthy plant foods. Our results reinforce the importance of a preference for healthier plant foods in terms of micronutrient intake adequacy in adults.
## Conflict of interest:
There is no conflicts of interest.
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|
---
title: The architecture of Cidec-mediated interfaces between lipid droplets
authors:
- Iva Ganeva
- Koini Lim
- Jerome Boulanger
- Patrick C. Hoffmann
- Olivia Muriel
- Alicia C. Borgeaud
- Wim J.H. Hagen
- David B. Savage
- Wanda Kukulski
journal: Cell Reports
year: 2023
pmcid: PMC9989828
doi: 10.1016/j.celrep.2023.112107
license: CC BY 4.0
---
# The architecture of Cidec-mediated interfaces between lipid droplets
## Summary
Lipid droplets (LDs) are intracellular organelles responsible for storing surplus energy as neutral lipids. Their size and number vary enormously. In white adipocytes, LDs can reach 100 μm in diameter, occupying >$90\%$ of the cell. Cidec, which is strictly required for the formation of large LDs, is concentrated at interfaces between adjacent LDs and facilitates directional flux of neutral lipids from the smaller to the larger LD. The mechanism of lipid transfer is unclear, in part because the architecture of interfaces between LDs remains elusive. Here we visualize interfaces between LDs by electron cryo-tomography and analyze the kinetics of lipid transfer by quantitative live fluorescence microscopy. We show that transfer occurs through closely apposed monolayers, is slowed down by increasing the distance between the monolayers, and follows exponential kinetics. Our data corroborate the notion that Cidec facilitates pressure-driven transfer of neutral lipids through two “leaky” monolayers between LDs.
## Graphical abstract
## Highlights
•Cidec mediates close (≈10 nm) apposition of lipid droplets for neutral lipid transfer•Tagging Cidec increases the distance between droplets and slows lipid transfer•Cidec-mediated transfer of neutral lipids accelerates with exponential kinetics
## Abstract
Cidec is required for formation of unilocular lipid droplets in white adipocytes, but exactly how it does so remains unclear. Ganeva et al. visualize Cidec-mediated droplet growth and observe that lipid transfer between droplets accelerates while interacting lipid droplets remain distinct entities, suggesting that Cidec facilitates pressure-driven lipid transfer.
## Introduction
White adipocytes, which are responsible for regulated fat storage and release in vertebrates, feature a single large lipid droplet (LD) filling up almost their entire cytoplasm. This distinctive morphology is a key factor in optimizing energy storage capacity and reducing the relative surface area, thereby enabling tight regulation of lipid storage and release from the droplet.1 Excessive or deficient lipid storage is a hallmark of several serious and highly prevalent metabolic diseases such as diabetes, non-alcoholic fatty liver disease, and atherosclerosis. Both human and mouse loss-of-function genetic data suggest that *Cidec is* strictly required for the formation of unilocular white adipocytes.2,3,4,5 Specifically, many white adipocytes in a patient with a nonsense CIDEC mutation (E186X) were shown to be multilocular,4 and, in Cidec null mice, all white adipocytes were multilocular.2,3 Gain-of-function studies involving expression of Cidec-GFP in heterologous cells also showed that Cidec consistently enlarged LDs, although the droplets remain multilocular in all these instances.6,7,8,9,10 These studies also showed that Cidec localized on the surface of LDs and accumulated at interfaces between LDs, where it facilitated net transfer of neutral lipid from the smaller to the larger LD. Barneda et al. extended these observations by suggesting that an amphipathic helix of the homologous protein Cidea was involved in recruiting the protein to the LD surface, in perturbing the phospholipid monolayer, and in facilitating neutral lipid transfer across the interface.9
## Results
To emulate the requirement for Cidec, we first used Cidec null mouse embryonic fibroblast (MEF)-derived adipocytes2 and expressed Cidec-EGFP during adipogenic differentiation using a lentiviral Tet-ON inducible system. Upon expression, Cidec-EGFP was targeted to the surface of LDs and enriched at interfaces between LDs (Figure 1A). The volume of LDs increased approximately 30-fold compared with LDs in cells that were not induced (Figure 1A) (median −Dox 0.86 μm3, median +Dox 31.79 μm3, $p \leq 0.0001$, $$n = 75$$ cells per condition). Expression of cytosolic EGFP had no effect on LDs (Figure S1A). These results show that induced expression of Cidec-EGFP in MEF-derived Cidec null adipocytes recapitulates the localization and activity of Cidec on LDs, consistent with previous observations in other cell types.6,8,9,10,12 We sought to exploit this experimental paradigm to investigate the architecture of the interface between LDs by cellular electron cryo-tomography (cryo-ET). Previous immunoelectron microscopy showed the accumulation of Cidec between LDs, hence the interfaces were termed contact sites, but it had remained elusive whether the cores of the contacting LDs are continuous or the monolayers are fused.8 Recent developments in cellular cryo-ET provide unprecedented sample preservation and resolution to visualize cellular interiors in 3D.13 These methodological advances are ideal to resolve cellular structures such as LD monolayers in an unperturbed, near-native state.14,15 MEF-derived adipocytes are too “thick” to be directly visualized by cryo-ET and require growth to high confluency, which renders vitrification difficult. Therefore, we transferred the Tet-ON inducible expression of Cidec-EGFP to HeLa cells, for which cryo-focused ion beam (cryo-FIB) milling workflows to thin cells for cryo-ET are well established.13,16 Expression of Cidec in heterologous cells that do not express any adipocyte-specific proteins induces LD enlargement, indicating that Cidec alone is sufficient for this process.8,9,10 We “fed” Cidec-EGFP-expressing HeLa cells with oleic acid for 24 h to trigger LD formation. Although LDs in HeLa cells are considerably smaller than in MEF-derived adipocytes, Cidec-EGFP expression increased LD volumes approximately 4.5-fold (Figure 1B) (median −Dox, 0.34 μm3, $$n = 69$$ cells; median +Dox, 1.3 μm3, $$n = 67$$ cells, $p \leq 0.0001$), whereas expression of cytosolic EGFP had no effect (Figure S1B). These results verify that expression of Cidec-EGFP in HeLa cells is sufficient to replicate its function in LD enlargement. Figure 1Induced expression of Cidec-EGFP increases LD sizes in Cidec null MEF-derived adipocytes and in HeLa cells(A) FM of fixed Cidec null MEF-derived adipocytes inducibly expressing Cidec-EGFP (green). LDs were labeled with LipidTOX Deep Red dye (magenta). Cidec null MEFs were differentiated into mature adipocytes over the course of 10 days in the continuous presence (upper) or absence (lower) of doxycycline. The plot shows mean LD volumes per cell. Dots represent individual cells, color coded according to experiment. Black lines correspond to the median of all cells with interquartile range (IQR) ($$n = 75$$ per condition, from three experiments: −Dox 0.86 μm3, IQR 0.98 μm3; +Dox 31.79 μm3, IQR 41.81 μm3).(B) FM of fixed HeLa cells inducibly expressing Cidec-EGFP (green). LDs were stained with LipidTOX Deep Red dye (magenta). ( Upper) HeLa cells induced with doxycycline for Cidec-EGFP expression. ( Lower) HeLa cells in the absence of doxycycline induction. Cells were fixed 24 h after the addition of oleic acid and doxycycline. The plot shows mean LD volumes per cell. Dots represent individual cells, color coded according to experiment. Black lines correspond to the median of all cells with IQR (−Dox, $$n = 69$$, from three experiments, median 0.34 μm3, IQR 0.39 μm3; +Dox, $$n = 67$$, from three experiments, median 1.30 μm3, IQR 1.04 μm3).(C) HeLa cells inducibly expressing Cidec-EGFP (green) and LDs labeled with LipidTOX Deep Red (magenta), grown on a cryo-EM grid and imaged by cryo-FM (first and second panel). Second panel corresponds to a magnified view of the area indicated with a green square in the first panel. Regions for cryo-FIB milling were chosen based on LD size (white arrows) and Cidec-EGFP enrichment at LD interfaces. Note that the large, round, green signal likely corresponds to autofluorescence, known to occur in mammalian cells imaged by cryo-FM.11 Third panel: Cryo-SEM overview image of the lamella generated by cryo-FIB milling from the HeLa cell shown in first and second panel. Interacting LDs identified by cryo-FM are visible in the resulting lamella (white arrows). Milled regions were imaged by cryo-EM to identify LDs in close proximity to each other (fourth panel) and then subjected to cryo-ET (fifth panel, corresponds to the same virtual tomographic slice as shown in Figure 2B, third panel). Corresponding areas in different images are indicated by blue and purple squares, respectively. Scale bars in (A) and (B), 2.5 μm. Scale bars in (C), from left to right, 10 μm, 2.5 μm, 5 μm, 500 nm, 150 nm.
We vitrified HeLa cells on electron cryo-microscopy (cryo-EM) grids after feeding them with oleic acid and inducing Cidec-EGFP expression. To identify LD interfaces at which Cidec-EGFP is enriched, we imaged the vitrified cells by fluorescence microscopy (FM) at cryogenic temperatures (cryo-FM) (Figure 1C). Subsequently, we thinned corresponding cell regions by cryo-FIB milling and subjected them to cryo-ET (Figure 1C).13 This approach ensured that we visualized bona fide Cidec-EGFP-marked LD interfaces in a near-native state and at high resolution. In the resulting tomograms, we observed LDs with neutral lipid cores of dense, amorphous appearance (Figure 2), previously attributed to a mixture of triacylglycerol (TAG) and small amounts of cholesterol esters.14 The surrounding monolayers were resolved as single dark lines, compared with the typical two lines visible for bilayers (Figure S2A). We frequently found mitochondria and endoplasmic reticulum (ER) in close proximity to the LDs, likely representing organelle contact sites important for lipid metabolism (Figure 2).17 Where two LDs were closely apposed, we often observed deformations of the otherwise nearly spherical shape of the LDs. These large-scale morphologies varied among the interfaces we imaged and corresponded to either minimal deformation (Figure 2A; Video S1), flattening of both LDs (Figure 2B; Video S2), one LD locally protruding and inducing an indentation in the other LD (Figure 2C; Video S3), or the smaller LD locally imposing its curvature by inducing an indentation in the larger LD (Figure 2D; Video S4).Figure 2Electron cryo-tomography of LD interfaces in HeLa cells expressing Cidec-EGFP(A–D) HeLa cells inducibly expressing Cidec-EGFP were vitrified, screened for LD interfaces by cryo-FM, thinned by cryo-FIB milling, and imaged by cryo-ET. The shape of the LDs is deformed where two LDs are closely apposed. The different observed morphologies are classified as follows: (A) minimal deformation of the monolayers; (B) flattening of both LDs forming an interface, same example as shown in Figure 1C; (C) protrusion of one LD into the other LD, resulting in an indentation; (D) indentation of the larger LD caused by the smaller LD. First column from left: virtual slices through tomograms acquired at areas where LDs are in close proximity (Video S1. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2A, Video S2. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2B, Video S3. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2C, Video S4. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2D). Dashed squares indicate areas corresponding to magnified images in third column. Second column: segmentation models of LDs and membranous organelles in proximity to LDs. Pink and magenta shades, LDs; yellow, outer mitochondrial membrane (OMM); green, inner mitochondrial membrane (IMM); turquoise, ER; blue, other membranous organelles (Other). Third column: close-ups of interfaces between LDs. Note that these are different virtual slices than in the first column. Fourth and fifth columns: close-ups of monolayers at the interface (fourth column) and outside the interface (fifth column). In (A), the third, fourth, and fifth panels are derived from the same virtual slice. In (C), the third and fifth panels are derived from the same virtual slice. In (D), the third and fourth panels are derived from the same virtual slice. Scale bars in (A)–(D), 150 nm in first column, 100 nm in third column, 25 nm in fourth and fifth columns.
Video S1. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2AThe video presents virtual slices along the z axis of the tomographic volume. The white bounding box indicates the dimensions of the tomographic volume, which are 1.43 and 1.38 μm in x and y, respectively.
Video S2. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2BThe video presents virtual slices along the z axis of the tomographic volume. The white bounding box indicates the dimensions of the tomographic volume, which are 1.43 and 1.38 μm in x and y, respectively.
Video S3. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2CThe video presents virtual slices along the z axis of the tomographic volume. The white bounding box indicates the dimensions of the tomographic volume, which are 1.38 and 1.43 μm in x and y, respectively.
Video S4. Electron cryo-tomogram of a HeLa cell expressing Cidec-EGFP, related to Figure 2DThe video presents virtual slices along the z axis of the tomographic volume. The white bounding box indicates the dimensions of the tomographic volume, which are 1.43 and 1.38 μm in x and y, respectively.
In all cases, the two phospholipid monolayers appeared as separate entities. We determined that the mean distance between closely apposed monolayers forming LD-LD interfaces was 10.9 nm ($$n = 21$$ interfaces, SD 2.2 nm) (see STAR Methods and Figure S4B). Furthermore, we often observed a dense layer of material between the monolayers, likely corresponding to proteins (Figures 2A–2D, third panel). Given the dimensions of the interface and the dense protein packing, we hypothesized that the bulkiness of the Cidec-EGFP construct might influence the architecture of the interface.
To address the influence of the EGFP moiety, we generated a HeLa cell line in which we inducibly expressed untagged Cidec. Using cryo-FM, we identified LipidTOX-labeled LDs closely apposed to each other, hence likely engaged in an LD-LD interface (Figures S2B and S2C). We targeted these areas by cryo-FIB milling (Figure S2D) and subsequent cryo-ET. We found that the overall architecture of LD interfaces was consistent between HeLa cells expressing Cidec-EGFP and untagged Cidec (Figures 3A–3C). As for Cidec-EGFP, the two apposed monolayers were visible as two separate entities in cells expressing untagged Cidec (Figures 3A–3C, third panels; Video S5. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3A, Video S6. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3B, Video S7. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3C).Figure 3Electron cryo-tomography of LD interfaces in HeLa cells expressing Cidec(A–C) HeLa cells inducibly expressing untagged Cidec were vitrified, screened for enlarged LDs in close proximity by cryo-FM, thinned by cryo-FIB milling, and imaged by cryo-ET. The shape of the LDs is deformed where two LDs are closely apposed. The different observed morphologies are classified as follows: (A) minimal deformation of the monolayers; (B) flattening of both LDs, forming an interface; (C) protrusion of one LD into the other LD, resulting in an indentation. First column from left: virtual slices through tomograms acquired at areas where LDs are in close proximity (Video S5. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3A, Video S6. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3B, Video S7. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3C). Dashed squares indicate areas corresponding to magnified images in third column. Second column: segmentation models of LDs and membranous organelles in proximity of LDs. Pink and magenta shades, LDs; yellow, OMM; green, IMM; turquoise, ER; blue, other membranous organelles (Other). Third column: close-ups of interfaces between LDs (different virtual slices than in the first column). Fourth and fifth column: close-ups of monolayers at the interface (fourth column; in A and C this is the same virtual slice as in the third column) and outside the interface (fifth column).(D) The different interface morphologies plotted as a ratio of the diameter of the larger LD to the diameter of the smaller LD. Green dots indicate Cidec-EGFP data, gray dots indicate untagged Cidec data. Black lines represent mean with SD (minimal deformation, $$n = 9$$, mean 1.28, SD 0.21; indentation, $$n = 4$$, mean 4.78, SD 0.98; indentation and protrusion, $$n = 5$$, mean 1.57, SD 0.19; flattened, $$n = 11$$, mean 1.63, SD 0.50). Only LD pairs where a reliable diameter measurement was possible (see STAR Methods) were included in this quantification. Two interfaces, both from the same Cidec-EGFP tomogram, were therefore excluded.(E) Model representation of the observed monolayer disturbances at LD interfaces from both Cidec-EGFP and untagged *Cidec data* combined, binned into three arbitrary classes to group minimal, intermediate and maximal deformations. Only LD-LD interfaces in which the monolayers were clearly visible and well preserved both within and outside the interface were considered in this analysis ($$n = 22$$ interfaces). Four interfaces from two Cidec-EGFP tomograms and five interfaces from three untagged Cidec tomograms were excluded due to insufficient vitrification. Among those excluded is the tomogram shown in (C).(F) Median distances between each LD pair, for Cidec-EGFP (green dots) and untagged Cidec (gray dots). Black lines represent the mean with SD (Cidec-EGFP, 10.9 nm, SD 2.2 nm, $$n = 21$$ interfaces; Cidec, 8.7 nm, SD 2.1 nm, $$n = 10$$ interfaces).Scale bars in (A)–(C), 150 nm in first column, 100 nm in third column, 25 nm in fourth and fifth columns.
Video S5. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3AThe video presents virtual slices along the z axis of the tomographic volume. The white bounding boxes indicate the dimensions of the tomographic volume, which are 1.43 and 1.38 μm in x and y, respectively.
Video S6. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3BThe video presents virtual slices along the z axis of the tomographic volume. The white bounding boxes indicate the dimensions of the tomographic volume, which are 1.43 and 1.38 μm in x and y, respectively.
Video S7. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3CThe video presents virtual slices along the z axis of the tomographic volume. The white bounding boxes indicate the dimensions of the tomographic volumes, which are 1.43 and 1.38 μm in x and y, respectively.
The LD pairs in cells expressing untagged Cidec displayed similar large-scale morphologies to those observed for Cidec-EGFP, indicating that these deformations are inherent to Cidec-mediated LD-LD interfaces (Figures 3A–3C; Video S5. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3A, Video S6. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3B, Video S7. Electron cryo-tomogram of a HeLa cell expressing untagged Cidec, related to Figure 3C). We thus analyzed whether the different morphologies correlated with the ratio of diameters of the LDs engaged in the interface, or with the absolute sizes of the LDs (see STAR Methods). Minimal deformation, flattening, and protruding LDs were morphologies found at ratios of large-to-small LD diameter between 1 and 2.5 (Figure 3D). In contrast, small undeformed LDs forming an indentation in large LDs were found when the diameter ratio was between 3.6 and 5.7. This type of morphology was exclusively associated with LDs of 400 nm or less in diameter, and such small LDs were never associated with other morphologies. These data suggest that, at diameters of less than 400 nm, LDs maintain a spherical shape when interacting with large LDs and impose their curvature locally on the interacting LD. The variability associated with all other morphologies indicates that, when LDs are larger than 400 nm, deformations do not depend on the sizes of interacting LDs (diameter ratio of small LDs indenting large LD versus any other morphology, $p \leq 0.0001$).
The close apposition of LDs and the appearance of an interface could potentially be caused by crowding of the large amounts of LDs within the cell, rather than by Cidec-mediated tethering. To address this possibility, we acquired cryo-EM data from cells fed with oleic acid in which we did not induce Cidec-EGFP. We found that, in the absence of Cidec-EGFP, there was minimal clustering of LDs (Figures S3A and S3D). In cells expressing Cidec-EGFP (Figures S3A and S3B) or untagged Cidec (Figures S3A and S3C), more than half of the LDs observed in overview images were engaged in an interface, whereas only about $15\%$ of LDs in cells not expressing Cidec-EGFP were in close proximity to each other (Figures S3A and S3D) ($$p \leq 0.0002$$). In keeping with the more scattered distribution of LDs in cells not expressing Cidec-EGFP, we rarely detected LDs in overview cryo-EM images of lamellae from these cells, resulting in a small total number of LDs (Figure S3A). These data support previous findings suggesting that Cidec promotes tethering of LDs,7 and indicate that the occurrence of LD-LD interfaces is a consequence of Cidec expression.
Neither for Cidec-EGFP nor for untagged Cidec did we observe a continuity between the two apposing monolayers that would be indicative of a stable fusion or pore formation. While the apposing monolayers seemed complete, their appearance within the LD interface sometimes differed from the appearance on the rest of the LD surface (Figures 2A–2D and 3A–3C, fourth and fifth panels). In areas not involved in interfaces, the monolayers often appeared smooth, whereas, within the interfaces, the monolayers exhibited waviness and nanometer-scale irregularities, indicating local disturbances. The extent of the disturbances varied between different interfaces (Figure 3E) and was only assessed in tomograms with optimally vitrified interfaces (see STAR Methods). While the majority of assessed interfaces showed rather minimal waviness (Figures 2A and 3A), $9\%$ displayed more extensive deformations of the monolayer (Figures 2C and 2D) ($$n = 22$$ interfaces). Within the interfaces with extended deformations, the disturbances were not evenly distributed over the entire monolayer. Instead, these interfaces exhibited areas with variedly pronounced deformations (Figure S4A). Furthermore, the distances we measured between the monolayers varied throughout the contact area, also indicating local heterogeneity within the interfaces (Figures S4B–S4F). These observations suggest that TAG transfer is likely to occur through two locally disturbed, yet largely intact, phospholipid monolayers.
When we measured the distances between the monolayers, we found that, overall, the monolayers were closer for untagged Cidec than for Cidec-EGFP (mean of distances: untagged Cidec 8.7 nm, SD 2.1 nm, $$n = 10$$ interfaces, $$p \leq 0.010$$ compared with Cidec-EGFP) (Figure 3F). The observed difference compared with Cidec-EGFP interfaces is in good agreement with the dimensions of EGFP molecules,18 suggesting that the increase in distance is due to the space taken up by EGFP moieties. Furthermore, the layer between the monolayers displayed lower visibility and density than for Cidec-EGFP, possibly in part reflecting the smaller molecular weight of the construct. These results suggest that the spacing between LD monolayers is determined by dense packing of Cidec molecules and is influenced by the size of the Cidec construct mediating the interaction.
Having identified determinants of the architecture of the LD interface, we next sought to link them to neutral lipid transfer function. We hypothesized that, if Cidec-EGFP has an effect due to the bulky size of the tag (27 kDa) compared with untagged Cidec, Cidec conjugated to SUMOstar may have a reduced effect due to the intermediate size of the tag (12 kDa). By titrating doxycycline dosage, comparable Cidec, Cidec-SUMOstar, and Cidec-EGFP transcript levels were confirmed for all three stable HeLa cell lines (Figure S5A). For FM imaging, we fixed cells expressing untagged Cidec and Cidec-SUMOstar 24 h after oleic acid and doxycycline were added (Figure 4A). Similarly to Cidec-EGFP (Figure 1B), inducing expression of either Cidec-SUMOstar or Cidec increased the LD volume ($p \leq 0.0001$ for Cidec-SUMOstar, $p \leq 0.0001$ for Cidec; Figure 4B). All three constructs reduced the number of LDs per cell ($p \leq 0.0001$ for Cidec-EGFP, $p \leq 0.0001$ for Cidec-SUMOstar, $p \leq 0.0001$ for Cidec; Figure 4C). No increase in LD volume ($$p \leq 0.9933$$, Figure 4B) or reduction in LD number ($$p \leq 0.8477$$, Figure 4C) was observed upon induction of cytosolic EGFP expression. When comparing cells expressing the different constructs, we found that expression of untagged Cidec resulted in the greatest increase in LD volume (median of mean LD volumes/cell: EGFP, 0.31 μm3; Cidec-EGFP, 1.30 μm3; Cidec-SUMOstar, 2.05 μm3; Cidec, 3.30 μm3; $p \leq 0.0001$ for group comparison; n: 67–73 cells) (Figure 4B) and strongest reduction in LD number (median of mean LD number/cell: EGFP 108 LDs/cell, Cidec-EGFP 45 LDs/cell, Cidec-SUMOstar 28 LDs/cell, Cidec 18 LDs/cell; $p \leq 0.0001$ for group comparison; n: 67–73 cells) (Figure 4C). These results confirm that all three constructs enlarge LDs and reduce their number per cell, albeit with varying efficiency. Figure 4Quantitative FM of HeLa cells expressing Cidec-EGFP, Cidec-SUMOstar, or untagged Cidec(A) Representative FM images of fixed HeLa cells, loaded with oleic acid, either doxycycline induced (+Dox) or uninduced (−Dox) for EGFP, Cidec-EGFP, Cidec-SUMOstar, or Cidec expression for 24 h prior to fixing and imaging. Scale bars, 10 μm.(B and C) Quantification of mean LD volume per cell (B) or mean LD number per cell (C) from 3D FM images of fixed HeLa cells, loaded with oleic acid, either uninduced (−Dox) or doxycycline induced (+Dox) for EGFP, Cidec-EGFP, Cidec-SUMOstar, or Cidec expression 24 h prior to fixing and imaging. Each dot represents the mean LD volume (B) or LD number (C) in one cell. Different colors correspond to three different experimental repeats. In each experiment, 11–35 cells were analyzed per condition. Black lines represent the medians of all data points with IQR. Medians in (B), EGFP, −Dox 0.46 μm3, IQR 0.28 μm3; +Dox 0.31 μm3, IQR 0.35 μm3; Cidec-EGFP, −Dox 0.34 μm3, IQR 0.39 μm3; +Dox 1.30 μm3, IQR 1.04 μm3; Cidec-SUMOstar, −Dox 0.40 μm3, IQR 0.27 μm3 +Dox 2.05 μm3, IQR 1.62 μm3; Cidec, −Dox 0.46 μm3, IQR 0.3 μm3, +Dox 3.30 μm3, IQR 2.98 μm3. Cidec-EGFP data are the same as plotted in Figure 1B. Medians in (C), EGFP, −Dox 122.5 LDs/cell, IQR 97.55 LDs/cell, +Dox 108 LDs/cell, IQR 88.0 LDs/cell; Cidec-EGFP, −Dox 127.0 LDs/cell, IQR 65.5 LDs/cell, +Dox 45.0 LDs/cell, IQR 39 LDs/cell; Cidec-SUMOstar, −Dox 119.0 LDs/cell, IQR 88.6 LDs/cell, +Dox 28.0 LDs/cell, IQR 16 LDs/cell, Cidec, −Dox 118.0 LDs/cell, IQR 56 LDs/cell, +Dox 18.0 LDs/cell, IQR 17 LDs/cell.(D) The rate of lipid transfer from donors to acceptors, as the average change of donor volume over the course of the transfer event. Measurements were done three-dimensionally in live cells imaged by time-lapse FM of LipidTOX signals (Video S8. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-EGFP, related to Figure 4D, Video S9. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-SUMOstar, related to Figures 4D and 5, Video S10. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing untagged Cidec, related to Figures 4D and 5). Live FM started 2 h after doxycycline-induced Cidec and Cidec-SUMOstar expression, and 24 h after doxycycline-induced Cidec-EGFP expression. Lipid transfer rates of individual events are represented by dots, color coded according to experimental repeat. Black lines represent median with IQR (Cidec-EGFP, 1.12 μm3/h, IQR 3.18 μm3/h, $$n = 104$$ from five independent experiments each analyzing 4–43 cells; Cidec-SUMOstar, 4.25 μm3/h, IQR 4.42 μm3/h, $$n = 76$$ from three independent experiments each analyzing 18–36 cells; Cidec, mean 17.57 μm3/h, IQR 14.62 μm3/h, $$n = 88$$ from three independent experiments each analyzing 23–34 cells). Only donors involved in active lipid transport and in contact with a single acceptor were considered.
We next considered the impact of the constructs on the rate of lipid transfer as this has implications for the in vivo physiology of LD growth. Prior work reported that, in 3T3-L1 pre-adipocytes overexpressing Cidec-EGFP, lipid exchange occurred at average rates of 0.13 μm3/s, whereas, in differentiated adipocytes expressing endogenous Cidec, average lipid exchange rates of 5.6 μm3/s were reported.8 These rates described bidirectional lipid exchange. When examining net lipid transfer from the smaller to the larger LD, a rate of 4.8 μm3/h was determined in pre-adipocytes.8 Thus, assuming a constant net transfer rate, an LD of 5 μm in diameter would require about 14 h to transfer its entire contents to another LD. We collected live-cell FM images of LDs, starting 2 h after induction of expression of Cidec and Cidec-SUMOstar. For Cidec-EGFP, LDs were very small at this time point, so we started live FM 24 h after induction. We identified LD pairs engaged in active lipid transfer by the close proximity of LD cores labeled with LipidTOX (Video S8. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-EGFP, related to Figure 4D, Video S9. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-SUMOstar, related to Figures 4D and 5, Video S10. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing untagged Cidec, related to Figures 4D and 5). We initially analyzed 100 pairs of juxtaposed LDs in Cidec and Cidec-EGFP-expressing, lipid-loaded cells. The data indicated that >$90\%$ of LDs were engaged in active lipid transfer in this context; the implication being that the majority of juxtaposed LDs we have imaged by cryo-ET (Figures 2 and 3) represent active lipid transfer events. All three Cidec constructs mediated lipid transfer in a net directional manner from the smaller LDs (donors) to the larger LDs (acceptors), as expected from previous reports.7,8 We measured the change of donor volume over time (see STAR Methods). Importantly, only donors that were in contact with a single acceptor were taken into account. For Cidec-EGFP, we found the volume reduction of the donor, and hence the rate of net lipid transfer averaged over the course of the transfer event, to be 1.12 μm3/h (median, $$n = 104$$ transfer events) (Figure 4D), again indicating that a complete transfer of neutral lipid content from donors to acceptors required hours, similar to previous reports.8 For Cidec-SUMOstar, the donor volume reduction rate averaged over the course of the transfer was 4.25 μm3/h (median, $$n = 76$$ transfer events), while, in the presence of untagged Cidec, the donor volume reduction rate was 17.57 μm3/h (median, $$n = 88$$ transfer events) (Figure 4D). This latter rate is approximately 15 times faster than for Cidec-EGFP ($p \leq 0.0001$) and indicates that untagged Cidec transfers neutral lipids between donor and acceptor LDs within minutes. For all three constructs, the volume reduction rate averaged over the course of the transfer was higher the larger the initial size of the donor LD (Figures S5B–S5D), consistent with previous findings.19 Taken together, these data suggest that the average rate of net lipid transfer is considerably slower in cells expressing Cidec with a bulkier tag and greater distance between the monolayers, although it is possible that properties in addition to simple size of the tag may also be involved.
Video S8. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-EGFP, related to Figure 4DVideo frames correspond to maximum projection images of z stacks, which were acquired at intervals of 2.5 min. Imaging started 24 h post doxycycline induction. Scale bar, 2 μm.
Video S9. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing Cidec-SUMOstar, related to Figures 4D and 5Video frames correspond to maximum projection images of z stacks, which were acquired at intervals of 20 s. Imaging started 2 h post doxycycline induction. Scale bar, 2 μm.
Video S10. Live FM of LDs, stained with LipidTOX Deep Red, in HeLa cells expressing untagged Cidec, related to Figures 4D and 5Video frames correspond to maximum projection images of z-stacks, which were acquired at intervals of 20 s. Imaging started 2 h post doxycycline induction. Scale bar, 2 μm.
We next investigated the Cidec-mediated change in LD volume over time by analyzing the kinetics of the process to obtain further insights into the underlying transfer mechanism. Based on the directionality of the net TAG flux from the small to the large LD, it was previously suggested that the process could be pressure driven.8 We implemented a semi-automated image analysis pipeline (see STAR Methods), which we applied to the live FM data obtained for untagged Cidec and Cidec-SUMOstar. Due to the relatively slow merging of LDs in Cidec-EGFP cells and the resulting use of different imaging settings, we did not include Cidec-EGFP in this analysis. Our analysis automatically determined the time points when a pair of LDs engaging in active lipid transfer first came into contact and when the lipid transfer event was completed (Figures 5A and 5B). For pairs identified as engaged in transfer, we obtained LD volumes in each movie frame and plotted them against time (Figures 5C–5E). The resulting curves showed that the change in LD volume of both donors and acceptors accelerated over time and hence followed exponential kinetics. This was the case both for untagged Cidec (Figures 5C and 5D) and Cidec-SUMOstar (Figure 5E). We fitted an exponential function to the curves and calculated rate constants from the fit (R values), which are a measure of the acceleration of lipid transfer (see STAR Methods). The smaller the R value, the slower the whole transfer event remained over the course of time. Within an LD pair engaged in transfer, the R value of the donor should correspond to the R value of the acceptor, unless the LDs are engaged in more than one transfer event. Hence, for each event, we plotted logarithmically the R values of donor versus acceptor (Figure S5E). For the majority of events, donor and acceptor R values were very similar. To exclude events likely involved in multiple transfers, we further considered only those transfer events for which the absolute value of log10(RAcceptor)/log10(RDonor) − 1 was smaller than 0.4 (Figure S5E, filled circles). Among these events, the median RDonor value was 0.022/s for untagged Cidec and 0.002/s for Cidec-SUMOstar (Cidec, $$n = 52$$; Cidec-SUMOstar, $$n = 14$$; Figure 5F). Thus collectively, untagged Cidec events showed a faster change of volume over time than Cidec-SUMOstar events ($p \leq 0.0001$). However, there was considerable variability in RDonor between events. We found that RDonor depended in an exponential fashion on the starting volume of the donor LD (Figure 5G). This effect was not obvious for the starting volume of the acceptor and RAcceptor (Figure 5H). Furthermore, RDonor did not correlate with the ratio between the starting volumes of the acceptor and the donor (Figure S5F). Hence the initial size of the donor LD influences how rapidly the lipid transfer speed increases during a transfer event, while neither the initial size of the acceptor nor the size difference between the two LDs have a dominant influence on the changes in transfer speed. Figure 5Analysis of LD volume changes over time shows that neutral lipid transfer follows exponential kinetics(A and B) Time course live FM of HeLa cells expressing untagged Cidec (A) and Cidec-SUMOstar (B). Representative LD pairs engaged in active lipid transfer. Panels are maximum projections of a z-stack. Scale bars, 2.5 μm.(C–E) Individual LDs were segmented out and tracked over the course of the movies. Traces indicate the volume change of individual LDs over time, in Cidec- (C and D) and Cidec-SUMOstar-expressing (E) cells. Gray overlays on x axes highlight the time when LDs are in contact (“event time”) and involved in active transfer. Overlaid on the traces, dots represent the individual time points when the two LDs were detected as being in close contact (both LDs detected within the same pixel). Black lines overlaid on the dots correspond to the exponential fit, which is used to determine R values (shown in F).(F) The R values calculated from the exponential fits of donors for Cidec ($$n = 52$$) and Cidec-SUMOstar ($$n = 14$$). Each data point corresponds to one transfer event. Black lines correspond to median and IQR (Cidec, $$n = 52$$; median, 0.022/s, IQR 0.031; Cidec-SUMOstar, $$n = 14$$; median, 0.002/s, IQR 0.007, $p \leq 0.0001$).(G) The R values calculated from the exponential fits of donors (as shown in C–E) plotted against the volumes of the donors at the beginning of the transfer event. Each point corresponds to an individual donor. Gray points indicate untagged Cidec and blue points Cidec-SUMOstar.(H) The R values calculated from the exponential fits of acceptors (as shown in C–E) plotted against the volumes of the acceptors at the beginning of the transfer event. Each point corresponds to an individual acceptor. Gray points indicate untagged Cidec and blue points Cidec-SUMOstar. Note that the analysis in this figure is derived from same live-cell imaging data as analyzed in Figure 4D.
Furthermore, these results suggest that the transfer of lipids at LD-LD interfaces follows exponential kinetics, starting slowly and accelerating toward the end. While this behavior is robust and retained in the presence of protein tags, the magnitude of the transfer rate is altered by various properties of the protein at the interface. These results are in line with the cryo-ET data, which show that monolayer integrity and overall architecture are consistent features of LD interfaces, while the magnitude of the gap between the monolayers is influenced by the bulkiness of the protein at the interface. The function and organization of Cidec could be further affected by other properties of the EGFP and SUMOstar tags.
The variability in transfer speed and acceleration between individual events is dependent on the volume of the donor at the start of the transfer event. Other contributing factors could be interface morphology and surface area, or the extent of Cidec enrichment at the interface.19
## Discussion
The contents of LD cores are among the most hydrophobic molecules found in cells. Thus how merging of LDs in the aqueous cytosol is controlled poses a unique cell biological problem. By the close apposition of monolayers, LD interfaces show resemblance to membrane contact sites.8 However, the distances between monolayers that we report here are considerably smaller than those reported for membrane contact sites involved in lipid transfer.20 Furthermore, in contrast to lipid transfer proteins at membrane contact sites,21 *Cidec is* not known to have a lipid transfer domain. Hence currently available data suggest that the transfer of TAGs between LDs is likely driven by a fundamentally different mechanism from other lipid transfer events. Our observations suggest that transfer of neutral lipids likely occurs through two largely intact monolayers, rather than a stable fusion pore, which would be expected to result in a visible continuity between the monolayers and a mixing of the TAG cores throughout the interface. We cannot entirely exclude the possibility of transient monolayer fusion events on a nanometer scale. However, as transfer occurs within minutes in cells expressing untagged Cidec, and as we have visualized a total of 31 interfaces (including interfaces mediated by both Cidec-EGFP and untagged Cidec) that were likely involved in active transfer, without ever detecting continuity between the two interfacing monolayers, fusion events are rather unlikely in our view. This suggestion is also supported by data reported by Gong et al.8 showing that, although *Cidec is* highly mobile on the surface of LDs, it very rarely relocates to a second LD.
Our cryo-ET observations point to the occurrence of molecular-level disturbances in the monolayers, which could reflect local leakiness allowing passage of TAG molecules, in line with previous molecular dynamics simulations.9 We speculate that the leakiness could be caused by the dense yet uneven packing of Cidec within and between the two monolayers. In accordance with this idea, it has recently been suggested that Cidec accumulates in patchy, lipid-permeable plates between LDs.19 The overall intactness of the monolayers might be important for retaining a difference in surface tension between the two LDs. This difference generates Laplace pressure, which has been proposed to be the driving force for the transfer.8,22 Nevertheless, key data confirming such a model were missing. In fact, it has been reported that the lipid transfer rate decreases over time and as the donor volume decreases, suggesting that pressure cannot be the sole driving force.19,22 Our finding that the transfer accelerates by exponential kinetics provides prerequisite evidence for a pressure-driven transfer model. Moreover, as the pressure is expected to inversely correlate with LD size, our observation that the smaller the donor LD is at the start of the event, the more the transfer accelerates, supports a pressure-driven model. In line with this, our observation that LDs smaller than 400 nm do not deform in shape but impose their curvature on larger LDs suggests that small LDs have a greater internal pressure or surface tension than large LDs. The discrepancy to previous findings may derive from differences in the experimental setup or in the measurement method.19,22 In summary, high-spatial-resolution cryo-ET of Cidec-mediated LD interfaces revealed a unique membrane contact site architecture characterized by closely opposed (approximately 10 nm), locally disturbed, but overall intact phospholipid monolayers. Furthermore, high-temporal-resolution live FM revealed that net lipid transfer follows exponential kinetics. These observations are consistent with the transfer of TAGs through the LD monolayers being driven by pressure, and with a form of facilitated Ostwald ripening possibly being the molecular mechanism underpinning the unilocularity of white adipocytes.
## Limitations of the study
Here, we have studied the architecture of interfaces between LDs in human cells. As a model system we used HeLa cells instead of differentiated adipocytes. This was necessary since adipocytes require higher cell confluence and feature very large LDs, preventing efficient vitrification and subsequent processing for cryo-ET. The study of overexpressed Cidec in HeLa cells mimics the process of LD coalescence only to a certain extent, as unilocularity is never achieved, and the total amount of neutral lipids stored in LDs per cell is lower than, e.g., in MEF-derived adipocytes. In order to image regions with enlarged LDs, the cells were subjected to thinning by cryo-FIB milling. While cryo-FIB milling is a powerful method to visualize areas deep inside cells by cryo-ET, it is limited in throughput and hence dataset sizes, limiting statistical power. A further drawback of our cryo-ET data is that, for cells expressing untagged Cidec, the presence of the protein at the interfaces could not be confirmed by cryo-FM, rendering data collection less efficient. When collecting the cryo-ET data, presence of LD-LD interfaces was prioritized over ideal sample preservation. Large LDs are located in thick cell areas, which are more difficult to vitrify. Thus, some of our tomograms contained areas of insufficient vitrification and were therefore excluded from certain analyses that required optimal preservation of the monolayers. Furthermore, an intrinsic limitation of cryo-ET is the anisotropic resolution of tomograms. Together with the small size of Cidec (27 kDa), this issue precluded analysis of Cidec structure and organization within the dense protein layer in the LD interfaces. Another limitation of this study is that we could not determine the protein expression levels for the different Cidec constructs due to the lack of suitable antibodies. However, the mRNA levels of all three used constructs are similar, and we do not expect differences in protein levels to affect our conclusions.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERBacterial and virus strainsOne Shot TOP10 Chemically Competent E.coliThermo FisherCat#C404003One Shot Stbl3 Chemically Competent E.coliThermo FisherCat#C737303Chemicals, peptides, and recombinant proteinsHCS LipidTOX Deep Red Neutral Lipid StainThermo FisherCat#H34477Oleic acidMerckCat#O3008Phusion High-Fidelity DNA PolymeraseThermo FisherCat#F530LDNA Ligation Kit, Mighty MixTakara BioCat#6023DMEM, high glucoseThermo FisherCat#11960044DMEM, high glucose, GlutaMAXThermo FisherCat#10569010Tetracycline-free FBSPan BiotechCat#P30-3602PyruvateThermo FisherCat#11360070L-glutamineMerckCat#59202Cβ-mercaptoethanolMerckCat#M3148BiotinMerckCat#B4639D-pantothenic acidMerckCat#P5155RosiglitazoneMerckCat#R24083-isobutyl-1-methylxanthineMerckCat#I5879DexamethasoneMerckCat#D4902Hygromycin BInvitrogenCat#10687010MycoAlert mycoplasma detection kitLonzaCat#LT07-418Gateway LR Clonase II enzyme mixThermo FisherCat#11791020Non-essential amino acidsMerckCat#M7145Lipofectamine LTX with Plus ReagentThermo FisherCat#15338100Fugene 6 transfection reagentPromegaCat#E2691UltraCULTURE serum-free cell culture mediumLonzaCat#BE12-725FRQ1 RNase-Free DNasePromegaCat#M610ALunaScript RT SuperMix KitNEBCat#E3010LTaqMan Universal PCR Master MixThermo FisherCat#4304437FormaldehydeMerckCat#47608VECTASHIELD Antifade Mounting MediumVector LaboratoriesCat#H-1000Prolong Gold Antifade MountantThermo FisherCat#P36931Penicillin-StreptomycinMerckCat#P4333Actrapid human InsulinNovo NordiskN/ADoxycyclineClontechCat#631311PuromycinCambridge BioscienceCat#P025-P026PolybreneMerck MilliporeCat#TR-1003-GSodium pyruvateMerckCat#S8636Critical commercial assaysRNeasy Mini KitQIAgenCat#74106Deposited dataRepresentative tomogram of Cidec-EGFP expressing HeLa cellsThis paper; deposited at EMDBEMD-16455Representative tomogram of Cidec expressing HeLa cellsThis paper; deposited at EMDBEMD-16454Raw tilt images and tomograms of Cidec-EGFP expressing HeLa cellsThis paper; deposited at EMPIAREMPIAR-11394Raw tilt images and tomograms of Cidec expressing HeLa cellsThis paper; deposited at EMPIAREMPIAR-11393Live cell fluorescence images of HeLa cells expressing Cidec constructsThis paper; deposited at Zenodohttps://doi.org/10.5281/zenodo.7534719Experimental models: Cell linesHeLa transduced with EGFPThis paperN/AHeLa transduced with Cidec untaggedThis paperN/AHeLa transduced with Cidec-EGFPAder et al.16N/AHeLa transduced with Cidec-SUMOstarThis paperN/ACidec null mouse embryonic fibroblasts (MEFs)Gift from Masato Kasuga Nishino et al.2N/ACidec null MEF transduced with Cidec-EGFPThis paperN/ACidec null MEF transduced with EGFPThis paperN/ABOSC 23 retroviral packaging cellsATCCCat#CRL-11270HEK293T cellsECACCCat#12022001OligonucleotidesCidecThermo FisherMm01184685_g1GAPDHThermo FisherHs02758991_g1Recombinant DNAGateway Entry vector, pEN-TmcsGift from Iain D. C. Fraser Shin et al.23Addgene no. 25751pEGFPN3 vectorClontechCat#6080-1Gateway Destination vector, pSLIK-hygromycinGift from Iain D. C. Fraser Shin et al.23Addgene no. 25737pMDLg/pRREGift from Didier Trono Dull et al.24Addgene no.12251pRSV-RevGift from Didier Trono Dull et al.24Addgene no.12253pVSV-G (also known as pMD2.G)Gift from Didier TronoAddgene no.12259pBABE-mPPARγ2This paperN/ApBABE-EGFPThis paperN/ASoftware and algorithmsPrismGraphPadN/AFijiSchindelin et al.25https://imagej.net/software/fiji/Bitplane Imaris software version 9.6.0Oxford InstrumentsN/AMATLABMathWorksN/AIMODKremer et al., Mastronarde et al.26,27https://bio3d.colorado.edu/imod/SerialEMMastronarde et al.28https://bio3d.colorado.edu/SerialEM/Custom MATLAB scripts for analysis of distances between LDsThis paperhttps://gitlab.com/jboulanger/bioimglabCustom MATLAB scripts for analysis of transfer kinetics in live FMThis paperhttps://gitlab.com/jboulanger/ldtrackOtherQuantifoil holey carbon EM grids (gold, 200 mesh, R$\frac{2}{2}$)www.quantifoil.comN/A
## Lead contact
Request for resources and further information should be directed to and will be fulfilled by the lead contact, Wanda Kukulski ([email protected]).
## Materials availability
Cell lines generated in this study are available from the lead contact upon request with a completed Materials Transfer Agreement.
## Cell lines
Primary Cidec null MEFs were cultured at 37°C, $5\%$ CO2 in high glucose DMEM (Thermo Fisher, 11960044) supplemented with $10\%$ Tetracycline-free heat-inactivated FBS (Pan Biotech, P30-3602), 100 U/mL penicillin, 100 μg/mL streptomycin (Merch, P4333), 2 mM L-glutamine (Merck, 59202C), 1x non-essential amino acids (Merck, M7145), 1 mM sodium pyruvate (Merck, S8636), and 50 μM β-mercaptoethanol (Merck, M3148). The MEFs were immortalised by transfection of 2 μg of simian virus 40 large T-antigen-expressing vector employing Fugene 6 transfection reagent (Promega, E2691) followed by five rounds of a 1 in 10 split to achieve $\frac{1}{100}$,000-fold splitting. Untransfected primary MEFs that underwent the $\frac{1}{100}$,000-fold splitting were used as a control to ensure that all surviving cells were immortalised. Immortalised Cidec null MEFs were then transduced with pBABE-mPPARγ2 to ensure they had the potency to differentiate into adipocytes. BOSC 23 retroviral packaging cells were ∼$70\%$ confluent when transfected with 12 μg of pBABE-mPPARγ2 or pBABE-EGFP plasmid DNA using Fugene 6 transfection reagent (Promega, E2691). Media containing secreted retrovirus was collected 72 h post-transfection and filtered through 0.45 μm syringe filters. The filtered retroviral stocks were used to transduce ∼50–$60\%$ confluent immortalised Cidec null MEFs with the addition of 12 μg/mL polybrene (Merck Millipore, TR-1003-G). Cidec null MEFs transduced with pBABE-EGFP acted as an indicator of transduction efficiency. Puromycin (Cambridge Bioscience, P025-P026) selection was initiated 24 h post-transduction at a concentration of 4 μg/mL.
To induce differentiation of Cidec null MEFs into adipocytes, cells were grown to 2-days post-confluency, then induced to differentiate in culture medium supplemented with 8 μg/mL biotin (Merck, B4639), 8 μg/mL D-pantothenic acid (Merck, P5155), 1 μM rosiglitazone (Merck, R2408), 0.5 mM 3-isobutyl-1-methylxanthine (Merck, I5879), 1 μM dexamethasone (Merck, D4902) and 1 μM insulin (Novo Nordisk). Two days thereafter, culture medium was changed to contain 8 μg/mL biotin, 8 μg/mL D-pantothenic acid, 1 μM rosiglitazone and 1 μM insulin. From day 4 of differentiation onwards, culture medium was changed every 2 days until the cells were used for experiments.
HeLa Doxycycline-inducible stable cell lines were grown as an adherent culture at 37°C, $5\%$ CO2 in a high glucose DMEM media containing pyruvate (Thermo Fisher, 11360070), GlutaMAX (Thermo Fisher, 10569010). The media was additionally supplemented with $10\%$ Tet-approved heat-inactivated FBS, 10 mM HEPES pH 7.2 and 0.2 mg/mL hygromycin B (Invitrogen, 10687010). Cell lines were regularly tested for mycoplasma infection using the MycoAlert mycoplasma detection kit (Lonza, LT07-418). The HeLa cell line expressing Cidec-EGFP was authenticated by Eurofins using PCR-single locus technology.
## Cloning
EGFP and mouse Pparγ2 cDNA were amplified by PCR using Phusion High-Fidelity DNA Polymerase (Thermo Fisher, F530L) and each cloned into pBABE-puro retroviral expression vector using SnaBI and SalI restriction sites. All Cidec constructs used in this study contain the *Mus musculus* Cidec sequence. Mouse Cidec cDNA was amplified by PCR using Phusion High-Fidelity DNA Polymerase (Thermo Fisher, F530L). The untagged Cidec transcript flanked by SacII and NotI restriction sites at the amino- and carboxyl-terminus, respectively, was cloned into Gateway Entry vector, pEN-Tmcs, using a DNA Ligation Kit, Mighty Mix (Takara Bio, 6023) and transformed into One Shot TOP10 Chemically Competent E.coli (Thermo Fisher, C404003). For EGFP tagging at the carboxyl-terminus, a SacII and BamHI restriction site flanked Cidec sequence was inserted into a pEGFPN3 vector (Clontech, 6080-1). For SUMOstar tagging at the carboxyl-terminus, a BamHI and SalI restriction site flanked Cidec sequence, a SalI and NotI flanked SUMOstar sequence, and a NotI and XbaI flanked Twin-strep-tag sequence were inserted into a pACEMam1 vector by enzyme restriction cloning. The sequence-verified insert was subcloned into a pEN-Tmcs (Addgene, 25751)23 vector using SacII and NotI restriction sites. In order to generate expression clones, sequence-verified inserts along with Tet-responsive elements flanked by attL sites in the Gateway Entry vector were recombined into a Gateway Destination vector,23 pSLIK-hygromycin containing attR sites using Gateway LR Clonase II enzyme mix (Thermo Fisher, 11791020). Recombination reactions were transformed into One Shot Stbl3 Chemically Competent E.coli (Thermo Fisher, C737303).
## Generation of Cidec null MEF and HeLa cell lines with Doxycycline-inducible expression of Cidec constructs
Cidec null MEFs and HeLa cell lines with Doxycycline-inducible expression of Cidec (untagged, SUMOstar- and EGFP-tagged Cidec) were generated by lentiviral transduction. *To* generate lentiviruses, HEK293T cells at approximately $70\%$ confluency were transfected using Lipofectamine LTX with Plus Reagent (Thermo Fisher, 15338100) according to the manufacturer’s protocol. A typical transfection reaction included 7.5 μg pMDLg/pRRE (Addgene, 12251), 7.5 μg pRSV-Rev (Addgene 12253),24 5 μg pVSV-G (Addgene, 12259), 1 μg pEGFP and 10 μg pSLIK-hygromycin plasmid DNA (with integrated untagged, SUMOstar- and EGFP-tagged Cidec cDNA sequences). 24 h post-transfection, the culture medium was removed and the cells were replenished with UltraCULTURE serum-free cell culture medium (Lonza, BE12-725F). Medium containing secreted lentivirus was collected every 24 h for a total of 72 h and stored at 4°C. Lentivirus-containing media was centrifuged at 2000 g at 4°C for 20 min and the supernatant was filtered through 0.45 μm syringe filters.
To concentrate the lentiviral supernatant, Centricon Plus-70 Centrifugal Filters (Merck Millipore, UFC710008) were used according to the manufacturer’s protocol. Sample filter cups were pre-rinsed with PBS and lentiviral supernatant was loaded onto the sample filter cups, sealed and centrifuged at 3500 g for 30 min at 15°C. To recover the concentrated lentivirus, the concentrate collection cups were inverted and placed on top of the sample filter cups, and then centrifuged at 3500 g for 10 min at 15°C. The concentrated lentivirus was either used to transduce Cidec null MEFs and HeLa cells at 2 to 3 different viral titers, or aliquoted into cryovials for long term storage at −80°C. Cells were selected 24 h post-lentiviral transduction using 200 μg/mL of hygromycin B (Invitrogen, 10687010). Non-lentivirus transduced cells were used as a control for antibiotic selection. The HeLa cell line expressing Cidec-EGFP has been reported by us before.16,31
## Determination of mRNA transcript levels of Cidec constructs in HeLa cells
In order to determine and achieve comparable mRNA expression levels of Cidec in untagged, SUMOstar- and EGFP-tagged HeLa cell lines, Doxycycline (Clontech, 631311) of 0.5, 1.0 or 2.0 μg/mL was added to seeded cells in the presence of 200 μM of oleic acid (Merck, O3008). 24 h post-induction, RNA was harvested using an RNeasy Mini Kit (QIAgen, 74,106) by following the manufacturer’s protocol. 400 ng of RNA was treated with 1 unit of RQ1 RNase-Free DNase (Promega, M610A) at 37°C for 30 min and was inactivated by RQ1 DNase Stop Solution at 65°C for 10 min. *To* generate cDNA standards and a negative control for reverse transcription, 1 μg of pooled RNA was prepared. RNA was reverse transcribed into cDNA using a LunaScript RT SuperMix Kit (NEB, E3010L) by following the manufacturer’s protocol, using thermocycling conditions as follows: 25°C for 2 min, 55°C for 10 min and 95°C for 1 min. cDNA was diluted 10x and qPCR was performed on an Applied Biosystem QuantStudio 7 Flex Real-Time PCR System. A typical TaqMan qPCR reaction included 1x TaqMan Universal PCR Master Mix (Thermo Fisher, 4304437) and 1x TaqMan Gene Expression Assay, in which GAPDH was used as a housekeeping gene (Cidec: Mm01184685_g1; GAPDH: Hs02758991_g1). mRNA levels were normalized to the levels in untagged Cidec line untreated with Doxycycline. From this, it was determined that 0.5 μg/mL of Doxycycline in untagged Cidec and Cidec-EGFP lines induced comparable mRNA expression levels of Cidec as 2.0 μg/mL of Doxycycline in the Cidec-SUMOstar line. Thus, these Doxycycline concentrations were used for the described fixed and live cell imaging experiments.
## Fixed cell imaging
HeLa cells and MEF cells were cultured in the corresponding media mentioned in the section “cell lines”. Cells were seeded onto ethanol-treated glass coverslips in 12-well plates. Cidec null MEFs were differentiated into mature adipocytes according to the protocol above. 1 μg/mL of Doxycycline was either added or omitted throughout the course of differentiation. An hour before fixation, LDs were stained with 1x HCS LipidTOX Deep Red Neutral Lipid Stain (Thermo Fisher, H34477) at 37°C. To study the effects of Cidec on LD enlargement at a fixed time point in HeLa cells expressing Doxycycline-inducible Cidec constructs, the day after seeding cells were washed twice with PBS and loaded with 200 μM oleic acid and various Doxycycline concentrations (0.5–2 μg/mL) in order to achieve comparable Cidec transcript levels (see above). 23 h post-Doxycycline induction, LDs were stained with 1x HCS LipidTOX Deep Red Neutral Lipid Stain for an hour at 37°C.
Cells were fixed with $4\%$ (v/v) formaldehyde (Merck, 47608) diluted in PBS for 15 min at room temperature. Cells were then washed 3 times for 5 min with PBS and mounted using VECTASHIELD Antifade Mounting Medium (Vector Laboratories, H-1000) or Prolong Gold Antifade Mountant with DAPI (Thermo Fisher, P36931). For Cidec null MEF-derived adipocytes, 2-dimensional images were acquired using a Leica SP8 confocal microscope. EGFP and LipidTOX Deep Red Neutral Lipid Stain were excited at 488 and 637 nm, and emission signals were collected at 495–535 and 645–700 nm, respectively. For HeLa cell lines, 3-dimensional images were acquired using a Leica SP8 confocal microscope at 0.3–0.4 μm z sections. EGFP and LipidTOX Deep Red Neutral Lipid Stain were excited at 488 nm and 637 nm, and emission signals were collected at 495–550 nm and 645–690 nm, respectively. Experiments were repeated three times for Cidec null MEFs-derived adipocytes with 25 cells being analyzed for LD volume in each experiment and three times for HeLa cells with at least 11 cells being analyzed in each experiment.
## Analysis of LD sizes and number in fixed cell fluorescence images
In 2-dimensional images of fixed Cidec null MEFs-derived adipocytes, LD sizes were derived by measuring LD diameters using Fiji software25 based on LipidTOX Deep Red dye staining. A line was drawn across the LDs on focal planes and these measurements were used to calculate LD volumes by assuming that LDs are spherical in shape. In images of fixed HeLa cells, LD sizes and numbers were determined by using Bitplane Imaris software version 9.6.0 (Oxford Instruments), based on LipidTOX Deep Red staining. The 3-dimensional images were segmented by ‘Spots’ creation wizard with ‘Different Spot Sizes (Region Growing)’ algorithm setting enabled. ‘ Estimated XY Diameter’ was set between 0.6 and 2.0 μm with ‘Background Subtraction’ enabled. Images were further filtered using ‘Quality’ filter type of at least 2.7 and ‘Spot Region Type’ of ‘Absolute Intensity’. Diameters of the ‘Spot Regions’ were determined from ‘Region Border’ at thresholds of 16–35. LD numbers per cell were obtained based on the number of spots detected.
## Live cell imaging
HeLa cells were seeded onto 35 mm imaging dishes with a polymer coverslip bottom (Ibidi, 81156). 24 h before live cell imaging was performed, 200 μM oleic acid was added to cells in culture media supplemented with $10\%$ Tetracycline-free FBS (Pan Biotech, P30-3602), 2 mM L-glutamine (Merck, 59202C), 100 units/mL penicillin and 0.1 mg/mL streptomycin (Merck, P4333) and 200 μg/mL Hygromycin B (Merck Millipore, 400052). Two hours before imaging of HeLa cells with Doxycycline-inducible expression of untagged Cidec or Cidec-SUMOstar, 0.5 or 2 μg/mL of Doxycycline, respectively, was added to the cells in the presence of 1x HCS LipidTOX Deep Red Neutral Lipid Stain. In the case of Cidec-EGFP, 0.5 μg/mL of Doxycycline was added 24 h before imaging. Live cell imaging was performed on a Leica SP8 confocal microscope in a chamber maintained at 37°C with $5\%$ CO2, using a 63x objective with NA = 1.4, and using an additional two-fold or three-fold zoom on the microscope level. LipidTOX Deep Red Neutral Lipid Stain was excited at 633 nm, and the emission signal was collected 640–700 nm. For HeLa cells expressing untagged Cidec or Cidec-SUMOstar, multiple positions of 3-dimensional images were acquired at 20 s intervals at 0.4 μm z sections for at least 2 h, while for Cidec-EGFP, 3-dimensional images were acquired at 2.5-min intervals for at least 20 h. During all acquisitions of untagged Cidec and Cidec-SUMOstar data, a three-fold zoom was used, resulting in a pixel size of 120 nm, while in 3 out of 4 data acquisition sessions for Cidec-EGFP, a two-fold zoom on the microscope was used, resulting in a pixel size of 180 nm, as compared to 120 nm for all other data. The experiments were performed on different days with cells cultured individually.
## Quantification of frequency of active lipid transfer events
To determine the frequency of two juxtaposed LDs being engaged in active lipid transfer, 100 pairs of contacting LDs in oleic acid fed HeLa cells expressing Cidec-EGFP or untagged Cidec were analyzed from live cell fluorescence images. Maximum projection images generated by Fiji from 3D stacks were used. For each LD analyzed, the average of two or more measured diameters was used to derive the volume of the lipid donors, assuming that LDs are spherical in shape. A change in LD volume over the imaging period was considered as a lipid transfer event.
## Manual analysis of lipid transfer rate
To determine the rate of neutral lipid transfer, maximum projection images were generated from the 3D stacks using Fiji25 and the diameters of donor LDs were measured at least at two different positions using Fiji. The average of the two or more measured diameters was used to derive the volume of the lipid donors. The change of the lipid donor volume over time (Δvolume/Δtime) was calculated by assuming that LDs are spherical in shape. In all cases, the rate of neutral lipid transfer was determined for donors that underwent active lipid transfer and were tethered to a single acceptor. In the case of Cidec-EGFP, only LD pairs with enriched Cidec-EGFP at the LD interfaces were analyzed.
## Semi-automated analysis of lipid transfer rate
Kinetics of neutral lipid transfer were analyzed with a custom-made MATLAB (MathWorks) script. Movies were acquired as described above and individual LDs were further segmented out and tracked over the course of the 3D image sequence. In the script, an event at time tmax is defined as a merging of two tracks into a single one, and the donor LD is identified as the LD whose volume will vanish at tmax. The jump in the volume of the donor at the end of the event corresponds to the volume of the donor being equal to the volume of the acceptor once transfer is completed. The evolution of the volume of the donor droplet over time is modeled as Vd(t)=Vd0(1−exp(Rd(t−tmax)) where Vd0 is the initial volume of the donor and *Rd is* the transfer rate (RDonor). Similarly, the volume of the acceptor droplet is modeled as Va(t)=Va0+(Vamax−Va0)×exp(Ra(t−tmax)) where Va0 and Vamax are respectively the initial and final volume of the acceptor and *Ra is* the transfer rate (RAcceptor). In both cases, a least-square procedure allows to retrieve the 5 parameters for each event. Each LD pair is considered as an individual event. To exclude outliers, corresponding to LDs in contact with more than one other LD, the rate Rd for the donor was plotted against the rate Ra for the acceptor for each event. We considered only those transfer events for which |log10Ra/log10Rd−1|<0.4, thereby excluding events likely to be involved in multiple transfers.
## Sample preparation for cryo-FM and cryo-FIB milling
HeLa Doxycycline-inducible stable cell lines were grown for approximately 48 h on holey carbon gold EM grids (200 mesh, R$\frac{2}{2}$, Quantifoil) prior to plunge freezing. 24 h after seeding, HeLa cells expressing Cidec-EGFP were fed with 0.2 mM oleic acid (Sigma, OA O3008) and induced with 1 μg/mL Doxycycline for Cidec-EGFP expression or grown further in the absence of Doxycycline. 16 h later these cells were stained for 1 h with HCS LipidTOX Deep Red Neutral Lipid Stain (Thermo Fisher, H34477) and plunge frozen. HeLa cells expressing untagged Cidec were grown for 24 h and then fed with 0.2 mM oleic acid. 24 h later, these cells were induced with 1 μg/mL Doxycycline for Cidec expression. 30 min post-induction, cells were stained for 1 h with LipidTOX Deep Red dye and 1 h 30 min post-induction, cells were plunge frozen. Plunge freezing was performed with a home-built manual plunger and cryostat.32 *To this* end, grids were manually back side blotted with Whatman filter paper No 1 and immediately vitrified in liquid ethane. Grids were screened at −195°C for ice quality and for regions featuring cells of interest by cryo-FM in a Leica EM cryo-CLEM system (Leica Microsystems) in a humidity-controlled room. The system used to image Cidec-EGFP and untagged Cidec expressing cells was equipped with an HCX PL APO 50x cryo objective with 0.9 NA (Leica Microsystems), an Orca Flash 4.0 V2 sCMOS camera (Hamamatsu Photonics), a Sola Light Engine (Lumencor), an L5 filter set (Leica Microsystems) for the detection of EGFP and a Y5 filter set (Leica Microsystems) for LipidTOX Deep Red detection. The system used to image cells not expressing Cidec-EGFP was equipped with an HCX PL APO 50x cryo objective with 0.9 NA (Leica Microsystems), a DFC9000 GT sCMOS camera (Leica Microsystems), an EL 6000 light source (Leica Microsystems), and a Y5 filter set (Leica Microsystems) for LipidTOX Deep Red detection. 1.5 × 1.5 mm montages of the central part of the grids were taken. These montages were later manually correlated with scanning electron beam micrographs and served for identifying the regions of interest for lamella preparation by cryo-FIB milling. Z-stacks in 1 μm steps were acquired of regions of interest corresponding to cells with enlarged LDs and Cidec-EGFP accumulation at LD-LD interfaces.
## Cryo-FIB milling
Thin lamellae of HeLa cells expressing either Cidec-EGFP or untagged Cidec or of cells not induced for expression of Cidec-EGFP were generated by cryo-FIB milling performed with a Scios DualBeam FIB/SEM (FEI) equipped with a Quorum stage (PP3010T) or an Aquilos 2 Cryo FIB (Thermo Scientific) using a similar procedure as published before.33 In the Scios, grids were coated with organometallic platinum using a gas injection system for 30 s at 13 mm working distance and 25° stage tilt. In the Aquilos, used only for lamella preparation of non-induced cells, grids were first splutter coated at 30 mA, 0.1 kV for 15 s. Subsequently the grids were subjected to GIS coating for 1 min at a working distance of 10 mm, which corresponds to a platinum layer of approximately 1 μm. In both systems, the electron beam was used for locating the cells of interest at 5 kV voltage and 13 pA current and for imaging to check progression of milling at 2 kV voltage and 13 pA current. Milling with the ion beam was performed stepwise. For rough milling the voltage was kept at 30 kV throughout all steps and milling was performed simultaneously from the top and the bottom of the lamella. The current and stage position were adjusted as follows: 1) 1 nA, 35° stage tilt until a lamella thickness of 20 μm; 2) 0.5 nA, 25° tilt until 12 μm; 3) 0.3 nA, 17° tilt until 3 μm; and 4) 0.1 nA, 17° tilt until 1 μm. For the fine milling steps, the voltage was lowered to 16 kV and the current to 23 pA. The stage was first tilted to 18° and the lamella was milled only from the top, then the stage was tilted to 16° and the lamella was milled only from the bottom. These two steps resulted in a lamella thickness of approximately 0.5 μm. Finally, the stage was tilted back to 17° and the lamella was milled simultaneously from the top and the bottom to a target thickness below 0.3 μm.
## Electron cryo-tomography (cryo-ET)
Cryo-ET data of FIB-milled cells was acquired on three different Titan Krios microscopes (Thermo Scientific), all equipped with a K2 direct electron detector and a BioQuantum energy filter (Gatan). SerialEM was used for acquisition.28 To identify the positions of the lamellae, low-magnification montages of the central part of the grid were acquired, with the detector operated in linear mode. Then2.3, 5.1 or 5.5 nm pixel size montages of individual lamellae were taken and used for finding interfaces between LDs. Tilt series were acquired from 0° to ±60° (maximum) using a dose-symmetric acquisition scheme,34 1° increment and a pixel size of 3.7, 3.5 or 3.4 Å. The detector was operated in counting mode. Images were acquired in a tilt group size of 4. The target defocus was set to −5 μm. The dose per tilt series image was adjusted to 1–1.2 e−/A2 and the target dose rate at the detector was kept around 4 e−/px/s. For a subset of the tilt series, we used exposure fractionationation into 3 frames per tilt image. Alignment of exposure fractionated frames was performed in IMOD using alignframes. Tilt series were aligned in IMOD using patch tracking.26,27 Final tomograms were reconstructed at 7.5, 7.1 or 6.7 Å pixel size using SIRT reconstruction with 10 iterations. For presentation in figure panels and movies, a median filter was applied to virtual slices. The cryo-EM data presented in the manuscript for Cidec-EGFP was obtained from 30 cells expressing Cidec-EGFP, plunge-frozen on at least 8 different days, and 15 tomograms containing LD interfaces were acquired on 10 of these cells. The cryo-EM data presented for Cidec was obtained from 15 cells expressing Cidec, frozen on 3 days, and 7 tomograms containing LD interfaces were acquired on 7 of these cells. The cryo-EM data presented of cells not expressing Cidec-EGFP was obtained from 5 cells frozen on 2 different days, and one representative tomogram, showing an LD but not containing LD interfaces, is included in the manuscript. Three of the tomograms of cells expressing Cidec-EGFP were previously used in unrelated projects published before.16,31
## Analysis of distances between lipid droplets
Distance measurements were performed in electron cryo-tomograms using a custom-made MATLAB (MathWorks) script. The mrc volume was converted to 8-bit tif images, wich were subsequently subjected to a median filter with radius 2 px in Fiji to enhance visibility of the monolayers. Control points along each monolayer were manually defined at positions where the monolayers were best visible. The monolayers were traced every 7.5 or 7.1 Å in z direction through the tomographic volume (see Figure S4B). Based on these points, the surface of each monolayer was interpolated on a 2 nm regular grid. The distance between points of the interpolated surfaces were computed, and various descriptive statistics were obtained. In particular, heatmaps of the distances (Figures S4C and S4D), minimum and maximum distance as well as the mean, median and the standard deviation of the distances were calculated. Each interface between an LD pair is considered as an individual event. Distances measured between the regularly spaced points of the interpolated surfaces were plotted as scatterplots for each individual interface (Figures S4E and S4F). Calculated medians of the distance at these interfaces were plotted as a scatterplot. For the analysis, we chose to use medians, because the distance measurements are not Gaussian distributions.
## Determination of lipid droplet diameters
LD diameters used to calculate the ratios shown in Figure 3D were estimated in IMOD applying imodcurvature to a model consisting of points picked along the LD monolayer in a single virtual tomographic slice. We assumed that LDs are spherical in shape as in the analysis of FM images (see above). If the equatorial plane of the LD appeared to be included in the tomogram, a single imodcurvature readout from the corresponding virtual slice was used to estimate the LD diameter. If the LD segment contained in the tomographic volume did not include the equatorial plane, two imodcurvature radii a and b were determined on two different virtual slices spaced in z-direction by z nm. The LD radius r was then calculated using the formula: r=√(a2+(a2−b2−z22∗z)2). In one case of a large LD forming two interfaces with smaller LDs, the radius of the large LD could not be estimated because the fraction of the LD contained in the tomogram was too small. The two corresponding interfaces were therefore excluded from the analysis in Figure 3D.
## Determination of degree of monolayer deformation
The extent of the disturbance of the monolayers analyzed in Figure 3E was assessed only in tomograms of optimal quality and cellular preservation. Due to insufficient vitrification of the areas of interest, a total of 5 tomograms were excluded. The exclusion concerns 4 interfaces from 2 Cidec-EGFP tomograms and 5 interfaces from 3 untagged Cidec tomograms. The waviness of the monolayers at the LD interface was assessed qualitatively and attributed to one of three arbitrary classes: minimal, intermediate, and maximal deformations. Intermediate and maximal deformations were attributed if the extend of monolayer deformation appeared higher in the interface than in the monolayer outside of the interface.
## Quantification and statistical analysis
All p values reported in the text are based on statistical tests performed on the data presented in the corresponding Figure panels. In all applicable instances, we plotted data from independent experiments in different colors, similarly to ‘superplots’ (Figures 1A, 1B, 4B–4D).35 All statistical tests and plots were done in Graphpad Prism. Statistical tests have been applied on the data shown as follows. The datasets in Figures 1A and 1B did not pass normality tests. We therefore used a non-parametric two-tailed Mann-Whitney test to compare -*Dox versus* + Dox. In Figure 3D the dataset with high enough n passed a normality test hence we used an ordinary one-way ANOVA test to compare all other morphologies against ‘indentation' (comparison against a control column). In Figure 3F, we used an unpaired parametric two-tailed t test to compare two independent groups, which are sampled from Gaussian distributions. The datasets in Figures 4B and 4C did not pass normality tests and we therefore used a Mann-Whitney test to compare two groups: -*Dox versus* + Dox for each individual construct. As we assessed the effect only in one direction (increase in mean LD volume in 4B and decrease in mean LD number in 4C), we chose one-tailed Mann-Whitney tests for the two-group-comparisons. We used a non-parametric Kruskal-Wallis test to perform multiple comparisons between all constructs in both 4B and 4C (+Dox). The datasets shown in Figure 4D did not pass normality tests and consequently we used a non-parametric Kruskal-Wallis test to perform multiple comparisons of three independent groups. In Figure 5F, two independent groups with non-gaussian distributions were compared using a non-parametric two-tailed Mann-Whitney test. In Figure S3A, a Chi square contingency test was used to compare two groups of categorical variables.
## Supplemental information
Document S1. Figures S1–S5 Document S2. Article plus supplemental information
## Data and code availability
Data and code are publicly available as follows: Representative electron cryo-tomograms are deposited at the Electron Microscopy Data Bank (EMDB).29 Raw tilt images and tomographic reconstructions of analyzed electron cryo-tomograms are deposited at the Electron Microscopy Public Image Archive (EMPIAR).30 Live fluorescence microscopy data is deposited at Zenodo. All original code has been deposited on gitlab. All accession numbers, DOIs and URLs are listed in the key resources table. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
## Author contributions
K.L. and I.G., conceptualization, methodology, investigation, formal analysis, and writing – original draft; J.B., custom software development, formal analysis, and writing – review & editing; P.C.H. and W.J.H.H., data acquisition and writing – review & editing; O.M. and A.C.B., sample preparation and writing – review & editing; W.K. and D.B.S., conceptualization, methodology, investigation, formal analysis, supervision, and writing – original draft.
## Declaration of interests
The authors declare no competing interests.
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|
---
title: Association of Education With Dementia Incidence Stratified by Ethnicity and
Nativity in a Cohort of Older Asian American Individuals
authors:
- Eleanor Hayes-Larson
- Ryo Ikesu
- Joseph Fong
- Taylor M. Mobley
- Gilbert C. Gee
- Ron Brookmeyer
- Rachel A. Whitmer
- Paola Gilsanz
- Elizabeth Rose Mayeda
journal: JAMA Network Open
year: 2023
pmcid: PMC9989900
doi: 10.1001/jamanetworkopen.2023.1661
license: CC BY 4.0
---
# Association of Education With Dementia Incidence Stratified by Ethnicity and Nativity in a Cohort of Older Asian American Individuals
## Key Points
### Question
Does the association of education with dementia differ by ethnicity and nativity among Asian American individuals?
### Findings
In this cohort study of 14 749 Asian American individuals who were members of a large integrated health care delivery system, college degree attainment was associated with lower dementia incidence, with no difference by nativity. Associations varied across Asian ethnicities but were less precise.
### Meaning
These findings suggest that college degree attainment was associated with lower dementia incidence, with similar associations across nativity, but more work is needed to understand determinants of dementia in Asian American individuals.
## Abstract
This cohort study examines the association of education with dementia in a large cohort of Asian American individuals, stratifying by ethnicity and nativity.
### Importance
High education protects against dementia, but returns on educational attainment may be different across sociodemographic groups owing to various social factors. Asian American individuals are a growing and diverse group, but little research has assessed dementia determinants in this population.
### Objective
To examine the association of education with dementia in a large cohort of Asian American individuals, stratifying by ethnicity and nativity.
### Design, Setting, and Participants
This cohort study used electronic health record (EHR) and survey data from the Research Program on Genes, Environment, and Health and the California Men’s Health Study surveys [2002-2020]. Data are from Kaiser Permanente Northern California, an integrated health care delivery system. This study used a volunteer sample who completed the surveys. Participants included Chinese, Filipino, and Japanese individuals who were aged 60 to less than 90 years without a dementia diagnosis in the EHR at the time of the survey (baseline) and who had 2 years of health plan coverage before baseline. Data analysis was performed from December 2021 to December 2022.
### Exposures
The main exposure was educational attainment (college degree or higher vs less than a college degree), and the main stratification variables were Asian ethnicity and nativity (born in the US or born outside the US).
### Main Outcomes and Measures
The primary outcome was incident dementia diagnosis in the EHR. Dementia incidence rates were estimated by ethnicity and nativity, and Cox proportional hazards and Aalen additive hazards models were fitted for the association of college degree or higher vs less than a college degree with time to dementia, adjusting for age (timescale), sex, nativity, and an interaction between nativity and college degree.
### Results
Among 14 749 individuals, the mean (SD) age at baseline was 70.6 (7.3) years, 8174 ($55.4\%$) were female, and 6931 ($47.0\%$) had attained a college degree. Overall, among individuals born in the US, those with a college degree had $12\%$ lower dementia incidence (HR, 0.88; $95\%$ CI, 0.75-1.03) compared with those without at least a college degree, although the confidence interval included the null. The HR for individuals born outside the US was 0.82 ($95\%$ CI, 0.72-0.92; $$P \leq .46$$ for the college degree by nativity interaction). The findings were similar across ethnicity and nativity groups except for Japanese individuals born outside the US.
### Conclusions and Relevance
These findings suggest that college degree attainment was associated with lower dementia incidence, with similar associations across nativity. More work is needed to understand determinants of dementia in Asian American individuals and to elucidate mechanisms linking educational attainment and dementia.
## Introduction
Low education is a robust and modifiable factor associated with the risk of dementia.1,2,3,4 However, the health benefits of higher educational attainment may be different across sociodemographic groups.5 These differential returns may be due to processes such as resource substitution (in which the disadvantaged group benefits more from education because other health-preserving resources are less available to them) or opportunity constraints (in which the disadvantaged group benefits less from education because the opportunities afforded by it are more restricted because of such factors as structural and interpersonal racism).5,6 In dementia, returns on education are further impacted by the contribution of education to cognitive reserve and resilience, which may vary according to individual educational experiences.6,7 Understanding variability in the impact of educational attainment on dementia incidence is important to understand and mitigate sources of racial and ethnic disparities in dementia incidence and inform prevention strategies for all groups.6,8 Asian American individuals are a diverse and growing group who are critically understudied in health research. Some evidence suggests that the incidence of dementia among Asian American individuals is lower than that among most other racial and ethnic groups,9,10 but there is little evidence on determinants of dementia, including the impact of education, among Asian American individuals as a whole or in specific ethnic groups. Furthermore, Asian American individuals born outside the US may experience distinct educational and social trajectories that affect the potential health-related benefits of education.11 For example, bilingualism could yield additional protection against dementia, whereas more limited labor market opportunities for immigrants vs individuals born in the US with the same educational attainment could reduce benefits.12,13,14,15,16,17 A large proportion of Asian American individuals are born outside the US, with substantial heterogeneity across ethnic groups.
The aim of this cohort study was to evaluate the association of education with dementia incidence by Asian ethnicity and nativity, to evaluate potential differential associations of education and dementia incidence across these groups. We hypothesized that education would be protective against dementia for all groups, but more weakly for Asian American individuals born outside the US, consistent with opportunity constraints.
## Study Design and Sample
This study analyzed a cohort of 184 929 White and Asian members of Kaiser Permanente Northern California (KPNC), an integrated health care delivery system. Participants were aged 60 to less than 90 years and had 2 years of prior continuous health plan coverage at the time they completed 1 of 2 health surveys: either the California Men’s Health Study (administered 2002-2003)18 or the Kaiser Permanente Research Program on Genes, Environment, and Health Survey (administered 2007-2009).19,20 The analytic sample was restricted to 15 004 individuals reporting Chinese, Filipino, and Japanese ethnicities (see next section for how race and ethnicity were defined) because these Asian ethnic groups had sufficient sample size of both individuals born in the US and those born outside the US. Other Asian ethnicities all had fewer than 15 dementia cases in at least 1 nativity stratum, resulting in unstable estimates, and were not included in the analyses. Participants were excluded if they had a prior electronic health record diagnosis of dementia (Alzheimer disease, vascular, and nonspecific dementias) at the time of their survey.
Written informed consent for survey participation was documented by KPNC at return of the completed survey. Analysis of the deidentified data was approved by the University of California, Los Angeles institutional review board. This manuscript follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline21 and the RECORD reporting guidelines22 for the use of routinely collected health data.
## Race and Ethnicity
Race and ethnicity were self-reported in the survey questions asking participants to mark all groups that best described their race or ethnicity. Asian American individuals were classified by the Asian ethnicities they reported (Asian Indian/South Asian, Chinese, Filipino, Japanese, Korean, and Vietnamese or other Southeast Asian). Participants who reported 1 Asian ethnicity with or without another non-Asian race or ethnicity were classified as the Asian ethnicity they reported, and participants who endorsed multiple Asian ethnicities (with or without a non-Asian race or ethnicity) were classified as multiethnic Asian.
## Primary Exposure: Educational Attainment
The main exposure was educational attainment, self-reported from the surveys. Participants were asked, “*What is* the highest level of school that you have completed?” with response options “Grade school (grades 1-8),” “Some high school (grades 9-11),” “High school or GED [general educational development],” “Technical/trade school,” “Some college,” “College,” or “Graduate school.” Because of the small sample sizes in some strata, we analyzed educational attainment dichotomously as college degree or higher vs less than college degree. A sensitivity analysis also examined educational attainment trichotomized as less than high school degree; high school degree, GED, or some college; and college degree or higher.
## Primary Outcome: Age at Dementia Onset
We defined incident dementia diagnoses during follow-up using International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes (eTable 1 in Supplement 1) for Alzheimer disease, vascular dementia, and nonspecific dementia, and extracted diagnoses from electronic health records for inpatient, emergency, and outpatient settings, excluding laboratory-only and radiology-only encounters. A similar battery of ICD-9 codes was reported to have a sensitivity of $77\%$ and a specificity of $95\%$ compared with a consensus diagnosis of dementia in a health care system in Seattle, Washington.23 In Medicare claims data, the use of a similar battery of ICD-9 codes for identifying cases had a sensitivity of $87\%$ in a sample of patients with Alzheimer disease who participated in the Consortium to *Establish a* Registry for Alzheimer’s Disease.24 Follow-up was censored for participant death, lapse in health plan coverage 90 days or longer, or end of study period. Deaths were identified from the KPNC mortality database, which aggregates data from KPNC clinical and administrative sources, the National Death Index, California State death records, and Social Security Administration records. Because all event ages after 90 years were top-coded to deidentify the data, we used imputed event times after age 90 years, as previously reported for this cohort.25
## Additional Measures
We were interested in effect measure modification by nativity, which was self-reported in the KPNC surveys with the question, “Were you born in the United States?” ( yes or no). Potential confounders obtained from electronic health records included age at survey completion, sex, and height (see eAppendix 1 in Supplement 1 for information for details on height data cleaning). We considered height a proxy for a number of early-life constructs, including nutritional environment and childhood socioeconomic status, and it is associated with cognitive outcomes.26,27,28 To describe the sample, we also report household size, household size–adjusted income, marital status, self-reported health, and smoking status from the KPNC surveys, but do not adjust for these variables, because they may be mediators of the effect of educational attainment on dementia incidence.
## Statistical Analysis
Data analysis was performed from December 2021 to December 2022. All analyses were conducted for the sample overall and stratified by Asian ethnicity. We first estimated dementia incidence rates (crude and age-standardized using the 2000 US Census population aged ≥60 years as the standard) by nativity and educational attainment. We used Cox proportional hazards to estimate hazard ratios (HRs) and Aalen additive hazards models to estimate hazard differences (HDs) for the association between education and dementia incidence. Models were adjusted for age using age as the timescale (starting at survey age) and also were adjusted for sex. To assess for effect measure modification by nativity, all models included an interaction term between education and nativity; statistical tests for interaction were 2-sided, and $P \leq .05$ was used as the a priori level of significance. Sensitivity analyses included [1] models additionally adjusted for height, [2] models using time on study as time scale and controlling for baseline survey age, and [3] models using a 3-level version of education as described already.
Missing data were handled with multiple imputation (eAppendix 2 and eTable 2 in Supplement 1).29,30 Analyses were conducted using R statistical software version 4.1.3 (R Project for Statistical Computing). All statistical code is available elsewhere.31
## Results
The sample of 14 749 individuals included 6415 Chinese, 5020 Filipino, and 3314 Japanese individuals. The mean (SD) age at baseline was 70.6 (7.3) years, and the mean (SD) follow-up time was 9.9 (4.6) years. Overall, $55.4\%$ of the sample (8174 individuals) was female, and $47.0\%$ (6931 individuals) had attained a college degree. Filipino individuals were more likely to have a college degree (2758 individuals [$54.9\%$] vs 2818 Chinese individuals [$43.9\%$] and 1355 Japanese individuals [$40.9\%$]) (Table 1) and to be born outside the US (4381 Filipino individuals [$87.3\%$] vs 4312 Chinese individuals [$67.2\%$] and 929 Japanese individuals [$28.0\%$]). Other sociodemographic variables at baseline also varied by ethnicity. For example, Filipino individuals were less likely to be at least 65 years old (ie, Medicare eligible), household income per person was highest among Japanese individuals, household size was largest among Filipino individuals, and Chinese individuals were most likely to be married or living as married. Sample characteristics after multiple imputation and stratified by educational attainment and nativity are shown in eTables 3 and 4 in Supplement 1.
**Table 1.**
| Characteristic | Participants, No. (%) | Participants, No. (%).1 | Participants, No. (%).2 | Participants, No. (%).3 |
| --- | --- | --- | --- | --- |
| Characteristic | Overall (N = 14 749) | Chinese (n = 6415) | Filipino (n = 5020) | Japanese (n = 3314) |
| Age at survey, mean (SD), y | 70.6 (7.3) | 70.6 (7.2) | 69.4 (6.8) | 72.6 (7.7) |
| Aged ≥65 y | 10 706 (72.6) | 4693 (73.2) | 3407 (67.9) | 2606 (78.6) |
| Sex | | | | |
| Female | 8174 (55.4) | 3245 (50.6) | 2850 (56.8) | 2079 (62.7) |
| Male | 6575 (44.6) | 3170 (49.4) | 2170 (43.2) | 1235 (37.3) |
| Educational attainment of college degree or higher | 6931 (47.0) | 2818 (43.9) | 2758 (54.9) | 1355 (40.9) |
| Missing | 795 (5.4) | 293 (4.6) | 296 (5.9) | 206 (6.2) |
| Born outside the US | 9622 (65.2) | 4312 (67.2) | 4381 (87.3) | 929 (28.0) |
| Missing | 378 (2.6) | 174 (2.7) | 145 (2.9) | 59 (1.8) |
| Household income per person, mean (SD), $ | 46 135 (28 114) | 47 125 (29 342) | 39 740 (24 809) | 54 211 (28 147) |
| Missing | 2117 (14.4) | 860 (13.4) | 691 (13.8) | 566 (17.1) |
| Height, mean (SD), in | 63.3 (3.2) | 63.8 (3.2) | 63.0 (3.1) | 62.8 (3.3) |
| Missing | 484 (3.3) | 173 (2.7) | 203 (4.0) | 108 (3.3) |
| Size of household | | | | |
| Living alone | 2041 (13.8) | 856 (13.3) | 422 (8.4) | 763 (23.0) |
| 2 Individuals | 4768 (32.3) | 2185 (34.1) | 1156 (23.0) | 1427 (43.1) |
| ≥3 Individuals | 7429 (50.4) | 3154 (49.2) | 3254 (64.8) | 1021 (30.8) |
| Missing | 511 (3.5) | 220 (3.4) | 188 (3.7) | 103 (3.1) |
| Married or living as if married | 10 710 (72.6) | 4944 (77.1) | 3586 (71.4) | 2180 (65.8) |
| Missing | 162 (1.1) | 91 (1.4) | 43 (0.9) | 28 (0.8) |
| Smoking status | | | | |
| Never | 9216 (62.5) | 4320 (67.3) | 3190 (63.5) | 1706 (51.5) |
| Former | 3514 (23.8) | 1259 (19.6) | 1062 (21.2) | 1193 (36.0) |
| Current | 648 (4.4) | 257 (4.0) | 228 (4.5) | 163 (4.9) |
| Missing | 1371 (9.3) | 579 (9.0) | 540 (10.8) | 252 (7.6) |
| General health | | | | |
| Excellent or very good | 4093 (27.8) | 1696 (26.4) | 1257 (25.0) | 1140 (34.4) |
| Good | 6254 (42.4) | 2723 (42.4) | 2110 (42.0) | 1421 (42.9) |
| Fair or poor | 3478 (23.6) | 1554 (24.2) | 1297 (25.8) | 627 (18.9) |
| Missing | 924 (6.3) | 442 (6.9) | 356 (7.1) | 126 (3.8) |
| Retired, yes | 9675 (65.6) | 4406 (68.7) | 2908 (57.9) | 2361 (71.2) |
| Self-reported stroke, yes | 734 (5.0) | 285 (4.4) | 278 (5.5) | 171 (5.2) |
| Self-reported hypertension, yes | 6950 (47.1) | 2786 (43.4) | 2636 (52.5) | 1528 (46.1) |
| Self-reported diabetes, yes | 3104 (21.0) | 1085 (16.9) | 1373 (27.4) | 646 (19.5) |
| End of follow-up event | | | | |
| Administratively censored | 7033 (47.7) | 3303 (51.5) | 2248 (44.8) | 1482 (44.7) |
| Censored at age ≥90 ya | 770 (5.2) | 341 (5.3) | 171 (3.4) | 258 (7.8) |
| Death | 2420 (16.4) | 1066 (16.6) | 766 (15.3) | 588 (17.7) |
| Dementia | 1895 (12.8) | 769 (12.0) | 566 (11.3) | 560 (16.9) |
| End of membership | 2631 (17.8) | 936 (14.6) | 1269 (25.3) | 426 (12.9) |
| Follow-up time, mean (SD), y | 9.9 (4.6) | 10.5 (4.6) | 9.3 (4.7) | 9.8 (4.6) |
Overall, dementia incidence rates were higher among those with less than a college degree, with smaller differences after age standardization. Among individuals born outside the US in the sample, dementia incidence rates were 9.3 cases per 1000 person-years (PY) among those with a college degree and 14.7 cases per 1000 PY among those with less than a college degree. Similarly, among the individuals born in the US, dementia incidence rates were 10.8 cases per 1000 PY among those with a college degree and 19.2 cases per 1000 PY among those with less than a college degree. After age standardization, dementia incidence rates were 8.7 cases per 1000 PY among individuals born outside the US with a college degree, 10.7 cases per 1000 PY among individuals born outside the US without a college degree, 8.2 cases per 1000 PY among individuals born in the US with a college degree, and 10.4 cases per 1000 PY among individuals born in the US without a college degree. Ethnicity-specific dementia incidence rates are given in eTable 5 in Supplement 1. Absolute rates varied, but trends for education were similar across groups.
In Cox proportional hazards models, college degree attainment was associated with lower dementia incidence among Asian American individuals born both in the US and born outside the US (Table 2 and Figure 1). Among individuals born in the US, those with a college degree had $12\%$ lower dementia incidence (HR, 0.88; $95\%$ CI, 0.75-1.03) compared with those without at least a college degree, although the confidence interval included the null. The HR for the individuals born outside the US was 0.82 ($95\%$ CI, 0.72-0.92; $$P \leq .46$$ for the college degree by nativity interaction). Although less precise, associations varied across Asian ethnicities, with larger nativity differences in the association between college degree and dementia among Chinese individuals (HR, 0.84 [$95\%$ CI, 0.65-1.08] for individuals born in the US; HR, 0.67; [$95\%$ CI, 0.55-0.82] for individuals born outside the US) than for Filipino or Japanese individuals. Of note, results for Filipino individuals born in the US and Japanese individuals born outside the US had wide $95\%$ CIs that included both positive and negative associations.
Associations on the additive scale showed similar trends (Table 3 and Figure 2). Overall, having a college degree was associated with fewer dementia cases per 1000 PY among Asian individuals born both inside and outside the US (HD, −1.53 cases per 1000 PY [$95\%$ CI, −3.73 to 0.67 cases per 1000 PY] among individuals born in the US; HD, −2.49 cases per 1000 PY [$95\%$ CI, −3.90 to −1.07 cases per 1000 PY] among individuals born outside the US; $$P \leq .47$$ for college degree by nativity interaction). The association between a college degree and lower dementia incidence was largest among Chinese individuals, and differences in associations by nativity were small across all ethnic groups. As with results on the HR scale, estimates in Filipino individuals born in the US and Japanese individuals born outside the US were particularly imprecise. Sensitivity analyses with [1] models additionally adjusted for height, [2] models using time on study and controlled for baseline age, and [3] models using a 3-level version of education yielded similar results (eTables 6-8 and eFigures 1 and 2 in Supplement 1).
## Discussion
The aim of this cohort study was to estimate the association of education and dementia incidence in a large cohort of Asian American individuals and to evaluate whether nativity modified this association. We found similar protective associations of having a college degree with dementia incidence by nativity on both multiplicative and additive scales and no clear differences across Asian ethnic groups, although estimates for Filipino individuals born in the US and Japanese individuals born outside the US were imprecise.
Despite the importance of education as a determinant of dementia and potential for heterogeneity in its impact on late-life cognitive function by race and ethnicity,6,7 to our knowledge, our study is the first to report the association between education and dementia incidence in multiple Asian American ethnic groups. We showed that college degree attainment vs less than a college degree was protective overall and for Chinese and Filipino individuals regardless of nativity. Among Japanese individuals born outside the US, dementia incidence was slightly higher in those with vs without a college degree, but the sample size of Japanese individuals born outside the US was small and the $95\%$ CIs were wide, including both positive and negative associations. To our knowledge, there are no estimates of the association between education and dementia incidence in Chinese and Filipino American individuals. Prior work in the Kame cohort of Japanese American individuals in King County, Washington, showed that each additional year of education was associated with $6\%$ lower incidence of dementia,32 but that study did not stratify by nativity. Although our estimates are slightly smaller than those in other literature, comparison of estimates across studies is complicated by heterogeneity in measurement and operationalization of educational attainment.4 Our work builds on other work showing differential returns on education for health among Asian American individuals and, to our knowledge, is the first to examine dementia outcomes. Reduced benefits of education for cardiovascular health have been shown among Asian American individuals born inside vs outside the US who were aged 25 years and older in California.33 Other work11 among Asian American individuals showed that higher education was associated with better self-rated health, but found differences in this association by country of origin and duration in the US (eg, stronger educational gradients in self-rated health among Filipino individuals born inside vs outside the US, but no educational gradient for Japanese individuals born in the US). In contrast, we did not observe substantial variability across nativity. We examined both multiplicative and additive scales to ensure that our conclusions were not scale dependent34 and conducted a sensitivity analysis with a 3-level version of educational attainment. The lack of differences in educational benefits by nativity could reflect true robustness of the education-dementia association, or could reflect bias resulting from differences in education-related factors (eg, whether education occurred in the US or internationally, which likely varies by ethnicity, and measures of quality of education were not collected in the surveys) or different sources of confounding (eg, selective migration by those with social advantage). Understanding the mechanisms through which education is protective against dementia (eg, through mediation analyses), was beyond the scope of this article, but is an important next step for this work. Measures of potential mediators in this data set have limitations; for example, although income across the life course may mediate the impact of education on dementia risk through conferring financial resources, income measured at a single time point in late life may have different meanings depending on retirement status and sources of income and may not clearly capture financial advantage.35
## Limitations
Limitations of the study include lack of data on immigration-related details (eg, age of or reason for migration). Historical patterns and reasons for immigration vary across Asian ethnic groups, and we could not assess the potential association of these factors with dementia incidence in the individuals born outside the US.25 Most Japanese American individuals were born in the US and are the descendants of immigrants who arrived in the US before the passage of the 1924 National Origins Act, which largely prohibited immigration from Asian countries, and most Chinese and Filipino American individuals arrived in the US after 1965 because of major changes to US immigration policies with the passage of the Immigration and Nationality Act.36 We also did not have information on languages spoken by participants or specific early-life confounders of the education-dementia association. However, in sensitivity analyses, we were able to adjust for height, which may be a proxy for a variety of potential early-life confounders (eg, socioeconomic status).26,27,28 In addition, our time-to-dementia outcome was derived from dementia diagnoses in the electronic health record, rather than from data collected specifically for research purposes. Dementia diagnosis may be subject to misclassification, and if missed diagnoses were differential by educational attainment (eg, more missed cases in low education groups, as has been shown among Medicare beneficiaries37), our estimates of the protective association with educational attainment would be conservative (biased toward the null). Similarly, if dementia diagnoses were more likely to be missed among individuals born outside the US, particularly individuals born outside the US without a college degree, we would expect our estimates of the protective association of educational attainment would be particularly biased toward the null in the individuals born outside the US, which would strengthen our finding that associations between education and dementia do not appear to be attenuated in Asian American individuals born outside the US vs those born in the US. In addition, KPNC members all have health insurance (although we did not have measures of insurance type, such as Medicare vs employer-based, that could provide further insight on social advantage), and prior work25 in this survey cohort has shown that participants were healthier and more likely to be English-speaking (surveys were administered in English, Spanish, and Chinese) than the *California* general population of older adults, which may limit the generalizability of our results to this population.
## Conclusions
Asian American individuals remain a critically understudied population in both dementia research and health research more broadly. This study, the first to our knowledge to examine the association of educational attainment with dementia incidence across multiple Asian ethnic groups, showed that education is an important modifiable determinant of dementia in Asian American individuals, with similar associations in individuals born outside the US vs individuals born in the US. Future work should focus on understanding the reasons for the lower dementia incidence rates among Asian American individuals vs other racial and ethnic groups, including both distribution of risk and protective factors and potential differential impact, and continue to explore the pathways by which education impacts dementia risk, including mediation by social factors such as income and impacts on cognitive reserve and resilience.
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|
---
title: Is Sunshine Vitamin Related to Adolescent Depression? A Cross-Sectional Study
of Vitamin D Status and Depression Among Rural Adolescents
journal: Cureus
year: 2023
pmcid: PMC9989901
doi: 10.7759/cureus.34639
license: CC BY 3.0
---
# Is Sunshine Vitamin Related to Adolescent Depression? A Cross-Sectional Study of Vitamin D Status and Depression Among Rural Adolescents
## Abstract
Background: *Adolescence is* the phase of rapid transition of the body. The requirement of all minerals and vitamins changes in this phase of life so does Vitamin D. Despite Vitamin D being abundantly available, its deficiency, which can cause innumerable side effects on the body, is extremely common among the general population.
Material and methods: The present study was a cross-sectional study carried out from January 2021 to July 2022 for two years at various government rural high schools in Kolar, Karnataka, India. All adolescents who were aged 11-18 years and studying in 9th and 10th standards were included in the study after consent and assent. Adolescent boys and girls with any pre-existing mental health illness were excluded from the study. To assess depression, Beck's Depression Inventory (BDI-II) was used. Vitamin D3 levels were assessed by using VITROS Immunodiagnostic products using a 25-OH Total reagent pack. All data were entered in a Microsoft Excel sheet (Redmond, USA) and analyzed using IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. To check for the association between factors, Chi-square was applied with a level of significance defined as a p-value less than 0.05.
Results: Out of 451 students, 272 ($60.3\%$) belonged to the 15-year age group, 224 ($49.7\%$) were boys, 235 ($52.1\%$) were studying in 10th standard, 323 (71.6 %) belonged to nuclear families, 379 ($84\%$) were non-vegetarian by diet, 222 ($49.2\%$) had sun exposure in the afternoon, and 156 ($34.6\%$) had a sun exposure of fewer than 60 minutes, 133 ($29.5\%$) had severe depression according to Beck's Depression Inventory-II. One hundred sixty-two ($35.9\%$) had insufficient Vitamin D3 levels (12-20 ng/ml), and 66 ($14.6\%$) had deficient levels of Vitamin D3 (less than 12 ng/dl). There was a statistically significant association between depression and Vitamin D3 levels.
Conclusion: There are innumerable causes of adolescent depression. The present study shows Vitamin D levels were statistically associated with depression among adolescents. Vitamin D supplementation of at least 600 international units, which is the recommended dietary allowance (RDA), could be beneficial in tackling Vitamin D to sufficiency status (20-100 ng/ml) and also indirectly address Adolescent Depression. Better study designs, like randomized control trials showing Vitamin D intervention and its possible curative role in adolescent depression, are required to establish the causal association.
## Introduction
Traditionally labeled as sunshine Vitamin, Vitamin D is endogenously produced in the skin when exposed to Ultraviolet B rays. Few food products like dairy products, eggs, fish, and cod liver oil also contain natural vitamin D. In most countries, exiguous foods like milk and cheese are also fortified with Vitamin D [1]. Despite this abundantly available Vitamin D, its deficiency is extremely common among the general population. A few reasons identified for this possible silent epidemic in various parts of the world and also in a tropical country like India where there is abundant sunshine were body covering habits due to religious beliefs, staying indoors for the majority of daytime with little or no physical activity, lack of open spaces and direct access to sunlight in high human density habitations resulting in the high prevalence [2,3]. Of various above-mentioned factors, a few pieces of research also mention obesity as a possible risk factor for Vitamin D deficiency among adolescents and young individuals. The proposed hypothesis for Vitamin D deficiency is more absorption of fat-soluble Vitamin D into adipose tissue [4]. Added to this, seasonal Vitamin D deficiency also has an inconsequential role [5].
Adolescence is the phase of rapid transition of the body. The requirement of all minerals and vitamins changes in this phase of life so does Vitamin D. The health implications of Vitamin D in terms of bone health are increasingly understood, yet its impact, particularly on mental health, is unclear. Although recent data has shown rangy corroboration that Vitamin D has an important impact on the pathophysiology and progression of serious chronic illness, especially on mental health. Contemporary evidence has been established that stunted Vitamin D levels are associated with depression, poor mood, and other mental disorders [6]. Individuals with normal levels of Vitamin D, which is 30-100 ng/dl, have a much lower probability of developing depression [7-9]. A study done in Norway has shown that Vitamin D deficiency is very common among psychogeriatric patients, independent of the diagnostic category [10]. A study done on the elderly showed that Low 25(OH)D was independently associated with a greater increase in depressive symptom scores and incident depression in community-dwelling older adults [11]. With this background, the study was started to find out the association between depression and Vitamin D status among rural adolescent boys and girls.
## Materials and methods
The present study was a cross-sectional study carried out from January 2021 to December 2022 for two years at various government rural high schools in Kolar, Karnataka, India. Twenty rural schools in Kolar were selected. A study done in India on school children has shown the prevalence of Vitamin D deficiency as high as $81\%$(p). With an error of $5\%$ and $95\%$ confidence interval sample size was calculated, which was 243 [12]. The sample size was calculated using Open Epi software Version 3.01. All adolescents who were aged 11-18 years studying in 9th and 10th standards were included in the study after consent and assent. Adolescent boys and girls with any pre-existing mental health illness, like already diagnosed severe depression or a history of any suicidal tendency or suicidal attempts in the past, were excluded from the study. To assess socio-demographic status, a pretested semi-structured questionnaire was used. To assess depression, Beck's Depression Inventory (BDI-II) was used, which is a 21 items Likert scale. According to Beck's Depression scale, the scores for each of the 21 questions are added up. The highest possible total for the whole test would be 63, and the lowest possible score for the test would be zero. Various categories of depression, according to Beck, would be based on summed-up scores, i.e., 0-10, which is considered normal, 11-16 is mild mood disturbance, 17-20 is borderline clinical depression, 21-30 is moderate depression, 31-40 is severe depression, and more than 40 is extreme depression [13]. The Indian Academy of Pediatricians (IAP) guidelines suggested cutoff for Vitamin D was used in the present study, which is less than 12 ng/dl as deficient, 12-20 ng/dl as insufficient, and more than 20 ng/ml as sufficient for a tropical country like India. The International Association of Endocrinology defined a vitamin D level of 21-29 ng/mL as insufficiency and less than 20 ng/ml as a deficiency [14]. All school children were interviewed by the Assistant professor from the department of community medicine, who had prior experience in using the BDI scale. Venous blood was taken by an experienced lab technician with all aseptic precautions, transported within the vaccine carrier box with the temperature well maintained according to the temperature range, and analyzed in Central Diagnostic Laboratory Services, Biochemistry Department, Sri Devaraj Urs Medical College, SDUAHER, Kolar. All precautions were taken to avoid any hemolysis of blood during fresh blood withdrawal and also during transportation. Vitamin D3 levels were assessed using VITROS immunodiagnostic products using a 25-OH total reagent pack. Students with clinical depression after the interview were referred to a psychiatrist for any further clinical intervention. The study was started after Central Ethics Committee approval (CEC SDUAHER/Res. Proj$\frac{.173}{2020}$-21). Informed written consent/assent was taken from the school children by informing them about the benefits and risks involved in the study. Autonomy was maintained for study participants making participation in the study voluntary. Confidentiality was also maintained as the participants’ names and personal details were not recorded.
## Results
Out of 451 adolescent rural school students, 272 ($60.3\%$) were from the 15-year age group, 224 ($49.7\%$) were boys, 235 ($52.1\%$) were studying in 10th standard, 323 (71.6 %) belonged to nuclear families (a family group consisting of parents and their children, typically living in one home residence) and rest belonged to joint families (a family that consists of two or more generations from the same paternal or maternal line that shares a home and lives together), 379 ($84\%$) were non-vegetarian by diet whose diet contains meat which could be red, poultry, seafood, or the flesh of any other animal and rest of the school children were vegetarians who do not take any animal source of protein, 222 ($49.2\%$) had sun exposure in the afternoon, and 156 ($34.6\%$) had sun exposure of fewer than 60 minutes (Table 1).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Frequency | Percent |
| --- | --- | --- | --- |
| Age in years | 14 | 50 | 11.1 |
| Age in years | 15 | 272 | 60.3 |
| Age in years | 16 | 117 | 25.9 |
| Age in years | 17 | 12 | 2.7 |
| Gender | Boys | 224 | 49.7 |
| Gender | Girls | 227 | 50.3 |
| Class studying | 9 | 216 | 47.9 |
| Class studying | 10 | 235 | 52.1 |
| Type of family | Nuclear | 323 | 71.6 |
| Type of family | Joint | 128 | 28.4 |
| Diet | Vegetarian | 72 | 16.0 |
| Diet | Non-vegetarian | 379 | 84.0 |
| Timing of outdoor activity | Afternoon | 222 | 49.2 |
| Timing of outdoor activity | Evening | 229 | 50.8 |
| Duration | Less than 30 minutes per day | 156 | 34.6 |
| Duration | More than 30 minutes per day | 295 | 65.4 |
Out of 451 students, 90 ($20\%$) had moderate depression, and 133 ($29.5\%$) had severe depression, according to Beck's Depression Inventory-II (Table 2).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Frequency | Percent |
| --- | --- | --- | --- |
| Beck's depression Inventory | Mild Mood disturbance | 168 | 37.3 |
| Beck's depression Inventory | Borderline Clinical depression | 60 | 13.3 |
| Beck's depression Inventory | Moderate | 90 | 20.0 |
| Beck's depression Inventory | Severe | 133 | 29.5 |
| Beck's depression Inventory | Total | 451 | 100.0 |
Out of 451 students, 162 ($35.9\%$) had insufficient Vitamin D3 levels, and 66 ($14.6\%$) had deficient levels (Table 3).
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Cutoff values | Frequency | Percent |
| --- | --- | --- | --- | --- |
| Vitamin-D3 Levels | Deficient | Less than 12 ng/ml | 66 | 14.6 |
| Vitamin-D3 Levels | Insufficient | 12 to 20 ng/dl | 162 | 35.9 |
| Vitamin-D3 Levels | Sufficient | 20-100ng/dl | 223 | 49.4 |
| Vitamin-D3 Levels | Total | | 451 | 100.0 |
$63.1\%$ of female adolescent school children had minimal depression, $71.1\%$ of 10th standard students had moderate depression, $63.7\%$ of those adolescent school children who work out (exercise) during the evening had minimal depression, and all these factors had a statistically significant association with p-value less than 0.01 (Table 4). There was a statistically significant association between depression and Vitamin D3 levels (Table 5).
Among adolescent school children, those who were studying in 9th standard and exercising in the afternoon had higher odds of having mild depression. Among adolescent school children, those who were exercising in the afternoon had higher odds of having moderate depression. Among adolescent school children, those who were females and had Vitamin D deficiency had higher odds of having severe depression (Table 6).
**Table 6**
| BDI | Socio-demographic Factors | B | P value | Odds ratio | Lower Bound | Upper Bound |
| --- | --- | --- | --- | --- | --- | --- |
| Borderline Clinical Depression | Male | 0.591 | 0.085 | 1.807 | 0.922 | 3.54 |
| Borderline Clinical Depression | 9th standard | 1.427 | 0.001 | 4.165 | 1.84 | 9.429 |
| Borderline Clinical Depression | Nuclear | 0.384 | 0.309 | 1.468 | 0.7 | 3.079 |
| Borderline Clinical Depression | Vegetarian | -0.258 | 0.582 | 0.773 | 0.309 | 1.932 |
| Borderline Clinical Depression | Afternoon | 0.967 | 0.008 | 2.631 | 1.288 | 5.375 |
| Borderline Clinical Depression | Vitamin D Insufficiency | -0.507 | 0.341 | 0.602 | 0.212 | 1.711 |
| Borderline Clinical Depression | Vitamin D Deficiency | -0.147 | 0.711 | 0.864 | 0.397 | 1.877 |
| Moderate Depression | Male | 0.152 | 0.611 | 1.164 | 0.648 | 2.09 |
| Moderate Depression | 9th standard | -0.652 | 0.071 | 0.521 | 0.257 | 1.057 |
| Moderate Depression | Nuclear | -0.17 | 0.598 | 0.844 | 0.449 | 1.585 |
| Moderate Depression | Vegetarian | 0.407 | 0.249 | 1.503 | 0.752 | 3.003 |
| Moderate Depression | Afternoon | 0.834 | 0.009 | 2.302 | 1.234 | 4.296 |
| Moderate Depression | Vitamin D Insufficiency | -1.163 | 0.01 | 0.312 | 0.129 | 0.754 |
| Moderate Depression | Vitamin D Deficiency | -0.051 | 0.878 | 0.95 | 0.494 | 1.828 |
| Severe Depression | Female | 1.231 | 0.001 | 3.423 | 2.002 | 5.853 |
| Severe Depression | 9th standard | 0.738 | 0.019 | 2.091 | 1.126 | 3.884 |
| Severe Depression | Nuclear | -0.189 | 0.516 | 0.828 | 0.468 | 1.465 |
| Severe Depression | Vegetarian | -0.021 | 0.951 | 0.979 | 0.495 | 1.936 |
| Severe Depression | Afternoon | 0.994 | 0.001 | 2.703 | 1.538 | 4.75 |
| Severe Depression | Vitamin D Insufficiency | -0.452 | 0.293 | 0.637 | 0.274 | 1.478 |
| Severe Depression | Vitamin D Deficiency | 1.039 | 0.001 | 2.826 | 1.562 | 5.115 |
## Discussion
The present study was a cross-sectional study carried out among rural adolescent school students for two years. Four hundred fifty-one rural high school students took part in the study. The majority were 15 years boys studying in 10th standard. Students from nuclear families were common, 222 ($49.2\%$) had sun exposure in the afternoon, and 156 ($34.6\%$) had sun exposure for less than 60 minutes. Out of 451 rural high school students, $20\%$ had moderate depression, and $29.5\%$ had severe depression, according to Beck's Depression Inventory-II. The present study showed that Vitamin D deficiency had a statistically significant association with depression, with students who were studying in the 9th standard and exercising in the afternoon having higher odds of minimal depression, those who were exercising in the afternoon having higher odds of moderate depression and those who were females and had Vitamin D deficiency (VDD) had higher odds of having severe depression.
Studies have shown that Vitamin D-deficient people have increased odds of having clinically significant depression. Various studies conducted in different parts of the world suggest that irrespective of nutrition intake, longitudinal and latitudinal variation for sun exposure, skin pigmentation, and gender, there is a clear causal relationship between vitamin D status and depression among the healthy general population and establishing that Vitamin D is crucial to mental health [15-20]. Regardless of this sufficient evidence, biological mechanisms coupling Vitamin D levels and mental health status are still not fully understood. There is a shred of sizable evidence that neurons and glia in many parts of the brain, like the cingulate cortex and hippocampus have Vitamin D receptors which are involved in neuroimmunomodulation, regulation of neurotrophic factors, neuroprotection, neuroplasticity, and brain development, demonstrating that vitamin D might be associated with depression. The neoteric hypothesis proposes that an elevation in neuronal calcium level is a major component accountable for driving the onset of depression, where it is suggested that Vitamin D maintains calcium homeostasis and hence its deficiency may contribute to the onset of depression [21,22]. Oxidative stress and neuro-inflammation alterations cause invigoration of peripheral macrophages and central microglia, dysfunction of the hypothalamus-pituitary-adrenal (HPA) axis, and hypercortisolemia causing dendritic growth, synaptic plasticity, and deterioration in synaptic communication which is inhibited by abundant Vitamin D levels by secreting neurotransmitters, especially dopamine and exhibiting its neuro-modulatory and neuroprotective effects [23]. Cellular biology explains that the wide distribution of Vitamin D receptors and 1-α-hydroxylase throughout the brain allows for the local production of activated Vitamin D regulating the nerve growth factor and glial cell line-derived neurotrophic factor, which orchestrates the cellular architecture of the brain [24]. It is also said that activated Vitamin D has neuroprotective effects via neuromodulation, anti-inflammatory, anti-ischemic, and anti-oxidant properties. Other evidence is that Vitamin D induces the expression of the serotonin-synthesizing gene tryptophan hydroxylase 2 while repressing the expression of tryptophan hydroxylase 1, which plays a definite role in serotonin synthesis, establishing a thin link formation of serotonin and Vitamin D levels, thus fostering its supplementation might play a significant role in depression and its treatment [25,26]. Various systematic reviews and meta-analyses suggest that Vitamin D status is clinically and statistically associated with depression [27-29]. Most mental illnesses start at an early age, and the majority of cases are undiagnosed. The physiological impact of suboptimal nutrition on brain function is not fully understood, but adequate concentrations of both macro- and micronutrients are needed for optimal brain function. An evidence gap map has spotted the beneficial effect of Vitamin D on mental health conditions [30].
The strengths of the present study include a validated questionnaire that was used to assess depression among adolescent boys and girls. A standard diagnostic test was used to assess Vitamin D deficiency. The present study would be the first of this type to relate vitamin deficiency with depression, especially among rural adolescents. The study has many limitations. The study uses BDI, which assesses only symptoms of depression as a static measure. A relatively smaller sample size taken from the same geographic terrain hinders the generalization of study results. The temporal association between depression and Vitamin D deficiency (VDD) can only be established with better study designs which were not done in the present study as the present study was a cross-sectional study. There are various other factors like anemia, social factors like relation with father, mother, and school performance which could have played a role in depression, which were not assessed in the present study. The present study recommends Vitamin D supplementation at schools as the majority of adolescent students had VDD despite abundant sources.
## Conclusions
Vitamin D deficiency is extremely common among adolescents, and it remains unaddressed. Depression in adolescents is very common, and causes for depression in this age group could be many. With all evidence suggesting that Vitamin D is associated with depression, Vitamin D supplementation can be a metaphor for tackling adolescent depression. More evidence should be generated with a better study design to establish Vitamin D status and its role in depression, especially in adolescents.
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|
---
title: Adherence to Healthy Lifestyle Prior to Infection and Risk of Post–COVID-19
Condition
authors:
- Siwen Wang
- Yanping Li
- Yiyang Yue
- Changzheng Yuan
- Jae Hee Kang
- Jorge E. Chavarro
- Shilpa N. Bhupathiraju
- Andrea L. Roberts
journal: JAMA Internal Medicine
year: 2023
pmcid: PMC9989904
doi: 10.1001/jamainternmed.2022.6555
license: CC BY 4.0
---
# Adherence to Healthy Lifestyle Prior to Infection and Risk of Post–COVID-19 Condition
## Key Points
### Question
Is a healthy lifestyle (healthy body mass index, never smoking, high-quality diet, moderate alcohol intake, regular exercise, and adequate sleep) prior to SARS-CoV-2 infection protective of post–COVID-19 condition (PCC)?
### Findings
In this prospective cohort study of 1981 women who reported a positive SARS-CoV-2 test from April 2020 to November 2021, adherence to a healthy lifestyle prior to infection was inversely associated with risk of PCC in a dose-dependent manner. Compared with those who did not have any healthy lifestyle factors, those with 5 or 6 had half the risk of PCC.
### Meaning
Preinfection healthy lifestyle was associated with a substantially decreased risk of PCC.
## Abstract
This prospective cohort study investigates the association between healthy lifestyle factors prior to SARS-CoV-2 infection and risk of post–COVID-19 condition.
### Importance
Few modifiable risk factors for post–COVID-19 condition (PCC) have been identified.
### Objective
To investigate the association between healthy lifestyle factors prior to SARS-CoV-2 infection and risk of PCC.
### Design, Setting, and Participants
In this prospective cohort study, 32 249 women in the Nurses’ Health Study II cohort reported preinfection lifestyle habits in 2015 and 2017. Healthy lifestyle factors included healthy body mass index (BMI, 18.5-24.9; calculated as weight in kilograms divided by height in meters squared), never smoking, at least 150 minutes per week of moderate to vigorous physical activity, moderate alcohol intake (5 to 15 g/d), high diet quality (upper $40\%$ of Alternate Healthy Eating Index–2010 score), and adequate sleep (7 to 9 h/d).
### Main Outcomes and Measures
SARS-CoV-2 infection (confirmed by test) and PCC (at least 4 weeks of symptoms) were self-reported on 7 periodic surveys administered from April 2020 to November 2021. Among participants with SARS-CoV-2 infection, the relative risk (RR) of PCC in association with the number of healthy lifestyle factors (0 to 6) was estimated using Poisson regression and adjusting for demographic factors and comorbidities.
### Results
A total of 1981 women with a positive SARS-CoV-2 test over 19 months of follow-up were documented. Among those participants, mean age was 64.7 years (SD, 4.6; range, 55-75); $97.4\%$ ($$n = 1929$$) were White; and $42.8\%$ ($$n = 848$$) were active health care workers. Among these, 871 ($44.0\%$) developed PCC. Healthy lifestyle was associated with lower risk of PCC in a dose-dependent manner. Compared with women without any healthy lifestyle factors, those with 5 to 6 had $49\%$ lower risk (RR, 0.51; $95\%$ CI, 0.33-0.78) of PCC. In a model mutually adjusted for all lifestyle factors, BMI and sleep were independently associated with risk of PCC (BMI, 18.5-24.9 vs others, RR, 0.85; $95\%$ CI, 0.73-1.00, $$P \leq .046$$; sleep, 7-9 h/d vs others, RR, 0.83; $95\%$ CI, 0.72-0.95, $$P \leq .008$$). If these associations were causal, $36.0\%$ of PCC cases would have been prevented if all participants had 5 to 6 healthy lifestyle factors (population attributable risk percentage, $36.0\%$; $95\%$ CI, $14.1\%$-$52.7\%$). Results were comparable when PCC was defined as symptoms of at least 2-month duration or having ongoing symptoms at the time of PCC assessment.
### Conclusions and Relevance
In this prospective cohort study, pre-infection healthy lifestyle was associated with a substantially lower risk of PCC. Future research should investigate whether lifestyle interventions may reduce risk of developing PCC or mitigate symptoms among individuals with PCC or possibly other postinfection syndromes.
## Introduction
Post–COVID-19 condition (PCC), informally known as long COVID, is defined as having COVID-19 symptoms for at least 4 weeks after initial SARS-CoV-2 infection.1 This condition is estimated to affect $20\%$ to $40\%$ of individuals with COVID-19.2,3 The prevalence is higher among persons who were not vaccinated against COVID-19 or who were hospitalized for COVID-19 during the acute phase, reaching $50\%$ to $70\%$.4,5,6 The condition has a wide range of respiratory, cardiovascular, metabolic, gastrointestinal, neurological, and psychiatric manifestations, which can influence daily functioning.7 With ongoing waves of SARS-CoV-2 infection, PCC has created a serious public health burden, with an estimated 8 to 23 million Americans having developed PCC.8 Thus, better understanding of PCC causes is critical.
Persistent inflammation has been implicated in PCC symptoms related to multiple organs.9,10 Inflammatory factors have also been associated with other postinfection syndromes, such as postviral fatigue syndrome.11,12 Healthy lifestyle factors, including healthy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared),13 abstinence from cigarette smoking,14 a healthy diet,15 moderate alcohol consumption,16 regular exercise,17 and adequate sleep,18 have been identified as protective against inflammation. Adherence to multiple healthy lifestyle factors is associated with less severe COVID-19 disease as well as lower mortality from infectious diseases (including COVID-19), in a dose-dependent manner.19,20 Prior studies have found healthy BMI was associated with lower risk of PCC and inconsistent associations between smoking and PCC.21,22,23 The association between multiple healthy lifestyle factors prior to infection and risk of PCC has not been established.
In this prospective cohort study, we investigated the association of adherence to modifiable risk factors prior to infection (eg, healthy BMI, never smoking, healthy diet, moderate alcohol consumption, regular exercise, and adequate sleep) with the risk of developing PCC among participants subsequently infected with SARS-CoV-2. We further examined the extent to which established risk factors for COVID-19 severity (eg, hypertension, asthma)21,24 might account for possible associations. In addition, among individuals with PCC, we explored whether preinfection healthy lifestyle was associated with number of PCC symptoms and PCC-related daily life impairment.
## Methods
Participants were from an ongoing longitudinal cohort, the Nurses’ Health Study II, which in 1989 enrolled 116 429 female nurses residing in the US aged 25 to 42 years.25 Biennial follow-up questionnaires are sent to query lifestyle characteristics and health. Response rates exceeded $85\%$ at each follow-up cycle. In April 2020 (termed baseline henceforth), a COVID-19 substudy invitation was sent to active cohort participants to assess health during the pandemic, with monthly and quarterly follow-up surveys administered through November 2021 (55 925 invited, 39 137 [$71\%$] responded) (eFigure 1 in the Supplement).
Of women who responded to the COVID-19 substudy baseline and final questionnaires, 32 249 women had returned the 2017 biennial questionnaire querying lifestyle factors. During 19 months of follow-up, 2303 participants ($7.1\%$) reported a positive SARS-CoV-2 test (antibody, antigen, or PCR [polymerase chain reaction]) and the date of that test. In main analyses, we excluded 89 participants who did not have complete information about lifestyle factors and 233 participants who did not answer the PCC question, leaving 1981 participants (eFigure 2 in the Supplement).
The study was approved by the Brigham and Women's Hospital institutional review board. Return of questionnaires implied informed consent. Results are reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
## Assessment of Healthy Lifestyle
Six potentially modifiable lifestyle factors were assessed, including BMI, smoking, alcohol consumption, diet, physical activity, and sleep (2015 for diet and alcohol intake, 2017 for others). Self-report of weight and height has been validated in this cohort.26 Smoking was queried every 2 years, and we characterized lifetime smoking history as never, past, or current smoking. In a validation study, toenail nicotine level was strongly associated with reported smoking level (Spearman r, 0.63).27 Diet in the past year was measured using a validated semiquantitative food frequency questionnaire (FFQ).28,29 To characterize overall diet quality, we used the Alternative Healthy Eating Index (AHEI-2010), which is based on empirical evidence30 (higher score indicates healthier diet), excluding the alcohol component. Alcoholic beverage consumption was also collected by the FFQ. Physical activity was assessed using a validated questionnaire.31 For each participant, we estimated the average time spent in the past year in moderate to vigorous recreational activities (eg, running, jogging, cycling, tennis, squash, racquet ball, swimming, weight or resistance training, brisk walking, and other vigorous activities). We queried average sleep in a 24-hour period, with response options ranging from less than 5 to at least 10 hours. Daily sleep duration has been validated.32
## Healthy Lifestyle Score
We defined 6 healthy lifestyle factors as healthy body weight (BMI, 18.5-24.9), never smoking, at least 150 minutes per week of moderate to vigorous physical activity, high diet quality (upper $40\%$ of AHEI-2010 score), moderate alcohol intake (5-15 g/d), and adequate sleep (7-9 h/d), in accordance with US government recommendations or prior evidence.33,34,35,36 For each of the 6 factors, we created a binary variable, with participants receiving a score of 1 if they met the criteria for healthy and 0 otherwise. We then calculated the total number of healthy lifestyle factors for each participant. Because only 36 women had all 6 healthy lifestyle factors, women with 5 or 6 factors were combined in analyses.
## SARS-CoV-2 Infection and PCC
SARS-CoV-2 infection and PCC ascertainment in this cohort has been described elsewhere.37 Briefly, past 7-day, 30-day, and 90-day positive SARS-CoV-2 test (antibody, antigen, or PCR) and hospitalization due to COVID-19 since March 1, 2020, were self-reported on each follow-up questionnaire in the COVID-19 substudy. Post–COVID-19 condition was assessed on the final substudy questionnaire, administered 12 months after baseline. Participants were asked, “Have you experienced any long-term COVID-19 symptoms (lasting for more than 4 weeks)?” If yes, participants were asked to indicate which symptoms they experienced (eMethods in the Supplement). Participants with self-reported PCC were asked: [1] whether symptoms were ongoing; [2] duration of symptoms (less than 2 months; 2-3 months; 4-5 months); and [3] how often the symptoms prevented them from carrying out daily activities.
## Covariates
Date of birth (ascertained in 1989), racial and ethnic group [1989], and partner’s educational attainment [1999] were self-reported. Census tract median income and percentage with bachelor’s degree or higher were assessed based on geocoded residence in 2009. Lifetime history of physician-diagnosed diseases has been updated biennially (eMethods in the Supplement). Frontline health care worker status was self-reported at COVID-19 substudy baseline. COVID-19 vaccination status and date of vaccination were assessed on quarterly follow-up questionnaires.
## Statistical Analysis
Among participants who reported a positive SARS-CoV-2 test over follow-up, we first compared sociodemographic factors and distribution of preinfection healthy lifestyle factors among those who responded to the PCC question vs those who did not. We then compared the prevalence of sociodemographic and lifestyle factors by the number of healthy lifestyle factors. Each variable was missing less than $5\%$. Indicator variables were used for any missing covariate information for categorical variables; no participants were missing data for continuous variables.
We estimated the relative risks (RRs) and $95\%$ CIs for the associations between the healthy lifestyle score and risk of PCC, adjusting for age, race and ethnicity, partners’ education, census tract median household income, census tract percentage population with bachelor’s degree or higher, health care worker status, and history of chronic diseases (fully adjusted model) using Poisson regression. We also estimated the RRs and $95\%$ CIs for the associations between individual healthy lifestyle factors (both as categorical/continuous variables and dichotomized) with risk of PCC. We further fit models mutually adjusted for all healthy lifestyle factors. In addition, we calculated the population attributable risk percentage (PAR). PAR estimates the proportion of PCC in this cohort that hypothetically would not have occurred if effect estimates reflected causal relationships and all participants were in the low-risk group.38 To calculate the PAR, we used RRs and $95\%$ CIs from the fully adjusted model including all 6 healthy lifestyle factors.
To estimate the PAR in the US population, we used the prevalence of the 6 lifestyle factors among women of the same age as our study (ages 55-75 years) in the nationally representative US National Health and Nutrition Examination Survey (NHANES, 2013-2014). Further, among persons who developed PCC, we compared frequency of PCC symptoms and daily-life impairment due to PCC by healthy lifestyle score.
We conducted 10 sensitivity analyses. First, we defined PCC as having symptoms lasting for more than 2 months and more than 4 months. Second, as not all participants may have had access to testing, we expanded the definition of SARS-CoV-2 infection to include participants having symptoms without a confirmed test (926 COVID-19 cases were added). Third, to investigate whether the observed associations were explained by the severity of acute phase disease, we excluded participants who had been hospitalized due to COVID-19. Fourth, we used multiple imputation for missing healthy lifestyle ($$n = 89$$) or PCC ($$n = 233$$) information. Fifth, to reduce possible recall bias, we restricted PCC cases to 633 participants who reported ongoing symptoms at the time of PCC assessment. Sixth, to distinguish PCC symptoms from symptoms related to sleep deprivation, we excluded participants reporting only psychological, cognitive, or neurological symptoms. Seventh, we excluded 497 persons reporting fatigue as one of their long-term symptoms (among whom 48 reported fatigue as their only symptom). Eighth, because risk of PCC may be reduced among those who were vaccinated against COVID-19, we additionally adjusted for vaccination status at time of infection.6 Ninth, as low-to-moderate vs no alcohol consumption has been associated with both better and worse health,39,40 we excluded alcohol from the healthy lifestyle score. Tenth, we investigated whether observed associations differed by health care worker status by adding a cross-product term to the model. All analyses were conducted in SAS statistical software, version 9.4 (SAS Institute). All statistical tests were 2-sided. Significance level was assessed at $P \leq .05.$
## Results
The mean (SD) age of 32 249 participants was 65.9 (4.5) years (range 55-75 years). Of those, $97.2\%$ were White, and $28.7\%$ were active health care workers. Participants missing PCC data ($$n = 233$$) (vs those nonmissing [$$n = 1981$$]) were more likely to be racial and ethnic minorities, have lower BMI, be health care workers, have higher socioeconomic status, sleep less, and be less likely to have type 2 diabetes (eTable 1 in the Supplement). We documented 1981 participants with a positive SARS-CoV-2 test over 19 months of follow-up. Among those participants, mean age was 64.7 years (SD, 4.6; range, 55-75); $97.4\%$ ($$n = 1929$$) were White; and $42.8\%$ ($$n = 848$$) were active health care workers. The median time from assessment of exposures (return of 2017 questionnaire) to SARS-CoV-2 infection was 35 months (IQR, 31 months-37 months).
Healthy lifestyle factors were weakly to moderately correlated with each other (Φ coefficient range, −0.03 to 0.24; eTable 2 in the Supplement). At baseline, those who had greater healthy lifestyle scores were younger, more likely to be White, had higher socioeconomic status, and had lower prevalence of comorbidities (Table 1). Of participants who reported a positive SARS-CoV-2 test during follow-up, $44.0\%$ ($$n = 871$$) reported PCC. Among these, $87.0\%$ ($$n = 758$$) reported symptoms lasting at least 2 months, and $56.5\%$ ($$n = 491$$) reported at least occasional daily life impairment related to PCC. The most common symptoms were fatigue ($57.1\%$, $$n = 497$$), smell or taste problems ($40.9\%$, $$n = 356$$), shortness of breath ($25.3\%$, $$n = 220$$), confusion/disorientation/brain fog ($21.6\%$, $$n = 188$$), and memory issues ($20.0\%$, $$n = 174$$).
**Table 1.**
| Characteristic | No. (%) (n = 1981) | No. (%) (n = 1981).1 | No. (%) (n = 1981).2 | No. (%) (n = 1981).3 | No. (%) (n = 1981).4 | No. (%) (n = 1981).5 |
| --- | --- | --- | --- | --- | --- | --- |
| Characteristic | No. of healthy lifestyle factors | No. of healthy lifestyle factors | No. of healthy lifestyle factors | No. of healthy lifestyle factors | No. of healthy lifestyle factors | No. of healthy lifestyle factors |
| Characteristic | 0 (n = 66) | 1 (n = 301) | 2 (n = 564) | 3 (n = 518) | 4 (n = 344) | 5 or 6 (n = 188) |
| Age, mean (SD), yb | 65.8 (4.3) | 65.2 (4.5) | 65.1 (4.6) | 64.3 (4.7) | 64.4 (4.4) | 64.0 (4.7) |
| Race, Whitec | 64 (96.8) | 291 (96.8) | 554 (98.2) | 495 (95.5) | 338 (98.3) | 187 (99.5) |
| Partner’s education ≤ high school | 14 (21.8) | 60 (19.8) | 108 (19.1) | 77 (14.8) | 37 (10.7) | 13 (7.1) |
| Census tract median household income, mean (SD), USD | 56 248 (17 550.8) | 59 814.9 (19 479.1) | 63 283.7 (22 513.7) | 66 416.3 (25 742.1) | 69 275.8 (26 415.7) | 71 252.7 (26 143.9) |
| Census tract % population with bachelor’s degree or higher, mean (SD) | 24.0 (14.9) | 24.7 (13.8) | 28.6 (15.9) | 31.8 (17.8) | 35.0 (17.7) | 36.7 (18.6) |
| BMI, mean (SD) | 32.5 (8.7) | 32.6 (6.4) | 30.2 (6.4) | 27.3 (5.9) | 25.3 (4.4) | 22.8 (2.6) |
| AHEI, mean (SD) | 52.1 (8.4) | 53.4 (10) | 56.8 (10.3) | 62.1 (11.7) | 66.7 (10.9) | 72.6 (8.5) |
| Alcohol, mean (SD), g/d | 4.0 (9.7) | 6.2 (14) | 6.7 (12) | 7.9 (11.7) | 7.2 (8.5) | 9.4 (7.7) |
| Physical activity, min/wk | 24.0 (33.0) | 63.3 (153.9) | 131 (258.9) | 266.8 (318.5) | 373.7 (339.5) | 478 (402.2) |
| Sleep, h/d | Sleep, h/d | Sleep, h/d | Sleep, h/d | Sleep, h/d | Sleep, h/d | Sleep, h/d |
| ≤5 | 11 (16.7) | 40 (13.3) | 56 (10.0) | 28 (5.5) | 15 (4.2) | 0 (0.0) |
| 6 | 51 (77.5) | 154 (51.0) | 116 (20.6) | 107 (20.6) | 43 (12.4) | 8 (4.0) |
| 7 | 0 (0) | 53 (17.7) | 213 (37.8) | 231 (44.6) | 193 (56.2) | 101 (53.8) |
| 8 | 0 (0) | 38 (12.7) | 137 (24.4) | 122 (23.5) | 80 (23.3) | 72 (38.4) |
| 9 | 0 (0) | 6 (2.1) | 37 (6.6) | 26 (5.0) | 13 (3.8) | 7 (3.7) |
| ≥10 | 4 (5.8) | 9 (3.1) | 4 (0.7) | 4 (0.8) | 0 (0.0) | 0 (0.0) |
| Smoking | Smoking | Smoking | Smoking | Smoking | Smoking | Smoking |
| Never | 0 (0.0) | 129 (42.7) | 360 (63.9) | 367 (70.9) | 280 (81.4) | 175 (92.9) |
| Past | 58 (87.6) | 161 (53.6) | 185 (32.9) | 146 (28.3) | 63 (18.3) | 13 (7.1) |
| Current | 8 (12.4) | 11 (3.7) | 18 (3.2) | 4 (0.8) | 1 (0.3) | 0 (0.0) |
| Frontline health care workerd | 26 (38.8) | 138 (45.7) | 259 (45.9) | 221 (42.7) | 135 (39.4) | 73 (38.8) |
| Lifetime history of comorbidities | Lifetime history of comorbidities | Lifetime history of comorbidities | Lifetime history of comorbidities | Lifetime history of comorbidities | Lifetime history of comorbidities | Lifetime history of comorbidities |
| High cholesterol | 52 (79.4) | 199 (66.1) | 349 (61.8) | 318 (61.4) | 193 (56.1) | 92 (49.1) |
| Diabetes | 18 (27.4) | 64 (21.2) | 80 (14.3) | 46 (8.9) | 15 (4.3) | 5 (2.4) |
| Hypertension | 42 (64.3) | 177 (58.7) | 276 (48.9) | 205 (39.6) | 103 (30.1) | 45 (23.9) |
| Asthma | 23 (35.5) | 83 (27.5) | 144 (25.6) | 112 (21.6) | 56 (16.2) | 33 (17.7) |
| Cancer | 18 (28.0) | 77 (25.4) | 103 (18.3) | 101 (19.4) | 81 (23.4) | 35 (18.7) |
| Chronic obstructive pulmonary disease | 12 (18.7) | 22 (7.3) | 28 (5.0) | 22 (4.2) | 10 (2.9) | 2 (1.3) |
| Cardiovascular diseasee | 12 (18.3) | 45 (14.8) | 50 (8.9) | 53 (10.2) | 17 (5.0) | 2 (1.3) |
| Inflammatory bowel disease | 2 (3.6) | 8 (2.7) | 21 (3.7) | 13 (2.5) | 9 (2.6) | 2.6 (5) |
| Hospitalization due to COVID-19 | 8 (12.0) | 23 (7.8) | 36 (6.4) | 21 (4.0) | 6 (1.6) | 5 (2.6) |
| COVID-19 vaccination (first dose) by the time of infection | 6 (9.5) | 15 (5.0) | 38 (6.7) | 35 (6.8) | 24 (6.9) | 7 (3.6) |
Risk of PCC was lower with increasing number of healthy lifestyle factors (Figure 1; $P \leq .001$ for trend). Compared with women who did not adhere to any healthy lifestyle factors, those having 5 or 6 factors had a $49\%$ lower risk of PCC (RR, 0.51; $95\%$ CI, 0.33-0.78, Figure 1). Assuming a causal relationship, the PAR for healthy lifestyle was $36.0\%$ ($95\%$ CI, $14.1\%$-$52.7\%$).
**Figure 1.:** *Number of Healthy Lifestyle Factors Prior to the Pandemic and Risk of Post–COVID-19 Condition (PCC) Among Participants Who Reported a Positive SARS-CoV-2 Test During Follow-up, the Nurses’ Health Study II, 2015-2021P for trend analysis used indicator levels as a continuous variable. Error bars indicate 95% CIs.aIn the 1981 female participants, healthy lifestyle factors include healthy body weight (body mass index, 18.5-24.9; calculated as weight in kilograms divided by height in meters squared), never smoking, at least 150 min/wk of moderate to vigorous physical activity, high diet quality (upper 40% of Alternative Healthy Eating Index [AHEI]–2010 score), moderate alcohol intake (5-15 g/d), and adequate sleep (7-9 h/d). Diet and alcohol intake were assessed in 2015; all other lifestyle factors were assessed in 2017.bAdjusted for age; race and ethnicity; health care worker status; partner’s education; census tract median household income; census tract percentage population with bachelor’s degree or higher; and history of chronic obstructive pulmonary disease, cancer, diabetes, asthma, hypertension, high cholesterol, cardiovascular diseases, and inflammatory bowel disease.*
In analyses examining each healthy lifestyle factor separately, BMI, smoking, diet, and physical activity were associated with risk of PCC in models adjusting for demographic factors (Table 2, Model 1). Compared to low or high levels, moderate alcohol consumption and 7 to 9 h/d sleep had the lowest risk of PCC (Table 2, Model 1). When further adjusted for socioeconomic status, health care worker status, and comorbidities, the associations were slightly attenuated (Table 2, Model 2). When we categorized the 6 lifestyle factors as binary variables, BMI and sleep were independently associated with risk of PCC in models mutually adjusted for all factors (Table 2, Model 3; Table 3). If these relationships were causal, the PAR ($95\%$ CI) for overweight/obesity was $10.3\%$ ($95\%$ CI, $0.2\%$-$19.8\%$) and for inadequate sleep was $6.6\%$ ($95\%$ CI, $1.8\%$-$11.5\%$, Table 3). PARs for other healthy lifestyle factors ranged from $2.4\%$ to $4.5\%$. Results were comparable when we used the prevalence of healthy lifestyle factors in NHANES for PAR calculation (eTable 3 in the Supplement).
Results were similar in sensitivity analyses defining PCC as having symptoms lasting at least 2 months or at least 4 months; including self-presumed COVID-19 cases; excluding persons who had been hospitalized due to COVID-19; using multiple imputation; restricting cases to participants with ongoing symptoms; excluding PCC cases with only psychological, cognitive, or neurological symptoms; and additionally adjusting for vaccination status (eTable 4 in the Supplement). A weaker association was observed when we excluded participants endorsing fatigue or excluded alcohol intake from healthy lifestyle factors (eTable 5 in the Supplement). The association did not differ by health care worker status (eTable 6 in the Supplement, $$P \leq .77$$ for interaction).
Among participants who developed PCC, all COVID-19 symptoms were less prevalent in participants with higher healthy lifestyle scores, except for smell or taste problems and headache (0-4 factors, mean [SD] number of symptoms, 2.7 [1.8]; 5-6 factors, mean [SD] number of symptoms, 2.3 [1.6], Figure 2). Adherence to 5 to 6 vs 0 to 4 healthy lifestyle factors was associated with lower risk of daily life impairment due to PCC, although the CI was wide (RR, 0.70; $95\%$ CI, 0.44-1.12).
**Figure 2.:** *Post–COVID-19 Condition (PCC) Symptoms According to Number of Healthy Lifestyle Factors Prior to the Pandemic Among Persons Who Developed PCC, the Nurses’ Health Study II, 2015-2021Healthy lifestyle factors include healthy body weight (body mass index, 18.5-24.9; calculated as weight in kilograms divided by height in meters squared), never smoking, at least 150 min/wk of moderate to vigorous physical activity, high diet quality (upper 40% of Alternative Healthy Eating Index [AHEI]–2010 score), moderate alcohol intake (5-15 g/d), and adequate sleep (7-9 h/d). Diet and alcohol intake were assessed in 2015; all other lifestyle factors were assessed in 2017.*
## Discussion
In this prospective cohort study of women followed up for more than a year starting in April 2020, we found a beneficial dose-response association of a preinfection healthy lifestyle with risk of PCC, after accounting for sociodemographic factors and pre-existing conditions. Women endorsing 5 or 6 healthy lifestyle factors had approximately $50\%$ lower risk of PCC than those without any healthy lifestyle factors. These associations were mainly driven by healthy body weight and adequate sleep. The PAR for all 6 healthy lifestyle factors in combination was $36.0\%$, indicating that, if these associations were causal, $36.0\%$ of PCC cases would have been avoided if all participants had 5 or 6 healthy lifestyle factors prior to the pandemic.
Few studies have examined modifiable lifestyle factors preceding the pandemic as risk factors for PCC. Adherence to a healthy lifestyle has been associated with reduced risk of noncommunicable diseases and mortality,19,33 indicating a long-term health benefit. Specific to COVID-19, 2 prospective cohort studies using the UK Biobank (sample size approximately 400 000) found that a combination of lifestyle factors had a dose-response association with lower risk of COVID-19 hospitalization and mortality.19,20 Unhealthy lifestyle factors (smoking, physical inactivity, obesity, and alcohol drinking) in combination accounted for $51\%$ of severe COVID-19 in the United Kingdom population.20 The associations between healthy lifestyle score and risk of severe COVID was partly mediated by low-grade inflammation ($10\%$ to $16\%$), as indicated by levels of C-reactive protein, although biomarkers were collected 10 years prior to infection.20 Our findings additionally identified a dose-response protective association of a healthy lifestyle against development of PCC, independent of pre-existing conditions and severity of acute phase disease.
While our results suggest that each of the 6 healthy lifestyle factors measured were broadly associated with a lower risk of PCC, in analyses mutually adjusted for all lifestyle factors and comorbidities, BMI and sleep were most strongly associated with lower risk of PCC. Several individual lifestyle factors have been associated with risk of long-term COVID symptoms or slow recovery from COVID-19, including obesity, smoking, unhealthy diet, and poor-quality sleep,21,22,23,41,42,43 although findings were not consistent, and no studies to our knowledge mutually adjusted for a range of lifestyle factors.23 Several biological mechanisms may explain the associations we observed. First, each unhealthy lifestyle factor we examined has been associated with increased risk of chronic inflammation, including findings from our cohort.13,14,15,16,17,18,20,44,45,46,47,48,49,50,51,52,53 Sustained systemic inflammation has been implicated in the development of PCC.9 Chronic inflammation may predispose individuals to excessive release of cytokines after infection, subsequently increasing risk for long-term complications in multiple organs.54,55 Second, these unhealthy lifestyle factors dysregulate adaptive autoimmunity, which has been found in individuals with PCC.9,56,57,58,59,60 Third, unhealthy lifestyle factors (obesity, smoking, physical inactivity, and excessive alcohol intake) predispose to blood clotting abnormalities, another pathophysiological change observed in persons with PCC.61,62 It has also been postulated that healthy lifestyle may benefit both innate and adaptive immune responses.63,64,65 Strengths of our study include a prospective design in which healthy lifestyle factors were assessed prior to the pandemic using validated instruments. In addition, incident SARS-CoV-2 infection, hospitalization due to COVID-19, COVID-19 vaccination, and PCC were ascertained during an active phase of the pandemic with monthly and quarterly follow-up over 19 months. Our study provides valuable population-based evidence for the association between healthy lifestyle and PCC.
## Limitations
Our study has several limitations. First, our cohort was comprised of middle-aged female nurses who were predominantly White, limiting generalizability. Moreover, because the incidence of PCC may differ by infecting strains,66 we cannot be certain that associations we found apply to PCC resulting from subsequent COVID-19 strains. In addition, we do not have information about multiple infections. Second, PCC information was not missing at random, which might have introduced bias. However, results were comparable in analyses using multiple imputation. Third, as SARS-CoV-2 infection and PCC were self-reported, misclassification may have occurred. Nevertheless, validity of self-reported health information is high in this cohort. Fourth, because PCC is still poorly understood, and there is no reference standard (or consensus) for the diagnosis of PCC, it is practically difficult to link symptoms with COVID-19 and to ascertain PCC cases.67 Finally, because asymptomatic cases are less likely to be detected, we likely underestimated the true prevalence of COVID-19 infections.
The findings for PAR should be interpreted with caution. The PAR relies on a causal interpretation of the association between healthy lifestyle factors and risk of PCC. As our study is observational, residual confounding likely remains, despite adjustment for multiple potential confounders. Although these healthy lifestyle factors are potentially modifiable, they are difficult to change;68 thus, eliminating this PAR may not be achievable. In addition, PAR is a population-specific calculation contingent on the prevalence of the exposures and their association with risk of disease; thus, the PAR calculated here may not apply to other populations. However, the prevalence of healthy lifestyle factors was comparable in female participants of the same age in the nationally representative NHANES cohort.
## Conclusions
The findings of this prospective cohort study indicate that adherence to a healthy lifestyle was associated with substantially reduced risk of developing PCC among individuals subsequently infected with SARS-CoV-2. If the associations we found were causal, among healthy lifestyle factors, maintaining a healthy weight and having adequate sleep duration may confer the greatest benefit for prevention of PCC. Future research should investigate whether implementing lifestyle interventions decreases risk of PCC or benefits persons with PCC or other chronic postinfection syndromes.
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|
---
title: Comparing Effectiveness and Safety of SGLT2 Inhibitors vs DPP-4 Inhibitors
in Patients With Type 2 Diabetes and Varying Baseline HbA1c Levels
authors:
- Elvira D’Andrea
- Deborah J. Wexler
- Seoyoung C. Kim
- Julie M. Paik
- Ethan Alt
- Elisabetta Patorno
journal: JAMA Internal Medicine
year: 2023
pmcid: PMC9989905
doi: 10.1001/jamainternmed.2022.6664
license: CC BY 4.0
---
# Comparing Effectiveness and Safety of SGLT2 Inhibitors vs DPP-4 Inhibitors in Patients With Type 2 Diabetes and Varying Baseline HbA1c Levels
## Key Points
### Question
Does the effectiveness and safety of sodium-glucose cotransporter 2 inhibitors (SGLT2i) differ from that of dipeptidyl peptidase 4 inhibitors (DPP-4i) in patients with type 2 diabetes (T2D) overall and at varying baseline hemoglobin A1c (HbA1c) levels?
### Findings
In this large new-user comparative effectiveness and safety research study including 87 274 propensity-scored matched adults with T2D, SGLT2i treatment initiators had a reduced risk of major cardiovascular events, heart failure, and acute kidney injury and an increased risk of genital infections and diabetic ketoacidosis compared with DPP-4i treatment initiators, regardless of their baseline HbA1c level.
### Meaning
Despite concern that use of SGLT2i at higher HbA1c levels would cause excess risk, the findings of this study suggest that patients with T2D can benefit from the use of SGLT2i regardless of glycemic control, with the expected adverse effect profile when compared with DPP-4i, with no additional risk of adverse effects in patients with elevated HbA1c levels.
## Abstract
This comparative effectiveness study evaluates cardiovascular effectiveness and safety of SGLT2 inhibitors vs DPP-4 inhibitors in adults with type 2 diabetes overall and at varying baseline HbA1c levels.
### Importance
Sodium-glucose cotransporter 2 inhibitor (SGLT2i) therapy has been associated with cardiovascular benefits and a few adverse events; however, whether the comparative effectiveness and safety profiles vary with differences in baseline hemoglobin A1c (HbA1c) levels is unknown.
### Objective
To compare cardiovascular effectiveness and safety of treatment with SGLT2i vs dipeptidyl peptidase 4 inhibitor (DPP-4i) in adults with type 2 diabetes (T2D) [1] overall and [2] at varying baseline HbA1c levels.
### Design, Setting, and Participants
A new-user comparative effectiveness and safety research study was conducted among 144 614 commercially insured adults, initiating treatment with SGLT2i or DPP-4i and with a recorded T2D diagnosis at baseline and at least 1 HbA1c laboratory result recorded within 3 months before treatment initiation.
### Interventions
The intervention consisted of the initiation of treatment with SGLT2i or DPP-4i.
### Main Outcomes and Measures
Primary outcomes were a composite of myocardial infarction, stroke, or all-cause death (modified major adverse cardiovascular events [MACE]) and hospitalization for heart failure (HHF). Safety outcomes were hypovolemia, fractures, falls, genital infections, diabetic ketoacidosis (DKA), acute kidney injury (AKI), and lower-limb amputation. Incidence rate (IR) per 1000 person-years, hazard ratios (HR) and rate differences (RD) with their $95\%$ CIs were estimated controlling for 128 covariates.
### Results
A total of 144 614 eligible adults (mean [SD] age, 62 [12.4] years; $54\%$ male participants) with T2D initiating treatment with a SGLT2i ($$n = 60$$ 523) or a DPP-4i ($$n = 84$$ 091) were identified; 44 099 had an HbA1c baseline value of less than $7.5\%$, 52 986 between $7.5\%$ and $9\%$, and 47 529 greater than $9\%$. Overall, 87 274 eligible patients were 1:1 propensity score–matched: 24 052 with HbA1c less than $7.5\%$; 32 290 with HbA1c between $7.5\%$ and $9\%$; and 30 932 with HbA1c greater than $9\%$ (to convert percentage of total hemoglobin to proportion of total hemoglobin, multiply by 0.01). The initiation of SGLT2i vs DPP-4i was associated with a reduction in the risk of modified MACE (IR per 1000 person-years 17.13 vs 20.18, respectively; HR, 0.85; $95\%$ CI, 0.75-0.95; RD, −3.02; $95\%$ CI, −5.23 to –0.80) and HHF (IR per 1000 person-years 3.68 vs 8.08, respectively; HR, 0.46; $95\%$ CI, 0.35 to 0.57; RD −4.37; $95\%$ CI, −5.62 to −3.12) over a mean follow-up of 8 months, with no evidence of treatment effect heterogeneity across the HbA1c levels. Treatment with SGLT2i showed an increased risk of genital infections and DKA and a reduced AKI risk compared with DPP-4i. Findings were consistent by HbA1c levels, except for a more pronounced risk of genital infections associated with SGLT2i for HbA1c levels of $7.5\%$ to $9\%$ (IR per 1000 person-years 68.5 vs 22.8, respectively; HR, 3.10; $95\%$ CI, 2.68-3.58; RD, 46.22; $95\%$ CI, 40.54-51.90).
### Conclusions and Relevance
In this comparative effectiveness and safety research study among adults with T2D, SGLT2i vs DPP-4i treatment initiators had a reduced risk of modified MACE and HHF, an increased risk of genital infections and DKA, and a lower risk of AKI, regardless of baseline HbA1c.
## Introduction
Type 2 diabetes (T2D) affects more than $11\%$ of the US population and is associated with increased morbidity and mortality from cardiovascular and kidney disease.1,2 Mitigating the risk of these complications is a priority in the management of diabetes. In large-scale postmarketing randomized cardiovascular outcome trials (CVOTs), sodium-glucose cotransporter 2 inhibitors (SGLT2i) have demonstrated a cardiorenal protective effect in patients with T2D and established cardiovascular or kidney conditions.3,4,5,6 These benefits have been reproduced in real-world evidence studies, which encompass a patient population with a broader spectrum of cardiovascular risk as seen in clinical practice.7,8 However, it is still unclear whether patients with different levels of hyperglycemia can similarly benefit from the use of SGLT2i.
Cardiovascular outcome trials of SGLT2i have explored potential for treatment effect heterogeneity by hyperglycemia, defined as baseline assessment of glycated hemoglobin (HbA1c), identifying some potential variation in the cardiovascular effects of SGLT2i by HbA1c level.3,4,5,6,9 However, subgroup analyses within CVOTs are generally underpowered to detect meaningful differences,9 and patients with uncontrolled diabetes were often underrepresented due to strict inclusion criteria.3,4,6 Further, since SGLT2i induce a glycosuric response by reducing kidney tubular glucose reabsorption, those medications can have a more pronounced effect on hyperglycemia in patients with poor glycemic control due to the increased amount of filtered glucose.10 The accompanying diuretic and natriuretic effect of SGLT2 inhibition may lead to a more marked improvement in volume status in patients with elevated vs controlled glycemia resulting in a lower risk for heart failure. Conversely, the higher concentration of glucose in the urine in patients with severe hyperglycemia could lead to an increased risk of adverse effects such as mycotic infections, volume depletion due to the osmotic diuresis induced by glycosuria, consequent increased risk of falls and fractures, and diabetic ketoacidosis (DKA).11 *In this* large comparative effectiveness and safety research study of patients with T2D, we evaluated cardiovascular and safety events associated with the initiation of SGLT2i treatment compared with dipeptidyl peptidase 4 inhibitor (DPP-4i), which clearly lack glycosuric adverse effects and are generally considered safe [1] in the overall population and [2] across subgroups of patients with controlled, above-target, or elevated HbA1c levels at baseline.
## Study Design and Data Source
We performed a new-user comparative effectiveness and safety research study using a US health insurance data set (deidentified Optum Clinformatics Data Mart Database) with nationwide commercial coverage including Medicare Advantage plans. Through linkage with national laboratory test provider chains, results for outpatient laboratory tests are available for a subset of approximately $45\%$ of beneficiaries (including laboratory test results of HbA1c), representative of the full insured population (see eMethods and eTable 1 in Supplement 1). The Mass General Brigham institutional review board provided ethics approval. Informed consent was waived because the study used deidentified secondary data.
## Study Population
The study population included patients 18 years and older who initiated treatment with a SGLT2i (canagliflozin, dapagliflozin, empagliflozin, or ertugliflozin) or a DPP-4i (alogliptin, saxagliptin, linagliptin, or sitagliptin) between April 1, 2013 (consistent with the US Food and Drug Administration [FDA] approval of the first SGLT2i), and June 30, 2021. Treatment with DPP-4i was selected as the comparator because these medications are also frequently used as second-line therapy for T2D, have similar out-of-pocket costs as SGLT2i but a different mechanism of action, which does not involve inhibition of kidney glucose reabsorption and osmotic diuresis, and have shown no association with atherosclerotic cardiovascular outcomes. Cohort entry was the day of the first filled prescription of either SGLT2i or DPP-4i, with no use in the previous 6 months. Study eligibility was limited to patients with at least 6 months of continuous health plan enrollment, a recorded T2D diagnosis before cohort entry, and at least 1 HbA1c laboratory result recorded within 3 months before cohort entry. We excluded patients with records of type 1, secondary, or gestational diabetes; malignant neoplasms; end-stage kidney disease; kidney replacement therapy; no laboratory results for creatinine; or nursing home residence within 6 months preceding cohort entry (eFigure 1 and eTable 2 in Supplement 1). Based on the most recent HbA1c baseline value, we identified 3 different subcohorts which comprised patients with controlled (HbA1c <$7.5\%$), above-target (HbA1c $7.5\%$-$9\%$), or elevated (HbA1c >$9\%$) glycemia, respectively (to convert percentage of total hemoglobin to proportion of total hemoglobin, multiply by 0.01). The cutoffs for HbA1c stratification were chosen by both inspecting terciles of the HbA1c distribution among SGLT2i treatment initiators and considering the thresholds currently recommended to define controlled vs uncontrolled hyperglycemia.12,13
## Outcomes and Follow-up
The primary effectiveness outcomes were [1] modified major adverse cardiovascular events (MACE), a composite cardiovascular end point of myocardial infarction, ischemic or hemorrhagic stroke, and all-cause death, and [2] hospitalization for heart failure (HHF). Secondary effectiveness outcomes were myocardial infarction, ischemic or hemorrhagic stroke, and all-cause mortality. In prior studies, the positive predictive values of claims-based algorithms were at least $87\%$ for myocardial infarction and stroke,14,15,16 and $84\%$ to $100\%$ for HHF.17 Safety outcomes included hypovolemia, nonvertebral fractures, falls, genital infections, DKA, acute kidney injury (AKI), and lower-limb amputations. Definitions were either validated against medical records18,19,20,21 or used in prior pharmacoepidemiologic studies assessing SGLT2i22,23,24 (eTable 3 in Supplement 1).
Adopting an as-treated approach, the follow-up started the day after cohort entry and continued until treatment discontinuation (allowing a 30-day grace period after termination of the last prescription’s supply), switch to or augmentation with a drug in the comparator class, occurrence of study outcome, death, end of continuous health plan enrollment, or end of available data, whichever came first.
## Baseline Patient Characteristics
Patient characteristics were selected a priori as potential confounders and measured at treatment initiation (demographics), as last recorded value within 3 months before cohort entry (HbA1c), or within 6 months before cohort entry (all other patient characteristics). Covariates included demographics, cardiovascular and other comorbidities, general health state indexes, such as combined comorbidity score and claims-based frailty index,25,26 HbA1c laboratory results, diabetes-specific complications, use of glucose-lowering and other medications, indicators of health care utilization as proxy for disease state, surveillance, and intensity of care. Based on baseline creatinine laboratory results, we calculated the estimated glomerular filtration rate (eGFRCr) using a version of the creatinine-based Chronic Kidney Disease–Epidemiology Collaboration (CKD-EPI) equation without the race term as correction factor.27,28 Other laboratory test results were also measured at baseline but were available for only a subset of the study population (see eTables 4-6 in Supplement 1 for a complete list of the baseline covariates).
## Statistical Analysis
Propensity score (PS) matching was used to control for confounding. The PS for initiating SGLT2i vs DPP-4i therapy was calculated within each HbA1c subcohort separately through a logistic regression model with 128 prespecified covariates. Laboratory data, except for HbA1c and eGFRCr, were not included in the model because of the substantial proportion of missing information. Initiators of SGLT2i therapy were 1:1 matched to initiators of DPP-4i therapy on their estimated PS within each HbA1c subcohort using the nearest neighbor approach with a caliper width of 0.01 on the PS scale. Covariate balance was assessed with standardized differences, with meaningful imbalances set at values higher than $10\%$.29,30 We also reviewed the balance in laboratory test results not included in the PS model, to evaluate potential residual confounding after PS matching.
We tabulated numbers of events, incidence rates (IRs), and rate differences (RDs) per 1000 person-years. Hazard ratios (HRs) and $95\%$ CIs were estimated by Cox proportional hazard models. We used Kaplan-Meier methods to plot cumulative incidence of primary outcomes and log-rank tests to compare hazard rates between drug classes. Two-sided P values for homogeneity were obtained by performing Wald tests and values <.05 were considered indicative of treatment heterogeneity.
We inspected the robustness of the main findings through sensitivity analyses (see eMethods in Supplement 1), addressing potential informative censoring, time-lag bias,31 unmeasured confounding for high risk for recurrence, and DPP-4i effects on HHF (since saxagliptin and alogliptin showed an increased HHF rate in CVOTs,32,33 which resulted in an FDA warning,34 we conducted a sensitivity analysis for the HHF outcome redefining the comparator group as sitagliptin only).
All analyses were implemented using Aetion Evidence Platform (Aetion Inc) and Stata statistical software, version 15.1 (StataCorp LLC).
## Study Population and Baseline Characteristics
A total of 144 614 eligible adults (mean [SD] age, 62 [12.4] years; $54\%$ male participants) with T2D initiating treatment with a SGLT2i ($$n = 60$$ 523) or a DPP-4i ($$n = 84$$ 091) were identified; 44 099 had an HbA1c baseline value of less than $7.5\%$, 52 986 between $7.5\%$ and $9\%$, and 47 529 greater than $9\%$. Overall, patients newly prescribed SGLT2i vs DPP-4i were younger, more likely to have obesity, a higher eGFRCr, and to be treated with more than 1 glucose-lowering medication (particularly glucagon-like peptide-1 receptor agonists and insulin), and less likely to have a diagnosis of diabetic nephropathy or CKD (eTables 4-6 in Supplement 1).
After PS matching, 87 274 patients were retained: 24 052 with glycemia at target (HbA1c <$7.5\%$ mean, $6.8\%$), 32 290 with glycemia above target (HbA1c $7.5\%$-$9\%$ mean, $8.2\%$), and 30 932 with elevated glycemia (HbA1c >$9\%$ mean, $10.6\%$) (Figure 1); all baseline characteristics were well balanced (Table), including the laboratory test results not included in the PS model (eTables 4-6 in Supplement 1), and the PS distributions overlapped completely (eFigure 2 in Supplement 1).
**Figure 1.:** *Study FlowchartEligible population included in the study cohort initiating SGLT2 or DPP-4 inhibitors between April 2013 and June 2021 before and after matching, overall and stratified by HbA1c baseline level. DPP-4 indicates dipeptidyl peptidase-4; HbA1c, hemoglobin A1c; PS, propensity score; SGLT2, sodium-glucose cotransporter 2.* TABLE_PLACEHOLDER:Table.
In the overall population, $6.7\%$ had moderate to advanced CKD, $8.7\%$ had a history of mycotic infection, $0.7\%$ had a history of fractures, $2.1\%$ had a history of falls, and $14.0\%$ were prescribed insulin on the day of cohort entry. Patients with HbA1c levels of more than $9\%$ treated with SGLT2i were younger than those with HbA1c levels between $7.5\%$ and $9\%$ and those with HbA1c levels less than $7.5\%$ (57.7 vs 62.0 vs 62.5 years, respectively), mostly male participants ($57.2\%$ vs $54.5\%$ vs $50.2\%$), less frequently White ($46.2\%$ vs $51.8\%$ vs $53.7\%$), and more likely to receive insulin ($20.1\%$ vs $14.0\%$ vs $7.8\%$). They had higher eGFRCr (83.2 vs 77.4 vs 74.9) and lower burden of comorbidities and frailty. Compared with SGLT2i treatment initiators with HbA1c levels of less than $7.5\%$ and greater than $9\%$, those with HbA1c levels between $7.5\%$ and $9\%$ had higher prevalence of diabetes-related complications such as diabetic nephropathy ($15.3\%$ vs $15.1\%$ vs $13.2\%$) and retinopathy ($7.8\%$ vs $5.6\%$ vs $7.3\%$) and lower prevalence of untreated diabetes at baseline (Table).
Duration of follow-up on treatment varied slightly based on the outcome. In the overall population, the mean follow-up was 240 days for modified MACE and 241 days for HHF. Most patients were censored due to treatment discontinuation (approximately $60\%$). Details on follow-up and censoring reasons are reported in eTable 7 in Supplement 1.
## Primary Effectiveness Outcomes Analyses
After PS matching, the IRs per 1000 person-years for modified MACE were overall 17.13 vs 20.18 in SGLT2i vs DPP-4i initiators, respectively, showing among new users of SGLT2i vs DPP-4i a $15\%$ decreased risk (HR, 0.85; $95\%$ CI, 0.75-0.95), or 3 fewer events in 1000 person-years (RD –3.02; $95\%$ CI, –5.23 to −0.80). The results across subgroups were consistent with the overall findings with no evidence of effect heterogeneity (HbA1c <$7.5\%$ HR, 0.84; $95\%$ CI, 0.66-1.07; HbA1c $7.5\%$-$9\%$ HR, 0.88; $95\%$ CI, 0.72-1.07; and HbA1c >$9\%$ HR, 0.83; $95\%$ CI, 0.68-1.00; P for homogeneity =.91), although the degree of uncertainty was higher and the point estimates less precise due to the reduced statistical power (Figure 2).
**Figure 2.:** *Primary and Secondary Effectiveness Outcomes in 1:1 Propensity Score–Matched Patients Initiating SGLT2 Inhibitor vs DPP-4 Inhibitor Therapy Stratified by HbA1c LevelsNumber of events and incidence rates (IR) by treatment group and the point estimates of the effect sizes are shown overall and for HbA1c subcohorts. Hazard ratios are indicated by squares; 95% CIs, by horizontal lines. DPP-4 indicates dipeptidyl peptidase 4; HbA1c, hemoglobin A1c; HR, hazard ratio; MACE, major adverse cardiovascular events; PY, person-years; RD, rate difference; SGLT2, sodium-glucose cotransporter 2.aModified MACE is composite outcome including myocardial infarction and stroke events and all-cause deaths. A detailed definition is reported in Supplement 1.*
Overall, 3.68 vs 8.08 HHF events per 1000 person-years were estimated in SGLT2i vs DPP-4i treatment initiators, respectively. The initiation of SGLT2i vs DPP-4i was associated with a $54\%$ decreased risk of HHF (HR, 0.46; $95\%$ CI, 0.35-0.57), corresponding to approximately 4 fewer cases per 1000 person-years (RD −4.37; $95\%$ CI, −5.62 to −3.12). This was consistent across subgroups with no evidence of effect heterogeneity (HbA1c <$7.5\%$ HR, 0.48; $95\%$ CI, 0.33-0.72; HbA1c $7.5\%$-$9\%$ HR, 0.44; $95\%$ CI, 0.30-0.64; HbA1c >$9\%$ HR, 0.47; $95\%$ CI, 0.31-0.71; $$P \leq .95$$ for homogeneity) (Figure 2).
Kaplan-Meier curves comparing the cumulative incidence of modified MACE and HHF between initiators of SGLT2 vs DPP-4i therapy were consistent with these results and across subgroups (Figure 3 and eFigure 3 in Supplement 1). Clinical benefits were observed within the first 3 months of follow-up (Figure 3).
**Figure 3.:** *Cumulative Incidence of Modified MACE and HHF Comparing 1:1 Propensity Score–Matched Patients Initiating SGLT2 Inhibitor vs DPP-4 Inhibitor TherapyDPP-4 indicates dipeptidyl peptidase 4; HHF, hospitalization for heart failure; MACE, major adverse cardiovascular events; SGLT2, sodium-glucose cotransporter 2.*
## Secondary Effectiveness Outcomes Analyses
No overall differences between SGLT2i and DPP-4i treatments in the risk of myocardial infarction (HR, 0.92; $95\%$ CI, 0.74-1.09) or stroke (HR, 0.86; $95\%$ CI, 0.65-1.08) were found. In the overall population, the initiation of treatment with SGLT2i vs DPP-4i was associated with a $26\%$ reduced risk of all-cause mortality (HR, 0.74; $95\%$ CI, 0.59-0.88), corresponding to 2 fewer deaths per 1000 person-years. No evidence of effect heterogeneity on either the relative or the absolute scale was found between subgroups for any of the secondary outcomes (Figure 2).
## Safety Outcomes Analyses
The risks of hypovolemia, nonvertebral fractures, falls and lower-limb amputations were similar among patients initiating treatment with SGLT2i vs DPP-4i (Figure 4). In the overall population, SGLT2i vs DPP-4i initiators had a 2.17-fold increased risk of genital infections (HR, 2.17; $95\%$ CI, 1.98-2.36), corresponding to approximately 38 additional events per 1000 person-years, with evidence of treatment effect heterogeneity across subgroups on both the relative and absolute scales ($P \leq .01$ for homogeneity). Patients with HbA1c levels of $7.5\%$ to $9\%$ had a 3.1-fold increased risk for yeast infections (IR per 1000 person-years 68.5 vs 22.8, respectively; HR, 3.10; $95\%$ CI, 2.68-3.58) vs an approximately 2-fold increased risk in patients with HbA1c levels of less than $7.5\%$ (HR, 2.41; $95\%$ CI, 2.04-2.85) and greater than $9\%$ (HR, 1.82; $95\%$ CI, 1.60-2.08), corresponding to RD of 46.22 ($95\%$ CI, 40.54-51.90) for HbA1c $7.5\%$-$9\%$, vs 33.96 ($95\%$ CI, 27.69-40.23) for HbA1c <$7.5\%$, and 29.66 ($95\%$ CI, 23.00-36.32) for HbA1c >$9\%$ additional cases per 1000 person-years. Overall, a 1.7-fold increased risk of DKA was found in SGLT2i treatment initiators (HR, 1.73; $95\%$ CI, 1.06-2.43). Although the estimates are less precise and the uncertainty is higher due to the low number of DKA events within each HbA1c subgroup, the stratified results appear consistent with the overall finding (Figure 4). In the overall cohort, a $27\%$ decreased risk of AKI was associated with the initiation of SGLT2i vs DPP-4i (HR, 0.73; $95\%$ CI, 0.66-0.81), corresponding to approximately 8 fewer cases per 1000 patient-years. Similar results were obtained in subgroup analyses by HbA1c with no evidence of treatment effect heterogeneity by HbA1c (Figure 4).
**Figure 4.:** *Safety Outcomes in 1:1 Propensity Score–Matched Patients Initiating SGLT2 Inhibitor vs DPP-4 Inhibitor Therapy Stratified by HbA1c LevelsNumber of events and incidence rates (IR) by treatment group and point estimates of the effect sizes are shown overall and for HbA1c subcohorts. Hazard ratios are indicated by squares; 95% CIs, by horizontal lines. DPP-4 indicates dipeptidyl peptidase 4; HbA1c, hemoglobin A1c; HR, hazard ratio; PY, person-years; RD, rate difference; SGLT2, sodium-glucose cotransporter 2.*
## Sensitivity Analyses
Findings remained consistent when an intention-to-treat approach was adopted and when the internal validity of the effectiveness and safety outcome analyses was tested (eTables 8-9 in Supplement 1) with some fluctuations in point estimates driven by the small number of events in the subgroup analyses by HbA1c. No difference in treatment effect was found in patients with vs without cardiovascular diseases.
## Discussion
In this large comparative effectiveness and safety research study of 87 274 adults with T2D, including 24 052 with controlled HbA1c levels, 32 290 with above-target HbA1c levels, and 30 932 with elevated baseline HbA1c levels, we found that [1] initiating treatment with SGLT2i was associated with a reduced risk of modified MACE, HHF, and AKI and a higher risk of genital infections and DKA compared with DPP-4i; and that [2] the results did not vary based on preexposure HbA1c levels for most outcomes evaluated. Although individual characteristics differed among subgroups (for example patients with elevated HbA1c were younger, more likely to receive insulin, had a higher eGFRCr and fewer comorbidities than others), benefits and adverse effects of SGLT2i vs DPP-4i were similar across HbA1c subcohorts.
Overall, patients receiving a SGLT2i had a $15\%$ lower risk of the composite of myocardial infarction, stroke, or death from all causes (approximately 3 fewer cases per 1000 person-years) and a $54\%$ lower risk of HHF (approximately 4 fewer cases per 1000 person-years) than those receiving a DPP-4i. These findings with respect to the modified MACE outcome parallel those of the placebo-controlled CANVAS trial for canagliflozin,4 and the placebo-controlled EMPA-REG OUTCOME trial for empagliflozin,3 and of large cohort studies comparing SGLT2i vs DPP-4i.7,8,35,36,37 Similarly, our HHF results are in line with those from CVOTs,3,4,5,6 and large comparative effectiveness studies.37,38 The effect estimates were consistent across HbA1c subgroups for both modified MACE (HR range, 0.83-0.88), in line with exploratory analyses from a network meta-analysis39 and HHF (HR range, 0.46-0.48).
The safety analyses showed that overall patients initiating a SGLT2i had a higher risk of genital infections and DKA, both of which are known adverse effects of these medications,21,22,40 and a lower risk of AKI, a previously observed benefit,22,41,42 than those receiving a DPP-4i. Rates of hypovolemia, falls, bone fracture events, and lower-limb amputations were similar in the SGLT2i and DPP-4i groups. The analysis of the safety profile of SGLT2i across patients with different HbA1c levels, which has not been investigated in CVOTs, is a main strength of our study. Medications in the SGLT2i class are responsible for pharmacologically induced renal glycosuria by suppressing sodium and glucose reabsorption in the proximal tubule. Given that the urinary glucose concentration and consequent osmotic diuresis is higher in SGLT2i users with uncontrolled glycemia than in those with better controlled glycemia, a common hypothesis is that the risk of hypovolemia (and consequent falls and fractures) from polyuria, genitourinary infections from glucosuria, amputations from a reduced limb perfusion due to hypotension and an increased risk of peripheral ischemia due to hemoconcentration and hyperviscosity, and DKA from decreased plasma glucose and insulin release might have been further increased in patients with elevated HbA1c compared with patients with lower HbA1c.43,44,45 The findings of this study do not support this hypothesis. Results were largely consistent across all HbA1c subcohorts, except for some evidence of treatment effect heterogeneity for genital infections, with the highest risk observed in patients with baseline HbA1c levels between $7.5\%$ and $9\%$. This subgroup had the highest prevalence of both diabetes-related complications and prescriptions of glucose-lowering medications, suggesting a more advanced stage of diabetes compared with other subgroups. This may explain the increased risk of yeast infections observed in these patients.
This study augments the evidence provided by CVOTs of SGLT2i, showing that patients with severe uncontrolled diabetes can benefit from the use of these medications in a fashion similar to patients with better controlled glycemia, with no further increase in the risk of adverse effects. The population of patients identified in this study is 5 to 9 times larger than the populations included in the CVOTs. Because of the larger sample, we were able to identify 3 subgroups of patients with different ranges of HbA1c, and thus explore with more granularity and reduced level of uncertainty the influence of increasing glycemic levels on the safety and effectiveness of SGLT2i treatment. Another strength of this study is better generalizability of the findings to routine care. Several studies reported that a considerable number of patients with T2D cared for in clinical practice do not have characteristics similar to the patient populations included in CVOTs.46,47,48 A recent review showed that if the enrollment criteria of CANVAS,4 EMPA-REG OUTCOME,3 and VERTIS-CV6 were applied to the real-world population, only $17\%$ to $36\%$ of patients with T2D would have been eligible, with only $49.5\%$ of real-world patients with T2D eligible for a CVOT with broader inclusion criteria such as the DECLARE-TIMI-58 trial.48 Further, while CVOTs restrict to patients with cardiorenal diseases or multiple risk factors to achieve adequate statistical power in the time frame of the trials, we examined the comparative effectiveness of these drugs across the broader spectrum of cardiovascular risk. Lastly, adopting an active-comparator new-user design largely reduced the risk of biased findings, increasing the study validity.49,50
## Limitations
This study has limitations. First, residual confounding for unmeasured characteristics, such as duration of diabetes or body mass index, cannot be entirely ruled out. However, we observed that covariates not included in the PS model (laboratory test results available only for a subset of the analytic cohort) were balanced after adjustment. Additionally, compared with other large cohort studies that compared SGLT2 vs DPP-4i,7,8,22,23,24,38,40,41 we addressed potential confounding by diabetes severity and kidney function by controlling for eGFRCr. Second, the stratification by HbA1c levels reduced precision of some outcome estimates within subgroups, such as for modified MACE and DKA. However, the direction and magnitude of the effect for these outcomes were consistent with the overall findings, supporting the lack of effect modification by HbA1c. Third, as this study is based on routine care use of SGLT2i or DPP-4i, the mean follow-up (ie, time on treatment) was shorter compared with CVOTs, which introduce substantial measures to improve treatment adherence. Contrary to randomized clinical trials that require long follow-up to accumulate sufficient events for powered analyses, the size of this study population allowed us to generate overall results with high precision despite a shorter length of follow-up. Additionally, several trials showed that SGLT2i rapidly reduced the risk of cardiovascular death or HHF in patients with T2D, with benefits that are sustained over time.51,52,53,54 Thus, assuming no time-varying hazards, these results should be generalizable to longer-term findings. Fourth, potential for outcome misclassification cannot be entirely excluded; however, the validated outcome definitions used in this study have high positive predictive value and specificity and are not expected to differ by treatment group. Last, we could not evaluate cardiovascular death due to the lack of information on cause of death in the data. Death for all causes may be limited by incomplete death records in this data set, though we would not expect this to differ by treatment groups.
## Conclusions
In this large comparative effectiveness and safety study of 87 274 adults with T2D, patients who initiated SGLT2i therapy had a reduced risk of MACE, HHF, and AKI, and an increased risk of genital infections and DKA, compared with DPP-4i. The cardiovascular effectiveness and safety of SGLT2i vs DPP-4i did not vary based on baseline HbA1c levels. This study complements the evidence provided by CVOTs by showing that patients with T2D can benefit from the use of SGLT2i regardless of glycemic control, with no additional increase in the risk of adverse effects in patients with above-target or elevated HbA1c levels, compared with DPP-4i initiators with similar glycemic control.
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|
---
title: 'Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning
Analyses of UK Biobank'
journal: JMIR Public Health and Surveillance
year: 2023
pmcid: PMC9989910
doi: 10.2196/43419
license: CC BY 4.0
---
# Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank
## Abstract
### Background
Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice.
### Objective
We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide.
### Methods
Based on the prospective cohort of UK Biobank, we included 223 ($0.06\%$) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 ($1.18\%$) controls (1:20) without such records. We similarly identified 833 ($0.22\%$) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 ($4.42\%$) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality.
### Results
Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for $91.7\%$ ($$n = 11$$) and $80.7\%$ ($$n = 25$$) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively.
### Conclusions
We established applicable machine learning–based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality.
## Introduction
According to the estimation of the Global Burden of Disease study, approximately 800,000 people die by suicide every year [1], which translates to the astonishing number of 1 person dying by suicide every 40 seconds. In the United Kingdom, there were 5691 deaths by suicide registered in England and Wales in 2019, which corresponded to an age-standardized rate of 11 deaths per 100,000 people [2]. Importantly, behind the number of suicidal deaths, there is a much higher incidence of suicide attempts requiring further research. From 2000 to 2010, a prospective study using data from 5 emergency departments in the United Kingdom identified 38,415 individuals who presented at an emergency department following a suicide attempt [3], among which only 261 ($0.7\%$) died. This finding implies that the population targeted for suicide prevention, such as timely psychological support, is considerably larger. However, only $28\%$ of people who attempt suicide in the United Kingdom have previously received psychiatric services [4]. Therefore, it is urgent to improve the identification of individuals at high risk for suicidality to improve suicide prevention.
The previous research suggests that the mechanisms of suicidality are complex and multifactorial [5], likely involving interactions between genetic, psychological (including traumatic experiences), and socioeconomic or other environmental factors [6,7]. This report might explain the suboptimal accuracy of suicidality prediction based on traditional statistical models, for example, with the area under the receiver operating characteristic (AUROC) curve reported to be 0.58 in a meta-analysis of 367 studies, which was only slightly better than a prediction of chance [8].
Alternatively, as tools that can deal with multidimensional data, artificial intelligence techniques (including machine learning) that have been widely used to uncover predictions of multiple diseases [9-11] might have the potential to improve the prediction of suicidality. Indeed, based on data from electronic medical records and mental health questionnaires, as well as sociodemographic factors, researchers have constructed machine learning models that obtained good performance (AUROC=0.590-0.930) for suicidality prediction in the high-risk population [12]. Likewise, more recent efforts to predict suicide attempts or deaths in the general population using this approach have yielded promising results, showing AUROC curves of 0.80 and 0.88 among men and women, respectively, in a Danish population and an AUROC curve of 0.857 among participants in the National Alcohol Epidemiological Survey in the United States [13,14]. However, prior studies did not consider several important factors, such as genetic background [7] and lifestyle factors (eg, diet, physical activity, and sleep) [15,16]. In addition, all these existing models require many input variables (2554 and 2978 inputted features for the Danish and US study, respectively), which have limited implications for daily practice.
Taking advantage of enriched information about suicidality and environmental factors, as well as the available individual-level genotyping data in UK Biobank, we aimed to construct applicable models using a machine learning approach (ie, with limited features) to predict suicidal behavior over both the short and long term. To test the robustness of our models, we validate them among individuals with different genetic risks of suicide.
## Data Source
A prospective UK Biobank cohort recruited 502,507 participants aged 40 to 69 years across the United Kingdom between 2006 and 2010 [17], which coincides with a high-risk age group of suicide among men and women [18]. At recruitment, all participants filled out questionnaires covering information on sociodemographic, lifestyle, and health–related factors, with a physical examination and collection of biological samples performed during the initial assessment. After recruitment, a proportion was invited several years later to repeat the assessment. In that study, 20,334 participants received a first repeated assessment in 2012 and 2013 and 51,131 received a second repeated assessment visit in 2014.
To track health-related outcomes, UK *Biobank data* have been linked periodically to multiple national registries with the participants’ consent [17]. The inpatient hospital data were obtained through linked hospital records in England, Scotland, and Wales, which were mapped from the Hospital Episode Statistics in England, the Scottish Morbidity Record, and the Patient Episode Database in Wales [19]. Primary care data were obtained from multiple data suppliers, including the Phoenix Partnership and Egton Medical Information Systems, which cover approximately $45\%$ of UK Biobank participants [20]. The mortality data were obtained from national death registers, such as the National Health Services (NHS) Digital Registry and the NHS Central Registry [21].
In this study, among the 502,507 UK Biobank participants, we excluded 48 individuals who had withdrawn from the UK Biobank. To ensure the measurement of genetic susceptibility for suicidality, 376,878 individuals with White ancestry and eligible genotyping data were included in the analysis (Figure 1A). Specifically, the polygenic risk score (PRS) was used as an index of genetic susceptibility, which was generated based on the genome-wide association study (GWAS) summary statistics (ie, effect sizes and standard errors for the variants) from an independent sample of 50,264 Danish residents involving 6,024 cases with an incidence of suicide attempt and 44,240 controls [22]. In addition to removing individuals with nonhomogenous European ancestry, this GWAS study applied principal components of genetic ancestry to take into account the effect of population stratification. We computed the PRS using LDPred2, a method of PRS calculation based on a matrix of correlations between genetic variants, which is faster, more accurate, and more robust than the LDPred14 [23]. In a validation step, the calculated PRS showed a high consistency with the studied phenotype (ie, suicidal behaviors) in our study population, yielding a mean area under the curve of 0.550 and an odds ratio of 2.34 ($95\%$ CI 1.66-3.29) by a unit increase in the PRS. During the analysis, we defined the genetic risk levels of suicidality as low (<1st tertile of the PRS), moderate (1st-2nd tertile), and high (>2nd tertile).
**Figure 1:** *Flowchart of the study. AUROC: area under the receiver operator curve; CV: corss validation; LightGBM: light gradient-boosting machine; NPV: negative predictive value; PCA: principal component analysis; PPV: positive predictive value.*
## Ethics Approval
UK Biobank has full ethical approval from the NHS National Research Ethics Service (16/NW/0274), and informed consent was obtained before data collection from each participant. This study was also approved by the biomedical research ethics committee of West China Hospital [2019-1171].
## Ascertainment of Suicidal Behaviors
To expand the application of our models to suicide prevention, both suicide attempts and deaths identified during the study period were considered suicidal behaviors of interest, which is consistent with previous studies [24,25]. Specifically, death by suicide was defined as death with suicide as the underlying cause of death and documented by its correspondence to the International Classification of Diseases 9th revision (ICD-9) and 10th revision (ICD-10) codes (ie, ICD-10: X60-84 and Y10-34; ICD-9: E950-958) [24,25] in the death register. Suicide attempts were considered as hospital admissions with a diagnosis of intentional self-harm (ICD 10: X60-84 and ICD-9: E950-958) or self-harm of undetermined intent (ICD-10: Y10-34) [24,25]. With relatively stable age- and sex-standardized incidence rates, the absolute number of suicide attempts and deaths was high within the first year of enrollment and dropped gradually to half that number in 6th year (Figure S1 of Multimedia Appendix 1 [26-28]). Thus, the outcomes of interest were suicidal behaviors occurring within 1 year (ie, short term) and 1 to 6 years (ie, long term) after the recruitment. We considered individuals with suicide attempts before the recruitment as those having a history of suicide attempts.
## Data Set Construction
We constructed separate data sets for predicting suicidal behaviors within 1 year and 1 to 6 years. For the short-term risk prediction, we identified cases of suicide attempts or deaths at least 1 time within 1 year after recruitment ($$n = 223$$). Controls ($$n = 4460$$) were randomly selected (1:20 allocation ratio) from the remaining participants who were eligible, alive, and free of suicidal behaviors 1 year after the recruitment, resulting in a data set consisting of 4683 participants (Figure 1A). The same strategies were applied to constructing data sets for long-term (ie, 1 to 6 years) suicide risk prediction, yielding a full data set of 17,493 participants, with 833 ($4.8\%$) and 16,660 ($95.2\%$) cases and controls, respectively.
The 2 aforementioned data sets were then used as discovery data sets for model training and the assessment of internal validity. We additionally used a subsample comprising 50,310 participants of White ancestry from UK Biobank who participated in the repeat assessments. Among this subsample, there were 12 ($0.02\%$) and 31 ($0.06\%$) individuals who attempted or died by suicide within 1 year or during 1 to 6 years after their repeat measurements, respectively, as the validation data set for assessing external validity.
## Feature Processing and Filtering
Taking full advantage of the diversity of variables in UK Biobank, we generated a feature list involving multidimensional factors. Due to difficulties obtaining individual genetic data in the real world, we did not involve the PRS in the construction of the prediction models, but we subsequently validated the suicide prediction models with the subgroups of varying (ie, high and low) genetic susceptibility to suicidality to demonstrate their robustness. Information regarding sociodemographic, environmental, and psychosocial factors was derived from the data collected at recruitment using the touchscreen or face-to-face interview questionnaires. For categorical variables (eg, “*In* general, how would you rate your overall health?”), UK Biobank assigns negative values to categories denoting missingness (ie, −1 refers to “Prefer not to answer,” and −3 refers to “Do not know”). Therefore, we recorded those negative values as “NA.” Specifically, instead of directly using variables collected through a generic diet questionnaire, we identified dietary patterns based on the results of principal component analysis with varimax rotation (Figure S2 of Multimedia Appendix 1). They were referred to as the prudent, western, and open-sandwich patterns [26], yielding variables with top factor loadings in each component (Table S1 in Multimedia Appendix 1). Medical data included the physical examinations (eg, pulse rate, blood pressure, and grip strength of both hands) conducted during the initial medical center visit, and we calculated mean values when multiple records existed. Additionally, a history of psychiatric disorders was defined as any previous diagnosis of psychiatric disorders before baseline (ICD-10: any F), which was identified through self-reported, hospital inpatient, and primary care data. To consider the influence of somatic fitness, we generated time-varying (0 to 1 and 1 to 4 years before the recruitment) dichotomous variables for each subtype of severe somatic diseases [29]. For the analyses of the total study population, the level of genetic susceptibility to suicidality (low, moderate, or high) was also considered a candidate feature.
After excluding variables with over $15\%$ of missing or irrelevant data (eg, device ID, seated boxing height, and hair color), we included a total of 143 features. The coding book of the included features is shown in Table S2 of Multimedia Appendix 1.
## Model Training and Validation
We constructed prediction models using all eligible features. The balanced bagging algorithm is proven to have good performance for classification models with class-imbalanced data [14]. Moreover, the light gradient-boosting machine (LightGBM) [30], as a gradient-boosting algorithm, has been widely applied in machine learning research due to its fast computational speed, high accuracy, and ability to handle missing values [11]. Therefore, considering the data imbalance and the existence of missing values, we used the balanced bagging LightGBM approach to achieve high classification accuracy and fast computation speed, which bagged 1000 balanced LightGBM classifiers (ie, using “class_weight” =“balanced”) after random downsampling [31]. We tuned the parameters by using stratified 10-fold cross-validation and grid-search, with the best combination of hyperparameters shown in the Methods section of Multimedia Appendix 1. Each of the 1000 balanced LightGBM classifiers randomly selected subsamples from the group of the minority class (ie, those who had suicidal behavior) and matched samples with the same size from the group of the majority class (ie, those who had no suicidal behavior) to construct case-control samples (ie, the in-bag set). The randomly selected case-control samples were applied to train balanced LightGBM classifiers, and the remaining sample, referred to as the out-of-bag (OOB) set, was used to estimate the prediction of the suicide risk score of the OOB set.
We defined the OOB set as the internal validation set. Specifically, we aggregated the predicted suicide risk scores of the OOB set from the 1000 balanced LightGBM classifiers to estimate the internal validated predicted error [32], and we regarded the models with the highest OOB AUROCs as optimal. Then, we computed the predicted suicide risk scores of the externally validated data sets from the repeated assessments for the optimal model. Due to the lack of agreement regarding which of the risk thresholds of classification provides the most sufficient clinical utility, we computed the AUROC [13,24], sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at different suicide risk score thresholds.
## Model Explanation
Interpretations of the models were measured using the Shapley Additive Explanations (SHAP) approach, which quantifies the relationship of the input features with the outcome [33]. Specifically, we computed the contribution of all the features to the studied suicidal behaviors for each participant and assigned each feature an importance score (ie, a SHAP value) after considering its interactions with the remaining features. The absolute values of the average SHAP values were presented as a bar plot illustrating the relative importance of these input features for the models’ predictions at the population level.
## Applicable Prediction Models
To facilitate the application of the prediction models, we conducted feature reduction by illustrating the changes in the prediction accuracy of the models with different numbers of input features (ie, those with top 10, 20, 50, and 100 SHAP values) [34,35]. As shown in Figure S3 of Multimedia Appendix 1, the models for predicting suicidal behaviors within 1 year and from 1 to 6 years both achieved overall good performance when the input feature dimension with the highest SHAP value was increased to 20, so we considered the models with 20 input features as the applicable prediction models which might facilitate the future implication.
## Model Validation Among Individuals With Different Genetic Susceptibilities
To illustrate the robustness of the suicide prediction models, we validated both full and applicable models in the whole population as well as subgroups of varying (ie, high and low) genetic susceptibility to suicidality by computing the OOB performance of these models.
We performed the data set construction and calculation of the PRS using R software version 3.6.1 (Lucent Technologies Co). The machine learning model development was achieved using Python software version 3.6 (Software Foundation), imbalanced-learn 0.9.0, and lightgbm version 3.2.1. We conducted the model interpretation analysis using SHAP version 0.38.1. We then analyzed the models’ performance and plot creation using scikit-learn version 1.0.2 and matplotlib version 3.3.2, respectively.
## Study Population Characteristics
The data sets for the prediction of suicidal behavior prediction within 1 year and for 1 to 6 years showed largely comparable characteristics at baseline (Table 1). We obtained similar ages, with mean ages of 56.75 (SD 8.03) and 56.65 (SD 7.99) years, respectively, and female-to-male sex distributions of 1:1.13 and 1:1.20, respectively. However, the characteristics of the validation sample for external validity (ie, individuals involved in the repeat assessments) were different from the discovery sample (ie, individuals recruited in the initial assessment visit), characterized by older age, more likely to have a history of psychiatric disorders, and lived in their own accommodation at time of data collection (Table 1).
**Table 1**
| Characteristics | Characteristics.1 | Characteristics.2 | Discovery | Discovery.1 | Discovery.2 | Discovery.3 | External validation (n=50,310) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | | Within 1 year (n=4683) | Within 1 year (n=4683) | 1 to 6 years (n=17,493) | 1 to 6 years (n=17,493) | |
| Age (years), mean (SD) | Age (years), mean (SD) | Age (years), mean (SD) | 56.75 (8.03) | 56.75 (8.03) | 56.65 (7.99) | 56.65 (7.99) | 63.24 (7.49) |
| Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) |
| | Female | 2480 (53) | 2480 (53) | 9537 (54.5) | 9537 (54.5) | 25,675 (51) | 25,675 (51) |
| | Male | 2203 (47) | 2203 (47) | 7956 (45.5) | 7956 (45.5) | 24,635 (49) | 24,635 (49) |
| History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) | History of psychiatric disorders, n (%) |
| | No | 3760 (80.3) | 3760 (80.3) | 14,371 (82.2) | 14,371 (82.2) | 39,226 (78) | 39,226 (78) |
| | Yes | 923 (19.7) | 923 (19.7) | 3122 (17.8) | 3122 (17.8) | 11,084 (22) | 11,084 (22) |
| History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) | History of suicide attempt n (%) |
| | No | 4574 (97.7) | 4574 (97.7) | 17,270 (98.7) | 17,270 (98.7) | 50,108 (99.6) | 50,108 (99.6) |
| | Yes | 109 (2.3) | 109 (2.3) | 223 (1.3) | 223 (1.3) | 202 (0.4) | 202 (0.4) |
| Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression? n (%) |
| | No | 3999 (85.4) | 3999 (85.4) | 15,188 (86.8) | 15,188 (86.8) | 45,372 (90.2) | 45,372 (90.2) |
| | Yes | 660 (14.1) | 660 (14.1) | 2248 (12.9) | 2248 (12.9) | 4556 (9.1) | 4556 (9.1) |
| | Missing | 24 (0.5) | 24 (0.5) | 57 (0.3) | 57 (0.3) | 382 (0.8) | 382 (0.8) |
| Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) | Have you ever seen a general practitioner for nerves, anxiety, tension, or depression? n (%) |
| | No | 2882 (61.5) | 2882 (61.5) | 11,181 (63.9) | 11,181 (63.9) | 34,168 (67.9) | 34,168 (67.9) |
| | Yes | 1768 (37.8) | 1768 (37.8) | 6215 (35.5) | 6215 (35.5) | 15,685 (31.2) | 15,685 (31.2) |
| | Missing | 33 (0.7) | 33 (0.7) | 97 (0.6) | 97 (0.6) | 457 (0.9) | 457 (0.9) |
| In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) | In the past, how often have you smoked tobacco? n (%) |
| | Smoked on most or all days | 1212 (25.9) | 1212 (25.9) | 4237 (24.2) | 4237 (24.2) | 11,900 (23.7) | 11,900 (23.7) |
| | Smoked occasionally | 573 (12.2) | 573 (12.2) | 2231 (12.8) | 2231 (12.8) | 6061 (12) | 6061 (12) |
| | Just tried once or twice | 685 (14.6) | 685 (14.6) | 2634 (15.1) | 2634 (15.1) | 7983 (15.9) | 7983 (15.9) |
| | I have never smoked | 1806 (38.6) | 1806 (38.6) | 6897 (39.4) | 6897 (39.4) | 22,789 (45.3) | 22,789 (45.3) |
| | Missing | 407 (8.7) | 407 (8.7) | 1494 (8.5) | 1494 (8.5) | 1577 (3.1) | 1577 (3.1) |
| Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) | Do you live in your own accommodation? n (%) |
| | No | 2194 (46.9) | 2194 (46.9) | 8136 (46.5) | 8136 (46.5) | 11,670 (23.2) | 11,670 (23.2) |
| | Yes | 2414 (51.5) | 2414 (51.5) | 9155 (52.3) | 9155 (52.3) | 38,114 (75.8) | 38,114 (75.8) |
| | Missing | 75 (1.6) | 75 (1.6) | 202 (1.2) | 202 (1.2) | 526 (1) | 526 (1) |
| Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) | Average annual total household income before taxa, n (%) |
| | Less than £18,000 (US $16,676) | 945 (20.2) | 945 (20.2) | 3410 (19.5) | 3410 (19.5) | 6502 (12.9) | 6502 (12.9) |
| | £18,000 to £30,999 (US $16,676 to $28,718) | 1045 (22.3) | 1045 (22.3) | 3802 (21.7) | 3802 (21.7) | 13,250 (26.3) | 13,250 (26.3) |
| | £31,000 to £51,999 (US $28,719 to $48,173) | 1073 (22.9) | 1073 (22.9) | 3958 (22.6) | 3958 (22.6) | 13,577 (27) | 13,577 (27) |
| | £52,000 to £100,000 (US $48,174 to $92,642) | 816 (17.4) | 816 (17.4) | 3113 (17.8) | 3113 (17.8) | 9529 (18.9) | 9529 (18.9) |
| | Greater than £100,000 (US $92,642) | 194 (4.1) | 194 (4.1) | 797 (4.6) | 797 (4.6) | 2614 (5.2) | 2614 (5.2) |
| | Missing | 610 (13) | 610 (13) | 2413 (13.8) | 2413 (13.8) | 4838 (9.6) | 4838 (9.6) |
| Cases, n (%) | Cases, n (%) | Cases, n (%) | 223 (47.62) | 223 (47.62) | 833 (47.62) | 833 (47.62) | 12 (0.02)b and 31 (0.06)c |
## Prediction Models Involving All Features
The internal validated AUROC of the prediction models involving all features was 0.888 ($95\%$ CI 0.863-0.914) for the prediction of suicidal behaviors within 1 year and 0.852 ($95\%$ CI 0.838-0.867) for 1 to 6 years (Figure 2). Figure 2 shows values of sensitivity, specificity, and predictive indices over a series of risk thresholds. For instance, at the 0.70 risk threshold, the short- and long-term sensitivities were, respectively, $57.85\%$ and $54.74\%$, the specificities were $95.11\%$ and $94.05\%$, the PPVs were $37.18\%$ and $31.49\%$, and the NPVs were $97.83\%$ and $97.65\%$. Furthermore, the externally validated AUROC curves were 0.919 ($95\%$ CI 0.852-0.985) for the model predicting suicidal behaviors within 1 year and 0.892 ($95\%$ CI 0.844-0.940) for the model predicting suicidal behaviors between 1 and 6 years, indicating the robustness of the prediction models.
Regarding the importance of features measured using SHAP values, similar to age, family income, and body fat percentage, mental health–related factors (eg, history of psychiatric disorders, history of suicide attempt, etc) were top ranked in both models within 1 year (7 among the top 20 features) and during 1 to 6 years (8 among the top 20 features). However, notable differences were observed with respect to some lifestyle and social factors (eg, “How many years of using a mobile phone at least once per week to make or receive calls?” and “Age you first had sexual intercourse”), as these seemed to have greater importance for only the prediction models within 1 year but not 1 to 6 years (Figure 3). In contrast, some general health–related factors (ie, “*In* general how would you rate your overall health?” and “Compared with 1 year ago, has your weight changed?”) seemed only important for the 1-to-6 years prediction model. Detailed information on the included features is shown in Table S2 of Multimedia Appendix 1.
**Figure 2:** *The performance of prediction models using all input features and top 20 features. The area under the receiver operating characteristic (AUROC) curve. The tables showed the internal validation performance (ie, sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of suicide prediction models at different classified thresholds.* **Figure 3:** *The comparison of top 20 features identified in suicide risk prediction full models for within 1 year and 1-to-6 years.
The dark blue and yellow bar represent the relatively importance of these input features for the prediction, respectively. And the numbers next to the bars are corresponding to the ranking of top 20 features. The detailed information of the included features is shown in Table S2 in Multimedia Appendix 1.*
## Prediction Models Involving the Top 20 Features
Figure S3 of Multimedia Appendix 1 displays the indices of model performance for the models involving different numbers of the top features (ie, top 20, 40, 60, and 100). Accordingly, the 2 models with the top 20 input features were considered optimal (Figure 2). The AUROC curves for their internal and external validations for the within 1-year suicide prediction were 0.897 ($95\%$ CI 0.874-0.920) and 0.901 ($95\%$ CI 0.821-0.981), respectively. For the 1-to-6 years prediction, the corresponding estimate was 0.854 ($95\%$ CI 0.840-0.868) and 0.885 ($95\%$ CI 0.834-0.936), respectively. Based on the applicable models, we found individuals in the top quintile of predicted risk accounting for $91.7\%$ ($$n = 11$$) and $80.7\%$ ($$n = 25$$) of all cases of suicide attempts or deaths within 1 year and during 1 to 6 years, respectively.
## Models for Individuals With Different Genetic Susceptibilities
Using both full and simplified prediction models, we obtained a comparable prediction accuracy for individuals with low and high genetic susceptibilities to suicidality (Figures S4 and S5 of Multimedia Appendix 1). For instance, for short-term risk prediction, the AUROC curves for models with the top 20 involved features were 0.907 and 0.885 for the high and low genetic susceptibility groups, respectively. The corresponding numbers for the long-term risk prediction were 0.869 and 0.822, respectively.
## Principal Findings
In this study on a community-based UK Biobank cohort of over 0.5 million UK residents aged 40 to 69 years (covering the age group with a high risk of suicide [18]), we established machine learning–based models to accurately predict both short- and long-term risks of suicide attempts and deaths (AUROC=0.892-0.919). Importantly, our applicable models achieved high predictive accuracy across populations with varying genetic susceptibility to suicide with a limited number (ie, 20) of phenotypic features that could be accessed easily through practice. Specifically, we found that individuals with the top $20\%$ of predicted risks comprised over $80\%$ of real cases of suicide attempts or deaths, suggesting that our approach may be a cost-effective way to identify high-risk middle-aged individuals who should be targeted for suicide prevention. In addition, besides some well-known suicide risk factors (ie, mental health–related conditions), these established models provide novel insights into factors driving suicidal behaviors, revealing that some lifestyle and social factors (eg, cell phone use frequency, etc) may be risk factors for suicidal behaviors in the short-term, while self-reported general health ratings are more important for the prediction of long-term suicidal risk.
In line with 2 previous studies focusing on machine learning–based suicide risk prediction in the general population using data from Danish health registers [13] and the National Alcohol Epidemiological Survey of the United States [14], our results identified mental health–related factors (ie, prior suicide attempt, history of psychiatric disorders, and past emotion) and sociodemographic factors (ie, age and family income) as top features for suicide risk prediction. However, benefiting from the enriched data in UK Biobank, particularly items related to neuroticism, lifestyle, social contacts, and self-rated general health, our prediction models achieved improved performance. In addition, the comparison of features that matter for short- versus longer-term suicide risk was not addressed in prior investigations. Similar efforts have been made in some specific populations (eg, patients receiving psychiatric [24] or other medical care [36] and soldiers [37]), though with only comparable predictive accuracy (ie, the AUROC curves ranged between 0.77 and 0.93) with more homogeneous clinical populations.
Consistent with our findings, neuroticism was reported as a risk factor for suicidal behaviors in a previous study, with plausible mechanisms of shared genetic components [38]. Likewise, severe somatic diseases, disabilities, or physical weakness have consistently been reported to be associated with higher suicide risk, which is possibly due to the chronic stress associated with these diagnoses and living with these diseases [7]. Previous efforts exploring the association between BMI and suicidality have led to inconsistent results [39], and the association between body fat and suicidality has remained largely unexplored. Nevertheless, our findings of the association between body fat percentage and suicidality gain support from a Mendelian randomization analysis, which revealed a causal link between a high percentage of body fat and depression [40].
Our attempts to construct separate models for the prediction of both short- and long-term suicide risks indicated that the models generally achieved better prediction accuracy for the more immediate period before the suicide attempt or death, which is in line with the findings of prior studies concerning time-varying suicide risk assessments [24,41]. While factors directly reflecting mental health impairment show consistent importance for both short- and long-term suicidal risk prediction, the significance of lifestyle and social factors (eg, the frequency of using a cell phone to make or receive calls) was mainly observed for short-term risk (ie, within 1 year), indicating the role of lower social support and social relations among individuals with suicide risk [7]. Additionally, our findings on the association between self-reported health ratings and long-term suicide risk are in line with the results of the Danish study, which also found that medical diagnoses and medications related to some somatic illnesses (eg, infection and respiratory diseases) measured 48 months before suicide were more important indicators of suicide risk than those measured 6 months earlier [13].
## Strengths and Weaknesses
The major merits of our study include the use of multidimensional data (including individual-level genotyping data) from a large community-based cohort of UK Biobank. The application of the machine learning approach, together with the use of SHAP values for feature interpretation, enabled us to identify the most informative variables that maximized the efficiency of the data for an accurate prediction of suicide risk. The imbalance in the sample sizes of the cases and the controls was mitigated by randomly downsampling and setting class weights for imbalanced classes in LightGBM during the training step [30,42]. Further, we improved the feasibility of our prediction models by using the feature reduction process, where accurate classification was achieved with only 20 features. Although no similar data from independent samples could be used for external validation, the validity of our models was demonstrated in a subgroup of UK Biobank participants who repeated surveys many years after the baseline measurement (showing different basic characteristics compared to the discovery data set), as well as the subpopulations stratified by their level of genetic susceptibility to suicidality.
A notable limitation of this study is the absence of data from emergency care departments, which were the main source for suicide case identification in previous studies [13,43]. Therefore, our study focused on suicidal behaviors resulting in hospitalization or death, and those with less severe consequences require further investigation. In addition, it is difficult to distinguish suicide attempts from nonsuicidal intentional self-harm based on ICD codes, as clinical diagnoses tended to be consequence oriented (ie, leading to life-threatening harm or not) or dependent on self-reported reasoning on intent. Moreover, such outcome ascertainment strategies have been demonstrated to suffer from poor sensitivity, resulting in a risk of underestimation of suicidal cases, as well as attenuated associations between studied exposures and suicidal outcomes [44]. Nevertheless, as this is the most feasible method to identify suicidal behavior, similar definitions and ascertainment of suicidal behaviors have been widely used in other large community- or population-based studies with a similar focus [24,25]. Furthermore, we only used the LightGBM as the base estimator for bagging, mainly due to its capability to handle missing values and achieve high discrimination accuracy [30]. It is possible that other machine learning approaches (eg, deep neural network), with some common methods of feature engineering (eg, standardization, one-hot encoding), might obtain better performance at the price of model interpretability. Finally, the UK Biobank study recruited only $5.5\%$ of the invited individuals in the age range of 40 to 69 years, leading to a selection bias of the study population compared to the entirety of the population in the United Kingdom [45]. Consequently, the generalization of our findings to the total UK population and other populations cannot be made.
## Conclusions
In conclusion, based on a UK Biobank cohort, we established clinically applicable machine learning–based models for accurately predicting both short- and long-term risks of suicidal behaviors. The good performance of the models for subgroups with different genetic susceptibilities to suicidality highlights the possibility of applying these models to high-risk individual identification in the general middle-aged population, which may facilitate the development of cost-effective suicide prevention.
## Data Availability
Data from the UK Biobank are available to all researchers upon submitted application. All codes associated with the current submission are available and can be requested by contacting the corresponding authors.
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|
---
title: Beta cell regeneration upon magainin and growth hormone treatment as a possible
alternative to insulin therapy
authors:
- Azam Moosavi
- Razieh Yazdanparast
journal: FEBS Open Bio
year: 2023
pmcid: PMC9989927
doi: 10.1002/2211-5463.13556
license: CC BY 4.0
---
# Beta cell regeneration upon magainin and growth hormone treatment as a possible alternative to insulin therapy
## Body
Diabetes treatment is currently a universal challenge. In type 1 diabetes mellitus, the progressive loss of functional β cell mass occurs. Therefore, one main goal in the treatment of affected individuals is based on compensation of the endogenous insulin pool size via expanding the functional β cell population [1].
Toward this goal, the maturation of various cell sources capable of differentiation, dedifferentiation and trans‐differentiation has been suggested. Many transcription factors play roles in these events such as pancreatic and duodenal homeobox 1 [2], neurogenin 3 [3], aristaless related homeobox (ARX) and paired box 4 (PAX4) [4]. Among them, ARX and PAX4 have received more attention because of their roles in the final differentiation step of β cells. Studies have also shown that ARX inactivation in pancreatic glucagon+ cells can transform them into β‐like cells [5].
Among the antidiabetic agents under investigation, antimicrobial peptides have received extensive attention [6, 7]. Magainin‐AM2 (Mag II), as an orthologue of magainin‐2 from amphibians, is a cationic peptide with the lowest hemolytic activity, assumed to work via cell membrane depolarization, increasing intracellular calcium [6, 8] and enhancing the release of Glucagon‐like peptide 1 (GLP‐1) from GLUTag cells, followed by insulin‐release from the treated cells [9, 10]. Furthermore, Yazdanparast et al. [ 11] have reported the significant roles of Mag II on enhancing the mouse hypothalamic GABA content. Recently, Collombat et al. [ 12] have also shown that sustain use of GABA induces α to ß‐like functional cells. Regarding such information, we were interested in determining whether the endogenously‐induced GABA, via Mag modulation of the relevant main signaling elements, could bring about β cell regeneration. Our goal was supported by considering the fact that the majority of Mag investigations have been performed on type 2 diabetes mellitus with no reports on their roles in β cell regeneration. On the other hand, regarding the overlap among the signaling pathways of Mags and growth hormone (GH), as shown in Fig. 1 and Fig. S1, it would be interesting and beneficial to evaluate their combined influence on the regeneration of pancreatic β cells.
**Fig. 1:** *Key signaling elements under the influence of GH and/or Mag based on the literature [6, 21, 22].*
Our data clearly indicated that the simultaneous use of Mag then GH enhanced more effectively the number of mice pancreatic β cells compared to those of mice treated only with Mag or GH.
## Abstract
Insulin therapy, pancreas transplantation and β cell regeneration are among the suggested treatment strategies for type 1 diabetes. It has been shown that some antimicrobial peptides have the potential to increase insulin release and to improve glucose tolerance, although the mechanism by which they promote the regeneration of damaged pancreatic cells to functional β‐like cells remains unknown. To answer this question, we evaluated the in vivo effects of magainin‐AM2 and growth hormone (GH) on the regeneration of streptozotocin (STZ)‐damaged mouse pancreas. Treatment with magainin‐AM2 and GH ameliorated the effects of STZ on fasting blood glucose and glucose tolerance test values, and also resulted in a significant increase in total cell counts (α and β) and the number of insulin+ and glucagon+ cells per islet and a decrease in the number of T and B cells. In addition, we observed a 1.43‐ and 2.21‐fold increase in expression of paired box 4, one of the main factors for α to β‐like cell conversion, in normal‐ and diabetes‐treated mice, respectively. Similarly, expression of P‐S6 and extracellular signal‐regulated kinases 1 and 2, required for cell proliferation/differentiation, increased by 3.27‐ and 2.19‐fold among the diabetes‐treated and control diabetic mice, respectively. Furthermore, in all experiments, amelioration of the effects of STZ were greatest upon Mag treatment followed by GH administration. The present in vivo data provide evidence in support of the possibility of pharmaceutical induction of α cell production and their trans‐differentiation to functional β‐like cells.
In the present study, normal and diabetic mice were treated with magainin, growth hormone or both. Theresults obtained, along with the observed improvement of fasting blood glucose and glucose tolerance test vallues, demonstrated compensation of β cells via induction of α/β cell production and their trans‐differentiation to functional β‐like cells. The best improvement was observed in the group treated with both magainin (Mag) and growth hormone (GH).
## Study design
Type 1 diabetes mellitus was induced via i.p. injection of Balb/C male mice (weighing approximately 27–32 g) with multiple low doses of streptozotocin (STZ) (40 mg·kg−1) and the control group received an equal volume of the vehicle [13]. All mice were kept under standard conditions (at a controlled temperature of 21 ± 2 °C, under a 12: 12 h light/dark photocycle, with ad libitum access to food and water) in the animal house, five per cage. The normal (N) ($$n = 20$$) and diabetic (D) ($$n = 20$$) groups were each divided into four subgroups ($$n = 5$$) for i.p. injections. The N1 and D1 received 6.7 mg·kg−1·day−1 GH for 14 days. The N2 and D2 received 0.185 mg·kg−1 Mag II daily for 28 days. The N3 and D3 first received 0.185 mg·kg−1 Mag II for 28 days followed by 6.7 mg·kg−1 GH for 14 days. The N4 and D4 received equal volume of physiological saline. Fasting bloood glucose (FBS) measurements were achieved before the treatments and weekly during the treatments. The glucose tolerance test (GTT) tests were performed at the end of each treatment. The pancreatic tissue of each mouse was quickly removed and placed in cold phosphate‐buffered saline (PBS). Pancreatic samples were collected for both extraction and storage at −80 °C and the remaining immersed in $4\%$ paraformaldehyde for further investigation. Also hematoxylin and eosin staining and immunohistochemistry (IHC) were conducted as described previously [14]. All protocols especially animal maintenance and manipulation were conducted according to the guidelines of animal ethics committee of University of Tehran with approval ID of IR.UT.SCIENCE.1401.005.
## FBS/GTT measurements
The FBS levels were measured after fasting the mice for 6 h using an Accu‐Chek glucometer (Roche, Basel, Switzerland) via the tail vein. After the last day of treatment, FBS measurements were followed by a glucose injection (2 g·kg−1, i.p.). Then, at the indicated times, blood glucose levels were evaluated with an Accu‐Chek glucometer via the tail vein.
## Pancreatic protein extraction and western blotting
Pancreatic protein was extracted using a previously published protocol [15]. Each pancreatic tissue was extracted in the buffer containing Tris‐HCl (100 mm, pH 7.5), EDTA (10 mm), sodium pyrophosphate (10 mm), sodium fluoride (0.1 mm), sodium orthovanadate (10 mm), phenylmethanesulfonyl fluoride (2 mm) and aprotinin (10 mg·mL−1). Each homogenized pancreatic tissue was vortexed for 30 min at 4 °C. The homogenates were centrifuged at 10500 g for 20 min at 4 °C. The protein content of each supernatant was determined by Lowry's method [16] with fetal bovine serum as the calibrating agent. Each pancreatic extract was divided into aliquots and stored at −80 °C for future use.
For western blots, an aliquot of each pancreatic extract was added to the loading buffer (5 × loading buffer: 3 mL of $20\%$ SDS added to 3.75 mL of 1 m Tris buffer at pH 6.8, 9 mg of bromophenol blue, 1.16 g dithiothreitol and 4.5 mL of glycerol, made up to a final volume of 15 mL with dH20) and boiled for 5 min at 95 °C and then immersed in ice. Following SDS/PAGE, the resolved proteins were transferred to the poly(vinylidene difluoride) blotting membrane. The poly(vinylidene difluoride) membrane was then non‐specifically blocked with fat free milk and, after overnight incubation at 4 °C with primary antibody, horseradish peroxidase‐conjugated secondary antibody was added and incubated with gentle mixing for 120 min at room temperature. Finally, the images were developed based on a western blotting–electrochemiluminescence protocol [17].
## Hematoxylin and eosin staining and immunohistochemistry assay
Hematoxylin abd eosin staining of each fixed 6‐μm pancreatic section was achieved as described previously [14]. After embedding in paraffin, each tissue fixed in $4\%$ paraformaldehyde was cut into 6‐μm sections and applied to slides. For hematoxylin and eosin staining, tissues were subjected to rehydration, incubation in hematoxylin (2.5 min), rinsing with water, dipping in $0.5\%$ HCl/$70\%$ ethanol (v/v), rinsing with water, immersion in $0.2\%$ NaHCO3, rinsing in water, dipping in $0.1\%$ eosin for 20 s, rinsing briefly with water and, finally, dehydration and mounting.
For IHC evaluations, after embedding in paraffin, each tissue fixed in $4\%$ paraformaldehyde was cut into 6‐μm sections and applied to slides. The sections were deparaffinized, rehydrated and then permeabilized in $0.2\%$ Triton X‐100 for 5 min. Then, the permeabilized sections were blocked in PBS containing $10\%$ inactivated fetal bovine serum for 90 min. Primary antibodies were provided in their required dilution in the same medium, applied on sections and then incubated overnight at 4 °C. After overnight incubation, each slide was incubated for 90 min with the appropriate secondary antibody after washing in PBS. Secondary antibodies were diluted in PBS containing $10\%$ inactivated fetal bovine serum. Slides were viewed by fluorescence microscopy after washing in PBS and mounting with 4′,6‐diamidino‐2‐phenylindole [14].
The primary antibodies, including anti‐p‐extracellular signal‐regulated kinase (ERK)$\frac{1}{2}$ antibody sc‐81492 (dilution 1: 1000), goat anti‐PAX4 antibody (ab101721) (dilution 1: 1000), rabbit anti‐signal transducer and activator of transcription (STAT)5a antibody (ab30648) (dilution 1: 1000), rabbit anti‐GAPDH antibody (ab181602) (dilution 1: 1000), rabbit anti‐insulin antibody (ab63820) (dilution 1: 500), mouse anti‐glucagon antibody sc‐514592 (dilution 1: 500), mouse anti‐vimentin antibody sc‐6260 (dilution 1: 500), rabbit recombinant anti‐Ki67 (ab197547) (dilution 1: 500), rat anti‐CD3 antibody (ab11089) (dilution 1: 500), mouse anti‐CD19 antibody (sc‐373897) (dilution 1: 500), were used in western blotting and IHC assays. Also, all secondary antibodies were utilized at a concentration of 1: 1000, including goat anti‐rat IgG H&L (ab6840), goat anti‐rabbit IgG H&L (ab6717), goat anti‐rabbit IgG H&L (ab72465), goat anti‐mouse IgG H&L (ab6785) and goat anti‐mouse IgG H&L (ab6787).
## Statistical analysis
All values are depicted as the mean ± SEM. $P \leq 0.05$ was considered statistically significant. The arbitrary optical density unit was acquired using imagej, version 1.46 (NIH, Bethesda, MD, USA). Data were analyzed using prism (GraphPad Software Inc., San Diego, CA, USA) by determining whether they followed a normal distribution using a D'Agostino‐*Pearson omnibus* normality test. If not, an unpaired/non‐parametric Mann–Whitney test was used. If positive, an unpaired t‐test (two groups being compared) or unpaired analysis of variance (ANOVA) (several groups compared simultaneously) was used assuming Gaussian distribution.
## Influence of Mag and GH on mice FBS and GTT
The slight increase in the mean percentage of FBS changes for normal mice (Fig. 2A) is not significant. By contrast, significant changes were observed for diabetic groups. In D4 diabetic mice, the mean percentage of FBS changes increased by $49.2\%$, whereas those of D1, D2 and D3 mice decreased by $8.2\%$, $47.9\%$ and $49.6\%$, respectively. In other words, the approximate improvements in the fasting blood sugar for treated diabetic groups compared to untreated control diabetic ones were $57.4\%$, $97.1\%$ and $98.8\%$. Figure 2B shows that the rise in blood sugar occurs in the first 30 min following glucose injection and this levels off to that of untreated normal ones at 120 min. This is in contrast to the rise of blood glucose in 15 min for the D1, D2 and D3 groups (Fig. 2C). Also, blood sugar levels for the untreated diabetic mice were higher than other groups at all defined times. As is evident from Fig. 2C, the blood glucose levels of D2 and D3 groups, in the first 90 and 120 min following glucose injection, had no significant difference with N4 (normal, untreated group).
**Fig. 2:** *Comparative results of FBS and GTT among diabetic‐ and normal‐treated mice relative to their relevant controls. Comparison of the mean percentage of fasting blood glucose changes (A), the mean blood glucose at GTT defined intervals among treated‐ and untreated‐normal (B) and diabetic (C) mice. ***P < 0.001, **P < 0.01, *P < 0.05; ns, no significant (ANOVA). All data are depicted as the mean ± SEM (n = 5). N1 (normal, GH treated), N2 (normal, Mag treated), N3 (normal, Mag – GH treated), N4 (normal, saline treated), D1 (diabetic, GH treated), D2 (diabetic, Mag treated), D3 (diabetic, Mag + GH treated), D4 (diabetic, saline treated).*
## Evaluation of islet regeneration in diabetic mice
As shown in Fig. 3A,B, GH, Mag and their combination improved the size of islets in both normal and diabetic mice. In Fig. 3C, the fold increase of cell count per islet is shown for normal and diabetic treated groups versus their matched controls. The fold increase is significant only in treated diabetic mice compared to untreated diabetic ones (D4). Also, and based on comparative analysis presented in Fig. 3D, only in the diabetic and normal mice treated with Mag and then growth hormone (D3 and N3), the average number of cells per islet increased by 2.94‐ and 1.84‐fold, respectively, relative to their relevant controls (D4 and N4). For groups treated solely with Mag or GH, the increase was not significant compared to their relevant controls (Fig. 3D).
**Fig. 3:** *Hematoxylin and eosin staining of pancreatic sections at 40×, 100× and 400× magnification. Microscopic images of pancreatic tissue sections of treated‐normal and its control (A), treated‐diabetic and its control (B) are shown. Quantitative fold comparison of cell numbers per islet in each normal (C) and diabetic (D) treated groups versus their matched controls are shown. ***P < 0.001, **P < 0.01, *P < 0.05 (ANOVA). All data are depicted as the mean ± SEM (n = 5). Scale bars = 20, 50 and 200 μm (as indicated). Animal group classifications are as shown in the legend to Fig. 2.*
## Effects of GH, Mag and Mag plus GH on key signaling elements
Regarding the improvements in the mice pancreatic islet sizes and the number of cells per islet upon treatment with GH, Mag or their combination (Fig. 3), we evaluated the expression of ERK, STAT5, S6 and PAX4 signals following the Mag/GH treatments. In normal mice, there was no significant difference in P‐ERK levels for the N1 and N2 groups. However, the P‐ERK level in the N3 group significantly increased by approximately 1.31‐fold relative to the N4 group. With almost the same pattern, the extent of activation of ERK was highest for the D3 groups by 1.91‐fold relative to the relevant controls (Fig. 4A,B). Inspection of the western blot images (Fig. 4A) indicates that phosphorylation of STAT5 for all diabetic groups is significantly suppressed relative to all normal groups. However, the extent of P‐STAT5 is potentiated by almost 1.61‐ and 2.76‐fold upon co‐treatment with GH and Mag for the N3 and D3 groups relative to their relevant controls (N4 and D4), respectively (Fig. 4A,C). Similarly, the expression of P‐S6 significantly increased by 2.19‐fold for the N3 group relative to the N4 group, and D3 group 3.29‐fold relative to the D4 group (Fig. 4A,D). The expression level of PAX4, as the main marker for α to ß cell conversion, was also increased among the N1, N2 and N3 groups by almost 1.35‐, 1.25‐ and 1.43‐fold, respectively, whereas those of D1 and D2 groups were small and not significant. However, PAX4 expression increased by almost 2.21‐fold for the D3 group (Fig. 4A,E).
**Fig. 4:** *Western blot analyses of P‐ERK, P‐STAT5, P‐S6 and PAX4 in normal‐ and diabetic‐treated mice relative to their relevant controls. A prototype image of the immunoblot of pancreatic P‐ERK, P‐STAT5, P‐S6, PAX4 and GAPDH is shown in (A). Comparative immunoblot analysis for pancreatic P‐ERK (B), P‐STAT5 (C), P‐S6 (D) and PAX4 (E) is shown. ****P < 0.0001, ***P < 0.001 **P < 0.01, *P < 0.05; ns, no significant (ANOVA). All data are depicted as the mean ± SEM (n = 3–5). Analyses of immunoblot results were achieved using fiji‐java6 (https://downloads.imagej.net/fiji/Life-Line/fiji-java6-20170530.zip) and prism, version 5. Animal group classifications are as shown in the legend to Fig. 2.*
## Compensation of β cell mass among treated diabetic mice
Inspection of Fig. 5 supports α and ß‐like cell mass augmentation upon each treatment among the N and D groups, with higher variation among the N3 and D3 groups relative to their relevant controls. This is evident from Fig. 5A,B. The fold of cell/pixel counts of each group versus their controls presented in Fig. 5C. Comparative results of the mean cell count per islet are presented in Fig. 5D,E. As shown in Fig. 5C, the insulin+ and glucagon+ cells, among all normal‐treated groups, increased by almost 1.05‐ to 2.5‐fold relative to those of the N4 group. The regeneration of α and ß‐like cells among all diabetic‐treated groups is even more pronounced (between 2.19‐ and 4.75‐fold) relative to those of D4 mice. Also, Ins + and Glu + Cell count/Islet fold (bihormonal cells), as a marker of α to ß cell trans‐differentiation, significantly increased in the D3 compared to D4 groups.
**Fig. 5:** *Magainin and GH induce insulin+ and glucagon+ cell regeneration in pancreatic islets. (A, B) Immunohistochemical staining performed on pancreas sections. (A) Normal treated/untreated. (C) Fold increase of insulin+/glucagon+, both cell count/pixel in normal‐ and diabetic‐treated mice versus their matched controls. (D, E) Respectively comparing the mean of insulin+ and glucagon+ cell count/islet of each group with results of other groups. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 (one‐way ANOVA, n = 3). All data are depicted as the mean ± SEM in (C) to (E). Scale bar = 20 μm. Animal group classifications are as shown in the legend to Fig. 2.*
Similarly, for immunohistochemical evaluation in normal and diabetic mice, Fig. 6A,B, shows that the number of Ki 67+ pixel count increased by 1.55‐fold (N3) and 1.67‐fold (D3) and that of vimentin+ pixels rose by 1.49‐fold (N3) and 2.21‐fold (D3). These data clearly support a higher proliferation and differentiation of epithelial to mesenchymal cells. It is clear that the combination treatments (N3 and D3) provide better results compared to the N1, N2, D1 and D2 groups (Fig. 6C,D).
**Fig. 6:** *Magainin and GH induced ki67+ and vimentin+ cells of pancreas. (A, B) Immunohistochemical staining performed on pancreas sections. (A) Normal treated/untreated mice. (B) Diabetic treated/untreated. (C, D) Comparing the percentage of ki67+ and vimentin+ pixels of each group with those of other groups. **P < 0.01, ***P < 0.001 (one‐way ANOVA, n = 3). All data are depicted as the mean ± SEM in (C) and (D). Scale bar = 20 μm. Animal group classifications are as shown in the legend to Fig. 2.*
## Suppression of the immune T and B cell numbers following treatments
*The* general cell surface antigens of T and B lymphocytes are known as CD3+ and CD19+, respectively. Figure 7 indicates that the diabetic D4 mice have the most CD3+ and CD19+ pixels. The results summarized in Fig. 7C,D confirm that combination treatment with Mag and then GH is more effective with respect to decreasing the number of T and B cells. Qualitative comparison of Figs 6B and 7A demonstrates that the amount of CD3+ and CD19+ pixels effectively decreased by $60\%$ and $58\%$ in D3 mice and also by $57\%$ and $54\%$ among N3 mice, respectively (Fig. 7C,D).
**Fig. 7:** *Magainin and GH induce reduction of CD19+ and CD3+ cells in pancreas. (A, B) Immunohistochemical staining performed on pancreas sections. (A) Normal treated/untreated mice. (B) Diabetic treated/untreated mice. (C, D) Alteration of CD19+ and CD3+ pixels in normal and diabetic treated mice and their matched controls. ***P < 0.001 (one‐way ANOVA, n = 3). All data depicted as mean ± SEM in (C) and (D). Scale bar = 20 μm. Animal group classifications are as shown in the legend to Fig. 2.*
## Discussion
Despite numerous reports on GH functions, there are a few studies on the anti‐diabetic effect of magainins. Magainin‐AM1 and ‐AM2, via stimulating GLP‐1 release from GluTag cells [9], can improve β cell mass maintenance by prohibiting apoptosis and enhancing cell proliferation [18, 19]. In addition, Mag‐AM2 enhances insulin release from mouse β cells via depolarization of the cell membranes and enhancing intracellular calcium [6]. Magainin‐II is also capable of increasing mice hypothalamic GABA content via direct activation of GAD, which produces GABA from l‐glutamate [11]. Recently, it was shown that GABA administration to STZ‐treated diabetic mice has led to regeneration of α and ß‐like cells and α cell conversion to ß‐like functional cells among mice pancreatic islets [12]. Furthermore, another recent study [20] indicates that, in a model of multiple low dose STZ‐induced diabetes, GLP‐1 (based on a meta‐analysis), insulin and GABA‐induced α and ß cell proliferation and α‐ to ß‐cell trans‐differentiation, whereas the proliferation ratio of ß cell/α cell increased only in GABA treated models. Moreover, α‐cell apoptosis was higher than that of ß cells in all groups. Our present data, in addition to being in line with the data in the literature, clearly confirms that endogenous induction of GABA [12] and GLP‐1 (Fig. S1) brings about the same results as claimed previously [12, 20].
Detailed inspection of the data in Fig. 4C indicates that fold increase of insulin+ cells is lower than the fold increase of glucagon+ cells among all normal‐treated groups. Inspection of the same counts among the diabetes‐treated groups, however, indicates that the fold increase of insulin+ cells is higher than the fold increase of glucagon+ cells. These data indicate that Mag, and/or its combination treatment, enhances the production of α cells among healthy animals but does not convert all of them to ß‐like cells. In other words, the requisite for conversion of α to ß‐like cells is the loss or ablation of ß cells, which occurs among the STZ‐treated animals, as also claimed previously [12].
Production of a higher than normal number of α and/or ß‐like cells might activate the immune system, which possesses the memory of ß cell antigens. To check this possibility, we evaluated the total population of T and B cells among all N and D subgroups. The results (Fig. 7) indicated that the mean pixel counts of CD19+ and CD3+among all treated groups were lower than their relevant control groups (N4 and D4). These data clearly indicate that not only is the immune system is not ignited to the level of detection following Mag, and/or Mag+ GH treatments, but also it is partially suppressed among the STZ‐induced diabetic mice.
In conclusion, the present in vivo data provide evidence regarding the possibility of the pharmaceutical induction of α/β cell production and their trans‐differentiation to functional β‐ like cells without igniting the immune system via administration of Mag and, to a better extent, via combined administration of Mag and GH. These findings could finally pave the path toward an alternative insulin replacement therapy pending scientific maturation of the subject at the clinical level.
## Conflicts of interest
The authors declare that they have no conflicts of interest.
## Author contributions
AM and RY contributed to the study conception and design. AM was responsible for material preparation and data collection. AM and RY conducted the analyses. AM wrote the first draft of the manuscript, with major editing by RY.
## Data accessibility
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: Enhancement of polyethylene glycol‐cell fusion efficiency by novel application
of transient pressure using a jet injector
authors:
- Chin Yang Chang
- Jiayu A. Tai
- Yuko Sakaguchi
- Tomoyuki Nishikawa
- Yayoi Hirayama
- Kunihiko Yamashita
journal: FEBS Open Bio
year: 2023
pmcid: PMC9989930
doi: 10.1002/2211-5463.13557
license: CC BY 4.0
---
# Enhancement of polyethylene glycol‐cell fusion efficiency by novel application of transient pressure using a jet injector
## Body
Cell–cell fusion is a biological method to fuse two or more cell types to become a single hybrid cell by either spontaneous or artificial means [1]. It is a useful method with potentially many applications in biotechnology and medical research, such as cancer immunotherapy, antibody engineering, and regenerative medicine in diabetes therapy [2, 3]. In cancer immunotherapy, cell fusion technology is used to fuse dendritic cells (DC) with tumor cells to produce DC vaccines with reactivity against tumor antigens for cancer therapy [4, 5]. Another common cell fusion application is in antibody engineering. Naturally occurring antibody‐producing B cells have finite life span and limited antibody production. In monoclonal antibody research, B cells producing specific monoclonal antibodies are fused with myeloma cell line to produce a hybrid cell line (or hybridoma) that can proliferate and permanently produce and secrete these specific monoclonal antibodies for use in research or large‐scale production. Cell fusion can also be used to produce hybrid hybridomas that produce bispecific antibodies, which target two antigens or epitopes. Therefore, cell fusion is an important tool in biomedical research.
There are several methods to induce cell fusion, the three most commonly used methods include (a) biological method (virus‐mediated fusion), (b) physical method (electrofusion), and (c) chemical‐based methods (PEG fusion) [6]. Each method has its advantages and disadvantages. In biological virus‐based fusion, viruses with fusion glycoproteins on the viral envelope such as *Sendai virus* or vesicular stomatitis virus are used to initiate fusion between adjacent cells by utilizing the innate viral cell fusion machinery [7, 8]. But cell fusion efficiency using this method is dependent on the initial receptor binding on the target cell surface which brings the viral surface in close contact with the cell surface before the fusion protein can initiate cell fusion [9], so if target cells do not express these binding receptors cell fusion efficiency may be low. Additionally, this method may not be appropriate for fusion involving immune cells due to possible immune stimulation from the virus itself [10]. Electrofusion is a physical method that uses short high voltage electrical pulses to destabilize cell membranes of target cells leading to higher membrane permeability, which improves the probability of the merging of adjacent cell membranes to result in fused cells [11]. This method is both relatively efficient and simple, but it requires costly instrumentation.
Chemical‐based cell fusion method using polyethylene glycol (PEG) is low cost, widely available and does not require expensive machinery [12, 13]. There are a wide range of molecular weights of PEG available, of which the most commonly used for cell fusion is between 1000 and 4000 [14, 15]. In addition to the molecular weight, the concentration of PEG is also an important factor for cell fusion. Higher PEG concentrations produce higher fusion efficiencies, but are also associated with higher cytotoxicity and lower cell viabilities [16, 17]. So, even though PEG is affordable and simple to use, one of the most important problems to overcome is its relatively poor fusion rate.
The jet injector is a needleless drug delivery system with a long history of development [18]. Unlike the conventional syringe injection, jet injector commonly uses instantaneous high pressure to eject a liquid substance to penetrate the skin. Depending on the desired injection depth or injection site, there are various physical means to generate the instantaneous energy to propel injection, such as compressed air or gas, mechanical, pyrotechnic propulsion using gunpowder ignition [19, 20, 21]. More recently, due to the urgent development of new vaccines against SARS‐CoV‐2, jet injectors have garnered increased attention, especially for use to deliver DNA‐based vaccines [22, 23]. Jet devices are not restricted to being vehicles for injection purposes. With more development and modifications, jet devices may provide more potential applications in the biomedical field.
In this article, we try to improve the existing PEG fusion method, especially to the shaking process portion of the cell fusion process. We hypothesize that pressurizing cells within a closed volume vessel can replace the shaking process of the standard PEG fusion method. Based on our results, both mechanical pressure and jet device pressure can replace the shaking process to affect cell fusion. This improvement would not only simplify the PEG cell fusion process, but could also improve overall fusion efficiency and cell viability. We also demonstrated in this study that this new fusion method can be successfully applied to produce DC–tumor cell fusion vaccines and hybridomas, showing that this is a reliable new method and has the potential to improve applications that require cell fusion in the medical field.
## Abstract
Cell–cell fusion involves the fusion of somatic cells into a single hybrid cell. It is not only a physiological process but also an important cell engineering technology which can be applied to various fields, such as regenerative medicine, antibody engineering, genetic engineering, and cancer therapy. There are three major methods of cell fusion: electrical cell fusion, polyethylene glycol (PEG) cell fusion, and virus‐mediated cell fusion. Although PEG cell fusion is the most economical approach and does not require expensive instrumentation, it has a poor fusion rate and induces a high rate of cell cytotoxicity. To improve the fusion rate of the PEG method, we combined it with the pyro‐drive jet injector (PJI). PJI provides instant pressure instead of cell agitation to increase the probability of cell‐to‐cell contact and shorten the distance between cells in the process of cell fusion. Here, we report that this improved fusion method not only decreased cell cytotoxicity during the fusion process, but also increased fusion rate compared with the conventional PEG method. Furthermore, we tested the functionality of cells fused using the PJI‐PEG method and found them to be comparable to those fused using the conventional PEG method in terms of their application for dendritic cell (DC)‐tumor cell fusion vaccine production; in addition, the PJI‐PEG method demonstrated excellent performance in hybridoma cell preparation. Taken together, our data indicate that this method improves cell fusion efficiency as compared to the PEG method and thus has the potential for use in various applications that require cell fusion technology.
Polyethylene glycol (PEG) is commonly used to fuse cells for various applications. In this study, we found that the use of instantaneous pressure can replace the repetitive cell shaking and dilution process in the conventional PEG fusion method and improve overall fusion efficiency and survival rate of fused cells.
## Animals and cell lines
Female 6‐week‐old BALB/c mice (CLEA Japan Inc., Tokyo, Japan) were used in the study. All mice were maintained in a temperature‐controlled, pathogen‐free room and were handled according to the approved protocols and guidelines of the Animal Committee of Osaka University (Suita, Japan). All animal experiments performed were approved by The Institute of Experimental Animal Sciences, Faculty of Medicine, Osaka University (Approval number: J007418‐004). NS‐1 and 4T1 mammary carcinoma cell lines were maintained in Roswell Park Memorial Institute 1640 (RPMI1640) medium (Nacalai Tesque Inc., Kyoto, Japan), and MC38 colon adenocarcinoma and B16‐F10 melanoma cell lines were maintained in Dulbecco's Modified Eagle Medium (DMEM) (Nacalai Tesque Inc.). Both RPMI1640 and DMEM complete media were supplemented with $10\%$ fetal bovine serum (BioWest, Nuaille, France) and 0.1 mg·mL−1 penicillin–streptomycin mixed solution (Nacalai Tesque Inc.). All cell lines and hybrid cells were cultured at 37 °C in a humidified atmosphere of $5\%$ CO2. Experimental animal sacrifice by carbon dioxide animal euthanasia.
## Splenocyte and DC preparation
Spleens were harvested from naive C57BL/6N mice, and the splenocytes derived from the spleens were filtered through a 40‐μm mesh sieve and hemolyzed in hemolysis buffer (Immuno‐Biological Laboratories Co., Ltd., Gunma, Japan). Mouse DCs were isolated by flushing out the bone marrow of the tibia and femur with RPMI1640 medium and then filtered through a 40‐μm mesh sieve. After washing, bone marrow cells were cultured in complete RPMI1640 medium containing 10 ng·mL−1 of recombinant mouse GM‐CSF (Wako, Osaka, Japan), as described previously [24]. Culture medium were replaced on Days 2 and 4. On Day 6, nonadherent and loosely adherent proliferating cells were harvested and identified as DCs by evaluating CD11c expression using flow cytometry.
## PEG cell fusion method
Fusion of NS‐1 cells and splenocytes was performed using PEG1500 (Roche, Basel, Switzerland) according to the manufacturer's instructions. Briefly, NS‐1 cells and splenocytes were mixed at a ratio of 1: 3 (total 8 × 106 cells) and gently agitated by flicking tube 10 times to mix cells, followed by the addition of 100 μL of PEG1500. After PEG addition, the cells were further gently agitated (2 revolutions·s−1) in a 37 °C water bath for 90 s and then diluted with equal volume of RPMI1640 medium. Dilution with gentle agitation (2 revolutions·s−1) was repeated twice more and then finally rested for 10 min in a 37 °C incubator. For mixed only control group, NS‐1 cells and splenocytes were mixed in the ratio mentioned above along with the addition of PEG1500, then immediately washed with 10 mL medium, then centrifuged to remove supernatant and suspended sample in FACS running buffer. For lower PEG concentration group, manufacturer recommended working concentration of PEG1500 was diluted 1: 3 with PBS to obtain $25\%$ working concentration PEG.
## Ball drop cell fusion method
Similar to PEG method, NS‐1 cells and splenocytes were mixed at a ratio of 1: 3 (total 8 × 106) by flicking tube 10 times, followed by the addition of PEG1500. Cell–PEG mixture was loaded into the device container (Fig. S1A) and placed into the ball drop device (Fig. S1C). The ball (50 g) will drop from two different height conditions: 30 and 70 mm (duration: 0.6 and 0.8 ms) to affect cell fusion. After ball drop cell fusion, cell fusion mixtures were returned to RPMI1640 medium immediately for recovery in a 37 °C incubator.
## PJI cell fusion method
PJI fusion was performed using the modified Actranza device (Fig. S1A,B; Daicel Corporation, Osaka, Japan). NS‐1 cells and splenocytes were mixed at a ratio of 1: 3 and then gently agitated by flicking tube 10 times to mix cells, followed by the addition of 100 μL of PEG1500. Cell–PEG mixture was loaded into the device container (Fig. S1A), and the device was fired to initiate cell fusion (duration: 0.5 ms), and the resultant cell mixture was recovered into RPMI1640 medium in a 37 °C incubator. The fusion cell was observed with a BZ‐X710 microscope (Keyence Corporation, Osaka, Japan). In 3D imaging, the single fluorescent light is a nonfused cell (DiO: green fluorescence or DiD: red fluorescence), and the fluorescent overlap appears yellow as a fusion cell.
## Cell fusion efficiency analysis
To analyze cell fusion efficiency using flow cytometry, NS‐1 cells were prestained with DiR (Thermo Fisher Scientific, Waltham, MA, USA) and splenocytes with BV421‐CD45 (BioLegend Inc., San Diego, CA, USA) prior to fusion and the resultant fusion efficiency was analyzed using CytoFLEX flow cytometer (Beckman Coulter Life Sciences, Brea, CA, USA) and CytExpert software (Beckman Coulter Life Sciences) by calculating the percentage of DiR and BV‐421 double‐positive cells. To determine cell viability after fusion, fused cell mixtures were cultured for 24 h and then stained with trypan blue (Nacalai Tesque Inc.) and the percentage of live/dead cells were counted using TC20 automated cell counter (Bio‐Rad, Hercules, CA, USA). For additional analysis of fusion between different types of cells using PJI, 4T1, MC38, and NS‐1 cells were prestained with 20 μL·mL−1 of DiI, DiD, and DiR, respectively (all from Thermo Fisher Scientific) for 20 min, while splenocytes were prestained with FITC‐CD45 (1: 100 dilution; BioLegend Inc.) for 10 min. After cell staining, 4T1/NS‐1 (1: 3), MC38/splenocyte (1: 3), and 4T1/splenocyte (1: 3) fusion pairs were subjected to PEG or PJI fusion method as mentioned previously.
## DC–tumor hybrid cell vaccine preparation and in vivo antitumor effect challenge
Bone marrow‐derived DCs were stimulated with LPS (100 μg·mL−1) for 24 h as mentioned previously [4]. B16‐F10 melanoma cells were irradiated at 100 Gray to inactivate cell proliferation. DC–tumor cell fusion was performed by mixing DCs and inactivated B16‐F10 melanoma cells at a ratio of 1: 2 (total of 9 × 106 cells) and subjected to PEG fusion or PJI fusion as mentioned above. To assess DC–tumor cell vaccine functionality in tumor treatment challenge, 1 × 106 viable B16‐F10 melanoma cells were injected intradermally on the backs of C57BL/6N mice. On days 2 and 7, tumor‐inoculated mice were given 2 doses of the DC–tumor hybrid cell vaccine intradermally on the opposite flank. Tumor growth was observed over time, and the tumor volume was measured in a blinded manner using slide calipers and was calculated using the following formula: tumor volume (mm3) = length × (width)$\frac{2}{2.}$
## ELISpot assay
Spleens from DC–tumor cell fusion vaccine‐treated mice were isolated 10 days after the last DC–tumor cell fusion vaccine injection. Splenocytes were isolated from the spleens as described previously. To stimulate splenocytes, B16‐F10 melanoma cells were treated with 15 μg·mL−1 mitomycin C (Nacalai Tesque Inc.) for 45 min. Splenocytes harvested from vaccine‐treated mice and mitomycin C‐treated B16‐F10 melanoma cells were mixed at a ratio of 10: 1 and co‐cultured for 48 h. Nonadherent splenocytes were collected, and ELISpot assay was performed using the Mouse IFN‐γ Development Module (R&D Systems, Minneapolis, MN, USA) and the ELISpot Blue Color Module (R&D Systems). The numbers of IFN‐γ‐secreting cells were subsequently counted.
## Hybridoma production
C57BL/6N mice were injected with 10 μg OVA recombinant protein (Wako) mixed with alum adjuvant (Thermo Fisher Scientific) on days 0 and 14. On day 28, mouse splenocytes were harvested and fusion with NS‐1 myeloma cells was performed using the PEG or PJI method as mentioned above. After fusion, the cells were cultured in HAT selection medium (RPMI1640 HAT Supplement 1×) (Thermo Fisher Scientific) supplemented with $20\%$ Hyclone fetal bovine serum (Cytiva, Washington, DC, USA), 0.1 mg·mL−1 penicillin–streptomycin mixed solution (Nacalai Tesque Inc.) and $10\%$ Hybridoma Cloning Supplement (Santa Cruz Biotechnology, Santa Cruz, TX, USA) for 1 week. Hybridoma colonies were observed using the BZ‐X710 microscope (Keyence Corporation), and hybridoma cell fusion success rate was calculated by observing total number of wells with hybridoma presence.
## Statistical analysis
Statistical analyses were conducted using Student's two‐tailed unpaired t‐test with graphpad (Boston, MA, USA), and P‐values < 0.05 were considered statistically significant.
## Using instantaneous pressure to modify PEG fusion method
The conventional PEG‐mediated cell fusion (PEG‐F) method is a commonly used method for cell fusion. Although it has the advantage of economical convenience, there is room for improvement with regard to the inconsistent and low fusion efficiency [25]. Previous research has shown that physical pressure can shorten the cell‐to‐cell distance [26], which promotes cell contact. So, we hypothesize whether application of acute pressure on cells can replace the cell shaking process in PEG‐F. To test this hypothesis, it was necessary to design a simple setup to generate acute pressure on cells. We designed a ‘ball drop’ apparatus (Fig. S1C). The rationale for this mechanism is when the ‘ball’ or weighted object is released from the top, the impact on the cell fusion container below would generate an instantaneous pressure on the cells inside the container. We also prepared two different ‘ball’ weights to generate different impact pressures to see how different pressures can affect cell fusion efficiency. In comparison with the standard PEG‐F method where PEG was first slowly added to NS‐1 myeloma cells and mouse splenocytes, then gently shaken for 20 min to promote cell‐to‐cell contact for cell fusion, in our new method, PEG‐added NS‐1 myeloma cells and mouse splenocyte mixture was directly loaded into and subjected to instantaneous pressure using our apparatus (Fig. 1A). Following cell fusion process, all cell mixtures were recovered and analyzed for fusion‐positive cells using flow cytometry. We found that acute pressure from falling weights was sufficient to induce cell fusion and is at least comparable with the standard PEG‐F method despite the shorter fusion time (Fig. 1B and Fig. S2). So, this suggests that pressure can promote cell fusion and potentially shorten the standard process.
**Fig. 1:** *Standard PEG, modified PEG fusion process and cell fusion efficiency. (A) Fusion process by PEG fusion method (left) and the modified PEG fusion process by ‘ball drop’ method or jet injector method (right). (B) After PEG was added to NS‐1 myeloma and mouse splenocyte cell mixture, cell fusion was carried out either without shaking or incubation (Mix), by standard PEG fusion (PEG‐F) method or by ‘ball drop’ method at two pressure conditions, as labeled in figure. After cell fusion, all groups were analyzed by flow cytometry (n = 4 each). Data are expressed as the mean ± SD. P‐values were analyzed by a two‐tailed Student's t‐test. * indicates P < 0.05. NS indicates not significant.*
## Jet injector as a pressure generator in PEG‐F modification
Next, to further explore the use of pressure to aid in cell fusion, we designed a more refined apparatus setup capable of generating various pressure conditions to evaluate optimal conditions for cell fusion. Pyro‐drive jet injector (PJI) is a needleless injection device that uses controlled gunpowder ignition‐powered momentum to propel a plunger fitted to a syringe‐like container to instantaneously eject its preloaded contents through the skin. Injection pressure can be varied by adjusting gunpowder ratios in the device. We modified the PJI as a pressure generator source and replaced the container with one that is closed system to hold cells for cell fusion (Fig. S1A,B). To test whether the modified PJI apparatus can facilitate cell fusion, NS‐1 cells and mouse splenocytes were mixed with PEG and applied to the device. Fused cells were observed after PJI‐mediated fusion (PJI‐F), similar to PEG‐F method, showing that the modified PJI device can be used to complement PEG to induce cell fusion (Fig. 2 and Fig. S3).
**Fig. 2:** *Splenocyte fusion with NS‐1 cell using jet device with PEG method. Cell fusion was performed using mouse splenocytes (DiO: green fluorescence) and NS‐1 cells (DiD: red fluorescence) with PEG either without shaking/incubation (Mix), by standard PEG‐F or by pyro‐drive jet injector‐mediated fusion (PJI‐F) method. After fusion, presence of fused cells was confirmed by fluorescence microscopy. Microscopy at 10× magnification.*
## Positive correlation between pressure intensity and fusion efficiency
To evaluate the effect of pressure on cell fusion efficiency, we prepared a range of PJIs with increasing amounts of pyro powder (15–110 mg), which produce corresponding pressure ranging from 2.5 to 30.7 MPa. PJI‐F method resulted in higher numbers of fused cells across all tested pressure conditions, in comparison with PEG‐F method (Fig. 3A). Cell viability after the fusion process is also crucial in the evaluation of cell fusion, due to potential cytotoxicity of PEG. We found that cell viability was higher using the PJI‐F method across all tested pressure conditions compared with the standard PEG‐F method, although PJI‐F cell viability is reduced when exposed to higher pressure conditions than compared to lower pressure conditions (Fig. 3B). Standard PEG‐F fusion efficiency is dependent on PEG concentration used; we also found that even at lower PEG concentration, PJI‐F was still more efficient than PEG‐F (Fig. S4). From these results, we have demonstrated that PJI‐F is both more efficient and likely less cytotoxic than standard PEG‐F method and that the acute pressure caused by PJI is sufficient to improve cell fusion efficiency. Additionally, we also applied PJI‐F to fuse several different combinations of cells aside from NS‐1/mouse splenocytes. We used PEG‐F and PJI‐F methods to fuse mouse mammary cell line 4T1 with mouse splenocytes, mouse colon cancer cell line MC38 with mouse splenocytes and 4T1 with NS‐1 (Fig. 4). We found that although fusion efficiency varied between different cell combinations, PJI‐F was still more efficient than PEG‐F in inducing cell fusion in all tested groups.
**Fig. 3:** *Increased pressure by PJI enhanced cell fusion efficiency, but excessive pressure reduced cell viability. (A) Cell fusion efficiency of fluorescent stained NS‐1 (DiR) and mouse splenocytes (CD45‐BV421) using standard PEG‐F method or PJI‐F method fitted with different doses of pyro powder (15–110 mg) corresponding to generating a range of different pressure conditions (2.5–30.7 MPa) and then analyzed by flow cytometry. (B) Cell viability in fused cell mixture after cell fusion described in (A) analyzed by trypan blue staining. Both cell fusion efficiency and cell viability results are graphed (n = 4 per group), and mean values are indicated above each column. Data are expressed as the mean ± SD. P‐values were analyzed by a two‐tailed Student's t‐test. * indicates P < 0.05. NS indicates not significant.* **Fig. 4:** *Cell fusion efficiency by PEG‐F and PJI‐F on different cell types. (A) Fusion of 4T1 cells and mouse splenocytes, (B) MC38 cells and mouse splenocytes, and (C) 4T1 cells and NS‐1 cells using standard PEG‐F method or PJI‐F (6.26 MPa) method; then, cell fusion efficiency was analyzed by flow cytometry. All cell fusion results are graphed (n = 4 per group), and mean values are indicated above each column. Data are expressed as the mean ± SD. P‐values were analyzed by a two‐tailed Student's t‐test. * indicates P < 0.05. NS indicates not significant.*
## PJI‐F can produce viable DC vaccine
Besides fusion efficiency and cell viability, it is also important to assess the functionality of fused cells by PJI‐F method. Cell fusion is commonly used in the production of DC vaccines (DC–tumor cell) and antibody‐producing hybridomas (B cell‐myeloma cell). To properly evaluate PJI‐F method for cell fusion, successfully fused cells by PJI‐F method should function no different from those made using the standard PEG‐F method. In DC–tumor cell fusion vaccines (DC vaccines), DCs gain tumor‐associated antigens from fusing with tumor cells and process them for immune presentation to other immune cells, leading to immune activation against tumor cells. We produced DC vaccine by fusing DCs with B16‐F10 melanoma cells using the PJI‐F method. To test whether PJI‐F DC vaccine can stimulate the activation of tumor‐specific immune response, we injected DC vaccines into naive C57BL/6N mice (Fig. 5A) and then harvested splenocytes from these vaccinated mice 10 days later. The splenocytes were restimulated with inactivated B16‐F10 cells and analyzed using IFN‐γ ELISpot assay. Our results showed that PJI‐F DC vaccine induced significantly more tumor‐specific IFN‐γ‐secreting splenocytes in naive mice compared to those made by PEG‐F (Fig. 5B). This suggests that PJI‐F DC vaccine can stimulate potent tumor‐specific immune activation. Next, to see whether these DC vaccines can affect tumor growth, we first inoculated C57BL/6N mice with B16‐F10 tumor cells on their left flank. Five days after, we challenged DC vaccine on the right flank two times at a four‐day interval. We found that both PJI‐F and PEG‐F DC vaccines suppressed B16‐F10 tumor growth (Fig. 5C). From these results, we have demonstrated that PJI‐F can produce viable DC vaccine that has antigen presentation function and can elicit antitumor effect.
**Fig. 5:** *PJI‐F‐generated DC–tumor cell fusion vaccine elicited antitumor effect. (A) Schematic diagram of DC vaccine mouse tumor model challenge. C57BL/6N mice were subcutaneously injected on the left side with 2 × 105 B16‐F10 melanoma cells. DC vaccines were intradermally administered on the opposite side on days 5 and 10. (B) DC–tumor cell fusion vaccine (DC vaccine) was generated by fusing mouse bone marrow‐derived splenocytes with B16‐F10 melanoma cells via nonshaken PEG method (Mix), PEG‐F method (V PEG‐F) or PJI‐F method (V PJI‐F). Naive C57BL/6N mice were immunized with DC vaccine and mouse splenocytes from each group (n = 4) were harvested 10 days later. Tumor‐specific IFN‐γ‐secreting T cell activation was analyzed using IFN‐γ ELISPOT assay. Nonvaccine‐treated mice (PBS) were used as a control group. * indicates P < 0.05. (C) Tumor size after DC vaccine administration was monitored (all n = 5). Tumor volumes are shown as mean ± SD. P‐values were analyzed by a two‐tailed Student's t‐test. * indicates P < 0.05. NS indicates not significant.*
## PJI‐F method can produce hybridoma more efficiently than PEG‐F method
Additionally, we also evaluated the use of PJI‐F method in the application of hybridoma production. To produce hybridoma, we fused NS‐1 myeloma cells and recombinant OVA peptide‐vaccinated mouse splenocytes using PJI‐F or PEG‐F method and then cultured the cells in HAT selection medium for 1 week. We observed that PJI‐F hybridoma colonies were indistinguishable from those from PEG‐F method (Fig. 6A) but PJI‐F method produced significantly more wells containing hybridoma colonies compared with PEG‐F method (> 2.5 fold) (Fig. 6B), indicating that PJI‐F method can produce hybridoma more efficiently than PEG‐F method. Therefore, PJI‐F performed better than PEG‐F in both fusion cell vaccine and hybridoma production. Based on all our findings, PJI‐F is a reliable method to improve cell fusion applications.
**Fig. 6:** *Hybridoma generation by PJI‐F method. (A) Hybridomas were generated by fusing NS‐1 myeloma cells with mouse splenocytes from recombinant OVA‐immunized BALB/c mice using standard PEG‐F method or PJI‐F method and cultured in HAT selection medium for 1 week. After HAT selection, hybridoma colony morphology was observed under the microscope. (B) Hybridoma generation efficiency was calculated by total number of hybridoma‐positive wells per plate, and results are represented graphically (n = 10). Data are expressed as the mean ± SD. P‐values were analyzed by a two‐tailed Student's t‐test. ** indicates P < 0.001.*
## Discussion
With the rapid development of antibody‐based drugs and regenerative medicine, cell fusion technology will become increasingly important and commonplace. PEG‐F may be an imperfect method, but the cost‐effectiveness is an attractive factor. One main problem of PEG‐F is the low cell fusion efficiency. Cell fusion efficiency using PEG‐F method is strongly dependent on cell type and skill proficiency [25]. In this research, we attempted to improve the efficiency of cell fusion using PEG method and reduce skill disparity by making the fusion process more standardized and consistent. To resolve these problems, we replaced the skill‐dependent operation (shaking process) during the PEG fusion process with the use of simplified steps (instantaneous pressure) to facilitate cell fusion (Fig. 1). Although effective cell fusion rates may vary between cell types, our results showed PJI‐F can achieve approximately 25–$55\%$ higher fusion efficiency than PEG‐F method (Figs 3A and 4). One potential reason for the variability of fusion efficiency in different types of cells may be related to cell size. For example, of the four types of cells used in our fusion experiments, we found that cell sizes are as follows: MC38 > NS‐1 > 4T1 > splenocyte using flow cytometry analysis (Fig. S5). These findings, along with the fusion efficiencies of specific combinations of these cells (Fig. 4), strongly suggest cell size may affect fusion efficiency. Based on our results, 4T1 fused with NS‐1, a larger sized cell, more efficiently than compared with fusion with smaller sized splenocytes.
The prolonged incubation with gentle shaking of cell mixture and PEG is the most critical step in the standard PEG cell fusion process. During this process, the mixed cells make essential cell‐to‐cell contact that promotes cell fusion, but the same conditions also affect overall cell viability due to PEG cytotoxicity. Therefore, to increase fusion cell viability, an important factor to consider would be reduction in the duration of cell contact with PEG. Since previous research indicated that high pressure causes molecular distance to shrink [27], we hypothesized whether application of instantaneous pressure could replace the shaking process during cell fusion (Fig. 1). Using jet device to generate pressure, we demonstrated successful cell‐to‐cell contact and detection of fused cells (Fig. 3 and Fig. S3). Although more detailed 3D examination of some ‘positive’ fused cells revealed false positives of close proximity cells rather than true fused cells (Fig. S3, white outlined arrows), other analytical approaches such as flow cytometry may be more conclusive in analyzing fusion efficiency (Figs 4 and 5). Cell fusion efficiency was positively correlated with increased pressure (Fig. 3A). However, excessively high pressure is detrimental for fusion process. Our results showed that when the pressurized output of the device is higher than 6 MPa, resultant cell viability started to decline. This indicates that stronger pressure may contribute to cell damage, so the balance between fusion efficiency and cell viability would determine optimal fusion conditions. Our results showed that the pressure of approximately 6 MPa is the best condition for cell fusion on our modified device. We also demonstrated that weak pressure (5.3 MPa) generated by ‘ball drop’ method was sufficient to boost cell fusion (Fig. 1B), but not as effective compared to low‐setting PJI (2.5 MPa) (Fig. 3A). One major difference between the ball drop method and PJI method, besides generated pressure, is the duration of pressurization. The free fall ball drop method exerted pressurization for a duration of approximately 0.8 ms, whereas PJI ignition exerted pressurization for < 0.5 ms. This suggests that pressurization duration may also affect overall fusion efficiency.
In standard PEG cell fusion method, the concentration of PEG used also affected cell fusion efficiency [17]. Our results showed that even when the amount of PEG‐1500 used was reduced to $25\%$, PJI‐F method improved cell fusion efficiency more effectively than PEG‐F method alone (Fig. S4). Therefore, instantaneous pressure can enhance cell fusion process regardless of PEG used. So, as long as optimal pressure conditions can be supplied, cell fusion using PEG can be improved, via similar pressure‐generating devices.
Here, we described using PJI‐F in two of the most common applications for cell fusion, DC vaccine and hybridoma production. Even though PJI‐F DC vaccine induced higher antigen‐specific immunity than PEG‐F DC vaccine, the overall functional antitumor effect was on par with PEG‐F DC vaccine in the tumor challenge model (Fig. 5B,C). This could be due to limitations of the fusion vaccine or the associated activated T cell effects alone in the complex tumor microenvironment. Both vaccines induced sufficient overall antitumor immune activation regardless of an increase in a specific subset of activated T cells arising in PJI‐F DC vaccine. But this overall significant suppression in tumor growth by both DC vaccines proves how effective DC vaccines could potentially be in antitumor therapy. And that application of pressure via PJI can improve the production of DC vaccines.
We have also shown that PJI‐F method could successfully be used to produce hybridomas. In hybridoma fusion, the resultant fused hybridoma cells should preserve cell properties of the parental cells, such as antibody production ability from immunized mouse splenocytes and immortal cell life span from the myeloma cell line. We found that PJI‐F method produced more than twice the number of hybridomas compared with standard PEG‐F method (Fig. 6A,B). This could be mainly due to the shorter cell fusion duration in the presence of PEG in the PJI‐F method, so cell viability is higher. Hence, PJI fusion method is a more efficient method for hybridoma production.
Recently, due to the emergence of COVID‐19, vaccines against this new infectious disease had been rapidly developed [28, 29, 30]. Taking advantage of the new vaccine research push, new types of vaccines, such as RNA or DNA‐based vaccines [31], have also come to the fore. Nucleic acid‐based vaccines can be developed rapidly, but often requires special carriers or injection methods to deliver these vaccines into cells [32, 33, 34, 35]. Jet injector is one of the many methods capable of injecting nucleic acid vaccines and has shown actual potential in delivering COVID‐19 vaccine in clinical settings [36, 37]. Besides being used as a delivery system, jet injector can also be used in other applications. Here, we have demonstrated a novel application of a modified jet device as a pressure generator to facilitate cell fusion. Although we did not compare the results of our PEG‐improved method with other existing fusion methods directly in this study due to disparate fusion conditions, we have shown that PJI‐F fusion method is an effective method over the traditional PEG fusion method.
In summary, instantaneous pressure could enhance cell fusion efficiency as well as simplify PEG fusion process and improve cell fusion applications such as DC fusion vaccines and hybridoma technology. Our findings showed that instantaneous pressure can complement PEG fusion and is a reliable method for existing and future applications involving cell fusion.
## Conflict of interest
The authors declare no conflict of interest.
## Author contributions
CYC was responsible for research project conceptualization, designed all experiments, collected data, and performed all analyses. JAT wrote the manuscript, involved in experimental discussion and troubleshooting, and obtained results for Figs 2A, 4 and 5. YS performed the double validation experiment and provided experimental assistance. TN involved in research discussions and preparation of microscopy samples. YH involved in ball drop experiment, equipment preparation and pressure measurement. KY involved in experimental assistance and animal management.
## Data accessibility
The raw data supporting the conclusions of this study are available from the corresponding author upon reasonable request.
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|
---
title: 'Obesity and Kidney Transplant Candidates: An Outcome Analysis Based on Body
Mass Index'
journal: Cureus
year: 2023
pmcid: PMC9989980
doi: 10.7759/cureus.34640
license: CC BY 3.0
---
# Obesity and Kidney Transplant Candidates: An Outcome Analysis Based on Body Mass Index
## Abstract
Background *Obesity is* a well-established risk factor for a decline in renal function and post-operative complications. Also, obese patients suffer worse outcomes such as higher rates of wound complications, longer hospital stays, and delayed graft function (DGF) when compared to nonobese patients.
The correlation between having a high BMI and the postoperative outcomes of kidney transplantation has not been investigated yet in Saudi Arabia. There is scarce evidence that patients with obesity who have undergone kidney transplantation are devoid of any complications before, during, or after their procedure.
Methodology A retrospective cross-sectional study was conducted using charts of nearly 142 patients in King Abdullah Specialist Children's Hospital in Riyadh, who had kidney transplant surgery in the organ transplantation department. All Obese patients with BMI >29.9 who underwent Kidney Transplant Surgery in King Abdulaziz Medical City from 2015 to 2022 were used. Details of hospital admissions were retrieved.
Results A total of 142 patients fulfilling the inclusion criteria were included. There was a significant difference between patients regarding pre-surgical history where all cases ($100\%$; 2) with class three obesity were hypertensive and on dialysis versus ($77.8\%$; 21) and ($70.4\%$; 19) of class two obesity and ($86.7\%$; 98) and ($78.8\%$; 89) of class one obesity cases, respectively ($$P \leq 0.041$$). Regarding medical history, hypertension was reported among 121 ($85\%$), followed by dialysis ($77\%$; 110), diabetes mellitus (DM) ($52\%$; 74), dyslipidemia ($24\%$; 35), endocrine diseases ($15\%$; 22), and cardiovascular diseases ($16\%$; 23).
Considering post-transplant complications, $14.1\%$ [20] of the study cases had DM ($16.8\%$ of obese class one, $3.7\%$ of obese class two, and none of obese class three; $$P \leq 0.996$$) and urinary tract infection (UTI) among $7\%$ [10] of the cases ($6.2\%$ of obese class one, $11.1\%$ of obese class two, and none of obese class three; $$P \leq 0.996$$). All these differences according to patients' BMI were statistically insignificant.
Conclusion Obese patients are more likely to experience difficult intraoperative management along with a complicated postoperative course due to numerous concomitant comorbidities. Post-transplant DM (PTDM) was the most prominent post-transplant complication followed by UTI. A remarkable reduction in serum creatinine and blood urea nitrogen (BUN) has been observed at the time of discharge and after six months compared to pre-transplant measurements.
## Introduction
Obesity, the incidence of which has risen sharply over the past decade encompassing $24.7\%$ of the Saudi population [1], is a well-established risk factor for worsening kidney function in both healthy individuals and those with a history of kidney transplantation [2]. Besides the current local prevalence, it is also estimated that by 2030, $38\%$ of the world’s adult population will be overweight and $20\%$ will be obese [3]. Obesity is widely assessed by the body mass index (BMI), defined as weight in kg/height in meters squared in adults, and the BMI percentile adjusted for age and sex in children, although it is argued that no standardized definition is applicable for the pediatric population. Despite the definition of obesity as excessive adiposity, there is no consensus on how to define obesity using fat mass calculation or fat percentage. The American Society of Clinical endocrinologists defines obesity as a body fat percentage of>$35\%$ in women and >$25\%$ in men [4].
Normative research has historically examined the wide-ranging complications and comorbidities of obesity in which kidney disease is rather remarkable. A study suggested that visceral obesity is an independent risk factor for end-stage renal disease (ESRD). It is also associated with a rise in relative risk with increasing body mass index (BMI) [5], as demonstrated by the increasing number of obese patients who have ESRD and are registered for kidney transplantation.
Next, post-operative complications and outcomes are worse in obese patients when compared with their non-obese counterparts, namely an increased risk of delayed graft function (DGF) with obesity [6]. However, even though morbidly obese patients have demonstrated some benefit from kidney transplantation compared to their medically treated counterparts [7], Weight bias in kidney transplantation waitlisting is still often seen. This bias is likely owing to short-term complications such as higher rates of wound complications, more extended hospital stays, and DGF [8] that could be associated with the BMI-based selection of transplant candidates endorsed by the practice guidelines of the American Society of Transplantation.
The correlation between having a high BMI and the postoperative outcomes of kidney transplantation has not been investigated yet in the Kingdom of Saudi Arabia. It is still little to no evidence that obese patients who have undergone kidney transplantation are devoid of any complications before, during, or after their procedure. Thus, this study’s aim is to shed light on the vagueness of the topic and hopefully help provide better conclusions for medical practitioners and the patients themselves.
## Materials and methods
This was a retrospective cross-sectional study, that was conducted by reviewing the charts of all the obese patients, whose BMI ≥ 30, who underwent kidney transplantation at King Abdullah Specialist Children's Hospital in Riyadh between January 2015 and March 2022. The total number of patients that were eligible and accessible for enrolment in the study was 142. Patients with a history of previous transplants, multi-organ transplant recipients with, a BMI less than 30, and those who underwent kidney transplants before 2015 were excluded. The reason 2015 was the cut-off is the unavailability of electronic records before 2015. The BESTCare system charts were reviewed by the co-authors after obtaining ethical approval from King Abdullah International Medical Research Centre (KAIMRC). The data were extracted on a google form, then recorded on an excel sheet designed to include all the variables needed. Outcome variables that were collected from the patient’s pre-operative assessment forms included demographic data, length of stay in the hospital, duration of dialysis, and presence of medical co-morbidities. Confidentiality of all patients was maintained throughout the study as no identifiers were collected, and each patient was assigned a serial number.
The data were collected, reviewed, and then fed to Statistical Package for Social Sciences version 21 (SPSS; IBM Corp., Armonk, NY). All statistical methods used were two-tailed with an alpha level of 0.05 considering significance if the p-value was less than 0.05. Descriptive analysis was done by prescribing frequency distribution and percentage for study variables including patients' body mass index, bio-demographic data, medical history, renal dialysis duration and causes, and renal transplant-related outcome (hospital stay, complications, graft function, and fate). Also, cross-tabulation for assessing the effect of patients' body mass index on their renal transplant surgery outcome was conducted using an exact probability test due to small frequency distributions. Serum creatinine level and BUN at different phases were displayed using the range, mean with standard deviation with repeated measures ANOVA for significance.
## Results
A total of 142 patients fulfilling the inclusion criteria were included. Exact 141 were Saudi Arabian, and only one was non-Saudi. Regarding medical history, hypertension (HTN) was reported among 121 ($85.2\%$), followed by dialysis ($77.5\%$; 110), diabetes mellitus (DM) ($52.1\%$; 74), dyslipidemia ($24.6\%$; 35), endocrine diseases ($15.5\%$; 22), and cardiovascular diseases, other than hypertension, ($16.2\%$; 23). Exact 41 ($39.4\%$) of the study patients had renal dialysis for less than two years, 41 ($39.4\%$) for two to five years, and 22 ($21.2\%$) for more than five years. As for the causes of ESRD, the most reported were DM ($22.5\%$; 32), hypertension ($10.6\%$;15), focal segmental glomerulosclerosis (FSGS) ($6.3\%$; 9), and IgA nephropathy ($4.9\%$; 7) (Table 1, Figure 1).
There was a significant difference between patients regarding pre-surgical history where all cases with class three obesity were hypertensive and on dialysis versus ($77.8\%$; 21) and ($70.4\%$; 19) of class two obesity and ($86.7\%$; 98) and ($78.8\%$; 89) of class one obesity cases, respectively ($$P \leq 0.041$$). There was no significant difference regarding ing duration of renal dialysis and causes of ESRD (Table 2).
**Table 2**
| Biomedical data | Biomedical data.1 | BMI Before Transplant | BMI Before Transplant.1 | BMI Before Transplant.2 | BMI Before Transplant.3 | BMI Before Transplant.4 | BMI Before Transplant.5 | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Biomedical data | Biomedical data | Obese class 1 | Obese class 1 | Obese class 2 | Obese class 2 | Obese class 3 | Obese class 3 | P-value |
| Biomedical data | Biomedical data | No | % | No | % | No | % | P-value |
| Pre-surgical history | | 1 | 0.9 | 1 | 3.7 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Cardiovascular Diseases | 17 | 15.0 | 5 | 18.5 | 1 | 50.0 | 0.041* |
| Pre-surgical history | Respiratory Disease | 11 | 9.7 | 2 | 7.4 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Hematological Disease | 3 | 2.7 | 0 | 0.0 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Oncological Disease | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Rheumatological Disease | 3 | 2.7 | 5 | 18.5 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Neurological Disease | 5 | 4.4 | 5 | 18.5 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Renal Disease | 10 | 8.8 | 3 | 11.1 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Reproductive Disease | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Endocrine Disease | 15 | 13.3 | 6 | 22.2 | 1 | 50.0 | 0.041* |
| Pre-surgical history | GIT Disease | 2 | 1.8 | 1 | 3.7 | 0 | 0.0 | 0.041* |
| Pre-surgical history | MSK Disease | 4 | 3.5 | 1 | 3.7 | 1 | 50.0 | 0.041* |
| Pre-surgical history | Hepatobiliary | 6 | 5.3 | 2 | 7.4 | 1 | 50.0 | 0.041* |
| Pre-surgical history | Dermatology | 0 | 0.0 | 1 | 3.7 | 0 | 0.0 | 0.041* |
| Pre-surgical history | Dialysis | 89 | 78.8 | 19 | 70.4 | 2 | 100.0 | 0.041* |
| Pre-surgical history | Hypertension | 98 | 86.7 | 21 | 77.8 | 2 | 100.0 | 0.041* |
| Pre-surgical history | Diabetes Mellitus type 2 | 59 | 52.2 | 14 | 51.9 | 1 | 50.0 | 0.041* |
| Pre-surgical history | Dyslipidemia | 28 | 24.8 | 6 | 22.2 | 1 | 50.0 | 0.041* |
| Duration of renal dialysis (years) | < 2 years | 35 | 41.2 | 4 | 23.5 | 2 | 100.0 | 0.194 |
| Duration of renal dialysis (years) | 2-5 years | 34 | 40.0 | 7 | 41.2 | 0 | 0.0 | 0.194 |
| Duration of renal dialysis (years) | > 5 years | 16 | 18.8 | 6 | 35.3 | 0 | 0.0 | 0.194 |
| Cause of ESRD | Unknown | 54 | 47.8 | 10 | 37.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | IgA nephropathy | 5 | 4.4 | 1 | 3.7 | 1 | 50.0 | 0.704 |
| Cause of ESRD | HTN | 14 | 12.4 | 1 | 3.7 | 0 | 0.0 | 0.704 |
| Cause of ESRD | DM | 24 | 21.2 | 7 | 25.9 | 1 | 50.0 | 0.704 |
| Cause of ESRD | FSGS | 6 | 5.3 | 3 | 11.1 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Lupus nephritis | 0 | 0.0 | 1 | 3.7 | 0 | 0.0 | 0.704 |
| Cause of ESRD | ADPKD | 3 | 2.7 | 3 | 11.1 | 0 | 0.0 | 0.704 |
| Cause of ESRD | CKD | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | IgA Vasculitis | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Badet Bidel Syndrome | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Nephrotic syndrome | 1 | 0.9 | 1 | 3.7 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Diffuse Global sclerosis with interstitial fibrosis and tubular atrophy | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Immune mediated Glomerulonephritis | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Posterior urethral valve | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Atrophic kidney | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Crescent forming glomerulonephritis (Ragnar) | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Reflux nephropathy | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Renal Agenesis | 0 | 0.0 | 1 | 3.7 | 0 | 0.0 | 0.704 |
| Cause of ESRD | Obesity | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.704 |
A total of $93.7\%$ [133] of the study patients had no pre-surgical complications which were $92\%$ [104] for class one obesity, all of class two and three obesity patients ($$n = 27$$) with no statistical significance ($$P \leq 0.998$$) (Table 3).
**Table 3**
| Pre-surgical complications | Unnamed: 1 | Unnamed: 2 | BMI Before Transplant | BMI Before Transplant.1 | BMI Before Transplant.2 | BMI Before Transplant.3 | BMI Before Transplant.4 | Unnamed: 8 | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pre-surgical complications | Total | Total | Obese class 1 | Obese class 1 | Obese class 2 | Obese class 2 | Obese class 3 | Obese class 3 | P-value |
| Pre-surgical complications | No | % | No | % | No | % | No | % | P-value |
| | 133 | 93.7 | 104 | 92.0 | 27 | 100.0 | 2 | 100.0 | 0.998 |
| Hypotension | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Pulmonary Edema | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Low Hb | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Gross hematuria | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Hypocalcemia | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Thrombosis right AV fistula | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Diabetic retinopathy, neuropathy, nephropathy | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Bilateral jugular vein occlusion | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Left ventricular clot | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Hyperkalemia | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.998 |
| Anuria | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0.998 |
As for the length of hospital stay, $35.9\%$ [46] stayed for less than one week ($36.3\%$ of obese class one, $33.3\%$ of obese class two, and $50\%$ of obese class three; $$P \leq 0.789$$) while $25\%$ [32] of the study cases stayed for more than two weeks ($23.5\%$ of obese class one, $33.3\%$ of obese class two, and none of obese class three; $$P \leq 0.789$$). As for graft function, it was stable among $81.7\%$ [116] of study cases ($79.6\%$ of obese class one, $88.9\%$ of obese class two, and all obese class three; $$P \leq 0.692$$). Considering post-transplant complications, $14.1\%$ [20] of the study cases had DM ($16.8\%$ of obese class one, $3.7\%$ of obese class two, and none of obese class three; $$P \leq 0.996$$) and UTI among $7\%$ [10] of the cases ($6.2\%$ of obese class one, $11.1\%$ of obese class two, and none of obese class three; $$P \leq 0.996$$). As for patients' fate, only one case ($0.7\%$) died which was obese class one. All these differences according to patients' BMI were statistically insignificant (Table 4).
**Table 4**
| Outcome | Outcome.1 | Total | Total.1 | BMI Before Transplant | BMI Before Transplant.1 | BMI Before Transplant.2 | BMI Before Transplant.3 | BMI Before Transplant.4 | BMI Before Transplant.5 | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Outcome | Outcome | Total | Total | Obese class 1 | Obese class 1 | Obese class 2 | Obese class 2 | Obese class 3 | Obese class 3 | P-value |
| Outcome | Outcome | No | % | No | % | No | % | No | % | P-value |
| Length of hospital stay | < 1 week | 46 | 35.9 | 37 | 36.3 | 8 | 33.3 | 1 | 50.0 | 0.789 |
| Length of hospital stay | 1-2 weeks | 50 | 39.1 | 41 | 40.2 | 8 | 33.3 | 1 | 50.0 | 0.789 |
| Length of hospital stay | > 2 weeks | 32 | 25.0 | 24 | 23.5 | 8 | 33.3 | 0 | 0.0 | 0.789 |
| Graft Function | Stable | 116 | 81.7 | 90 | 79.6 | 24 | 88.9 | 2 | 100.0 | 0.692 |
| Graft Function | Failure | 2 | 1.4 | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.692 |
| Graft Function | Delayed graft function | 2 | 1.4 | 1 | 0.9 | 1 | 3.7 | 0 | 0.0 | 0.692 |
| Graft Function | Unknown | 22 | 15.5 | 20 | 17.7 | 2 | 7.4 | 0 | 0.0 | 0.692 |
| Post-Transplant Complications | | 97 | 68.3 | 76 | 67.3 | 19 | 70.4 | 2 | 100.0 | 0.996 |
| Post-Transplant Complications | HTN | 3 | 2.1 | 2 | 1.8 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | DM | 20 | 14.1 | 19 | 16.8 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Dyslipidemia | 3 | 2.1 | 1 | 0.9 | 2 | 7.4 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Chronic low K | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Loosing diarrhea | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Hyperthyroidism | 1 | 0.7 | 0 | 0.0 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | NASH | 2 | 1.4 | 1 | 0.9 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Acute tubular necrosis | 3 | 2.1 | 2 | 1.8 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Hypocalcemia | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Hypercalcemia | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Hypomagnesaemia / low mg | 2 | 1.4 | 1 | 0.9 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Membranous Nephropathy | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Hydronephrosis | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Kaposi’s Sarcoma | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | GU Complications | 4 | 2.8 | 3 | 2.7 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Neurological Complications | 4 | 2.8 | 3 | 2.7 | 1 | 3.7 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | CVS Complications | 4 | 2.8 | 2 | 1.8 | 2 | 7.4 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Infectious Complications | 3 | 2.1 | 3 | 2.7 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | MSK Complications | 3 | 2.1 | 3 | 2.7 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Dermatological Complications | 2 | 1.4 | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | Respiratory Complications | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.996 |
| Post-Transplant Complications | UTI | 10 | 7.0 | 7 | 6.2 | 3 | 11.1 | 0 | 0.0 | 0.996 |
| Deceased / Alive | Alive | 141 | 99.3 | 112 | 99.1 | 27 | 100.0 | 2 | 100.0 | 0.879 |
| Deceased / Alive | Deceased | 1 | 0.7 | 1 | 0.9 | 0 | 0.0 | 0 | 0.0 | 0.879 |
Serum creatinine level showed a significant reduction from 845 ± 271.8 before transplant to 107.2 ± 97.0 one-year after surgery ($$P \leq 0.001$$). Also, BUN was significantly decreased from 21.6 ± 9.2 before surgery to 6.3 ± 3.7 one-year after transplant surgery ($$P \leq 0.001$$) (Table 5).
**Table 5**
| Lab finding | Range | Mean | SD | P-value |
| --- | --- | --- | --- | --- |
| Serum creatinine | | | | 0.001* |
| Before surgery | 212-1,708 | 845.9 | 271.8 | 0.001* |
| At discharge | 56-794 | 136.9 | 114.9 | 0.001* |
| 6m post-surgery | 56-707 | 110.5 | 82.0 | 0.001* |
| 1-year post-surgery | 53-883 | 107.2 | 97.0 | 0.001* |
| BUN | | | | 0.001* |
| Before surgery | 4.8-44.7 | 21.6 | 9.2 | 0.001* |
| At discharge | 2.8-38.7 | 9.5 | 6.4 | 0.001* |
| 6m post-surgery | 2.0-17.8 | 6.3 | 2.4 | 0.001* |
| 1-year post-surgery | 2.4-31.7 | 6.3 | 3.7 | 0.001* |
## Discussion
When looking at the obesity classes before and after surgery, we found that the class one population had decreased in number by almost half after surgery. On the other hand, class three showed an increase in number. The decrease in class one was divided by either patients gaining or losing weight and then classified into their respective categories. One study supports the fact that successful transplantations are associated with higher weight and BMI without an elevation in lean body mass [9] These findings might help surgeons in looking at transplantation as an important element that influences weight. However, the reasons and factors determining whether a patient gains or loses weight are not clear from our data, due to its retrospective nature, and need further study. Furthermore, an important point to consider is waist circumference measurement since BMI alone or in combination with waist circumference could be associated with different results [10]. This supports the questionable role of BMI significance in determining the eligibility for transplantation since many centers reject patients based on BMI alone [11], and its lack of ability to differentiate between muscle mass and fat mass [12] puts its use into further question.
Our data shows that the most common condition in the population’s history was hypertension, yet the most common cause of ESRD was diabetes. This shows the major effect of diabetes before transplantation. After transplantation, however, cardiovascular disease has the largest impact on the survival of both the graft and the patient [13]. Lentine et al. found cardiovascular events increase with the increase in BMI [14]. The previous notion is important to keep in mind in that BMI could help predict associated comorbidities, and the fact that obesity is a risk factor for conditions such as hypertension [15], supports that idea. For classes one and two, we found a significant association between BMI and the patient being hypertensive or on dialysis, but a small fraction was free of them. In contrast, class three patients all had a history of being hypertensive. This finding could justify why some centers have a lower limit for transplants.
The majority of obese class one patient had no pre-renal transplant complications. However, some of the patient's experienced hypotension, anemia, hyperkalemia, and other pre-surgical complications. In the pre-operative assessment of obese patients who underwent bariatric surgery, it was found that obesity correlates with multiple comorbidities. These comorbidities can make intraoperative management difficult and complicate the post-operative course. Some obesity-related conditions include diabetes, gastroesophageal reflux disease, heart disease, and many others. Thus, it is important to evaluate such patients pre-operatively to identify these conditions and manage them accordingly to avoid post-operative complications [16]. On the other hand, all patients classified as obesity class two and class three did not present with pre-renal transplant complications.
Among our patients who were obese before undergoing renal transplant surgeries, obese class one patients have shown similarity in the length of hospital stay (LOS) by which 41 patients stayed for one to two weeks, followed by 37 patients that stayed for less than a week, and 24 patient stayed for more than two weeks. For obese class two patients, the length of stay was distributed equally between them in which eight patients stayed for less than a week, for one to two weeks, and more than two weeks. Only two patients with obesity class three stayed for less than a week and one to two weeks. This shows according to our data that there is no correlation between the progression of obesity class and the length of hospital stay which has been shown in another study that LOS is significantly longer in patients with BMI more than or equal to 35 compared to patients with BMI less than 35 [17]. As for graft function, most of the study cases had a stable function. Moreover, four cases of obesity class one and two have experienced either failure or DGF, and despite this study containing only two cases of obesity class three, all of them had stable graft function. It was documented in some articles that higher pre-transplant BMI was associated with the risk of DGF [18].
Post-transplant DM (PTDM) was the most notable complication post-transplant, and all PTDM cases were shown in obese class one patients except for one case which was in obese class two. PTDM is documented as a frequent post-transplant complication in allograft kidney recipients. It can be attributed to modifiable and non-modifiable risk factors. In non-modifiable risk factors, it is similar in the way of development of type two diabetes in the general population. However, modifiable risk factors can include perioperative stress, vitamin D deficiency, cytomegalovirus or hepatitis C, and immunosuppressive medications like glucocorticoids, mTOR inhibitors, and calcineurin inhibitors [19]. Urinary tract infections (UTI) were also noted post-transplant, they are an important factor that may lead to increased graft failure and morbidity. UTI occurs in $25\%$ following one year of transplant in kidney transplant recipients and is responsible for $45\%$ of infectious complications, which can worsen the quality of life and can potentially impair graft function [20]. Other post-transplant complications noted were hypertension, acute tubular necrosis, dyslipidemia, and others.
Both serum creatinine and blood urea nitrogen (BUN) have shown a significant reduction from the period preceding the surgery to the time of discharge in which the mean reduction was around sixfolds and twofolds for serum creatinine and BUN levels, respectively. A notable reduction was also noted after six months of discharge in both serum creatinine and BUN. Following one year after renal transplant surgery, there was nearly a plateau of mean values of both entities which were within the normal ranges.
## Conclusions
Our paper highlights the interrelation between successful transplantation and change in BMI classes post-op as an alteration in said classes was noted in the majority of our patients. However, no correlation has been established with regard to obesity class and length of hospital stay.
Hypertension was the most common comorbidity in our sample, yet diabetes was more likely to be the cause behind ESRD. PTDM was the most prominent post-transplant medical complication followed by UTI. With respect to kidney function, a remarkable reduction in serum creatinine and BUN has been observed at the time of discharge and after six months compared to pre-transplant measurements. DGF or failure was infrequent and has been noted in only a handful of cases, as most patients had stable grafts throughout their course.
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|
---
title: 'Glycaemia in low-premixed insulin analogue type 2 diabetes patients in a real-world
setting: are the CGM targets met?'
authors:
- Mitja Krajnc
- Nika Aleksandra Kravos Tramšek
journal: European Journal of Medical Research
year: 2023
pmcid: PMC9990036
doi: 10.1186/s40001-023-01081-y
license: CC BY 4.0
---
# Glycaemia in low-premixed insulin analogue type 2 diabetes patients in a real-world setting: are the CGM targets met?
## Abstract
### Background
There are insufficient data on continuous glucose monitoring (CGM) in nonintensive insulin therapy patients. Using CGM and the recommended CGM targets, we wanted to evaluate low-premix insulin analogue therapy (biphasic aspart/NovoMix 30 and biphasic lispro 25/Humalog Mix 25) in real-world type 2 diabetes patients for glycaemic efficacy and especially hypoglycaemia.
### Methods
The prospective observational study was performed on 35 patients who were treated with a low-premixed insulin. We used the Dexcom G6 system for CGM (9.6 ± 1 days) to measure the clinically relevant CGM parameters: glycaemic variability (%CV), TBR (time below range) < 3.0 mmol/$l = 54$ mg/dl (level 2 hypoglycaemia), TBR 3.0–3.8 (= 54–69 mg/dl), TIR (time in range) 3.9–10–0 mmol/l (70–180 mg/dl), TAR (time above range) 10–13.9 mmol/l (180–250 mg/dl) and TAR > 13.9 mmol/l (250 mg/dl). We also assessed clinical and demographic characteristics, laboratory HbA1c, fasting blood glucose, peak postprandial glucose values, and the percentage of hypoglycaemia between 00:00 and 06:00.
### Results
In our patients, the average ± SD age was 70.4 ± 9.2 years, diabetes duration 17.4 ± 7.1 years, $51\%$ were females, average daily insulin dose was 46.4 units ($80\%$ received biphasic aspart). The average ± SD TIR was 62.1 ± $12.2\%$, TBR < 3.0 mmol/l 0.8 ± $2.0\%$, TBR 3.0–3.8 mmol/l 1.5 ± $1.5\%$, TAR 10–13.9 mmol/l 29.2 ± $12.4\%$, TAR > 13.9 mmol/l 6.4 ± $7.2\%$ and %CV 29.9 ± $7.1\%$. The average time in hypoglycemia was 33.1 min daily in our patients (11.5 min in the level 2 range). In the older/high-risk population, the TBR/TIR/TAR/level 2 TAR targets were met in $\frac{40}{80}$/77/$80\%$, respectively. For the general T2D people, level 2 TBR/TBR/TIR/TAR/level 2 TAR would be met in $\frac{74}{83}$/$\frac{34}{77}$/$49\%$. Average fasting blood glucose was 8.0 ± 2.5 mmol/l (144 ± 45 mg/dl), BMI 31.3 ± 5.1 kg/m2, daily insulin dose 46.4 ± 12.1 units, HbA1c 57.4 ± 5.4 mmol/mol (7.4 ± $0.7\%$). The glycaemic variability goal was met in $80\%$ (with $66\%$ meeting the lower $33\%$ CV goal). 17 ± $12\%$ of hypoglycaemia was nocturnal. People with TBR > $4\%$ were significantly older.
### Conclusions
Most of our type 2 diabetes patients, treated with low-premixed insulin, did not meet the recommended TBR target for older/high-risk patients while meeting the TIR and TAR targets. Nevertheless, the time spent in (total and nocturnal) hypoglycemia was short. The study indicates that the general type 2 diabetes population targets would mostly be met for TBR and %CV in our patients but not the TIR and TAR targets. CGM appears to be a useful clinical tool in these patients.
## Introduction
From 2018 to 2030, the world insulin requirements are expected to increase parallel to the incidence of diabetes by more than $20\%$ [1]. In modern patient-centred and individualised type 2 diabetes (T2D) management, it is necessary to address the adverse effects of medications, key person characteristics, the complexity of regimens (including frequency), access, cost and availability of drugs [2, 3]. The most significant reduction in HbA1c is seen with insulin regimens and GLP1 receptor agonists [4]. Insulins do not offer additional cardiovascular or renal benefits, cause weight gain and require subcutaneous injections. The currently recommended way of starting insulin therapy in people with T2D is the addition of basal insulin to the prior pharmacological treatment in conjunction with revisiting health behaviour and diabetes self-management education and support. Only when combination therapies with basal insulin are no longer sufficient, the next step is to further intensify therapy with prandial insulin [2, 3, 5].
Historically, premixed insulins have frequently been prescribed to persons with T2D. 2012 ADA and EASD recommendations stated that the most convenient strategy for insulin initiation was a single basal insulin injection, and the most precise and flexible prandial coverage was possible with basal-bolus therapy. Premixed insulin, consisting of a fixed combination of intermediate insulin with a rapid analogue or the regular insulin, was considered somewhat inflexible but appropriate for certain patients who ate regularly and needed a simplified approach beyond basal insulin. *In* general, when compared with basal insulin alone, premixed regimens tend to lower HbA1c to a more significant degree, but often at the expense of more hypoglycemia and weight gain. Disadvantages include the inability to titrate the shorter from the longer acting component of the prescribed formulations [6, 7]. There is less weight gain, less hypoglycemia and lower insulin dose with premixed insulin analogues compared to basal-bolus therapy [3, 8]. Still, the choice of an insulin regimen for initiation or intensification of therapy is a subjective decision, that should consider the duration of diabetes, symptoms of hyperglycemia, lifestyle, drug therapy, glycaemic status (with patterns of glycaemia, risk of hypoglycemia and glycaemic variability) and patient preference [9]. Premixed insulin is sometimes considered the best option in patients who are unwilling or unable to adhere to the increased number of injections and monitoring required with basal plus/basal-bolus regimens [10]. Two doses of premixed insulin are a simple, convenient means of spreading insulin across the day and are usually considered when basal or basal-plus insulin is no longer sufficient to reach an individual's A1c target [3]. Despite national and international guidelines, the proportions of insulin regimens differ substantially between European countries. Premixed insulins are still widely used in some countries [11]. E. g. premix use, already high as a starting regimen, was used by one-third of the participants after 4 years in Northern Europe in CREDIT non-interventional study, with authors suggesting a lower number of injections as a probable cause [12].
In our study, we wanted to evaluate low-premix analogue therapy (biphasic insulin aspart, containing $30\%$ soluble insulin aspart and $70\%$ protamine-crystallized insulin aspart/NovoMix 30 and biphasic lispro 25, containing $25\%$ soluble insulin lispro and $75\%$ protamine-crystallized insulin lispro/Humalog Mix 25) for glycaemic efficacy and the occurrence of hypoglycaemia. We performed the study in a real-world setting, using a continuous glucose monitoring (CGM) system. The results would help us consider the potential need for a change in selected antihyperglycaemic therapy. Following the guidance [2, 3, 13], combination therapy including a basal insulin, basal-bolus regimen and fixed basal insulin/GLP1 receptor antagonist combination could all be considered the antihyperglycaemic alternatives of premixed insulin.
Numerous studies have demonstrated significant clinical benefits of CGM use regardless of insulin delivery method, also for people with T2D [14, 15]. Nevertheless, CGM data in patients treated with premixed insulin are scarce, and the evidence is insufficient for people with a nonintensive insulin regimen [3, 16]. The primary CGM characteristic of effective and safe glucose control is high time in range (TIR) and low time below range (TBR), in particular for blood glucose (BG) below 3.0 mmol/l (54 mg/dl). The international guidance on targets for assessment of glycemic control for the general adult population with T2D recommends aiming for > $70\%$ of readings in TIR 3.9–10.0 mmol/l (70–180 mg/dl), < $4\%$ of readings below 3.9 mmol/l; < $1\%$ of readings below 3.0 mmol/l (54 mg/dl, level 2 hypoglycaemia), < $25\%$ of readings above 10.0 mmol/l (180 mg/dl) and < $5\%$ of readings above 13.9 mmol/l (250 mg/dl, level 2 hyperglycaemia). For older and high-risk people with T2D, the targets are less stringent in the interest of safety: > $50\%$ of readings should be in TIR, less than $1\%$ below 3.9 mmol/l and less than $50\%$ above 10.0 mmol/l (with less than $10\%$ above 13.9 mmol/l). Evidence regarding CGM for this group is lacking but the elevated risk of hypoglycemia has been well-documented. The goal for glycaemic variability (coefficient of variation) should be ≤ $36\%$ [14]. Hypoglycemia is especially a threat during the night in patients with insulin-treated T2D, which may lead to increased mortality, anxiety, poor adherence, and hypoglycemia unawareness [17]. Nocturnal hypoglycemia is usually defined by a time window for which the international consensus recommendation is from midnight to 6 am, which generally includes the duration of nighttime sleep and the longest inter-prandial interval [18].
While CGM provides insights into the daily glucose fluctuations, it can also be used to calculate an estimated HbA1c and expressed as glucose management indicator (GMI) [19]. Clinical studies are an excellent application for intermittent CGM, and CGM parameters are nowadays regarded as mandatory for documenting clinical trials [20]. Through a broad spectrum of glucose-derived data, CGM is a valuable tool for clinically evaluating glucose-lowering medications [21].
## Methods
We conducted the prospective observational study on 35 persons with T2D treated in our institution's diabetology outpatient clinic (Maribor University Medical Centre provides secondary and tertiary medical care for northeastern Slovenia; Slovenian insulin-treated patients are not usually followed-up in primary care). Our participants were randomly selected from all premixed insulin patients from June to December 2022. Simple random sampling was performed by a computer programme that generated a random number, corresponding to a patient file number. All the patients regularly received diabetes self-management education and support (with a more extended structured programme at an insulin initiation and then periodically at follow-up visits and when requested by a patient or a healthcare professional). Our institution's medical ethics committee approved the study in October 2021. We calculated the sample size to estimate a simple mean for TBR < 3.0 mmol/l (54 mg/dl) and TBR 3.0–3.8 mmol/l (54–69 mg/dl) to be at least 25, with the assumptions of standard deviation 1.0 (based on pilot data) and required size of standard error 0.2.
The inclusion criteria were the following: signed written informed consent after the thorough familiarisation with the study, age above 18 years, T2D diagnosed for at least 6 months, current treatment with a premixed insulin analogue preparation: BiAsp 30/biphasic aspart 30 (NovoMix 30 by Novo Nordisk) or biphasic lispro mix 25 (Humalog Mix 25 by Eli Lilly). The exclusion criteria were HbA1c above 85.8 mmol/mol ($10.0\%$), current use of a concomitant medication that could affect glycaemia or sensor accuracy (e.g., a systemic glucocorticoid, hydroxyurea, high doses of paracetamol), end-stage renal disease, pregnancy and breastfeeding. One-quarter of eligible patients refused to participate in the study.
In our participants, we collected data on the following parameters: age, sex, T2D duration, daily insulin dose and number of daily injections, concomitant use of metformin, an SGLT2 inhibitor or an incretin-based medication, fasting BG and HbA1c before the sensor insertion, body weight and body mass index and a known diagnosis of at least one diabetic microvascular and/or at least one macrovascular complication. Fasting BG and IFCC-standardized HbA1c were measured in our institution's central laboratory within 10 days before a sensor insertion.
For continuous glucose monitoring, we used a real-time Dexcom G6 personal system (Dexcom, Inc., San Diego, California). It does not require calibration and can be worn for up to 10 days, which was also our goal. Its overall MARD (mean absolute relative difference) is $9.0\%$ [20, 21]. We used devices in a blinded fashion, with the participants educated on the proper conduct for interstitial glucose data collection via a receiver. The participants were also instructed to lead their usual daily routine and to continue with their current diabetes and other treatments. A Dexcom G6 sensor was inserted on day 1 by an experienced nurse educator with an auto-applicator, and the participants were instructed on good sensor care. A sensor was removed after 10 days (or earlier if there were technical difficulties or a participant requested it). The specialized Dexcom Clarity software was used. From the CGM data, we recorded the percentage of TIR, TBR (3.0–3.8 mmol/l, 54–70 mg/dl), TBR (< 3.0 mmol/l, 54 mg/dl), time above range (TAR) (10–13.9 mmol/l, 180–250 mg/dl), TAR (> 13.9 mmol/l, 250 mg/dl), glycaemic variability % (CV%) and glucose management indicator (GMI). For each participant, we also visually determined average peak postprandial BG after breakfast, lunch and dinner (within 3 h postprandial period) from the Dexcom Clarity CGM graph, based on the participants' self-reported times of the individual main meals. From the Dexcom Clarity analysis, we also recorded the percentage of total TBR occurring during the nocturnal period (between 00:00 and 06:00; nocturnal hypoglycemia).
We performed statistical analysis with SPSS Statistics, version 29.0.0.0 (Chicago, IL, USA). We analysed data to acquire averages, standard deviations, minimum and maximum values of the chosen parameters, and the frequencies stratified by clinically relevant intervals for a selected variable. We performed Wilcoxon rank sum test to assess whether the examined parameters differed significantly between the two TBR groups (TBR < $4\%$ vs. ≥ $4\%$). P value of < 0.05 was considered to be statistically significant.
## Results
The study included 35 participants. Clinical and demographic characteristics are shown in Table 1. The mean age of the participants was 70.4 years, $51\%$ were women, and all were white; the mean diabetes duration was 17.4 years and the insulin treatment duration was 7.6 years. The average laboratory serum fasting glucose was 8.0 mmol/l (144 mg/dl) and the average HbA1c was 57.4 mmol/mol ($7.4\%$). The average total daily insulin dose was 46.4 units in 2.2 daily doses. In average, we acquired 9.6 days of sensor data in a participant (that is 336 patients-days in all the participants). $80\%$ of the participants were treated with biphasic aspart, the rest with biphasic lispro. Considering concomitant medications, $69\%$ were also treated with metformin, $31\%$ with a SGLT2 inhibitor, $17\%$ with a DPP-4 inhibitor, $6\%$ with a GLP-1 receptor agonist and none with a sulphonylurea or a glinide. $69\%$ of patients had at least one microvascular complication and $37\%$ had at least one macrovascular complication of diabetes. The mean BMI was 31.3 and the mean relative body change in the previous 5 years was + $3.6\%$.Table 1Clinical and demographic characteristics of our patients ($$n = 35$$)MinimumMaximumMeanStd. deviationAge (years)529070.49.2Diabetes duration (years)52817.47.1Insulin treatment duration (years)2197.63.4Fasting BG (mmol/l (mg/dl))2.4 (43.2)16.7 (300.6)8.0 [144]2.5 [45]HbA1c (mmol/mol (%))44.3 (6.2)74.9 (9.0)57.4 (7.4)5.4 (0.7)Body weight (kg)6111584.614.4Body mass index (kg/m2)244631.35.1Waist circumference (cm)8513211012Daily total insulin dose (units)247846.412.1Daily insulin doses (number/day)232.20.4CGM active sensor data (days)6119.61Relative body weight change in the previous 5 years (%)− 681.83.6N%Female sex1851Premixed insulinAspart 30: 28Lispro 25: 78020Metformin therapy2469SGLT2 inhibitor therapy1131DPP-4 inhibitor therapy617GLP-1 receptor agonist therapy26Insulin secretagogues therapy00HbA1c (mmol/mol (%))To 53 (7.0): 953 (7.0)–69.4 (8.5): 1969.4 (8.5) +: 7265420At least 1 microvascular complication of diabetes2469At least one macrovascular complication of diabetes1337CGM continuous glucose monitoring, BG blood glucose, N number In our participants, the average TIR was $62.1\%$, TBR $2.3\%$ and TAR $35.6\%$, with $0.8\%$ of time below 3.0 mmol/l and $6.4\%$ of time above 13.9 mmol/l. Average %CV was 29.9. Mean laboratory HbA1c was equal to the calculated glucose management indicator (7.4). The average glucose after lunch and dinner was 12.0 mmol/l (216 mg/dl). On average, $17\%$ of hypoglycaemia occurred in the period between 00:00 and 06:00. Table 2 presents our data in standardised CGM metrics (based on 14). Data on average peak postprandial glucose after three main meals and the percentage of total TBR during 00:00–06:00 period (nocturnal hypoglycaemia) are also included. Table 2Glycaemic data in standardised CGM metrics, average peak postprandial BG and percentage of nocturnal TBRMinimumMaximumMeanStd. deviationTIR, time in range 3.9–10.0 mmol/l (70–180 mg/dl) (%)428962.112.2TBR, time below range 3.0–3.8 mmol/l (54–70 mg/dl) (%)05.21.51.5TBR, time below range < 3.0 mmol/l (54 mg/dl) (%)010.40.82.0TAR, time above range 10.0–13.9 mmol/l (180–250 mg/dl) (%)11.058.229.212.4TAR, time above range > 13.9 mmol/l (250 mg/dl) (%)028.06.47.2glycaemic variability (%CV)15.847.729.97.1glucose management indicator (GMI)6.28.77.40.7BG after breakfast (mmol/l (mg/dl))8.0 [144]18.0 [324]11.2 (201.6)2.0 [36]BG after lunch (mmol/l (mg/dl))3.0 [54]19.0 [342]12.0 [216]2.8 (50.4)BG after dinner (mmol/l (mg/dl))9.0 [162]18.0 [324]12.0 [216]2.5 [45]Percentage of total TBR during 00:00–06:00 time period (nocturnal hypoglycaemia)0361712BG blood glucose, %CV coefficient of variation in % $34\%$ of the participants achieved the recommended general T2D glycaemic goals [14] for TIR, $83\%$ for TBR below 3.9 mmol/l ($74\%$ for values below 3.0 mmol/l) and $23\%$ for TAR ($51\%$ for values above 13.9 mmol/l). $83\%$ of the participants achieved older/high-risk population goals for TIR, $40\%$ for TBR, and $77\%$ for TAR ($80\%$ for values > 13.9 mmol/l). $80\%$ of the participants met the general %CV goal (of ≤ $36\%$). In Table 3, we present the stratified frequencies for selected glycaemic parameters. Table 3Stratified frequencies for selected glycaemic parametersN%TIR, time in range (3.9–10.0 mmol/l) (%) < $50\%$: 650–$70\%$: 17 > $70\%$: 12174934TBR, time below 3.9 mmol/l (70.2 mg/dl) (%) < $1\%$: 14 < $4\%$: 29≥$4\%$: 6408317TBR, time < 3.0 mmol/l (54 mg/dl) (%) < $1\%$: 261–$3\%$: 8 > $3\%$: 174233TAR, time above 10.0 mmol/l (180 mg/dl) (%) < $25\%$: 825–$50\%$: 19 > $50\%$: 8235423TAR, > 13.9 mmol/l (250 mg/dl) (%) < $5\%$: 185–$10\%$: 10 > $10\%$:7512920glycaemic variability (%CV)≤ $36\%$: 28 > $36\%$: 7 < $33\%$: 23802066percentage of TBR during the period 00:00–06:$000\%$: 221–$10\%$: 611–$30\%$: 530+%: $26317146\%$ CV coefficient of variation in %, TIR time in range, TBR time below range, TAR time above range We present the average CGM metrics of our participants in Fig. 1.Fig. 1The average CGM metrics of our participants. Times are presented as %. TAR: time above range (orange: >13.9 mmol/l, yellow: 10-13.9 mmol/l), TIR: time in range (green), TBR: time below range (light red: 3-3.8 mmol/l, dark red: <3.0 mmol/l)
We performed Wilcoxon rank sum test to determine whether the two TBR groups (TBR < $4\%$ vs. ≥ $4\%$) differed by age, sex, T2D duration, daily insulin dose, fasting BG, HbA1c, body weight or body mass index. Based on the sum of ranks for TBR ≥ $4\%$ ($$n = 9$$), p value of < 0.05 was found for age (T value 162). The differences in other parameters were insignificant ($p \leq 0.05$).
Twelve eligible and invited patients ($26\%$) refused to take part in the study. They tended to be older, male and with longer diabetes duration.
## Discussion
In our T2D real-world patients treated with a low-premixed insulin preparation, the average time spent in hypoglycemia was 33.1 min daily ($2.3\%$), with 11.5 min in level 2 hypoglycemia. In the general type 2 diabetes population, that would translate to reaching the CGM TBR targets [14] in the majority of patients: $74\%$ for level 2 and $83\%$ for all hypoglycemia. Our patients, however, were older on average [24–26] and at higher risk for hypoglycemia, which is more common in the elderly, with longer duration of diabetes and in insulin-treated people [3, 28]. In addition, in our population, people with TBR ≥ $4\%$ were significantly older than people with TBR < $4\%$ but did not differ in the other studied parameters. Considering the safer hypoglycaemia-preventing guidance for this group [14], only $40\%$ of patients would reach the more strict TBR target of less than $1\%$ below 3.9 mmol/l. Nevertheless, the average TBR was quite short in our study. Wang et al. measured average TBR < 3.9 mmol/l of $9.4\%$ for a premixed insulin group ($$n = 194$$) [26], and Margaritidis et al. the value of $6.3\%$ in a well-controlled group ($$n = 36$$) [27]. Lin et al. reported a TBR value of 0 for their 16 premix patients [29].
In our study, $17\%$ of hypoglycaemia occurred during the most vulnerable nocturnal period (between 00:00 and 06:00), which is less than is expected in higher risk patients, e.g., type 1 or advanced insulin-dependent type 2 diabetes patients [17, 18]. $63\%$ of the participants did not experience nocturnal hypoglycaemia at all. The lower risk of nocturnal hypoglycaemia in the participants could be linked to a ratio of prandial/basal insulin, residual beta cell function and insulin secretion, the more pronounced tendency to hyperglycaemia in premix patients and behavioural modification. There are emerging data that some CGM parameters could help prevent nocturnal hypoglycaemia in insulin-treated T2D patients [17].
In contrast, the TIR targets for high-risk T2D people [14] were mostly (in $80\%$) met in our patients, with only $34\%$ meeting the general population target. Wang et al. showed a similar TIR of $59.8\%$ for their group [26]. Lin et al. reported a TIR of $39.0\%$ for premix-treated patients [29], and Margaritidis et al. measured a value of $74.2\%$ [27]. The TAR target for high-risk people was met in $77\%$ of our group. $77\%$ of patients did not meet the more stringent general population hyperglycaemia goal, with $49\%$ not satisfying the level 2 hyperglycaemia goal. Wang et al. also reported level 2 hyperglycemia $3.8\%$ of the time [26]. In the study by Margaritidis et al., TAR was $19.6\%$ in a well-controlled group [27]. Lin et al. reported a TAR of $61.0\%$ [29]. *The* general glycaemic variability target was met in $80\%$ of our patients. Some studies suggest that the lower %CV value of < $33\%$ provides additional protection against hypoglycaemia in patients receiving insulin [14, 30], which was reached by $66\%$ of our patients. Wang et al. report a higher average %CV of $36.1\%$ for premix patients [26]; in the study by Margaritidis, it was $34.2\%$ [27]. In the study by Lin et al., it was $24\%$ [29].
There is a growing body of evidence suggesting that CGM use confers benefits in individuals who are treated with less-intensive therapies, among them better glycaemic control (e.g., lower HbA1c), reduced resource utilisation and costs, and also (beyond diabetes) CGM-based behavioural interventions and quality of life [31]. Our results indicate that CGM is clinically useful in individual premix-treated patients to reveal hypo- and hyperglycaemic patterns and time outside the range. That could be especially important in older/high-risk people with multiple hypoglycemic risk factors. As always, glycaemic targets should be individualised, and rarely is that more applicable than in the personal use of CGM and the data it provides [32]. Undoubtedly, insulin therapy would often be modified in clinical practice based on a CGM report, e. g. a premix changed for other medication and/or additional diabetes self-management education and support offered. Considering the findings of some studies, in some patients, different insulin regimens could be more effective and safer than premixed insulin in achieving the CGM-based targets [25–27]. Many people with T2D and less-intensive therapies could benefit from CGM use, with both safety and efficacy improved. We expect future research (including cost–benefit analyses) to expand the role of CGM also in premixed-insulin patients.
The standard glycemic parameters (measured HbA1c, preprandial and postprandial glucose) were all slightly above the recommended targets for the general type 2 diabetes population but satisfactory for older adults [3, 5, 13]. The patients received standard guideline-informed concomitant antihyperglycaemic therapy that was not expected to cause or contribute to hypoglycemia [2, 3, 5].
Our sample size was small due to the limitations in study staff and personal access to the outpatient clinic after COVID-19. In addition, a significant number of invitees ($26\%$) refused to participate in the study, which could result in sampling bias and affect the generalizability of the results. We also did not include the patients with the worst glycaemic control, since a timely therapeutical intervention was considered the priority. Our study is also purely descriptive. On the other hand, we presented evidence from a real-world setting based on the best available quality CGM and laboratory results. GMI and laboratory HbA1c were identical in our patients, indicating stable glucose control over a period longer than 10 days [17, 33] and suggesting the patients continued with their usual glycaemic control routine while using a glucose sensor. TIR and laboratory HbA1c also corresponded well, since the HbA1c value of 57.4 mmol/mol ($7.4\%$) indicates the TIR of approx. $60\%$ in general [32, 33].
## Conclusions
Our real-world observational study in T2D patients treated with low-premixed insulin shows that most of them did not meet the recommended TBR target for older/high-risk patients while reaching the TIR and TAR targets. Nevertheless, the time spent in hypoglycemia (also nocturnal) was relatively short. The study indicates that the general T2D population targets would mostly be met for TBR and %CV in our population but not the TIR and TAR targets. In our premix-treated patients, CGM appears to be a valuable and informative clinical tool for managing and selecting antihyperglycaemic therapy.
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|
---
title: Exon architecture controls mRNA m6A suppression and gene expression
authors:
- P. Cody He
- Jiangbo Wei
- Xiaoyang Dou
- Bryan T. Harada
- Zijie Zhang
- Ruiqi Ge
- Chang Liu
- Li-Sheng Zhang
- Xianbin Yu
- Shuai Wang
- Ruitu Lyu
- Zhongyu Zou
- Mengjie Chen
- Chuan He
journal: Science (New York, N.Y.)
year: 2023
pmcid: PMC9990141
doi: 10.1126/science.abj9090
license: CC BY 4.0
---
# Exon architecture controls mRNA m6A suppression and gene expression
## Body
N6–methyladenosine (m6A), the most prevalent mRNA modification in mammals, influences wide-ranging aspects of gene expression in diverse physiological and pathophysiological processes (1–3). The METTL3-METTL14 methyltransferase complex installs m6A methylation on mRNA in a common DRACH (D = A, G, or U; R= A or G; H= A, C, or U) sequence motif, but only a fraction of DRACH sequences (~$5\%$) in a subset of cellular transcripts are selected for methylation [4]. Additionally, m6A exhibits a marked regional bias in its transcriptomic distribution, being strongly enriched in unusually long internal exons and near stop codons [5, 6]. Despite the central importance of specific m6A deposition in m6A-mediated gene regulation, the mechanistic basis for m6A specificity has remained poorly understood.
In this study, we discover the existence of prevalent regulatory mechanisms that restrict m6A methylation to specific transcript regions through targeted suppression of m6A in unmethylated regions. We find that pre-mRNA splicing selectively suppresses m6A deposition in average-length internal exons, but not in longer exons. We identify Exon Junction Complexes (EJC) as major m6A suppressors that mediate this effect and control several key characteristics of global m6A specificity. EJC depletion results in pervasive aberrant methylation of mRNAs and m6A-mediated transcript destabilization. EJCs, together with interacting proteins, package and protect long stretches of proximal RNA from cellular methylation deposition, which may represent a general mechanism by which exon architecture and EJC positioning determine local mRNA accessibility to regulatory machineries.
## Abstract
N6–methyladenosine (m6A) is the most abundant mRNA modification and plays crucial roles in diverse physiological processes. Utilizing a Massively Parallel Assay for m6A (MPm6A), we discover that m6A specificity is globally regulated by “suppressors” that prevent m6A deposition in unmethylated transcriptome regions. We identify Exon Junction Complexes (EJCs) as m6A suppressors that protect exon junction-proximal RNA within coding sequences from methylation and regulate mRNA stability through m6A suppression. EJC suppression of m6A underlies multiple global characteristics of mRNA m6A specificity, with the local range of EJC protection sufficient to suppress m6A deposition in average-length internal exons, but not in long internal and terminal exons. EJC-suppressed methylation sites co-localize with EJC-suppressed splice sites, suggesting that exon architecture broadly determines local mRNA accessibility to regulatory complexes.
## Massively Parallel Assay for m6A
The extent to which global m6A specificity is controlled has important implications for m6A regulation but is poorly understood [4]. We approached this problem by asking: is the local sequence surrounding an m6A methylated site, when uncoupled from its endogenous context, sufficient to specify methylation at that site? Conversely, is the local sequence surrounding an unmethylated DRACH site, when uncoupled from its endogenous context, sufficient to prevent methylation at that site?
To assess this systematically on an epitranscriptome-wide scale, we developed a Massively Parallel Reporter Assay (MPRA) that enables high-throughput assessment of the m6A methylation status of thousands of designed sequences, which we termed Massively Parallel assay for m6A (MPm6A) (Fig. 1A and fig. S1A). In the MPm6A workflow, thousands of endogenously methylated m6A sites and endogenously unmethylated DRACH sites, with 102 nucleotides of sequence surrounding each site, were synthesized and cloned into the 3′UTR of a plasmid-based intronless GFP transgene. For each sequence, we also designed a corresponding negative control sequence in which all DRACH motifs were mutated to prevent methylation. The sequences were expressed and then m6A methylated through transfection into cells, or, when specified, through in vitro transcription and in vitro m6A methylation. The methylation status of each individual sequence was assessed by its enrichment following m6A-immunoprecipitation (IP) of mRNA. We selected 6,897 HeLa m6A sites and 3,058 unmethylated DRACH sites to assay in HeLa cells and validated the assay’s precision and accuracy (fig. S1, B to D).
## Widespread mRNA m6A suppression controls m6A epitranscriptome specificity
When we compared the methylation levels of the endogenously methylated sequences to their negative control sequences, we found that $92.8\%$ of the sequences exhibited significant methylation in this reporter assay (Fig. 1B and fig. S1E), indicating that most endogenously methylated sites do not strictly require their larger surrounding native context for methylation. Unexpectedly, $90.2\%$ of endogenously unmethylated sequences also exhibited significant methylation (Fig. 1B and fig. S1E). The MPm6A enrichment scores of the endogenously unmethylated group were similar to the endogenously methylated group, despite their diverging endogenous methylation states (Fig. 1B). We observed similar results when the sequences were in vitro transcribed and methylated with recombinant METTL3-METTL14 (fig. S2). Thus, thousands of endogenously unmethylated DRACH sites became methylated when they were uncoupled from their endogenous contexts and expressed in an artificial reporter context. We term these identified sites “suppressed m6A sites”. We validated these results for three selected sequences (fig. S1F), and confirmed that methylation was not notably influenced by the CMV promoter of the MPm6A plasmid (fig. S3). We observed similar results when sequences were expressed within CDS or 5′UTR, though m6A enrichment was significantly lower for many sequences when placed in the CDS or 5′UTR versus in the 3′UTR (fig. S4, A to D). This suggests that 5′ regions are generally less conducive for m6A methylation than 3′ regions (fig. S4E). Collectively, these results reveal the existence of thousands of suppressed m6A sites that are silenced by unknown mechanisms.
We noted that suppressed m6A sites were enriched in the CDS and 3′UTR and were depleted near the stop codon, which is the reverse of endogenous m6A site enrichment (Fig. 1C and fig. S5A). Further, suppressed m6A sites in internal exons reside within much shorter exons (median = 167 nt) than endogenous m6A sites (median = 915 nt) (Fig. 1D). These observations suggest that endogenous m6A enrichment in long internal exons may be a consequence of suppression of m6A deposition in shorter internal exons, which comprise most exons ($90\%$ of internal exons are < 246 nt) (Fig. 1E). 942 genes containing suppressed m6A sites did not contain any endogenous m6A peaks on their transcripts (fig. S5B). Suppression of these sites appears to involve suppression of m6A deposition rather than active demethylation, as binding sites for RBM15, a METTL3-METTL14 methyltransferase complex accessory subunit, were highly enriched near endogenous m6A sites compared to suppressed m6A sites (fig. S6A), while FTO and ALKBH5 binding sites were not significantly enriched near suppressed sites and exhibited little binding near suppressed sites overall (fig. S6B). These results were unexpected as previous reports on m6A specificity had mainly focused on activating mechanisms (7–12). Our MPm6A assay suggests the existence of unknown m6A “suppressors” that govern global m6A specificity by suppressing m6A deposition.
## Pre-mRNA splicing suppresses m6A methylation proximal to splice sites
We next examined the relative enrichment of binding sites for 120 RBPs near endogenously methylated versus suppressed m6A sites to identify candidate suppressors [13]. Several spliceosome components (BUD13, SF3B4, EFTUD2) were significantly enriched near suppressed sites, suggesting that splicing may suppress m6A (fig. S6A). Because suppressed m6A sites in CDS primarily reside within average-length internal exons, we hypothesized that the splicing of average-length internal exons may suppress m6A methylation. To test this, we cloned a suppressed m6A site from an average-length internal exon in the CRY1 gene (fig. S7A) into a rabbit beta-globin minigene reporter (BG), as well as a version with the introns removed (BG Δi1,i2). First, we cloned the suppressed CRY1 site and $\frac{50}{51}$ nt of flanking sequence into the internal exon, or in the last exon of these constructs. Notably, the spliced construct strongly suppressed methylation of the sequence placed within the internal exon, but not within the last exon (fig. S7B). Removal of either intron (BG Δi1 CRY1 102, BG Δi2 CRY1 102) resulted in partial loss of suppression, indicating that splicing of both introns contributed to methylation suppression (Fig. 2A). Consistent with this notion, deletion of all splice sites also resulted in a decrease in m6A suppression (fig. S7C). Cloning in 912 nt of the CRY1 exonic sequence surrounding the suppressed site into the internal exon (BG CRY1 912), forming a long internal exon, resulted in a loss of suppression (Fig. 2A). We hypothesized that the suppression is dependent on the proximity of the m6A site, located within the center of the exon, to splice sites. Expanding the length of the BG CRY1 102 internal exon by cloning in larger amounts of CRY1 flanking exonic sequence resulted in a progressive loss of suppression, with a > 476 nt internal exon unable to suppress m6A (Fig. 2B and fig. S7D). These results reveal a causal role for pre-mRNA splicing in m6A regulation.
## Exon junction complexes control m6A epitranscriptome specificity
We next sought to understand the mechanism by which splicing suppresses m6A deposition. Exon junction complexes (EJCs) are deposited by spliceosomes onto mRNA ~24 nt upstream of exon-exon junctions and plays multifaceted roles in gene expression regulation [14, 15]. Notably, two recent studies reported that EJCs efficiently block splicing at proximal aberrant splice sites [16, 17]. Additionally, EJCs, together with interacting serine and arginine-rich (SR) proteins, package and compact mRNA and can protect long stretches of proximal RNA from nuclease accessibility in vitro, and also block 5′ to 3′ exonuclease degradation in vivo [18, 19]. We reasoned that suppressed m6A sites within average-length internal exons are within relatively close proximity to both an upstream and downstream EJC. Conversely, m6A sites within long internal exons and near stop codons (which generally reside in long last exons) can be hundreds of nucleotides away from the nearest EJC. We therefore hypothesized that EJCs could mediate the splice site-proximal suppression of m6A we observed.
We knocked down (KD) the core EJC factor EIF4A3 in HeLa cells and assessed the effect on m6A deposition transcriptome-wide using m6A-MeRIP-seq. Notably, 24,350 regions were significantly hypermethylated upon EIF4A3 KD, while 3,140 regions were hypomethylated (Fig. 3A). $39\%$ of these hypermethylated regions exhibited a greater than 8-fold increase in m6A enrichment compared to the non-targeting siRNA control. We knocked down RBM8A, another core EJC factor [20], and observed similar, though relatively milder, transcriptome-wide m6A changes, with 14,034 significantly hypermethylated regions observed, of which $57\%$ overlapped with hypermethylated regions observed in EIF4A3 KD (Fig. 3A and fig. S8, A and B). The relatively milder m6A changes upon RBM8A KD may result from relatively lower KD efficiency (table S1) or may indicate a stronger requirement of EIF4A3 for suppression. Concordant with these transcriptome-wide m6A changes, using UHPLC-QQQ-MS/MS, we found that EIF4A3 KD increased global levels of m6A in polyadenylated RNA by two-fold, while RBM8A KD resulted in a ~$25\%$ increase (fig. S8C).
$94\%$ of hypermethylated regions from EIF4A3 KD and $82\%$ of hypermethylated regions from RBM8A KD did not overlap with m6A peaks identified under the non-targeting siRNA control conditions, suggesting that these regions contain newly methylated suppressed m6A sites (Fig. 3, A and B, and fig. S8D). Indeed, out of 1,024 CDS sequences identified by MPm6A to contain suppressed m6A sites, $46\%$ become methylated upon EIF4A3 and/or RBM8A KD, including the CRY1 suppressed site (fig. S8E), with three selected suppressed sites validated (fig. S8, F and G) [21]. Furthermore, EIF4A3 KD substantially alleviated the previously observed m6A suppression within the internal exon of BG CRY1 102 (fig. S8H).
Consistent with our model, newly methylated and hypermethylated regions were highly enriched in average-length internal exons within CDSs (Fig. 3, C to E, and fig. S8, I and J), with transcriptome-wide increases in m6A enrichment in exon junction-proximal regions observed (fig. S9, A and B) upon EIF4A3 or RBM8A KD. EIF4A3 KD disrupted m6A epitranscriptome specificity globally, resulting in substantial loss of enrichment of m6A peaks in long internal exons and increased density of m6A in the CDS relative to the stop codon (Fig. 3, D and E). It was previously reported that the peak of m6A density near stop codons on metagene plots can be more precisely visualized as an increased enrichment 150 nt past the start of last exons [6]. EIF4A3 KD resulted in a global increase in m6A enrichment < 150 nt past the start of last exons (fig. S9, A to C), indicating that EJC suppression of methylation proximal to last exon-exon junctions is responsible for the characteristic m6A peak density near stop codons. While most transcripts exhibited hypermethylation and contained one or more endogenous m6A peaks upon EIF4A3 KD, over a thousand transcripts that ordinarily lack endogenous m6A peaks also gained aberrant m6A methylation upon EIF4A3 KD, revealing a major role for EJCs in suppressing m6A deposition on the subset of transcripts that ordinarily are not subject to m6A regulation (fig. S9, D to F).
The widespread suppression of m6A by the EJCs also implies that many m6A are deposited following splicing, which we confirmed using pulse-chase metabolic labeling experiments and UHPLC-QQQ-MS/MS (supplementary text and fig. S10). *Two* genes used in gene therapies for mucopolysaccharidosis type II and spinal muscular atrophy, IDS and SMN, contain EJC-suppressed m6A sites in their mRNAs, respectively. As expected, when these mRNAs were expressed from cDNA constructs, and thus not bound by EJCs, they were significantly hypermethylated relative to the corresponding endogenous mRNAs (fig. S11). Further, lncRNAs that contain three or more exons globally exhibit EJC suppression of m6A in internal exons, while those with two or less do not (supplementary text and fig. S12). We depleted EIF4A3 with a different siRNA in HeLa cells, and knocked down EIF4A3 in HEK293T cells as well as in a glioblastoma cancer cell line (U87) that is sensitive to EIF4A3 perturbation [22], and observed similar transcriptome-wide m6A changes in each case (figs. S13 to S15). Altogether, our results indicate that spliceosomes widely suppress m6A methylation via deposition of EJCs that protect proximal RNA from methylation.
## EJCs regulate mRNA stability by suppressing m6A methylation
m6A is known to mainly accelerate mRNA degradation via the reader protein YTHDF2 [23, 24]. Accordingly, we observed globally reduced mRNA half-life of hypermethylated transcripts (~$90\%$) upon EIF4A3 KD (Fig. 4, A and B). Consistently, we observed generally increased YTHDF2 binding on hypermethylated mRNAs, accompanied with the decreased mRNA half-life (Fig. 4C). YTHDF2 KD could rescue accelerated degradation of YTHDF2 target transcripts upon EIF4A3 KD (fig. S16). Further, the density of EJC-loading on transcripts (estimated by the number of exons within CDS regions per 1 kb) correlated with transcriptome-wide mRNA stability (fig. S17). Higher EJC density on transcripts tended to correlate with reduced m6A methylation and higher mRNA stability, and the strength of this correlation was diminished by Mettl3 KO (fig. S17, A and B).
We also found that METTL3 depletion could generally reduce the expression level changes of hypermethylated genes upon EJC depletion in HeLa cells (supplementary text and fig. S18), indicating that these EJC-dependent gene expression changes are at least in part mediated by m6A methylation.
While the vast majority of hypermethylated transcripts were destabilized by EIF4A3 KD, a small subset of hypermethylated transcripts were stabilized (Fig. 4A). One example is p53, which mediates neurodevelopmental defects in mouse models of EJC haploinsufficiency [25]. The TP53 transcript was hypermethylated but also up-regulated upon EIF4A3 KD. Mechanistically, we observed increased binding to TP53 mRNA by IGF2BP proteins, which are known to stabilize methylated transcripts (supplementary text and fig. S19). In summary, while the predominant effect of EJC-mediated m6A suppression is to stabilize mRNAs by preventing the YTHDF2-mediated decay, in a minority of instances hypermethylated transcripts can be stabilized by other mechanisms, such as binding by IGF2BPs [26].
Consistent with a general role for m6A in promoting translation [12, 27], EIF4A3 KD led to slightly increased translation efficiency of hypermethylated transcripts, with more highly hypermethylated transcripts exhibiting greater increases in translation efficiency (fig. S20), although the impact was modest relative to the effects observed on mRNA stability.
## Differential m6A methylation across tissues and species through EJC suppression
Our model suggests that the cellular EJC levels may impact global mRNA m6A deposition in different tissues. Indeed, we observed a negative correlation between EIF4A3 expression level and global mRNA m6A modification level in 25 different human tissues with available transcriptome-wide m6A profiles (fig. S21A) [28]. We examined the top $10\%$ of genes with the strongest correlations and found that the majority (> $70\%$) exhibited a negative correlation between m6A and EIF4A3 levels in different tissues. Further, m6A levels of these genes also negatively correlated with their transcript abundances (fig. S21B). Similar trends were also observed in mouse tissues (fig. S21C). These results further support m6A suppression by EJCs and subsequently mRNA stability regulation in mammalian tissues.
Notably, we observed the lowest EIF4A3 expressions in brain tissues, which exhibited the highest overall mRNA m6A levels (fig. S21A). We further compared the methylome of the human cerebellum (lowest EIF4A3 level and highest overall mRNA m6A) with that of the heart (higher EIF4A3 level and lower overall mRNA m6A). Regions that are hypermethylated in the cerebellum (compared to heart) reside within short internal exons (fig. S21D), suggesting reduced m6A suppression due to low EIF4A3 expression in cerebellum. This association between high m6A level and low EIF4A3 expression in cerebellum was attenuated upon depletion of METTL3 (fig. S21C). These observations further indicate the widespread suppression by EJCs contributes to tissue-specific m6A deposition. We also found that a subset of EJC-suppressed m6A sites physiologically escape suppression in certain tissues via methylation of alternative transcript isoforms. These isoforms contain longer exons and thus altered EJC positioning; methylation of these isoforms generates tissue-specific m6A patterns (supplementary text and fig. S22).
Lastly, the effect of exon-intron architecture on mRNA stability may have co-evolved with YTHDF2 in vertebrates. The strong correlation between EJC loading, represented by the number of exons, and mRNA level across tissues is maintained across humans, mice, and zebrafish, but not fly and worm, which lack YTHDF2 orthologs (supplementary text and fig. S23).
## EJCs and peripheral EJC factor RNPS1 protect exon junction-proximal RNA regions from aberrant mRNA processing
We did not observe interactions between the methyltransferase complex and EJC complexes (fig. S24), suggesting that steric hindrance from EJCs, rather than a specific inhibitory interaction, accounts for methylation suppression. Nuclear EJCs bound with the peripheral EJC factor RNPS1 multimerize and associate with wide variety of SR and SR-like proteins to package and compact mRNA into higher-order, megadalton-scale mRNPs that ensheathe proximal RNA well beyond the canonical EJC deposition sites [18, 29, 30]. Tens to hundreds of nucleotides of proximal RNA could be protected by this mega-complex from nuclease digestion due to this packaging [18, 31]. To examine whether the mRNA packaging function of the EJC-mediates suppression of proximal methylation, we isolated EJCs/EJC-bound RNA from cellular extracts, digested away physically accessible RNA with in vitro nuclease treatment, and then measured m6A levels on the EJC-protected RNA footprints (fig. S25, A and B). EJC-protected footprints were strongly depleted of m6A, indicating that these inaccessible RNA regions are largely protected from m6A deposition within cells (fig. S25C). EJCs also protected these footprints from in vitro methylation by recombinant METTL3-METTL14 (fig. S25D). This was not due to general inhibition of methyltransferase activity or lack of methylatable sites on the EJC footprints, as free, unmethylated RNA spiked into the methylation reaction as well as deproteinized footprints were both robustly methylated (fig. S25, D and E). Therefore, EJCs suppress local m6A deposition by packaging proximal RNA.
We next asked whether the peripheral EJC factor RNPS1, which associates with high molecular weight EJCs in these highly packaged mRNP structures [29], plays a role. RNPS1 knockdown led to substantial transcript m6A hypermethylation within average-length internal exons and CDS regions (Fig. 5, A to C, and fig. S26, A to C). We detected fewer hypermethylated regions overall compared to depletion of the core EJC factors; however, we did observe high overlap ($45\%$) between siRNPS1 hypermethylated regions and siEIF4A3/siRBM8A hypermethylated regions (Fig. 5C and fig. S26C). In contrast, depletion of UPF1, a central NMD factor that interacts with the EJC in the cytoplasm, did not result in m6A changes similar to those of the core EJC (fig. S27).
The ability of EJCs to protect proximal RNA regions from methylation resembles the recently characterized EJC- and RNPS1-mediated suppression of proximal aberrant splice sites and recursive splicing [16, 17]. Transcriptome-wide, EJC-suppressed splice sites significantly colocalize with EJC-suppressed m6A sites (supplementary text; Fig. 4, C to E; fig. S26, D to F; and table S2). Altogether, these results suggest that RNPS1-associated EJCs suppress both local cellular m6A methylation and splicing through packaging of proximal RNA and point to exon architecture as an important determinant of local RNA accessibility to regulatory machineries. Additionally, beyond components of the m6A methyltransferase complex, a number of other RBPs also exhibit preferential binding at long internal exons, suggesting that EJCs may regulate mRNA accessibility to a broader range of mRNA regulators in addition to the splicing and m6A methylation machineries through their mRNA packaging function (supplementary text and fig. S28).
## Discussion
Previously identified m6A effector proteins fall broadly into three categories according to their activities: “writers”, which catalyze m6A methylation, “readers”, which preferentially bind m6A, and “erasers”, which reverse m6A methylation. Here we establish the EJCs as a member of a new class of m6A regulators: “suppressors”, which broadly suppress the deposition of m6A (fig. S28). EJCs appear to be a major regulator of m6A deposition that mediate multiple key aspects of global m6A epitranscriptome specificity, including enrichment of m6A in long internal exons, depletion of m6A in CDSs and enrichment of m6A in last exons near stop codons, and methylation selectivity for transcripts possessing long internal exons. This mechanism may also explain the high abundance of m6A on certain non-coding RNAs, such as LINE-1 elements that are generally unspliced and thus not bound by the EJCs [32, 33]. Further, our systematic analysis of m6A determinants using MPm6A may suggest the existence of additional m6A suppressing pathways, including m6A suppression within the CDS, as EIF4A3 KD does not appear to completely restore methylation to unspliced levels (supplementary text, fig. S8H, and fig. S29).
Our results point to exon length within transcripts as a functionally relevant element for post-transcriptional gene expression regulation. Mammalian EJCs stably bind the vast majority of pre-translational mRNAs in the transcriptome at closely spaced intervals. Long internal exons and terminal exons, which usually encode UTRs, are notably free of EJCs. This widespread binding, in conjunction with their mRNA packaging function, appears to uniquely position EJCs to broadly determine mRNA accessibility to regulatory machineries, such as the m6A methylation and splicing machineries (fig. S30). Our work has relevance for the use of cDNA expression constructs in research studies and gene therapies, as loss of endogenous mRNA exon architecture and EJC protection results in m6A hypermethylation (fig. S11), which could modulate gene expression outcome. Finally, our study also suggests that exon length and architecture co-evolved with mRNA processing steps as an additional regulatory layer of gene expression.
## Funding:
National Institutes of Health HG008935 (C.H.); National Institutes of Health grant T32 HD007009 (P.C.H); National Institutes of Health grant F32 CA221007 (B.T.H); C.H. is an investigator of the Howard Hughes Medical Institute.
## Data and materials availability:
Raw and processed data can be found at NCBI GEO accession GSE162199. Custom scripts available on Zenodo [34]. All other data are available in the manuscript or the supplementary materials.
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|
---
title: The effectiveness of treadmill and swimming exercise in an animal model of
osteoarthritis
authors:
- Leandro Almeida da Silva
- Anand Thirupathi
- Mateus Cardoso Colares
- Daniela Pacheco dos Santos Haupenthal
- Ligia Milanez Venturini
- Maria Eduarda Anastácio Borges Corrêa
- Gustavo de Bem Silveira
- Alessandro Haupenthal
- Fernando Russo Costa do Bomfim
- Thiago Antônio Moretti de Andrade
- Yaodong Gu
- Paulo Cesar Lock Silveira
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9990173
doi: 10.3389/fphys.2023.1101159
license: CC BY 4.0
---
# The effectiveness of treadmill and swimming exercise in an animal model of osteoarthritis
## Abstract
Introduction: Osteoarthritis (OA) is considered an inflammatory and degenerative joint disease, characterized by loss of hyaline joint cartilage and adjacent bone remodeling with the formation of osteophytes, accompanied by various degrees of functional limitation and reduction in the quality of life of individuals. The objective of this work was to investigate the effects of treatment with physical exercise on the treadmill and swimming in an animal model of osteoarthritis.
Methods: Forty-eight male Wistar rats were divided ($$n = 12$$ per group): Sham (S); Osteoarthritis (OA); Osteoarthritis + Treadmill (OA + T); Osteoarthritis + Swimming (OA + S). The mechanical model of OA was induced by median meniscectomy. Thirty days later, the animals started the physical exercise protocols. Both protocols were performed at moderate intensity. Forty-eight hours after the end of the exercise protocols, all animals were anesthetized and euthanized for histological, molecular, and biochemical parameters analysis.
Results: Physical exercise performed on a treadmill was more effective in attenuating the action of pro-inflammatory cytokines (IFN-γ, TNF-α, IL1-β, and IL6) and positively regulating anti-inflammatories such as IL4, IL10, and TGF-β in relation to other groups.
Discussion: In addition to maintaining a more balanced oxi-reductive environment within the joint, treadmill exercise provided a more satisfactory morphological outcome regarding the number of chondrocytes in the histological evaluation. As an outcome, better results were found in groups submitted to exercise, mostly treadmill exercise.
## 1 Introduction
Osteoarthritis (OA) is considered an inflammatory and degenerative joint disease, characterized by loss of hyaline joint cartilage and adjacent bone remodeling with the formation of osteophytes, accompanied by various degrees of functional limitation and reduction in the quality of life of individuals (Green et al., 2018; Rios et al., 2018). Given its avascular nature, cartilage is a tissue of difficult repair, which constitutes a major therapeutic challenge due to the high incidence in the world population (Shikichi et al., 1999; IWANAGA et al., 2000).
Recent studies on the pathophysiology of the disease indicate that the initiation and progression of OA are directly linked to the occurrence of an inflammatory process, cartilage fragmentation, and a state of oxidative stress in the joint environment (Henrotin et al., 2005; Okin and Medzhitov, 2012; Li et al., 2013; Zahan et al., 2020).
Removal of the meniscus encourages instabilities and overload on the point of load distribution in the cartilage, stimulating mechanical stress, inflammatory processes, and oxidative stress (Serra and Soler, 2019; Filho et al., 2021). In this way, the model works to promote strong OA triggering factors, similar to the involvement in human disease (Logerstedt et al., 2010; McCoy, 2015; Filho et al., 2021).
Regular physical exercise is well established in the literature as an important therapeutic ally in the prevention or treatment of several chronic diseases (RADak et al., 1999; Petersen and Pedersen, 2005; Cifuentes et al., 2010; Green et al., 2018; Rios et al., 2018), including OA. The moderate intensity mechanical stress provided by exercise seems to positively modulate signaling pathways of the main inflammatory mediators involved in the pathophysiology of OA (IL1-b, TNF-a, IL6, and IFN-γ), which seems to favor the regulation of the synthesis of proteoglycans and collagen, attenuating the process of joint wear (Pinho et al., 2010; Finsterer, 2012; Lindsay et al., 2015). In addition, exercise generates an anabolic/protective response through increased expression of anti-inflammatory cytokines (IL10 and IL4) and growth factors (TGF-b) (Sellam and Berenbaum, 2010; Musumeci, 2016; Rios et al., 2018) and activation of the antioxidant defense system (SOD and GSH), generating attenuation in the production of reactive oxygen species (ROS) by chondrocytes (Kühn et al., 2003; Henrotin et al., 2005; Altindag et al., 2007; Altay et al., 2015; Zahan et al., 2020).
Although the beneficial effects of physical exercises in this pathology are well established in the literature, there is still no agreement on which type of exercise brings more benefits in the treatment and management of OA (Batterham et al., 2011; Al-Hashem et al., 2017; Assis et al., 2018; Chen et al., 2020; Kolasinski et al., 2020; Tian et al., 2021). Therefore, from a literature review, it was identified that the most studied exercise protocols in animal models use treadmill protocols (Cifuentes et al., 2010; Moriyama et al., 2012; Assis et al., 2015; Assis et al., 2016; Assis et al., 2018) and swimming (Cechella et al., 2014; Assis et al., 2016; Tomazoni et al., 2016; Tomazoni et al., 2017; Assis et al., 2018; Hsieh and Yang, 2018).
Thus, this study aimed to investigate and compare the effects of two types of physical exercises widely used in the literature (treadmill vs. swimming) in a mechanical model of osteoarthritis to better understand the effects of these different modalities on the inflammatory response, oxidative stress markers and morphological variables present in OA.
## 2 Materials and methods
All experimental procedures involving animals were performed in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (Bethesda, MD, United States) and with the approval of the Ethics Committee of the university (Universidade do Extremo Sul Catarinense—UNESC) with protocol number $\frac{51}{2020.}$ All animal experiments comply with the ARRIVE guidelines (Percie du Sert et al., 2020).
## 2.1 Animals
Forty-eight male Wistar animals (2 months old, 250–300 g) were kept at a controlled temperature of 20 ± 2°C, with a $\frac{12}{12}$ h light/dark cycle and free access to food and water. The animals were randomly assigned to four experimental groups ($$n = 12$$ per group) as follows: Sham (without OA model induction), Osteoarthritis (OA); OA + Treadmill (T); OA + Swimming (S).
The number of animals was based on a review of studies with animal models (Galois et al., 2004; Moriyama et al., 2012; Assis et al., 2015; McCoy, 2015; Assis et al., 2016; Tomazoni et al., 2016; Al-Hashem et al., 2017; Tomazoni et al., 2017; Assis et al., 2018; Castrogiovanni et al., 2019; Serra and Soler, 2019; Chen et al., 2020; Percie du Sert et al., 2020; Tian et al., 2021), for the possibility of a difference of up to $20\%$–$25\%$ in the parameters to be analyzed between the groups, with a variance of up to $10\%$ of the means, calculated using the EDA tool (du Sert et al., 2017), resulting in a sample size of 12 animals per group for biochemical and histological evaluations (7 animals per group for biochemical tests and 5 animals per group for histology analyses).
## 2.2 Osteoarthritis model
Rats were anesthetized with $4\%$ isoflurane. The right knee was shaved, aseptically prepared with $90\%$ alcohol, and exposed for surgery. For all groups, the same surgical approach was performed according to the standard incision performed in arthroplasty, prosthesis placement, and treatment of severe OA procedures in humans. This approach was also carried out in a previous experiment by this research group (Filho et al., 2021). It involves an anterior surgical approach to the knee, followed by medial parapatellar arthrotomy and lateral patellar dislocation, allowing access to the medial compartment of the knee of the animals (INSALL, 1971).
In OA groups, a meniscectomy of the medial meniscus was performed. Complete resection of the medial meniscus of the right hind limb was performed with a cold scalpel blade. In the Sham group, only the surgical approach was performed, without meniscectomy, followed by incision closure in two planes. There was no access to the lateral compartment of the joint and no additional ligament resection in any of the procedures. The central ligaments of the knee (anterior and posterior cruciate) and collateral ligaments (lateral and medial) were preserved. After reducing the patellar dislocation, the surgical incisions were closed in two planes with mono nylon sutures.
## 2.3 Intervention
Thirty days after the meniscectomy, the animals started the physical exercise protocols. The animals in the OA + T group were submitted to the prescription of treadmill exercise, according to a protocol adapted from Cifuentes et al. [ 2010], as can be seen in Table 1. The beginning took place with a week of adaptation (week 0), with each training session consisting of 10 min per day, on alternate days of the week, with a speed of 10 m/min, intending to adapt the animals to the protocol and the belt movement. During this week of adaptation, the animals received electrostimulation (0.2 mA), which served to stimulate the animal to walk and instruct it to move on the treadmill (Castrogiovanni et al., 2019).
**TABLE 1**
| Period | Velocity | Inclination (%) | Duration |
| --- | --- | --- | --- |
| Adaptation | 10 m/min | 1 | 10 min |
| Week 01 | 13 m/min | 1 | 30 min |
| Week 02 | 13 m/min | 1 | 30 min |
| Week 03 | 16 m/min | 1 | 30 min |
| Week 04 | 16 m/min | 1 | 50 min |
| Week 05 | 16 m/min | 1 | 50 min |
| Week 06 | 16 m/min | 1 | 50 min |
The treadmill speed was 13 m/min, without incline. In weeks 01 and 02, the running time was 30 min; in weeks 03, 04, 05, and 06 the speed was 16 m/min. The execution time in these weeks was 30 min in week 03 and 50 min in weeks 04, 05, and 06. These training intensities and volumes correspond to light-moderate intensities, corresponding to approximately $50\%$ and $60\%$ of VO2max (Sutton et al., 2001; Cifuentes et al., 2010).
The animals in the OA + S group were submitted to the prescription of swimming exercise, in a specific tank designed for this type of study, with water at a temperature of 32°C, according to the protocol: the animals were submitted to an adaptation period of 20 min per day, during the first adaptation week (week 0), on alternate days. After the adaptation week, the animals started the swimming program, which developed on alternate days of the week, for 06 weeks. The protocol used was adapted from Cechella et al. [ 2014] and Hsieh and Yang [2018] and lasted 20 min per day, being carried out in alternating sessions during the weekdays, as can be seen in Table 2.
**TABLE 2**
| Period | Duration | Overload (%) |
| --- | --- | --- |
| Adaptation | 20 min | 0 |
| Week 01 | 20 min | 3 |
| Week 02 | 20 min | 3 |
| Week 03 | 20 min | 3 |
| Week 04 | 20 min | 5 |
| Week 05 | 20 min | 5 |
| Week 06 | 20 min | 5 |
At the beginning of each training week, the animals were weighed and an overload, equivalent to the percentage of overload for the week, was attached to the tail of the animals with the aid of a sealed eppendorf containing lead. In weeks 01, 02, and 03, an overload equivalent to $3\%$ of the body weight of each animal was used. For this, each rodent was weighed and its weight was recorded for weekly monitoring. In weeks 04, 05, and 06 an overload of $5\%$ of body weight was placed on each animal.
The animals were considered unfit for training when they showed suffering like pain or discomfort that led them to: on the treadmill, not being able to follow the pace of the treadmill speed or dragging the paw (with OA) on the treadmill; in swimming, not being able to keep the head above the water surface or not performing swimming movements using the paw used for the OA model.
## 2.4 Euthanasia
After these procedures, the animals were anesthetized with $4\%$ isoflurane and killed by guillotine decapitation 48 h after the last training session (Figure 1), with the removal of gastrocnemius muscle samples to evaluate energy metabolism, a tissue sample from the joint in which all intra-capsular tissues of the joint were homogenized using a 7.4 pH sodium phosphate buffer (PBS) for biochemical analysis and distal femoral bony epiphysis with the cartilaginous surface, proximal tibial bony epiphysis with the cartilaginous surface, in addition to the lateral meniscus, for histological analyses.
**FIGURE 1:** *Timeline.*
## 2.5.1 Energy metabolism
Succinate Dehydrogenase Activity—Krebs Cycle: The activity of the enzyme succinate dehydrogenase was determined according to the method described by Fischer et al. [ 1985].
The activity of mitochondrial respiratory chain enzymes: Complex I activity was evaluated by the method described by Cassina and Radi [1996]. Complex II activity was measured by the method described by Fischer et al. [ 1985].
## 2.5.2 Intracellular determination of reactive oxygen species (ROS) and nitric oxide
The production of hydroperoxides was determined by the intracellular formation of 2′,7′-dichlorofluorescein (DCFHDA) from the oxidation of 2′,7′-dichlorodihydrofluorescein diacetate (H2DCFDA) by ROS (Dong et al., 2010).
The endothelial function was assessed by evaluating the nitric oxide levels by measuring its stable nitrite metabolite and quantified by spectrophotometer at 540 nm as described in the literature (Chae et al., 2004).
Both of the techniques were made with a standard curve in which the test resulted in high linearity of the samples, above 0.98, proving the high sensitivity.
## 2.5.3 Determination of oxidative damage marker levels
The oxidative damage to protein was measured by the determination of carbonyl groups, based on a reaction with dinitrophenylhydrazine (DNTP), and the carbonyl contents were determined by measuring the absorbance at 370 nm (Levine et al., 1990).
Total thiol content was determined using the 5,5-dithiobis (2-nitrobenzoic acid) (2-nitrobenzoic acid) (DTNB) method, the absorbance at 412 nm was measured, and the amount of TNB formed (equivalent to the amount of sulfhydryl (SH) groups) was calculated (Aksenov and Markesbery, 2001).
## 2.5.4 Determination of antioxidant defenses
SOD activity was quantified by inhibiting the oxidation of adrenaline and measured in a SpectraMax i3xELISA reader at 480 nm. Values were expressed as unit SOD/mg protein (U/mg protein) (Bannister and Calabrese, 1987).
Glutathione levels were measured through a reaction between DTNB and thiols, promoting color development as a result. Total glutathione (GSH) levels were expressed in µmol per mg of protein based on absorbance at 412 nm (Hissin and Hilf, 1976). This technique was made with a standard curve in which the test resulted in high linearity of the samples, above 0.98, proving the high sensitivity.
## 2.5.5 Protein content
The protein content was determined using Folin phenol reagent (phosphomolybdic–phosphotungstic reagent) by Lowry et al. [ 1951]. The bovine serum albumin was used to perform a standard curve. The results were expressed as mg protein (mg).
## 2.5.6 Determination of the cytokine content using ELISA
The samples were processed and then the plate was sensitized for further incubation with the antibody. To measure cytokines (IFN-γ, TNF-α, IL1-β, IL6, IL4, IL10, and TGF-β) the enzyme-linked immunoabsorbent assay (Duoset ELISA) capture method (R&D system, Inc., Minneapolis, United States) was used.
## 2.6 Histological analysis
Samples extracted from the femoral condyle and tibial plateau regions of the right hind limb were soaked in $10\%$ paraformaldehyde (PFA) solution in 0.1 M phosphate buffer (pH 7.4). Subsequently, they were fixed for 24 h in the same solution (PFA $10\%$), and embedded in paraffin after decalcification in $10\%$ formic acid, dehydration, and bleaching, and sectioned into 5 µm thick sections. Histological quantifications of chondrocyte number, cartilage thickness, and cartilage-cartilage contact measure were performed by hematoxylin-eosin (H&E) staining (Moscardi et al., 2018). Slides were read under an optical microscope (Eclipse 50i, Nikon, Melville, NY, United States), at ×600 magnification, and four ocular fields were captured per slice (5 animals/group). Images were recorded using a *Nikon camera* (Sight DS-5M-L1, Melville, NY, United States) and analyzed using NIH ImageJ 1.36b software (NIH, Bethesda, MD, United States). The measurement of the chondrocyte number was determined in an area of 104 μm2 (Gonçalves et al., 2021). The measurement of cartilage-cartilage contact and cartilage thickness were both measured in the medial region of the joint.
To assess the degree of cartilage damage, samples were stained with Alcian Blue–Safranin O in five fields per slide per animal of each experimental group and analyzed according to the OARSI score described by Pritzker et al. [ 2006]: 0–4 damage to cartilage, 5–6 additional damage to subchondral bones.
Histology analyzes were performed by a histopathologist who evaluated the number of chondrocytes count, measurement of cartilage thickness, measurement of cartilage-cartilage contact, and OARSI scale classification. These analyzes were performed blindly using numerical codes in the experimental groups.
## 2.7 Statistical analysis
Data are expressed as the mean ± standard error of the mean (SEM), evaluated by the Shapiro-Wilk normality test, and analyzed statistically by one-way analysis of variance (ANOVA) tests, followed by Tukey post-hoc test. The significance level for statistical tests is $p \leq 0.05.$ GraphPad Prism version 7.0 (developed by GraphPad Software Inc.) was used as a statistical package.
## 3.1 Energy metabolism
Figure 2 shows the levels of Succinate dehydrogenase (SDH) and complexes I and II of the electron transport chain (ETC). In Figure 2A, it is observed a significant increase in SDH in the OA + T and OA + S groups compared to the Sham group ($p \leq 0.05$). It is also possible to observe a statistical difference between the two, OA + T and OA + S, in relation to the OA group ($p \leq 0.01$). In Figure 2B it is observed a significant increase in the activity of complex I in the OA + T group compared to the Sham group ($p \leq 0.05$) and in relation to the OA group ($p \leq 0.001$). In Figure 2C, it is observed a significant increase in the activity of complex II in the OA + T group compared to the Sham group ($p \leq 0.05$) and in relation to the OA group ($p \leq 0.01$).
**FIGURE 2:** *Activity of the components of the respiratory chain. (A) SDH; (B) Complex I; (C) Complex II. Abbreviations: OA, osteoarthritis; SDH, succinate dehydrogenase. Data are presented as mean ± SEM, in which: #
p < 0.05 vs. Sham Group; **p < 0.01 vs. OA Group; ***p < 0.001 vs. OA Group; (One-way ANOVA followed by Tukey post-hoc test).*
## 3.2 Oxidants levels
In Figure 3A it is observed a significative elevation of DCF in OA, OA + T, and OA + S groups in relation to Sham, while OA + T showed a significant reduction when compared to the OA group ($p \leq 0.05$). In Figure 3B, nitrite levels showed significant increases in the OA and OA + S groups when compared to Sham, while the OA + T group showed a significant reduction in nitrite concentrations when compared to the OA group ($p \leq 0.001$).
**FIGURE 3:** *Oxidants. (A) DCF; (B) Nitrite. Abbreviations: DCF, dichlorofluorescein; OA, osteoarthritis. Data are presented as mean ± SEM, in which: #
p < 0.05 vs. Sham Group; *p < 0.05 vs. OA Group; ***p < 0.001 vs. OA Group; (One-way ANOVA followed by Tukey post-hoc test).*
## 3.3 Oxidative damage and antioxidants levels
To assess oxidative damage, carbonyl levels and sulfhydryl content were analyzed (Figure 4). In 4A it is observed a significant increase of carbonyl was in the OA, OA + T, and OA + S groups in relation to the Sham group ($p \leq 0.05$). In 4B it is observed a significant reduction of sulfhydryl in the OA, OA + T, and OA + S groups in relation to the Sham group, while the OA + T group showed a significant increase when compared to the OA group ($p \leq 0.05$).
**FIGURE 4:** *Oxidative damage and antioxidant system levels. (A) Carbonyl; (B) Sulfhydryl; (C); SOD; (D) GSH. Abbreviations: GSH, reduced glutathione; SOD, superoxide dismutase. Data are presented as mean ± SEM, in which: #
p < 0.05 vs. Sham Group; *p < 0.05 vs. OA Group; **p < 0.01 vs. OA Group; ****p < 0.0001 vs. OA Group; (One-way ANOVA followed by Tukey post-hoc test).*
To assess the activity of the antioxidant system, SOD activity and GSH levels were measured. In 4C it is observed a significant reduction of SOD in the OA group in relation to the Sham group, while the OA + T and OA + S groups presented significant increases in SOD when compared to the OA group ($p \leq 0.0001$). In 4D it is observed a significant reduction of GSH in the OA group, in relation to the Sham group ($p \leq 0.05$). The OA + T group showed a significant increase ($p \leq 0.01$) when compared to the OA group. The OA + S group also showed significant increases in SOD when compared to the OA group ($p \leq 0.05$).
## 3.4 Pro-inflammatory cytokines
Figure 5 shows the levels of pro-inflammatory cytokines IFN-γ, IL1-β, TNF-α, and IL6. It is observed in Figure 5A that the OA and OA + S groups showed significant increases in IFN-γ in relation to the Sham group, while the OA + T group showed a significant decrease in relation to the OA group ($p \leq 0.05$). In 5B it is observed a significant increase of IL1-β in the OA group in relation to Sham ($p \leq 0.05$), while OA + T and OA + S groups showed significant reductions in the levels of IL1-β when compared to the OA group ($p \leq 0.0001$ e $p \leq 0.001$), respectively. In 5C, it is shown that TNF-α showed a significant increase in the OA group compared to the Sham group ($p \leq 0.05$), while the OA + T and OA + S groups showed significant reductions in this marker when compared to the OA group ($p \leq 0.001$). In 5D, we observed a significant increase in IL6 in the OA and OA + S group compared to the Sham group ($p \leq 0.05$). A decrease in IL6 concentration was observed in both types of physical exercise when compared to the OA group. The OA + S group presented ($p \leq 0.01$), while the OA + T group presented ($p \leq 0.0001$).
**FIGURE 5:** *Pro-inflammatory cytokines. (A) IFN-ʎ; (B) IL1-β; (C) TNF-α; (D) IL6. Abbreviations: IL, interleukin; IFN, interferon; OA, osteoarthritis; TNF, tumor necrosis factor. Data are presented as mean ± SEM, in which: #
p < 0.05 vs. Sham Group; *p < 0.05 vs. OA Group; **p < 0.01 vs. OA Group; ***p < 0.001 vs. OA Group; ****p < 0.0001 vs. OA Group; (One-way ANOVA followed by Tukey post-hoc test).*
## 3.5 Anti-inflammatory cytokines
Figure 6 shows the levels of anti-inflammatory cytokines IL4, IL10, and TGF-β. In 6A, it is observed a significant decrease of IL4 in the OA, OA + T, and OA + S groups in relation to Sham ($p \leq 0.05$), while the OA + T group showed a significant increase in IL4 levels when compared to the OA group ($p \leq 0.0001$). In 6B it is observed a significant decrease of IL10 in the OA group in relation to Sham ($p \leq 0.05$) and increase of IL10 in OA + T group in relation to OA group ($p \leq 0.0001$). In Figure 6C it is observed a significant decrease in TGF-β in the OA and OA + S groups compared to the Sham group, while the OA + T group showed a significant increase in TGF-β levels when compared to the OA group ($p \leq 0.0001$).
**FIGURE 6:** *Anti-inflammatory cytokines. (A) IL4; (B) IL10; (C) TGF-β. Abbreviations: IL, interleukin; OA, osteoarthritis; TGF, transforming growth factor. Data are presented as mean ± SEM, in which: #
p < 0.05 vs. Sham Group; *p < 0.05 vs. OA Group; ****p < 0.0001 vs. OA Group; (One-way ANOVA followed by Tukey post-hoc test).*
## 3.6 Histological analysis
In Figure 7A, there are representative images of the histological analysis. In Figure 7B, we evaluated the mean number of chondrocytes inside the lacuna (40 µm). We observed that the OA and OA + S groups showed a significant decrease in the number of chondrocytes per lacuna when compared to the Sham group ($p \leq 0.05$). On the other hand, the OA + T group showed a significant increase in chondrocytes per lacuna when compared to OA group ($p \leq 0.001$). In Figure 7C, no significant change was observed between the groups in terms of cartilage thickness. In Figure 7D, in the cartilage-cartilage contact measure, the OA, OA + T, and OA + S groups showed statistically significant reductions when compared to Sham ($p \leq 0.05$).
**FIGURE 7:** *Histological analysis. (A) Representative images where Arrows = surface cartilage thickness; Arrowhead = chondrocytes inside the lacuna; Black line = cartilage thickness; (B) Chondrocyte mean; (C) Cartilage thickness; (D) Cartilage-cartilage contact; (E) Score OARSI. Data are presented as mean ± SEM, in which: #
p < 0.05 v.s Sham Group; ***p < 0.001 v.s OA Group; (One-way ANOVA followed by Tukey post hoc test).*
In Figure 7E, the assessment of the degrees of cartilage injury according to the classification of the Osteoarthritis Research Society International (OARSI). In this evaluation, all groups submitted to the surgical model of OA had a higher degree of injury compared to the sham group ($p \leq 0.05$). However, the exercise intervention groups resulted in a lower degree of injury when compared to the OA group ($p \leq 0.01$).
Please see Supplementary Image 2 for a summary of all results.
## 4 Discussion
In the present study, moderate-intensity exercise interventions were used in a mechanical model of OA in Wistar rats. To analyze whether the protocols were able to generate physiological changes in the treated animals, succinate dehydrogenase (SDH), a metabolic marker of mitochondrial activity in the Krebs cycle and the electron transport chain (ETC) (Chilibeck et al., 1998) was evaluated. This marker showed high levels of activity in the two exercised groups compared to the control, showing that both physical exercise protocols provided changes in the aerobic pattern of the animals and suggesting that the exercise intervention was able to generate an increase in mitochondrial activity (Chilibeck et al., 1998; Kang et al., 2009; da Luz Scheffer and Latini, 2020) and greater activity in complexes I and II in ETC, that is, confirming that the physical exercise protocols produced metabolic alterations.
In addition, the proposed exercise was able to promote a reduction in inflammatory parameters and oxidative stress, generating less tissue damage, especially when performed on a treadmill. The swimming exercise, despite promoting movement resistance, causes less impact on the joint due to the presence of thrust generated by the water column (Cechella et al., 2014; Hsieh and Yang, 2018). On the other hand, treadmill exercise seems to enable adequate mechanical resistance and impact intensity, modulating anabolic signaling within the joint environment (Shikichi et al., 1999; IWANAGA et al., 2000; Henrotin et al., 2005; Green et al., 2018). The impact on the joint is related to OA triggering factors, in adequate proportions it stimulates the best perfusion of nutrients and oxygen between cartilage and synovial fluid (Castrogiovanni et al., 2019).
Corroborating the results of the present research, other sources also point out the decrease in the production of markers such as TNF-α and IL1β and the increase in cytokines such as IL10 and IL4 as an effect of moderate exercise, reinforcing the anti-inflammatory nature of the modality (Pinho et al., 2010; Tomazoni et al., 2017; da Luz Scheffer and Latini, 2020; Cerqueira et al., 2020; Baker et al., 2011; Woodell May and Sommerfeld, 2020; Gleeson et al., 2011).
In this study, it was shown that both moderate exercises on a treadmill and swimming were able to modulate the pro-inflammatory process from the reduction of cytokines such as TNF-α, IL1-β, and IL6. Furthermore, in the specific case of treadmill exercise, there was a decrease in IFN-γ. Such circumstances point to a possible faster phenotypic switch from M1 to M2, attenuating the acute inflammatory phase and, together, providing an “anti-catabolic” environment within the joint (Gleeson et al., 2011).
Furthermore, the reduction of damage caused by sustained inflammation to articular cartilage may also be associated with the pleiotropic action of IL6. Despite being considered an originally pro-inflammatory cytokine, research indicates that, during the training action, myocytes produce IL6 with an anti-inflammatory profile controlled by the action of CA2+ and glycogen-activated protein kinase (MAPK) (da Luz Scheffer and Latini, 2020; Gleeson et al., 2011; Benatti and Pedersen, 2015). This IL6 can induce negative feedback that inhibits the production of TNF-α by type “A” joint synovitis cells. In addition, it also promotes the production of the IL1-β antagonist receptor, the cytokine called IL1-RA, and the anti-inflammatory interleukin IL10, contributing to the minimization of the acute inflammatory process (Gleeson et al., 2011; Benatti and Pedersen, 2015; Castrogiovanni et al., 2019).
In the study, a reduction of IL6 was found in the samples of the groups treated with exercise, mainly in the exercise groups performed on the treadmill. This may be because IL6 with an anti-inflammatory profile peaks during muscle contraction during exercise, followed by an important gradual decrease soon after the end of the activity (Fischer, 2006; Benatti and Pedersen, 2015). Thus, as the material for analysis of this cytokine was removed 48 h after the last exercise session, it is understood that the evaluated IL6 has a pro-inflammatory profile probably associated with the characteristics of osteoarthritic disease.
However, it is estimated that during the training of the treated groups, IL6 at times of muscle contraction behaved as an anti-inflammatory cytokine that corroborated the increase in the production of anti-inflammatory cytokines, the inhibition of TNF-α and restriction of the action of IL1-β, mainly due to the chronic adaptive effect after sequential acute sessions of moderate-intensity exercise (Silva et al., 2018).
Furthermore, the results presented here confirm the increase in the concentrations of anti-inflammatory cytokines, among them IL10 and IL4, acting with a chondroprotective action in the trained groups.
IL10 is involved in the decrease of the expression of MMPs and the decrease in the synthesis of IL1-β and TNF-α (Molnar et al., 2021). It is synthesized by immune cells and chondrocytes, playing a prominent role in the physiological maintenance of ECM cartilage, as it has chondroprotective properties through stimulation of type II collagen synthesis (Mostafa Mtairag et al., 2001; Schulze-Tanzil et al., 2009; Wojdasiewicz et al., 2014). Literature data indicate that moderate exercise therapy can positively modulate IL10 synthesis, thus blocking joint damage (Fernandes et al., 2002; Mathiessen and Conaghan, 2017). Another mechanism involved in the intra-articular production of IL10 refers to the phenotypic change from M1 to M2 macrophages, stimulated by physical training. This change from M1 to M2 allows the synthesis of IL10, providing the emergence of a chondroprotective anabolic environment, triggered by the cartilage exposure to appropriate tensions, together with a positive outcome of anti-inflammatory effects from the prescription of moderate exercise (Mostafa Mtairag et al., 2001; Fernandes et al., 2002; Mathiessen and Conaghan, 2017).
As for IL4, its signaling pathway is not yet fully understood, but it is speculated that IL4 production is associated with Th2 cells that infiltrate the synovial membrane during the OA process (Ishii et al., 2002). It has been described to play an important role in joint chondroprotection, also inhibiting the secretion of MMPs, thus ultimately minimizing the degradation of proteoglycans, observed in the natural course of untreated OA (van Meegeren et al., 2012).
In addition to the presence of chronic inflammatory conditions, capable of generating morphological changes in the joint structure, the etiopathogenesis and progression of OA seem to be directly related to the cellular redox balance of the articular cartilage components (Bondeson et al., 2010; Woodell May and Sommerfeld, 2020).
The intensity of exercise execution is a relevant factor in the production of ROS (Zahan et al., 2020; Cifuentes et al., 2010; da Luz Scheffer and Latini, 2020). When performed at moderate intensity (Sutton et al., 2001; Leandro et al., 2007; Cifuentes et al., 2010), physical exercise can modulate the oxidative stress parameters either by increasing the activity of antioxidant enzymes, or by decreasing the production of oxidants, or even by lower the impairment of oxidized proteins (Leeuwenburgh and Heinecke, 2001; Filho et al., 2021), as observed in the groups submitted to exercise.
The benefits generated by exercise practice against oxidative stress markers can be explained by the mechanism of the adequacy of the antioxidant defense enzymatic system (SOD and GPX) and by the increase in tissue resistance to oxidative damage developed by physical exercise.
In the present study, physical exercise also caused a reduction in the levels of Dichlorofluorescein (DCF), an indirect marker of hydrogen peroxide (H2O2), which, within the chondrocytes, is capable of suspending the synthesis of proteoglycans and altering the synthesis of ATP in the CTE (Johnson et al., 2000; Migita et al., 2001), damaging the articular cartilage. In addition to the benefits mentioned above, physical exercise increases the activity of proteasomes, considered protein complexes directly related to the protein repair process against OS present in the osteoarthritic joint (RADak et al., 1999; da Luz Scheffer and Latini, 2020).
The proposed intervention protocols provided biochemical and molecular changes in the treated groups, modulating the inflammatory process and the formation of oxidative stress, through the stimulus imposed by moderate exercise in osteoarthritic joints. As a consequence of this process of improvement of inflammatory conditions and oxidative stress, the beginning of tissue repair is expected, which aims to reconstitute the injured tissues during the OA process.
In this therapeutic evolution of joint tissue repair, the chondrocyte plays a key role, being involved both in catabolic processes, becoming a source of MMP production, and in anabolic processes such as involvement in the synthesis of collagen II, proteoglycans, and growth factors within of the joint environment.
In parallel, growth factors such as TGF-β can stimulate chondrocytes to express cartilage-specific ECM molecules such as type II collagen and proteoglycans, promoting a tissue repair cycle (Silva et al., 2009; da Silva et al., 2009). This growth factor is usually associated with chondrogenesis, chondrocyte proliferation, accumulation of ECM components, and terminal differentiation (Van Der Kraan, 2018). Thus, from this interaction, it can be interpreted that the greater number of chondrocytes and levels of TGF-β found in the results of the study may be the result of this relationship to which treadmill exercise was able to modulate and which possibly had an influence of the anabolic states from the previously described anti-inflammatory and antioxidant actions.
In addition, experimental studies have shown that moderate physical exercise modulates IL1-β expression and increases the number of chondrocytes in histological sections of animals that underwent moderate physical training (Akkiraju and Nohe, 2015; Martins et al., 2019). Reinforcing this characteristic, research has revealed, through bioinformatics, that moderate-intensity exercise generates lower gene expression of caspase-3 and NF-kB in the joint. The first is related to cellular apoptosis and the second is associated with the secretion of pro-inflammatory cytokines such as IL1-β, IL6, and TNF-α (Galois et al., 2004; Qian et al., 2014; Yang et al., 2019; Lu et al., 2021). That is, the findings of the present study are in line with the literature and reaffirm this scenario, while an increase in the number of chondrocytes was observed compared to the OA group, especially in the group of physical exercise on the treadmill. It is estimated that this circumstance possibly stems from the better environment of cellular homeostasis provided by the mechanical action of treadmill physical exercise which, when appropriate, provides better delivery of nutrients and oxygen to the cartilage.
On the other hand, it was confirmed that OA plays pathophysiological mechanisms involved in the apoptosis of chondrocytes, since, when analyzing the quantity by a lacuna of these cells, a significant decrease is found in the OA group in relation to the Sham group.
Although no significant histological changes were found in the total thickness of cartilage and cartilage-cartilage contact measure between the exercised groups and the OA group, the degrees of injury in the OARSI score and the analysis of biochemical parameters suggest that the osteoarthritis group may have with the progressive degradation of cartilage, characteristic of the disease. On the other hand, exercise, especially when performed on a treadmill, seems to exert a protective factor on articular cartilage. Thus, it is considered a resource capable of preventing the progression and/or changing the speed and severity of tissue damage due to the control of factors associated with the etiology and progression of the disease, such as inflammatory parameters and oxidative stress, in addition to the benefit obtained by the practice, expressed by the increase in the number of chondrocytes and the lower degree of injury when compared to the OA group.
However, the treatment of OA is, therefore, a major therapeutic challenge due to the avascular nature and low cellularity of the cartilaginous tissue, which makes the tissue repair process difficult. Physical exercise, when compared with other treatment methods, such as surgical and pharmacological procedures, should be considered an effective form of management in the treatment and control of disease progression. In addition, it has the advantage of being a non-invasive approach, which, when properly prescribed, does not present side effects and generates systemic benefits, not restricted to the affected joint tissue.
Despite the biochemical and histological analyzes demonstrating protective effects on joint cartilage obtained by the protocols used, new studies containing the evaluation of cell signaling pathways may be useful for a more comprehensive understanding of the mechanisms of action of exercise on OA, based on the evaluation of aggrecanases and collagenases involved in the degenerative process, analysis of gene expression associated with anabolic and catabolic aspects of OA such as type II collagen and metalloproteinases, which may be considered limitations of the present study.
## 5 Conclusion
Moderate physical exercise was able to positively modulate inflammatory states, and cellular redox states, and, in the case of exercise on treadmill, it provided an increase in the number of chondrocytes. Thus, moderate exercise, especially on the treadmill, can be considered a convenient method for the treatment of osteoarthritis, capable of also providing benefits to numerous systems, not restricted only to the musculoskeletal system.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Ethics Committee of the university (Universidade do Extremo Sul Catarinense—UNESC) with protocol number $\frac{51}{2020.}$
## Author contributions
LS—conceptualization, data acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing—original draft, writing—review and editing; MCC—investigation, methodology, project administration, validation, writing—review and editing; DH—validation, writing—review and editing; LV—investigation and validation; MEC—investigation, validation and writing—review and editing; GS—investigation and validation; AH—validation; FB—validation; TA—validation; AT—validation; YG—funding acquisition and validation; PS—conceptualization, formal analysis, funding acquisition, project administration, software, supervision, validation, writing—review and editing.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer YX declared a shared affiliation with the authors LS, AT, YG, PS to the handling editor at the time of review.
## 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/fphys.2023.1101159/full#supplementary-material
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|
---
title: Diurnal pattern of breaks in sedentary time and the physical function of older
adults
authors:
- Ting-Fu Lai
- Yung Liao
- Chien-Yu Lin
- Ming-Chun Hsueh
- Mohammad Javad Koohsari
- Ai Shibata
- Koichiro Oka
- Ding-Cheng Chan
journal: Archives of Public Health
year: 2023
pmcid: PMC9990209
doi: 10.1186/s13690-023-01050-1
license: CC BY 4.0
---
# Diurnal pattern of breaks in sedentary time and the physical function of older adults
## Abstract
### Background
The association of breaks in sedentary time with outcomes of physical function can vary according to the time of day. We examined the association of the diurnal pattern of breaks in sedentary time with physical function outcomes in older adults.
### Methods
A cross-sectional analysis was conducted among 115 older adults (≥60 years). The overall and time-specific breaks (morning: 06:00–12:00; afternoon: 12:00–18:00; evening: 18:00–24:00) in sedentary time were assessed using a triaxial accelerometer (Actigraph GT3X+). A break in sedentary time was defined as at least 1 min where the accelerometer registered ≥100 cpm following a sedentary period. Five physical function outcomes were assessed: handgrip strength (dynamometer), balance ability (single leg stance), gait speed (11-m walking), basic functional mobility (time up and go), and lower-limb strength (five times sit-to-stand). Generalized linear models were used to examine the associations of the overall and time-specific breaks in sedentary time with the physical function outcomes.
### Results
Participants showed an average of 69.4 breaks in sedentary time during the day. Less frequent breaks in the evening (19.3) were found than that in the morning (24.3) and the afternoon (25.3) ($p \leq 0.05$). Breaks in sedentary time during the day were associated with less time on gait speed in older adults (exp (β) = 0.92, $95\%$ confidence interval [CI] 0.86–0.98; $p \leq 0.01$). Time-specific analysis showed that breaks in sedentary time were associated with less time on gait speed (exp (β) = 0.94, $95\%$ CI 0.91–0.97; $p \leq 0.01$), basic functional mobility (exp (β) = 0.93, $95\%$ CI 0.89–0.97; $p \leq 0.01$), and lower-limb strength (exp (β) = 0.92, $95\%$ CI 0.87–0.97; $p \leq 0.01$) in the evening only.
### Conclusion
A break in sedentary time, particularly during the evening, was associated with better lower extremity strength in older adults. Further strategies to interrupt sedentary time with frequent breaks, with an emphasis on evening hours, can be helpful to maintain and improve physical function in older adults.
## Background
The number and proportion of the individuals 65 years and older are growing worldwide—the number is expected to more than double from 2019 to 2050, and the proportion is projected to rise from $9\%$ in 2019 to $16\%$ in 2050 [1]. Aging is associated with some decline in health status [2]. A decline in physical function is considered the main reason for a decline in physical independence and an increased risk of disability [3]. Globally, it is estimated that more than $46\%$ of adults over 60 years of age have disabilities and at least 250 million had moderate to severe disability in 2012 [4]. The impact of disability on older adults places a heavy burden of medical expenses [5] and mental stress on family caregivers [6]. Identifying modifiable correlates of physical function may be promising in preventing disability in older adults and reducing the socioeconomic burden.
An updated global guideline on physical activity and sedentary behavior suggests that reduced sedentary time is independent of preventing a decline in physical function in older people [7]. Breaking up prolonged sedentary time can provide opportunities to intervene rather than increasing physical activity. Targeting the older population would be a priority, as some evidence shows this age group may spend most of their waking hours in sedentary behavior [8]. A recent meta-analysis suggests that increased breaks in sedentary time are associated with increased muscle strength in older adults [9]. However, there are limited studies investigating the diurnal pattern in older adults of the association between breaks in sedentary time and physical function [10]. The time spent on physical activity can be restricted in the older population more so than in younger groups due to physiological deterioration. Therefore, investigations of diurnal break patterns in sedentary time and physical function could contribute to the information on the recommended time of day for the projected additional benefits, while designing effective interventions or strategies to maintain and improve muscle function in older adults [11].
This study investigated the associations between breaks in sedentary time, during the day and at different times of the day, and physical function outcomes among older adults. It has been shown that older adults with higher levels of energy intake (e.g., carbohydrate intake with glucose fluctuations) in the evening are associated with lower muscle mass than that in the morning and afternoon [12, 13]. Therefore, it is important to examine whether frequent breaks in sedentary time, with more energy expenditure, during the evening have the most marked association with physical function in older adults than that at other times of the day.
## Participants
We recruited older adults in the community aged 60 years and over, using local advertisements and voice announcements at the community centers of 28 selected neighborhoods in Taipei City, Taiwan. Those who were unable to walk independently (i.e., a need to use walking assistance equipment or help with someone’s arm) were excluded from this study. Detailed recruitment procedures have been reported in a previous study [14]. The minimum sample size required was determined using effect size of $d = 0.25$, with power of 0.8, and the alpha level was set to 0.05 using the G*Power software. To acquire the sample size that should be recruited in the beginning, the minimum sample size calculated was then back-calculated after considering the attrition rate of $10\%$. The number of participants recruited in the beginning was at least 111. A total of 130 older adults participated in the study; 115 had on-site examinations and wore a three-axis accelerometer for seven consecutive days with sufficient valid days. A valid day was defined as wearing an actigraphy device for 10 h or more during waking hours. Data with at least 4 valid days (including 1 weekend day) were included in the analysis. This approach was consistent with previous studies [14, 15]. Informed consent was obtained from each participant before participation in the study. The Research Ethics Committee of the National Taiwan Normal University (REC number: 201706HM020) approved the study.
## Outcomes
Physical function outcomes, including upper extremity strength (i.e., handgrip strength), balance ability, and lower extremity strength (i.e., gait speed, basic functional mobility, and lower-limb strength), were assessed by five independent on-site examinations [16, 17]. The handgrip strength of both hands was measured in turn using the hydraulic hand dynamometer (Jamar Plus+ Digital Hand Dynamometer 5632–13). We asked the participants to sit with their back supported, hips and knees flexed at 90°, feet in contact with the ground, elbows flexed at 90°, with forearms and wrists in neutral position. The participants were then asked to hold the dynamometer and squeeze with as much force as possible. The optimum performance of higher strength was selected from two attempts with a 1 min break in between. We measured balance ability using single leg stance test. The participants were asked to stand on one leg with shoes and their eyes open, lift one leg off the floor, and the time taken until they lowered their foot down to the floor was recorded [18]. The maximum time was recorded as 60 s if the time was prolonged more than 60 s. To measure gait speed, each participant was asked to walk 11 m in one direction as fast as possible [19]. The shorter walk time in the central 5 m was estimated as the optimum gait speed. To assess basic functional mobility, we also asked participants to rise from a conventional chair (seat height: 43 cm), walk 3 m forward, turn, return to the chair, and sit down as quickly as possible [20]. To assess their lower-limb strength, participants were asked to sit on a conventional chair (same as basic functional mobility) and repeat stand up and sit down 5 times, as fast as possible [21]. Each participant repeated the same procedure twice for basic functional mobility and lower-limb strength. The optimum performance selected was the shortest time for each measurement.
## Exposures
Time spent on sedentary behaviors (≤ 99 cpm) was measured by a triaxial accelerometer (ActiGraph GT3X+) worn on the waist [22]. A break in a sedentary time was defined as at least 1 min when the accelerometer registered ≥100 cpm following a sedentary period, according to previous definitions [23, 24]. We followed the data collection and processing criteria procedure suggested by a systematic review of standard protocols for the use of accelerometers [25]. Non-wear time was identified while the periods of more than 60 consecutive minutes of zero counts. The participants were also asked to assess their sleep time and duration using a sleep log. Time intervals during the waking hours were identified, in which sedentary breaks occurred in the morning (06:00–12:00), afternoon (12:00–18:00) and evening (18:00–24:00) based on previous studies [26, 27]. ActiLife software version 6.0 was used to extract the accelerometer data; all data were processed using 60-s time-spans with a default sampling frequency of 30 Hz.
## Covariates
Interviewer-administered questionnaires were used to collect the age of the participants (60–74 or ≥ 75 years), sex, marital status (married or not married), living status (living alone or with others), educational level (having a university degree or lower), health status and habitual behaviors. The height and weight of the participants was measured by trained personnel. Health status included general health measured by a five-point Likert scale (1-very bad to 5-very good in response to, “*In* general, do you consider yourself to be healthy?”), mental health (a yes/no response to “Did you frequently feel depressed in the past month?”), and physical health. Responses to general health questions with at least three points were classified as “good,” and others as “bad.” Physical health identified chronic disease, such as diabetes, hypertension, and hyperlipidemia. Body mass index (BMI) was calculated using height and weight and classified into “normal (18–24 kg/m2)” or “overweight (> 24 kg/m2)” [28]. Habitual behaviors were alcohol consumption, tobacco use, and a balanced diet according to the Taiwan national standard [28]. The time measured by the accelerometer in moderate to vigorous physical activity (MVPA), identified as ≥2020 cpm [22], was classified as “sufficient (≥ 150 min/week)” and “insufficient (< 150 min/week)” [7]. The total sedentary time and wear-time of the accelerometer were also included.
## Statistical analysis
We used descriptive statistics to show the mean and standard deviation (SD) of the physical function outcomes. We selected covariates for the different outcomes of physical function using independent sample t-tests. The relevant covariates for each physical function outcome were identified when there were statistical differences in the characteristics of the participants and were adjusted in the corresponding models (see Table 1. Descriptive statistics between the characteristics of the participants and outcomes of physical function ($$n = 115$$)). *All* generalized linear models were further controlled for overall MVPA, sedentary time, and monitor wear-time. Repeated analysis of variance (ANOVA) was used to determine the difference in breaks in sedentary time across the three different times of the day, due to dependent events. The bar charts with the mean and SD of the overall breaks and time-specific breaks in sedentary time were presented. Generalized linear models specifying a gamma distribution with a log link were used to examine the associations of breaks in sedentary time with physical function outcomes. The antilogarithms of the regression coefficients (exp[β]) and $95\%$ confidence intervals (CIs) were estimated after controlling for appropriate covariates. Regression coefficients represented proportional changes in strength or time spent performing physical function. All analyses were performed using IBM SPSS Statistics 23.0. The significance level was established at $p \leq 0.05.$Table 1Descriptive statistics between the characteristics of the participants and outcomes of physical function ($$n = 115$$)CharacteristicsTotalN (%)Outcome of physical functionHandgrip strength (kg), mean (SD)Balance ability (sec), mean (SD)Gait speed (sec), mean (SD)Basic functional ability (sec), mean (SD)Lower- limb strength (sec), mean (SD)Sex*$p \leq 0.01$p = 0.35*$$p \leq 0.01$$$p \leq 0.31$p = 0.79 Men31 (27.0)33.2 (6.0)39.9 (23.1)2.8 (0.5)6.8 (1.8)7.6 (2.1) Women84 (73.0)21.5 (3.5)35.3 (23.1)3.1 (0.7)7.2 (1.8)7.4 (2.7)Age group (year)$$p \leq 0.20$$*$p \leq 0.01$*$p \leq 0.01$*$p \leq 0.01$*$$p \leq 0.03$$ 60–7494 (81.7)25.1 (6.7)40.8 (22.2)2.9 (0.6)6.7 (1.6)7.2 (2.6) ≥ 7521 (18.3)22.9 (6.7)17.5 (16.9)3.6 (0.7)8.6 (1.9)8.5 (2.2)BMIp = 0.50p = 0.05*$p \leq 0.01$*$p \leq 0.01$p = 0.15 Normal59 (51.3)24.3 (6.1)40.6 (21.5)2.8 (0.6)6.6 (1.4)7.1 (2.4) Overweight56 (48.7)25.1 (7.4)32.2 (24.0)3.2 (0.7)7.5 (2.0)7.8 (2.7)Marital status*$p \leq 0.01$*$$p \leq 0.01$$*$$p \leq 0.04$$$p \leq 0.17$p = 0.96 Married77 (67.0)25.9 (7.5)40.3 (22.5)2.9 (0.6)6.9 (1.8)7.4 (2.7) Not married38 (33.0)22.3 (4.2)28.9 (22.5)3.2 (0.7)7.4 (1.7)7.5 (2.3)Living statusp = 0.10p = 0.53p = 0.43p = 0.27*$$p \leq 0.03$$ Living alone12 (10.4)22.8 (3.5)32.6 (22.6)3.1 (0.8)7.6 (2.1)9.0 (3.5) Living with others103 (89.6)24.9 (7.0)37.0 (23.2)3.0 (0.6)7.0 (1.7)7.3 (2.4)Educational levelp = 0.33*$p \leq 0.01$*$p \leq 0.01$p = 0.05p = 0.59 University degree26 (22.6)25.8 (8.1)49.7 (18.1)2.6 (0.5)6.2 (1.2)7.2 (2.9) Lower than university degree89 (77.4)24.4 (6.3)32.7 (23.0)3.1 (0.7)7.3 (1.8)7.5 (2.5)Employmentp = 0.10p = 0.53p = 0.63p = 0.26p = 0.43 *With a* full-time job4 (3.5)19.2 (1.7)29.3 (28.9)3.2 (0.7)8.0 (2.1)8.5 (3.7) *Without a* full-time job111 (96.5)24.9 (6.8)36.8 (23.0)3.0 (0.7)7.0 (1.8)7.4 (2.5)General health*$$p \leq 0.02$$*$p \leq 0.01$*$p \leq 0.01$*$p \leq 0.01$*$$p \leq 0.01$$ Good36 (31.3)27.1 (7.6)44.8 (20.8)2.7 (0.6)6.4 (1.6)6.6 (1.9) Bad79 (68.7)23.6 (6.1)32.8 (23.2)3.1 (0.6)7.4 (1.8)7.9 (2.7)Frequent depressed*$p \leq 0.01$*$$p \leq 0.01$$$p \leq 0.14$p = 0.08p = 0.27 Yes15 (13.0)21.2 (4.3)22.3 (20.8)3.3 (0.7)7.8 (2.2)8.4 (3.7) No100 (87.0)25.2 (6.9)38.7 (22.7)3.0 (0.6)6.9 (1.7)7.3 (2.3)Diabetesp = 0.53*$$p \leq 0.03$$$p \leq 0.47$p = 0.18p = 0.29 Yes19 (16.5)23.8 (6.1)26.3 (22.6)3.1 (0.7)7.6 (2.1)8.0 (3.5) No96 (83.5)24.9 (6.9)38.5 (22.7)3.0 (0.7)7.0 (1.7)7.3 (2.4)Hypertensionp = 0.50p = 0.28p = 0.40p = 0.25p = 0.33 Yes45 (39.1)25.2 (6.9)33.6 (23.9)3.1 (0.7)7.3 (1.7)7.7 (2.6) No70 (60.9)24.3 (6.7)38.4 (22.5)3.0 (0.7)6.9 (1.8)7.3 (2.5)Hyperlipidemiap = 0.99p = 0.28p = 0.14p = 0.06p = 0.42 Yes33 (28.7)24.7 (6.8)32.8 (23.7)3.1 (0.7)7.6 (1.9)7.8 (2.5) No82 (71.3)24.7 (6.8)38.0 (22.8)2.9 (0.6)6.9 (1.7)7.3 (2.6)Alcohol usep = 0.50*$$p \leq 0.03$$$p \leq 0.73$p = 0.62p = 0.93 Yes9 (7.8)26.2 (7.2)20.7 (21.1)3.1 (0.7)7.3 (2.3)7.4 (1.8) No106 (92.2)24.6 (6.7)37.9 (22.8)3.0 (0.7)1.7 (1.7)7.5 (2.6)Cigarette use*$p \leq 0.01$p = 0.81p = 0.17p = 0.29p = 0.27 Yes7 (6.1)31.5 (8.3)38.6 (27.0)2.7 (0.4)6.4 (1.1)6.4 (1.5) No108 (93.9)24.2 (6.4)36.4 (22.9)3.0 (0.7)7.1 (1.8)7.5 (2.6)Dietary intake*$p \leq 0.01$p = 0.34*$$p \leq 0.03$$$p \leq 0.18$p = 0.79 Balanced85 (73.9)25.6 (7.1)37.8 (23.1)2.9 (0.6)6.9 (1.7)7.4 (2.7) Unbalanced30 (26.1)22.1 (4.7)33.1 (22.9)3.2 (0.8)7.4 (2.0)7.6 (2.3)MVPA*$p \leq 0.01$*$p \leq 0.01$*$p \leq 0.01$*$p \leq 0.01$*$p \leq 0.01$ Insufficient59 (51.3)23.1 (6.5)30.3 (24.0)3.3 (0.7)7.7 (2.0)8.1 (3.1) Sufficient56 (48.7)26.4 (6.7)43.1 (20.2)2.7 (0.5)6.4 (1.2)6.8 (1.7)BMI body mass index, SD standard deviation, MVPA Moderate-to-vigorous physical activity*$p \leq 0.05$
## Results
Almost three-quarters of the participants were women ($73.0\%$), and more than four-fifths were aged 60–74 years ($81.7\%$). Table 1 shows that there were some differences in physical function performance between the characteristics of the participants. For example, those who met the recommended level of physical activity showed higher handgrip strength (26.4 kg vs 23.1 kg), remained balanced for longer (43.1 s vs 30.3 s), walked faster for the same distance (2.7 s vs 3.3 s), moved faster for basic functional mobility (6.4 s vs 7.7 s), and changed their posture faster in the sit-stand test (6.8 s vs 8.1 s). The time spent using the accelerometer was 15.4 h and the total sedentary time during a day averaged 10.1 h. Sedentary time was distributed equally in the morning (2.9 h), afternoon (3.6 h), and in the evening (3.1 h). Figure 1 shows an average of 69.4 (SD = 13.5) breaks in sedentary time during the day. The breaks in sedentary time were similar in the morning (24.3 ± 6.7) and the afternoon (25.3 ± 5.1), but less frequent in the evening (19.3 ± 5.5) ($p \leq 0.05$).Fig. 1One way ANOVA test was applied for Fig. 1. Data were expressed as mean ± SD An increase in one SD of breaks in sedentary time during the day was associated with a shorter time on the gait speed test (exp (β) = 0.92, $95\%$ CI 0.86–0.98; $p \leq 0.01$), but not associated with other physical function outcomes (Table 2). Time-specific analysis showed that an increase in SD of breaks in sedentary time during the evening was associated with a shorter time on the gait speed test (exp (β) = 0.94, $95\%$ CI 0.91–0.97), basic functional mobility (exp (β) = 0.93, $95\%$ CI 0.89–0.97), and lower-limb strength (exp (β) = 0.92, $95\%$ CI 0.87–0.97). There were no associations between the study variables in the morning or afternoon. Table 2Associations of the overall and time-specific breaks in sedentary time with the outcomes of physical function ($$n = 115$$)Outcome of physical functionOverall breaks in sedentary time during the day (z-score)Time-specific breaks in sedentary time during the day (z-score)MorningAfternoonEvening(06:00–12:00)(12:00–18:00)(18:00–24:00)Exp (β)($95\%$ CI)pExp (β)($95\%$ CI)pExp (β)($95\%$ CI)pExp (β)($95\%$ CI)p1Handgrip strength (kg)a0.98(0.92, 1.05)0.611.00(0.95, 1.05)0.991.01(0.97, 1.05)0.670.99(0.95, 1.02)0.442Balance ability (s)a1.16(0.88, 1.52)0.291.18(0.95, 1.47)0.140.97(0.81, 1.16)0.691.06(0.89, 1.27)0.503Gait speed (s) b0.92(0.86, 0.96)< 0.010.97(0.93, 1.02)0.240.99(0.96, 1.03)0.810.94(0.91, 0.97)< 0.014Basic functional mobility (s)b0.95(0.89, 1.02)0.141.00(0.95, 1.05)0.900.99(0.94, 1.03)0.580.93(0.89, 0.97)< 0.015Lower-limb strength (s)b0.91(0.83, 1.00)0.0560.98(0.91, 1.05)0.561.00(0.93, 1.07)0.910.92(0.87, 0.97)< 0.01exp (β): antilogarithms of the regression coefficients; CI: confidence interval. The estimates with $p \leq 0.05$ was highlighted in bold1 Adjusted for sex, marital status, self-rated health, depression, tobacco use, dietary intake, MVPA, sedentary time, and monitor wear time2 Adjusted for age group, marital status, education level, self-rated health, depression, diabetes, alcohol use, MVPA, sedentary time, and monitor wear time3 Adjusted for sex, age group, BMI, marital status, educational level, self-rated health, dietary intake, MVPA, sedentary time, and monitor wear time4 Adjusted for age group, BMI, self-rated health, MVPA, sedentary time, and monitor wear time5 Adjusted for age group, living status, MVPA, sedentary time, and monitor wear timea A positive association indicates a better physical function, accompanied by a higher break in sedentary timeb A negative association indicates a better physical function, accompanied by a higher break in sedentary time
## Discussion
This is the first study, to our knowledge, to examine the associations of the diurnal pattern of breaks in sedentary time with the outcomes of physical function in older adults. The overall breaks in sedentary time throughout the day was associated with better gait speed test and breaks in sedentary time in the evening were associated with better lower extremity strength, indicated by outcomes of gait speed, basic functional mobility, and lower-limb strength.
In keeping with the present study findings, a previous study also showed that frequent breaks in sedentary time were associated with improved lower extremity function [29]. It has been suggested that breaking up sedentary time with frequent active breaks throughout the day can lead to increased skeletal muscle strength due to increased opportunities for muscular contractions [9]. However, in contrast to some previous studies [30, 31], we found no association with the overall breaks in sedentary time during the day with basic function mobility or lower-limb strength. A possible explanation is that a stronger stimulus than a muscular contraction originates from a break in sedentary time may be needed to induce an improvement in physical function outcomes, such as handgrip strength and lower limb strength [32]. Furthermore, a previous study also indicated that a break in sedentary time was not related to subjective physical function, taking into account MVPA and the duration of the sedentary period [33]. Another possible explanation for the non-significant associations found in our study could be that the participants’ characteristics [30, 31]. The mean age of the participants in the present study was 70 years, while those in previous studies were 73 and 75 years, respectively [30, 31]. An average time of 7.7 s for functional mobility was shown by Sardinha et al. [ 31] and an average time of 11.3 s for lower limb strength was shown by Wilson et al. [ 30]. The younger participants in this study generally performed better physical funtion outcomes with spending shorter average time on basic functional mobility (7.1 s) and lower limb strength (7.46 s) than that in previous studies using the same measures [30, 31].
Regarding different times of day, the better outcomes of lower extremity strength, including gait speed, basic functional mobility, and lower limb strength, were associated with a frequent break in sedentary time in the evening, but not in the morning or afternoon. Although no similar studies have investigated the association between breaks in sedentary time and physical function outcomes across times of the day, a recent study suggested that the breaks in sedentary time in the evening showed positive associations with a higher percentage of optimum glycemic indices, in patients with diabetes, after dinner and at bedtime [34]. Glucose fluctuations, independently associated with sarcopenia, low muscle mass, less grip strength, and slow gait speed, in patients with diabetes [13], could be related to physical function. Generally, people consume more carbohydrates at dinner leading to higher glucose fluctuations than with the other two meals of the day [12], bearing in mind it is crucial to stabilize blood glucose levels of patients with diabetes in the evening. Older people who take more breaks from sedentary time in the evening are more likely to have better glucose management and thus contribute to optimum physical function outcomes. Future research is needed to investigate the underlying pathways between breaks in sedentary time, glycemic fluctuations, and physical function outcomes among older adults. Furthermore, a previous study indicated that older adults reached the highest level of physical activity during the day to perform daily tasks such as running errands and voluntary exercise [35]. Higher levels of physical activity during the day can be accompanied by frequent breaks in sedentary time in the morning and afternoon than in the evening. The smaller variations in the breaks in sedentary time may attenuate the relationships in question during the day.
One strength of this study was to use objective measures to assess both the breaks in sedentary time and the outcomes of physical function. There are some limitations to consider when interpreting the results. First, the sample size was relatively small, and most of the participants were women; the participants may not be representative of all older adults in Taiwan. Future research with a large number of participants and representative sample that investigates the relationships is needed. Second, the three different times during the day were identified based on the assumption of waking hours when people can change behavior (e.g., from sitting to standing). There may be inconsistencies in the onset and duration of sleep of participants. However, our data showed that almost $90\%$ of the participants slept and covered the period, 00:00–06:00, without any sedentary or physically active behavior. Third, the accelerometer data for identifying breaks in sedentary time used a sampling frequency (i.e., 30 Hz) following previous research. However, the setting for sampling frequency may be too wide to identify changes in the position, particularly among older adults. Future studies using accelerometer data with a setting for lower frequency are needed. Fourth, there may be some covariates not assessed. For example, a previous study from Portugal has shown that older women performed more frequent breaks in sedentary time were less likely to present unfavorable waist circumference but not associated with BMI [36]. In this study, we measured BMI and considered it as the covariates in the regression models when applicable. Further unmeasured covariates and indexes should be considered. Finally, the cross-sectional association between the breaks in sedentary time and the outcomes of physical function observed in this study could not imply causality.
## Conclusions
The study found that older adults who had more frequent breaks in sedentary time during the evening had better outcomes of lower extremity strength, including gait speed, basic functional mobility, and lower-limb strength. The findings suggest that increases in evening breaks while sedentary may be beneficial to the strength of the lower extremities in older adults. Strategies or interventions relevant to interrupting sedentary time with frequent breaks in the evening must be developed to maintain and improve the physical functional ability of older adults.
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|
---
title: 'Metformin attenuates symptoms of osteoarthritis: role of genetic diversity
of Bcl2 and CXCL16 in OA'
authors:
- Nahid Alimoradi
- Mohammad Tahami
- Negar Firouzabadi
- Elham Haem
- Amin Ramezani
journal: Arthritis Research & Therapy
year: 2023
pmcid: PMC9990216
doi: 10.1186/s13075-023-03025-7
license: CC BY 4.0
---
# Metformin attenuates symptoms of osteoarthritis: role of genetic diversity of Bcl2 and CXCL16 in OA
## Abstract
### Objective
This study aimed to evaluate the effectiveness of metformin versus placebo in overweight patients with knee osteoarthritis (OA). In addition, to assess the effects of inflammatory mediators and apoptotic proteins in the pathogenesis of OA, the genetic polymorphisms of two genes, one related to apoptosis (rs2279115 of Bcl-2) and the other related to inflammation (rs2277680 of CXCL-16), were investigated.
### Methods
In this double-blind placebo-controlled clinical trial, patients were randomly divided to two groups, one group receiving metformin ($$n = 44$$) and the other one receiving an identical inert placebo ($$n = 44$$) for 4 consecutive months (starting dose 0.5 g/day for the first week, increase to 1 g/day for the second week, and further increase to 1.5 g/day for the remaining period). Another group of healthy individuals ($$n = 92$$) with no history and diagnosis of OA were included in this study in order to evaluate the role of genetics in OA. The outcome of treatment regimen was evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The frequency of variants of rs2277680 (A181V) and rs2279115 (938C>A) were determined in extracted DNAs using PCR-RFLP method.
### Results
Our results indicated an increase in scores of pain (P ≤ 0.0001), activity of daily living (ADL) (P ≤ 0.0001), sport and recreation (Sport/Rec) (P ≤ 0.0001), and quality of life (QOL) ($$P \leq 0.003$$) and total scores of the KOOS questionnaire in the metformin group compared to the placebo group. Susceptibility to OA was associated with age, gender, family history, CC genotype of 938C>A (Pa = 0.001; OR = 5.2; $95\%$ CI = 2.0–13.7), and GG+GA genotypes of A181V (Pa = 0.04; OR = 2.1; $95\%$ CI = 1.1–10.5). The C allele of 938C>A (Pa = 0.04; OR = 2.2; $95\%$ CI = 1.1–9.8) and G allele of A181V (Pa = 0.02; OR = 2.2; $95\%$ CI = 1.1–4.8) were also associated with OA.
### Conclusion
Our findings support the possible beneficial effects of metformin on improving pain, ADL, Sport/Rec, and QOL in OA patients. Our findings support the association between the CC genotype of Bcl-2 and GG+GA genotypes of CXCL-16 and OA.
## Introduction
Osteoarthritis (OA) is a multifactorial progressive degenerative disease that is associated with synovial inflammation and bone remodeling and leads to impaired functional characteristics of the joint cartilage [1]. Inflammatory mediators and factors such as cytokines and chemokines disturb the extracellular matrix (ECM) homeostasis which has a vital role in the pathogenesis of OA [2]. Development of articular cartilage degeneration leads to progressive joint space narrowing and is associated with disability and pain in the joints [3]. OA occurs most commonly in the knee joints, which severely affects the quality and quantity of life of patients [4]. The prevalence of OA significantly increases with age with higher rates in women over 45 [5].
Racial/ethnic disparities in OA are well-recognized. Several studies have shown that non-Hispanic Black (NHB) individuals experience higher rates of symptomatic and radiographic OA, higher age-related average pain intensity, and higher levels of disability than their non-Hispanic White (NHW) counterparts, in addition to higher pain sensitivity [6–8]. The role of genetics in various illnesses has been well documented [9–13]. In addition, role of genetics in OA has been supported by many studies, predominantly the single nucleotide polymorphisms (SNPs). Genetic variations could be associated with the OA progression or response to drug therapy among different ethnicities. Since different pathways and molecular mechanisms are involved in the pathogenesis of OA, various genes can affect one’s risk for developing OA [14–16].
Bcl-2 is an anti-apoptotic protein that suppresses apoptosis by inhibiting the action of Bcl-2 associated X protein (BAX), a member of the Bcl-2 family that promotes apoptosis via the mitochondrial-mediated pathway. The ratio of Bcl-2 to BAX expression indicates cellular susceptibility to apoptosis [17–19]. In a report studying the effects of an autophagy inhibitor in the induced-OA animal models, increased autophagy in cartilages of knee joints was associated with inhibition of the Akt/mTOR signaling pathway and increased Bcl-2/Bax ratio [20]. Higher level of apoptotic cartilage death in the OA involved area than in the non-involved area better reflect their role in the pathogenesis of OA [21]. In fact, a negative correlation was observed between Bcl-2/BAX ratio, aging, and OA development [22–25].
The Bcl-2 polymorphism (-938 C> A, rs2279115), a functional SNP on chromosome 18q21.33, is located in the P2 promoter region. It has been reported that the wild type of this SNP may inhibit gene transcription and thus negatively affect Bcl-2 levels [26–28]. However, no study has yet evaluated the association of the Bcl-2 rs2279115 gene polymorphism with susceptibility and severity of knee OA.
Various studies have shown that the occurrence and development of OA are closely associated with the abnormal balance between autophagy and apoptosis in the cartilage [29–31]. Recent studies have shown that the regulation of autophagy is associated with the AMPK/mTORC1 signaling pathways. Upregulation of mTOR leads to the inhibition of autophagy, while the apoptosis of OA chondrocytes is increased which plays an important role in the pathogenesis of OA [32–35]. AMPK is a regulator of cellular energy that maintains a balance between catabolism and anabolism. Decreased control of AMPK over mTOR leads to increased mTOR activity, which may eventually lead to several age-related diseases, including diabetes and OA [36–40]. Inhibition of autophagy has been shown to increase inflammatory activity and lead to increased IL-1β production, which increases mTOR expression in OA chondrocytes [41, 42].
CXCL-16 is a chemokine secreted by synovial macrophages and fibroblasts that mediates the chemo-attraction of neutrophils, lymphocytes, and monocytes into the synovium [43–45]. Soluble CXCL-16, when bound to CXCR6, activates Akt, which may, directly and indirectly, activate its downstream key protein, serine/threonine kinase, mTOR [46, 47]. In studies, serum levels of CXCL-16 were associated with areas of bone apposition/reparative proliferation in temporomandibular joint osteoarthritis (TMJOA) and chronic rheumatoid arthritis-induced inflammation [45, 48]. Studies have reported significant expression of CXCL-16 mRNA in synovial tissue and fluid of OA compared with controls [44, 49]. The CXCL-16 functional SNP, called A181V (rs2277680) on chromosome 17p13, is located in exon 4 of the gene encoding the CXCL16 stalk and encodes the A3V mutation, in which it converts alanine to valine at codon 181 (A181V), and has a strong effect on the structure of the CXCL-16 protein [50, 51]. To date, no study has evaluated the association of A181V (rs2277680) SNP with knee OA.
Metformin is a safe and tolerable oral drug known as the first-line treatment for type 2 diabetes [52]. It can also be beneficial for a number of illness from metabolic to age-related diseases due to its effect on cellular processes [53–59]. According to the role of AMPK in cartilage homeostasis, metformin as an AMPK activator can protect inflammatory cell death of chondrocytes and attenuate cartilage degeneration and development of OA, as evidenced by in vivo and in vitro results [60–63].
To date, there has been no effective non-surgical treatment for OA, and existing therapies such as analgesics and nonsteroidal anti-inflammatory drugs (NSAIDs) are only used to reduce pain and swelling. It is noteworthy that surgical therapy is the last option for patients with end-stage OA [64].
To evaluate the outcome of any therapeutic interventions for knee OA, it is recommended to use multi-item knee-specific outcome measures, such as knee injury and osteoarthritis outcome score (KOOS) to provide a broader picture of the clinical condition [65–67].
The aim of this study was to assess the effect of metformin versus placebo on symptoms of knee OA in overweight patients diagnosed with knee OA in a randomized, double-blind, placebo-controlled trial in a 4-month period using the KOOS questionnaire. Moreover, considering the role of autophagy, AMPK, and the mTOR pathways in OA as well as the effect of Bcl-2, and CXCL-16 in regulation of the mTOR pathway, we aimed to investigate the role of genetic diversity of Bcl-2 and CXCL-16 in OA.
## Participants and treatment protocol
This study was a randomized double-blind placebo-controlled clinical trial with the IRCT code of IRCT20150202020908N2. Ethical approval was obtained from the Research Ethics Committee of Shiraz University of Medical Sciences with the ethical code of IR.SUMS.REC.1398.1010. This study was conducted in accordance with the Declaration of Helsinki.
The participants were selected among the patients with knee pain referred to knee clinic of Motahari complex, Shiraz, Iran.
All patients were examined by an orthopedic knee surgeon during the first visit, and a focused physical examination and history taking were performed. Standard standing anteroposterior (AP) and lateral radiograph were taken and grade of OA was defined according to Kellgren-Lawrence (K-L) classification. Those who had grade 4 were scheduled for total knee replacement surgery. Analgesics and physical rehabilitation were prescribed for patients with K-L grade ≤ 3 who had not received any treatment prior to their referral. In case the patients were not responsive to the above-mentioned treatment protocol, they were reassessed to determine whether they are eligible to enter the study.
Inclusion criteria included BMI > 25 and grade ≤ 3 of OA according to the K-L classification. Exclusion criteria included patients < 18 years of age, cancer, autoimmune diseases, type 2 diabetes, alcoholism, inflammatory bowel disease (IBD), hepatic or renal dysfunction, previous intra-articular knee injections, and metformin use or history of use in the last 2 months. Regarding medication history, patients did not use any anti-inflammatory agent or any other drugs with anti-inflammatory properties except for NSAIDs.
All participants filled out the informed consent form prior to entry of the study. Demographic variables of the participants such as age, sex, BMI, family history, medical conditions, and drug history were collected (Table 1).Table 1Demographic data of OA patients and healthy controlsVariablesOA ($$n = 88$$)Non-OA ($$n = 92$$)Total ($$n = 180$$)Pa (< 0.05)Sex,n(%) Female60 (68.2)41 (44.6)105 (58.3)0.005 Male28 (31.8)51 (55.4)75 (41.7)Age (years)48.3 ± 9.233.0 ± 7.740.5 ± 11.40.0001BMI (kg/m2)29.1 ± 3.425.6 ± 4.927.3 ± 4.60.213Cardiovascular dx,n(%)21 (23.9)3 (3.3)24 (13.3)0.284Family history (%)58 (65.9)11 (11.9)69 (38.3)0.003Smoking16 (18.2)16 (17.4)32 (17.8)0.890Pa adjusted P-value, BMI body mass index A total of 88 cases diagnosed with OA were enrolled in this study; OA patients were randomly divided to two groups, one group receiving metformin ($$n = 44$$) and the other group receiving an identical inert placebo ($$n = 44$$) for 4 consecutive months (starting dose 0.5 g/day for the first week, increase to 1 g/day for the second week, and further increase to 1.5 g/day for the remaining period).
Allocation of patients was based on a simple randomization method of two sets of envelopes with the name of the drug/placebo prescribed inside. Envelopes were shuffled, and the clinician sequentially opened the envelopes to determine the treatment group for each patient. Both the prescribing physician and the patients were blinded to the treatment as well as the analyst who measured the response rate.
In order to assess the possible association of genetic variants of Bcl-2 and CXCL-16 and OA, healthy people with no history of disease were invited to enroll the study by a public announcement at Motahari Medical Complex. Absence of OA was confirmed by physical examination, use of the KOOS questionnaire, and complementary radiologic evaluation when there was a doubt. Finally, 92 healthy non-OA participants were included as a negative control group.
## Questionnaire
KOOS, a valid and reliable questionnaire that has been developed for use in practice and research to evaluate the short-term and long-term outcomes of knee injury such as osteoarthritis, was used to assess the effect of treatment on OA outcome. The questionnaire comprises 42 items in 5 subscales which include pain, other symptoms, function in daily living [3], function in sport and recreation (Sport/Rec), and knee-related quality of life (QOL) [65]. The participants completed the paper versions of the KOOS at the beginning and at the end of the study period. All items were scored on a scale of 0 to 4 and summed, where 0 indicates no difficulty and 4 indicates severe difficulty. The initial raw data from each subscale was converted to a scale from 0 to 100 (worst to best) [68].
## Genotyping
Alongside, another group of healthy individuals ($$n = 92$$) with no history and diagnosis of OA was included in this study in order to evaluate the role of genetics in OA. Blood sample was collected from each participant in an EDTA tube, and genomic DNA was extracted from whole blood leukocytes by the salting out extraction method previously described [69]. The quality of extracted DNA was assessed using NanoDrop®, which ensured high-quality DNA for subsequent genotyping experiments. The detailed sequence information of the study SNPs is mentioned in Table 2. Analysis of SNP of polymorphism rs2277680 and rs2279115 were carried out via the polymerase chain reaction-restricted fragment length polymorphism (PCR-RFLP) method. Genotyping of both SNPs was carried out with a slight modification of previously described protocols [51, 70]. A total of 50 ng genomic DNA was mixed with 0.2 pmol of each PCR primer in a total volume of 25 μl containing 12.5 μl Master Mix (2X) (Ampliqon, Denmark). PCR cycling conditions were as follows: an initial denaturation at 95 °C for 5min, followed by 35 cycles at 95 °C for 40–45 s, 64 °C for 40 s, and 72 °C for the 30s (rs2277680), and 30 cycles at 95 °C for 45 s, 58°C for 40 s, 72 °C for the 30s (rs2279115), and a final extension at 72 °C for 7 min (rs2277680) and 72 °C for 10 min (rs2279115). After PCR amplification, PCR products of rs2277680 and rs2279115 polymorphisms were incubated with restriction enzymes PvuII (Fermentas, Lithuania) at 37 °C, for 16 h, and BccI (New England Biolabs, England) at 37 °C for 1 h. Digested fragments were separated by electrophoresis on a $3\%$ agarose gel (Invitrogen® Ultra-Pure, Waltham, Massachusetts) and visualized in a UV transilluminator. Table 2List of forward and reverse primers for PCR-RFLP of rs2277680 and rs2279115 and their associated restriction enzymes and DNA fragmentsPolymorphismPrimer sequence (5′-3′)TA (°C)Restriction enzymeDNA fragment size (bp)References938C>A (rs2279115)F-CTGCCTTCATTTATCCAGCAR-GGCGGCAGATGAATTACAA58 °CBccI37°C/1h$\frac{300}{189}$/111[1]A181V (rs2277680)F-CAGAAGCATTTACTTCCTACCAGR-ACTGTGGCTGATGTCCTGGCT64 °CPvuII37°C/16h$\frac{281}{206}$/75[2]TA annealing temperature, bp base pair
## Statistical analysis
Data were analyzed using the SPSS® 21.0 for Windows (SPSS Inc., Chicago, Illinois) software. Due to a number of dropouts in the metformin group, an intent-to-treat analysis was performed. Continuous variables are demonstrated as mean ± SD, and categorical variables are presented in percentages (%). Mann-Whitney test was used to analyze non-parametric data, while t-test was used for parametric data analysis. In order to rule out the false positive issue in the analysis of the KOOS subscales, T2-Hotelling multivariate test was performed. Gene-disease association was assessed using odds ratio (OR) and $95\%$ confidence interval (CI) estimation based on the following genetic contrast/models: [1] allele contrast (C-allele vs A-allele, and G-allele vs A-allele); [2] recessive model (CC vs CA+AA and GG vs GA+AA); [3] dominant model (CC+CA vs AA and GG+GA vs AA) for the genetic polymorphisms of the two SNPs, rs2279115 of Bcl-2 and rs2277680 of CXCL-16, respectively. Distributions of genotypes was calculated by chi-square (χ2) test. Adjusted P-values were calculated using multiple logistic regression analysis. P-value < 0.05 was considered as statistically significant.
## Results
A total of 88 patients diagnosed with OA based on KOOS scale were enrolled in our randomized clinical trial. Ninety-two healthy participants were recruited as well. Twenty-seven patients in the metformin group and 44 patients in the placebo group completed the study. The drop-out in the metformin group was because of lack of tolerance due to side effects of metformin such as dizziness, lightheadedness, lethargy, or digestive problems which occurred by increase in the dosage of the drug up to 1500 mg/day.
All the enrolled participants completed the KOOS questionnaire (Table 1). Among the total of 180 enrolled participants, 44 were metformin users (age, 49.3 ± 9.3 years; $77.3\%$ female, $22.7\%$ male), 44 were placebo users (age, 47.3 ± 9.0 years; 68.2 female, 31.8 male), and 92 were healthy controls (age, 33.0 ± 7.7 years; 44.6 female, 55.4 male). The mean BMI of the metformin, placebo, and healthy control group were 29.1 ± 3.0 kg/m2, 29.0 ± 3.8 kg/m2, and 25.6 ± 4.9 kg/m2 respectively.
In terms of age, gender, and family history, there was a significant difference between OA patients (metformin and placebo) and healthy controls (P ≤ 0.001, P ≤ 0.005, and P ≤ 0.003, respectively) (Table 1), while no significant difference was observed between metformin and placebo groups (P ≥ 0.05) regarding these parameters (Table 3). Also, there was no significant differences between OA patients and healthy control regarding smoking status ($$P \leq 0.890$$). Additionally, the use of metformin for 4 months did not cause significant differences in BMI or weight change between participants in metformin group ($$P \leq 0.951$$).Table 3Demographic data of metformin and placebo groupsVariablesMetformin ($$n = 44$$)Placebo ($$n = 44$$)Total ($$n = 88$$)Pc (< 0.05)Sex,n(%) Female34 (77.3)30 (68.2)64 (72.7)0.344 Male10 (22.7)14 (31.8)24 (27.3)Age (years)49.3 ± 9.347.2 ± 9.048.3 ± 9.20.277BMI (kg/m2)29.1 ± 3.129.0 ± 3.829.1 ± 3.40.828Cardiovascular dx,n(%)13 (29.5)8 (18.2)21 (23.9)0.494Family history (%)30 (63.6)32 (72.7)62 (70.5)0.447K-L grade,n(%)16 (36.4) 120 (45.5)36 (40.9)0.229 221 (47.7)22 [50]43 (48.9) 37 (15.9)2 (4.5)9 (10.2)Pc P-value for chi-square test, BMI body mass index The mean baseline KOOS scores of patients in metformin, placebo, and healthy control groups were 52.7 ± 18.4, 52.7 ± 19.0, and 86.8 ± 10.0, respectively. There was a significant difference between symptoms, pain, ADL, Sport/Rec, and QOL scores in patients with OA and the healthy control group at baseline (P ≤ 0.0001), as expected. The mean total KOOS scores of patients in the metformin and placebo group at the end of the study were 65.8 ± 19.8 and 51.0 ± 18.2, respectively (P ≤ 0.0001), while there was no significant difference in the mean of KOOS scores between the two groups prior to intervention (P ≥ 0.05) (Table 4). Treatment with metformin resulted in a significant improvement in pain, ADL, Sport/Rec, and QOL scores of the KOOS questionnaire at the end of the study compared to the pre-treatment time (time 0) within the same group (P ≤ 0.0001) (Table 4).Table 4The KOOS scores pre and post-treatment in metformin and placebo groupsKOOS scalesMean ± SDPmMean ± SDPpP mpMetformin ($$n = 44$$)Placebo ($$n = 44$$)Pre-treatmentPost-treatmentPre-treatmentPost-treatmentPost-treatmentTotal52.7 ± 18.465.8 ± 19.80.000152.7 ± 19.051.0 ± 18.20.0550.0001Symptoms77.7 ± 14.978.7 ± 17.40.66475.6 ± 17.373.6 ± 17.50.0140.166Pain54.9 ± 18.870.8 ± 19.10.000151.8 ± 19.050.5 ± 19.00.070.0001ADL55.7 ± 20.872.9 ± 19.20.000155.1 ± 24.156.3 ± 20.40.3620.0001Sport/Rec35.1 ± 29.053.0 ± 31.30.000137.8 ± 28.632.3 ± 23.70.0010.0001QOL39.6 ± 20.353.6 ± 26.00.00241.1 ± 20.541.6 ± 21.60.4720.003Pm, the calculated P value between the pre and post-treatment times in the metformin group; Pp, the calculated P value between the pre and post-treatment in placebo group; P mp, the calculated P value between post-treatment times of the metformin and placebo groups In detail, a significant increase in the item of swelling in symptoms score was observed in the metformin group between time zero and the end of the study ($$P \leq 0.006$$); however, in the total score of symptoms, no significant difference was observed. Treatment with placebo resulted in a reduction in the symptoms and Sport/Rec scores compared to the pre-treatment values ($$P \leq 0.014$$, and P ≤ 0.001, respectively). There was no significant difference in all calculated KOOS scores in metformin and placebo groups at zero time (P ≥ 0.05), while after treatment, all scores except for symptom scores were significantly different, and the beneficial effects of metformin treatment compared to placebo treatment were clear (P ≤ 0.05) (Table 4). The P-value of T2-Hotelling test was < 0.001. As expected, the scores of metformin users at the end of the study were still significantly different from the healthy subjects (P ≤ 0.0001).
Genotype and allele distribution of Bcl-2 and CXCL-16 gene polymorphism are presented in Table 5. P-values after adjusting for confounders (age, sex, and family history) are provided as Pa. As shown, for Bcl-2 gene, significant association with OA was found in a recessive model (Pa = 0.001; OR = 5.2; $95\%$ CI = 2–13.7 for CC vs CA+AA). For CXCL-16 gene, significant association with OA was found in a dominant model (Pa = 0.04; OR = 2.1; $95\%$ CI = 1.1–10.5 for GG+GA vs AA).Table 5Genotype and allele distribution in OA patients and healthy controlsSNPFrequencies (%)PcOR; $95\%$CIPaOR; $95\%$CISubjectsOAControl ($$n = 92$$)Metformin ($$n = 44$$)Placebo ($$n = 44$$)Genotype938C>A (Bcl-2)CC21 (47.7)28 (63.6)28 (30.4)0.00082.9; 1.9–5.40.0015.2; 2.0–13.7CA15 (34.1)12 (27.3)54 (58.7)AA8 (18.2)4 (9.1)10 (10.9)AlleleC57 (64.8)68 (77.3)110 (59.8)0.021.7; 1.1.–2.60.042.2; 1.1–9.8A31 (35.2)20 (22.7)74 (40.2)GenotypeA181V (CXCL-16)GG20 (45.5)20 (45.5)28 (30.5)0.0073.4; 1.4–8.50.042.1; 1.1–10.5GA19 (43.2)22 (50.0)43 (46.7)AA5 (11.3)2 (4.5)21 (22.8)AlleleG59 (67.0)62 (70.5)99 (53.8)0.0041.9; 1.2–2.90.022.2; 1.1–4.8A29 (33.0)26 (29.5)85 (46.2)Pc P-value for chi-square test, Pa adjusted P-value, OR odds ratio, CI confidence interval The C allele of 938C>A (Pa = 0.04; OR = 2.2; $95\%$ CI = 1.1–9.8) and G allele of A181V (Pa = 0.02; OR = 2.2; $95\%$ CI = 1.1–4.8) were associated with OA.
## Discussion
In this study, we conducted a double-blind placebo-controlled clinical trial to determine whether metformin intervention can affect OA, ameliorate signs, and pain or modify activity in overweight non-diabetic knee OA patients within 4 months of treatment. We also assessed the role of genetic variations of Bcl-2 and CXCL-16 in OA. The results of our study demonstrated significant beneficial effects of metformin on the general conditions of OA patients. Metformin also caused a significant reduction in knee swelling. On the other hand, placebo treatment did not produce beneficial effects on the condition of OA patients.
The results of another clinical trial in OA patients confirm the beneficial effects of metformin such as changes in western Ontario and McMaster universities osteoarthritis index (WOMAC) score and visual analog scale (VAS) knee pain with a maximum dose of 2 g/day for 24 months [71]. In another study, the results showed that metformin (1000 mg/day for 12 weeks) in combination with meloxicam (15 mg/day) significantly improved KOOS components compared to the setting that meloxicam was used alone in OA patients [72]. Recently, some studies have reported the therapeutic influence of metformin on reducing cartilage degeneration and joint pain in animal models and cohort studies [73–76]. In our study, with pain reduction following metformin administration, pain consequences such as a disability in activities of daily living, exercise, physical reactions, and quality of life also improved in OA patients. In this regard, the effects of metformin in reducing pain and edema which was observed in our study can be attributed to many factors, including its anti-inflammatory and antioxidant effects [77–79]. mTOR signaling has been reported to sensitize the nervous system in conditions such as chronic pain along provoking inflammation [80, 81]. Additionally, the role of AMPK in reduction of pain and inflammation is clear. As known, metformin inhibits the mTOR pathway and its downstream pathways by enhancing AMPK phosphorylation. It also maintains the balance between apoptosis and autophagy of chondrocytes and other cells by activating AMPK and reduces cartilage degeneration by affecting the mTOR pathway and its downstream pathways [39, 61–63, 74]. As a result, metformin reduces pain and edema by inhibiting apoptosis, reducing oxidative stress, and increasing autophagy [79, 81–83].
Other findings of our study were that the carriers of CC genotype of 938C>A were significantly more prone to OA. Bcl-2 gene consists of two promoters, P1 and P2; the P2 promoter plays a regulatory role on the P1 promoter and reduces its activity. The SNP 938C>A is located on the P2 promoter. Previous studies suggest that the C allele increases the activity of the P2 promoter and thus inhibits gene transcription by the P1 promoter and promotes apoptosis by reducing Bcl-2 gene expression [26–28]. The A allele is reported to be associated with increased cancer risk by increasing the expression level of Bcl-2 and inhibiting apoptosis [28, 84–86]. According to different studies, Bcl-2 mRNA expression level is significantly decreased in the cartilage tissue of patients with high degree of OA or in animal models; however, in other studies, this reduction of Bcl-2 level was not supported statistically [22, 87–89]. The only SNP studied previously in OA patients is the Bcl-2 -717 C>A polymorphism where its homozygous mutant genotype was reported to be associated with decreased expression of Bcl-2, promotion of apoptosis, and development of OA [90]. Bcl-2 is one of the important elements of interaction between the apoptotic and autophagy systems [91]. It has been reported that binding of the autophagy protein Atg4B to Bcl-2 leads to degradation of Bcl-2-Beclin-1 and induces Bcl-2-Beclin1 dissociation. The release of Beclin-1 and the subsequent binding to PI3KC3 regulates autophagy initiation which finally leads to switch from apoptosis to autophagy [92–94]. According to the effect of CC genotype in increasing apoptosis by reducing the production of Bcl-2, the probability of developing OA may increase in individuals carrying this genotype [22, 88, 90].
Regarding CXCL16 genetic variant, A181V, our results advocate the association between GG+GA genotypes as well as its associated allele, the G allele, and OA. Missense mutation of rs2277680 (A181V) causes G→A substitutions at positions 4585312 on chromosome 17, resulting in an alanine to valine substitution at codon 181 (A181V) of the CXCL-16 protein which is at its mucin-type stalk region [51]. This substitution is located in the interior of the CXCL-16 protein and affects the conformation of the chemokine domain and reduces the size of its active site, which is associated with a loss of CXCR6-mediated adhesion in human monocytes and chemotactic activity [51, 95]. The presence of this mutation was reported to be associated with an increased risk for the development of sepsis and multiple organ dysfunction syndrome (MODS) in patients with major trauma as well as acute liver failure (ALF) in patients with Hepatitis B virus (HBV) infection, as a result of lack of natural killer T (NKT) cells activation [95]. CXCL-16 is a pro-inflammatory cytokine with angiogenic properties which stimulates synovial neovascularization [96, 97]. In chronic joint inflammation of OA, the balance between pro- and anti-inflammatory cytokines is tilted towards pro-inflammatory cytokines, which ultimately leads to the initiation of angiogenesis and bone remodeling [98, 99]. Neovascularization plays an important role in the pathology of OA, and its degree is related to the degree of OA [100, 101]. The formation of new vessels, in turn, leads to increased leukocyte recruitment and migration of neurons to the joint. These new neurons are sensitized to pain through mechanical stress, hypoxia, and inflammation. Finally, the stimulation of synovial neovascularization leads to the recurrence of pain and inflammation in the joint and exacerbation of OA [102, 103]. One study demonstrated that CXCL-16/CXCR6 chemokine signaling is associated with invasive growth and angiogenic activities in PCa cells by inducing the activation of Akt/mTOR pathways [104].
The G allele of A181V has been reported to result in the production of CXCL-16 protein with an effective protein-receptor interaction site (CXCL-16/CXCR6). This effective binding increases synovial inflammation and neovascularization induced by CXCL-16 in individuals carrying these genotypes, which eventually leads to the development of OA [95, 97, 98].
It is noteworthy that metformin modulates Beclin1–Bcl-2 complex dissociation, which regulates initiation of autophagy leading to switch from apoptosis to autophagy in an AMPK-dependent and AMPK-independent manners. In the AMPK-dependent manner, metformin phosphorylates MAPK8 which mediates Bcl-2 phosphorylation and in the AMPK-independent manner metformin inactivates STAT3, an upstream protein of Bcl-2 [105–107]. Alternatively, metformin can inhibit mTOR phosphorylation leading to the activation of ULK1, which phosphorylates Beclin-1, thus enhancing Beclin-1-PI3KC3 complex formation and autophagy induction [80, 108]. Moreover, it has been reported that metformin decreases the mRNA expression and plasma levels of CXCL-16 [108-110]. Accordingly, if our study be replicated in a larger population of patients with OA, it is possible that patients respond in different degrees to metformin. Hence, the role of studied variants of Bcl-2 and CXCL-16, as targets of metformin, in response to metformin treatment can be assessed in future studies.
As the study limitation, we should allude to the relatively small sample size of the enrolled subjects. However, positive results showing the beneficial effects of somewhat short-term treatment of OA in non-diabetic patients opens up a venue to explore the effect of metformin in larger sample sizes. Alongside, the association between genetic variants of Bcl-2 and CXCL-16 and OA was investigated for the first time which may provide preliminary insights for further investigations in various ethnicities and larger study groups.
## Conclusion
A 4-month period of treatment of non-diabetic OA patients with metformin provided significant beneficial effects. Due to good safety profile and limited and mostly tolerable side effects of metformin, this oral hypoglycemic agent may be considered as an alternative or adjuvant treatment of OA patients. Additionally, significant associations with OA susceptibility were observed in carriers of different variants of 938C>A and A181V polymorphisms. We identified a possible risk genotype, wild genotype (CC) of Bcl-2 and wild allele (G) of CXCL-16 for OA susceptibility. Assessing patients more at risk of OA based on their genetic pattern may provide clues for considering preventive options in patients more at risk.
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|
---
title: Accelular nanofibrous bilayer scaffold intrapenetrated with polydopamine network
and implemented into a full-thickness wound of a white-pig model affects inflammation
and healing process
authors:
- Katarína Kacvinská
- Veronika Pavliňáková
- Petr Poláček
- Lenka Michlovská
- Veronika Hefka Blahnová
- Eva Filová
- Martin Knoz
- Břetislav Lipový
- Jakub Holoubek
- Martin Faldyna
- Zdeněk Pavlovský
- Monika Vícenová
- Michaela Cvanová
- Jiří Jarkovský
- Lucy Vojtová
journal: Journal of Nanobiotechnology
year: 2023
pmcid: PMC9990222
doi: 10.1186/s12951-023-01822-5
license: CC BY 4.0
---
# Accelular nanofibrous bilayer scaffold intrapenetrated with polydopamine network and implemented into a full-thickness wound of a white-pig model affects inflammation and healing process
## Abstract
Treatment of complete loss of skin thickness requires expensive cellular materials and limited skin grafts used as temporary coverage. This paper presents an acellular bilayer scaffold modified with polydopamine (PDA), which is designed to mimic a missing dermis and a basement membrane (BM). The alternate dermis is made from freeze-dried collagen and chitosan (Coll/Chit) or collagen and a calcium salt of oxidized cellulose (Coll/CaOC). Alternate BM is made from electrospun gelatin (Gel), polycaprolactone (PCL), and CaOC. Morphological and mechanical analyzes have shown that PDA significantly improved the elasticity and strength of collagen microfibrils, which favorably affected swelling capacity and porosity. PDA significantly supported and maintained metabolic activity, proliferation, and viability of the murine fibroblast cell lines. The in vivo experiment carried out in a domestic Large white pig model resulted in the expression of pro-inflammatory cytokines in the first 1–2 weeks, giving the idea that PDA and/or CaOC trigger the early stages of inflammation. Otherwise, in later stages, PDA caused a reduction in inflammation with the expression of the anti-inflammatory molecule IL10 and the transforming growth factor β (TGFβ1), which could support the formation of fibroblasts. Similarities in treatment with native porcine skin suggested that the bilayer can be used as an implant for full-thickness skin wounds and thus eliminate the use of skin grafts.
## Background
Wound healing is an integrated and complex process that begins immediately after injury and involves the release of a large number of regulatory molecules, including pro-inflammatory cytokines, growth factors, and low molecular weight compounds from the serum of injured blood vessels and degranulated platelets [1, 2]. The epidermal defect itself is called a superficial wound, a defect in the epidermis and dermis together with damage to blood vessels, sweat glands, etc. is called a partial thickness wound, while damage to the subcutaneous fat layer is called a full thickness wound and it leads to extensive loss of skin, hair follicles, and glands [3]. There are many studies that are applied with extensive research to the treatment of partial and full-thickness wounds using different materials made of porous foams, hydrogels, or nanofibrous layers of synthetic materials (poly(ethylene glycol) (PEG), poly(ε-caprolactone) (PCL), poly(lactic-co-glycolic acid) (PLGA), poly(lactic acid) (PLA), poly(vinyl alcohol) (PVA), polyurethane films, or silk fibroin) [4–6] and/or natural materials based on collagen (Coll), gelatin (Gel), cellulose, alginate, chitosan, hyaluronan, fibrin or fucoidan materials [7–9].
Coll has been widely used in many applications because the naturally occurring protein consists of three α-domains (polypeptide chains) that provide the main mechanical support for cell attachment and has excellent biocompatibility and biodegradability [10–14]. However, Coll-based scaffolds face rapid biodegradation rates and low mechanical strength. Chemical cross-linking is the most effective strategy to promote stability. Carbodiimides have been widely investigated as suitable crosslinkers for collagen scaffolds [15–17]. Another possibility is the combination of Coll with natural and/or synthetic materials, which brings new functional possibilities to tissue engineering applications [18]. Chitosan (Chit) is a biodegradable, non-toxic, and antibacterial material with a homeostatic effect. Chit is often used in combination with Coll; it accelerates fibroblast formation and enhances early phase reactions related to healing [19, 20]. In our previous studies, [21, 22] we evaluated Coll/Chit scaffolds enriched with fibroblast growth factor 2 (FGF 2) and further in combination with selenium nanoparticles (SeNPs). The results showed support for fibroblast attachment and metabolic activity. In addition, the scaffolds exhibited antibacterial activity against three strains of bacteria, Escherichia Coli (E. coli), *Staphylococcus aureus* (S. aureus), and methicillin-resistant S. aureus (MRSA). Chit can be processed in various forms such as films, hydrogels, fibers, powders, and micro/nanoparticles used in skin tissue engineering [23, 24]. Oxidized cellulose is a biodegradable polymer, with non-immunogenicity, and it promotes the healing of chronic wounds [25, 26]. In combination with Coll, it reduces pro-inflammatory interleukins, reactive oxygen species, and binds to metal ions with increasing concentrations of growth factors and proteinase inhibitors [27]. The addition of calcium salt of oxidized cellulose (CaOC) to electrospun nanofibers provided a unique inhibitory effect on E. coli bacteria [28]. Poly(ε-caprolactone) (PCL) is a synthetic, biocompatible, linear aliphatic polyester that is hydrophobic, it degrades relatively slowly, and has good mechanical properties. PCL scaffolds have been used as in vivo tissue implants for various medical applications and have shown great potential for wound healing, bone tissue engineering, cardiovascular tissue engineering, and nerve regeneration [29, 30]. Dopamine is a molecule that forms natural adhesion between the material surfaces or sticks small molecules. It is synthesized in the body by cells and has an amino acid sequence similar to that of mussel protein, which has the ability to bind to many surfaces in an aqueous environment [31]. An attractive property of dopamine is its auto-polymerization, which has been reported to occur in Tris buffer with pH of 8.5, where dopamine leads to polydopamine (PDA) films and nanofibers [32–34].
In recent years, studies based on multilayer scaffolds in the treatment of full-thickness wounds have advantages over a single layer dressing because they can functionally replace both dermal and epidermal components. Acellular bilayer materials were prepared by a combination of natural and synthetic materials, e.g., Chit/PCL nanofibrous mats, PLLA-microporous disc [35]. Furthermore, the Coll/Chit scaffold enriched with recombinant human vascular endothelial growth factor (rhVEGF) and antibacterial gentamicin were encapsulated in PLGA microspheres [36]. A trilayer Chit-based scaffold was prepared to more accurately replicate full-thickness skin striation than a single or bilayer scaffold, which required weeks of co-culture of fibroblasts and keratinocytes to achieve similar striation [37]. There are many other existing studies that consider the potential use of acellular multilayered scaffolds, not only in skin tissue, [38–40] but also in vascular tissue [41] and bone tissue engineering [42].
This study aims to develop an acellular PDA-modified bilayer scaffold and to enhance mechanical and biological support in full-thickness porcine skin wound reconstruction. The bilayer is made of porous Coll/polysaccharide foam (Chit or CaOC) with the aim of mimicking a dermis-like structure and a basal membrane-like structure, characterized by a nanofibrous layer made of biocompatible polymers gelatin, PCL, and CaOC. The PDA-modified bilayer significantly changes mechanical properties and promotes stability, leading to different water absorption and material morphology. These changes also allowed cells to proliferate with maintained viability. In vitro evaluation of cytotoxicity and metabolic and proliferation activity of murine fibroblasts demonstrated a non-cytotoxic effect of implanted bilayers. In this study, the healing process was monitored for a longer period of time, as well as the effect of PDA after transplantation, which shows the histological analysis of inflammatory and anti-inflammatory cytokines. PDA can enhance the inflammatory phase at the beginning of wound healing and shows possible support for the expansion of growth factor and anti-inflammatory cytokines in the middle and later stages of wound healing compared to native porcine skin treatment.
## Materials and chemicals
Bovine Collagen type I, 8 wt$.\%$ aqueous solution (Coll, Collado s.r.o., Brno, Czech Republic), chitosan from shrimp shells, $70\%$ DDA, low viscosity (Chit, Sigma-Aldrich, Darmstadt, Germany), calcium salt of oxidized cellulose–degree of oxidation 16–$24\%$ and Mn = 350 kg/mol (CaOC, Synthesia, Pardubice, Czech Republic), acetic acid ($99\%$, Penta s.r.o, Chrudim, Czech Republic), poly(ε-caprolactone) (PCL, 80 kg/mol), gelatin (Gel, Type B, Bioreagent, powder from bovine skin), N-(3-Dimethylaminopropyl)-N´-ethylcarbodiimide hydrochloride (EDC), N-hydroxysuccinimide (NHS), $98\%$ dopamine hydrochloride, tris (hydroxymethyl) aminomethane hydrochloride, ethanol p.a. $99.8\%$, sodium phosphate dibasic for molecular biology (≥ 98,$5\%$), sodium chloride, calcium chloride, sodium phosphate dibasic dodecahydrate (Na2HPO4 ·12H2O), potassium dihydrogen phosphate (KH2PO4), potassium chloride (KCl), collagenase from Clostridium histolyticum, lysozyme human, the murine fibroblast cell lines 3T3-A31, Dulbecco's modified eagle medium DMEM (D6429), fetal bovine serum FBS (F7524), 2′,7′-bis (2-carboxyethyl)-5[6]-carboxyfluorescein acetoxymethyl ester (BCECF-AM), propidium iodide (P4864), (all from Sigma Aldrich, Darmstadt, Germany), penicillin/streptomycin (15140–122) and DiOC6[3] (D273), (Life Technologies, Eugene, OR, USA), octenidine solution (Octenisept®, Schülke, Germany), Butomidor® inj. ( butorphanol tartrate, Vétoquinol, Czech republic), Domitor®, Medetomidine, Orion corporation, Finland) Propofol® (Propofolum $1\%$, Fresenius Kabi Deutschland, Bad Homburg, Germany), Metacam® (meloxicam, Boehringer Ingelheim Vetmedica, Ingelheim/Rhein, Germany), Enroxil® (Enrofloxacin, Krka, Novo mesto, Slovenia) Betadine®, ($2.5\%$ solution of povidone iodine, EGIS Pharmaceuticals PLC, Budapest, Hungary) were used as received without further purification.
## Preparation of porous foams and cross-linked bilayers
Porous foams were prepared according to previous work [43]. Briefly, the calculated amount of Coll (0.5 wt$.\%$) and suitable polysaccharide (0.5 wt$.\%$) in the weight ratio of 1:1 was slowly homogenized. Material suspensions were freeze-dried on an Epsilon 2-10D machine (Martin Christ, Osterode am Hartz, Germany). A fibrous layer was electrospun on the surface of the lyophilized porous foam according to [28] modified with the addition of PCL. Nanofibers were prepared as follows: the Gel/PCL/CaOC $\frac{70}{30}$/10 polymer solution was prepared in concentrated glacial acetic acid and stirred overnight. Electrospinning was performed using a laboratory Nanospider NS LAB 500 machine (Department of Physical Electronics, Masaryk University, Brno, Czech Republic). The setting parameters were as follows: flow rate of 25 mm‧min−1, applied voltage of 60 kV, the distance between the spinning and collecting electrode was set to 15 cm, and the spinning electrode was rotated at a speed of 5 rpm. The ambient conditions were 23 °C, 980 kPa, and $40\%$ humidity. A cross-linking agent of the carbodiimide system in ethanol (EDC/NHS in a molar ratio $\frac{2}{1}$) was used to cross-link the porous foam and the nanofibrous layer. After 2 h of the cross-linking process, the bilayer was washed twice with 0.1 M Na2HPO4 followed three times with ultrapure water to remove by-products. Subsequently, the bilayers were freeze-dried and stored in desiccators prior use.
## Preparation of PDA-coated cross-linked bilayers
Porous foams and nanofibrous layers were prepared as described in 3.2.1. Briefly, a nanofibrous layer was electrospun on the freeze-dried porous foam and both parts were cross-linked and washed after 2 h. A solution of dopamine hydrochloride (2 mg.mL−1 in 0.01 M Tris HCl) was prepared before the second freeze-drying process. Tris HCl was used as an initiator for dopamine polymerization, resulting in black polydopamine (PDA) [31]. Cross-linked bilayers were immersed in a solution of dopamine hydrochloride and maintained for another 24 h under aerobic conditions. The PDA-intrapenetrated samples were then washed 5 times in water to remove residual unbound dopamine, and subsequently the samples were lyophilized again. Samples were always prepared either in a volume of 500 μL in 24-well plates for in vitro testing (only porous foams), or in a volume of 80 mL in 12 × 12 cm square plastic plates for biomechanical evaluation (bilayers). All types of prepared samples are summarized in Table 1. An explanation of sample abbreviations is as follows: The letter N indicates the presence of a cross-linked nanofibrous layer (e.g., the abbreviation of Coll/Chit-N/PDA belongs to the bilayer formed by Coll/Chit foam with cross-linked nanofibers, coated with PDA). The letters NX indicate the presence of a non-cross-linked nanofibrous layer, as it can be seen in Table 1.Table 1Summary of prepared samplesPorous foam compositionAbbreviation of foamCollagen foamCollCollagen/Chitosan foamColl/ChitCollagen/CaOC foamColl/CaOCPorous foam coated with PDAAbbreviation of foamCollagen foam coated with PDAColl/PDACollagen/Chitosan coated with PDAColl/Chit/PDACollagen/CaOC coated with PDAColl/CaOC/PDANon-cross-linked bilayers*Abbreviation of bilayerCollagen bilayer (foam + nanofibers)Coll-NXCollagen/Chitosan bilayer (foam + nanofibers)Coll/Chit-NXCollagen/CaOC bilayer (foam + nanofibers)Coll/CaOC-NXCross-linked bilayersAbbreviation of bilayerCollagen bilayer (foam + nanofibers)Coll-NCollagen/Chitosan bilayer (foam + nanofibers)Coll/Chit-NCollagen/CaOC bilayer (foam + nanofibers)Coll/CaOC-NCross-linked bilayers coated with PDAAbbreviation of bilayerCollagen bilayer (foam + nanofibers and PDA coating)Coll-N/PDACollagen/Chitosan bilayer (foam + nanofibers and PDA coating)Coll/Chit-N/PDACollagen/CaOC bilayer (foam + nanofibers and PDA coating)Coll/CaOC-N/PDA* All non-cross-linked bilayers are excluded from following experiments due to the low mechanical properties and are mentioned only in SEM observation
## Structure and morphology
A scanning electron microscope MIRA3 (TESCAN, Brno, Czech Republic) was used to study the morphology and adhesion of the prepared bilayers. Images were taken in a secondary electron emission mode, the scan mode was DEPTH, the beam density was 10 and the high voltage was 10 kV. The working distance was set to 15 mm. The surface of the samples was coated with a 20 nm thin layer of Au/Pd using EM ACE 600 (Leica Microsystems, Wetzlar, Germany). The pore size was characterized from SEM images using ImageJ software and SEM Image Pore Extractor (SEMIPE). A minimum of five and a maximum of ten images with the same resolution were taken from each sample. From each image, 40–90 pores were measured. Data were evaluated using a 2-sample T-test, which assumes unequal variances and unequal sample sizes. The level of significance was set at *$p \leq 0.05$ **$p \leq 0.01$and ***$p \leq 0.001.$
## Fourier-transformed infrared study results
A Fourier-transformed infrared spectroscopy (FTIR) with attenuated total reflectance (ATR-FTIR, Vertex $\frac{70}{70}$v, Bruker, Billerica, MA, USA) was performed to characterize material composition of porous foam with and without PDA and material composition of the PDA bilayers. Presented ATR-FTIR spectra were taken from averaging 32 scans with a spectral resolution of 2 cm−1. The displayed spectra in the wavenumber range of 4000–500 cm−1 were normalized using min–max normalization (OPUS software, Bruker, Billerica, MA, USA). The ATR-FTIR spectra were measured under evacuated conditions from all samples, each placed on a diamond ATR crystal.
## Dynamic mechanical analysis
An RSA G2 dynamic mechanical analyzer (TA Instruments Inc., New Castle, USA) was used to measure the tensile properties of prepared bilayers. The prepared samples were cut into strips with a length of 40 mm and a width of 10 mm. Thickness varies with the type of sample (0.20 mm–0.60 mm) and samples were measured with Digital Caliper 0–150 mm (Groningen, Netherlands). The first biomechanical tests were performed at room temperature at 23 °C and the second test involved constant hydration conditions with phosphate buffer at 36 °C in a built chamber surrounding the grip. Before each measurement, 10 min of swelling was allowed for each sample to swell the bilayers. The relationship between stress and strain is shown along with the corresponding elastic modulus. Data analysis using Microsoft Excel was used for the statistical evaluation of five samples of the same bilayer. Data were evaluated using a 2-sample T-test, which assumes unequal variances and unequal sample sizes. The level of significance was set at ** $p \leq 0.01$and ***$p \leq 0.001.$
## Swelling capacity
The bilayers were cut into 1 × 2 cm strips and immersed in a water solution to test their hydrolytic stability under ambient conditions. Each sample was weighted before immersion (Wi). The weight of swollen samples (Ws) was also recorded after gently removing the surface water with filter paper at several intervals: 1, 2, 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, and 180 min. A swelling ratio was calculated to define the exact amount of swelling caused by water absorption, and the swelling curve was obtained. The swelling ratio was calculated according to Eq [1]1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Swelling\;\,Ratio = \frac{{W_{s} }}{{W_{i} }}$$\end{document}SwellingRatio=WsWi The samples were measured in triplicates and the results are shown as mean ± standard deviation.
## Enzymatic stability
Collagenase from *Clostridium histolyticum* was used to investigate in vitro degradation studies of chemically cross-linked bilayers. Degradation was carried out in prepared phosphate buffered saline (PBS) at physiological pH of 7.4 at 37 °C. After one hour of swelling, samples were removed from the PBS, subsequently weighted, and placed in the collagenase solution ($c = 2.2$ mg∙L−1). After every 2, 4, 8, 24, 48, 72, 96, 120 and 144 h, excess PBS was blotted onto the filter paper, following the weight notation, as well as the percentage of weight loss calculation using Eq. [ 2]2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Weight\;Loss = 100 - \left({\frac{{W_{i} \cdot 100}}{{W_{s} }}} \right)\;[\%]$$\end{document}WeightLoss=100-Wi·100Ws[%]where Ws represents the weight of the scaffold after 1 h of swelling and Wi represents the weight of the digested scaffold. Three measurements of each type of sample were recorded and shown as mean ± standard deviation.
## In vitro cytotoxicity assessment
Murine fibroblast cell lines 3T3-A31 were cultured in culture medium containing DMEM (high glucose, D6429, Sigma-Aldrich, St. Louis, MO, USA), $10\%$ FBS, and $1\%$ penicillin/streptomycin. 70,000 cells/scaffold (with a diameter of 10 mm and a height of 4–5 mm) were seeded for a period of 14 days.
Metabolic activity was determined by the CellTiter 96® Aqueous One Solution Cell Proliferation (MTS) metabolic assay (CellTiter 96® Aqueous One Solution Cell Proliferation Assay, Promega corp., Madison, WI, USA), where the MTS tetrazolium compound was added directly to the cell culture medium in a 1:5 ratio. Metabolically active cells reduced the MTS reagent and generated a colored formazan dye that is soluble in cell culture medium. Formazan dye was quantified by measuring the absorbance at 490 nm, reference 690 nm using Tecan Infinite M200 Pro. The samples were carried out in biological quadruplicates; the results are shown as mean ± standard deviation.
The Quant-iT™ dsDNA Assay Reagent (Invitrogen) assay determined cell proliferation, as it quantified the amount of double-stranded DNA. The assay contains a fluorescent dye activated once it is bound to dsDNA. Fluorescence was measured at λex = 485 nm and λem = 523 nm. The samples were carried out in biological quadruplicates; the results are shown as mean ± standard deviation.
Cell viability was assessed by live-dead staining of three samples. 2′,7′-bis (2-carboxyethyl)-5[6]-carboxyfluorescein acetoxymethyl ester (BCECF, Sigma-Aldrich, Saint Luis, MO, USA) was used to visualize the membranes of living cells and propidium iodide to visualize the nuclei of dead cells. Samples were observed using a Zeiss LSM 880 Airyscan confocal microscope. Excitation/emission was set as follows: BCECF λex = 488 nm/λem = 505–545 nm, PI λex = 560 nm/λem ˃ 575 nm.
The cell distribution on the scaffold and the morphology were observed using 3,3'-Dihexyloxacarbocyanine Iodide (Invitrogen™) DiOC6[3]/propidium iodide (ThermoFisher Scientific™) staining. Excitation/emission was set as follows: DiOC6[3] λex = 488 nm/λem = 505–545 nm, PI λex = 560 nm/λem ˃ 575 nm. The signal from the nuclei was further used to determine the depth of penetration of the cell into the scaffold.
The statistical significance was performed using one-way analysis of variance (ANOVA) in Sigma Stat software 3.5 (Systat Software, California, USA).
## In vivo experiment
The in vivo experimental part was performed in a total of 2 female pigs (Sus Scrofa domesticus) of the production hybrid line with an initial weight of 70 ± 5 kg. All phases of the experiment lasted 6 months. The pigs were supplied by a local production company approved by the Ministry of Agriculture of the Czech Republic and housed at the Veterinary Research Institute (Brno, Czech Republic) in experimental stables certified by the Ministry of Agriculture of the Czech Republic. The study was carried out according to the Declaration of Helsinki and was approved by the Institutional Review Board of the Veterinary Research Institute (protocol code $\frac{12}{2016}$ with approval from 21 April 2016) and by the Branch Commission for Animal Welfare of the Ministry of Agriculture of the Czech Republic (permission number $\frac{34715}{2016}$-MZE-17214 from 15 June 2016). Upon arrival at the research facility, the pigs were housed for two weeks prior to the experimental procedure challenge and housed individually in stainless-steel cages located in isolated rooms with controlled regime and independent ventilation. Rooms were kept at a temperature of 21 °C, a relative humidity in the range of 40–$60\%$, and a ventilation of approximately 15 air changes per hour.
All surgical procedures were performed under general anesthesia. Pre-medication and analgesia during the surgery were performed by Butomidor inj. ( butorphanol tartrate) at a dose of 0.1 mg‧kg−1 b.w. s.c. Anesthesia was performed with Medetomidine at a dose of 0.5 mg‧kg−1 body weight. Furthermore, general anesthesia was maintained throughout the surgery by continuous administration of Propofol $1\%$ at a dose (8–15 mg‧kg−1 b.w. i.v.). Immediately after surgery, the analgesic Metacam (meloxicam) was used at a dose of 0.1 mg‧kg-1 b.w. s.c. once a day for three consecutive days. Experimental animals received systematic antibiotic therapy (Enrofloxacin 15 mg‧kg-1 b.w. once daily i.m. for 10 days). After shaving the fur off the back of the pig, antisepsis of the donor area was performed with a $2.5\%$ solution of povidone iodine. Subsequently, using an electrodermatome (Zimmer Biomet® Air Dermatome, Zimmer Biomet, Indiana, USA), a 0.20 mm thin split- thickness skin graft (STSG) was removed with an area of 8 × 8 cm in square at 6 sites of planned skin defects. A sharp excision of an area measuring 8 × 8 cm of full-thickness skin (2–2.3 cm depth) was performed at the site of the removed skin grafts (Fig. 1). This was followed by the application and fixation of the nanostructured scaffold and its STSG cover. The nanostructured scaffold consists of Coll and CaOC-based foam as the dermis layer (thickness around 2 mm) and the nanofibers layer as a basal membrane (thickness around 200 µm). The scaffold on the right was PDA-coated (Fig. 1e) and on the left was without PDA. An epidermal graft without scaffold was used as a control. All implants were fixed to the wound using a skin stapler (Single-use Skin Stapler B. Braun®, B. Braun, Germany), covered with greasy tulle and mule moistened with Octenidine solution, and secured with a pressure bandage. Microsoft Excel and its = RAND() function were used to randomize the types of biomaterials used in individual full-thickness skin defects. Fig. 1Creation of 6 full-thickness skin wounds 8 × 8 cm a scheme of wounds location, b split-thickness skin graft (STSG) donor site, c full-thickness excision, d wound bed preparation prior to scaffold application, e bilayer scaffold made of collagen/oxidized cellulose foam with cross-linked nanofibers—Coll/CaOC-N (left) and bilayer scaffold coated with polydopamine—Coll/CaOC-N/PDA (right) application directly to wound bed, f application of STSG on bilayer scaffold, covering the full-thickness skin defect in one-step procedure The first dressing change followed on the seventh postoperative day, when the viability of the graft was verified. The defects were then tied with a wet mule and a greasy tulle. Histological and immunohistological samples were taken under general anesthesia at individual stages of defect healing on the 7th and 14th postoperative day and then in the 3rd and 6th postoperative month. The post-incision defect closure was performed by direct absorbable suture.
## Histological analysis and qPCR analysis of tissue samples
Formaldehyde-fixed, paraffin-embedded tissue samples were processed for 2 histological sections per sample and stained with hematoxilin and eosin. Light microscopy was used to evaluate the histological images. For performance, mRNA was stabilized in tissue samples with an RNA Later kit (Quiagen, The Netherlands). Total RNA was isolated using an RNeasy Mini Kit (Quiagen, The Netherlands) from 4 samples per group and reverse transcribed with the oligo-dT primer and MMLV (Invitrogen, USA) reverse transcriptase. Primers for all genes (e.g., IL1β, TNFα, TGFβ1, IL10) and the reference gene (HPRT) were used in previous publications of the team [44–46]. Based on the results obtained from histological analyzes, other genes (examination of genes associated with cell death) were also considered. For RT-PCR, a Light Cycler 480 (Roche, Switzerland) was used. Each PCR reaction consisted of QuantiTect Sybr Green master mix (Quiagen, The Netherlands), 1 μM of each primer and 1.0 μL of cDNA in a total volume of 10 μL. Each sample was run in duplicate. The expression of a particular gene was calculated as a multiple of the expression of the reference gene using the following formula: [1/(2Ct GOI)]/[1/(2CtHPRT)].
The mean expression of the HRT unit of pro-inflammatory, anti-inflammatory cytokines and growth factors was compared using the T-test. Immunohistological samples with the Coll/CaOC, samples with the addition of PDA and control group were compared in each time point separately.
## PDA influences the structure and morphology of the bilayer
In this study, cross-linked bilayers and PDA-coated cross-linked bilayers were prepared and morphologically compared with non-cross-linked bilayers. Figure 2 shows SEM visualizations of non-cross-linked bilayers and PDA-coated cross-linked bilayers to show the significant effect of both interventions cross-linking and PDA coating. The nanofibrous layer is placed on the surface of the sample (yellow arrow). A porous scaffold structure can be seen below it (red arrow). Figure 3 shows a detailed SEM visualization of the adhesion between the nanofibrous layer and the porous foam. Figure 4 shows the nanofibrous structure of the bilayer. Non-cross-linked nanofibers are smooth with random fiber orientation and with fiber diameter in the range of 370–500 nm (Fig. 4(NX)). Cross-linked nanofibers partially lost their fibrous structure, and the fibers are already firmly attached to each other, exhibiting a more uniform structure (Fig. 4(N)). Non-covalent self-assembly of dopamine in cross-linking bilayers produced PDA precipitates deposited on nanofibers and almost continuously covered the entire fibrous area (Fig. 4(N/PDA)). A slight diversity was observed only for the Coll/CaOC-N/PDA sample. In this case, PDA formed a continuous film on the surface of the bilayer. Fig. 2SEM visualization of bilayers made of porous foam and nanofibers. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The next mark ‘NX’ represents the non-cross-linked bilayer. The mark ‘PDA’ represents the polydopamine coating on cross-linked bilayers. The nanofibrous layer is placed on the surface of the sample (yellow arrow). A porous scaffold structure can be seen below it (red arrow)Fig. 3Detailed SEM visualization of adhesion between porous foam and nanofibers. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The next mark ‘NX’ represents the non-cross-linked bilayer. The mark ‘PDA’ represents the polydopamine coating on cross-linked bilayers. The nanofibrous layer is placed on the surface of the sample (yellow arrow). A porous scaffold structure can be seen below it (red arrow)Fig. 4SEM visualization of surface of nanofibrous layers. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The next mark ‘NX’ represents the non-cross-linked bilayer. The mark ‘PDA’ represents the polydopamine coating on cross-linked bilayers Figure 5a–d presents the effect of PDA and cross-linking on total bilayer thickness and pore sizes. Here, it is evident that the cross-linking process as well as the PDA addition lead to a decrease in the bilayer thickness; the range of thickness is between 0.3 and 2.2 mm (Fig. 5a). The thickness of these samples was in most cases half that of the original non-cross-linked bilayer. Figure 5b shows the pore sizes of porous foams without the presence of nanofibers, where the pore sizes are in the range of 50–250 µm and where PDA significantly decreases the pore sizes of all foams. The pore sizes of the porous foams were also measured from the cross-sectional area of the bilayers Fig. 5c. A decrease in thickness is followed by a decrease in the pore sizes, while the pores change shape to a thin ellipsoid. Here, there is no significance after cross-linking, only PDA presents significant results in pore reduction in some bilayers. The pore size of the bilayers was reduced from 250 to 80 µm for Coll-NX/PDA and Coll-N/PDA, from 230 to 100 µm for Coll/Chit-NX/PDA and Coll/Chit-N/PDA and from 120 to 60 µm for Coll/CaOC-NX/PDA and Coll/CaOC-N/PDA, respectively. The pore sizes on the surface of the nanofibers are in the range of 0.7–4 µm, influenced by PDA and/or cross-linking (Fig. 5d).Fig. 5Effect of cross-linking and PDA coating on the bilayer thickness a; effect of PDA coating on the pore size of porous foams b; effect of cross-linking and PDA coating on the pore sizes of foams measured from the cross-sectional area of bilayers c, and pore sizes of nanofibers d. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The next mark ‘NX’ represents the non-cross-linked bilayer. The mark ‘PDA’ represents the polydopamine coating on cross-linked bilayers
## PDA influences the structure integrity of collagen
Figure 6a shows the ATR-FTIR spectra of the PDA and non-PDA Coll, Coll/Chit and Coll/CaOC foamed samples, which consist of the characteristic absorptions related to the O–H group and the N–H stretching bonds including the typical collagen amide A at 3325 cm−1 and the amide B at 2924 cm−1. Amide I is attributed to the stretching vibrations of the C=O groups at 1600–1800 cm−1. The vibrations of the N–H bands and the vibrations of the C–N are associated with amide II (1470–1570 cm−1). The vibrations of the C–N stretching, the N–H bending, and the vibrations of the CH3 groups belong to amide III at 1250–1350 cm−1 [47]. Collagen amide bonds A, B, I, II, and III are confirmed in all types of PDA and non-PDA samples. The presented absorption spectra look very similar in the whole wavenumber range. It is clear that the amount of individual biopolymers (Coll, CaOC, Chit) is large compared to the amount of PDA coating layer, so that very often the bands of PDA are overlap with the bands of collagen. The addition of PDA has already been characterized by the C–O–H of the catechol groups of PDA at 1410 cm−1 and the indole rings visible at about 1350 cm−1, which has also been shown to depend on the concentration of PDA (from 0.5 to 10 mg.mL−1) [48–50]. The amount of PDA added (2 mg.mL−1) in our experiments and the washing process during the preparation of the samples led to a lower final concentration of PDA in the samples that just coated the biopolymer fibers with thin layer. Figure 6b shows in more detail representative spectra of the PDA and the non-PDA coated Coll/Chit sample with a highlighted band of the PDA indole ring in the region between 1230 and 1350 cm−1 assigned to the C–N–C stretching. The presence of Chit and CaOC are clearly seen as vibration of carbon–oxygen single bond, denoted as C–O at 1076 cm−1 and 1031 cm−1, respectively. Fig. 6Absorption FTIR spectra of porous foam and porous foam coated with PDA a; a more detailed explanatory overlapping absorption FTIR spectra of Coll/Chit and Coll/Chit/PDA porous foam for a better explanation of PDA bonding b
## PDA influences the mechanical behaviour of bilayers
The mechanical properties of the material are critical not only for cell responses, but also for handling during surgery. The following tensile curves (Fig. 7a–d) represent the stress–strain behavior of the cross-linked bilayers, and their representatives differ with the additional PDA coating. Non-cross-linked nanofibers were excluded from the measurement because of weak adhesion between the layers. Each tensile curve is characterized by a linear region with continuously increasing stress. The strain also increases and the material is in elastic deformation. In this region, the modulus of elasticity (the elastic modulus E) is constant and describes the stiffness of the material. Upon further increase in stress, the material transitioned from elastic deformation to yielding region, indicating the limit of elastic behavior and the onset of fiber failure. Eventually, each curve reached a point of ultimate tensile strength (UTS), the point representing the highest load that the material can handle. After UTS, the material is weakened, which is represented as a tearing phase. During the measurement in the dry state, the PDA stiffened the bilayer, and the tearing phase is faster than under hydrated conditions, which is seen mainly in the Coll/CaOC-N and Coll/CaOC-N/PDA bilayers in Fig. 7b and d. The tensile curves obtained during the measurement in the hydrated state (Fig. 7c and d) more clearly revealed a yielding region where the material reaches a certain degree of permanent destructiveness, observed mainly in the Coll/Chit-N and Coll/Chit-N/PDA bilayer. Under moisture conditions, similar to the conditions of a real environment, the elongation of the PDA-modified bilayer was greater than that of the cross-linked representatives, but with significantly lower applied UTS. The hydrated state allows the material to expand, and the retained water can act as a plasticizing agent. The elongation reached at least $50\%$ of the original length, mainly for the Chit material. On the contrary, in the dry state, the addition of PDA reduced elasticity and the elongation of the PDA-modified bilayers was significantly lower than that of the non-PDA samples. Fig. 7Stress–strain curves of cross-linked bilayers during both dry a, b and wet c, d measurements influenced by PDA addition. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The mark ‘PDA’ represents additional polydopamine coating on bilayer All the values obtained for elongation, UTS, and the elastic moduli are depicted in Fig. 8. *In* general, PDA significantly affected the stiffness of each bilayer in both environments. Higher UTSs were measured in the presence of PDA, resulting in the highest mechanical resistance. The most resistant between the PDA bilayers was observed for the Coll/CaOC-N/PDA sample with UTS of 0.40 ± 0.07 MPa and maximum strain of 2.53 ± $0.01\%$ followed by the Coll-N/PDA bilayer with UTS of 0.28 ± 0.09 MPa and strain of 2.74 ± $0.01\%$ and finally the Coll/Chit-N/PDA bilayer with UTS of 0.16 ± 0.06 MPa and strain of 4.45 ± $0.01\%$ (Fig. 8a and b). The hydrated state caused a decrease in stiffness; Coll/CaOC-N/PDA could maintain the structure at UTS of 14.09 ± 3.20 kPa and could achieve a maximum elongation of 13.30 ± $0.17\%$, Coll-N/PDA resisted at UTS of 37.34 ± 7.23 kPa, while achieving a maximum elongation of 12.9 ± $0.06\%$, Coll/Chit-N/PDA with UTS of 36.41 ± 9.82 kPa and with an elongation of 36.90 ± $2.34\%$ (Fig. 8c and d). Figures 8e and fb represent the elastic moduli of all prepared bilayers. The modulus is orders of magnitude lower in the hydrated state with E ranging between 67 and 258 kPa. In the dry state, the E values of the bilayers are in the range of 0.7 MPa to 19.0 MPa. However, the addition of PDA significantly enhances the resistivity of material in the hydrated state. Fig. 8The summary of the ultimate tensile strength (UTS) values a, c; maximum elongations b, d and elastic modulus e, f measured in both dry and hydrated state. Collagen foam with cross-linked nanofibers is represented as Coll-N; collagen/chitosan foam with cross-linked nanofibers is represented as Coll/Chit-N; and collagen/oxidized cellulose foam with cross-linked nanofibers is represented as Coll/CaOC-N. The mark ‘PDA’ represents additional polydopamine coating on bilayers
## PDA influences the swelling behaviour and enzymatic degradation of the porous foam
Figure 9 shows the swelling process and the enzymatic degradation of the porous foams. The highest swelling capacity was attributed to Coll, followed by Coll/Chit, and finally Coll/CaOC foam. In principle, Coll fibers have a good affinity to water and swell when water is absorbed. Once *Coll is* cross-linked with Chit, the swelling ratio decreases with the number of hydrophilic groups. CaOC with reduced hydrophilicity because of its calcium salt led to reduced water uptake. As shown in Fig. 9a, PDA significantly reduced the swelling ratios of all porous foams. Fig. 9Swelling behavior a and enzymatic degradation b of porous foams. The collagen foam is Coll; collagen/chitosan foam is Coll/Chit and collagen/oxidized cellulose foam is Coll/CaOC. The mark ‘PDA’ represents additional polydopamine coating on porous foams Enzymatic degradation is an important parameter to observe the stability of the designed scaffold in in vitro conditions. Degradation occurred in the enzymatic environment of collagenase, which is effective against Coll. The degradation process ended when sample handling was restricted. The addition of PDA significantly increased the lifetime of the Coll/PDA foam by 18 days, Coll/Chit/PDA needed 47 days and Coll/CaOC/PDA fully digested after only 9 days (Fig. 9b). Each scaffold achieved more than $95\%$ weight loss at different times due to enzymatic degradation of Coll and hydrolytic degradation of polysaccharide.
## PDA influences the viability and proliferation of mouse fibroblasts in porous foam
Only porous layers were tested in vitro, because this layer is intended for the formation of fibroblasts and mainly to monitor the effect of the addition of PDA on cell processes. The metabolic assay showed that the highest sustained metabolic activity of the cultured cells (on day 14) was characterized in materials in the presence of PDA, Fig. 10a. Significantly more metabolically active cells were found in Coll/PDA compared to Coll/Chit/PDA and Coll/CaOC/PDA, respectively, on day 14. Cells in the Coll/CaOC/PDA sample proliferated strongly between day 7 and day 14 (Fig. 10b); therefore, they may have reached confluence associated with a decrease in metabolic activity (Fig. 10a). However, a good activity was demonstrated by the Coll/CaOC/PDA sample on day 7. As evident in Fig. 10b, the dsDNA quantification assay showed that the cell proliferation was remarkable in Coll/CaOC coated with PDA, with double-stranded DNA reaching 749 ng of dsDNA per scaffold on day 14 (Fig. 10b). PDA also significantly supported the proliferative activity of cells seeded on Coll and Coll/Chit scaffolds. In this case, the CaOC type of cellulose has shown very remarkable results even without the presence of PDA compared to that of Chit or Coll alone, probably due to the presence of calcium, essential element for cells. Fig. 10Metabolic activity a and proliferation b of murine fibroblasts cell line 3T3-A31 seeded on porous foams. The collagen foam is Coll; collagen/chitosan foam is Coll/Chit and collagen/oxidized cellulose foam is Coll/CaOC. The mark ‘PDA’ represents additional polydopamine coating on porous foams. Statistical significance marked with a group name ($p \leq 0.05$) and asterisk group ($p \leq 0.05$) and name of group with asterisk ($p \leq 0.001$), triangle sign for the highest value ($p \leq 0.001$) Fibroblast viability was performed by staining the cytoplasm of viable cells (expressed by green fluorescence) and the nuclei of nonviable cells (expressed by red fluorescence) (Fig. 11a–f). Propidium iodide was used since the cytoplasmic membrane of living cells is not permeable to it. Thus, living and dead cells can be distinguished. A significant difference was observed especially in the Coll/CaOC group without and with PDA. The PDA coating resulted in an increase in the number of viable cells. PDA also appears to promote the formation of cytoplasmic protrusions and their spread in PDA treated samples compared to the non-PDA treated samples (Fig. 12a–f). The cell cytoplasm surrounded the nucleus more closely in groups without PDA. PDA treatment in the Coll/CaOC scaffold appeared to promote the formation of prominent cytoplasmic structures. A color-coded projection of the cells evaluated from confocal microscopy has shown the cells in deeper layers (Fig. 13). The deeper layers are shown in green and yellow color and show that the fibroblasts were in the deepest layers present in the Coll/Chit foam coated with PDA (Fig. 13e). The cells were up to 60–80 µm deep. While in all other groups, they mostly migrated only around 20 µm deep. The higher amount of cells in deeper cell layers may arise from a larger pore size or open pores, and from better nutrition diffusion from the upper part of the scaffolds. This suggests that the scaffold is suitable for cell migration to deeper layers and allows sufficient medium flow to maintain cell viability. Fig. 11Live-Dead assay of murine fibroblast cell line 3T3-A31 seeded on porous foams on the 14th experimental day. Collagen foam is Coll; collagen/chitosan foam is Coll/Chit, and collagen/oxidized cellulose foam is Coll/CaOC. The mark ‘PDA’ represents an additional polydopamine coating on porous foams. The cytoplasm of living cells (green fluorescence) and dead cells (red fluorescence). Scale bar 100 µm, objective 10 × Fig. 12The cell distribution on the scaffolds—day 14. Collagen foam is Coll; collagen/chitosan foam is Coll/Chit, and collagen/oxidized cellulose foam is Coll/CaOC. The mark ‘PDA’ represents an additional polydopamine coating on porous foams. Cytoplasmic membranes (DiOC6[3], green signal), cell nuclei (propidium iodide, red signal). Scale bar 50 µm, objective 20 × Fig. 13Color-coded depth projection based on the signal of cells stained with propidium iodide and DiOC6[3] on day 14. Collagen foam is Coll; collagen/chitosan foam is Coll/Chit, and collagen/oxidized cellulose foam is Coll/CaOC. The mark ‘PDA’ represents an additional polydopamine coating on porous foams. Depth view from 0 µm (blue) to 120 µm (red)
## Wound healing of porcine skin with bilayers
Coll/CaOC-N and Coll/CaOC-N/PDA bilayers were selectively chosen for in vivo experiments considering their in vitro results. Figure 14a. shows photographic monitoring of wound closure and contraction after application of control group, native porcine skin. Figure 14b shows the photographic monitoring of wound closure and contraction after application of bilayers. This wound was divided into two parts; on the left side, the Coll/CaOC-N bilayer was applied, and on the right side, the Coll/CaOC-N/PDA bilayer was applied. By month 6, the wounds had healed and contracted. On the right side, where the PDA-based bilayer was applied, hair growth was observed since month 3. Moreover, from visual observation, the resulting scar is more evident from the control group. Fig. 14Closure of full-thickness wounds after implementation of control group—native porcine skin a and bilayer made of collagen/oxidized cellulose foam with cross-linked nanofibers—Coll/CaOC-N (b-left) and bilayer coated with polydopamine—Coll/CaOC-N/PDA (b-right) for 6 months
## PDA influences the expression of inflammatory cytokines during wound healing
The expression of all cytokines in response to PDA treatment during the first three weeks after material transplantation, after month 3 and month 6 is shown in Fig. 15. The level of pro-inflammatory cytokine TNFα at week 1 was significantly higher for material involving PDA compared to sample without PDA or control ($p \leq 0.05$). Week 2 and week 3 did not show significance for TNFα when PDA was added. However, at the end of healing (month 3), the expression of TNFα was significantly higher for the sample treated with PDA compared to the control ($p \leq 0.05$). At month 6, TNFα significantly increased, again for the PDA sample, compared to the control and the non-PDA variant ($p \leq 0.005$ and $p \leq 0.001$). The expression of other pro-inflammatory cytokine IL1β for the PDA sample at week 1 was significantly higher that of the control ($p \leq 0.05$). At week 2, IL1β increased significantly for the PDA sample compared to the non-PDA and control ($p \leq 0.001$). This expression was then reduced and was comparable to that of the non-PDA material. Another pro-inflammatory cytokine IL17 was significantly higher at week 1 for the PDA material ($p \leq 0.05$), while month 6 showed a significant decrease in IL17 level ($p \leq 0.05$), compared to the non-PDA variant. In contrast, PDA was found to promote the elimination of inflammation, as it supported the expression of the anti-inflammatory molecule IL10 (month 3) during healing ($p \leq 0.005$). Another cytokine TGFβ1 (transforming growth factor) was found to be significantly expressed in the healing stages of the material with added PDA (besides week 2). The level of the last cytokine MMP9 (an enzyme of the matrix metalloproteinase (MMP) family) increased in the presence of PDA to a significantly higher level at week 3, compared to the control. Expressions of MMP9 in the rest stages of wound healing were comparable between materials and not significantly different. Fig. 15Time dependence of mean expression of the HRT unit of pro-inflammatory cytokines TNFα (a), IL1β (b), IL17 (c), MMP9 (f), anti-inflammatory cytokines IL10 (d) and growth factor TGFβ1 (e). Each panel represents a specific time of cytokines expression (1 week–6 months) and implanted material, where a bilayer made of collagen/oxidized cellulose cross-linked with nanofibers coated with polydopamine is Coll/CaOC-N/PDA (orange color panel), a bilayer made of collagen/oxidized cellulose cross-linked with nanofibers is Coll/CaOC-N (green color panel).(The panel of the control group (yellow color panel) is the native porcine skin. Statistical significance (*$p \leq 0.05$), (**$p \leq 0.005$), (***$p \leq 0.001$) An early inflammation is also evident in the histological pictures in Fig. 16, the first week after implantation of Coll/CaOC-N and Coll/CaOC-N/PDA bilayers compared to the control. Here, a superficial skin defect is evident and the dermis is inflammably cellular with the implanted material. After two weeks, there is still a skin defect on the surface and the cellular granulation tissue is rich in capillaries and fibroblastic tissue. The content of residual implanted material, multinucleated macrophages, and fibroblast-like cells is presented in the surroundings. After one month, there is a fibrous cellular scar with well-organized fibroblast formation and mild chronic inflammation without implant material. The surface defect is already re-epithelialized. 6 months of healing shows an organized fibrous scar with a lack of fibroblastic cells and capillaries in the dermis. On the wound surface is the epidermis of the usual structure presented. Fig. 16Histological sections at various times of tissue harvesting after implantation of the control group-native porcine skin a, a bilayer of collagen/oxidized cellulose cross-linked with nanofibers (Coll/CaOC-N) presented as a well-organized scar tissue b, collagen/oxidized cellulose cross-linked with nanofibers coated with polydopamine (Coll/CaOC-N/PDA) the presence of macrophages and fibroblast-like cells in the 1st and the 2nd week after application, collagen-rich tissue with low presence of fibroblast-like cells 6 months after application c. Sections were stained with H&E, photographs at 200 × magnification. Remnants of the nanofiber layers are depicted in a black arrow
## Discussion
To date, of all acellular scaffolds, only human acellular dermis products, such as clinically proven AlloDerm and DermaMatrix appear to be the best option in skin tissue [51, 52]. Implantation of acellular scaffolds with cell cultures of fibroblasts and/or keratinocytes is associated with enormous costs and difficult regulations [53]. Currently, an example of a cell-free extracellular matrix represents Integra, made of Coll and chondroitin-6-sulfate with a silicone backing [54]. This matrix has also been seeded with autologous fibroblasts and keratinocytes, but it is not yet commercially available. It is time-deficient, as it requires 3 to 4 weeks for cultivation. Similarly, 2 weeks of keratinocyte cultivation is required by similar material [55].
In this research, bilayer acellular scaffolds were fabricated and modified with PDA to enhance biomechanical and biological properties and to see the healing effects of porcine skin, as well as in vitro tests with murine fibroblasts. The nanofibrous layer functionally and structurally represents the BM, and the porous foam represents the dermis layer. Fabricated nanofibrous BM is characterized by small pores and a very thin thickness 0.2 mm, compared to the fabricated dermis layer, which is about 2 mm. Its main objective is to block bacteria and ensure that fibroblasts do not travel from the dermis part to the epidermis part, as well as to support adhesion to a possible epidermal autograft. Porous foam with larger pores and higher thickness ensures fibroblast adhesion, proliferation, nutrient support, and general filling of the wound bed. PDA is obtained by auto-polymerization of dopamine and has already shown some advanced effects on material properties in tissue engineering applications, mainly in terms of mechanical and cellular performance [56, 57]. In this work, the PDA improved the mechanical properties of all bilayers under both dry and hydrated conditions. PDA resulted in an increase in UTS of approximately $58\%$, $62\%$, and $35\%$ for Coll-N/PDA, Coll/Chit-N/PDA, and Coll/CaOC-N/PDA bilayers, respectively. In the hydrated state, PDA enhances the firmness mainly in Coll/Chit-N/PDA (by $61\%$). The hydrated environment provided to the PDA-coated bilayers with higher viscoelasticity. One of the striking features of healthy skin is its ability to return to normal after being stretched. The hydrated state and PDA significantly supported fibers elongation, which was considered beneficial because the hydrated conditions mimic the real tissue environment. The Coll/Chit-N/PDA bilayer was stretched by more than $80\%$ in the hydrated state compared to the dry state. Samples Coll-N/PDA and Coll/CaOC-N/PDA elongated by more than 79–$81\%$ under hydrated conditions, respectively. Adding PDA to an already existing network of collagen and polysaccharide enhanced the mechanical rigidity of the material. PDA contains abundant hydroxyl groups of catechol and active amino groups, which can easily penetrate the network and physically cross-link with residues of protein/cellulose functional groups and form an interpenetrated polymer network. This type of network shows significantly better mechanical properties than "ordinary" polymer networks [58–60].
PDA has also slowed the enzymatic degradation, while the swelling capacity and pore size were reduced. Although the pore structure was reduced, and the material became more rigid, the Coll/CaOC/PDA showed the highest proliferative activity between day 7 and day 14 and satisfactory metabolic activity during the experimental period. Oxidized cellulose foams Coll/CaOC and Coll/CaOC/PDA, respectively, revealed favorable properties for initial cell adhesion and proliferation with possible further formation of the confluent cell layer supported by PDA. Greater spread of the cytoplasm and supported cell viability were also observed in all samples after PDA coating. In our experiments, we used the static culture of the scaffolds. In the majority of 3D scaffolds cultivated under static conditions, dead cells are visible after long-term cultivation, probably due to slow medium diffusion. In addition, the physico-chemical properties of the scaffold surface or scaffold degradation may negatively alter cell adhesion, proliferation, and viability of the cells. In our samples with PDA, significantly ($p \leq 0.05$) increased metabolic activity of cells was observed. This is in good agreement with Paccelli et al. [ 61]. They reported an increased wettability of gellan gum hydrogel coated with PDA and subsequently increased cell adhesion, spreading, proliferation, and focal protein and cytoskeletal protein expression. Regarding the surface of the nanofibrous layer, PDA also changed its surface morphology and was able to create different topologies depending on the porous collagen/polysaccharide foam, on which the nanofibers were fabricated. For example, some untreated residuals of the slightly acidic CaOC of the Coll/CaOC foam could reduce the polymerization of dopamine and create a more homogeneous film of PDA compared to ball-like structures on the rest of nanofibers. This can further affect the diffusion of molecules.
In reconstructing skin tissue, it is essential to choose an appropriate wound in a preclinical animal model. Most in vivo studies, including bilayer dressings, are evaluated in small mammals, especially rats, mice, rabbits, or guinea pigs due to their cost and easy-to-handle processes [62–64]. Recently, in vivo studies were performed with dopamine-modified materials that have also been shown to heal wounds in these small mammals, including rats and mice [65–68]. To our knowledge, only a few studies have used the pig model, as a result of expensive studies and difficulties due to its size [69–71]. In addition, there are no studies that demonstrate the effect of dopamine on porcine skin. Furthermore, wound healing in the pig model differs from wound healing in small mammals [72, 73]. Many authors have already suggested that pigs should be the preferred animal model due to similarities between their epidermis and dermis with humans [74, 75]. In this work, a crucial question regarding PDA-coated bilayer material is its influence on porcine skin during wound healing, especially anti-inflammatory behavior, as it is still little known. The Coll/CaOC-N/PDA bilayer was selected for the experiment and was compared with its non-PDA variant Coll/CaOC-N and the control group (native porcine skin). PDA supports the early stages of inflammation, since the levels of the pro-inflammatory cytokines TNFα, IL1β, and IL17 were highly expressed. Pro-inflammatory cytokines are among the first factors that are produced in response to wounds, as they must participate in the inflammation phase of wound healing with a moderate immune response only [76]. Proper levels of pro-inflammatory cytokines prevent infection and accelerate normal wound healing [77]. The expression of the anti-inflammatory cytokine IL10 and the growth factor TGFβ1 is higher in the middle stages of wound healing, due to PDA. PDA also supported MMP9, which could help in angiogenesis and neovascularization processes in the middle stage of healing [78]. The expression of all cytokines in this study supported the idea that PDA triggers the early stages of inflammation (1–3 weeks) and prolongs inflammation at the end of healing (6 months). At this point, the overactivation of immune cells and their protease products could inhibit tissue formation.[1] An inflammatory reaction is a dynamic process with a reparation phase followed by a rebuilding phase associated with macrophages activation and the production of TGFβ1 as well as MMPs. So far, there has been no direct evidence for the role of polydopamine in macrophage activity. The main anti-inflammatory function of polydopamine is attributed to the ability to eliminate reactive oxygen species (ROS) [79]. The excessive amount of ROS produced by neutrophils at the wound site may destroy biological macromolecules, and thus it can cause a reduction of the production of anti-inflammatory molecules and contrary increase the production of pro-inflammatory cytokines. PDA is capable of capture electrons and scavenge reactive oxygen species (ROS) via its catechol groups, which reduce inflammation and promote tissue regeneration [80, 81]. Another potential mechanism is already proposed, based on PDA extracts, which may either act as a scavenger of ROS or can activate antioxidant protein HO-1 and thus inhibit the inflammatory response.[82] Based on the wound closure and contraction in Fig. 14 with wound recovery accompanied by hair growth, and less visual scarring from the PDA bilayer, it could be stated that the increased inflammation in the healing process had no effect on the speed of wound recovery. Although histological sections in Fig. 16 reveal minimal differences in histological skin tissue composition among native porcine skin, Coll/CaOC-N and Coll/CaOC-N/PDA, especially in the 6th month after implantation, the authors of the study find this favourable. According to histological sections, the maturation scar is advanced in Coll/CaOC-N and Coll/CaOC-N/PDA tissue samples compared to native porcine skin. The gross observations then favorize Coll/CaOC-N/PDA over Coll/CaOC-N samples, as mentioned above. Elgharably [83] used Coll gel to heal full-thickness excisional wounds of the porcine model and also showed a robust inflammatory response, which resolved in a timely manner followed by an improved proliferative phase, angiogenic result and post-wound tissue remodeling. Middelkoop et al. [ 84] studied Coll-based materials and synthetic materials in domestic pigs and pointed to the disadvantages of dressing treatments, which were not revealed in in vitro studies. Philandrianos et al. [ 85] showed that after implanted artificial dermal substitutes (Integra, ProDerm, Renoskin, Matriderm and Hyalomatrix) and the control group there was no differential effect on the contraction of full-thickness porcine wounds after 2 and 6 months of healing. Har-el et al. [ 86] conducted a similar study between an electrospun soy protein-based scaffold (SPS) and Tegaderm® implemented in a full-thickness wound in the pig model. SPS exhibited better re-epithelialization. Importantly, it should be taken into account that studies demonstrating no risk of PDA degradation products are lacking. Jin et al. [ 82] showed that the PDA extracts were mainly composed of dopamine, quinine and PDA segments. These degradable products of PDA showed no cytotoxicity, which is in good agreement with our study.
## Conclusions
This study evaluated the effect of dopamine coating in a fully resorbable and acellular bilayer scaffold made of polysaccharides and collagen. The PDA has a unique position in the creation of biomechanically engineered materials as it significantly changes the strength of the bilayer and promotes stability and viscoelasticity due to its polymeric network interpenetration. This also influences swelling capacity and porosity, which is considered desirable and beneficial for restoring skin function, since dopamine also supports fibroblast viability and proliferation. This research contributes to the findings that dopamine may enhance the inflammatory phase at the beginning of wound healing in the domestic pig model and shows possible support in the expansion of growth factor and anti-inflammatory cytokines in the middle and later stages of wound healing. Dopamine shows no toxicity during the healing process, and therefore the dopamine-modified bilayer is suitable as implantable material. Despite the remarkable results of the in vitro experiments, the question remains whether the utility of dopamine in vivo is not overestimated, as it has been shown to be resemble to the control group. The future perspective on the utility of dopamine is mainly supported by its remarkable results based on in vitro experiments with cells, which substantially prove the potential of dopamine. However, its mechanism of action is still not completely discovered and even this study failed to clarify the mechanism of dopamine, as well as its degradable products and possible influence on the body. This action at the molecular level needs to be discovered before any application, which would also provide a better understanding of its future incorporation into biomaterials, thus obtaining more pronounced in vivo results.
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---
title: Dietary diversity and associated factors among adolescent girls in Nifas Silk
Laphto sub city, Addis Ababa, Ethiopia, 2021
authors:
- Tarik Abebe
- Getachew Sale Mezgebu
- Fentaw Wassie Feleke
- Meseret Tamrat
journal: BMC Nutrition
year: 2023
pmcid: PMC9990237
doi: 10.1186/s40795-023-00693-1
license: CC BY 4.0
---
# Dietary diversity and associated factors among adolescent girls in Nifas Silk Laphto sub city, Addis Ababa, Ethiopia, 2021
## Abstract
### Background
Addressing the nutritional problems of adolescent girls is important as their nutritional status has a negative effect on the future generation. However, the evidence revealed the variation and unrelated data on the prevalence of dietary diversity and lack of including all adolescent age and community groups in Ethiopia. Hence, this study assessed dietary diversity and associated factors among adolescent girls in Nifas Silk Laphto Sub-city, Addis Ababa Ethiopia, 2021.
### Methods
A community-based cross-sectional study was conducted among 475 adolescent girls at Nifas Silk Laphto sub-city, Addis Ababa Ethiopia from July 1 to 30, 2021. Multistage cluster sampling was employed to select adolescent girls. Pretested questionnaires were used to collect the data. The data were checked for completeness and entered by Epidata version 3.1 and cleaned and analyzed by SPSS version 21.0. A multivariable binary logistic regression model was fitted to identify factors associated with dietary diversity scores. The degree of association was assessed using an odds ratio with a $95\%$ confidence interval and variables with p-value ≤ 0.05 were considered significant.
### Result
The mean and the standard deviation of dietary diversity scores were 4.70 and 1.21 respectively. The proportion of low dietary diversity scores among adolescent girls was $77.2\%$. Adolescent girls’ age, meal frequency, wealth index of household, and food insecurity were significant determinants of dietary diversity score.
### Conclusion
The magnitude of low dietary diversity scores was significantly higher in the study area. Adolescent girls’, meal frequency, wealth index, and food security status were predictors of dietary diversity score. School-based nutrition education and counseling and designing strategies for improving household food security programs are crucial.
## Introduction
Adolescence is a transitional period marked by rapid and sequential physical and mental changes that transform a small child into a young adult girl. This age is a stage of growth and development in the lifespan that needs adequate and proper quality food to meet the nutrient requirement for their physical, mental growth and development in addition to reproductive maturity [1].
Dietary diversity is the consumption of an adequate variety of food groups [2]. A monotonous diet lacks essential micronutrients and contributes to the burden of malnutrition and micronutrient deficiencies [3]. The problem is particularly critical in adolescents because they need energy and nutrient-dense foods to grow and develop both physically and mentally and to live a healthy life [4].
Globally, only $17\%$ of adolescents had got a diversified diet [5]. Similarly, in developing countries based on dietary diversity scores 23.5–$50\%$ of Iranian [6, 7] and $11.2\%$ of Zimbabwe [8] adolescents got diversified diet. In Ethiopian findings from Jimma, south west of Ethiopia showed that $61.3\%$ of adolescent girl students [9] and another study from Addis Ababa Yeka sub-city $43.3\%$ of high school adolescent girls had low dietary diversity scores [10].
Most of the women in parts of sub-Saharan Africa, including Ethiopia, enter pregnancy with a poor nutritional status. It has been found that most of the time, the women may enter pregnancy with iron deficiency anemia and may have other micronutrient deficiencies which adversely affect her health and that of the fetus like low birth weight, neural tube defect and others [11–13].
Researches documented that maternal education [14], school type, mothers occupation, nutritional knowledge [15], residence and wealth status [14, 15] were associated with dietary diversity of adolescents. Eating behaviors of adolescents are influenced by many factors, including peer influences, parental modeling, food preferences, cost, personal and cultural beliefs, mass media, and body image perception [16, 17]. Mostly, household diets are predominantly starchy staples with few animal products and seasonal fruits and vegetables [9, 18].
Improving adolescent girls’ nutrition has benefits other than reproduction; the well-being and long-term nutritional health of women are legitimate goals in themselves [11]. In many low- and middle-income countries (LMICs) the double burden of malnutrition is high among adolescent girls, leading to poor health outcomes for the adolescent herself and sustained intergenerational effects [19]. In Ethiopia adolescent girl’s nutrition promotion is lagging and should connect with health services on one side, and food security programs on the other. Moreover, the evidence revealed the variation and unrelated data on the prevalence of dietary diversity and lack of including all adolescent age groups. Therefore, this study was assessed the dietary diversity practice and associated factors among adolescent girls in Nifas Silk Laphto sub city, Addis Ababa, Ethiopia, 2021.
## Study area
The study was conducted at Nifas Silk Laphto sub city Addis Ababa city. Addis *Ababa is* the capital city of Ethiopia. The estimated population of the Addis Ababa city is 4.6 million. Nifas Silk Laphto sub city has 68.3 sq.km wide and total Population: 335,740, among them 158,126 are males and 177,614 female population. The sub-city also has 13 Woredas and 43,289 estimated household based on Nifas Silk sub city Data 2020 data [20].
## Study design and period
Community based cross-sectional study was conducted from July 1 to 30, 2021.
## Source and study population
All adolescent girls living in Nifas Silk Laphto sub-city, Addis Ababa Ethiopia were the source population. Meanwhile, randomly selected adolescent girls in Nifas Silk Laphto sub-city, Addis Ababa Ethiopia were study population. All adolescent girls living at least six months in randomly selected Ketenas at Nifas Silk Laphto sub-city, Addis Ababa Ethiopia included in the study. However, adolescent girls who were critically ill during the study and pregnant or lactating were excluded in the study.
## Sample size determination
The sample size was calculated for both prevalence and determinants of dietary diversity score. Finally, the maximum sample size was calculated by using the single population proportion formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=\frac{{\left(Z\frac{\mathrm{\alpha }}{2}\right)}^{2}P(1-P)}{{d}^{2}}$$\end{document}n=Zα22P(1-P)d2; By considering the proportion of low dietary diversity score 0.75 among adolescent girl students a study done in the context of urban Northwest Ethiopia: 2017 [21], a confidence level of $95\%$: 1.96, 1.5 design effect and a 0.05 margin of error the sample size became 432. By adding $10\%$ non-response rate, finally became 475.
## Sampling procedure
Multi-stage sampling technique was used to select the study participant. Adolescent girl from each household of selected Ketenas were identified using systematic sampling technique from Woredas youth center frame and home to home census survey until the required sample size fulfilled and the starting household were selected using a lottery method. If there were more than one adolescent per household, the study was conducted only from one of them by using lottery method (Fig. 1).Fig. 1Schematic representation of sampling procedure
## Operational definitions
Dietary diversity score was considered as high, if the adolescents’ girl received greater than or equal to five food groups, which was created by summing up the number of food groups consumed over a 24-h period by an individual. Otherwise it is low dietary diversity score [9, 21, 22].
Household food security status was classified by HFIAS tool into two levels i.e. food secured if the household respondents responded ‘no’ to all of the items and insecure if the head of the household responded to at least one ‘yes’ to items of 1–9 [23].
Nutritional knowledge from twelve nutritional related questions responses: above mean value was considered as good and those below mean value was also considered as poor [9].
## Data collection tools and procedure
Data were collected by a semi-structured questionnaire developed through reviewing of literatures from different sources for socio-economic factors, dietary practice, comorbidity, food insecurity and nutritional knowledge [9, 14, 21, 24–26].
Dietary diversity and meal pattern were measured by dietary diversity questionnaire of 24 h dietary recall developed from FAO 2011 Guidelines for measuring household and individual dietary diversity of nine food group (i.e. starchy staples, cereals and white tubers), dark green leafy vegetables, other vitamin A-rich vegetables and fruits, other vegetables and fruits, organ meat, flesh meat, egg, legumes, nuts and seeds, milk and milk products,) were used to obtain information on subject’s food intake [27]. Subjects were asked to recall all foods eaten and beverages taken in the previous 24 h inside and outside the home. The DDS score was created by summing up the number of food groups consumed over a 24-h period by an individual from nine food groups.
Nutritional knowledge was assessed by 12 questions which aimed to assess whether adolescent girls and her mothers have had enough knowledge about the nutrients, advantage of diversified food and cause of malnutrition. A total nutrition knowledge score was obtained by adding the responses, scoring. A correct response was given a score of one, and an incorrect one had given a score of zero [9].
The Household Food Insecurity Access Scale were measured by the Household Food Insecurity Access Scale (HFIAS) measurement tool. Each of the questions asked with a recall period of four weeks. The respondents were first asked an occurrence question that is, whether the condition in the question happened at all in the past four weeks (yes or no) [23].
Wealth index also classified into tertile (poor, medium and rich) based on the EDHS 2016 list of household items by principal component Analyses [28].
## Data quality control
The data collectors were three nurse and two midwives and trained for one day regarding the purpose of the study and the procedures to be followed for data collection. The questionnaire was prepared in English first and then translated to Amharic and back to English by language experts to check for consistency. The semi-structured questionnaire was checked to avoid printing errors before data collection started. The name of the data collectors was recorded so as to enhance the responsibility to any incomplete data. Data collectors were summit the collected data to supervisor in daily basis and the supervisors will check the completeness of the data. Pre-test was done on $5\%$ of the samples and through supervision during data collection.
## Data processing and analysis
The collected data were entered by using Epidata software version 3.1 and export, cleaned and analysed using SPSS version 21.0. Socio-demographic and other variables were presented by frequency tables and graphs. Binary logistic regression analysis was used to check association between dependent and independent variables. Bivariable binary logistic regression analysis was performed and variables with p-value < 0.25 in the bivariable analysis were exported to multivariate binary logistic regression analysis in order to screen strong predictors of dietary diversity score. The degree of association between dependent and independent variables was assessed using AOR at $95\%$ CI. P-value less than 0.05 was considered as statistically significant. Multivariable binary logistic regression was performed using backward method. The adequacy of the model to predict the outcome variables was checked by Hosmer–Lemeshow goodness-of-fit and the P-value of 0.197 which was greater than 0.05 indicates the data were good fit to the model. Multicollinearity was checked by using variance inflation factor (maximum VIF = 1.83) of less than ten considered as there was no threat of multicollinearity.
## Socio-demographic characteristics of adolescent girls
A total of 460 adolescent girls were participated in the study yielding a response rate of $96.8\%$. The mean (± SD) age of the respondents was 14.55 (± 2.89) years and nearly more than half ($51.1\%$) of the adolescent girls was in the age range of 10–14 years old. Among the total respondents 449 ($97.6\%$) of them were single and half of ($50.0\%$) attended at private school. Majority ($58.0\%$) of adolescents were attended primary school and nearly all ($92.0\%$) had media exposure like TV, radio, and other social Medias.
With respect to family related information’s, majority ($51.7\%$) of their parents were married. Among the total respondents, nearly half ($42.4\%$) of their mother educational status were college and above and nearly one third ($28.0\%$) of mothers were government employee by occupation. More than one third ($39.3\%$) of father educational status were college and above and 122($26.5\%$) were government employee. More than one third ($38.4\%$) had poor wealth index and majority 265($57.6\%$) of households were food secured (Table 1).Table 1Socio-demographic characteristic of adolescent girls in Nifas Silk Laphto Sub city, Addis Ababa Ethiopia, 2021 ($$n = 460$$)VariablesCategoryFrequencyPercentAgeEarly23551.1Middle9220.0Late13328.9Marital statusSingle44997.6Married112.4Type of school attendGovernment23050.0Private23050.0Adolescent girl educational levelPrimary school26758.0Secondary school7616.5Preparatory and above11725.4Media exposure like TV, radio, and other social mediasYes42392.0No378.0Marital status of parentMarried23851.7Single6013.0Divorced9119.8Widowed7115.5Educational status motherUnable to read and write4710.1Able to read and write378.0Primary school (grade 1–8)8518.5Secondary school (grade 9–12)9620.9College and above19542.5Occupation of motherHousewife10222.2Government employee12928.0Private organization8718.9Merchant9520.7Daily laborer4710.2Educational status of fatherUnable to read and write51.1Read and write only286.2Primary school (Grade 1–810322.5Secondary school (Grade 9–12)14331.1College and above18139.3Occupation of fatherFarmer132.8Government employee12226.5Private organization14030.4Merchant15233.0Othera337.1Head of householdFather23250.4Mother22649.1Others20.4Who decide the type of food prepared in homeFather36378.9Mother40.9Children7817.0Other153.2Family size≤420745.0 > 425355.0Wealth indexPoor17738.4Medium13028.3High15333.3Food securitySecure26557.6Insecure19542.3aOthers: daily laborer, students, retires
## Nutritional related knowledge and information’s
About half ($50.9\%$) of the adolescent girls and $52.2\%$ of their mothers had poor knowledge respectively. More than two-third ($78.7\%$) of adolescent girls was obtained nutritional related information from schools and nearly all ($85.2\%$) the respondents had not got any nutritional counseling from health and nutrition professionals. Few ($5.9\%$) of respondents had history of chronic disease like Diabetes, kidney, heart disease etc. ( Table 2).Table 2Nutritional Related knowledge among adolescent girls in Nifas Silk Laphto Sub city, Addis Ababa Ethiopia, 2021 ($$n = 460$$)VariablesCategoryFrequencyPercentAdolescent girls nutritional related knowledgePoor23450.9Good22649.1Maternal nutritional related knowledgePoor24052.2Good22047.8Source of nutritional related informationMass media6413.9Friends2.4Family327.0School36278.7Nutritional counseling from health professionalsYes6814.8No39285.2History of chronic disease like Diabetes mellitus, kidney etcYes275.9No43394.1
## Meal pattern of adolescent girls
With respect to meal frequency majority ($56.7\%$) of adolescent girls were more than three times per day. Regarding to snaking about 135($29.3\%$) of respondents had not any snaking habit and more than two-third ($79.8\%$) skipping their breakfast ≤ 2 times per week. Majority ($59.6\%$) of adolescent girls had eating out habit at least one times per week (Table 3).Table 3Meal pattern of adolescent girls in Nifas Silk Laphto Sub city, Addis Ababa Ethiopia, 2021 ($$n = 460$$)VariablesCategoryFrequencyPercentMeal frequency≤319943.3 > 326156.7SnakingNo13529.3Yes32570.7Breakfast skipping≤2 times per week36779.8 > 2 times per week9320.2Eating out/weekNo18640.4Yes27459.6Who influences your decision of mealParents6814.8Elder sibling112.4Friends/class mate15233.0No one (I decide on my own)22949.8
## Dietary diversity Score
The mean dietary diversity score of study participants was 4.70 (SD: ± 1.21 respectively. The prevalence of low dietary diversity score among adolescent girls were $77.2\%$ ($95\%$ CI: 73.3, 81.1). The majority $96.1\%$ and $65.2\%$ of adolescent girls consumed starch staples and Legumes/ Nuts respectively (Fig. 2).Fig. 2Types of food items consumed by adolescent girls in Nifas Silk Laphto sub city, Addis Ababa Ethiopia, 2021 ($$n = 460$$)
## Factors associated with dietary diversity score
Bivariable binary logistic regression analysis was done to assess association between individual independent variables and DDS to identify candidate variables for multivariable binary logistic regression. Age of adolescent girls, grade of adolescent girls, family size, meal frequency, adolescent girls and maternal nutritional related knowledge, marital status of parents, nutritional counseling, chronic disease, food security status and wealth index were significantly associated with (P-values < 0.25) and entered into multivariable binary logistic regression. Finally, adolescent girls age, meal frequency, wealth index, food security status were a statically significant predictors of dietary diversity score.
Adolescent girl found in the early age group were 4.19 times higher odds of low dietary diversity score than late adolescents [AOR, 4.19 ($95\%$ CI: 2.29, 7.66)]. Participants who had more than three meals per day were five times more likely to have low DDS than adolescent girls with meal frequency ≤ 3 times meal per day [AOR, 4.54 ($95\%$ CI: 2.29, 9.00)]. Adolescent girls living in the medium wealth index household were $73\%$ less likely to have low DDS compared with those in rich wealth index [AOR, 0.27 ($95\%$ CI: 0.12, 0.57)]. The finding showed that participants who were in food secure households were $70\%$ less likely having low DDS [AOR, 0.30 ($95\%$ CI: 0.16, 0.55)] (Table 4).Table 4Results of bivariable and multivariable binary logistic analysis of factors associated with dietary diversity score among adolescent girls in Nifas Silk Laphto sub city, Addis Ababa Ethiopia. 2021 ($$n = 460$$)PredicatorsDDSCOR($95\%$CI)P-valueAOR($95\%$CI)P-valueLowNo (%)HighNo (%)AgeEarly211246.01(3.48–10.4) < 0.0014.19(2.29–7.66) < 0.001**Middle65271.64(0.93–2.90)0.0851.55(0.81–2.95)0.143Late795411GradePrimary231364.16(2.49–6.98) < 0.0011.01(0.24–4.27)0.992Secondary53231.49(0.81–2.76)0.2011.04(0.37–2.79)0.978Preparatory and above714611Parents marital statusMarried172660.56(0.36–0.87)0.0100.75(0.43–1.31)0.312Other1833911Family size≤4173341.99(1.26–3.14)0.0031.08(0.56–209)0.613 > 41827111Wealth indexPoor133440.30(0.16–0.58) < 0.0010.77(0.36–1.72)0.485Medium83470.18(0.10–0.34) < 0.0010.27(0.12–0.57) < 0.001**Rich1391411Food security statusFood secure178870.21(0.12–0.35) < 0.0010.30(0.16–0.55)0.001*Food insecure1771811Adolescent girls nutritional knowledgePoor209254.58(2.79–7.53) < 0.0010.71(0.25–2.03)0.523Good1468011Maternal nutritional knowledgePoor201392.21(1.41–3.46)0.0011.56(0.91–2.65)0.103Good1546611Meal frequency≤3 times186137.79(4.20–14.4) < 0.0014.54(2.29–9.00) < 0.001** > 3 times1699211Nutritional counselingYes39290.32(0.19–0.56)0.0020.63(0.34–1.17)0.141No3167611Chronic diseaseYes16110.41(0.18–0.89)0.0260.69(0.26–1.81)0.450No3399411AOR Adjusted odds ratio, COR Crude odds ratio*Significant at P value < 0.05** significant at p value < 0.001
## Discussion
The findings of this study demonstrated that average dietary diversity score was 4.70(SD: ± 1.21) and the prevalence of low dietary diversity among adolescent girls was $77.2\%$. Adolescent girls age, meal frequency, wealth index, food security status were predictors of dietary diversity score.
The mean DDS was in line with a study in Jimma Town, South West Ethiopia which was 4.34(SD: ± 1.42) [9] and 4.69(SD: ± 1.46) in Ethiopian Gurage zone [14]. This result slight variation might occur because of the reference period difference to calculate DDS, the number of food groups included in the score, the study setting and lack of accessibility of diversified diet in the city.
The proportion of low dietary diversity was in line with a study done Gurage zone $73.2\%$ [14], Gondar adolescent $75.4\%$ [21] and higher when compared to another study done in Jimma town $12\%$ of school adolescents had low [25], Dembia, northwest Ethiopia $32.3\%$ [29], Addis Ababa, Yeka Sub-city $43.3\%$ [10], Woldia $49.1\%$ [30], South West Ethiopia $61.3\%$ [9], and Iranian 50–$76.5\%$ [6, 7]. The difference might be due to variations of geographical location, seasonal variability, and other socio-demographic factors. Furthermore, the disparity might happen due to socio-economic differences and the presence of food-based dietary guidelines for other countries like Iran which promote diversified food consumption. However, this finding was lower when compared to a study done in Zimbabwe $88.8\%$ [31] and global level which was only $17\%$ of adolescents got diversified diet [5]. The disparity might happen due to socio-economic differences of the study area, production, availability and cultural preference.
Nearly all ($96.1\%$) of adolescent girls consumed starchy staples. This finding was consistent with a study done in Gondar, Northwest Ethiopia: $97.7\%$ of adolescent girls consumed starchy staples [21]. Another study in Jimma town also supports the current study [9]. This is because cereals were produced in the majority areas and highly accessible in the market, and the dietary habit of developing nations is entirely depends on starchy staples [32].
Those adolescent girls who lived in the medium wealth index household were $73\%$ less likely to have low DDS compared with those in rich wealth index. This finding is inconsistent with studies conducted in Ethiopia Gurage zone and Jimma town [9, 14] and also in Dembia, northwest Ethiopia: inadequate dietary diversity was significantly associated with middle and high wealth category [29]. Although access is important, but the awareness of food-based dietary guidelines will probably have more effect. This might be due to variations of geographical location, seasonal variability, and market accessibility.
The finding also showed that participants who were in food secure households were $70\%$ less likely having low DDS. This result is consistent with other findings done in Gondar and Nigeria reveals there is a significant positive relationship as expected between food security level and dietary diversity [33, 34]. Food insecurity has been shown to reduce individual-level consumption of ASF, fruits, and vegetables largely due to a significantly lower total food expenditure than food secure households [35, 36]. This implies that as the food security status improved and dietary diversity will be increased. Furthermore, DDS is measure of food security, nutrition information, early warning system and target of intervention at Global or national level [27, 37].
Adolescent girls found in the early age group were four times more likely to have low dietary diversity score than late adolescents. In fact, as the age increase, the educational status also increasing and this in turn improved the knowledge of diversified diet of adolescent girls, they have a chance to get information on healthy dietary habit. This difference also might be due to an exposure to different nutrition-related health education in the current study area [25].
Participants who had more than three meals per day were five times more likely to have low DDS than adolescent girls with meal frequency ≤ 3 times meal per day. The result is in line with a study done in West Java [38]. This might be due to the fact that as the number of meals increased per day the probability of getting a diversified diet will be rise.
## Strength and limitation of the study
The findings of this community-based study have a significant contribution to address the nutritional problems of adolescent girls. However, the cross-sectional nature of this study limits us from determining causal effects as the study variables. The study assessed household and individual dietary diversity only for the last 24 h; hence, there might be lack of a correct reflection of the usual dietary habits of adolescents and also leads to social disability bias.
## Conclusion
The magnitude of inadequate dietary diversity score was higher in the study area. Adolescent girl age group, meal frequency, wealth index, food security status were predictors of dietary diversity score. The federal ministry of health should focus on strengthening micro-finance and small business enterprise to increase access to food via amplified income, design strategies on household food security program [e.g., Productive Safety Net Program (PSNP)]. Moreover, starting nutritional education and counseling at all age and grade level are crucial. A large scale community based study with large sample size and more strong study design should be conducted.
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---
title: 'coda4microbiome: compositional data analysis for microbiome cross-sectional
and longitudinal studies'
authors:
- M. Luz Calle
- Meritxell Pujolassos
- Antoni Susin
journal: BMC Bioinformatics
year: 2023
pmcid: PMC9990256
doi: 10.1186/s12859-023-05205-3
license: CC BY 4.0
---
# coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies
## Abstract
### Background
One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.
### Results
We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the “all-pairs log-ratio model”, the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data).
### Conclusions
coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN (https://cran.r-project.org/web/packages/coda4microbiome/) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12859-023-05205-3.
## Background
Although there are still many unknowns about the specific mechanisms of action of the human microbiome, there is growing evidence of its relevance in human health [24, 38]. In recent years, much progress has been made in microbiome research thanks to high-throughput DNA sequencing technologies that allow precise quantification of the composition of the microbiome. The study of the microbiome is considered a great opportunity for improving the current treatment of some diseases and for deriving microbial biomarkers that could be used as diagnostic or prognostic tools.
Microbiome composition is dynamic and the study of microbiome changes over time is of primary importance for understanding the relationship between microbiome and human phenotypes. Longitudinal studies are costly, both economically and logistically, but there is growing evidence of the limitations of cross-sectional studies for providing a full picture of the role of the microbime in human health. Microbiome longitudinal studies can be very valuable in this context, provided appropriate methods of analysis are used [34]. The analysis of microbiome data involves significant experimental and computational challenges [5]. One of them is the compositional nature of the data, which requires the use of specific methods of analysis [9, 17–19]. Compositional data refers to constraint multivariate non-negative data that carry relative information. Microbiome relative abundances (proportions) are constrained by a total sum equal to one. This total constraint induces strong dependencies among the observed abundances of the different taxa. In fact, the observed abundance of each taxon is not informative and only provide a relative measure of abundance when compared to the abundances of other taxa [36]. Ignoring the compositional nature of microbiome data can lead to spurious results [28, 37]. This is particularly critical in the context of microbiome longitudinal studies where compositions measured at different times can be affected by distinct batch effects and similar quality control or filtering protocols may yield to different sub-compositions at each time point.
Aitchison [2] laid the foundations of Compositional Data Analysis (CoDA), which relies on extracting the relative information of compositional data by comparing the parts of the composition. Logarithms of ratios between components (log-ratios) are the fundamental transformation in this framework [20, 31] and is known as the log-ratio approach.
Some methods used in microbiome analysis, such as ALDEx2 [13], LinDA [39, 40], ANCOM [26], ANCOM-BC [23], fastANCOM [39] and LOOCM [21], perform the log-ratio approach to identify differential abundant taxa between two study groups. Here we introduce coda4microbiome, a new R package for analyzing microbiome data within the CoDA framework in both, cross-sectional and longitudinal studies. coda4microbiome is an improvement of our previous algorithm, selbal [33], using a more flexible model and a more computationally efficient global variable selection method that results in a considerable reduction of computational time. coda4microbiome differs from most differential abundance (DA) testing methods that aim to characterize microbial communities by selecting taxa with significant different abundances between two study groups (e.g., controls vs cases). Like selbal, the aim of coda4microbiome is prediction, i.e., the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the “all-pairs log-ratio model”, the model containing all possible pairwise log-ratios.
For longitudinal data, pairwise log-ratios measured at different time points gives a curve profile or trajectory for each sample. A summary of the shape of these individual trajectories will be the basis for the analysis. More specifically, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories).
In both, cross-sectional and longitudinal studies, after reparameterization of the initial “all-pairs log-ratio model”, the inferred microbial signature is expressed as a function of the (log-transformed) initial variables in the form of a log-contrast model [3], i.e., a log-linear model with the constraint that the sum of the coefficients is equal to zero. The zero-sum constraint ensures the invariance principle required for compositional data analysis. These microbial signatures can be interpreted as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. For longitudinal data and a binary outcome (e.g. disease status), the signature provides two groups of taxa with different log-ratio trajectories for cases and controls.
The algorithm is implemented in the R package “code4microbiome” (https://cran.r-project.org/web/packages/coda4microbiome/). Several graphical representations of the results are provided that facilitate the interpretation of the analysis: plot of the log-ratio trajectories, plot of the signature (selected taxa and coefficients) and plot of the prediction accuracy of the model. In fact, coda4microbiome is not just an R package but a broader initiative that aims to bridge the gap between compositional data analysis and microbiome research. To this end, we are conducting training activities and developing materials that are available at the website of the project: https://malucalle.github.io/coda4microbiome/ The methodology for cross-sectional data is described in “Microbioal signature based on log-ratio analysis: cross-sectional studies” Section and illustrated with data from a pediatric Crohn's disease study (“Cross-sectional data: Crohn’s disease (CD) study” Section). The methodology for longitudinal data is described in “Microbial signature based on log-ratio analysis: longitudinal studies” Section and illustrated in “Longitudinal data: early childhood and the microbiome study” Section with data from the “Early childhood and the microbiome (ECAM) study” [7, 8]. We performed a simulation study for benchmarking of several microbiome analysis algorithms (“Simulation study” and “Simulation study results” Section) and applied coda4microbiome in several real datasets (“Real datasets analysis” Section).
## Microbioal signature based on log-ratio analysis: cross-sectional studies
Assume we have n subjects with phenotype \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y = \left({Y_{1}, \ldots,{ }Y_{n} } \right)$$\end{document}Y=Y1,…,Yn and denote by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_{i} = \left({X_{i1},{ }X_{i2}, \ldots,X_{iK} } \right)$$\end{document}Xi=Xi1,Xi2,…,XiK the microbiome composition of subject i for K taxa. \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 can represent either relative abundances (proportions) or raw read counts. We approach the identification of those taxa that are associated to the outcome through penalized regression on the “all-pairs log-ratio model”, a generalized linear model containing all possible pairwise log-ratios [6]:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left({E\left(Y \right)} \right) = \beta_{0} + \mathop \sum \limits_{1 \le j < k \le K} \beta_{jk} \cdot{\text{log}}\left({X_{j} /X_{k} } \right)$$\end{document}gEY=β0+∑1≤j<k≤Kβjk·logXj/Xk The regression coefficients in Eq. [ 1] are estimated to minimize a loss function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L\left(\beta \right)$$\end{document}Lβ subject to an elastic-net penalization term on the regression coefficients\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$:$$\end{document}:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta } = \mathop {{\text{argmin}}}\limits_{\beta } \left\{ {L\left(\beta \right) + \lambda_{1} ||\beta||_{2}^{2} + \lambda_{2} ||\beta||_{1} } \right\}$$\end{document}β^=argminβLβ+λ1||β||22+λ2||β||1 A common reparameterization of the penalization parameters is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda_{1} = \lambda \left({1 - \alpha } \right)/2$$\end{document}λ1=λ1-α/2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda_{2} = \lambda \alpha$$\end{document}λ2=λα where \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 amount of penalization and \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 the mixing between the two norms.
For the linear regression model the loss function is given by the residual sum of squares\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta } = \mathop {{\text{argmin}}}\limits_{\beta } \left\{ {Y - M\beta_{2}^{2} + \lambda_{1} ||\beta||_{2}^{2} + \lambda_{2} ||\beta||_{1} } \right\},$$\end{document}β^=argminβY-Mβ22+λ1||β||22+λ2||β||1,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M$$\end{document}M is the matrix of all pairwise log-ratios and has dimension \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 by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K\left({K - 1} \right)/2$$\end{document}KK-$\frac{1}{2.}$ The expression of the optimization problem [2] for other models, like the logistic regression, can be found in Friedman et al. [ 14]. We use the function cv.glmnet() from the R package glmnet [14] to solve [2] within a cross-validation process that provides the optimal value 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 with a default value for \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 equal to 0.9. Non-compositional covariates are previously modeled with Y and the fitted values are considered as “offset” in the penalized regression.
The result of the penalized optimization provides a set of selected pairs of taxa, those with a non-null estimated coefficient. The linear predictor of the generalized linear model [2] provides a microbial signature score for each individual, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i \in \left\{ {1, \ldots, n} \right\}$$\end{document}i∈1,…,n, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{i} = \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot{\text{log}}\left({X_{ij} /X_{ik} } \right)$$\end{document}Mi=∑1≤j<k≤Kβ^jk·logXij/Xik, which is associated with the phenotype \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y_{i}$$\end{document}Yi. Because of the linearity of the logarithm, the microbial signature M can be rewritten in terms of the selected single taxa which is more interpretable than in terms of pairs of taxa:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M = \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot {\text{log}}\left({X_{j} /X_{k} } \right) = \mathop \sum \limits_{$j = 1$}^{K} \hat{\theta }_{j} \cdot {\text{log}}\left({X_{j} } \right)$$\end{document}M=∑1≤j<k≤Kβ^jk·logXj/Xk=∑$j = 1$Kθ^j·logXjwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{j} = \mathop \sum \limits_{k = j + 1}^{K} \hat{\beta }_{jk} - \mathop \sum \limits_{$k = 1$}^{j - 1} \hat{\beta }_{kj}$$\end{document}θ^j=∑k=j+1Kβ^jk-∑$k = 1$j-1β^kj, that is, the sum of the coefficients \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta }$$\end{document}β^ that correspond to a log-ratio that involves component j [6].
It can be proved that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop \sum \limits_{$j = 1$}^{K} \hat{\theta }_{j} = 0$$\end{document}∑$j = 1$Kθ^$j = 0$ and thus, the microbial signature M is a log-contrast function involving the selected taxa (those with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{j} \ne 0)$$\end{document}θ^j≠0). This ensures the invariance principle required for proper compositional data analysis and it facilitates the interpretation of the microbial signature. Indeed, expression \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop \sum \limits_{$j = 1$}^{K} \hat{\theta }_{j} \cdot{\text{log}}\left({X_{j} } \right)$$\end{document}∑$j = 1$Kθ^j·logXj in [3] can be interpreted as a weighted balance between two groups of taxa, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2, the taxa with a positive coefficient vs those with a negative coefficient [36].
## Summary of log-ratio trajectories
Assume n subjects with fixed phenotype \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y = \left({Y_{1},{ } \ldots,{ }Y_{n} } \right)$$\end{document}Y=Y1,…,Yn. Subject i has been observed in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_{i} { }$$\end{document}Li time points, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({t_{i1},{ }t_{i2},{ } \ldots,{ }t_{{iL_{i} }} } \right)$$\end{document}ti1,ti2,…,tiLi. We denote by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_{i} \left({t_{ij} } \right) = \left({X_{i1} \left({t_{ij} } \right),{ }X_{i2} \left({t_{ij} } \right),{ } \ldots,X_{iK} \left({t_{ij} } \right)} \right)$$\end{document}Xitij=Xi1tij,Xi2tij,…,XiKtij the microbiome composition of subject i at time \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{ij}$$\end{document}tij, where K is the number of taxa which is assumed to be the same for all the individuals and all the time points. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_{i} \left({t_{ij} } \right)$$\end{document}Xitij can represent either relative abundances (proportions) or raw counts. We denote by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$logX_{i} \left({t_{ij} } \right)$$\end{document}logXitij the logarithm transformation of microbiome abundances after zero imputation [27]. The log-abundance trajectory of component A for individual i is denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$logX_{iA} = \left({logX_{iA} \left({t_{i1} } \right), logX_{i2A} \left({t_{i2} } \right), \ldots,logX_{iA} \left({t_{{iL_{i} }} } \right)} \right)$$\end{document}logXiA=logXiAti1,logXi2Ati2,…,logXiAtiLi and the log-ratio trajectory between components A and B for individual i is given by:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$logX_{iA} - logX_{iB} = \left({logX_{iA} \left({t_{i1} } \right) - logX_{iB} \left({t_{i1} } \right), logX_{iA} \left({t_{i2} } \right) - logX_{iB} \left({t_{i2} } \right), \ldots,logX_{iA} \left({t_{{iL_{i} }} } \right) - logX_{iB} \left({t_{{iL_{i} }} } \right)} \right)$$\end{document}logXiA-logXiB=logXiAti1-logXiBti1,logXiAti2-logXiBti2,…,logXiAtiLi-logXiBtiLi We summarize the log-ratio trajectory between components A and B for individual i within two time points \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_{1}$$\end{document}l1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_{2}$$\end{document}l2 as the integral of the log-ratio trajectory:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right) = \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} \left({logX_{iA} \left(t \right) - logX_{iB} \left(t \right)} \right) dt,$$\end{document}siA,B=∫l1l2logXiAt-logXiBtdt,where the values of the log-ratio for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t \notin \left({t_{i1}, t_{i2}, \ldots, t_{{iL_{i} }} } \right)$$\end{document}t∉ti1,ti2,…,tiLi are linearly interpolated.
We do not take the absolute value in Eq. [ 4] because the sign of the integral is informative: Positive values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right)$$\end{document}siA,B correspond to trajectories of component A above trajectories of component B, that is, larger relative abundances of A with respect to B, while negative values represent the opposite. Values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right)$$\end{document}siA,B around zero can represent similar abundances between A and B over time or a non-homogeneous trend between A and B within the observed region.
Another advantage of the summary \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right)$$\end{document}siA,B is computational. Since the integral is linear, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right)$$\end{document}siA,B is equal to the difference between the integrals of log-transformed microbiome abundances of taxa A and taxa B:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{i} \left({A,B} \right) = \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} logX_{iA} \left(t \right)dt - \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} logX_{iB} \left(t \right) dt$$\end{document}siA,B=∫l1l2logXiAtdt-∫l1l2logXiBtdt Thus, the number of integrals to be calculated is of the order of K, the number of taxa, instead of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K\left({K - 1} \right)/2$$\end{document}KK-$\frac{1}{2}$, the number of pairwise log-ratios.
## Microbial signature based on log-ratio analysis
To identify those log-ratios that are most associated with the outcome Y, we implement glm penalized regression on the log-ratio summaries of all pairs of taxa:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left({E\left(Y \right)} \right) = \beta_{0} + \mathop \sum \limits_{1 \le j < k \le K} \beta_{jk} \cdot s\left({j, k} \right)$$\end{document}gEY=β0+∑1≤j<k≤Kβjk·sj,kwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s\left({j,k} \right)$$\end{document}sj,k is the summary of the log-ratio trajectory corresponding to components \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_{j}$$\end{document}Xj and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_{k}$$\end{document}Xk.
Equation [5] is identical to Eq. [ 1] for cross-sectional studies except for the change of the pairwise log-ratios by the summary of the log-ratios trajectories. Thus, the inference and variable selection process is performed similarly with elastic-net penalized regression within a cross-validation process using cv.glmnet() from the R package glmnet [14].
For each individual, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i \in \left\{ {1, \ldots, n} \right\}$$\end{document}i∈1,…,n, the microbial signature score is given by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{i} = \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot s_{i} \left({j,k} \right)$$\end{document}Mi=∑1≤j<k≤Kβ^jk·sij,k. Because of the linearity of the integrals used as summaries of the log-ratio trajectories and following the same reparameterization than in Eq. [ 3], M can be rewritten in terms of the selected single taxa which is more interpretable than the selected pairs of components:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} M & = \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot s\left({j_{1},j_{2} } \right) \\ & = \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} logX_{j} \left(t \right)dt - \mathop \sum \limits_{1 \le j < k \le K } \hat{\beta }_{jk} \cdot \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} logX_{k} \left(t \right)dt \\ & = \mathop \sum \limits_{$k = 1$}^{K} \hat{\theta }_{j} \cdot \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} logX_{j} \left(t \right)dt \\ & = \mathop \smallint \limits_{{l_{1} }}^{{l_{2} }} \left({\mathop \sum \limits_{$k = 1$}^{K} \hat{\theta }_{j} \cdot logX_{j} \left(t \right)} \right)dt \\ \end{aligned}$$\end{document}M=∑1≤j<k≤Kβ^jk·sj1,j2=∑1≤j<k≤Kβ^jk·∫l1l2logXjtdt-∑1≤j<k≤Kβ^jk·∫l1l2logXktdt=∑$k = 1$Kθ^j·∫l1l2logXjtdt=∫l1l2∑$k = 1$Kθ^j·logXjtdtwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{j} = \mathop \sum \limits_{k = j + 1}^{K} \hat{\beta }_{jk} - \mathop \sum \limits_{$k = 1$}^{j - 1} \hat{\beta }_{kj}$$\end{document}θ^j=∑k=j+1Kβ^jk-∑$k = 1$j-1β^kj.
Since \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop \sum \limits_{$k = 1$}^{K} \hat{\theta }_{k} = 0$$\end{document}∑$k = 1$Kθ^$k = 0$, the microbial signature M is the integral of the trajectory of a log-contrast function involving the selected taxa (those with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{k} \ne 0)$$\end{document}θ^k≠0) and, similarly to the signatures for cross-sectional data, it can be interpreted as a weighted balance between two groups of taxa, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2, the taxa with a positive coefficient vs those with a negative coefficient.
## coda4microbiome main functions
The package coda4microbiome [10] contains several functions that implement the proposed algorithms. The method for the identification of microbial signatures in cross-sectional studies (“Microbioal signature based on log-ratio analysis: cross-sectional studies” Section) is implemented in function coda_glmnet() and the method for longitudinal data (“Microbial signature based on log-ratio analysis: longitudinal studies” Section) is implemented in function coda_glmnet_longitudinal().
The library also contains additional functions like plot_signature_curves() that provides a plot of the signature trajectories or filter_longitudinal() that filters those individuals and taxa with enough longitudinal information.
The coda4microbiome methodology is visually described with a pictogram in the supplementary material (Additional file 1: Fig. S1).
## Simulation study
We performed a case–control simulation study to evaluate the discrimination (or classification) performance and computational burden of coda4microbiome in comparison to other methods used for microbiome analysis: selbal [33], ANCOM-BC [23], ALDEx2 [13], DESeq2 [25], edgeR [32], metagenomeSeq [30], and LinDA [40].
Both, coda4microbiome and selbal, provide a classification model (microbial signature) that defines how the selected taxa are combined. For the other methods that only provide a set of differentially abundant taxa, the classification model was obtained by fitting a logistic regression model containing the DA taxa. Metagenomic simulated data was generated using faecal samples from the “Global Patterns” dataset [11] as template, following the data generation model described by Weiss et al. [ 37]. This model generates true positives taxa so that their relative abundances match their real abundance in the environment. As in Weiss et al. [ 37], some of the simulation parameters were fixed for all scenarios: the number of most prevalent taxa to keep in simulation template (2000 taxa), the number of true positive taxa (100 taxa), and the sequencing depth (2000 reads). Both categories had the same number of samples, being 50 or 100 samples in each group. The effect size of the true positive taxa was set to 1.25, 1.5, 2, 5, 10 or 20. This results in a total of 12 simulated scenarios, and we considered 10 replicates for each one. After simulated metagenomic datasets were generated, taxa with less than $5\%$ of prevalence among samples were removed, and 1 count was added to all taxa abundances to overcome problems with log-transformations.
To evaluate the discrimination accuracy of the different methods, a five-fold cross-validation process was applied. Samples in each simulation set were randomly grouped into five different cv-fold groups, ensuring the same number of cases and controls in each one. For every cross-validation fold, the train set includes all cv-fold groups except one, used for testing the model afterwards. The same cv-fold groups assignment in a simulation set were used for testing all the algorithms. For DA methods, a taxa selection step was performed on the train set based on the significance of the Benjamini–Hochberg adjusted p-value [4] with a threshold of 0.05. Relative abundances of selected taxa were used to fit a logistic regression model able to classify the two groups. coda4microbiome and selbal were trained on the train set and the obtained microbial signature was evaluated on the test set. For all methods, the measure of performance was the Area Under the ROC Curve (AUC). We also compared the number of taxa selected by each method and the computational time.
## Cross-sectional data: Crohn’s disease (CD) study
We illustrate coda4microbiome algorithm for cross-sectional studies with data from a pediatric Crohn’s disease (CD) study [16]. The dataset, available at coda4microbiome package, includes microbiome compositions of 975 individuals, 662 with CD and 313 without any symptoms. The abundance table agglomerated at the genus level contains 48 genera.
We implemented coda4microbiome::coda_glmnet() function to the Crohn’s dataset. The algorithm identifies that the outcome is binary and implements a penalized logistic regression. The results of the analysis provide a first plot (Fig. 1) showing the cross-validation accuracy (AUC) curve from cv.glmnet(). For the default lambda (“lambda.1se”), the algorithm selects 27 pairwise log-ratios that, as we will see later, correspond to 24 different taxa. Fig. 1Cross-validation accuracy curve for different degrees of penalization: Log-transformed penalization parameter (x axis), cross-validation AUC (y axis), and, on top of the plot, the number of selected variables for each penalization value. Highlighted with a vertical line the values of "lambda.min" and "lambda.1se" (default penalization value) The results of coda_glmnet include the number, the name, and the coefficients of the selected taxa. These can be visualized in a bar plot where the selected taxa and the corresponding coefficients are represented (Fig. 2).Fig. 2Microbial signature for Crohn’s disease: Taxa composing the microbial signature that best discriminates between Crohn's disease patients and controls. The magnitude of the coefficients represents the contribution of each variable to the model. ( green: positive coefficient and red: negative coefficient) A third plot describes the discrimination capacity of the selected microbial signature (Fig. 3). This is accompanied with three classification accuracy measures: the apparent AUC, i.e., the AUC of the signature applied to the same data that was used to generate the model, and the mean and sd of the cross-validation AUC obtained from the output of cv.glmnet(). For this dataset, the apparent AUC is 0.84 and the mean (sd) cross-validation AUC are 0.82 (0.0081).Fig. 3Box-plot and density plots representing the distribution of predicted values (microbial signature scores) for Crohn’s disease patients (orange) and controls (blue) When the outcome is a continuous numerical variable, coda_glmnet() function implements penalized linear regression and Fig. 3 is a scatter plot between predictions and the outcome values.
## Longitudinal data: early childhood and the microbiome study
To illustrate coda4microbiome for longitudinal studies we use data from the “Early childhood and the microbiome (ECAM) study” that followed a cohort of 43 U.S. infants during the first 2 years of life for the study of their microbial development and its association with early-life antibiotic exposures, cesarean section, and formula feeding [7, 8].
Metadata and microbiome data were downloaded from https://github.com/caporaso-lab/longitudinal-notebooks. Initially the data contained information on 43 child and 445 taxa at the genus level. We filtered those individuals and taxa with enough information for time-course profiling: we removed individuals with only one time-point observation and those taxa with less than 30 children ($70\%$ of individuals) with at least 3 non-zero observations over the follow-up period. After this filtering, the data reduced to 42 children and 37 taxa.
Here we focus on the effects of the diet on the early modulation of the microbiome by comparing microbiome profiles between children with breastmilk diet (bd) vs. formula milk diet (fd) in their first 3 months of life.
Using function coda_glmnet_longitudinal(), we identified a microbial signature with maximum discrimination accuracy between the two diet groups. The signature is defined by the relative abundances of two groups of taxa, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 is composed of 6 taxa (those with a positive coefficient in the regression model) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2 is composed of 2 taxa (those with a negative coefficient) (Table 1 and Fig. 4). Group \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 is mainly dominated by three taxa within the order Clostridiales (family Ruminococcaceae [2] and gender Blautia) and one taxon within the gender Actinomyces. Two taxa (g_Veillonella and f_Lachnospiraceae) have a coefficient close to zero and will have a very small contribution to the signature. Group \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2 is composed by two taxa within the genders Haemophilus and Staphylococcus. Table 1Taxa included in the microbial signature that best discriminates between the two diet groupsBalance groupCoefficientTaxanomic assignmentG10.3359p_Firmicutes;c_Clostridia;o_Clostridiales;f_Ruminococcaceae;g_10.2730p_Firmicutes;c_Clostridia;o_Clostridiales;f_Lachnospiraceae;g_Blautia0.2159p_Actinobacteria;c_Actinobacteria;o_Actinomycetales;f_Actinomycetaceae;g_Actinomyces0.1358p_Firmicutes;c_Clostridia;o_Clostridiales;f_Ruminococcaceae;g_20.0337p_Firmicutes;c_Clostridia;o_Clostridiales;f_Veillonellaceae;g_Veillonella0.0055p_Firmicutes;c_Clostridia;o_Clostridiales;f_Lachnospiraceae;g_G2 − 0.4327p_Proteobacteria;c_Gammaproteobacteria;o_Pasteurellales;f_Pasteurellaceae;g_Haemophilus − 0.5672p_Firmicutes;c_Bacilli;o_Bacillales;f_Staphylococcaceae;g_StaphylococcusFig. 4Taxa composing the microbial signature that best discriminates between the two diet groups (green: positive coefficient and red: negative coefficient) The trajectories of the microbial signature over the observed period are represented in Fig. 5, where the color of the curves corresponds to the diet group. Each trajectory represents the relative mean abundances between the two taxa groups for each child. We can see that the two groups are clearly separated. Those children under breastmilk diet (in orange) usually have trajectories below zero, which means they have more relative mean abundance of g_Haemophilus and g_Staphylococcus with respect to the relative abundance of taxa in group \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1, while children with formula milk diet (in blue) have more relative abundance of taxa in group \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 relative to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}.$$\end{document}G2.Fig. 5Relative abundance between group \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{2}$$\end{document}G2 during the first three months of life. Highlighted curves represent the mean value of the signature for each diet group (orange: breast milk diet, blue: formula milk diet) Figure 6 displays the distribution of the microbial signature scores for the two diet groups and offers a visual assessment of the (apparent) discrimination accuracy of the signature. Quantitatively, the apparent discrimination accuracy of the signature (i. e. the AUC of the signature applied to the same data that was used to generate the model) is 0.96 and the mean cross-validation AUC is 0.74 (sd = 0.10).Fig. 6Box-plot and density plots representing the distribution of predicted values (microbial signature scores) for the two diet groups (orange: breast milk diet, blue: formula milk diet) The results are consistent with previous studies on the association of the infant gut microbiome composition and breastmilk feeding practices. In Fehr et al. [ 12], *Haemophilus parainfluenzae* and *Staphylococcus were* found to be enriched with exclusive breastmilk feeding together with lower prevalence of Actinomyces at 3 months. Lachnospiraceae (Blautia) was enriched among infants who were no longer fed breastmilk. Similar results are reported in Laursen et al. [ 22] where the duration of exclusive breastfeeding was negatively correlated with genera within Lachnospiraceae (e.g., Blautia) and genera within Ruminococcaceae. Positive correlations with exclusive breastfeeding were observed for g_Bifidobacterium and Pasteurellaceae (Haemophilus).
## Simulation study results
Figure 7 show the number of selected taxa by each method for simulated datasets with different effect sizes (1.25, 1.5, 2, 5, 10 and 20) and 100 samples per group. Similar results are obtained for simulations with 50 samples per group (results not shown). For all methods, except for coda4microbiome and selbal, the larger the effect size, the more taxa are selected, as it is expected since the power of the DA tests increases with larger effect sizes. The opposite is true for coda4microbiome and selbal, as the effect size increases, less variables are needed in the model to obtain good classifications. Despite the fold effect and sample size, coda4microbiome finds a predictive microbial signature with less features than selbal, with similar AUCs. Fig. 7Mean number of selected taxa of the different methods for simulated datasets with different effect sizes (1.25, 1.5, 2, 5, 10 and 20) and 100 samples per group Figures 8 provides two different representations of the discrimination accuracy (AUC) of each method: a boxplot distribution and a line plot of the mean cv-AUC for the 10 replicates of each scenario and 50 samples per group. Figure 9 provides the same information for the case of 100 samples per group. The numerical results (mean and sd) are detailed in Table 2. coda4microbiome and selbal perform similarly in all scenarios. The higher classification accuracy of coda4microbiome and selbal is especially remarkable in scenarios with low effect size and a small sample size (Fig. 8). For an effect size equal to 1.25 the mean cv-AUC of these two methods is 0.763 and 0.795, respectively, while all the other methods have mean cv-AUC below 0.6. For an effect size of 1.5, coda4microbiome and selbal discrimination is 0.943 and 0.912, respectively, and only DESeq2 and edgeR have a good performance, though with lower discrimination values (0.851 and 0.841, respectively). All the other methods methods have mean cv-AUC below 0.6. For larger effect sizes the performance of all the methods if good (discrimination around 1) except for LinDA that has a poor performance in all the scenarios. Similar results are obtained for larger sample sizes (Fig. 9). In this case, DESeq2 and edgeR perform very well, with discrimination accuracy still slightly lower than coda4microbiome and selbal when the effect size is equal to 1.25 but slightly larger to coda4microbiome when the effect size is equal to 1.5. All methods, except LinDA, reach AUCs over 0.9 in simulations with a fold effect of 2, 5 or 10. On scenarios with very high fold effect, such as 20, classification performance decreases for most of the methods except for coda4microbiome, selbal and ALDEx2.Fig. 8Boxplots distribution and line plots of the mean cross-validation AUCs of every methodology for different effect sizes (1.25, 1.5, 2, 5, 10 and 20) and 50 samples per groupFig. 9Boxplots distribution and line plots of the mean cross-validation AUCs of every methodology for different effect sizes (1.25, 1.5, 2, 5, 10 and 20) and 100 samples per groupTable 2Mean and standard deviation discrimination accuracy (AUC) of the different methods for different effect sizesMethodn1 = n2 = 50n1 = n2 = 100Effect size1.251.52510201.251.5251020coda4microbiome0.763 (0.12)0.943 (0.1)0.988 (0.05)0.999 (0.01)1 [0]1 [0]0.776 (0.12)0.891 (0.08)0.987 (0.02)1 [0]1 [0]1 (0.03)selbal0.795 (0.12)0.912 (0.11)0.993 (0.07)1 [0]1 [0]1 [0]0.761 (0.11)0.924 (0.09)0.987 (0.01)1 (0.01)1 [0]1 [0]aldex20.59 (0.06)0.6 (0.16)0.946 (0.13)1 [0]1 [0]1 [0]0.629 (0.11)0.898 (0.16)0.982 (0.02)1 [0]1 [0]1 [0]ancombc0.573 (0.04)0.603 (0.06)0.963 (0.07)1 (0.01)1 [0]0.954 (0.1)0.569 (0.03)0.847 (0.13)0.971 (0.02)1 (0.01)1 [0]1 (0.01)DESeq20.56 (0.12)0.851 (0.15)0.973 (0.04)1 [0]1 (0.01)0.882 (0.09)0.672 (0.11)0.92 (0.07)0.989 (0.01)1 (0.01)1 [0]1 [0]edgeR0.581 (0.12)0.841 (0.14)0.974 (0.03)1 [0]1 (0.01)0.925 (0.11)0.692 (0.11)0.921 (0.07)0.988 (0.01)1 (0.01)1 [0]1 [0]metagenomeSeq0.573 (0.04)0.593 (0.14)0.962 (0.06)1 (0.01)0.997 (0.04)0.929 (0.07)0.591 (0.03)0.829 (0.12)0.982 (0.02)1 (0.01)1 (0.12)0.972 (0.13)LinDA0.583 (0.04)0.604 (0.08)0.7 (0.09)0.859 (0.06)0.907 (0.05)0.883 (0.1)0.575 (0.03)0.662 (0.07)0.729 (0.1)0.936 (0.07)0.976 (0.02)1 (0.01) Figure 10 show the computational times for each method for different effect sizes (1.25, 1.5, 2, 5, 10 and 20) and 100 samples per group. Similar results are obtained for simulations with 50 samples per group (results not shown). ANCOM-BC and selbal are the two methods that spent more time in the analysis. coda4microbiome is clearly more computationally efficient than selbal. Fig. 10Median computational times for each methodology in simulation datasets with 100 samples per group
## Real datasets analysis
In order to better compare the computational times of coda4microbiome over selbal, we applied both methods to 8 real datasets available at [28]. Table 3 shows the great improvement of coda4microbiome in comparison with selbal, especially when using the recommended function selbal.cv() that, as coda4microbiome, implements cross-validation in the analysis and thus, provides more robust results. In fact, because of the computational burden of selbal.cv(), we only could use selbal() function in the simulations. Table 3 is ordered according to the number of taxa in each dataset and this allows to easily see the high correlation between computational time and the number of features. Table 3Computational times for coda_glmnet() function from R package coda4microbiome and functions selbal() and selbal.cv() from R package selbalDatasetNumber of taxaTotal sample size (N1; N2)coda4microbime coda_glmnet()selbal()selbal.cv()cdi_schubert75237 [84;153]0.0570.2917.591ob_goodrich117613 [428;185]0.1120.75218.841hiv_noguerajulian140170 [28;142]0.0710.54314.962Ji_WTP_DS15559 [30;29]0.0880.3349.427Office242625 [341;284]0.5692.742119.696ArcticFreshwaters2741023 [540;483]0.7818.156145.931Blueberry41863 [24;39]0.9422.15568.607sw_sed_detender102578 [60;18]27.84113.76463.544
## Discussion
coda4microbiome algorithm represents an improvement of our previous algorithm selbal [33]. Both, coda4microbiome and selbal search for two groups of taxa, A and B, that are jointly associated with the outcome of interest Y. The main differences between both algorithms are [1] the model for combining the relative abundances of taxa in group A and B, [2] the process for selecting the taxa that will constitute the microbial signature and [3] the type of study that can be approached with each method:selbal expresses the microbial signature as an ilr balance between A and B [31], i.e., as the log-ratio of the geometric mean abundances of taxa in group A vs taxa in group B. Instead, coda4microbiome microbial signature is expressed as a log-contrast model where those taxa with a positive coefficient define group A, those with a negative coefficient define group B and those with a zero coefficient are not part of the microbial signature.selbal performs forward selection and coda4microbime implements elastic-net penalized regression variable selection.selbal is only available for cross-sectional studies while coda4microbime is implemented for both cross-sectional and longitudinal studies.
In summary, coda4microbiome improves selbal by considering a more general model (an ilr balance is a special log-contrast), a more powerful variable selection process (forward selection does not ensure a global optimum) and can be used in both, cross-sectional and longitudinal studies.
The results of our simulations indicate that when the aim is classification, DA tests followed by fitting a regression model with the selected significant taxa perform worse than coda4microbiome or selbal, which are methods specifically developed for model prediction. coda4microbiome performs very well even in situations where the fold change of the associated taxa is quite low (e.g. 1.25), which is probably the case for most of real microbiome associations. Under such small fold effects, other methods such as edgeR, DESeq2, ALEDx2, ANCOM-BC, MetagenomeSeq and LinDA perform poorly. Selbal instead, performs similarly to coda4microbiome with good discrimination accuracy for the same simulation scenarios. Though selbal and coda4microbiome have similar classification power, the latest requires less computational time which is an important advantage especially for datasets with a large number of features.
## Conclusions
We developed an R package for microbiome analysis that deals with the compositional nature of microbiome data in both, cross-sectional and longitudinal studies. coda4microbiome provides a set of functions to explore and study microbiome data within the CoDA framework, with a special focus on identification of microbial signatures that can serve as biomarkers of disease risk and prognostic. The results are expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The interpretability of results is of major importance in this context. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures.
The main difference between coda4microbiome and other CoDA methods that also employ the log-ratio approach, such as ALDEx2 [13], ANCOM-BC [23] or fastANCOM [39], is that they perform differential abundance testing while coda4microbiome is focused on prediction. coda4microbiome improves our previous algorithm, selbal [33]. Both have similar performance but coda4microbiome is more computationally efficient.
Longitudinal microbiome studies, especially those focused on the human microbiome, have usually low resolution: the number of individuals is small, each individual has few observation times, the observations of the different individuals are not made at exactly the same time, the data are very variable, the expected behavior of the abundance trajectories is not linear or quadratic, etc. This makes it difficult to justify and implement a parametric modeling of trajectories and limits the use of models for longitudinal data (time series, mixed models). In this context, a description of the trajectories such as the one we propose, although less precise, allows to extract valuable information from the data as we have shown in the example. Other longitudinal data modeling strategies [1, 15, 29, 35] could be used in longitudinal microbiome studies with higher resolution such as laboratory or animal experimental studies. Simulation studies should be performed to assess the performance of coda4microbiome for longitudinal microbiome data against other existing methods.
With this new R package, we aim to enhance microbiome analysis by taking into consideration the compositional nature of microbiome data through the use of compositional data analysis methods.
## Supplementary Information
Additional file 1. Pictogram of coda4microbiome algorithm.
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|
---
title: Association of gastric inhibitory polypeptide receptor (GIPR) gene polymorphism
with type 2 diabetes mellitus in iranian patients
authors:
- Saiedeh Erfanian
- Hamed Mir
- Amir Abdoli
- Abazar Roustazadeh
journal: BMC Medical Genomics
year: 2023
pmcid: PMC9990261
doi: 10.1186/s12920-023-01477-z
license: CC BY 4.0
---
# Association of gastric inhibitory polypeptide receptor (GIPR) gene polymorphism with type 2 diabetes mellitus in iranian patients
## Abstract
### Introduction
Gastric inhibitory polypeptide receptor (GIPR) encodes a G-protein coupled receptor for gastric inhibitory polypeptide (GIP), which was demonstrated to stimulate insulin secretion. Relation of GIPR gene variation to impaired insulin response has been suggested in previous studies. However, little information is available regarding GIPR polymorphisms and type 2 diabetes mellitus (T2DM). Hence, the aim of the study was to investigate single nucleotide polymorphisms (SNPs) in the promoter and coding regions of GIPR in Iranian T2DM patients.
### Materials and methods
Two hundred subjects including 100 healthy and 100 T2DM patients were recruited in the study. Genotypes and allele frequency of rs34125392, rs4380143 and rs1800437 in the promoter, 5ʹ UTR and coding region of GIPR were investigated by RFLP-PCR and Nested-PCR.
### Results
Our finding indicated that rs34125392 genotype distribution was statistically different between T2DM and healthy groups ($$P \leq 0.043$$). In addition, distribution of T/- + -/- versus TT was significantly different between the both groups ($$P \leq 0.021$$). Moreover, rs34125392 T/- genotype increased the risk of T2DM (OR = 2.68, $95\%$CI = 1.203–5.653, $$P \leq 0.015$$). However, allele frequency and genotype distributions of rs4380143 and rs1800437 were not statistically different between the groups ($P \leq 0.05$). Multivariate analysis showed that the tested polymorphisms had no effect on biochemical variables.
### Conclusion
We concluded that GIPR gene polymorphism is associated with T2DM. In addition; rs34125392 heterozygote genotype may increase the risk of T2DM. More studies with large sample size in other populations are recommended to show the ethnical relation of these polymorphisms to T2DM.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-023-01477-z.
## Introduction
Global studies indicated that 415 million people lived with diabetes mellitus (DM) until 2017. Estimations suggest that 608 million people are suffered from DM that $32\%$ of them are undiagnosed. More than $90\%$ of DM patients accounts for type 2 diabetes mellitus (T2DM) [1]. T2DM is a common metabolic disorders mainly caused by two mechanisms: reduced insulin secretion by pancreatic B cells or resistance of target tissues to insulin [2]. Any defect in one of these mechanisms could trigger a T2DM phenotype. The most popular risk factors for T2DM are combinations of genetic, environmental and metabolic factors [3, 4] Contribution of genes to T2DM is well studied previously and large-scale genotyping using the Metabochip indicated that some loci increase the susceptibility to T2DM [5]. Now the question is that whether the risk loci are confined to the ones that have been identified in previous studies?
Gastric inhibitory polypeptide receptor (GIPR, Gene ID: 2696) or glucose-dependent insulinotropic polypeptide receptor (https://www.uniprot.org/uniprotkb/P48546/entry) has several isoforms and encodes a G-protein coupled receptor for gastric inhibitory polypeptide (GIP), which was originally identified as a hormone that acts in gut extracts (https://www.ncbi.nlm.nih.gov/gene/2696) and inhibits the releasing of gastrin and subsequently gastric acid. GIPRs are located in pancreas and insulin-sensitive tissues such as adipocytes [6] and their interaction with GIP led to increases of lipoprotein lipase activity (LPL), fatty acid and glucose uptake. Recent studies demonstrated that GIP is secreted in response to oral glucose and stimulates insulin release [7]. Moreover, GIP induces fatty acid adsorption into adipocytes and inhibits lipolysis [8], induces resistance to insulin in adipose tissues [9–11], and the plasma level of GIP is increased in T2DM [12, 13].
GIPR gene located on 19q13.32 is present in β cells of Langerhans islands. Despite differential expression in extra pancreatic tissues, its expression in adipose tissues is relatively high [6]. Previous findings suggest that pharmacological activation of GIPR may have a therapeutic benefit on peripheral energy metabolism [14].
As indicated by recent studies, single nucleotide polymorphism in GIPR gene are related to changes in the secretion of hormones and adipokines in obese type 2 diabetic patients [15]. GIPR gene polymorphisms have been studied in other diseases including schizophrenia [16, 17], pre-diabetic and diabetic patients [18] metabolic syndrome [19] and obesity [20]. However, little information is available in diabetic patients in Iran. Hence, the aim of the present study was to investigate the association of the rs34125392, rs4380143 and rs1800437 polymorphisms in the promoter, 5ʹ UTR and coding region of GIPR gene with type 2 diabetes mellitus in an Iranian population.
## Subjects
Two hundred subjects referred to Peymanieh hospital (Jahrom city, Iran) including 100 healthy individuals and 100 type 2 diabetes mellitus patients were recruited in the study. Patients had a fasting blood glucose (FBG) > 125 mg/dl. However, subjects with underlying disease including cancer, liver and kidney disease, gastrointestinal tract disease and cardiovascular disease such as acute myocardial infarction were excluded from the study. Low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), total cholesterol (TC), triacylglycerol (TG) and FBG were measured by routine biochemical assays.
## DNA extraction and PCR-RFLP
Venous blood samples were drawn in an EDTA-containing tube and stored in -80 C until the DNA extraction. Salting out technique was used to extract the genomic DNA [21] and stored rapidly in -20. Three single nucleotide polymorphisms were investigated in the study. On the other hand, rs4380143 T > C with minor allele frequency (MAF) 0.3 was located on upstream promoter region, rs34125392 T>- with MAF 0.26 was located on 5ʹUTR region, and rs1800437 G > C (Glu to Gln) with MAF 0.2 was located on coding region. RFLP-PCR was applied to genotyping and allele identification. The sequences of primers used in the study are summarized in Table 1.
Table 1Sequence of primers used for detection of rs34125392, rs4380143 and rs1800437 in GIPR genePrimersSequenceGIPR5392. R5’-GGTGGGACAGCATGAGAGATTGTA − 3’GIPR5392.F5’-GTTATCTAGCAGCTAACCAGAGATGGA-3’GIPR0143. R5’-CCAAGAGTTGGAGACCAGCATGG -3’GIPR0143.F5’-CAGTTCCAACAACACTGTCAATCACC-3’GIPR0143.Nes. R5’-GTTCCAGTGCACTCCACTCTCAT − 3’GIPR0143.Nes. F5’-CAGGCTGGTCTCAAACTCCTG-3’GIPR00437. R5’-GCATTCTTGGCATTCTCCTGTCC − 3’GIPR00437.F5’-GAAGGAGCTGAGGAAGATCTCAAAGC-3’F: forward primer, R: Reverse primer, Nes: Nested-PCR Reactions were performed in a micro-tube with final volume of 25 µl containing 0.2 µg genomic DNA, 0.8 U Hot start Taq DNA polymerase and 1.5 mM MgCl2. The temperature cycles for rs34125392 were 940 C for 30 s, 630 C for 45 s and 720 C for 35 s for 30 cycles. The temperature cycles for rs4380143 were 940 C for 30 s, 650 C for 45 s and 720 C for 50 s for 30 cycles. The product of this step was the template for Nested-PCR with new pair of primers (Table 1) to detect rs43800143. The temperature cycles for Nested-PCR were 940 C for 30 s, 620 C for 25 s and 720 C for 35 s for 25 cycles. The temperature cycles for rs1800437 were 940 C for 30 s, 650 C for 45 s and 720 C for 20 s for 30 cycles. Initial incubation period was 5 min at 940 C and a final extension incubation step was set at 720 C for 5 min for all reactions.
Then, the PCR products of rs34125392, rs4380143 and rs1800437 were subjected to digestion with BtsI, FatI and BssSI, respectively (Newengland Biolab; 10U, overnight). The digested PCR products were run on $3\%$ agarose gel and visualized by UV transillumination after DNA green viewer staining.
## Statistical analysis
SPSS v.18 (Chicago) was used for statistical analysis. Normality of the data was checked by Kolmogorov-Smirnov test. Hardy-*Weinberg equilibrium* was performed to survey allele distribution. Logistic regression was used to survey odds ratios. The numeric data were reported as mean ± Standard Error (SE). Student t test was used to analyze the differences in FBG, HDL-C, LDL-C, TG and age between the patient and healthy groups. Chi square test was applied to investigate the differences of genotype, allele and sex frequencies between the groups. Also the differences of allele and genotype distribution in the men and woman of the both group was assayed by chi square test. Two-way multivariate analysis of variance (Two-way MANOVA) was performed to investigate the effect of independent variables (Polymorphisms) on dependent variables (Biochemical parameters such as FBG, TC, TG, HDL-C and LDL-C).*Wilks lambda* and Tukey tests were used in Two-way MANOVA. P value less than 0.05 was considered to be significant.
## Characteristics of the study population
The dataset generated and/or analyzed during the current study are available as Supplementary file 1. Two hundred subjects including 100 healthy individuals and 100 T2DM patients were recruited in the study. Characteristics of the study population are summarized in Table 2. Healthy subjects and T2DM patients had a different age ($$P \leq 0.029$$). Distribution of the sex between the both groups was not significantly different ($P \leq 0.05$). FBS, LDL-C, TC and TG were statistically different between the groups ($P \leq 0.001$). High density lipoprotein cholesterol was not different between T2DM and healthy subjects ($P \leq 0.05$).
Table 2Characteristics of study populationParametersControlN = 100T2DMN = 100P value Age(year) 54 ± 1.2357.3 ± 0.9P = 0.029 HDL-C(mg/dl) 46.6 ± 245.2 ± 3P > 0.05 LDL-C(mg/dl) 90.2 ± 3.02110.9 ± 4.34P < 0.001 TG(mg/dl) 104.6 ± 3.64159.6 ± 9.83P < 0.001 TC(mg/dl) 149.9 ± 3.3186.6 ± 5.33P < 0.001 FBS(mg/dl) 84.7 ± 2.42166.58 ± 7.09P < 0.001 Sex(male/female) $\frac{28}{7240}$/60P > 0.05FBS: fasting blood sugar; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; TC: total cholesterol; T2DM: type 2 diabetes mellitus: TG: triacylglycerol
## Genotype/Allele distribution
Distribution of genotypes and allele frequency of rs34125392, rs4380143 and rs1800437 are summarized in Table 3. rs34125392 genotype distribution was statistically different between T2DM and healthy groups($$P \leq 0.043$$). In addition, distribution of T/- + -/- versus TT was significantly different between the both groups ($$P \leq 0.021$$). Moreover, rs34125392 T/- genotype increased the risk of T2DM (OR = 2.68, $95\%$CI = 1.203–5.653, $$P \leq 0.015$$). However, allele frequency and distribution of rs34125392 genotype in men and women was not significantly different between the groups ($P \leq 0.05$).The effects of the rs34125392 polymorphism on biochemical parameters was investigated by Two-way MANOVA analysis (Table 4). The results showed that the polymorphism and its genotypes had no effect on biochemical parameters ($P \leq 0.05$).
Table 3Genotype and allele distribution of rs34125392, rs4380143 and 1,800,437 in study populationAllele/GenotypeControl($$n = 100$$)T2DM($$n = 100$$)P valuers34125392AlleleT97($48.5\%$)80($40\%$)NS-103($51.5\%$)120($60\%$)GenotypeT/T27($27\%$)13($13\%$)0.043T/-43($43\%$)54($54.9\%$)-/-30($30\%$)33($33\%$)T/- + -/-73870.021rs4380143AlleleT110($55\%$)102($51\%$)NSC90($45\%$)98($49\%$)GenotypeT/T23($23\%$)16($16\%$)NST/C65($65\%$)70($70\%$)C/C12($12\%$)14($14\%$)T/C + C/C7784NSrs1800437AlleleG154($23\%$)150($75\%$)NSC46($77\%$)50($25\%$)GenotypeG/G7($7\%$)5($5\%$)NSG/C32($32\%$40($40\%$)C/C61($61\%$)55($55\%$)G/C + C/C9395NS Table 4Two-way MANOVA between rs34125392 (T/T versus T/- + -/-), rs4380143 (T/T versus T/C + C/C) and rs1800437 (GG versus G/C + C/C) and biochemical variables in study populationBiochemical variables(*Control versus* Patients)rs34125392T/T vs. T/- + -/-rs4380143T/T vs. T/C + C/Crs1800437GG vs. G/C + C/CP valueP valueP valueHDL-CP = 0.617P = 0.637P = 0.670LDL-CP = 0.719P = 0.937P = 0.320TGP = 0.444P = 0.181P = 0.561TCP = 0.720P = 0.406P = 0.409FBSP = 0.118P = 0.061P = 0.592FBS: fasting blood sugar; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; TC: total cholesterol; TG: triacylglycerol; VS: versus On the other hand, allele frequency and genotype distribution of rs4380143 was not significantly different between healthy and T2DM subjects ($P \leq 0.05$). T/C + C/C versus TT were not different between the groups ($P \leq 0.05$). In addition, allele frequency and genotypes distribution of rs4380143 was the same in men and women between the groups ($P \leq 0.05$). There was no increased risk of rs4380143 for T2DM ($P \leq 0.05$). Two-way MANOVA analysis (Table 4) showed that rs4380143 and its genotypes had no effect on biochemical parameters ($P \leq 0.05$).
Moreover, rs1800437 genotype distribution and allele frequency was the same in the both groups ($P \leq 0.05$). G/C + C/C versus GG were not different between the groups ($P \leq 0.05$). We found no increased risk of genotypes and alleles for T2DM ($P \leq 0.05$). Two-way MANOVA analysis (Table 4) showed that rs1800437 and its genotypes had no effect on biochemical parameters ($P \leq 0.05$). Also the cumulative effects of tested polymorphisms on biochemical parameters were investigated. The findings revealed that the tested polymorphism and their genotypes had no effect on biochemical parameters (Data not shown; $P \leq 0.05$).
## Discussion
The main finding of our study was that rs34125392 in GIPR gene is associated with T2DM and this polymorphism increased the risk of the disease. Also multivariate analysis indicated that the tested polymorphisms and their genotypes had no effect on FBG, HDL-C, LDL-C, TC and TG. T2DM is a multifactorial disease which affects many people worldwide [22] and virtually no physician is found that has no patient with T2DM. Gene variations have a strong role in T2DM and the list of the genes that involved in the pathogenesis of the disease is increasing to date [23].
To the best of our knowledge this the first study that investigated rs34125392 and rs4380143 polymorphisms in T2DM. We searched PubMed, Google and dbSNP databases and found no study that included these polymorphisms in their studies. Our findings showed that the distribution of rs34125392 genotypes is different between the healthy and patient group. Also our finding indicated that rs34125392 T/- genotype may increase the risk of T2DM. Taking together, since this polymorphism is located on 5ʹUTR region of GIPR gene and we didn’t measure the expression level of GIPR gene, so the main question is that whether this gene variation could alter gene expression. Skuratovskaia et al. [ 15] investigated the association of GIPR gene polymorphism with plasma level of mediators which have a role in the regulation of carbohydrate metabolism in obese T2DM patients. They found that GIPR gene expression in adipose tissue of the small intestine mesentery in patients bearing rs2302382 CC and rs8111428 AA genotypes was decreased and this was in relation to increase level of leptin. They claimed that during normal expression plasma concentration of insulin and GIP in subjects bearing rs2302382 polymorphism and rs8111428 AG genotypes were increased. However, we found no increased risk of rs4380143 genotypes in T2DM and the distribution of alleles and genotypes were the same in patients and healthy groups.
Shalaby group [24] studied rs1800437 polymorphism in Egyptian T2DM patients. Their findings indicated that the distribution of C haplotype is statistically higher in patients than controls. They concluded that there is no association between this polymorphism and the risk of T2DM. In contrast to Shalaby group our finding showed that the distribution of rs1800437 genotypes and allele frequency in T2DM patients and healthy subjects were not different. This inconsistency may be related to different races. However, we found that there is no association between rs1800437 genotypes and alleles with T2DM. Jeannine group [25] investigated the association of variants in GIPR gene with impaired glucose homeostasis in obese children and adolescents from Berlin. They found an association between rs1800437 C allele and elevated homeostasis model of insulin resistance values. They concluded that GIPR gene variations are not related to childhood obesity but rs1800437 may have a potential role in glucose homeostasis. Recent studies are focused on the GIPR agonist to improve the extra pancreatic effects of GIP and its role in secretion of insulin. Nicholas group [26] performed a study to clarify whether the increased risk of coronary artery disease (CAD) is mediated via GIPR or is instead the result of linkage disequilibrium (LD) confounding between variants at the GIPR locus. They found that rs1800437 G allele is common among the fasting GIP levels, glycemic traits, and adiposity-related traits and it is independent of CAD and lipid traits.
T2DM patients have a high propensity for multiple comorbidities related to diabetes complications such as cardiovascular disease (CVD). Since there are substantial differences in lipid markers among the study’s baseline characteristics in our study and lipid markers has been proven to be a risk factor of CVD[27], so we analyzed the relation of the tested polymorphism with lipid parameters by Two-way MANOVA. Our findings indicate that there is no association between GIPR polymorphism and lipid parameters.
## Limitation of the study and suggestions
This study has some limitations including low sample size. While this is the first study in Iranian population, we suggest that future studies should be conduct with larger sample size. This was not a linkage study, so we suggest that future studies be conducted with direct haplotyping method to better show the relationship between polymorphism and its influence on biochemical parameters and T2DM.
## Conclusion
our findings indicated that GIPR gene polymorphism is associated with T2DM in Iranian patients. In addition, the results showed that rs34125392 T/- genotype may increase the risk of T2DM. However, the tested polymorphisms had no effect on biochemical parameters. Since rs34125392 polymorphism is located on 5ʹUTR region of GIPR gene, further studies are needed to show that whether genotype variation could alter GIPR phenotype. Future studies with larger sample size in other populations are recommended to show the ethnical relation of these polymorphisms to T2DM. Our sample size was relatively small so our findings should interpret with caution.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary file 1. The data sete analysed during the study to investigate genotype and allele frequency of rs34125392, rs4380143 and rs1800437 in GIPR gene
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|
---
title: 'RGT: a toolbox for the integrative analysis of high throughput regulatory
genomics data'
authors:
- Zhijian Li
- Chao-Chung Kuo
- Fabio Ticconi
- Mina Shaigan
- Julia Gehrmann
- Eduardo Gade Gusmao
- Manuel Allhoff
- Martin Manolov
- Martin Zenke
- Ivan G. Costa
journal: BMC Bioinformatics
year: 2023
pmcid: PMC9990262
doi: 10.1186/s12859-023-05184-5
license: CC BY 4.0
---
# RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data
## Abstract
### Background
Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein–DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner.
### Results
We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors.
### Conclusion
We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen. The documentation is available at: https://reg-gen.readthedocs.io
## Background
The combination of next-generation sequencing (NGS) with complex biochemistry techniques enables profiling of distinct epigenetic and regulatory features of cells in a genome-wide manner. Two examples are chromatin immunoprecipitation followed by sequencing (ChIP-seq) for protein–DNA interaction [1] and assay for transposase-accessible chromatin using sequencing (ATAC-seq) for open chromatin [2]. These techniques allow the studying of epigenetic dynamics in cellular processes such as cell differentiation [3, 4] and the characterization of the regulatory landscape of diseases such as human cancers [5]. Analysis of such data typically requires multi-step computational pipelines that usually include:low-level methods (read alignment, quality control),medium-level methods for detection of genomic regions with relevant epigenetic signals (processing of genomic profiles, peak calling, differential peak calling, computational footprinting), andhigh-level methods for visual representation and integrative analysis with further genomic data (association with gene expression and further epigenetic data, detection of transcription factor binding sites, and functional enrichment analysis).Fig. 1Example of a typical pipeline for the analysis of a transcription factor ChIP-seq experiment. First, the reads are aligned to the genome (step 1, low-level analysis). A peak caller receives these aligned reads as input and typically creates an intermediary representation called genomic signal. Based on this genomic signal, the peak caller then detects regions with a higher value than the background. These candidate peaks represent the regions with DNA–protein interaction sites (steps 2 and 3, medium level). Several downstream analyses are then performed, such as the detection of motif-predicted binding sites inside the peaks (step 4, high-level analysis) or line plots displaying average genomic signals of other ChIP-seq experiments around the predicted peaks or binding sites (step 5, high-level analysis) Figure 1 gives an example of a common ChIP-seq data analysis pipeline. It includes on the low level the use of a read aligner, such as BWA [6]; on the medium level a peak calling method, such as MACS2 [7], for the detection of regions with the presence of potential protein–DNA interactions; and on the high level a motif match procedure, such as FIMO [8], to find transcription factor binding sites inside peaks as well as R functions for the visualization of genomic signals, such as Genomics Ranges [9]. A similar pipeline for ATAC-seq data analysis is described in Additional file 1: Figure S1.
The definition of analysis pipelines depends on the biological study as well as on the used NGS technique. Its complexity, which includes the use of several bioinformatics tools that may require command-line usage and/or scripting skills, makes the analysis of epigenomics data so far less reproducible and inaccessible to non-experts. Moreover, the development of bioinformatics tools for medium-level analysis needs to take into account specific characteristics of the used NGS protocols [10, 11]. For example, ChIP-seq experiments require the computational estimation of the read extension sizes [11]. It also requires a signal correction with control experiments, as the local chromatin structure may influence the ChIP-seq signal [12]. In contrast, footprint analysis of ATAC-seq data does not require the estimation of read extension sizes, as the start of the read corresponds to the cleavage position. However, ATAC-seq analysis demands the correction of Tn5 cleavage bias [13]. Moreover, some aspects, such as PCR amplification artifacts, are shared by ChIP-seq and ATAC-seq experiments [11]. Clearly, the development of tools for the analysis of epigenetic data is greatly facilitated by a flexible and easy-to-handle computational library. This library should support genomic data I/O as well as usual pre-processing methods, such as fragment size estimation and the correction of sequence bias. Regarding high-level tasks, the library should provide structure to allow sequence analysis (i.e. motif matching), interval algebra (i.e. measuring overlap between peaks), or associating signals with regions (i.e. line plots showing signal strength around peaks).Fig. 2Overview of RGT core classes and tools. RGT provides three core classes to handle the genomic regions and signals. Each genomic region is represented by GenomicRegion class and multiple regions are represented by GenomicRegionSet class. The genomic signals are represented CoverageSet class. These classes serve as the core data structures of RGT for handling genomic regions and signals. Based on these classes, we developed several tools for analyzing regulatory genomics data as represented by different colors, namely, HINT for footprinting analysis of ATAC/DNase-seq data; RGT-viz for finding associations between chromatin experiments; TDF for DNA/RNA triplex domain finder; THOR for differential peak calling of ChIP-seq data; *Motif analysis* for transcription factor binding sites matching and enrichment
## Implementation
We developed RGT in Python by following the object-oriented approach. The core classes provide functionalities for handling data structures that are related to questions about regulatory genomics. Based on the cores, we implemented several computational tools to perform various downstream analyses (Fig. 2). These include previously described HINT tools for ATAC-seq/DNase-seq footprinting [13–15], the differential peak caller THOR [16] and a library to characterize triple helix mediated RNA-DNA interactions [17]. RGT also includes some functionalities such as motif binding sites prediction and enrichment analysis (Motif Analysis), as well as methods for association and visualization of genomic signals (RGT-Viz). We describe below the basic structures and the novel Motif Analysis and the RGT-Viz frameworks.
## Core classes
Analysis of high-throughput regulatory genomics data is mostly based on the manipulation of two common data structures: genomic signals which represent the abundance of sequencing reads on the genome and genomic regions which represents candidate regions. In RGT, we implemented three classes, i.e., GenomicRegion, GenomicRegionSet, and CoverageSet, to represent a single region, multiple regions, and genomic signals, respectively. In each of the classes, we implemented several functions to perform basic data processing. For example, CoverageSet provides functions for fragment extension estimation, signal smoothing, GC-content bias correction, and input DNA normalization. These procedures are crucial for the particular downstream analysis of chromatin sequencing data, such as peak calling and footprinting. For computational efficiency, functions related to GenomicRegionsSet and interval-related algebra have been implemented in C. Moreover, RGT contains I/O functions of common genomic file formats such as Binary Alignment Map (BAM) files for alignments of reads, (big)wig files for genomic profiles, and bed files for genomic regions by exploring pysam [18, 19] related functions.
These core classes provide a powerful infrastructure for the development of methods dealing with regulatory genomics data. As an example of the simplicity, versatility, and power of RGT, we include a tutorial on how to build a simple peak caller with less than 50 lines of codes: https://reg-gen.readthedocs.io/en/latest/rgt/tutorial-peak-calling.html.
## Finding associations between chromatin experiments with RGT-viz
A typical problem in regulatory genomics is to associate results of distinct experiments, i.e. overlap between distinct histone marks or a given histone mark in distinct cells. RGT-viz provides a collection of statistical tests and tools for the association and visualization of genomic data such as genomic regions and genomic signals (Fig. 3a).
In the tests of regions versus regions, a set of reference and query regions, both in BED format, are required as inputs. The aim is to evaluate the association between the reference and the query. For this, RGT-viz provides the following tests:Projection test This test compares a query set, i.e. ChIP-seq of transcription factors with a larger reference set, i.e. ChIP-seq peaks of a regulatory region (H3K4me3 or H3K4me1 marks). It estimates the overlap of the query to the reference and contrasts with the coverage of the reference in the complete genome. A binomial test is then used to indicate if the coverage of the query in the reference is higher than the reference of the reference to the genome [20] (Additional file 1: Fig. S2a).Intersection Test This test is based on measuring the intersection between a pair of genomic regions and comparing it to the expected intersection on random region sets. Random regions are obtained by evaluating permutations (with size equal to the input regions) of the union of regions in the pair of queries [21] (Additional file 1: Fig. S2b). The statistical test is based on empirical p-values. Combinatorial Test The combinatorial test is appropriate for two-way comparisons. For example, you want to check the proportion of peaks of two (or more) transcription factors on two (or more) cell types. For this, it creates a background distribution per reference sets (cells) by considering the union of all query sets (TFs) in that cell. It then creates count statistics per cell and compares if the number of binding sites in a cell for a given TF is higher than in another cell by using a Chi-squared test (Additional file 1: Fig. S2c).Jaccard Measure This measures the amount of overlap between the reference and the query using the Jaccard index (also called Jaccard similarity coefficient) [22]. Given two region sets A and B, it measures the ratio of intersecting base pairs in relation to the regions associated with the union of A and B. Through this Jaccard index, the amount of intersection can be expressed by a value between zero to one (Additional file 1: Fig. S2d). This test explores a randomization approach, i.e. random selection of genomic region sets with the same number/size regions, to estimate empirical p-values. Another important functionality is the visualization of distinct genomic signals, as described below. To visualize the signals in different regions, the following tools are provided:Boxplot It compares the number of fragments from different ChIP-seq experiments on the given region set. This can be used for example to contrast the signal of distinct ChIP-seq TFs over promoter regions (H3K4me3 peaks). Conceptually, the generation of a boxplot is simply counting the number of reads within the region set and then plotting these counts in boxplot (Additional file 1: Fig. S3a). RGT-Viz provides functionalities to normalize the individual libraries regarding library sizes. Lineplot and heatmap Line plot and heatmap are used to display the distribution of reads within a given region set. Specifically, each region is first extended with the given window size which defines the boundaries for plotting. Next, the coverage of reads on the given regions is calculated based on the given bin size and step size. Finally, the line plot or heatmap is generated. The line plot shows average signals over all regions in the region set while the heatmap displays the signals of all regions (Additional file 1: Fig. S3b-c).Fig. 3Overview of RGT-viz and motif analysis. a RGT-viz provides several tests for regions versus regions and visualization tools for regions versus signals by taking BED and BAM files as input. b Motif matching detects binding sites for a set of TFs against multiple genomic regions. The motifs were collected from public repositories such as UniPROBE, JASPAR, and HOCOMOCO. The position weight matrix (PWM) for each TF is used to calculate a binding affinity score per position. The genomic regions are usually obtained by peak calling based on ChIP-seq or ATAC-seq data
## Transcription factor motif matching and enrichment with motif analysis
Motif analysis is a framework to perform transcription factor motif matching and motif enrichment. Motif matching aims to find transcription factor binding sites (TFBSs) for a set of TFs in a set of genomic regions of interest (Fig. 3b). For this, RGT has its own class, i.e., MotifSet for storing TF motifs from known repositories, such as UniPROBE [23], JASPAR [24] and HOCOMOCO [25]. In addition, users are also allowed to add new motif repositories. RGT uses an efficient Motif Occurrence Detection Suite (MOODS) algorithm to find binding site locations and bit-scores [26]. Note that MOODS was originally implemented in C++ and we have adapted it to a Python package (https://pypi.org/project/MOODS-python/). Next, RGT uses a dynamic programming algorithm [27] to determine a bit-score cut-off threshold based on the false positive rate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-4}$$\end{document}10-4. The predicted binding sites can be obtained with p values between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-5}$$\end{document}10-5 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-3}$$\end{document}10-3.Fig. 4Case study of RGT-viz and motif analysis for DC development. a Dendritic cell development. DC develop from multipotent progenitors (MPPs), which commit into DC-restricted common dendritic cell progenitors (CDP). CDP differentiate into classical DC (cDC) and plasmacytoid DC (pDC). b Intersection test shows that the IRF8 binding sites in cDC and pDC are associated with the PU.1 binding sites in MPP, CDP, cDC, and pDC. c Line plots showing genomic signals of different histone modifications on the PU.1/IRF8 peaks in cDC. d Screenshot showing the top 5 TFs identified by motif enrichment analysis from the overlapping peaks between PU.1 and IRF8 in cDC cells The motif enrichment module evaluates which transcription factors are more likely to occur in certain genomic regions than in ”background regions” based on the motif-predicted binding sites (MPBS) from motif matching. To determine the significance, we performed Fisher’s exact test for each transcription factor and corrected the p values with the Benjamini–Hochberg procedure. More specifically, we provided three types of tests:Input regions versus Background regions *In this* test, all input regions are verified against background regions that are either user-provided or randomly generated with the same average length distribution as the original input regions. Gene-associated regions versus Non-gene-associated regions *In this* test, we would like to check whether a group of regions that are associated with genes of interest (e.g. up-regulated genes) is enriched for some transcription factors versus regions that are not associated with those genes. The input regions are divided into two groups by performing gene-region association that considers promoter-proximal regions, gene body, and distal regions. After the association, we perform a Fisher’s exact test followed by multiple testing corrections as mentioned in the previous analysis type. Promoter regions of input genes versus Background regions *In this* test, we take all provided genes, find their promoter regions in the target organism, and create a “target regions” BED file from those. A background file is created by using the promoter regions of all genes not included in the provided gene list. Next, motif matching is performed on the target and background regions and a Fisher’s exact test is executed. Finally, the enrichment regions are provided in an HTML interface.
## Additional tools based on RGT
Several additional tools that explored and extended classes from RGT to tackle specific regulatory genomics problems are available. HINT is a framework that uses open chromatin data to identify the active transcription factor binding sites (TFBS). We originally developed this method for DNase-seq data [14, 15] and later extended it to ATAC-seq data by taking the protocol-specific artifacts into account [13]. Footprint analysis requires base pair resolution signals in contrast to peak calling problems, which are based on signals on windows with more than 50 bps. Therefore, HINT has a GenomicSignal class, which deals with ATAC-seq, and DNA-seq signals such as cleavage bias correction, base pair counting, and signal smoothing. Moreover, HINT makes use of the previously described motif-matching functionality provided by RGT to characterize motifs related to ATAC-seq footprints. These can be explored in differential footprinting analysis to detect relevant TFs associated with different biological conditions. This method has been widely used to study, among others, cell differentiation [13, 28] and diseases [29–32].
THOR is a Hidden Markov Model-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates [16]. As a first step, THOR needs to create and normalize ChIP-seq signals from distinct experiments. Among others, THOR extended functionalities of the base class CoverageSet to a MultipleCoverageSet class to deal with multiple signals at a time and to provide global normalization methods, such as trimmed means of M-values (TMM). Finally, Triplex Domain Finder (TDF) characterizes the triplex-forming potential between RNA and DNA regions [17]. TDF explores functionality provided by RGT/RGT-viz to build statistical tests for characterizing DNA binding domains in lncRNAs.
## Investigating dendritic cell development with RGT-viz
We here provided a case study using RGT-viz to investigate dendritic cell (DC) development (Fig. 4a). We collected ChIP-seq data of the transcription factors PU.1 and IRF8, and five histone modifications (i.e., H3K4me1, H3K4me3, H3K9me3, H3K27me3, and H3K27ac) for each of the cell types [4, 16, 33, 34] (Additional file 1: Table S1). PU.1 is one of the master regulators of hematopoiesis and is expressed by all hematopoietic cells [35] and IRF8 is believed to co-bind with PU.1 to control the differentiation of DC progenitors towards specific DC sub-types [4, 36]. We mapped the sequencing reads to mm9 using BWA [6] and called the peaks with MACS2 [7].
We performed an intersection test between PU.1 and IRF8 peaks from different cell types to check for PU.1 and IRF8 co-binding during DC differentiation. Of note, IRF8 ChIP-seq only detected peaks in classical and plasmacytoid DC (cDC and pDC, respectively), as this TF is not expressed in multipotent progenitor (MPP) and expressed only at low levels in common DC progenitors (CDP).
This test reveals that PU.1 and IRF8 are significantly associated in all cell types, while the co-binding was two times higher as measured by \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 statistics in cDC than pDC. Moreover, we observed that overlap of binding sites of cDC IRF8 peaks is already quite high with CDP PU.1 peak. This indicates that PU.1 binding prepares the chromatin for IRF8 binding already in CDP, showing DC priming in CDP (Fig. 4b and Additional file 1: Table S2).
We next asked if the co-binding regions are associated with different regulatory regions (enhancers vs. promoters). For this, we defined the set of peaks with both PU.1. and IRF8 binding, or only with PU.1. or only IRF8 binding in cDC and pDC cells by using intersect and subtracting functions from the core class GenomicRegionSet of RGT. We then generated line plots of PU.1, IRF8, H3K4me1, and H3K4me3 on these three sets of regions in cDC (Fig. 4c). We observed that peaks with PU.1-IRF8 co-binding have higher ChIP-seq peaks for either factor indicating that co-binding strengthens the binding affinity of both TFs. Moreover, H3K4me1 signals are strong for PU.1 and IRF8 co-binding, while IRF8 only has stronger H3K4me3 marks. This suggests an association of PU.1 and IRF8 co-binding with enhancers, while IRF8 exclusive binding is more associated with promoters. These examples demonstrate how RGT-Viz can be used to explore associations and interpretation of genomic data.
We next performed motif matching and enrichment analysis on the PU.1 and IRF8 co-binding peaks in cDC (Fig. 4d). We observed that PU.1 (and ETS family) motifs were ranked at the top and an IRF family motif at fifth (IRF1; MA0050.2.IRF1). This demonstrates how motif analysis can recover expected regulatory players from regulatory sequences.
## Discussion
We presented the regulatory genomics toolbox (RGT), a versatile toolbox for analyzing high-throughput regulatory genomics data. RGT was programmed in an oriented-object fashion and its core classes provided functionalities to handle typical regulatory genomics data: regions and signals. Based on these core classes, RGT built distinct regulatory genomics tools, i.e., HINT for footprinting analysis, TDF for finding DNA–RNA triplex, THOR for ChIP-seq differential peak calling, motif analysis for TFBS matching and enrichment, and RGT-viz for regions association tests and data visualization. These tools have been used in several epigenomics and regulatory genomics works to study cell differentiation and regulation [28, 31, 37–42].
There are several methods providing functionality similar to RGT but they mostly focus on a subset of problems tackled by RGT (Additional file 1: Table S3). Bedtools is a well-known and efficient C tool for interval algebra. However, it provides no functionalities related to statistical tests, motif analysis, and visualization. Visualization and genomic signal processing are provided by the python-based Deeptools [43]. However, it lacks functionality related to interval algebra or motif analysis. pyDnase is a for genomic signal processing but with a focus on problems related to genomic footprinting [44]. Also, previous tools focus on providing command-line interfaces, while RGT provides both programming and command-line interfaces. Regarding R language, GenomicRanges is a library for interval algebra [45], while motif matching can be performed with motifmatchr [46]. We are not aware of any framework for genomic signal processing in R. Altogether, RGT is the most complete framework for chromatin sequencing data manipulation, which we are aware of.
We envision that RGT can facilitate the development of computational methods for the analysis of high-throughput regulatory genomics data as a powerful and flexible framework in the future.
## Supplementary Information
Additional file 1. Fig. S1. An example pipeline for the analysis of ATAC-seq data; Fig. S2. Schematics of regions versus regions tests in RGT-viz; Fig. S3. Schematics of plotting tools in RGT-viz; Fig. S4. An screenshot showing the results of motif enrichment analysis for TF PU.1 (also known as Spi1); Table S1. ChIP-seq data used in the dendritic cell development case study of RGT-viz and Table S2. Statistical results of intersection test between PU.1 and IRF8 ChIP-seq peaks across different cell types. The online version contains supplementary material available at 10.1186/s12859-023-05184-5.
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|
---
title: 'The impact of stroke on the ability to live an independent life at old age:
a community-based cohort study of Swedish men'
authors:
- Elias Lindvall
- Kristin Franzon
- Erik Lundström
- Lena Kilander
journal: BMC Geriatrics
year: 2023
pmcid: PMC9990268
doi: 10.1186/s12877-023-03817-1
license: CC BY 4.0
---
# The impact of stroke on the ability to live an independent life at old age: a community-based cohort study of Swedish men
## Abstract
### Introduction
Few studies with controls from the same cohort have investigated the impact of stroke on the ability to live an independent life at old age. We aimed to analyze how great an impact being a stroke survivor would have on cognition and disability. We also analyzed the predictive value of baseline cardiovascular risk factors.
### Methods
We included 1147 men, free from stroke, dementia, and disability, from the Uppsala Longitudinal Study of Adult Men, between 69–74 years of age. Follow-up data were collected between the ages of 85–89 years and were available for 481 of all 509 survivors. Data on stroke diagnosis were obtained through national registries. Dementia was diagnosed through a systematic review of medical charts and in accordance with the current diagnostic criteria. The primary outcome, preserved functions, was a composite outcome comprising four criteria: no dementia, independent in personal activities of daily living, ability to walk outside unassisted, and not living in an institution.
### Results
Among 481 survivors with outcome data, 64 ($13\%$) suffered a stroke during the follow-up. Only $31\%$ of stroke cases, compared to $72\%$ of non-stroke cases (adjusted OR 0.20 [$95\%$ CI 0.11–0.37]), had preserved functions. The chance of being free of dementia was $60\%$ lower in the stroke group, OR 0.40 [$95\%$ CI 0.22–0.72]. No cardiovascular risk factors were independently able to predict preserved functions among stroke cases.
### Conclusion
Stroke has long lasting consequences for many aspects of disability at very high age.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-023-03817-1.
## Introduction
Stroke is the third leading cause of disability worldwide [1]. Due to a higher proportion of patients being treated in stroke units and more effective secondary prevention, more people survive stroke, even in very high age, leaving an increasing number of individuals with varying degrees of sequelae [2]. Few studies have specifically addressed the impact of stroke on disability in people over 80 years. Although predictors of disability affecting daily activities after stroke have been studied, follow-up over several years seems to be scarce [3].
Approximately, 10–$30\%$ of stroke victims develop dementia within one year [4]. Stroke is a strong contributor to vascular dementia [5] and may accelerate neurodegeneration in Alzheimer’s disease [6].
Other common consequences of stroke in the oldest old are loss of the ability to walk outdoors and the need for assisted care facilities. These aspects have a negative impact on independency and quality of life [7], as well as societal costs. However, the extent to which stroke per se contributes to disability is not fully clear, since only a few longitudinal studies have compared subjects with and without stroke from the same population-based cohort.
The Uppsala Longitudinal Study of Adult Men (ULSAM) is a community-based cohort of Swedish men, who have been followed for more than four decades with a focus on age-associated disorders and independent aging [8, 9].
The aim of this study was to explore how stroke after 70 years of age affects the ability to live an independent life, in men surviving above age 85. Further, we analyzed whether the cardiovascular risk profile at age 70 predicted the long-term functional independence following the stroke. Our primary outcome – preserved functions – was defined as independency in personal activities of daily living (PADL), absence of dementia, being able to walk outdoors on your own, and living in your own home.
## Study design and population
All men living in the county of Uppsala, born between 1920–1924 ($$n = 2841$$), were offered to participate in the first ULSAM investigation during 1970–1974; specifically, $82\%$ ($\frac{2322}{2841}$) accepted the invitation. For the present study, 1221 men (mean age 71 years) in the third ULSAM investigation cycle (ULSAM-70) were eligible for inclusion (Fig. 1). We excluded individuals with a prior stroke diagnosis in the national in-patient registry ($$n = 38$$) and those who did not meet the criteria for preserved functions ($$n = 36$$), through a systemic review of questionnaires and medical charts. Hence, a total of 1147 men ($94\%$) were included in our baseline cohort. Fig. 1Flow chart of the study population.*ULSAM-88 years was finished in December 2009. Data from phone interviews, questionnaires and medical records were gathered up until March 2010 At follow-up after approximately sixteen years, a total of 638 individuals ($56\%$) had died; moreover, 28 survivors, representing $6\%$ of all stroke cases and $5\%$ of non-stroke cases, could not be classified regarding functions due to insufficient data in their medical charts. These men had only a few visits to general practitioners and hospitals. Furthermore, 481 individuals ($42\%$) had sufficient primary outcome data (‘main cohort’) to be classified regarding preserved functions or not. Out of these, 308 men ($64\%$) also participated in functional tests, such as gait speed, cognitive tests, and blood sampling (‘subgroup participating in tests’).
The outcome data were collected at the sixth ULSAM investigation face-to-face (ULSAM-88) and through telephone interviews and reviews of medical charts.
## Baseline data
The ULSAM data collection procedure is described in detail at the website (https://www.pubcare.uu.se/ulsam/Database). Baseline data were collected between August 1991 and May 1995 (for details, see additional file 1). Data on educational level, marital status, physical activity, smoking habits, living conditions, and PADL functions were collected through interviews and questionnaires. Research nurses recorded the blood pressure, electro-cardiogram, drew blood samples, and calculated the body mass index (BMI). Fasting blood glucose values were obtained from an oral glucose intolerance test. Diabetes [10] and hypertension [11] were defined in accordance with the international criteria. Drug prescription data were available through the Swedish prescribed drug registry. The Mini Mental State Examination (MMSE) [12] enhanced cued recall [13], temporal orientation [13] and Trail Making Test B (TMTB) [14] were administered. Charlson comorbidity index was calculated based on the comorbidities available in the International classification of diseases (ICD) from the Swedish in-patient registry [15, 16].
## Stroke
The first-ever stroke diagnoses were retrieved from the in-patient registry and defined as hospitalization with any stroke diagnosis (ICD version 8–10, see Additional file 1); before follow-up date in ULSAM-88 or before March 10, 2010 (participants with only medical chart data).
## Outcome data
All 509 surviving ULSAM participants were invited to ULSAM-88 at the Geriatric department, Uppsala University hospital between September 2008 and December 2009; and 308 participated at the hospital or had a home visit by a research nurse (‘subgroup participating in tests’). For individuals who did not participate in ULSAM-88 ($\frac{201}{509}$), phone interviews were conducted, as well as questionnaires and a review of medical charts from January 2010 to March 2010. The diagnostic workup for dementia was made according to standard clinical procedures at either the Memory Clinic (mandatory for a diagnosis of Lewy body dementia and frontotemporal dementia) or in primary care, and cases were identified by reviews of the medical records. Caregivers’ descriptions of cognitive problems, impact on IADL, MMSE and Clock test were always included. The cognitive tests administered to participants in ULSAM-88 were on separate occasions, and were not included in these diagnostic procedures. Dementia was diagnosed by two experienced geriatricians (LK and KF) who independently examined all records and cognitive data available at the end of ULSAM-88/March 2010. Established diagnostic criteria and neuroradiology were used to classify cases as Alzheimer’s disease [17], vascular dementia [18], mixed Alzheimer’s and vascular dementia, Lewy body dementia/*Parkinson dementia* [19, 20], or frontotemporal dementia [21]. A diagnosis of unspecified dementia was set in cases with insufficient information. A third geriatrician was consulted in case of discordance, and a majority decision was made.
A questionnaire was administered to acquire data on living conditions, everyday physical activity, and PADL-function. For test participants, research nurses collected data on gait speed [22], ability to stand without support for 30 seconds [23], and Timed Up-and-Go Test [24]. Further, they administered the MMSE [12], enhanced cued recall (16 items) and verbal fluency (animals) from the 7 Minute Screen [13], the Geriatric Depression Scale-20 [25]; and a 0–$100\%$ visual analogue quality of Life scale [26].
## Outcome definitions
The primary outcome – preserved functions – was defined as fulfilling the following 4 criteria, through data from questionnaires, medical charts, or phone interviews:Absence of a dementia diagnosisAble to walk outdoors unassistedIndependency in personal activities of daily livingLiving in your own home, i.e., not living in an institution or care facility Absence of a dementia diagnosis was included as a separate outcome for the predictive value of baseline variables analysis.
## Statistical analysis
Differences between the stroke and non-stroke participants were analyzed with Students t–test or chi-square, depending on the level of measurement for the predictor variable. When assumptions of chi-square were not met, Fisher’s exact test (nominal variables) or Likelihood ratio (ordinal variables) were used. Logistic regression was used to calculate the odds ratios (OR) and $95\%$ confidence intervals (CI) for the outcomes. All outcomes were adjusted for age. Preserved functions and dementia were also adjusted for education. The outcome differences for the subgroup participating in the tests were analyzed using T-test or chi-square. For secondary outcomes, p-values were adjusted for age using ANCOVA-test. Additionally, cognitive test data were adjusted for education using ANCOVA.
Logistic regression was used to determine the predictive value of baseline variables for preserved functions and non-dementia. The analysis was performed separately for each individual baseline predictor, and the results were presented as OR with $95\%$ CI. Continuous variables were analyzed as Z-scores so that the odds ratios would reflect increases in the standard deviation (SD).
A sensitivity analysis was performed to compare the rate of stroke in subjects with insufficient outcome data and the main cohort. All statistical analyses were performed using IBM SPSS Statistics Software (version 27 for PC; IBM Corp., NY, USA). Author ELi had access to all data, takes responsibility for data integrity and performed all statistical analyses.
## Ethical considerations
ULSAM collected data with ethical approval from the regional ethics board in Uppsala (1990–10-10, Dnr $\frac{251}{90}$ and 2008–01-23, Dnr $\frac{2007}{338}$). All participants had given their informed consent.
## Results
During follow-up, a total of 64 participants ($13\%$) suffered a stroke. Among these, $8\%$ had an intracerebral hemorrhage, and $2\%$ had a subarachnoid hemorrhage. The mean time from the first-ever stroke to follow-up was 5.7 years (SD = 4.8 years), and one-third ($\frac{21}{64}$) had a stroke before age 80. Twenty-six participants ($41\%$) had multiple hospital admissions due to the stroke. The proportion of stroke cases was slightly lower in the subgroup participating in tests ($10\%$) than in the main cohort ($13\%$). Baseline variables in the main cohort are presented in Table 1. As expected, the stroke cases had a higher systolic blood pressure and prevalence of hypertension, as well as atrial fibrillation. These differences were consistent in the subgroup participating in the tests, except for atrial fibrillation (data not shown).Table 1Baseline characteristics of subsequent stroke and non-stroke casesMain cohort ($$n = 481$$)Variables:Stroke ($$n = 64$$)Non-stroke ($$n = 417$$)p-valueMean age years (SD)$$n = 48171$.1$ (0.6)70.9 (0.6)0.034Educationn = 4810.354 < 8 years n39 ($61\%$)217 ($52\%$) 8–13 years n15 ($23\%$)131 ($31\%$) > 13 years n10 ($16\%$)69 ($17\%$)Living with a partnern = 46655 ($90\%$)355 ($88\%$)0.574Smokingn = 4670.942 Current n8 ($13\%$)58 ($15\%$) Former n31 ($50\%$)200 ($49\%$) Never n23 ($37\%$)147 ($36\%$)Leisure time physical activityn = 4580.863 Low n19 ($32\%$)133($33\%$) High n40 ($68\%$)266 ($67\%$)Systolic BP mm Hg (SD)$$n = 480150$$ [19]144 [18]0.029Diastolic BP mm Hg (SD)$$n = 48085$$ [10]83 [9]0.242HDL cholesterol mmol/L (SD)$$n = 4781$.31$ (0.36)1.31 (0.34)0.856LDL cholesterol mmol/L (SD)$$n = 4753$.99$ (0.91)3.87 (0.88)0.334BMI kg/m2 (SD)$$n = 48026$.2$ (3.4)26.2(3.1)0.982Diabetes nn = 48012 ($19\%$)52 ($13\%$)0.152Hypertension nn = 48048 ($76\%$)253 ($61\%$)0.018Atrial fibrillation nn = 4545 ($9\%$)9 ($2\%$)0.023MMSE score (SD)$$n = 38929$$ (1.3)29 (1.3)0.870TMT B seconds (SD)$$n = 423116$$ [43]111[44]0.397Charlson comorbidity indexn = 481 Index value 0 n49 ($77\%$)338 ($81\%$) Index value 1 n13 ($20\%$)69 ($17\%$) Index value 2–4 n2 ($3\%$)10 ($2\%$)Values are absolute numbers or means. Percentages represent proportions of all individuals with available data. SD Standard deviation. BP Blood pressure. MMSE Mini Mental State Examination. TMT B Trail making test B. BMI Body mass index. HDL High-density lipoprotein. LDL Low-density lipoprotein. Students t-test and chi-square were used for p-values Only one-third of the stroke victims ($31\%$), and more than two-thirds ($72\%$) of the non-stroke participants fulfilled the criteria for preserved functions (Table 2). Stroke decreased the odds of having preserved functions at age 85–89 by $80\%$, OR 0.20 ($95\%$ CI 0.11–0.37). The proportion of individuals without dementia was $84\%$ in men without a stroke and $63\%$ among those afflicted by a stroke, OR 0.40 ($95\%$ CI 0.22–0.72). For stroke cases with dementia, the frequency of either vascular dementia or mixed Alzheimer’s and vascular dementia was $71\%$. Out of the four components, the ability to walk outdoors without assistance was most often affected in stroke. The subgroup participating in tests had higher rates of preserved functions in both non-stroke and stroke individuals, reflecting the higher participation rate among unimpaired subjects. There was no difference in Enhanced cued recall or temporal orientation. Stroke individuals had lower scores in the quality of life scale (60 vs. 72), verbal fluency test (13 points vs. 16 points), and higher scores in the Geriatric Depression Scale-20 (3.9 points vs. 2.7 points).Table 2Outcome measurements in incident stroke and non-stroke participantsPrimary outcomeMain cohort ($$n = 481$$)Variables:TotalStrokeNon-strokeUnadjusted OR ($95\%$CI)Adjusted OR ($95\%$CI)$$n = 64$$,$13\%$$$n = 417$$, $87\%$Mean age at first ever stroke years (SD)81.7 (4.8)Preserved Functions n/total n$\frac{322}{481}$ ($67\%$)$\frac{20}{64}$ ($31\%$)$\frac{302}{417}$ ($72\%$)0.17 (0.01–0.31)0.20 (0.11–0.37)aNon dementia n/total n$\frac{388}{481}$ ($81\%$)$\frac{40}{64}$ ($63\%$)$\frac{348}{417}$ ($84\%$)0.33 (0.19–0.58)0.40 (0.22–0.72)aPADL independent n/total n$\frac{345}{423}$ ($72\%$)$\frac{24}{47}$ ($51\%$)$\frac{321}{376}$ ($85\%$)0.18 (0.09–0.34)0.19 (0.10–0.37)bWalking unassisted outdoors n/total n$\frac{353}{430}$ ($73\%$)$\frac{22}{47}$ ($47\%$)$\frac{331}{383}$ ($86\%$)0.14 (0.07–0.26)0.16 (0.08–0.31)bLiving at home n/total n$\frac{400}{470}$ ($83\%$)$\frac{37}{60}$ ($62\%$)$\frac{363}{410}$ ($89\%$)0.21 (0.11–0.38)0.24 (0.13–0.45)bSubgroup participating in tests ($$n = 308$$)Variables:StrokeNon-strokeUnadjusted OR ($95\%$CI)Adjusted OR ($95\%$CI)Preserved Functions n/total n$\frac{17}{31}$ ($55\%$)$\frac{235}{277}$ ($85\%$)0.22 (0.10–0.47)0.25 (0.11–0.56)aNon dementia n/total n$\frac{26}{31}$ ($84\%$)$\frac{259}{277}$ ($94\%$)0.36 (0.12–1.05)0.37 (0.13–1.12)aPADL independent n/total n$\frac{21}{31}$ ($68\%$)$\frac{252}{277}$ ($91\%$)0.21 (0.09–0.49)0.24 (0.10–0.58)bWalking unassisted outdoors n/total n$\frac{19}{31}$ ($61\%$)$\frac{258}{277}$ ($93\%$)0.12 (0.05–0.28)0.13 (0.06–0.32)bLiving at home n/total n$\frac{25}{31}$ ($81\%$)$\frac{270}{277}$ ($98\%$)0.11 (0.03–0.35)0.12 (0.04–0.38)bSecondary outcomesSubgroup participating in tests ($$n = 308$$)Variables:StrokeNon-strokeUnadjusted p-valueAdjusted p-valuen = 31, $10\%$$$n = 277$$, $90\%$Gait speed m/s (SD)$$n = 2511$.26$(0.38)1.37(0.32)0.3370.348bAble to stand without support nn = 28623 ($100\%$)262 ($100\%$)1.0000.998bTimed up and go seconds (SD)$$n = 25017$$ (12.2)14 (7.1)0.3810.210bMMSE score (SD)$$n = 30625$.8$ (5.0)27.0 (3.2)0.2150.205aVerbal fluency score (SD)$$n = 30313$$ (5.6)16 (5.3)0.0350.046aGeriatric depression scale-20 score (SD)$$n = 3003$.9$ (2.6)2.7 (2.5)0.0310.036bQuality of life scale score (SD)$$n = 28860$$ [20]72 [16]0.0070.002bValues are absolute numbers or means. Percentages represent proportions of all individuals with available data. Logistic regression was used to derive Odds Ratios (OR). PADL Personal activities of daily living. MMSE Mini mental state examination. Students t-test and chi-square were used. ANCOVA was used for adjusted p-valuesaAdjusted for age and educationbAdjusted for age No baseline variables were independent predictors of the preserved functions (Table 3) or dementia (Table 4) in the stroke group. However, among non-stroke individuals, hypertension (OR 0.53 [$95\%$ CI 0.33–0.84]) and slower performance on TMT B (OR 0.58 [$95\%$ CI 0.45–0.75]) were associated with lower odds, whereas higher MMSE scores (OR 1.47 [$95\%$ CI 1.11–1.93]) were associated with a greater chance of having preserved functions. Furthermore, hypertension (0.45 [$95\%$ CI 0.25–0.80]) and slow performance on the TMT B (OR 0.58 [$95\%$ CI 0.44–0.77]) were associated with lower odds of non-dementia. High MMSE scores (OR 1.78 [$95\%$ CI 1.31–2.44]) predicted higher odds of non-dementia. Table 3Baseline variables predicting Preserved FunctionsStroke cases ($$n = 64$$)Non-stroke cases ($$n = 417$$)Variables:PF ($$n = 20$$)Non-PF ($$n = 44$$)Unadjusted OR ($95\%$ CI)PF ($$n = 302$$)Non-PF ($$n = 115$$)Unadjusted OR ($95\%$ CI)Available dataAvailable dataEducationn = 64n = 417 < 8 years n12 ($60\%$)27 ($61\%$)ref157 ($52\%$)60($52\%$)Ref 8–13 years n5 ($25\%$)10 ($23\%$)1.13 (0.32–4.01)93 ($31\%$)38 ($33\%$)0.94 (0.58–1.51) > 13 years n3 ($15\%$)7 ($16\%$)0.96 (0.21–4.38)52 ($17\%$)17 ($15\%$)1.17 (0.63–2.18)Living with a partnern = 6119 ($95\%$)36 ($88\%$)2.64 (0.29–24.24)$$n = 405258$$ ($87\%$)97 ($90\%$)0.75 (0.37–1.52)Smokingn = 62n = 405 Current n2 ($10\%$)6 ($14\%$)ref40 ($14\%$)18 ($16\%$)Ref Former n10 ($50\%$)21 ($50\%$)1.43 (0.24–8.38)147 ($50\%$)53 ($48\%$)1.25 (0.66–2.36) Never n8 ($40\%$)15 ($36\%$)1.60 (0.26–9.83)107 ($36\%$)40 ($36\%$)1.20 (0.62–2.34)Leisure time physical activityn = 59n = 399 Low n4 ($21\%$)15($38\%$)ref99 ($34\%$)34 ($32\%$)Ref High n15 ($79\%$)25 ($63\%$)2.25 (0.63–8.05)194 ($66\%$)72 ($68\%$)0.93 (0.58–1.49)Systolic BP mm Hg (SD)$$n = 63148$$ [17]151 [21]0.87 (0.52–1.46)$$n = 417143$$ [18]147 [17]0.77 (0.62–0.97)Diastolic BP mm Hg (SD)$$n = 6383$$ [9]85 [10]0.75 (0.44–1.28)$$n = 41783$$ [9]84 [9]0.82 (0.66–1.02)HDL cholesterol mmol/L (SD)$$n = 631$.39$ (0.36)1.27 (0.34)1.37 (0.81–2.32)$$n = 4141$.29$ (0.33)1.31 (0.34)0.94 (0.76–1.18)LDL cholesterol mmol/L (SD)$$n = 634$.09$ (0.97)3.95 (0.89)1.17 (0.69–1.98)$$n = 4123$.88$ (0.90)3.87 (0.84)1.01 (0.81–1.26)BMI kg/m2 (SD)$$n = 6325$.6$ (3.4)26.4(3.5)0.76 (0.43–1.34)$$n = 41726$.1$ (2.9)26.6 (3.6)0.83 (0.65–1.04)Diabetes nn = 633 ($15\%$)9 ($21\%$)0.67 (0.16–2.79)$$n = 41734$$ ($11\%$)18($16\%$)0.68 (0.37–1.27)Hypertension nn = 6315 ($76\%$)33 ($77\%$)0.91 (0.26–3.13)$$n = 417171$$ ($57\%$)82 ($71\%$)0.53 (0.33–0.84)Atrial fibrillation nn = 581 ($5\%$)4 ($10\%$)0.49 (0.05–4.68)$$n = 3967$$ ($2\%$)2 ($2\%$)1.30 (0.27–6.37)MMSE score (SD)$$n = 5229$$ (1.0)28 (1.4)1.80 (0.80–4.05)$$n = 33729$$ (1.1)28 (1.6)1.47 (1.11–1.93)TMT B seconds (SD)$$n = 60115$$ [42]117[44]0.95 (0.51–1.77)$$n = 363105$$ [40]127 [49]0.58 (0.45–0.75)Charlson comorbidity indexn = 64n = 417 Index value 0 n16 ($80\%$)33 ($75\%$)0.49 (0.03–8.26)244 ($81\%$)94 ($82\%$)1.73 (0.48–6.27) Index value 1 n3 ($15\%$)10 ($23\%$)0.30 (0.01–6.38)52 ($17\%$)17 ($15\%$)2.04 (0.51–8.09) Index value 2–4 n1 ($5\%$)1 ($2\%$)ref6 ($2\%$)4 ($4\%$)RefOdds ratio (OR) reflect increases in standard deviation (SD) of the variable. Available data was used to calculate percentages. Logistic regression was used. PF Preserved functions, BP Blood pressure. MMSE Mini mental state examination. TMT B Trail Making Test B. BMI Body mass index. HDL High-density lipoprotein. LDL Low-density lipoproteinTable 4Baseline variables predicting non-dementiaStroke cases ($$n = 64$$)Non-stroke cases ($$n = 417$$)Variables:Non-dementia ($$n = 40$$)Dementia ($$n = 24$$)Unadjusted OR ($95\%$ CI)Non-dementia ($$n = 348$$)Dementia ($$n = 69$$)Unadjusted OR ($95\%$ CI)Available dataAvailable dataEducationn = 64n = 417 < 8 years n25 ($63\%$)14 ($58\%$)ref181 ($52\%$)36 ($52\%$)ref 8–13 years n8 ($20\%$)7 ($29\%$)0.64 (0.19–2.14)109 ($31\%$)22 ($32\%$)0.99 (0.55–1.76) > 13 years n7 ($18\%$)3 ($13\%$)1.31 (0.29–5.87)58 ($17\%$)11 ($16\%$)1.05 (0.50–2.19)Living with a partnern = 6134 ($90\%$)15 ($71\%$)3.40 (0.84–13.84)$$n = 405297$$ ($87\%$)58 ($91\%$)0.70 (0.28–1.71)Smokingn = 62n = 405 Current n4 ($10\%$)4 ($17\%$)ref48 ($14\%$)10 ($15\%$)ref Former n21 ($54\%$)10 ($44\%$)1.10 (0.43–10.17)166 ($49\%$)34 ($50\%$)1.02 (0.47–2.21) Never n14 ($36\%$)9 ($39\%$)1.56 (0.31–7.85)123 ($37\%$)24 ($35\%$)1.07 (0.48- 2.40)Leisure time physical activityn = 59n = 399 Low n10 ($28\%$)9($39\%$)ref117 ($35\%$)16 ($25\%$)Ref High n26 ($72\%$)14 ($61\%$)1.67 (0.55–5.07)219 ($65\%$)47 ($75\%$)0.64 (0.35–1.17)Systolic BP mm Hg (SD)$$n = 63152$$ [20]147 [20]1.27 (0.77–2.09)$$n = 417143$$ [18]148 [17]0.78 (0.60–1.01)Diastolic B Diastolic BP mm Hg(SD)$$n = 6385$$ [9]84 [11]1.16 (0.70–1.91)$$n = 41783$$ [9]84 [10]0.88 (0.68–1.15)HDL cholesterol mmol/L (SD)$$n = 631$.33$ (0.37)1.28 (0.33)1.17 (0.70–1.94)$$n = 4151$.30$ (0.33)1.33 (0.38)0.91 (0.70–1.19)LDL cholesterol mmol/L (SD)$$n = 634$.14$ (0.94)3.76 (0.82)1.58 (0.90–2.77)$$n = 4123$.88$ (0.88)3.85 (0.91)1.04 (0.80–1.35)BMI kg/m2 (SD)$$n = 6126$.3$ (3.5)26.0 (3.4)1.11 (0.66–1.86)$$n = 40726$.2$ (3.1)26.5 (3.4)0.90 (0.68–1.19)Diabetes nn = 637 ($20\%$)5 ($21\%$)0.83 (0.23–2.99)$$n = 41743$$ ($12\%$)9($13\%$)0.94 (0.44–2.03)Hypertension nn = 6332 ($82\%$)16 ($67\%$)2.29 (0.70–7.43)$$n = 417201$$ ($58\%$)52 ($75\%$)0.45 (0.25–0.80)Atrial fibrillation nn = 584 ($11\%$)1 ($5\%$)2.42 (0.25–23.25)$$n = 3967$$ ($2\%$)2 ($3\%$)0.66 (0.13–3.23)MMSE score (SD)$$n = 5229$$ (1.2)28 (1.3)1.43 (0.72–2.86)$$n = 33729$$ (1.2)28 (1.9)1.78 (1.31–2.44)TMT B seconds (SD)$$n = 60115$$ [42]117[44]0.95 (0.51–1.77)$$n = 363105$$ [40]127 [49]0.58 (0.45–0.75)Charlson comorbidity indexn = 64n = 417 Index value 0 n30 ($75\%$)19 ($79\%$)-281 ($81\%$)57 ($83\%$)0.55 (0.07–4.41) Index value 1 n8 ($20\%$)5 ($21\%$)-58 ($17\%$)11 ($16\%$)0.59 (0.07–5.10) Index value 2–4 n2 ($5\%$)0 ($0\%$)ref9 ($3\%$)1 ($1\%$)refOdds ratio (OR) reflect increases in standard deviation (SD) of the variable. Available data was used to calculate percentages. Logistic regression was used. PF Preserved functions, BP Blood pressure. MMSE Mini mental state examination. TMT B Trail Making Test B. BMI Body mass index. HDL High-density lipoprotein. LDL Low-density lipoprotein
## Sensitivity analysis
The stroke rate was similar in individuals who were unclassifiable due to insufficient medical chart data, as in the main cohort ($14\%$ vs. $13\%$, $$p \leq 0.779$$).
## Discussion
In this community-based cohort of men aged 85–59 years, free from stroke and disability at baseline, stroke during the follow up period was associated with $80\%$ lower odds of preserved functions, compared to stroke-free survivors. Stroke survivors had higher odds ratios for all four aspects of disability, i.e., dementia, dependency in PADL, loss of the ability to walk outdoors on your own, and institutionalization. These are vital for a person’s ability to pursue meaningful and joyous activities in their everyday life [7, 27].
Unsurprisingly, we found no other stroke study with a similar composite outcome; therefore, we will discuss cognition, loss of independence, and quality of life separately.
Dementia after stroke is by far the most studied. However, most reports are from hospital-based cohorts, with follow-up for 1–3 years. Moreover, they often lack a relevant control group, which is essential since other causes of functional impairment are common in very high age [28, 29].
To the best of our knowledge, only five previous studies have investigated the risk of dementia in stroke compared to stroke-free subjects from the same cohort [30–34]. In the Framingham study, with a mean age at entry of 79 years, stroke was associated with a twofold increase in the hazard ratio of dementia over 10 years of follow-up [30]. Cognitive impairment or dementia was present in $64\%$ of the stroke survivors compared to $21\%$ in the stroke-free subjects after 5 years of follow-up in the Canadian Study of Health and Aging [31]. The Rotterdam Scan Study included subjects with a mean age of 69 years, and the hazard ratio for dementia was doubled in those eleven percent who suffered a stroke during a mean of 7 years of follow-up [34]. In the Kungsholmen Study, all participants were more than 75 years at the time of inclusion and followed for 3 years. Both first-ever stroke (relative risk 2.4) and a history of stroke (relative risk 1.7) were associated with dementia [32]. Similar results are reported from the Icelandic MRI study [33]. In ULSAM, the OR for post-stroke dementia was 2.5. The overall prevalence of dementia was $19\%$, which is marginally lower than the expected prevalence in this age group [35].
Loss of independency in outdoor walking and managing PADL were more common than dementia in our study. Results from previous studies are inconclusive since the methods vary widely and follow-up periods are only for a few years. The modified Rankin *Scale is* probably the most common disability measurement in stroke trials [36]. Our outcome items are more precisely defined. Neurological deficits at onset, upper limb paresis, and high age are predictors of ADL function [3]. In a Swedish hospital-based cohort, one year after a first-ever stroke, one-fifth lived in nursing homes and more than one-third of the survivors were PADL dependent, i.e., less than in our cohort [37]. In Sweden, institutionalization is indicated when home care services are no longer able to provide the support needed, and it is not associated with socioeconomic status. The 5-year risk of institutionalization after stroke was $26\%$ in the Oxford Vascular Study, which is lower than in our study ($38\%$) [38]. This is probably explained by the fact that our participants were older at the time of follow-up (87 vs. 75 years).
We saw a negative impact on quality of life among those afflicted by stroke, as well as slightly higher ratings on the Geriatric Depression Scale. This shows that stroke certainly affects the well-being in those who survive beyond their eighties. Men with stroke had lower scores in verbal fluency but not in the MMSE. This may be because verbal fluency relies more on semantic memory, speech, executive function and cognitive speed than MMSE.
Although MMSE is not sensitive memory test, the difference in score might reflect a steeper cognitive decline for stroke survivors because of cerebrovascular disease [5, 39].
None of the baseline cardiovascular parameters were independent predictors of preserved functions in stroke cases. This is in line with previous studies where the addition of other cardiovascular parameters in a predictive model did not increase the general risk of dementia/cognitive decline post stroke [34, 40].
The limitations of this study include the lack of stroke specific data, such as stroke severity, infarct volume, and type of stroke. These are well known predictors for disability, at least for the first 5 years after a stroke [4, 41]. Generalizability may be affected due to recruitment from a single region and the lack of female participants; however, previous studies comparing the prevalence of post stroke cognitive impairment between men and women show no significant difference [34, 42]. We estimated approximately 100 stroke survivors, based on the rates of participation in ULSAM. However, we had fewer stroke cases, probably because many ULSAM participants have had their vascular risk factors addressed and treated since age 50. One major limitation is the small numbers participating in tests, although men in this age group are rare, and our study adds knowledge in this population. As a consequence the predictive value of baseline variables may have been underestimated.
The strengths include the fact that participants were included from a community-based population and free from stroke and disability at baseline. Data were collected prospectively, and register data allowed us to capture all hospitalized stroke cases. We were able to track the primary outcome in $94\%$ of the survivors through phone interviews and medical chart reviews. The study design allowed us to compare OR between stroke and non-stroke subjects, which is important in high age where other causes of disability are frequent. Other major strengths of this study are the homogeneity in age and the long-term follow-up of 15 years. Our study population was also older than in most other cohorts. The primary outcome was a composite variable, consisting of factors easy to measure and explain to the broad population, and most certainly relevant to patients and caregivers.
## Conclusion
Stroke has a significant impact on cognitive and physical function in stroke survivors above the age of 85. Hence, this reinforces the need for stroke prevention and active long-term rehabilitation to mitigate the consequences of stroke in older people.
## Supplementary Information
Additional file 1.
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|
---
title: Results of thermal osteonecrosis for implant removal on electron microscopy,
implant stability, and radiographic parameters – a rat study
authors:
- Kristian Kniha
- Eva Miriam Buhl
- Faruk Al-Sibai
- Stephan Christian Möhlhenrich
- Anna Bock
- Marius Heitzer
- Frank Hölzle
- Ali Modabber
journal: Head & Face Medicine
year: 2023
pmcid: PMC9990269
doi: 10.1186/s13005-023-00349-2
license: CC BY 4.0
---
# Results of thermal osteonecrosis for implant removal on electron microscopy, implant stability, and radiographic parameters – a rat study
## Abstract
### Background
This rat study aimed to evaluate the feasibility of temperature thresholds that affect peri-implant bone cells and morphology and the potential usefulness of thermal necrosis for inducing implant removal for a subsequent in vivo pig study.
### Methods
On one side, rat tibiae were thermally treated before implant insertion. The contralateral side was used as the control group without tempering. Temperatures of 4 °C, 3 °C, 2 °C, 48 °C, 49 °C, and 50 °C were evaluated with a tempering time of 1 min. Energy-dispersive X-ray spectroscopy (EDX) and transmission electron microscopy (TEM) analyses were performed.
### Results
The EDX analysis revealed significant increases in element weights at 50 °C (e.g., calcium, phosphate, sodium, and sulfur; $p \leq 0.01$). The results of the TEM analysis showed that at all the applied cold and warm temperatures, signs of cell damage were observed, including vacuolization, shrinkage, and detachment from the surrounding bone matrix. Some cells became necrotic, leaving the lacunae empty.
### Conclusions
Temperature of 50 °C led to irreversible cell death. The degree of damage was more significant at 50 °C and 2 °C than at 48 °C and 5 °C. Although this was a preliminary study, from the results, we identified that a temperature of 50 °C at a time interval of 60 min can lower the number of samples in a further study of thermo-explantation. Thus, the subsequent planned in vivo study in pigs, which will consider osseointegrated implants, is feasible.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13005-023-00349-2.
## Introduction
In the current literature, only few data support the use of a controlled thermoexplantation procedure for implant removal. No clear indications have been established on the exact temperature and time interval that would produce sufficient but minimal osteonecrosis around implants to remove them without trauma. Uncontrolled temperatures could easily lead to extensive jaw necrosis and would be more likely to cause severe inflammation rather than atraumatic explantation. Consequently, a precise temperature within the initial developmental range of bone necrosis must be selected for successful thermal explantation. Nevertheless, several publications have demonstrated the efficient loosening of osseointegrated implants using warm temperatures with, for example, ultra-high-frequency and laser surgical devices [1–3]. The heat input was uncontrolled and inconsistent in all the reported cases.
A recent systemic review analyzed the literature regarding the threshold values for thermal bone necrosis [4]. The authors concluded that no studies have indicated a specific threshold value for bone necrosis. For a tempering time of 1 min, the values ranged from 47 °C to 55 °C, but no threshold value has yet been established for cryoinsult. Thus, in the current literature, no clear evidence exists that support the use of a managed thermo-explantation technique or suggest the exact temperature and time interval around the implants that would generate adequate but minimal osteonecrosis to extract them without trauma. Dental implants that replace failed implants have lower survival rates. However, if they osseointegrate successfully, the method described here can be considered [5]. High temperatures could easily lead to substantial jaw necrosis and cause severe inflammation. Therefore, more in-depth and pre-clinical studies are needed to gain further insights to evaluate the potential usefulness of thermal necrosis for implant removal before it can be predictably applied to patients. In our preliminary cadaver study, temperatures of 51 °C and 5 °C at time intervals of 10 and 30 s were identified, respectively, which led to significant osseous matrix degeneration [6]. In addition, transmission electron microscopy (TEM) at 53 °C revealed a decalcification and swollen mitochondria, which lost the structure of their inner cristae.
The present rat study was conducted to verify these results in an in vivo setting and reduce the sample size for subsequent in vivo studies in the pig with osseointegrated implants. The primary aim of this study was to analyze feasible cold and warm temperatures and time intervals. For this purpose, mineralization of the peri-implant bone of the rat tibia was analyzed on energy-dispersive X-ray spectroscopy (EDX), and osteocyte morphology was analyzed for signs of injury on TEM. We hypothesized that an appropriate temperature and time interval for warm and cold temperatures would induce minimal peri-implant osteonecrosis. Furthermore, implant stability and bone loss over time were measured.
## Experimental protocol
This study included 48 adult male Sprague–Dawley rats, each weighing 450 g and aged 4 months (Janvier Labs, Le Genest-Saint-Isle, France). One examiner completed all the steps of the study experiment. This investigation was conducted in accordance with the guidelines of the European Parliament and the council on the protection of animals used for scientific purposes, ARRIVE (Animal Research: Reporting of In Vivo Experiments) [7], and Directive $\frac{2010}{63}$/EU. The study protocol received ethical approval and consent to participate from the appropriate local authority (Landesamt für Natur und Verbraucherschutz, Recklinghausen, Germany; Ref. 2019A276).
For a tempering time of 1 min, the six test groups were divided into cold and warm temperatures of 4 °C, 3 °C, 2 °C, 48 °C, 49 °C, and 50 °C. These temperatures were chosen based on the findings of a prior investigation [8]. In each group, six temperatures were randomly tested in eight animals. Each animal received one implant in each tibia (Modus 2.0 3-mm cortical screws, Medartis AG, Basel, Switzerland). Therefore, for each temperature, eight test und eight control samples were investigated, resulting in 12 groups with 96 implants. All the procedures were performed under general anesthesia. At 30 min before the start of surgery, the animals were weighed. Then, the animals received 0.03-mg buprenorphine subcutaneously (0.1-ml Temgesic/kg of body weight, Remedix GmbH, Germany). Anesthesia was induced with an inhalation narcotic in an induction box with isoflurane (4 vol$.\%$) and oxygen (vol. $30\%$) air mixture (Isofluran, Piramal GmbH, Hallbergmoos, Germany). Inhalation narcosis with an isoflurane (2 vol$.\%$) and oxygen ($30\%$) air mixture was continued via a nasal mask. After the induction of anesthesia, the animals were positioned on an adjustable warming mat.
Before implant insertion, one tibia was randomly chosen as the test side in each animal. Without tempering, the contralateral side was used as the control group. Before temperature application and implantation, the skin was shaved, disinfected, and cut with a scalpel after sterile draping. Implantation was performed after exposure of the tibia by predrilling with a pilot drill of 1.5-mm diameter under strict cooling with sterile saline (Medartis AG, Basel, Switzerland). In the case of the test group, the thermal treatment was administered after predrilling and before implant placement. The device exhibited a congruent clamp fit in the drill studs. A screw was inserted in accordance with the manufacturer's protocol by using a screwdriver and a torque of 15–20 Ncm. After primary stable insertion of the implants, resonance frequency analysis with hand-screwed individual smart pegs (Osstell, Gothenburg, Sweden) was performed to measure the primary stability after implant insertion (Fig. 1). Primary stability was measured with the implant stability quotient (ISQ) in four directions (i.e., from left to right and from front to back [9]), resulting in a calculated mean ISQ value. The wounds were closed with suture material. By using a standardized 90° dental X-ray computed tomography device (Dentsply Detrey, Germany, Konstanz), the distance between the alveolar crest at the implant and the implant shoulder was measured directly after surgery on both implant sides [10]. The measurements were taken between the implant shoulder and the greatest coronal level of the direct bone-to-implant contact (Fig. 2). All the measurements were performed using specialized computer software (ImageJ Version 1.51, National Institutes of Health, USA) [11]. Every day postoperatively, the animals were treated once with carprofen 4 mg/kg subcutaneously (Rimadyl, Zoetis GmbH, Berlin, Germany) for analgesia. At the second follow-up, 7 days after surgery, ISQ evaluation and radiographic imaging were repeated. Fig. 1A After implant insertion, the ISQ values were measured for the first time. B The mini-screws used. For the ISQ values, threads were cut on the head of the screw. C The second implant stability measurement was performed after 7 daysFig. 2A In the case of the test group, the thermal treatment was administered after predrilling. The surface of the device individually tempered the bone surface B After the thermo-treatment, implantation was performed. C The radiographic evaluation was performed directly after the surgery and after 7 days. D A standardized radiographic measurement procedure was used to measure the crestal bone changes around the implant body The EDX and TEM analyses included six drill samples, one for each of the following temperatures: 4 °C, 3 °C, 2 °C, 48 °C, 49 °C, and 50 °C. The EDX analysis was performed using a scanning electron microscope (SEM; ESEM XL 30 FEG; FEI, Eindhoven, the Netherlands) equipped with an EDX detector system (EDAX, Mahwah, NJ) in the backscatter mode, with an acceleration voltage of 15 kV. The samples for the SEM and EDX analyses were fixed in $3\%$ glutaraldehyde in 0.1 M Sorensen’s phosphate buffer, dehydrated in an ascending ethanol series ($30\%$–$100\%$), and dried at 37 °C, in accordance with the procedure used in a previously published study [6]. The EDX analysis was performed with an EDAX Genesis system (EDAX), at eight measurement points using a mean value for each sample.
For TEM, the samples were fixed in $3\%$ glutaraldehyde in 0.1 M Sorensen’s phosphate buffer and decalcified in 0.5 M EDTA. After post-fixation in $1\%$ OsO4 (Roth, Karlsruhe, Germany) in a 25 mM sucrose buffer, the samples were dehydrated in an ascending ethanol series, incubated in propylene oxide (Serva, Heidelberg, Germany) and embedded in Epon resin (Serva). Ultrathin Sects. ( 70–100 nm) were cut and stained with $0.5\%$ uranyl acetate and $1\%$ lead citrate (both EMS, Munich, Germany) for contrast enhancement. The samples were viewed at an acceleration voltage of 60 kV by using a Zeiss Leo 906 TEM service (Carl Zeiss, Oberkochen, Germany).
## Statistical analyses
Analyses were performed using the Prism 8 software for Mac OS X (GraphPad, La Jolla, CA) running on Apple OS X. The variables were analyzed using the Kolmogorov–Smirnov normality test. The Kruskal–Wallis and Dunn’s multiple comparison tests with adjustment were used to identify differences between the parameters.
Post hoc power analysis was performed with the G*Power software (Heinrich-Heine-Universität, Düsseldorf, Germany) using the post hoc analysis of variance with groups to determine a power of $100\%$ (parameter primary study aim EDX) based on the total sample size of 30 and six groups using an effect size of 11.23 and an α of 0.05.
## Results
For the EDX analysis, one sample for each of the six temperature/time intervals was used in the evaluation (Table 1). A significant increase in bone calcium content was observed in the warm temperature groups (48 °C–50 °C, $p \leq 0.01$), which increased when the cold temperatures were reduced but without any significant difference within the group. By contrast, the carbon content decreased parallel to the increase in calcium content. The calcium-to-carbon ratio was used to normalize the calcium content to the organic component and therefore reflected the degree of bone calcification, which increased with increasing temperatures. Moreover, the sodium content increased significantly with the temperature stimulus but without significant differences between the cold and warm groups. Like the calcium content, the phosphate value analogously reflected increases in the values with increasing damaging temperature stimulus (3 °C–50 °C, $$p \leq 0.01$$ and 48 °C–50 °C, $p \leq 0.01$). The calcium-to-phosphate ratio increased with decreasing cold and increasing warm temperatures. The sulfur content decreased when the temperature was decreased from 4 °C to 2 °C and from 48 °C to 50 °C [12]. TEM revealed that at all the applied cold and warm temperatures, the osteocytes in the lacunae of the bone within the treatment area showed signs of cell damage, including vacuolization, shrinkage, and detachment from the surrounding bone matrix. Some cells became necrotic, leaving empty lacunae behind (Fig. 3). The TEM images of osteocytes showed that both heat and cold treatments induced cellular damage. The damage at hot temperatures (48 °C, 49 °C, and 50 °C) was greater than that at cold temperatures (2 °C, 3 °C, and 4 °C). At warm temperatures, the cell injury worsened with the increase in temperature applied such that 50 °C led to irreversible cell death. The degree of damage was greater at 50 °C and 2 °C than at 48 °C and 5 °C.Table 1Descriptive and statistical values for EDX analysis of bone composition between the groups4 °C3 °C2 °C48 °C49 °C50 °Cp ValueWeight %ElementMeanSDMeanSDMeanSDMeanSDMeanSDMeanSDCalcium2.272.334.636.2029.3335.710.550.2213.6310.0825.314.782°–48° $p \leq 0.013$°–50° $$p \leq 0.014$$°–50° $p \leq 0.0148$°–50° $p \leq 0.0148$°–49° $p \leq 0.01$Carbon86.344.1285.837.2957.5634.4889.902.4474.0513.0648.706.894°–49° $$p \leq 0.044$$°–50° $p \leq 0.013$°–49° $$p \leq 0.043$$°–50° $p \leq 0.01$Oxygen7.453.015.962.935.224.078.012.544.251.8612.635.462°–50° $$p \leq 0.0449$$°–50° $p \leq 0.01$Sodium0.320.150.300.110.640.550.330.070.480.190.690.253°–50° $$p \leq 0.01$$Phosphate2.092.822.332.766.515.790.430.347.094.1812.401.982°–48° $$p \leq 0.013$$°–50° $$p \leq 0.014$$°–50° $p \leq 0.0148$°–50° $p \leq 0.0148$°–49° $p \leq 0.01$Sulphur1.521.770.940.460.740.850.790.260.500.370.270.173°–50° $p \leq 0.014$°–50° $$p \leq 0.022$$°–48° $p \leq 0.01$RatiosCa/C0.030.050.320.010.180.5Ca/P1.091.983.141.271.922.04Fig. 3A TEM images of bone cells at cold temperatures. The osteocytes (O) in the lacunae (L) of the bone at 4 °C (A), 3 °C (B), and (C) 2 °C show acute damages. Some are necrotic with empty lacunae, whereas others vacuolized (arrowhead), shrank, and lost attachment (arrow) to the bone matrix (M). Each picture shows the most representative osteocyte of the respective sample. B TEM images of bone cells at warm temperatures. The osteocytes (O) in the lacunae (L) of the bone at 50 °C (A), (B) 49 °C, and (C) 48 °C show acute damages with necrosis, shrinkage, detachment, and vacuolization, similarly to those treated with cold temperatures. The degree of damage was worst at 50 °C, where almost all osteocytes were gone. Each image shows the most representative osteocyte of the respective sample The measurement of peri-implant bone loss showed an increase in the distance from the bone attachment at the implant to the shoulder with increasing temperature stimulus. However, these differences were insignificant ($p \leq 0.05$; Fig. 4A).Fig. 4A The distance of the peri-implant bone loss is shown for the test and control groups after the 7-day follow-up. Furthermore, the measurement after implant insertion is presented. B The implant stability (ISQ) values are presented for the test and control groups after implant insertion and the 7-day follow-up The mean ISQ values reflect the same stability values for the implant placement and the control group after 7 days. An inhomogeneous result was evaluated in the test groups, as the ISQ values at 4 °C–3 °C decreased and those at 3 °C–2 °C increased again ($p \leq 0.05$). On the other hand, decreasing stability values were observed at temperatures of 48 °C–50 °C, but the differences were not statistically significant (Fig. 4B).
## Discussion
The impact of targeted heating or cooling was evaluated in this rat study to obtain evidence of the behavior of peri-implant bone necrosis for inducing implant removal. Eriksson and Albrektsson investigated the effects of heat on bone metabolism and published their data. They reported that arterial and venous hyperemia could be observed at short high-temperature surges, with a partial halt (hemostasis) of blood flow in numerous capillaries [13]. However, after 3 weeks, bone resorption was observed [13].
Mineral bone composition can indicate bone damage. Decreased Ca/P ratio is associated with induced bone loss [14], whereas the Ca/C ratio indicates the degree of calcification of the bone matrix at different stages in the respective measure [15]. These study results showed that both the Ca/P and Ca/C ratios increased with the worsening of the temperature-induced bone damage. In the damaged bone matrix, the Ca/C ratio also increased [15]. The warm temperature groups showed a substantial increase in bone calcium content, which also increased when the cold temperatures were reduced, but no significant within-group difference was found. In our study, the carbon content reflected the organic compound of the bone and decreased parallel to the increase in calcium content. Sulfur content, which is also correlated to bone physiology, decreased when the temperature decreased from 4 °C to 2 °C and from 48 °C to 50 °C [12].
The TEM images in another study showed that a temperature input of 53 °C resulted in decalcification and enlarged mitochondria, which lost their inner cristae structure [8]. In our investigation, warm temperatures also led to cell injury and irreversible cell death at 50 °C. Furthermore, the first-choice imaging modality should always be conventional radiography, as it provides an overview of the architecture and pathological states of the bone and soft tissues of the region of interest [16]. The distance between the implant shoulder and the bone contact with the implant was measured in this study to gather data on the behavior of the peri-implant bone level around zirconia implants [10]. Higher temperatures [17] led to not only loss of bone contact but also increases in the sizes of the infrabony pockets next to the treated implant. An increased distance over time may indicate bone loss and implant loosening. However, this study showed no significant differences over the 7-day follow-up period. In another study, the peri-implant pockets were significantly larger than those in the control group at 50 °C for 1 min [17]. A more extended follow-up for this parameter seems advisable for future study protocols. Furthermore, CT or magnetic resonance imaging might be more sensitive, as the sensitivity of plain film for detecting early stages of bone necrosis is rather low at $41\%$ [18].
Oscillations occurring as reactions to the implant-to-bone contact can indicate the bone-to-implant contact interface from the resonance frequency analysis (RFA). The ISQ is the unit of measurement for RFA [19]. In humans with real implant geometries, the values range from 1 to 100, with a high ISQ score (> 60) indicating good stability [9]. In this study, the ISQ system was tested on miniature implants, as a suitable thread could be cut on the mini-screws. The aim was to determine whether such implant loosening can be detected in small animal models as well. As a critical reflection, we can infer that the ISQ model was established for real implants in humans. Therefore, we can assume that the stability values are fundamentally lower than those in humans owing to the smaller size. This was also shown in our evaluation and should be considered when interpreting our data. An inhomogeneous result was evaluated in the test groups; nevertheless, at temperatures ranging from 48 °C to 50 °C, decreasing stability values were found, but the differences were not significant. In the following pig study with real implants, based on these findings, the ISQ value measurement could be used to detect implant loosening.
Furthermore, the implants used in this study were not osseointegrated bodies. As a result, identifying valid threshold temperature values for osseointegrated implants necessitates in vivo research. With regard to implant survival there are no adverse results in diabetes type I patients [20]. However, in terms of possible complications, more postoperative bleeding and wound infections have been described. Regarding thermo-explantation, increased bleeding may affect the temperature level. As a successful thermo-explantation can only be confirmed in osseointegrated implants, this rat study was conducted to minimize the sample sizes in later animal studies using pigs with osseointegrated implants. As soon as the temperature time interval can be narrowed down, further studies will analyze the effect of different implant designs and shoulder formations on the described method [21]. If a special device can be developed, then for the current occasion a sufficient reduction of air contamination should also be considered [22].
## Conclusion
The cell damage at hot temperatures (48 °C, 49 °C, and 50 °C) was greater than that at cold temperatures (2 °C, 3 °C, and 4 °C). Although this was a preliminary study, the temperatures and intervals identified in areas of both heat and cold could help lower the number of samples in further studies of thermo-explantation. At a temperature of 50 °C at a time interval of 60 s, significant bone composition changes were observed. This level may be used for future thermo-explantation. The subsequent planned in vivo pig study, which will consider osseointegrated implants, is feasible.
## Supplementary information
Additional file 1.Additional file 2.
## Compliance with ethical standards
The study protocol received ethical approval from the appropriate local authority (Landesamt für Natur und Verbraucherschutz, Recklinghausen, Germany; Ref. 2019A276).
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|
---
title: 'Exploring effectiveness of CBT in obese patients with binge eating disorder:
personality functioning is associated with clinically significant change'
authors:
- Laura van Riel
- Elske van den Berg
- Marike Polak
- Marjolein Geerts
- Jaap Peen
- Theo Ingenhoven
- Jack Dekker
journal: BMC Psychiatry
year: 2023
pmcid: PMC9990274
doi: 10.1186/s12888-023-04626-x
license: CC BY 4.0
---
# Exploring effectiveness of CBT in obese patients with binge eating disorder: personality functioning is associated with clinically significant change
## Abstract
### Background
Binge eating disorder (BED), as the most prevalent eating disorder, is strongly related to obesity and other somatic and psychiatric morbidity. Despite evidence-based treatments a considerable number of BED patients fail to recover. There is preliminary evidence for the association between psychodynamic personality functioning and personality traits on treatment outcome. However, research is limited and results are still contradictory. Identifying variables associated with treatment outcome could improve treatment programs. The aim of the study was to explore whether personality functioning or personality traits are associated with Cognitive Behavioral Therapy (CBT) outcome in obese female patients with BED or subthreshold BED.
### Methods
Eating disorder symptoms and clinical variables were assessed in 168 obese female patients with DSM-5 BED or subthreshold BED, referred to a 6-month outpatient CBT program in a pre-post measurement design. Personality functioning was assessed by the Developmental Profile Inventory (DPI), personality traits by the Temperament and Character Inventory (TCI). Treatment outcome was assessed by the Eating Disorder Examination-Questionnaire (EDE-Q) global score and self-reported binge eating frequency. According to the criteria of clinical significance, 140 treatment completers were categorized in four outcome groups (recovered, improved, unchanged, deteriorated).
### Results
EDE-Q global scores, self-reported binge eating frequency and BMI significantly decreased during CBT, where $44.3\%$ of patients showed clinically significant change in EDE-Q global score. Treatment outcome groups showed significant overall differences on the DPI Resistance and Dependence scales and the aggregated ‘neurotic’ scale. Significant overall differences were found between groups on TCI Harm avoidance, although post hoc t-tests were non-significant. Furthermore, multiple logistic regression analysis, controlling for mild to moderate depressive disorder and TCI harm avoidance showed that ‘neurotic’ personality functioning was a significant negative predictor of clinically significant change.
### Conclusion
Maladaptive (‘neurotic’) personality functioning is significantly associated with a less favorable outcome after CBT in patients with binge eating. Moreover, ‘neurotic’ personality functioning is a predictor of clinically significant change. Assessment of personality functioning and personality traits could support indication for more specified or augmented care, tailored towards the patients’ individual strengths and vulnerabilities.
### Trial registration
This study protocol was retrospectively evaluated and approved on 16-06-2022 by the Medical Ethical Review Committee (METC) of the Amsterdam Medical Centre (AMC). Reference number W22_219#22.271.
## Background
With a $0.9\%$ lifetime prevalence Binge Eating Disorder (BED) is the most common eating disorder in adults in the general population [1]. In DSM-5 BED is characterized by recurrent, weekly episodes of uncontrollable overeating and significant distress, without compensatory behaviors as present in bulimia nervosa [2]. In ‘subthreshold BED’ all of the criteria for BED are met except that patients binge, on average, less than once a week or for less than 3 months. BED is significantly associated with obesity, major comorbid psychiatric disorders, numerous medical disorders [3–9] and significant psychosocial impairments [4, 6, 9]. Studies on efficacy showed that both cognitive behavioral therapy (CBT) and interpersonal psychotherapy (IPT), have short- and long-term benefits for BED [11]. However still about half of the patients fail to fully respond [4, 12–17]. Increasing our understanding of variables associated with treatment outcome can contribute to the development of more effective, personalized, therapy programs.
With this focus in mind, the complex relationship between BED and personality pathology has been a subject in research for years and different models provide complementary perspectives. The interpersonal model of BED [18] implies that impaired interpersonal functioning reinforces low self-esteem and negative affect, which in turn triggers binge eating as a dysfunctional strategy to cope with negative feelings [19–23]. A recent study confirms that patients with BED report higher levels of interpersonal problems, in particular greater submissiveness, as compared to both obese people without BED and normal weight controls [24]. In addition, patients with more severe eating pathology reported lower global self-esteem [25]. Attachment theory [26] takes a developmental perspective regarding eating disorder symptoms, positing that repeated interactions based on attachment behaviors with caregivers result in internal working models of the self and other and specific adult attachment styles [27]. Understanding binge eating from the perspective of attachment theory is consistent with the interpersonal model of binge eating. Within this frame of reference interpersonal problems, resulting from insecure attachment, lead to negative affect and increased use of maladaptive coping which in turn may trigger binge eating in BED [28]. Recent studies confirm that emotion dysregulation and maladaptive coping mediate the relationship between attachment anxiety and binge eating [29–33].
This integrative perspective is in line with the current hybrid DSM-5 Alternative Model of Personality Disorders (AMPD) in which psychodynamically informed levels of personality functioning -as reflected by concepts of self and interpersonal functioning- are combined with personality traits [2]. A recent study exploring personality functioning and personality traits in patients with BED showed that patients with BED or subthreshold BED presented more maladaptive and less adaptive psychodynamic personality functioning as well as impaired personality traits, as compared to obese and normal weight community controls [34].
In addition to studies investigating the associations between BED and personality pathology there is some evidence for the predictive value of low self-esteem [35, 36], interpersonal problems [37, 38] or pathological personality traits [39–41] in BED treatment outcome. However, the amount of studies regarding the associations between personality functioning as well as concomitant personality traits on BED treatment outcome is very limited. Yet, extending our knowledge about this relationship could contribute to selection of those patients who could benefit from specific psychotherapeutic treatment offers or a more personalized approach based on individual strengths and vulnerabilities.
The aim of the current study was to evaluate the outcome of BED treatment using the criteria of clinical significance. In addition, we aimed to explore the associations between CBT treatment outcome and impairments in personality functioning as well as pathological personality traits in female obese patients with BED or subthreshold BED. We hypothesize that impaired personality functioning and impaired personality traits are associated with a less favorable treatment outcome in this patients’ population with binge eating pathology regarding eating disorder behaviors and cognitions and specifically binge eating frequency.
## Participants
The study included 168 obese women (BMI ≥ 30 kg/m2) with BED or subthreshold BED, aged 18–68 years ($M = 41$, SD = 12.6), who were referred between 2014 and 2017 for CBT to Novarum, a specialist treatment centre for obesity and eating disorders in Amsterdam, the Netherlands.
Diagnostic assessment included an initial telephone screening on eating disorder symptoms and other psychiatric symptoms and, if applicable, retrievement of information about former psychological treatments. Subsequently, a clinical interview by either a licensed and trained psychologist or psychiatrist was conducted, in which the presence of BED or subthreshold BED was determined, as well as relevant psychiatric comorbidity. The assessment for eating disorders included a semi-structured diagnostic interview based on the SCID-I eating disorder module as part of the Structured Clinical Interview for DSM-IV, Patient Edition (SCID-P) [42] including specific questions regarding criteria for binge eating as established in the Eating Disorder Examination (EDE) interview [43]. The diagnostic formulation and proposed treatment options were then confirmed by a multidisciplinary team. As DSM-5 was introduced in the Netherlands on January 2017, the former DSM-IV diagnosis of all participants were revised (by the psychiatrist of the research team) using the DSM-5 criteria for BED and subthreshold BED (as described by the DSM-5 category Other Specified Feeding and Eating Disorders (OSFED)).
Patients engaging in compensatory bulimic behaviors like vomiting or laxative misuse were not eligible for this study, nor were patients who were currently in concurrent treatment for BED or weight loss programs. Other exclusion criteria were severe comorbid psychiatric disorders (e.g. psychotic disorders, severe mood disorders, suicidality or substance use disorders), mental retardation and current pregnancy.
A total of 168 patients participated in the study, 156 of which completed their treatment program. 12 patients dropped out of treatment because of current life events, patient’s feeling that either the therapy did not meet their expectation in relief from complaints or the unilateral belief that she had already improved sufficiently. In some cases, the therapist thought a comorbid disorder required attention first. Of the 156 treatment completers, 7 patients were excluded from analysis due to missing data on one of the primary outcome variables, the EDE-Q global score or EDE-Q self-reported binge eating frequency. Remaining patients with normative EDE-Q global scores both at admission and discharge ($$n = 9$$) were not included in the analyses since improvement for this group was not applicable, leaving 140 patients for further analyses.
This study protocol was approved by the Medical Ethical Review Committee (METC) of the Amsterdam Medical Centre (AMC). All participants gave informed written consent before enrolment and received 15 euro compensation for their willing participation.
## Treatment program
The treatment program was based on the principles of CBT-E, Cognitive Behavioral Therapy Enhanced for eating disorders [44, 45]. Patients were offered 20 sessions of CBT once a week, either in a group (maximum nine patients) or individual. 86 patients ($61.4\%$) received the group treatment, 54 patients ($38.6\%$) the individual treatment. Weekly psychomotor therapy was added to the group program. The maximum absence allowed was 3 out of 20 sessions. If the client missed 4 or more sessions, they were regarded as treatment non-completers. Alongside these main sessions, all patients attended at least one additional informative group meeting (90 min) with their partner or a close relative. The main objective of this meeting was to enhance mutual understanding and support during the process of change. The main goal of the treatment program was to regain control over binge eating, establish a regular eating pattern, develop a more realistic body image, decrease body shape dissatisfaction and diminish the influence of shape and weight on self-esteem. Sessions consisted of discussing daily self-monitoring of eating behaviors, psycho-education and cognitive therapy. In addition, weight was monitored weekly. Weight loss was not the primary incentive of this treatment, but prevention of weight gain was. The group treatment had a half-open group format: new patients could enter every 10th week. All sessions were led by a CBT trained psychologist or psychotherapist.
## Measures
Prior to the treatment participants completed a set of self-rating questionnaires to assess eating disorder pathology, personality functioning and personality traits. Eating disorder pathology was also administered post-treatment.
The Eating Disorder Examination Questionnaire (EDE-Q) [46] is a 36-item self-report based on the Eating Disorder Examination (EDE) [43]. The EDE-Q generates frequency ratings for key eating disorder behaviors (binge-eating, self-induced vomiting, laxative misuse, and excessive exercise), dietary restraint, and concerns about weight, shape and eating. Higher scores on the EDE-Q are indicative of more severe eating disorder pathology. The self-reported binge eating frequency was measured by the EDE-Q. A global score was calculated by summing up and averaging all attitudinal items, so that each item has equal weight [47]. Psychometric analyses showed the EDE-Q global score to be moderately accurate in discriminating obese individuals with BED from those without BED [47]. The EDE-Q presents strong psychometric properties in terms of validity, internal consistency and test-retest reliability [47–50]. The validated Dutch version of EDE-Q was used in the current study [51].
The Temperament and Character Inventory (TCI) is a 240-item self-report for personality traits based on Cloninger’s psychobiological model [52, 53]. The TCI includes four dimensional temperament scales (‘Novelty seeking’, ‘Harm avoidance’, ‘Reward dependence’ and ‘Persistence’) and three character scales (‘Self-directedness’, ‘Cooperativeness’ and ‘Self-transcendence’). The TCI, as composed of 240 true/false items, has proven good internal consistency and test-retest reliability[54]. The validated Dutch version was used in the current study [55].
The Developmental Profile Inventory (DPI) [56] identifies levels of personality functioning, by the psychodynamic and behavioral patterns of an individual’s current functioning. This self-report questionnaire, based on the framework of the Developmental Profile [57, 58, 59], is organized hierarchically by nine levels of psychodynamic personality functioning. The DPI offers a strength-weakness analysis that can be helpful for meaningful case formulation, indication for psychotherapeutic treatment and identifying the most relevant psychodynamic focus during psychotherapeutic treatment. The 108 items (statements which the patient describes as being more or less applicable to him/her on a four-point Likert scale) refer to psychodynamic patterns in three domains (Self, Interpersonal Functioning and Problem-Solving Strategies) and generate scores over the nine subsequent hierarchically-ordered developmental levels (three ‘Adaptive’ levels: Maturity/Generativity, Solidarity, Individuation; three ‘Neurotic’ levels: Rivalry, Resistance, Dependence; and three ‘Primitive” levels: Egocentricity, Fragmentation, Lack of Structure). The DPI has shown adequate psychometric properties in terms of reliability and validity [60]. Dutch and English versions of the manualized DPI are available (https://www.developmental-profile.nl).
Additionally, all participants were measured bare-footed with medical scales and a stadiometer, through which BMI (kg/m2) was calculated. In addition, all patients were systematically assessed with respect to sociodemographic characteristics (age, gender, marital status, and educational level).
## Statistical analysis
In order to examine the associations between treatment outcome and personality functioning or personality traits we employed the concept of clinically significant change as proposed by Jacobson and Truax [61, 62]. Clinically significant change is defined as improvement after treatment that has taken the patient from a score typical of a clinical population to a score typical of a normal functioning population [63]. To qualify as clinically significantly improved the patient has to (a) show a reliable change (statistically significant improvement) and (b) cross the cut-off point for a clinically significant change [62]. First, a reliable change index (RCI) was calculated for the EDE-Q global score, reflecting what is considered a statistically significant and reliable change. In line with Dingemans [64], based on the method as described by Hiller [65] and clinical and general population normative data as provided by Aardoom [47], we computed an RCI of 0.79 for the EDE-Q global scores. Next, a clinical cut-off point (CO) was determined based on Dutch normative data provided by Aardoom [47], where we computed an estimated CO of 2.17 for the EDE-Q global scores. By combining the EDE-Q global score RCI (0.79) and clinical CO (2.17) four treatment outcome groups based on their EDE-Q global scores were determined: Recovered: patients with both statistically significant improvement (RCI ≥ 0.79) and clinically significant recovery (CO < 2.17; i.e., symptoms within the range of the normal population sample after therapy).Reliably improved: statistically significant and reliable improvement (RCI ≥ 0.79) between pre- and post-measurement, but not recovered. Unchanged: patients without statistically reliable individual improvement (RCI < 0.79).Deteriorated: statistically significant worsening (RCI ≤ − 0.79) of patients.
To investigate differences between pre- and post-measurement for the different treatment outcome groups, paired t-tests were computed.
Subsequently, we analyzed the associations between personality functioning and treatment outcome as well as the associations between personality traits and treatment outcome, by analyzing differences between the outcome groups on personality functioning (DPI) variables and personality traits (TCI) with separate ANOVAs and post hoc t-tests with Bonferroni corrected p-values.
As a final step, we conducted logistic regression analysis to determine the independent contributors to the odds of recovery after treatment in the whole sample. Recovery (i.e. clinically significant change) is defined by the different outcome groups (1 = recovered, 0 = not recovered). The possible predictors of recovery, which were statistically significant in single logistic regression analysis, were entered as predictors in a multiple logistic regression model (enter). Predictors that were tested as covariates were age, educational level (dummy variables), binge eating episodes, BMI, BED vs. subthreshold BED diagnosis, type of treatment (group or individual), and co-morbid DSM-5 psychiatric disorders with prevalence in our sample suited for logistic regression analysis (i.e., expected frequencies > 5), namely mild to moderate depressive disorder (present, absent) and personality disorder (present, absent). Remaining predictors were the different DPI levels of personality functioning and the various subscales of the TCI. Odds ratios (OR) and $95\%$ confidence intervals ($95\%$ CI) were calculated for predictors. For the multiple logistic regression model a receiver operating characteristic (ROC) curve was plotted to determine the predictive value of the model. Furthermore, Nagelkerke R2 was reported as an indication of explained variance. In the multiple regression model, predictors were standardized to enhance comparison between predictors in terms of relative importance.
All analyses were executed in IBM SPSS Statistics (Version 28) with a significance level of $p \leq .05.$ Where parametric testing was used, distribution of variables was assessed. In case skewness or kurtosis devided by its standard error exceeded the cut-off of 1.96, a non-parametric counterpart of the test was conducted to confirm parametric results [66].
## Study sample
Mean age at baseline was 41 years (SD = 12.6, range 18–68) years. Mean pretreatment BMI was 39.25 kg/m2 (SD = 5.98, range 30.11–56.70 kg/m2). $10\%$ completed elementary school or lower vocational school, $42.9\%$ high school or vocational school and $47.1\%$ (under) graduate school. 89 ($63.3\%$) participants were diagnosed with BED, 51 ($36.4\%$) with subthreshold BED.
Prevalence of co-morbid DSM-5 psychiatric disorders was as follows: $14.3\%$ for mild to moderate major depressive disorder, $2.1\%$ for anxiety disorder, $2.1\%$ for ADHD, $3.6\%$ for PTSD, $2.1\%$ for other DSM-5 symptom disorders. $11.4\%$ met the criteria for personality disorders according to DSM-5 (Section II): borderline personality disorder $3.6\%$, obsessive compulsive personality disorder $1.4\%$, avoidant personality disorder $2.1\%$ and other specified personality disorder $4.3\%$.
## Baseline differences between patients with BED and those with subthreshold BED
Pre-treatment, patients with BED ($$n = 89$$) did not differ significantly from those with subthreshold BED ($$n = 51$$) with respect to age, educational level and BMI. Patients with BED were relatively more often assigned to group therapy ($68.5\%$) than to individual therapy ($31.5\%$) as compared to patients with subthreshold BED ($49\%$ vs. $51\%$). In line with the clinical criteria for distinguishing between BED and subthreshold BED, patients with BED scored significantly higher with respect to the EDE-Q global score and binge eating frequencies. Furthermore, patients with BED scored significantly higher on the DPI levels of personality functioning Lack of Structure, Fragmentation, Egocentricity and Rivalry and the aggregated developmental levels, ‘Primitive’, ‘Neurotic’ and overall ‘Maladaptive’ functioning than subthreshold BED patients. In addition, patients with BED scored significantly higher with respect to the TCI personality trait Reward dependency.
As patients with BED showed a more maladaptive personality functioning profile, we investigated BED as potential covariate in the analysis of treatment outcome and in the prediction of treatment outcome.
## Baseline differences between treatment completers and treatment drop-outs
Treatment completers ($$n = 140$$) did not differ significantly from treatment non-completers ($$n = 12$$) with respect to age, educational level, EDE-Q global score, binge eating frequencies, BMI, DPI levels of personality functioning and type of treatment (group or individual). However, some significant differences in TCI personality traits existed: treatment drop-outs had significantly higher scores on Harm avoidance ($d = 0.61$) and Novelty seeking ($d = 0.69$) than patients who completed treatment. For the following part, only analyses of treatment completers will be considered.
## Improvement after treatment
Table 1 shows overall differences pre-and post-treatment with respect to EDE-Q global scores, EDE-Q self-reported binge eating frequency and BMI. Paired t-tests showed that patients’ EDE-Q global scores as well as their self-reported binge eating frequency significantly decreased during treatment ($p \leq .001$) with a large effect size. Notably, also patients’ BMI scores significantly decreased during treatment ($p \leq .001$), however with a small effect size.
Table 1Pretreatment and post-treatment means (SD) for treatment completers ($$n = 140$$)Pretreatment scoresPost-treatment scoresM (SD)M (SD)Test statistics p Hedges’s gavEDE-Q global score3.64 (0.80)2.25 (1.16)t[139] = 13.75p <.0011.40EDE-Q OBE scale scores7.12 (9.34)1.49 (2.80)t[137] = 7.425p <.0010.80BMI39.90 (5.97)38.63 (5.80)t[138] = 4.35p <.0010.10Notes: EDE-Q, Eating Disorder Examination Questionnaire; BMI, body mass index; p = p value. Hedges’s gav effect size for change scores: 0.20 = small; 0.50 = medium; 0.8 = large [67]. Given positive skewness of distributions of difference scores for the above three outcome variables (skewness/SE > 1.96), Wilcoxon tests were computed to confirm parametric results. Non-parametric test statistics for the EDE-Q global scores, binge eating frequency and BMI were, respectively, $z = 9.51$, $p \leq .001$, $z = 7.36$, $p \leq .001$, $z = 3.78$, $p \leq .001.$ Pre- and post-treatment differences were significant when considered separately for patients in group and individual therapy, as well as for patients with BED and those with subthreshold BED, with the exception of a non-significant difference in BMI for the subthreshold BED patient group.
## Assessment of treatment outcome according to the criteria of clinical significance
Table 2 shows differences for the treatment outcome groups with respect to EDE global scores, self-reported binge eating frequency as well as BMI. Since there was only a small number of deteriorated patients ($$n = 3$$), this group was combined with the unchanged patients ($$n = 44$$). Based on the EDE-Q global change scores, patients were distributed as follows over the change categories: 62 ($44.3\%$) patients recovered, 31 ($22.1\%$) were reliably improved and 47 ($33.6\%$) patients either remained unchanged or deteriorated.
As to be expected, in the groups classified as recovered or reliably improved, there was a significant decrease in EDE-Q global score with large effect sizes.
Regarding self-reported binge eating frequency (EDE-Q), the results indicate that the recovered and reliably improved groups show a statistically significant decrease with large effect sizes. In the unchanged/deteriorated group the decrease in self-reported binge eating frequency is significant with small to medium effect sizes. Note that in the recovered group the post-treatment mean frequency of binges is near to 0 ($M = 0.50$).
Finally, BMI decreased significantly in the recovered and reliably improved group, although with small effect sizes, while the unchanged/deteriorated group did not show a significant change in BMI.
Table 2Pretreatment and post-treatment means (SD) for outcome groups based on the EDE-Q global scorePretreatment scoresPost-treatment scoresOutcome variablesM (SD)M (SD)Test statistics p Hedges’s gavRecovered ($$n = 62$$)EDE-Q global score3.51 (0.83)1.13 (0.55)t[61] = 20.24p <.0013.35EDE-Q OBE scale scores5.92 (7.37)0.50 (1.13)t[61] = 6.12p <.0011.02BMI39.02 (5.25)38.12 (4.95)t[60] = 3.54p <.0010.15Improved ($$n = 31$$)EDE-Q global score4.26 (0.55)2.96 (0.39)t[30] = 15.07p <.0012.63EDE-Q OBE scale scores10.60 (10.18)1.90 (2.90)t[29] = 4.96p <.0011.13BMI38.21 (6.69)37.28 (6.63)t[30] = 2.91p =.0070.14Unchanged/EDE-Q global score3.42 (0.71)3.26 (0.67)t[46] = 2.49p =.0160.22deteriorated ($$n = 47$$)EDE-Q OBE scale scores6.48 (10.72)2.54 (3.76)t[45] = 2.57p =.0130.48BMI40.39 (6.30)40.19 (6.01)t[46] = 0.90p =.3710.03Notes: EDE-Q, Eating Disorder Examination Questionnaire; BMI, body mass index; p = p value. Hedges’s gav effect size: 0.20 = small; 0.50 = medium; 0.8 = large [67]. Analyses excluding deteriorated patients ($$n = 3$$) result in equal results in terms of significance. Given occurrences of positive skewness of distributions of difference scores for the above three outcome variables (skewness/SE > 1.96), Wilcoxon tests were computed to confirm all parametric results. Non-parametric test statistics resulted in p-values of comparable size as reported above, with the exception of a clearly smaller p-value for differences in the EDE-Q binge eating frequency in the unchanged/deteriorated group, with $z = 2.63$, $$p \leq .008$$
## Explaining clinical improvement by personality functioning and personality traits
The associations between clinical improvement and personality functioning and personality traits were investigated by univariate analyses of group differences between outcome groups. In addition, logistic regression analyses were performed to identify predictors of clinical recovery.
## Univariate analyses of differences between outcome groups
For univariate analyses (ANOVA) of group differences with respect to pretreatment personality functioning (DPI) and personality traits (TCI), significant outcomes are presented in Table 3.
Table 3Significant levels of personality functioning and personality traits for outcome groups based on EDE-Q global scoresRecovered ($$n = 62$$)Improved ($$n = 31$$)Unchanged/ deteriorated ($$n = 47$$)Test statisticsM (SD)M (SD)M (SD) F † η2 ‡Post hoc t-test §DPI ‘neurotic’ scale37.17 (16.41)45.24 (11.10)44.34 (15.34)4.37*0.061 < 2,3*DPI Resistance13.40 (5.64)16.33 (4.32)15.32 (5.51)3.60*0.051 < 2*DPI Dependence13.82 (6.91)17.03 (5.53)17.72 (6.63)5.42**0.071 < 3*TCI Harm avoidance21.86 (6.58)24.58 (6.04)24.59 (6.38)3.22*0.05nsNotes: DPI, Developmental Profile Inventory; TCI, Temperament and Character Inventory; † F obtained by ANOVA; due to missing values, the degrees of freedom for the residuals of the model vary between 136[thus, F[2, 136]] and 137[thus, F[2,137]]; * = p ≤.05; ** = p ≤.01; *** = p ≤.001. ‡ Cohen’s [1992]: eta-squared: 0.02 = small; 0.13 = medium; 0.26 = large. § Post hoc test p-values reported only for significant F-tests. Where Post hoc tests were non-significant ns was reported. Analyses excluding deteriorated patients ($$n = 3$$) show equal results in terms of significance. As there were no significant between-group pretreatment differences in age, educational level, BMI or therapy type (group, individual) analyses were not adjusted for any of these potential covariates Regarding DPI personality functioning, ANOVA showed that for one out of two maladaptive aggregated developmental levels, namely ‘neurotic’ functioning, and two out of nine subsequent developmental levels, namely the maladaptive levels of Resistance and Dependence, there were overall significant differences between outcome groups. Regarding TCI personality traits, one out of seven trait scales, namely Harm avoidance showed significant differences between outcome groups.
To assess pairwise differences between outcome groups post hoc t-tests with Bonferroni corrected p-values were performed. Compared to the recovered group, both the improved and unchanged/deteriorated groups had significantly higher scores on the aggregated ‘neurotic’ scale (resp. $$p \leq .048$$ and $$p \leq .044$$). In addition, the improved group had significantly higher scores on the scale Resistance ($$p \leq .041$$) and the unchanged/deteriorated group had significantly higher scores on Dependence ($$p \leq .007$$). Effect sizes for these differences were between the boundaries of small to medium. Regarding TCI personality traits significant overall differences were found between groups on Harm avoidance, with the lowest mean scores in the recovered group, however these paired differences did not remain significant after Bonferroni correction for multiple testing.
## Predictors of recovery
In single logistic regression analyses, mild to moderate depressive disorder (OR = 0.20, $95\%$ CI [0.06, 0.62]), DPI resistance (OR = 0.92, $95\%$ CI [0.87, 0.97]), DPI dependence (OR = 0.92, $95\%$ CI [0.86, 0.98]), the aggregated DPI ‘neurotic’ scale (OR = 0.92, $95\%$ CI [0.86, 0.98]) and TCI harm avoidance (OR = 0.97, $95\%$ CI [0.94, 0.99]) were significant predictors of recovery after treatment. Odds ratios below 1 indicate that for patients with a mild to moderate depressive disorder and higher scores on the aforementioned DPI scales and TCI harm avoidance the odds of being recovered after treatment decrease.
The results of the multiple logistic regression analysis on recovery are presented in Table 4. The predictors entered in the model were mild to moderate depressive disorder, the aggregated DPI ‘neurotic’ scale and TCI Harm avoidance. Significant predictors of recovery in this model were having a mild to moderate depressive disorder (OR = 0.58, $95\%$ CI [0.37, 0.90]) and the aggregated DPI ‘neurotic’ scale (OR = 0.65, $95\%$ CI [0.42, 0.99]). The ROC curve showed a significant AUC of 0.65 ($95\%$ CI, 0.55–0.74, $$p \leq .003$$), indicating significant predictive value of the multiple logistic regression model.
Note that both DPI levels of Resistance and Dependence are part of the aggregated DPI ‘neurotic’ scale. The aggregated score was preferred as a predictor in the multiple regression model over both separate scales. In a multiple regression model including both separate DPI scales, these predictors were not significant due to their intercorrelation ($r = .69$).
Table 4Multiple logistic regression to predict recoveryVariables B SE (B)Waldp-valueExp(B)Exp(B) $95\%$ CIDepressive disorder-0.550.235.860.0150.580.37, 0.90DPI ‘neurotic’ scale-0.440.223.990.0460.650.42, 0.99TCI Harm avoidance-0.150.220.460.4980.860.57, 1.32Constant-3.050.192.640.1040.74Notes. Nagelkerke R2 = 0.16, Chi2[3] = 17.52, $p \leq .001.$ Predictor variables were standardized prior to the analysis
## Discussion
The aim of the study was to explore the association between levels of personality functioning and specific personality traits and CBT treatment outcome for female obese patients with BED or subthreshold BED. First, we found that CBT was effective in reducing binge eating pathology (as measured with the EDE-Q global score and self-reported binge eating frequency), and in reaching a significant, although small, weight loss. Second, impaired (‘neurotic’) levels of personality functioning, -that is the aggregated ‘neurotic’ scale and the levels of Dependence and Resistance-, as well as higher scores on TCI Harm avoidance were significantly associated with a less favorable outcome after CBT in patients with binge eating. However after correction for multiple testing paired differences for TCI Harm avoidance were not significant.
According to the criteria of clinically significant change in our study a substantial group of patients recovered ($44.3\%$) or improved ($22.1\%$), however a considerable number of patients remained unchanged or deteriorated ($33.6\%$). Recovery in our study also concerned a significant and clinically meaningful reduction in frequency of binge-eating episodes as the mean frequency of binges in the recovered group was decreased after therapy towards near zero ($M = 0.50$ per 28 days), which is even below frequency of binge eating in the general population [51]. Remission rates are a valuable indicator of treatment success in BED. We have aimed to obtain a robust measure of clinically meaningful change by classifying patients in the four different outcome groups based on their EDE-Q global score. Here, you could see the proportion of patients in the ‘recovered’ group (i.e., $44.3\%$) as an indication of the remission rate in our study. This is supported by the finding that for “self-reported 28-day frequency of objective binge eating episodes” the mean pretreatment score of 5.92 decreased to a mean post-treatment score of 0.5 (near zero).
In addition, recovered patients reached a significant, but small decrease in BMI, although weight loss was not a therapy target in itself. These results are in line with previous studies showing that psychotherapy, mostly CBT, had significant post-treatment effects on binge-eating episodes, with recovery rates ranging from 54 to $63\%$ [4, 12, 14–16, 68]. In addition, these studies reported improved eating disorder psychopathology as reflected in participants’ susceptibility to hunger, cognitive control over eating, and overall concerns about eating, shape and weight when compared to inactive control groups (typically wait-list), whereas effects on weight loss effects was either non-significant or minimal [4, 12, 14–16, 68].
Our second main finding was that recovery after CBT treatment was significantly associated with lower pretreatment scores on ‘neurotic’ personality functioning, (the DPI aggregated ‘neurotic’ scale and the developmental levels Resistance and Dependence). Furthermore, multiple logistic regression analysis, controlling for mild to moderate depressive disorder and TCI Harm avoidance showed that ‘neurotic’ personality functioning was a significant negative predictor of clinically significant change. Where for patients with higher scores on ‘Neurotic’ personality functioning the odds of being recovered after treatment decrease.
Maladaptive ‘neurotic’ personality functioning is characterized by problems with the self (e.g. low self-esteem) and problematic patterns in interpersonal contact, characterized by a lack of autonomy, and by (the avoidance of) conflicts. Our results are in line with previous studies regarding self-esteem in transdiagnostic eating disorders samples, demonstrating that higher scores on self-esteem contributed to a more positive EDE-Q outcome [69, 70]. However, other studies found contradictory results. Vall & Wade [71] report in a meta-analysis that higher self-esteem predicted better outcomes at follow-up, but not at the end of treatment, possibly due to the large differences in the effects reported across the three included studies. Cooper and coworkers report that patients with lower base-line self-esteem achieved a better outcome with CBT-E [72] and in study by Grilo and coworkers [73] self-esteem was not found to be a predictor nor a moderator of CBT outcome. With respect to interpersonal problems, previous studies suggested that a greater extent of interpersonal problems prior to treatment predict more eating disorder pathology at post-treatment in overweight patients with BED [37, 38, 71]. These findings are in line with our results as we found in univariate analyses that higher pretreatment scores on the DPI ‘neurotic’ levels of personality functioning, in particular Resistance and Dependence, were significantly associated with a less favorable outcome after CBT. This was supported by multiple logistic regression analysis, were both mild to moderate depressive disorder and ‘neurotic’ personality functioning were significant negative predictors of clinically significant change.
Regarding personality traits, the results of our study showed significant overall differences on Harm avoidance. In line with our results for DPI personality functioning, the groups with poorer outcomes showed higher pretreatment scores on Harm avoidance, although pairwise differences were not statistically significant after Bonferroni correction. Furthermore, in multiple logistic regression analysis, controlling for mild to moderate depressive disorder and neurotic personality functioning, Harm avoidance was not a significant predictor of clinical recovery. Contradictory to our results, previous studies among patients with eating disorders showed that low ‘Harm avoidance’ predicted a poor clinical outcome in patients with Anorexia Nervosa and Bulimia Nervosa [74] and high ‘Harm avoidance’ predicted favorable clinical changes after a six-month therapy of Brief Adlerian Psychodynamic Psychotherapy for patients with Anorexia Nervosa and Bulimia Nervosa [75]. However, no studies investigated the association between BED treatment outcome and TCI personality traits yet.
As we found that pre-treatment patients with BED had a more maladaptive personality functioning profile than patients with subthreshold BED, we explored BED as potential covariate. Importantly, additional analyses ruled out BED as potential covariate in the analysis of treatment effect and the prediction analysis. However, as the current study was not powered for the comparison of BED and subthreshold BED patients, additional studies, with larger group sizes are necessary. Future research should investigate the suggestion that patients with BED have a more maladaptive personality functioning profile and, subsequently, inform about the relation between those differences and clinical change after CBT.
## Strengths & limitations
A definite strength of this study is the naturalistic treatment setting and the inclusion of a heterogeneous sample including female obese patients with both BED or subthreshold BED and therefore its relevance in clinical practice. Of additional value is the relatively large number of treatment completers. Another potential strength of the study includes the utilization of both statistical and clinical significance methods to explore therapeutic outcomes, reflecting the proportion of patients who not only statistically, but clinically significantly improved, reflecting recovery or improvement after treatment in a meaningful way. In the current study, strong psychometric properties of the EDE-Q global scores warrant the use of the RCI as indicator of non-zero true score change, however McAleavey [2022] presents a discussion of alternative approaches that might be more suited in other contexts [76].
This study is differentiating in its approach studying psychodynamic personality functioning as well as personality traits, which is in line with the AMPD of DSM-5 [2]. Furthermore, the level of personality functioning and core psychodynamic impairments were assessed using the DPI, a dimensional approach that has not yet been used in previous studies of obesity and BED. Moreover, we assessed personality traits using the TCI, a reliable questionnaire for the assessment of relevant personality traits in subjects with and without specific psychopathology [52]. These findings subsequent former study results, showing that obese patients with BED or subthreshold BED presented more maladaptive and less adaptive psychodynamic personality functioning as well impaired personality traits reflected by higher Temperament and Character Inventory (TCI) Harm avoidance and lower Self-directedness as compared to non-bingeing obese controls and normal weight controls [34].
This study has several limitations. First, personality variables and eating disorder symptoms were evaluated by self-report questionnaires, instruments that tend to overestimate the prevalence of psychopathology [46]. Second, due to sample size limitations in the data-analyses we did not distinguish between patients who received group CBT and who received individual CBT. However, there were no differences at baseline in relevant sociodemographic or clinical variables between both treatment groups. Third, the fact that the sample consisted solely of female patients might be considered as a limitation as it may not reflect the associations between personality and outcome of CBT treatment in men. Fourth, the study includes only analyses of treatment completers. Participants dropping out of treatment had significantly higher scores on Harm avoidance and Novelty seeking than patients who completed treatment. Assuming that patients who drop out of treatment are more likely to remain unchanged or deteriorate after treatment, the inclusion of these drop-outs in our analysis would have likely amplified the group differences found for the trait Harm avoidance. Note that even though the amount of drop-out in the current study was low ($$n = 12$$, $7.1\%$), drawing conclusions from these findings is tentative. The absolute treatment effect should be interpreted with caution, as the estimates were not based on an intent-to-treat analysis. Fifth, the limited sample size may have reduced the power of the study, which might have affected statistical significance of results, in particular for tests of pairwise differences with Bonferroni correction. Finally, our results reflect short term post-treatment outcomes and may not be generalized to prediction of longer-term follow-up. Larger studies with long-term follow-up are needed to replicate and extend our findings.
## Future recommendations
This study showed associations between treatment outcome and personality characteristics, however association does not imply causation. Therefore, more studies should be performed to inform about the causal relationship between personality functioning and personality traits and treatment outcome in patients with BED or subthreshold BED. Within this frame of reference, measuring treatment outcome according to the concept of clinical significance can be of additional meaningful clinical relevance.
Subsequently, future studies should explore the effects of additional psychotherapeutic interventions targeting maladaptive neurotic personality functioning on BED treatment outcome. Such therapeutic programs or additional interventions should aim to improve feelings of low self-worth and lack of autonomy, as well as enhancing interpersonal skills by decreasing the tendency to engage in passive (aggressive) submission or dependent behavior. As suggested by other research [77], the knowledge generated by such studies may be used in developing more effective, specifically tailored treatments for those patients with BED who fail to improve by the current evidence-based treatment programs. Such a shift in eating disorder research and clinical practice, as suggested by Muzi and coworkers [78] could encourage practitioners to adapt psychotherapy interventions to suit the specific transdiagnostic characteristics (e.g., personality features) of individual patients, to better meet their needs and enhance their therapeutic outcome [78]. A dimensional approach in which the level of psychodynamic personality functioning and personality traits are considered, aimed to optimize indication for personalized psychotherapeutic treatment interventions, help to frame multidimensional and nuanced case conceptualization and constitute the essence of the AMPD in the DSM-5.
## Conclusion
Impaired (‘neurotic’) levels of personality functioning were significantly associated with a less favorable outcome after CBT (treatment) in patients with binge eating. Furthermore, ‘neurotic’ personality functioning in addition to and mild to moderate depressive disorder were identified as negative predictors of clinically significant change. This suggests that for patients with higher scores on ‘neurotic’ personality functioning the odds of being recovered after treatment decrease. This finding suggests that assessment of personality functioning in patients with BED or subthreshold BED could support indication for more specified or augmented care, tailored towards the patients’ personal strengths and vulnerabilities.
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---
title: 'Maternal dietary components in the development of gestational diabetes mellitus:
a systematic review of observational studies to timely promotion of health'
authors:
- Victoria Lambert
- Sonia Edith Muñoz
- Carla Gil
- María Dolores Román
journal: Nutrition Journal
year: 2023
pmcid: PMC9990275
doi: 10.1186/s12937-023-00846-9
license: CC BY 4.0
---
# Maternal dietary components in the development of gestational diabetes mellitus: a systematic review of observational studies to timely promotion of health
## Abstract
### Background
There is ample evidence that considers diet as an important factor in the prevention of gestational diabetes mellitus (GDM). The aim of this review is to synthesise the existing evidence on the relationship between GDM and maternal dietary components.
### Methods
We performed a systematic bibliographic search in Medline, Latin American and Caribbean Health Sciences Literature (Lilacs) and the Latin American Nutrition Archive (ALAN) of regional and local literature, limiting the searches to observational studies published between 2016 and 2022. Search terms related to nutrients, foods, dietary patterns and the relationship to GDM risk were used. The review included 44 articles, 12 of which were from America. The articles considered different topics about maternal dietary components as follows: 14 are about nutrient intake, 8 about food intake, 4 combined nutrient and food analysis and 18 about dietary patterns.
### Results
Iron, processed meat and a low carbohydrate diet were positively associated with GDM. Antioxidant nutrients, folic acid, fruits, vegetables, legumes and eggs were negatively associated with GDM. Generally, western dietary patterns increase GDM risk, and prudent dietary patterns or plant-based diets decrease the risk.
### Conclusions
Diet is considered one of the causes of GDM. However, there is no homogeneity in how people eat nor in how researchers assess diet in different contextual conditions of the world.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12937-023-00846-9.
## Introduction
Gestational Diabetes Mellitus (GDM) is one of the most common complications that occur during pregnancy [1], it affects approximately 5–$17\%$ of pregnancies worldwide and is becoming a public health problem due to the great burden of the disease and its increasing prevalence [2]. GDM can be defined as the alteration of glucose tolerance of variable severity that begins or is recognized for the first time during the current pregnancy [3, 4]. Generally, this resolves when the pregnancy ends, but it makes the woman prone to the development of premature labour, caesarean sections, hypertensive disorders, a new development of GDM in subsequent pregnancies, obesity and metabolic syndrome, and an increased risk of developing type 2 diabetes and cardiovascular diseases in the years following her pregnancy [3–6]. On the other hand, babies born to mothers with GDM are at increased risk of developing foetal hyperinsulinemia, neonatal hypoglycaemia, jaundice, being large for their gestational age, and developing obesity and type 2 diabetes later in life thus generating a cycle that favours metabolic dysfunction through the generations [6–8].
In the aetiology of GDM, various factors are identified that interact in a complex causal network. It is known that maternal age, pre-pregnancy overweight and obesity, excessive weight gain during pregnancy, sedentary lifestyle are risk factors for its development [9, 10]. Recently, the association between diet and GDM has been studied, but the evidence is still unclear. It is particularly noteworthy that diet before and during pregnancy is a potentially modifiable factor that can modulate the risk of GDM [11–15]. Likewise, it has been evidenced this pathology has a significant economic impact in all countries, health systems and individuals, especially those with low incomes [3, 4]. The available evidence on the diet-GDM relationship is still scarce in the major world regions [15–17]. Hence, the objective of this review is to synthesise the evidence between nutrients, food, dietary patterns and other features of diet and the risk to develop GDM considering regional differences in eating habits.
## Search strategy
A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, an updated guideline for reporting systematic reviews. In the search we introduced terms referring to the relationship between components of the mother’s diet and GDM. We performed a systematic bibliographic search in MEDLINE and The Cochrane library for international publications, and Latin American and Caribbean Health Sciences Literature (Lilacs) or the Latin American Nutrition Archive (ALAN) for regional and local literature, limiting the searches to observational studies published since 2016.
## Search terminology
The search terms included Medical Subject Headings (MESH) terms and keywords as “diabetes, gestational” AND “diet, western”, “feeding behavior”, “diet”, “food”, “food industry”, “food and beverages”, “eating”, “energy intake”, “nutrients”, “diet records”, “dietary pattern”, “maternal diet”, “food frequency questionnaire”, “zinc”, “mineral”, “vitamin”, “nutrition”, “fruits”, “vegetables”, “vitamin pattern”, “dietary intake”, “flavonoids”, “antioxidant”, “iron” OR “meat”, “fiber” OR “fibre”, “fat” OR “fatty acids”, “micronutrients” OR “macronutrients”, “carotenoid” OR “vitamin A” OR “carotene”, “vitamin C” OR “vitamin D” OR “folate” OR “vitamin b2” OR “vitamin b6”, “calcium” OR “potassium”. The search was limited to human observational studies published up to December 2022. Reference lists from relevant articles and reviews were manually searched for potentially relevant citations not detected by the electronic search.
## Selection criteria and data extraction
The selected studies met the following inclusion criteria: full text and original study; observational study design like cohort, case-control, cross-sectional in women of reproductive age; and studies whose objectives, methodological designs and results included the association between maternal dietary components before or during pregnancy and development of GDM.
Studies reporting on dietary supplements were excluded. Studies reporting on abnormal glucose tolerance but not on GDM, review papers, conference abstracts and intervention studies were not included. Studies examining eating disorders, perceptions, sensations, clinical trials and qualitative methodological studies were not considered. Titles, abstracts and full-text articles identified from the literature search were screened for eligibility against inclusion and exclusion criteria.
Data were extracted for the evaluation of the study from the authors, year of publication, study design, number of women, number of GDM cases, recruitment location and period, baseline age, exclusion criteria, dietary factors and assessment method, screening method and diagnostic criteria. Finally, information on the results of the study was extracted: mean, SD, SE, OR, or $95\%$ CI of maternal dietary components together with the number of women in each group, effect estimates, and $95\%$ CIs for associations between dietary factors and GDM and confounding factors used in the analyses.
## Risk of bias and quality assessment
Quality assessment of the studies included was independently performed by two researchers and discrepancies were resolved by discussion with a third reviewer. The Newcastle-Ottawa Scale was used to evaluate the quality of assessment of exposure and outcome variables of interest [18]. View Supplementary Information.
## Data synthesis and analysis
Search results indicating the significance and direction of the associations observed were qualitatively summarised in tables for each maternal dietary component by study design. Information on study characteristics was extracted to describe studies and populations.
## Study selection
The selection process of the articles included in the review is summarised in Fig. 1. At first, according to the search terms entered, the search returned a total of 701 articles, of which we found 394 in PubMed, 38 in The Cochrane Library and 269 in Lilacs. A total of 657 duplicate articles and articles that did not meet the inclusion criteria based on title and abstract were removed. Finally, the total number of articles included in the review was reduced to 44, 5 were published in Latin America, 7 in North America, 8 in Europe, 22 in Asia, 1 in Africa and 1 in Oceania. The maternal dietary components considered were macro or micronutrients intake (14 articles), food intake (8 articles), 4 articles combined nutrient and food analysis, and 18 dietary patterns. Of all the articles from Latin America, 2 deal with macro-and micronutrient intakes, 1 with food intake and 2 articles with dietary patterns. Most studies addressing food from a dietary pattern approach come from East Asia. Fig. 1Flow chart with selection criteria of articles included in the systematic review on the association between dietary factors and GDM at the international and regional levels
## Study characteristics
Study characteristics, including a number of subjects, study type, population characteristics, maternal dietary component analysis and its effect on GDM are described in Tables 1, 2, 3, 4 and 5 according to a regional location. Results were mostly prospective cohort studies (26 studies), 13 were case-control studies and 5 were cross-sectional. The majority of the cross-sectional studies were Latin American. All the reports included women aged between 19 and 45 years old who visited hospitals or healthcare centres. The maternal dietary components were analysed before and during pregnancy in 3 articles, 9 articles before pregnancy and 32 articles during pregnancy. Table 1Summary of results from Asian observational studies on diet and gestational diabetesPaperRegional locationNumber of subjectsStudy typePopulation characteristicsMaternal dietary componentEffects of diet on GDAsadi M, et al. 2019 [19]Iran- Asian:278 cases:130 controls:148case-controlwomen aged 19–40 years who came from six healthcare centres between 2014 and 2015pre-pregnancy dietary patternPrudent dietary pattern: Odds Ratio (OR) = 0.88, $95\%$ Confidence Interval (CI): 0.44–0.99, p-trend = 0,02Sedaghat F, et al. 2017 [20]Iran- Asian: 388 cases:122 controls:266case-controlwomen aged 18–40 years who visited major general hospitals 2009–2010pre-pregnancy dietary patternWestern dietary pattern: OR = 1.97, $95\%$ CI: 1.27–3,04Lamyian M, et al. 2017 [21]Iran- Asian: 1026cohortwomen aged 18–45 years who visited 5 universities of medical sciences’ hospitals 2010–2011pre-pregnancy fast food consumptionTotal fast food consumption: OR = 2.12 $95\%$ CI: 1.12–5.43, p-trend = 0.03; french fries: OR = 2.18 $95\%$ CI: 1.05–4.70, p-trend = 0.12Zamani B. et al. 2019 [22]Iran- Asian:460 cases:200 controls:260case-controlwomen aged 22–44 years who visited the nutrition clinic in AL-Zahra and Shahid Beheshti hospitals, Isfahan. Gestational age between 25 and 28 weekspregnancy plant-based diet (PDI)Higher PDI score: OR = 0.47; $95\%$ CI: 0.28–0.78, $$P \leq 0.004$$Zareei S. et al. 2018 [23]Iran - Asian: 204 cases:104 controls:100case-controlwomen who visited the maternity ward of Valiasr Hospital in Fasa Town, 2016. No data about gestational agepregnancy dietary patternUnhealthy dietary pattern: OR = 2.838 $95\%$ CI: 1.039–7.751, p-value: 0.042; healthy dietary pattern: OR = 0.284,$95\%$ CI:0.096–0.838, p-value: 0.023Du HY et al. 2017 [24]China - Asian: 753cohortwomen who visited the Maternal and Child Health Care Hospital in China, 2013–2014. Gestational age between 5 and 15 weeks.pregnancy dietary patternWestern pattern: OR = 4.40, $95\%$ CI: 1.58–12.22, p-trend: 0.004); traditional pattern: OR = 4.88, $95\%$ CI: 1.79–13.32, p-trend: 0.002)Chen Q. et al. 2020 [25]China - Asian: 9556 cases:1464 controls:8092case-controlwomen who visited the First Affiliated Hospital of Shanxi Medical University in China, 2012–2016.pre-pregnancy and pregnancy dietary patternVegetable pattern 1 year prior to conception OR = 0.94; $95\%$ CI: 0.89–0.99, p-trend: 0,025; first trimester of pregnancy OR = 0.94; $95\%$ CI: 0.89–0.99, p-trend: 0,018; second trimester of pregnancy OR = 0.91; $95\%$ CI: 0.86–0.96, p-trend: < 0.001Zhou X et al. 2018 [26]China - Asian:2755cohortpregnant women from the Tongji Maternal and Child Health Cohort. They have visited the maternity clinic in one of three public hospitals in Wuhan, China, since 2013–2016. Gestational age between 8 and 16 weeks.pregnancy dietary patternHigh Fish-meat-eggs scores (OR for quartile 4 vs. quartile 1 = 1.83; $95\%$ CI 1.21, 2.79; $$p \leq 0$$·007); high rice-wheat-fruits scores (OR for quartile 3 v. quartile 1 = 0.54; $95\%$ CI 0.36, 0.83; $$p \leq 0.010$$)Dong H et al. 2021 [27]China - Asian: 1455cohortwomen > 12 weeks gestations who visited the Sichuan Provincial Hospital for Women and Children, Southwest China, 2017pregnancy dietary patternOverall Low carbohydrate dietary (LCD) pattern RR = 1.24, $95\%$ CI 1.01–1.52, $$p \leq 0.026$$Chen Q. et al. 2019 [28]China, Asian: 9556 cases:1464 controls:8092case-controlwomen who visited the First Affiliated Hospital of Shanxi Medical University in China, 2013–2016.pre-pregnancy and pregnancy vitamin dietary patternVitamin dietary pattern 1 year prior to conception: OR = 0.90; $95\%$ CI: 0.85–0.95, p-trend: < 0.0001; first trimester of pregnancy OR = 0.90; $95\%$ CI: 0.86–0.95, p-trend: < 0.0001; second trimester of pregnancy OR = 0.90; $95\%$ CI: 0.85–0.95, p-trend: < 0.0001Liu C et al. 2020 [29]China - Asian: 3009cohortwomen who visited the First Affiliated Hospital of Shanxi Medical University in China, 2013–2016. Gestational age < 16 weeks.pregnancy vitamin C intakeAbove adequate dietary vitamin C intake OR = 0.68, $95\%$ CI: 0.49–0.95.Saraf-Bank S et al. 2018 [30]Iran - Asian: 463 cases:200 controls:263case-controlpregnant women aged 22–44 years who visited the Nutrition Clinic of Isfahan and Shahid Beheshti Hospital as well as Azzahra Hospital. Gestational age between 5 and 28 weeks.dietary acid intakeHighest tertile of potential renal acid load (PRAL): OR = 9.27; $95\%$ CI: 4.00–21.46, p-trend: < 0,001Parast VM et al. 2017 [31]Iran - Asian: 80 cases:40 controls:40case-controlpregnant women who visited the Department of Obstetrics and Gynecology of the Shahid Beheshti Hospital, 2016. Gestational age between 24 and 28 weeksantioxidant nutrients intakeTotal capacity antioxidant (TAC): OR 9.6; $95\%$ CI: 3.4–26.8); p value: < 0.001; intakes of vitamin E OR = 1.5; $95\%$ CI: 1.2–1.9; p value: < 0.001; intakes of selenium OR = 8.2; $95\%$ CI: 1.3–52.0; p value: 0.026; intakes of zinc OR = 1.7; $95\%$ CI: 1.2–2.5; p value: < 0.001Gao Q et al. 2019 [32]China- Asian: 1978cohortpregnant women with maternal age > 18 years, who visited three public hospitals in Wuhan, 2013–2016. Gestational age between 8 and 16 weeks.carotenoids and lycopene intakeHighest quartile of lycopene intake OR = 0.50; $95\%$ CI 0.29, 0.86; p-trend = 0·007)Kyozuka H et al. 2021 [33]Japan - Asian: 92764cohortJapanese women from the Japan Environment and Children’s Study (JECS), 2011–2014.pre-pregnancy antioxidant nutrients intakeQuintil 5 Selenium intake OR = 1.15, $95\%$ CI: 1.01–1.30; quintil 1 Se intake: OR: 1.19, $95\%$ CI: 1.01–1.41Daneshzad E et al. 2020 [34]Iran - Asian: 463 cases:200 controls:263case-controlpregnant women aged 22–44 years who visited the Nutrition Clinic of Isfahan, Iran. Gestational age between 25 and 28 weeks.antioxidants and Vitamin C intake3 tertil of FRAP (ferric reducing ability of plasma) OR = 0.26, $95\%$ CI: 0.16–0.42; p = < 0.0001Aljanahi A et al. 2020 [35]Saudi Arabia - Asian: 121 cases:72 controls:49case-controlpregnant women aged 19–45 years who visited the King Fahad University Hospital, Maternal and Children Hospital and Family Medicine of Imam Abdulrahman Bin Faisal University. No data about gestational age.vitamin D intake, dairy products and eggs consumptionVitamin Ddietary intake is higher among controls compared to cases (p-value: 0.021); vitamin C and eggs intake is higher among cases compared to controls ($$p \leq 0.004$$; $$p \leq 0.040$$); fortified orange juice OR = 3.2; $95\%$ CI: 1.2–8.8, p-value: 0.026; fortified yogurt OR = 3; $95\%$ CI: 1.1–8.6, p-value: 0.04; low-fat milk OR = 3.2; $95\%$ CI 1.3–7.7; p-value 0.01; full-fat milk OR = 0.4; $95\%$ CI: 0.2–0.8; p-value: 0.017Li H, et al. 2021 [36]China - Asian: 2987cohortwomen with a median age of 28.5 ± 3.6 years old. They were from Clinic of the General Hospital of Chinese People’s Armed Police Forces, 2013–2014. Gestational age between 13 and 28 weeks.fruits, vegetable and fruit juice intake during pregnancyNo association with total fruit and vegetable consumption. A higher quantity of grape, melon, potatoes and fruit juice were positively associated with GDM. A higher quantity of apple, orange and potatoes were negatively associated with GDM ($p \leq 0$,05).Yong HY, et al. 2021 [37]Malaysia - Asian: 452cohortWomen from three maternal child health (MCH) clinics. No data about agepre-pregnancy and pregnancy consumption of beveragehigher fruit juice intake before pregnancy: AOR = 0.98, $95\%$ CI = 0.97–0.99. In the first trimester: AOR = 0.92, $95\%$ CI = 0.89–0.98)A higher intake of cultured-milk drinks before pregnancy: AOR = 1.03, $95\%$ CI = 1.01–1.08. In the first trimester: AOR = 1.07, $95\%$ CI = 1.02–1.12.Liu YH et al. 2022 [38]China - Asiacases: 143 controls: 345case-controlno data availablepregnancy dietary patternsDietary pattern 2: OR = 2.96, $95\%$ CI: 0.939–9.356, $$P \leq 0.004$$Wang H et al. 2021 [39]China - Asian: 2099cohortpregnant women were part of the participants in the Tongji Maternal and Child Health Cohort (TMCHC) study, 2013–2016. Gestational age between 8 and 16 weeks.pregnant plant-based diet index (PDI)Highest quartile of PDI: OR 0.43; $95\%$ CI 0.24, 0.77; $$p \leq 0.005$$Zhang X et al. 2021 [40]China- Asian: 9317cohortwomen from public hospitals with obstetric services in South China, 2014–2017.pre-pregnancy and pregnancy dietary glycemic index, glycemic load and fiber intakeHighest tertile respect to lowest tertile Glycemic index pre-pregnancy: OR 1.12 ($95\%$ CI 1.03, 1.19) $$p \leq 0.01$$ 1st trimester: OR 1.25 ($95\%$ CI 1.20, 1.33) $$p \leq 0.008$$ 2nd trimester: OR 1.29 ($95\%$ CI 1.21, 1.48) $$p \leq 0.005$$ Glycemic load pre-pregnancy: OR 1.15 ($95\%$ CI 1.08, 1.23) $$p \leq 0.02$$ 1st trimester: OR 1.23 ($95\%$ CI 1.10, 1.45) $$p \leq 0.01$$ 2nd trimester: OR 1.25 ($95\%$ CI 1.11, 1.40) $$p \leq 0.01$$ Fiber intake pre-pregnancy: OR 0.89 ($95\%$ CI 0.83, 0.94) $$p \leq 0.03$$ 1st trimester: OR 0.83 ($95\%$ CI 0.75, 0.91) $$p \leq 0.01$$ 2nd trimester: OR 0.82 ($95\%$ CI 0.73, 0.91) $$p \leq 0.01$$Table 2Summary of results from European observational studies on diet and gestational diabetesPaperRegional locationNumber of subjectsStudy typePopulation characteristicsMaternal dietary componentEffects of diet on GDTryggvadottir EA, et al. 2016 [41]Iceland - Europan:168cohortwomen aged 18 to 40 years who visited the Prenatal Diagnosis Unit at the National University Hospital, 2012–2013. Gestational age between 19 and 24 weeks.pregnancy dietary patternPrudent dietary pattern: Odds Ratio = 0.54; $95\%$ Confidence Interval (CI): 0.30, 0.98; Prudent pattern in overweight/obese before pregnancy: OR = 0.38; $95\%$ CI 0.18–0.83Kozlowska A, et al. 2018 [42]Polish- Europan:113cross-sectionalwomen > 20 weeks of gestation who visited the Medical University of Warsaw, 2016–2018.vitamin and mineral pregnancy dietary intakeMean vitamin C intake was higher in controls than among cases (p-value: 0.04). Mean calcium intake was higher in controls than among cases (p-value: 0.01)Bartáková V, et al. 2018 [43]Czech Republic - Europen:363 cases:293 controls:70case-controlwomen aged 29–36 years who visited the Diabetes Centre of the University Hospital Brno. Gestational age between 24 and 30 weeks.pregnancy food intakeDairy products OR = 3.149; $95\%$ CI: 1.180–8.403, p-value: 0.022; goodies OR = 7.600; $95\%$ CI: 0.996–57.964, p-value: 0.050; sweet beverages OR = 10.510; $95\%$ CI: 1.395–79.173, p-value: 0.022Donazar-Ezcurra M, et al.2017 [44]Spanish - Europen: 3455cohortwomen prevenient of The SUN project cohort, 2013–2015pre-pregnancy dietary patternsWestern dietary pattern: OR = 1,56; $95\%$ CI 1,00- 2,43Mari Sanchis A et al. 2018 [45]Spanish - Europen: 3298cohortwomen prevenient of The SUN project cohort, 2012–2014pre-pregnancy meat and iron intakeTotal meat consumption: OR = 1.67; $95\%$ CI 1.06–2.63; p-trend 0.010; red meat consumption: OR = 2.37; $95\%$ CI 1.49–3.78: p-trend< 0.001; processed meat consumption: OR = 2.01; $95\%$ CI 1.26–3.21; p-trend 0.003Petry CJ, et al. 2019 [46]United Kingdom- Europen: 865cohortpregnant women of 12 weeks of gestation. Cambridge Baby Growth Study (CBGS) recruits.eggs consumptionEggs consumption was negatively associated with GDM ($$p \leq 0.03$$)Nicolì F, et al. 2021 [47]Italy - Europen: 376cohortWomen from the Diabetes Clinic of the University Hospital of Pisa, 2019. No data about age or gestational ageConsumption of non- nutritive-sweetened soft drinksNon-nutritive-sweetened soft drinks intake: OR 1.814; $95\%$ CI: 1.145–2.874; $$p \leq 0.011$$Yuste Gomez A et al. 2022 [48]Spain - Europen: 103cohortwomen over 16 years old from La Paz University Hospital, no data available about follow-up time. Gestational age < 16 weeks.pregnancy food intakeDifferences in white bread consumption among pregnant women who develop GDM and controls ($$p \leq 0$$,012)Table 3Summary of results from North American observational studies on diet and gestational diabetesPaperRegional locationNumber of subjectsStudy typePopulation characteristicsMaternal dietary componentEffects of diet on GDBao W et al. 2017 [49]USA- - North American: 15225cohortpregnant women aged 24–44 years from the Nurses’ Health Study II cohort, 1991–2001pre pregnancy vitamin D intakeNo associationLi M. et al. 2019 [50]USA - North American: 14553cohortpregnant women aged 24–44 years from the Nurses’ Health Study II, 1991–2001.pre pregnancy food folate intakeAdequate total folate intake (‡400 mg/day) RR = 0.83; $95\%$ CI 0.72–0,95, $$p \leq 0.007$$)Shin D. et al. 2015 [10]USA- North Américan:253cohortpregnant women (16–41 years) included in the National Health and Nutrition Examination Survey (NHANES) 2003–2012. Gestational age of 20 weeks.pregnancy dietary patterns“High refined grains, fats, oils and fruit juice” pattern: OR = 4.9; $95\%$ CI 1.4–17.0, p-trend: 0.007; “high nuts, seeds, fat and soybean; low milk and cheese” pattern: OR = 7.5; $95\%$ CI 1.8–32.3, p-trend: 0.009; “high added sugar and organ meats; low fruits, vegetables and seafood” pattern: OR = 22.3; $95\%$ CI 3.9–127.4, p-trend: < 0.0001Osorio-Yáñez Citlalli et al. 2017 [51]USA - North American: 3414cohortpregnant women < 20 weeks of gestation who attending prenatal care clinics affiliated with the Swedish Medical Center and Tacoma General Hospital in Seattle and Tacoma, 1996–2008calcium and dairy products intakeCalcium intake: RR = 0·58; $95\%$ CI 0·38–0·90; $$p \leq 0$$·015; low fat dairy product RR = 0,57; $95\%$ CI: 0,32–1,02 $$p \leq 0$$,032; whole grains RR: 0,61; $95\%$ CI: 0,39- 0,95, $$P \leq 0$$·019Darling AM et al. 2016 [52]USA, Canada - North American: 7229cohortpregnant women from the Slone Epidemiology Center Birth Defects Study, in the United States and Canada, 1998–2008pre-pregnancy iron intakePreconceptional dietary heme-iron 2.53; $95\%$ CI: 1.70–3.78, p-trend: 0.02; preconceptional dietary non-heme iron OR = 0.53; $95\%$ CI: 0.34–0.83, p-trend: 0.13Chen Z et al. 2021 [53]United States - American: 14926cohortWomen from the Nurses’ Health Study II, 1991–2001prepregnancy Plant-based diet index (PDI)PDI: Q5 compared with Q1: RR 0.70 $95\%$ CI 0.56–0.87 ptrend = 0.0004. hPDI: the RR 0.75 $95\%$ CI 0.59–0.94, ptrend = 0.009. uPDI was not associatedLindsay KL, et al. 2022 [54]United States - American: 7997cohortwomen > 13 years old who came from eight U.S. medical centers between 2010 and 2013.pregnancy and prepregnancy alternative healthy eating index (pAHEI) - 2010higher adherence to an alternative healthy index (pAHEI): aOR = 0.986 $95\%$ CI = 0.973–0.998 $$p \leq 0.022$$Table 4Summary of results from African and Oceanian observational studies on diet and gestational diabetesPaperRegional locationNumber of subjectsStudy typePopulation characteristicsMaternal dietary componentEffects of diet on GDLooman M et al. 2019 [55]Australia - Oceanian:3607cohortwomen aged 25–30 years from the prospective Australian Longitudinal Study on Women’s Health cohort, 2003–2015.pre-pregnancy dietary micronutrient adequacyHighest quartile of the Micronutrient Adequacy Ratio: RR = 0.61, $95\%$ CI 0.43–0.86, p-trend 0.01.Mahjoub F et al. 2021 [56]Tunisia - African: 120 cases:60 controls:60case-controlpregnant women aged 26–37 years from the National Institute of Nutrition, 2018. Gestational age between 24 and 32 weeks.nutrient intake and adherence to a Mediterranean diet during pregnancyVitamin D intake: OR = 0.29 [0.15–0.54], $P \leq 10$–3)Table 5Summary of results from Latinamerican observational studies on diet and gestational diabetesPaperRegional locationNumber of subjectsStudy typePopulation characteristicsMaternal dietary componentEfectos sobre resultados maternosSartorelli DS et al. 2019 [57]Brazil - Latin American: 785cross-sectionalwomen aged ≥20 years, pre-pregnancy body mass index (BMI) ≥ 20 kg/m2 recruited in five laboratories, 2011–2012. Gestational age between 24 to 39 weeks.pregnancy minimally processed foods and ultra-processed foods intakeNo associationSartorelli DS et al. 2019 [57]Brazil - Latin American: 785cross-sectionalwomen aged ≥20 years, pre-pregnancy body mass index (BMI) ≥ 20 kg/m2 recruited in five laboratories, 2011–2012. Gestational age between 24 to 39 weekspregnancy dietary patternsDietary pattern 1 (high rice, beans, and vegetables, with low full-fat dairy products, biscuits, and sweets) Odds Ratio (OR) = 0.58; $95\%$ Confindece Intervale (CI) 0.36–0.95; $$p \leq 0.03$$Balbi M et al. 2019 [58]Brazil - Latin American: 785cross-sectionalwomen aged ≥20 years, pre-pregnancy body mass index (BMI) ≥ 20 kg/m2 recruited in five laboratories, 2011–2012. Gestational age between 24 to 39 weekspregnancy flavonoids intakeNo associationNascimento GR et al. 2016 [59]Brazil - Latin American: 838cohortpregnant women from a prenatal health care clinic at the Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), 2011–2014. Gestational age between 15 to 20 weeks.pregnancy dietary patternsNo associationBarbieri P. et al. 2016 [60]Brazil - Latin American: 799cross-sectionalpregnant women > 24 weeks of gestation who receiving care at the Public Health System of Ribeirao Preto (SP), Brazil, 2011–2012.pregnancy dietary fat quality∑n-3 Polyunsaturated fatty acids intake (PUFA) intake: OR = 0.21; $95\%$ CI 0.08–0.56, $$p \leq 0$$,002; α-linolenic intake: OR: 0.15; $95\%$ CI: 0.05–0.42, p = < 0.0001; PUFA intake: OR = 0.45; $95\%$ CI: 0.24–0.85, $$p \leq 0.04$$
## Quality assessment
The quality assessment ratings and scores of the studies included were carried out according to the Newcastle – Ottawa quality assessment Scale (NOS). Two researchers evaluated quality studies and a third reviewer resolved discrepancies. The Newcastle-Ottawa Scale was adapted to specifically evaluate the quality of exposure and outcome variables of our interest. View Supplementary information.
## Association between maternal dietary components and GDM
Some reports have suggested that pre-pregnancy nutritional status and weight gain during pregnancy can modulate the development of GDM [6, 17]. In recent years, diet and healthy nutrition were priorities to prevent adverse events in maternal and child health by the Global Health Alliance in Preconception, Pregnancy and Postpartum (HiPPP) [61]. Increasing evidence suggests that an unbalanced pre-pregnancy and pregnancy diet can have a substantial impact on the health outcomes of women and children and the effects of foetal nutrition may persist into adulthood, with possible intergenerational effects [62–64]. Likewise, various international studies have confirmed the existence of an association between some components of the diet and the incidence of GDM [11, 65]. Below we describe the results obtained on the various ways of studying the components of the diet associated with the risk of developing GDM.
## Energy intake
Some authors support the idea that the development of GDM is not caused by dietary nutrients but by the excess of energy [6, 63], because energy intake is the main determinant of gestational weight gain [17]. Thus, Daneshzad E et al. [ 2020] found that total energy intake was higher in women with GDM than in women without the condition ($P \leq 0.05$) [34]. Tryggvadottir EA et al. [ 2015], who studied the GDM-energy relationship from the dietary pattern perspective in 168 pregnant women, reported that those women with obesity ingested more daily energy (2206 ± 535 kcal) than those with overweight (2108 ± 459 kcal) and with normal weight (2160 ± 400 kcal) although energy intake was not associated with GDM [41].
## Macronutrients
Five reports address the relationship between GDM and carbohydrate, fibre, protein and fatty acid intake. Daneshzad E et al. 2020 show lower intakes of carbohydrates in women with GDM with respect to women without GDM ($p \leq 0.05$) [34]. A study conducted in China analysed pre-pregnancy and pregnancy dietary glycemic index, glycemic load and fibre intake. Highest tertile respect to lowest tertile glycemic index and glycemic load were protective regarding GDM risk, while fibre intake was promotive ($p \leq 0.05$) [40]. On the other hand, Zhou X et al. 2018, showed that high fish-meat-eggs scores, which were positively related to protein intake and inversely related to carbohydrate intake, were in turn associated with a higher risk of GDM [OR for quartile (Q) 4 v. quartile (Q) 1: 1.83; $95\%$ CI 1.21, 2.79; P trend = 0.007]. In contrast, high rice-wheat-fruits scores, which were positively related to carbohydrate intake and inversely related to protein intake, were associated with a lower risk of GDM (adjusted OR for Q3 vs Q1: 0.54; $95\%$ CI 0.36, 0.83; P trend = 0.010) [26].
With regard to fatty acids, Barbieri P. et al. 2016 found an inverse association between the highest intakes of total n-3 fatty acid, acid alpha-linolenic acid, and GDM [60]. Similarly, in a case-control study in Tunez [56], monounsaturated fatty acids and saturated fatty acids consumptions were significantly higher in the control group (2.3 ± 0.8 vs 1.7 ± 0.7, $p \leq 0$,05).
## Micronutrients
Many studies examine the association between dietary micronutrients and adverse maternal outcomes but only some of them evaluate their relationship with GDM. Chen Q. et al. 2019 showed that the “vitamin” pattern (characterised as the consumption of a diet rich in vitamin A, carotene, vitamin B2, vitamin B6, vitamin C, dietary fibre, folate, calcium, and potassium) was positively associated with GDM. For every $25\%$ of the increase in the vitamin factor score during 1 year prior to conception and the first trimester, the GDM risk decreased by $9\%$ (OR: 0.91, $95\%$CI: 0.86–0.96) and by $10\%$ (OR: 0.90, $95\%$CI: 0.85–0.95) during the second trimester [28]. In this sense, in another study, women in the highest quartile of the prepregnancy micronutrient adequacy ratio (constructed by vitamin A, folate, niacin, riboflavin, thiamin, vitamin C, vitamin E, calcium, iron, potassium, zinc, phosphorus and magnesium) had a $39\%$ lower risk of developing GDM compared to women in the lowest quartile (RR 0.61, $95\%$ CI 0.43–0.86, p for trend 0.01) [55]. On the other side, micronutrients in isolation were analysed. Folic acid, antioxidant nutrients, calcium and Vit D showed a protective effect. Besides that, iron showed a promoter effect and evidence of selenium was inconsistent.
## Protective micronutrients: folic acid, antioxidants, calcium and vitamin D
Evidence on folic acid intake and GDM varies in the literature. One work showed that pre-pregnancy food folate intake was not associated with GDM risk (P trend = 0.66) while an inverse association was found between GDM and pre-pregnancy total supplement and food folate intake [50].
The association between dietary components with antioxidant action and the development of GDM has been studied to a greater extent than other nutrients. Vitamin C consumption could have a protective effect against GDM. A cohort study showed that pregnant women with dietary vitamin C intake above the recommended level (more than 200 mg/day) experienced lower odds of GDM (OR 0.68, $95\%$ CI: 0.49–0.95) than those with just an adequate intake (115–200 mg/day) [29]. A cross-sectional study observed that the mean vitamin C intake was significantly higher in the control group than in women with GDM [42]. Furthermore, a case-control study observed that intakes of vitamin C, vitamins B6 and A, selenium, and manganese were significantly lower in women with GDM ($P \leq 0.05$) [34]. In the same way, other studies analysed vitamin E, selenium, zinc, magnesium, potassium, lycopene and flavonoids intake. A case-control study showed consumption of vitamin E ($p \leq 0.001$), selenium ($p \leq 0.05$) and zinc ($p \leq 0.001$) were significantly lower in women with GDM as compared to healthy pregnant women [31]. Moreover, a cohort study found that women with lycopene intake in the highest quartile reduced $5\%$ the risk of GDM (OR 0·50; $95\%$ CI 0·29, 0·86; P for trend = 0·007) compared with the lowest quartile [32]. Also, a cohort study observed a high prevalence of inadequate dietary micronutrient consumption for magnesium ($52.5\%$), potassium ($63.8\%$) and vitamin E in pregnant women ($78.6\%$), however, it was not associated with the risk of GDM [55]. Nor did a cross-sectional study find any association between flavonoids intake and GDM but it showed a very low intake of flavonoids in pregnant women [58].
One cross-sectional, two cohort and two case-control studies evaluated a protective effect of calcium and Vitamin D intake against GDM too. Another cross-sectional study found the mean calcium intake was significantly higher in the control group than among the cases [42]. One cohort study showed that, although not significantly, calcium intake was inversely associated with the risk of GDM (RR = 0·58; $95\%$ CI 0·38, 0·90; $$P \leq 0$$·015). Besides, in those women who consumed less than 1200 mg/day, increasing dietary intake by 200 mg/day reduced the risk of GDM by $22\%$ (RR = 0.78; $95\%$ CI: 0.61–0.99; p value = 0.042) [51]. The other cohort found dietary vitamin D intake and total supplement and dietary vitamin D intake were inversely associated with risk of developing GDM, although it was not significant [49]. The last two case-control studies, when compared in terms of intake, women with GDM presented lower intake of vitamin D in relation to the controls (2.3 ± 2.1 μg / j vs. 6.3 ± 3.3 μg / j, $P \leq 10$–3) [35, 56].
## Promoter micronutrients: iron and selenium
Regarding to promoter micronutrients, two cohort studies positively associated pre-pregnancy heme iron intake with GDM (OR = 2.21 $95\%$ CI 1.37–3.58, p-trend 0.003) [45] (OR 1.55; $95\%$ CI 0.98, 2.46) [49]. On the other hand, preconception dietary non-heme iron was associated with a decreased risk of GDM (OR: 0.48; $95\%$ CI 0.28, 0.81) [52]. As regards to selenium, a cohort study showed that pregnant women with intakes in the highest quintile (OR: 1.15, $95\%$ CI: 1.01–1.30) and also those in the lowest one presented increased risks of GDM (OR: 1.19, $95\%$ CI: 1.01–1.41), using quintile 3 as the reference [33].
## Food and other dietary features
Three case-control studies, six cohorts and one cross-sectional study found an association between food or meals and the risk of developing GDM. The case-control studies evaluated adherence to dietary acid load (calculated using several nutrient intakes such as phosphorus, protein, calcium, magnesium and potassium) and the mediterranean diet (adherence to vegetables, fruits, legumes, cereals and bread, pasta, rice; fish and seafood; meat, poultry; dairy products; alcohol and ratio MUFAs/SFAs), food consumption and the asociation with GDM risk [30, 43, 60]. Women with higher scores of dietary acid load and a low mediterranean diet score were more likely to have GDM during pregnancy (OR = 9,27; $95\%$ CI: 4.00–21.46) [30, 56]. Also, women with GDM exhibited significantly more frequent poultry, pork and smoked meat, dairy products and sweet beverages consumption. Women with GDM consumed less fresh vegetables compared to controls [43]. Another cohort study shows a positive association between higher quantities of grape, melon, and fruit juice and GDM, and a negative association between higher quantities of apple, orange and potatoes ($p \leq 0$,05) [36]. Two cohort studies found an association between risk of GDM, egg and fast food consumption [21, 46]..A negative association was shown between the frequency of egg consumption and GDM [46]. On the other hand, total fast-food (OR 2.12; $95\%$ CI 1.12–5.43) and french fries consumption (OR 2.18; $95\%$ CI 1.05–4.70) was associated with higher risk of GDM [21]. In the last cohort study, a difference in white bread consumption between women with and without GDM was found ($$p \leq 0$$,012) [48] Finally, the cross-sectional study assessed the association between risk of GDM and the intake of minimally processed and ultra-processed foods in Brazilian women, but no association was found [66]. Women with GDM were consuming more eggs ($$p \leq 0.040$$). It was also found that full-fat milk was negatively associated with GDM and low-fat milk, fortified yoghurt, and fortified orange juice were positively associated with GDM ($p \leq 0.05$) [35]. Regarding the beverage intake, a higher fruit juice intake before pregnancy (AOR = 0.98, $95\%$ CI = 0.97–0.99) and in the first trimester (AOR = 0.92, $95\%$ CI = 0.89–0.98) had a lower GDM risk. On the other hand, a higher non-nutritive-sweetened soft drinks intake (OR 1.814; $95\%$ CI: 1.145–2.874; $$p \leq 0.011$$) [37], a higher intake of cultured-milk drinks before pregnancy (AOR = 1.03, $95\%$ CI = 1.01–1.08) and during first trimester (AOR = 1.07, $95\%$ CI = 1.02–1.12) had an increased GDM risk [47].
## Prepregnancy and pregnancy dietary patterns
Analysing a diet, the dietary pattern approach allows combining different dietary components (nutrients, foods, food groups) into a single measure of dietary exposure. It provides information about the nature, quality, quantity, proportions and frequency of consumption of different foods and beverages that are dominant in an individual’s diet [67, 68]. Dietary patterns can be influenced by food availability and socio-cultural factors [69]; therefore, it is worth analysing their regional variations because, principally in Asia, two different dietary patterns, prudent and western, during pre-pregnancy and pregnancy and GDM risk were described in the literature.
First, two case-control studies and two cohort study evaluated the association between pre pregnancy dietary patterns and GDM. Asadi et al. 2019 identified that prudent dietary pattern (higher intakes of fruits, low-fat dairy, potato, egg, fish, poultry, nuts, organs meat and red meat) was inversely associated with GDM risk (OR = 0.88, $95\%$ CI: 0.44–0.99), and the western dietary pattern (higher intakes of sugar-sweetened beverages, refined grain products, fast foods, salty snacks, sweets and biscuit, mayonnaise and saturated oils) was significantly associated with GDM risk [19]. Unlike these findings, Sedaghat F, et al. 2017 found an association between western dietary pattern (high in sweets, jams, mayonnaise, soft drinks, salty snacks, solid fat, high-fat dairy products, potatoes, organ meat, eggs, red meat, processed foods, tea, and coffee) and GDM before and after adjustment for confounders (OR = 1.97, $95\%$ CI: 1.27–3.04, OR = 1.68, $95\%$ CI: 1.04–2.27), but they did not find a significant association of GDM with the prudent pattern (higher intake of liquid oils, legumes, nuts and seeds, fruits and dried fruits, fish and poultry whole, and refined grains) and risk of GDM [20]. In the same way, in a cohort study Donazar-Ezcurra M, et al. 2017 identified two prepregnancy dietary patterns, a western dietary pattern (high consumption of meat-based products and processed foods) and the Mediterranean dietary pattern (high consumption of vegetables, fruits, fish and non-processed foods), similar to Iranian prudent patterns. They found a positive association in the multivariable model between the highest quartile of adherence to western dietary pattern and GDM compared with the lowest quartile (OR 1·56; $95\%$ CI 1·00, 2·43), however they did not find an association between the Mediterranean dietary pattern and GDM incidence (OR 1·08; $95\%$ CI 0·68, 1·70) for the highest quartile compared with the lowest [44].
Second, two cohort and two case-control studies evaluated prepregnancy and pregnancy dietary patterns and GDM risk association in Asia. Chinese women with adherence to a vegetable dietary pattern (consumption of green leafy vegetables, cabbages, carrots, tomatoes, eggplants, potatoes, mushrooms, peppers, bamboo shoots, agarics, and garlic and bean products) prior to conception (OR 0.94; $95\%$ CI, 0.89 to 0.99), during the first trimester (OR, 0.94; $95\%$ CI, 0.88 to 0.99) and during the second trimester of pregnancy (OR, 0.91; $95\%$ CI, 0.86 to 0.96) lowered the GDM risk [25]. In the same sample, it was determined that the adherence to a vitamin-nutrient pattern (high intake of dietary vitamin A, carotene, vitamin B2, vitamin B6, vitamin C, dietary fibre, folate, calcium, and potassium) 1 year prior to conception (OR: 0.91, $95\%$CI: 0.86–0.96), in the first trimester (OR: 0.91, $95\%$CI: 0.86–0.96) and the second trimester of pregnancy (OR: 0.90, $95\%$CI: 0.85–0.95) decreased GDM risk [28]. Also, a higher adherence to a plant-based diet index in North America, decreased GDM risk (OR 0.43; $95\%$ CI 0.24, 0.77; $$p \leq 0.005$$) [53]. In the same way an association between higher adherence to an alternative healthy index (pAHEI) and lower GDM risk was found (aOR = 0.986 $95\%$ CI = 0.973–0.998 $$p \leq 0.022$$) [54].
Lastly, adherence to a pregnancy dietary pattern and its association with GDM risk was a bit more studied than the pre-pregnancy dietary pattern. Three case-control studies and eight cohort studies were found. In the Iranian case-control studies, a plant-based diet index (PDI), and a healthy and unhealthy dietary pattern were identified. Zamani B. et al. 2019 showed that adherence to a high plant-based diet index score was inversely associated with risk of GDM (OR = 0.47; $95\%$ CI: 0.28–0.78, $$P \leq 0.004$$) [22]. An unhealthy dietary pattern (high intake of mayonnaise, soda, pizza and sugar) was associated with GDM (OR = 2.838,$95\%$ CI:1.039–7.751), and the adherence to a healthy dietary pattern (high intake of leafy green vegetables, fruits, poultry and fish) in the Q4 had $149\%$ higher chance not to develop GDM (OR = 0.284,$95\%$ CI:0.096–0.838) compared with the Q1 [23]. Similar results were found in a study that analysed association between overall PDI, healthy PDI and GDM risk in North America (RR 0.70 $95\%$CI 0.56–0.87 $$p \leq 0.0004$$; RR 0.75 $95\%$ CI 0.59–0.94 $$p \leq 0.009$$) during 2010–2013 [39]. In this sense, a European cohort study identified the prudent dietary pattern (positive factor loadings for seafood; eggs, vegetables, fruits and berries, vegetable oils, nuts and seeds, pasta, breakfast cereals, and coffee, tea and cocoa powder, and negative factor loadings for soft drinks and french fries) was associated with a lower risk of GDM (OR: 0.54; $95\%$ CI: 0.30, 0.98), even if they included only overweight and obese women (OR: 0.31; $95\%$ CI: 0.13, 0.75) [41].
In a USA cohort study, three dietary patterns associated with increased risk for GDM were identified, the “high refined grains, fats, oils and fruit juice” pattern (AOR 4.9; $95\%$ CI 1.4–17.0), “high nuts, seeds, fat and soybean; low milk and cheese” pattern (AOR 7.5; $95\%$ CI 1.8–32.3) and the “high added sugar and organ meats; low fruits, vegetables and seafood” pattern (AOR 22.3; $95\%$ CI 3.9–127.4) [10].
In China, Zhou X et al. 2018 showed that adherence to high fish–meat–eggs scores, which were positively related to protein intake and inversely related to carbohydrate intake, were associated with a higher risk of GDM (OR for Q4 v. Q1: 1·83; $95\%$ CI 1·21, 2·79; Ptrend = 0·007). On the other hand, high rice-wheat–fruits scores, which were positively related to carbohydrate intake and inversely related to protein intake, were associated with a lower risk of GDM (OR for Q3 v. Q1: 0.54; $95\%$ CI 0.36, 0.83; P trend = 0.010) [26]. In this sense, another cohort study found the adherence to a low carbohydrate diet (< 70 g/day) with high consumption of animal protein was associated with GDM risk [27]. Also In China, Du HY et al. 2017 identified four dietary patterns. Compared with the prudent pattern, the Western pattern and the traditional pattern were associated with an increased risk of GDM (OR = 4.40, $95\%$ CI: 1.58–12.22; OR = 4.88, $95\%$ CI: 1.79–13.32). Compared to the lowest quartile, Q3 of the western pattern scores and Q3-Q4 of the traditional pattern scores were associated with a higher risk of GDM [24]. Another study conducted by Liu YH et al. 2022 found relationship between homocysteine-related dietary patterns (positive factor loadings for wheaten food, livestock meat, eggs and negative factor loading for coarse cereals, green leafy vegetables, dried fungi and algae, milk group and nuts) and higher GDM risk (OR = 2.96, $95\%$ CI: 0.939–9.356, $$P \leq 0.004$$) [38]. In the last two studies realised in Brazil, Nascimento GR et al. 2016 did not find an association between dietary patterns during early pregnancy and GDM [59], but Sartorelli DS et al. 2019 showed dietary pattern 1 (high rice, beans, and vegetables, with low full-fat dairy products, biscuits, and sweets) was inversely associated with GDM (OR 0.58; $95\%$ CI 0.36–0.95; $$p \leq 0.03$$) [57].
Pre-pregnancy and pregnancy dietary patterns characterised by fruits, vegetables, whole grains, fish and dairy products had a protective effect against GDM risk. A dietary pattern characterised by refined grains, sugar, fats, meat, processed food and snacks was associated with a higher risk of GDM.
## Discussion
This systematic review found a positive association between iron, processed meat and a low carbohydrate diet and GDM risk. Antioxidant nutrients, folic acid, fresh and dried fruits, vegetables, legumes and eggs were negatively associated with GDM. Generally, western dietary patterns increase GDM risk, and prudent dietary patterns or plant-based diets decrease the risk. It appears that a high intake of saturated fats at the expense of decreased carbohydrate intake is associated with an increased risk of GDM. Studies in both, humans and experimental animals, suggest that the adaptive phenotypic response to low-carbohydrate intake is insulin resistance [70]. These mechanisms, in sensitive organisms like pregnant women, are increased with diet exposure especially during this period [71]. However, these mechanisms need to be studied in greater depth.
As we have described, there is ample evidence considering diet an important factor in the prevention of GDM [72]. In this regard, national and international groups have identified preconception and pregnancy as key opportunities in the life course for health promotion and disease prevention [16, 61]. However, the current evidence about which nutrients, foods and diet characteristics are associated with the risk of developing GDM is based on a limited number of studies that are heterogeneous in design, sample size, exposure and outcome measures, and in the populations involved. Also, dietary components have been analysed in isolation, in food-groups or in dietary patterns.
Diet study from a dietary pattern approach is necessary because it makes it possible to study the associations between diet and the health-disease process, and to prevent incorrect interpretations of the results due to the complex interactions between the numerous components of the diet [15, 69]. Also, this approach is the most comprehensive and their results are the clearest for the development of health promotion actions due to their ability to capture the variability of food intake in a population influenced, in turn, by food availability and sociocultural factors. This could have better results and lower costs on health policies and clinical practice in developing countries [4–6, 9].
Most of the studies have been carried out in Asia, particularly in China and Iran, whose populations have lifestyles different from those of western countries, in addition to having genetic and cultural peculiarities. Likewise, in Africa, Oceania and Latin America the relationship between GDM and diet were poorly described. In addition, the GDM prevalence has been little described around the world too. Only in 2019, did the International Diabetes Federation (IDF) unify prevalence of hyperglycemia but not GDM prevalence [4]. However, the prevalence of GDM is estimated to increase [1, 3, 4].
As a limitation of the review, we found differences between the studies in the diagnostic criteria of GDM. Besides, the instruments for food data collection were validated but differently in each study because some of them used a food frequency questionnaire and others used a 24-hour dietary recall. Likewise, those kinds of instruments have measurement errors by memory bias in collection and the sample could have selection biases because most of the study populations were not drawn from a random sample, but from regions, cities or ethnic groups, which may limit the generalisability of the results. Although observational studies provide weaker evidence than other study designs, we focused on their analysis in order to synthesise evidence from feasible studies that could be conducted even in less socio-economically developed countries [73].
The results of this review are consistent with dietary recommendations for women of reproductive age or during pregnancy commonly indicated by healthcare professionals. Likewise, habitually there are recommendations for weight gain and symptoms treatments during pregnancy [74] and there is consensus on dietary recommendations for its treatment. However, there is no consensus on dietary recommendations for the prevention of GDM. We know the importance of proper nutrition as a pillar in the treatment of GDM, but it is necessary to highlight its importance in early pregnancy and even before pregnancy, in healthy women or women with associated risk factors and thus improve the quality of life of women and their offspring [75, 76].
As a conclusion, we consider that the physiology of pregnancy is homogeneous for all healthy women regardless of their place of residence. However, some will develop GDM, and some will not. Diet is considered one of the causes of GDM. However, there is no homogeneity in how people eat nor in how researchers assess diet. In this paper, we sought to build an integrated panorama of how habitual diet affects the risk of GDM as evaluated in different contextual conditions of the world.
## Supplementary Information
Additional file 1. The Newcastle-Ottawa quality assessment scale (NOS). Supplementary Table 6. Quality assessment of case-control studies on maternal dietary components and gestational diabetes. Supplementary Table 7. Quality assessment of cohort studies on maternal dietary components and gestational diabetes.
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|
---
title: 'Healthcare providers’ knowledge, attitude, and practice on quality of nutrition
care in hospitals from a developing country: a multicenter experience'
authors:
- Muna Shakhshir
- Abdulsalam Alkaiyat
journal: Journal of Health, Population, and Nutrition
year: 2023
pmcid: PMC9990276
doi: 10.1186/s41043-023-00355-9
license: CC BY 4.0
---
# Healthcare providers’ knowledge, attitude, and practice on quality of nutrition care in hospitals from a developing country: a multicenter experience
## Abstract
### Background
Despite the fact that malnutrition can affect both recovery and outcome in acute care patients, little is known about malnutrition in Palestine, and even less is known about the assessment of malnutrition knowledge, attitudes, and practices (M-KAP) toward healthcare providers and nutrition care quality measures in hospitalized patients. Therefore, this study aimed to evaluate the M-KAP of physicians and nurses in routine clinical care and determine the influencing factors.
### Methods
From April 1 to June 31, 2019, cross-sectional research was performed at governmental ($$n = 5$$) and non-governmental ($$n = 4$$) hospitals in the North West Bank of Palestine. Data were collected using a structured self-administered questionnaire from physicians and nurses to collect information on knowledge, attitude, and practices related to malnutrition and nutrition care, alongside sociodemographic characteristics.
### Results
A total of 405 physicians and nurses were participated in the study. Only $56\%$ of participants strongly agreed that nutrition was important, only $27\%$ strongly agreed that there should be nutrition screening, only $25\%$ felt food helped with recovery, and around $12\%$ felt nutrition as part of their job. Approximately $70\%$ of participants said they should refer to a dietitian, but only $23\%$ knew how and only $13\%$ knew when. The median knowledge/attitude score was 71, with an IQR ranging from 65.00 to 75.00, and the median practice score was 15.00 with an IQR of 13.00–18.00. The mean knowledge attitude practice score was 85.62 out of 128 with SD (9.50). Respondents who worked in non-governmental hospitals showed higher practice scores ($p \leq 0.05$), while staff nurses and ICU workers showed the highest practice score ($p \leq 0.001$). Respondents with younger age categories, working in non-governmental hospitals in the ICU as practical and staff nurses, showed the highest KAP score ($p \leq 0.05$). Significance positive correlations were found between respondents’ knowledge/attitude and practice scores regarding the quality of nutrition care in hospitals ($r = 0.384$, p value < 0.05). In addition, the result also revealed that almost half of respondents believed that the most important barriers to inadequate intake of food at the bedside are related to food appearance, taste, and aroma of meals served ($58.0\%$).
### Conclusions
The research revealed that inadequate knowledge was perceived as a barrier to effective nutrition care to the patient. Many beliefs and attitudes do not always translate into practice. Although the M-KAP of physicians and nurses is lower than in some other countries/studies, it highlights a strong need for more nutrition professionals in the hospital and increasing nutrition education to improve nutrition care in hospitals in Palestine. Furthermore, establishing a nutrition task force in hospitals elaborated by dietitians as the unique nutrition care provider will assure to implementation of a standardized nutrition care process.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-023-00355-9.
## Background
Nutritional care is a multidisciplinary responsibility of hospital staff, including managerial level, and its integration within healthcare workforce activities is essential [1]. Therefore, the nutrition care process (NCP) is a significant issue to dietetics professionals, and there are rising needs for implementation across the globe [2]. NCP refers to any interactive step-by-step pathway undertaken by a health professional and documented in the medical record to promote a patient's food-related behavior and subsequent health outcomes. NCP can be considered a problem-solving method and a systematic approach to the foundation of medical nutrition therapy, which can screen, assess, diagnose, treat, and evaluate nutrition-related problems and malnutrition-related processes [3]. As a result, poor nutrition care can cause harm or has the potential to cause harm to patients including malnutrition.
Malnutrition is prevalent globally, considered a burden on patients, families, hospitals, and the healthcare system, including economic burden [4]. European Society Of Parenteral and Enteral Nutrition (ESPEN) defines malnutrition seen in hospitalized patients as a combination of cachexia (disease-related) and malnutrition (inadequate consumption of nutrients) as opposed to malnutrition alone [5]. Thus, the diagnosis of malnutrition is based on a combination of at least one phenotype criterion (i.e., unintentional weight loss, low BMI, or reduced muscle mass) and one etiology criterion (i.e., reduced food intake, malabsorption, or severe disease with inflammation) [6, 7].
To avoid malnutrition, all healthcare providers, including hospital management, must work as one team [8]. For example, physicians are responsible for writing admission orders based on the present patient's condition, including food. Furthermore, nurses are the direct care staff in hospital wards who have the most day-to-day contacts with patients, and they frequently perform initial nutrition screening [9]. As such, they play critical roles in the continuing identification of patients at risk of malnutrition due to inadequate food consumption and in the administration of nutrition-supportive therapies to patients on their wards [10].
Quality of nutrition care is lacking in Palestine and considered a widespread challenge as many hospitalized patients are treated for many medical problems while having their nutritional needs ignored. To the best of our knowledge, no data were found on the prevalence of malnutrition in hospitalized patients and there is no previous research related to nutrition care in Palestine. Planning and formulating strategies and interventions necessitate a thorough understanding of what healthcare professionals know and practice in routine nutritional care and what personal factors and barriers affect nutrition practice and attitude. Although malnutrition can affect recovery and outcome in acute care patients [11], little is known about malnutrition in in Palestine and they are limited to non-hospitalized patients [12, 13] and even less is known about healthcare providers’ knowledge, attitudes, and practices (M-KAP) toward nutrition care in hospitalized patients.
This study was designed to assess the KAP of physicians and nurses toward quality measures of nutrition care in hospitalized patients as a developmental approach to improve the nutrition process and promote nutrition care plans in Palestine. Physicians and nurses were selected because they are considered as the vast majority of hospital staff, and the first healthcare provider that the patient comes in contact with despite that nutrition care is a multidisciplinary responsibility [14]. Additionally, the current study highlights the reasons for inadequate nutrition in hospitalized patients and share concerns about the importance of developing and directing change management strategies in hospital settings to complete the integrated cycle of quality of health care provided. The present research explores, for the first time in Palestine, the effect of measuring M-KAP of hospital staff in routine clinical care, as it is a useful method to provide valuable input to improve awareness of hospital staff, define staff responsibility, promote nutrition, identify focus areas, provide feedback and direction to optimize the nutrition care process and quality of care strategies [15].
## Study design
This is a cross-sectional study. Data were collected between April 1, 2019, and June 31, 2019.
## Settings
This study collected data from physicians and nurses in two hospitals types: governmental ($$n = 5$$) and non-governmental ($$n = 4$$) hospitals in the North West Bank of Palestine.
## Sample size
Sample Size was calculated using the Raosoft sample size calculator. $5\%$ margin of error with $95\%$ confidence interval, $50\%$ response distribution, and a population of 19,000 were used. The sample size calculated was 377. Eligible participants were nurses and physicians with clinical roles and direct patient contact in an inpatient department of the selected hospitals.
Participants were told about the study after satisfying sample selection criteria, and those who agreed to participate voluntarily were included in the sample.
## Population
Four hundred and five nurses (practical nurses (who provide assistance to doctors or registered nurses), registered (who provide direct care to patients) and midwifery) and physicians (residents and specialists) from governmental and non-governmental hospitals in the North West- Bank-Palestine were selected by a convenience sample method. Subjects were recruited based on a nonrandom sample based on Fig. 1. Dietitians and ancillary services practitioners were excluded as too many questions were not relevant, and their results would not represent the general staff in the unit. In addition, dietitians would be aware and trained about nutrition care. Therefore, including their opinions may skew the data to have a more positive opinion about the importance of nutrition, compared to other staff. Fig. 1Selection and sample size The questionnaire was applicable for eligible participants from nurses and physicians with clinical roles and direct patient contact in any inpatient departments of the selected hospital in the North West Bank (Nablus, Tulkarem, Qalqelia, Jenin, and Tubas).
## Tool (data collection form)
A questionnaire, adapted from a previous study [15], was used after translating to the Arabic language and validated. The original tool was developed by the More-2-eat (M2E) project, which measured Malnutrition Knowledge, Attitude, and Practice (M-KAP). This tool was established in accordance with integrated nutrition pathway in a cute care (INPAC) to represent critical prevention, detection, and treatment efforts [15, 16]. *In* general, hospitals utilized this instrument as a baseline measure to highlight where they needed to enhance nutrition care and illustrate change over time when improvements were made [17–19].
In this study, no special permission was required from the developers to reuse any part of this questionnaire to measure nurses' and physicians' knowledge, attitude, and practice regarding malnutrition and nutrition care.
This questionnaire consisted of six parts:The first section included sociodemographic information such as the participant's age, gender, specialty, years of experience, type of hospital, and units they worked in. The second section consisted of 15 questions about the knowledge and perception of nurses and physicians concerning malnutrition and nutrition care. Scores were added from questions 1–15 to get the knowledge score (range 15–75).The third section consisted of 5 questions about attitude. Scores were added from questions 16–20 to get the attitude score (range 5–25). Scores were also added from questions 1–20 to get KA score (range 20–100).The fourth section consisted of 7 questions about nutrition care practice at the patient's bedside. In this section, scores were added from 21–27 to get the practice score (range 7–28).The fifth section investigated the perceptions regarding the most important reasons why patients may not eat in the hospital unit [10, 20]. Nine options were listed for the participants to choose from them. The sixth section investigated the perceptions regarding the most important reasons why patients may get insufficient nutrition support (tube feeding, artificial nutrition) [10, 20]. Again, nine options were listed for the participants to choose from them. The last sections (i.e., fifth and sixth sections) were shortened and slightly modified to make them relevant to Palestinian hospitals and staff. Concerning questions relating to the most important reasons why patients may not eat in the hospital unit and reasons why patients may get insufficient nutrition support, responses included “yes”, or “no”.
The questionnaire included five options for the knowledge and attitude part, ranging from "strongly disagree" to "strongly agree," as follows: strongly disagree = 1, somewhat disagree = 2, sometimes = 3, somewhat agree = 4, strongly agree = 5. The first, eighth, thirteenth, and fifteenth questions were reverse coded. Respondents had four alternatives in the practice section: "never," "sometimes," "often," and "always," with the practice score being "never = 1", "sometime = 2", "often = 3", and "always = 4". The overall KAP score was computed using questions 1–27 and the subscale total, with higher scores indicating more positive K, A, and P.
For the knowledge and attitude section, the questionnaire provided five choices ranging from 'strongly disagree' to 'strongly agree' as follows: strongly disagree = 1, somewhat disagree = 2, sometimes = 3, somewhat agree = 4, strongly agree = 5. Questions (1, 8, 13, and 15) were reverse coded. In the practice section, the respondents had 4 options: 'never'; 'sometimes'; 'often' and 'always', for the practice score: never = 1, sometimes = 2, often = 3, always = 4. While the total KAP score was obtained from questions 1–27, and the subscale total was calculated so that higher scores indicated more positive K, A, and P.
## Validity and reliability of the tool
The original form of the questionnaire was translated and back-translated following World Health Organization guidelines [21]. The Arabic version can be found at the end of this manuscript (Additional file 1).A focus group panel involved ten qualified nurses and physicians who were meeting the inclusion criteria, reviewed and evaluated the final questions' face and content validity, assessed the definition of wards, medical terminology, clear sequences of statements where the aim, objectives, and tool discussed. Before conducting the study, a modified questionnaire was tested with a pilot sample of five physicians and five nurses; data from the pilot sample were not included in the analysis. The questionnaire was revised for clarity and ease of use, and no changes were recommended. Cronbach alpha was used to check consistency between questions and was found to be accepted as follows: knowledge ($68\%$), attitude ($67\%$), practice ($80.5\%$), and KAP ($77\%$).
## Data collection procedure
The questionnaire was self-administered. Each survey took ten minutes to complete. The researchers gave the participants some background information about the research project, and where necessary, they explained some of the questions in the questionnaire. Participants were given a consent form that explained the objective of the study and guaranteed confidentiality. Participants have the option of participating or not.
## Ethical consideration and human subjects’ protection
Permission for the study was obtained from the Institutional Review Board (IRB) of An-Najah National University, Ministry of Health, and hospitals included in the study, and any other authorities concerned. All procedures were carried out in accordance with the Helsinki Declaration's ethical standards. Participants were informed about the purpose and benefits of the research. All data have been collected with confidentiality. The IRB authorized the research protocol (including the verbal consent process) and did not need written consent because the research has no harm or physical risk to participants. Everyone who took part was notified that their data will be coded and anonymized.
## Statistical analysis
The IBM SPSS software version 23 was used to enter, clean, manage, and analyze data. Frequencies and means were computed for categorical and continuous data. Descriptive statistics were employed to determine the response frequency and describe the sample. According to data normality, KAP was shown as the entire mean, whereas median was shown as an individual for K, A, and P. For numerical variables, data were given as mean and standard deviation (SD) or median and interquartile range (IQR), and for nominal variables, the frequency with percentages. The Kolmogorov–Smirnov test was used to ensure that all results were normal. The independent sample t-test and ANOVA were used for data with a normal distribution. In contrast, for data with a non-normal distribution, the Mann–Whitney U test and the Kruskal–Wallis H test were used. The Pearson correlation coefficient was used to look into the possibility of a relationship between two continuous variables (malnutrition knowledge, attitude, and practice scores). Staff roles, specialization, type of hospital, units, and years of experience were all expected to impact K, A, and P and therefore the KAP scores; thus, samples were spread among units to see whether there were any connections. As needed, statistical tests to assess relationships and significance were employed. Significant was defined as a p value of less than 0.05.
## Sociodemographic data
Demographic information for the sample is presented in Table 1. A total of four hundred and thirteen questionnaires were collected from the governmental 235 ($58.02\%$) and non-governmental hospitals ($41.98\%$) in seven of the hospital units: surgical, internal, pediatric, gynecology and obstetrics, intensive care unit (ICU), emergency and other departments as follows ($23.95\%$, $20.49\%$, $14.81\%$, $13.58\%$, $12.58\%$, $9.38\%$, $5.19\%$, respectively). Eight of the respondents were excluded from the results as they did not directly contact patients inwards. Respondents were mostly male ($60.00\%$). The age of respondents was equally distributed between 21 and 69 years old, the mean age of the respondents was 32.77 ± 9.09 years, and the median was 30 years with an interquartile range of 27.0–36.0.Table 1Demographics data of the sample ($$n = 405$$)VariableNumber (%)GenderMale243 (60.00)Female162 (40.00)Age categories (year) < 30194 (47.90)30–39135 (33.33)40–4945 (11.11)50–5924 (5.93) ≥ 606 (1.48)Type of hospitalNon-governmental170 (41.98)Governmental235(58.02)UnitsICU51(12.58)Surgical97(23.95)Internal83(20.49)Gynecology and obstetric55(13.58)Pediatric60(14.81)Emergency38(9.38)Others21 (5.19)Job titleResident physician109 (26.91)Specialist physician64 (15.80)Practical nurse49 (12.10)Staff nurse/registered nurse150 (37.04)Nurse-midwife33 (8.15)Contract typeFull time375 (92.59)Part-time30 (7.41)Years of experience < 389 (21.98)03-Oct202 (49.88) > 10114 (28.15) Physicians ($42.71\%$) and nurses (57. $29\%$) were asked to complete the survey. Two groups of physicians and three groups of nurses participated in the study: specialist physician ($15.80\%$), practical nurse ($12.10\%$), and nurse-midwife ($8.15\%$), where most respondents were from registered nurses ($37.04\%$) and resident physicians ($26.91\%$). The majority ($92.59\%$) was full-time contract type. Around half ($49.88\%$) of the respondents had job experience between three to ten years.
## Knowledge of nurses and physicians for malnutrition and nutrition care
The median knowledge score of nurses and physicians for malnutrition and nutrition care is 53.00 with an interquartile range of 49.00–57.00. Both age and hospital's units showed a significant association with knowledge ($p \leq 0.05$) (Table 2). On the other hand, there was no significant association between gender, type of hospital, job title, and years of experience. Respondents in surgical, internal, pediatric, and ICU reported better knowledge, in the previous order, more so than other hospital units. Respondents in young and middle adulthood showed good knowledge than older adulthood. Knowledge increased in critical care units ($p \leq 0.05$).Table 2Association between sociodemographic factors and all domains of malnutrition knowledge, attitudes, and practices (M-KAP)VariablesNumber (%)Knowledge score median (Q1–Q3)Attitude score median (Q1–Q3)Knowledge and attitude scorePractice score median (Q1–Q3)KAP scoreN = 405Median (Q1–Q3)Mean (SD)GenderMale243 ($60\%$)54 (49.00–57.00)$,a18 (16.00–19.00)$,a71 (66.00–75.00)$,a15 (13.00–17.00)$,a85.59 (9.72)$,bFemale162 ($40\%$)53 (49.00–56.00)18 (15.00–20.00)70 (65.00–75.00)15 (13.00–18.00)85.67 (9.18)Age categories < 30194 ($47.90\%$)54 (49.00–57.00)*,c18 (15.00–20.00) *,c71.00 (65.00–76.00) $,c15 (13.00–18.00) $,c86.10 (9.41) #,d30–39135 ($33.33\%$)53 (49.00–57.00)18 (15.00–19.00)70.00 (66.00–75.00)15 (13.00–18.00)85.85 (9. 23)40–4945 ($11.11\%$)54 (50.00–56.00)18 (17.00–20.00)72.00 (68.00–75.00)15 (13.50–16.50)86. 22 (8.56)50–5924 ($5.93\%$)49 (45.5–54.75)18 (16.00–19.00)67.50 (64.00–73.75)16 (13.00–17.00)82.16 (9.01) > 606 ($1.48\%$)43.5 (41.00–53.50)19 (14.25–19.25)62.50 (56.00–72.50)11 (5.25–16.75)73.83 (18. 23)Type of hospitalNon-governmental170 ($41.98\%$)54 (49.00–57.00) $,a18.50 (16.00–20.00) #,a71.50 (67.00–76. 25) *,a16 (13.00–19.00) *,a86.95 (9.84) *,bGovernmental235($58.02\%$)53 (49.00–56.00)17.00 (15.00–19.00)70.00 (65.00–75.00)15 (13.00–17.00)84.66 (9.15)UnitsICU51($12.58\%$)55 (50.00–59.00) #,c17 (15.00–19.00) $,c71 (68.00–77.00) #,c18 (15.00–20.00) #,c89.07 (9.45) #,dSurgical97($23.95\%$)54 (48.50–57.00)18 (16.00–20.00)71 (65.00–76.00)15 (13.00–17.00)85. 23 (8.74)Internal83($20.49\%$)54 (50.00–58.00)18 (16.00–20.00)73 (67.00–76.00)15 (13.00–17.00)87.06 (10.93)Gynecology & obstetric55($13.58\%$)51 (48.00–54.00)17 (14.00–19.00)68 (63.00–73.00)14 (10.00–16.00)81.30 (9.75)Pediatric60($14.81\%$)54 (50.00–58.00)18 (17.00–20.00)72 (67.00–76.00)15 (12.00–17.00)86.55 (8. 26)Emergency38($9.38\%$)51 (49.00–54.25)17 (16.00–19.00)68 (65.00–72. 25)15 (14.00–17.00)83.94 (6.61)Others21 ($5.19\%$)52 (50.00–55.50)17 (15.50–19.00)70 (66.50–73.00)16 (10.50–18.50)85.09 (10.30)Job titleResident physician109 ($26.91\%$)54 (49.00–57.00) $,c18 (16.00–19.00) $,c72 (67.00–75.00) $,c14 (12.00–16.00) #,c84.77 (8. 22) #,dSpecialist physician64 ($15.80\%$)52 (49.00–54.00)18 (16.00–19.00)70 (67.00–73.00)13 (11.00–15.00)82.84 (8.34)Practical nurse49 ($12.10\%$)53 (49.50–55.00)18 (15.00–20.00)70 (64.00–75.50)17 (15.00–20.00)87.08 (8.84)Staff nurse150 ($37.04\%$)54 (49.00–58.00)18 (15.00–20.00)71 (65.00–76.50)17 (14.00–19.00)87.62 (10.80)Nurse-midwife33 ($8.15\%$)52 (48.50–54.00)17 (13.50–19.00)69 (63.00–72.50)14 (12.00–17.00)82.57 (8.03)Contract typeFull time375 ($92.59\%$)53 (49.00–57.00) $,a18 (16.00–19.00) $,a71 (65.00–75.00) $,a15 (13.00–18.00) $,a85.50 (9.57) $,bPart time30 ($7.41\%$)54 (49.75–57. 25)18.50 (15.75–20.00)70 (66.75–76.00)16 (14.00–19.00)87. 20 (8.61)Years of experience < 389 ($21.98\%$)53 (49.00- 57.00) $,c18 (16.00–20.00) $,c70 (65.50–75.00) $,c14 (12.50–17.50) $,c85.43 (9.03) $,d03-Oct202 ($49.88\%$)54 (49.00–57.00)18 (15.00–19.00)71 (66.00–76.00)15 (13.00–18.00)85.85 (9.68) > 10114 ($28.15\%$)52 (49.00–56.00)18 (16.00–20.00)71(65.00–75.00)16 (13.50–17.50)85.37 (9.60)$, not Significant (p value ≥ 0.05); *, p value < 0.05; #, p value ≤ 0.01astatistical significance of differences calculated using the Mann–Whiney U testbstatistical significance of differences calculated using the independent sample t-testcstatistical significance of differences calculated using the Kruskal–Wallis testdstatistical significance of differences calculated using the one-way ANOVA In response to knowledge about malnutrition, almost half of those surveyed ($56\%$) strongly agreed that nutrition is important. The patient's weight should be taken on admission ($50.6\%$). In comparison, only a quarter of respondents ($26.9\%$) believed that patients should be screened for malnutrition on admission, and only $19.8\%$ believed that patient's weight should be taken on discharge. On the other hand, only $9.6\%$ strongly agreed, and $39.3\%$ somewhat agreed that malnutrition is a high priority in their hospitals; a quarter of respondents believed that malnutrition patients needed to follow up in the community after discharge ($23.2\%$), and they are highly needed to be given an adequate amount of food in the hospital to enhance recovery ($25.7\%$); Additional file 2: Table S1 summarizes participant’s knowledge responses in detail (Additional file 2: Table S1).
## Attitudes regarding malnutrition and nutrition care
The median attitudes score regarding malnutrition and nutrition care is 18.00 with an interquartile range of 16.00–20.00. A quarter of respondents perceived how to refer to a dietitian ($23.2\%$), but a minority of respondents knew when to refer ($13.1\%$) and when the patient was at risk or malnourished ($11.9\%$). $9.6\%$ of participants strongly indicated that they knew some strategies to support patients' food intake at mealtime, while $51.1\%$ agreed that they need more training to better support the patients' nutrition needs. Table S2 summarizes participant’s attitude responses in detail (Additional file 2: Table S2).
Table 2 shows that the only significant association was between attitude and type of hospital (Mann–Whitney U test, p-value < 0.05). Respondents who worked in non-governmental hospitals reported a better attitude (median = 18.50) than those in governmental hospitals (median = 17.00). Gender, age, specialty, units, years of experience, and contract type did not significantly affect attitude.
Taken together, the results on knowledge and attitude showed that the median KA score is 71 with an interquartile range of 65.00–75.00. Table 2 illustrates the significant association between knowledge/attitude and two of the demographics: Types of hospitals (Mann–Whitney, $p \leq 0.05$) and units (Kruskal–Wallis test, $p \leq 0.05$). Respondents who worked in non-governmental hospitals reported better knowledge/attitude (median = 71.50) than those in a governmental hospital (median = 70.00). Respondents who worked in internal (median = 73), pediatric (median = 72), ICU (median = 71), surgical (median = 71), reported higher knowledge/attitude level than those who worked in other departments, gynecology and emergency (median = 70, 68, and 68, respectively).
## Practices regarding malnutrition and nutrition care
The median practices score regarding malnutrition and nutrition care is 15.00 with an interquartile range of 13.00–18.00. Surprisingly, a minority of respondents always provide adequate nutrition care to the patient at the bedside during mealtime; the most striking observation to emerge is that only $14.6\%$ of responders have been realigned their tasks, so they do not give interruption the patient at meal time. On the other hand, $16.8\%$ of respondents check whether the patient has all that he needs to eat, only $8.4\%$ of respondents help a patient with opening food packages, and $9.9\%$ assist the patient in eating if he needs help, while almost $5\%$ visits and check patients during their mealtime to see how well they are eating and give encouragement to a patient's family to bring food from home for the patient is permitted. Only $7.7\%$ of the respondents provided malnourished patients nutrition education material on discharge. Additional file 2: Table S3 summarizes participant’s practice responses in detail (Additional file 2: Table S3).
The results, as shown in Table 2, indicate that the type of hospital was significantly associated with practice toward nutrition care at the bedside with $p \leq 0.05$ (Mann–Whitney U test) in addition to specialty and hospital's units were significantly associated with it (Kruskal–Wallis test, $p \leq 0.05$). Other demographics did not significantly associate with practices like gender, age, and years of experience. Higher practice on nutrition care was detected in non-governmental hospitals (median = 16) than governmental hospitals (median = 15). Staff and practical nurse participants reported higher practice than resident doctors, nurse midwives, and specialist doctors (median = 14, 14, and 13, respectively). ICU participants reported higher practice (median = 18) than other hospital units with significant differences.
## Knowledge, attitude, and practice (KAP) regarding malnutrition and nutrition care
Overall, these results indicate that the mean KAP score was 85.62 ± 9.50, with a minimum of 45 and a maximum of 113. Table 2 presents an overview of the statistically significant association between sociodemographic data and total KAP. No statistical difference has been shown between sexes, years of experience, and respondents' contract type. Table 2 illustrates that age, specialty, and units were significantly associated with knowledge, attitude, and practice toward nutrition care (one-way ANOVA, $p \leq 0.05$) in addition to the type of hospital was significantly associated with it (independent sample t-test, $p \leq 0.05$). Respondents from non-governmental hospitals reported higher scores (mean = 86.95) more than governmental hospital participants (mean = 84.66). Respondents in adulthood groups (< 30, 30–39, and 40–49 years old) reported higher KAP score (mean = 86.10, 85.85, and 86.22, respectively) more than older adulthood groups (50–59 and above 60 years old) (mean = 82.16, 73.83, respectively). Respondents in the ICU units reported higher KAP score (mean = 89.07) followed by internal unit (mean = 87.06), pediatric unit (mean = 86.55), and surgical unit (mean = 85.23), other departments (mean = 85.09), emergency (mean = 83.94), and gynecology and obstetrics unit (mean = 81.30). Staff and practical nurse participants reported higher KAP scores (mean = 87.62, 87.08) followed by resident doctors (mean = 84.77), specialist doctors (mean = 82.84), and nurse-midwife (mean = 82.57).
## The correlations between knowledge, attitude, and practice scores regarding the quality of nutrition care
Between respondents' knowledge and attitude scores, there was a significant moderate positive correlation ($r = 0.134$, $p \leq 0.001$). According to the findings, individuals with greater knowledge were more likely to affect nutrition care positively. There was a significant moderate positive correlation between respondents' knowledge and practice scores ($r = 0.273$, $p \leq 0.001$). These findings suggest that individuals with high understanding were more likely to conduct good nutrition care. Knowledge/attitude and practice had a modest but significant positive association ($r = 0.348$, $p \leq 0.001$). According to the findings, respondents with excellent knowledge/attitude were more likely to have good nutrition care practices. There was a modest but significant positive connection between respondents' attitudes and practice ratings for nutrition care ($r = 0.266$, $p \leq 0.001$), indicating that those with a positive attitude are more likely to have more practice (Table 3).Table 3Correlations between knowledge, attitude, and practiceCorrelationsPearson correlationp-valueKnowledge/attitude0.1340.007Knowledge/practice0. 2730.001Knowledge, attitude/practice0.3480.001Attitude/practice0. 2660.001
## Barriers to adequate in-hospital nutrition and nutrition support
The results also indicated that almost half of the respondents believe that the most important barriers to inadequate intake of food are related to food appearance, taste and aroma of meals served ($58.0\%$), patient medical condition ($56.3\%$), the temperature of meals ($55.6\%$), patients need an assistant at mealtime ($54.8\%$), interruption during the mealtime ($53.1\%$), patients are not well-positioned to eat ($48.4\%$), lack of documentation ($47.9\%$), and $38.0\%$ of respondents referred the reason to miscoordination of tray delivery between foodservice and nursing. However, the most surprising barrier was the indifference to having patients’ adequate food intake ($42.2\%$); (Fig. 2).Fig. 2The most important barriers why patients may not eat in hospital unit On the other hand, the research has touched on the reasons for insufficient nutrition support in hospitalized patients. The results indicated that most of the respondents believed that the most important reasons related to the technically difficult issues; for example, proximal GI obstruction, multiple upper abdominal operations and gastrectomy that may affect insertion ($83.0\%$), complications like catheter removal and hyperglycemia ($82.7\%$), unaware of the importance of nutrition ($82.5\%$), no clear definition of the job description ($80.5\%$), malnourished patients are not identified ($79.0\%$), lack of documentation ($78.3\%$), too expensive ($68.1\%$), indifference ($67.9\%$) and time-consuming ($66.4\%$); (Fig. 3).Fig. 3The most important barriers why patients may get insufficient nutrition support (tube feeding, artificial nutrition) Hospitalized patients, regardless of their BMI, may suffer from malnutrition because of reduced dietary intake due to illness-induced poor appetite, gastrointestinal symptoms, reduced ability to chew or swallow, or patients have missed meals due to interruptions or investigation, and nothing by mouth (NPO) status for diagnostic and therapeutic procedures [37]. On the other hand, adequate food and energy intake was an important factor determining LOS and patient clinical status [57, 58]. However, this was not always done in practice, and energy goals were frequently not met due to many barriers related to insufficient nutritional intake at the patient bedside. In this study, similar results have been found. For example, the lowest score was obtained for nutrition practice at the bedside ($55.32\%$) compared to knowledge and attitude scores ($71.8\%$, $68.2\%$), respectively. In contrast, many barriers affect sufficient dietary intake and nutrition support at the bedside. For example, the research revealed that the most important barriers to inadequate intake were related to food quality at the bedside, i.e., food appearance ($58.0\%$). In comparison, illness effects on food intake ($56.3\%$), patients were unable to open packages/unwrapping ($54.8\%$) and meals interrupted by staff ($53.1\%$). These results were equal to the most common barriers to insufficient food intake in the surgical and medical units of 18 Canadian hospitals of acute care but from patient’s point of view [11]. On the contrary, lack of awareness, lack of experience in critical care (technically difficult with too many complications), resource constraints such as time and money were the most common barriers for insufficient nutrition support, similar to a Canadian study in the ICU [59]. In addition, inadequate knowledge and confidence were seen as barriers to providing patients with appropriate nutrition treatment [60].
Results confirmed a high need for training courses to improve the knowledge and practice of nutrition care in hospitals as many beliefs and attitudes did not always translate into practice. In addition, low staff priority to nutrition care due to lack of time, many jobs to do, and the task are not relevant have been reported in much previous research and is highly needed for further study.
The absence of nutrition care has an impact on both patients and staff. Patients' nutritional requirements were ignored while being treated for medical problems. Furthermore, most patients are unaware of the critical role that good nutrition plays in their treatment and recovery from illness. Patients in need of nutrition therapy were unaware of the appropriate diet and texture of the provided food corresponding to their medical condition and the potential food–drug interactions that could jeopardize their medical status. As a result, dietary education and patient information should be given top importance in educational efforts at all levels. Unfortunately, only $7.7\%$ of responders in this research give nutrition instruction materials to malnourished patients. Even though a Cochrane review of 36 studies published in 2008 examined the evidence surrounding dietary advice and nutritional intake of adults with illness-related malnutrition, the findings compared a combination of dietary advice, dietary supplements, or no advice with outcome measures. They concluded that dietary advice with nutritional supplements might be more effective than advice alone or no advice [61].
## Discussion
This is the first study to offer some important information on malnutrition and the quality of nutrition care services for patients in hospital settings in Palestine. The patient's nutritional status is still not considered a medical priority despite numerous advances in clinical care. The importance and originality of this study, which evaluates the level of knowledge, attitude, and practices related to malnutrition and nutrition treatment among Palestinian nurses and physicians working in hospitals and see whether they were at acceptable levels. The findings of this work may contribute to the field of nutrition management systems in clinical care practice.
Unfortunately, as indicated in the literature review, no studies have been conducted to evaluate physicians' and nurses' knowledge, attitudes, and behaviors on nutrition treatment and malnutrition among hospitalized patients in Palestine. Moreover, nutrition care in hospitals has received little attention in Palestine due to the gradual effects of nutrition. Common barriers include lack of nutrition knowledge among healthcare providers, lack of clearly defined nutrition responsibilities in planning and managing nutrition care, and lack of nutrition specialists in hospitals. To date, four of the nine hospitals in this study do not include nutrition specialists among their staff. Furthermore, only one hospital from the ones mentioned above screened patients for any possible risk indicator of malnutrition.
Malnutrition is associated with negative outcomes for patients, including increased risk of immune suppression [22], higher infection and complicated rate, increased muscle loss [23], increased risk of pressure ulcer, and impaired wound healing [22], longer hospital stay, higher treatment costs and increased morbidity and mortality [22, 24–29]. To address hospital malnutrition, the More-2-Eat implementation project (M2E) has developed from the year 2015 to 2017 an evidence-based integrated nutrition pathway in a cute care (INPAC); to guide all healthcare staff in the prevention, detection, and treatment of malnutrition in medical and surgical patients and to support practice improvement from direct care staff to policy management level [16, 30]. Furthermore, meal service to the patient is an integral part of nutrition care. Improving meal intake and minimizing barriers to inadequate food intake are essential and relevant to the patient and hospital outcomes [10, 11, 20]. Therefore, poor nutrition care pathways can cause decreased patient satisfaction, which may, in turn, lead to decreased food consumption, unintended weight loss, and other complications [31, 32].
## Knowledge, attitude and practice of nurses and physicians concerning malnutrition and nutrition care
This study showed that the quality of nutrition care at hospitals is in the early stage; the result has shown that approximately half of the respondents ($56.0\%$) strongly agreed that nutrition is important for the patient's recovery and management of the disease. The result is lower than a similar study that reported that most respondents ($88\%$) strongly agreed that nutrition is important [15]. Practical nurses and other health professionals, including general practitioners, have shown similar views in other studies where they perceived that nutrition is important for chronic disease management and supported best practice guidelines (Australian Governmental Department of Health and Aging 2003) to improve nutrition care for the management of patients with chronic diseases [33–35].
Malnutrition is a common and highly prevalent condition among patients in acute hospital settings [36]. However, it continues to be an underdiagnosed and largely under-recognized health problem in many hospital settings [37–40]. This study confirms the previous findings since only $9.6\%$ of respondents strongly believed malnutrition is a high priority. Most respondents ($79.0\%$) said that a barrier to nutrition care was that malnourished patients were not identified. Screening all patients for malnutrition is essential to identify patients at risk of malnutrition and develop a care plan [41]. However, in this study, only $26.9\%$ strongly agreed that all patients should be screened; furthermore, only one of nine hospitals has a nutrition policy and screening tool for malnutrition. In comparison, half of the respondents ($50.6\%$) strongly agreed that patient's weight should be taken on admission, and only $19.8\%$ strongly agreed that patient's weight is necessary at discharge. Results are less than similar research that has been conducted in Canada, which reported the results with the above-mentioned dependent variables as follows ($20\%$, $49\%$, $69\%$, and $28\%$), respectively [15]. This might be due to lack of hospital nutrition policy, lack of nutrition knowledge, difficulty identifying patients at nutritional risk as supported by previous research [42], and the absence of dietitians in addition to evidence-based screening and assessment tools as a key first step for best practice in Palestine.
It is worth mentioning that respondents showed lower mean scores toward questions related to nutrition care responsibility than other related questions in the questionnaire. This finding contradicts the previous study, which has suggested that nutrition care is multidisciplinary responsibility [14]. Our study reported that $30.6\%$ of respondents believed that nutrition care is the only role of a dietitian, and most of them ($78\%$) believed that malnutrition patients should have an individualized treatment by a dietitian but only $23\%$ knew how and only $13\%$ knew when. Only $38.7\%$ of the respondents agreed that all staff involved in patient care could help set up the tray, and $45.75\%$ of the respondents agreed that they could provide hands-on assistance to eat when necessary. Some of the hospitals have a dietitian. This finding demonstrates the need for more dietitians/nutrition professionals in hospitals along with the need for education about how and when to refer to a dietitian.
In our study, the mean KAP score was 85.62 ± 9.50, with a minimum of 45 and a maximum of 113, which seems to be less than similar research, which found that the score was ($\frac{93.6}{128}$) (range: 51–124). This finding may be translated to a lower perceived and actual quality of nutrition care [15].
This research revealed a significantly meaningful positive correlation between nutrition knowledge, attitude, and practice regarding nutrition care in hospitals. The result is consistent with a previous Croatian study published in 2018 that showed a statistically significant difference in the median number of positive attitudes of general practitioners based on additional education in nutrition and the implementation of nutrition care practice in everyday work with patients [43]. The KAP described here are essential for providing successful nutritional care in malnourished patients, and improving these factors may result in improved patient outcomes. These results are in line with previous research which found that the KAP questionnaire significantly enhanced after the implementation of the malnutrition screening tool [19, 44, 45].
Even though the correlations between knowledge, attitude, and practice were all positive and statistically significant in this study, unfortunately, many beliefs and attitudes did not always translate into practice. For example, several studies in a systematic review study published in 2013 reported a conflict between nurses' theoretical recognition and actual implementation of nutrition guidelines [46, 47]. This study is consistent with previous studies [46, 47] that found most respondents ($76.1\%$) agreed that giving malnourished patients adequate food will enhance their recovery. However, only $4.9\%$ visit and check a patient during their mealtime to see how well they are eating. In addition, $60.5\%$ agreed that interruption during mealtime could negatively affect food intake, and only $14.6\%$ realign their task, so they do not interrupt a patient during mealtime.
Considering the nutrition field is an interesting and appreciated field in the hospital, the results confirmed that lack of nutrition knowledge is a barrier to insufficient and inappropriate nutritional practice. It was observed from several lines of evidence that increased knowledge level will lead to an increase in examined patients and detection of malnutrition [42, 48]. As a result, there is a high need for training courses to improve knowledge, attitudes, and practice regarding nutrition care in hospitals. Nurses were more likely to feel positive about nutrition care as a part of their responsibilities after receiving recent professional training in the field [49].
## Factors affecting knowledge, attitude, and practice
All nurse respondents were ward nurses rather than from the other nursing positions, and more than half of the 232 nurses were female ($56.4\%$). Thus, the results seem close to other research with a similar representation from academic and community hospitals [10].
A study has shown a significant relationship between age categories and knowledge and total KAP score, similar to other studies that found a significant relationship between nurses' age and level of nutrition knowledge. Those older nurses show higher average knowledge scores [50, 51]. In contrast, younger participants showed higher median and mean scores than the other older ones in this study. This could be due to the emerging higher education support system both at school and universities that shed light on the importance of nutrition care.
Types of hospitals in which respondents worked were not significantly associated with nutrition knowledge. This might be because all staff came almost from the same educational level. On the contrary, there was a significant association between types of hospital and attitude, knowledge/attitude, practice, and total KAP score. Non-governmental hospitals show better knowledge/attitude, practice, and total KAP score than governmental hospitals. This might be due to continuous training, dietitians being involved in nutrition care, and the presence of nutrition policy and available screening tools [19, 44, 45, 52].
In this study, there was a significant correlation between the respondents' units and the level of nutrition knowledge, knowledge/attitude, practice score, and total KAP scores. In addition, the ICU unit was obtained the highest mean and median score, similar to a study conducted in the Middle East, which revealed that ICU nurses scored higher than internal medicine nurses toward knowledge and perceived quality of nutrition care [53]. This might be due to nutrition self-course or awareness due to a sense of responsibility toward high-risk patients in the ICU. Therefore, their nutrition status is heavily dependent on what the healthcare providers know and behave to achieve a higher level of nutrition care [54, 55].
It is worth mentioning that a significant relationship was found between the specialty of the respondents and practice in addition to the total KAP score. Practical and staff nurses showed a higher score than the physician did. This result verifies previous findings that ensuring optimal nutrition care depends heavily on nurses who play a pivotal role in ensuring adequate nutritional care is delivered to the patient at the bedside [53].
On the other hand, there was no significant difference in total KAP score for years of practice, similar to previous findings [15, 48, 56]. Furthermore, it was reported that no significant difference between years of nursing experience and clinical nutrition knowledge ($$p \leq 0.827$$). This may confirm that education is better than clinical experience in the case of nutrition care.
## Strengths and limitations
This study is the first in Palestine to evaluate knowledge, attitudes, and practice levels regarding nutrition care for healthcare providers in hospital settings. It shed light on the importance of a standardized nutrition care process to manage malnutrition and increase the quality of nutrition care. In addition, the diversity of respondents included different healthcare sectors.
The most important limitation lies in the fact that the data were obtained through a self-administered questionnaire. The respondents may react to being well educated, and the work environment is well suited to nutrition care. Therefore, results could overestimate the attitude and practice score due to recall bias. It is worth mentioning that the questionnaire asked questions related to perceptions of nurses and physicians and self-perceived attitudes and practices and may not be representative of what occurs in real life, the actual barriers, or their significance. In addition, the analyzed results from the snapshot timing may not be representative. To investigate the effect of healthcare provider training and education on nutritional status, attitudes and behaviors needed to be analyzed over time [62, 63]. The convenience of sample methods may have limited the generalizability of the current study.
## Conclusions
The main goal of the current study was to evaluate knowledge, attitude, and practices regarding malnutrition and quality of care in addition to the most important staff perceptions of patient barriers to food intake and/or insufficient nutrition support in hospital settings, North Palestine. This study showed that the respondents generally had low nutritional KAP scores. Inadequate knowledge was perceived to be a barrier to effective nutrition care to the patient. In addition, many beliefs and attitudes do not always translate into practice. Therefore, barriers to effective nutrition care must be followed by the administration managers. It is recommended that hospitals establish a nutrition task force based on NCP or INPAC pathway that can engage and improve the nutrition care process for patients during their stay from admission to discharge [10, 20, 64–66]. Availability of high-quality documentation of the nutrition care process is essential from the moment of the patient's admission to the ward to the time of discharge, especially since recognizing malnutrition in hospitalized patients is not often a priority in clinical practice in Palestine. Additionally, nutrition knowledge is necessary to improve nutrition practice, but nutrition knowledge seems insufficient to change practice in routine clinical care. Furthermore, encouraging changes in the system to increase nutrition education among hospital staff, encourage implementation of nutrition screening tools, and increase the presence of dietitians in the hospitals.
## Supplementary Information
Additional file 1. The Arabic version of Knowledge, Attitude and Practice scale for measuring Quality of Nutrition Care in Hospitals. Additional file 2. Table S1: Distribution of responses to each knowledge question with a five-point Likert scale ranked from 1 to 5 (Strongly disagree, disagree, not sure, agree, and strongly agree). Table S2: Distribution of responses to each attitude question with a five-point Likert scale ranked from 1 to 5 (Strongly disagree, disagree, not sure, agree, and strongly agree). Table S3: Distribution of responses to each practice question with a five-point Likert scale ranked from 1 to 4 (Never, sometimes, often, and always).
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|
---
title: 'The relationship between diet quality indices and odds of breast cancer in
women: a case–control study'
authors:
- Mohammad Hassan Sohouli
- Genevieve Buckland
- Cain C. T. Clark
- Heitor O. Santos
- Felipe L. Athayde
- Vahid Sanati
- Leila Janani
- Akram Sadat Sajadian
- Mitra Zarrati
journal: BMC Women's Health
year: 2023
pmcid: PMC9990286
doi: 10.1186/s12905-023-02242-1
license: CC BY 4.0
---
# The relationship between diet quality indices and odds of breast cancer in women: a case–control study
## Abstract
Dietary quality is an important factor in the etiology of breast cancer (BrCa), but further studies are required to better elucidate this relationship. Accordingly, we sought to analyze if diet quality, assessed using the Diet Quality Index-International (DQI-I), Mean Adequacy Ratio (MAR), and Dietary Energy Density (DED), was related to BrCa. In this Hospital-based case–control study, 253 patients with BrCa and 267 non BrCa controls were enrolled. Individual food consumption data from a food frequency questionnaire was used to calculate the Diet Quality Indices (DQI). Employing a case–control design, odds ratios (ORs) and $95\%$ confidence intervals (CIs) were obtained, and a dose–response analysis investigated. After adjusting for potential confounders, those in the highest quartile of the MAR index had significantly lower odds of BrCa than those in the lowest (OR = 0.42, $95\%$ CI 0.23–0.78; P for trend = 0.007). Although there was no association between individual quartiles of the DQI-I and BrCa, there was evidence of a significant trend across all the quartile categories (P for trend = 0.030).There was no significant association was found between DED index and the odds of BrCa in the crude and fully adjusted models. We found that higher MAR indices were associated with decreased odds of BrCa, Therefore, the dietary patterns reflected by these scores may serve as a possible guide to preventing BrCa in Iranian women.
## Introduction
Breast cancer (BrCa) is the most prevalent of all cancers, and the second leading cause of death after lung cancer [1]. Annually, more than 1.1 million new cases of BrCa are diagnosed among women, which is equivalent to $10\%$ of all new cancers, and $23\%$ of all cancers specifically in women [2]. Among the most important risk factors associated with BrCa include genetic risk factors, family history of cancer, smoking, sedentary lifestyle, obesity, hormone therapy, and various aspects of diet [3–5].
Extensive research has focused on the role of lifestyle-related factors, especially nutrition and diet, as preventative measures for BrCa because these factors are potentially modifiable [6]. Although studies have shown that a higher intake of saturated fatty acid and cholesterol, and a lower intake of antioxidant micronutrients, such as vitamin E, C, D, magnesium, calcium, and zinc, are associated with higher risk of BrCa [7, 8], the evidence for these specific nutrients is still inconsistent [6].
Findings of studies have shown that BrCa patients generally consume fewer vegetables, fruit, and whole grains, which are known as the main components of the DASH (Dietary Approaches to Stop Hypertension) and Mediterranean diet, than women without BrCa [9, 10]. Indeed, adherence to healthy lifestyle recommendations, including dietary guidelines, appears to be lower in those diagnosed with BrCa [11].
Research into dietary patterns, which reflect the characteristics of the whole diet rather than just specific nutrients or foods, can be advantageous because food and nutrients in the diet are generally consumed together and may therefore have interactive and synergistic effects on each other. In addition, dietary patterns are often more appropriate for extrapolation into the real-world scenario [9, 12]. Dietary Quality Indices (DQI), such as the Diet Quality Index-International (DQI-I), Mean Adequacy Ratio (MAR), and Dietary Energy Density (DED), which are indicative of the whole diet characteristics, were created to address concerns about chronic diet-related diseases [13–15]. DQI-I is a composite index at the individual diet level that was created in 2003 in order to compare the adequate amounts of diets among different cultures of countries and includes 4 components of diversity, adequacy, moderation, and balance. Another index includes DED that is defined to estimate the amount of energy in a given weight of food. MAR is also calculated based on the ratio of nutrient adequacy for energy intake and other nutrients such as vitamin A, calcium, zinc, vitamin C, riboflavin, thiamine, iron, phosphorus, magnesium, protein, potassium, and fat. It has been suggested that individuals with higher DQI-I scores may follow a healthier eating pattern, which may be attributed to a higher intake of fruits, vegetables, and dietary phytochemicals, therein reducing the risk of BrCa [16]. Previous research on diet quality indices in cancer, in particular BrCa, is fairly limited. For example, one study found no significant relationship between the DED index and the risk of BrCa [17]. Although in another study this index was associated with an increased positive risk of this disease in postmenopausal women [18], in contrast to our study, this relationship was not studied in all women. For other indexes, studies focused on other cancers, or older versions of these indexes were used to examine this association.
These DQI indices are useful tools for identifying and estimating the quality of diet in different societies with different dietary patterns around the world and their association with chronic nutrition-related diseases such as cancer, diabetes, and fatty liver disease [13, 17, 19, 20]. The relationship between diet quality and BrCa is particularly relevant to study in Iran, due to the increasing rates of BrCa as well as the unique features of the Iranian diet (for example, bulky meals, high intake of refined grains, hydrogenated fats and high percentage of energy intake from carbohydrates),the rapid nutritional transition in this region [21], and high health care costs associated with chronic diseases, especially cancer. To our knowledge, studies on investigated how these dietary quality indices (DQI-I, MAR and DED) relate to BrCa are limited. As well as other related indices that have investigated this relationship, in terms of components, they are different from our desired indices, which can cause different findings. Therefore, the present study evaluate the association between three DQI and BrCa in a hospital-based case–control study.
## Methods
This case–control study was performed on 253 BrCa patients and 267 controls, that had as of late (2019–2020) been alluded to the Hazrat Rasoul and Taleghani hospitals, Tehran, Iran. The minimum required sample size was calculated based on the ability to detect an OR of 2 with a case to control ratio of 1:1, $90\%$ power, and a type I error (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{\alpha }$$\end{document}α) rate of $5\%$ (250 participants in each group). BrCa patients were newly (< 6 months) diagnosed by an oncologist based on histopathological features of breast tumors [22]. Inclusion criteria for the case group included the following: 1) Breast cancer confirmed by an oncologist and pathology results; 2) A maximum of 6 months have passed since the diagnosis of BrCa (BrCa patients were newly); 3) Willingness to cooperate in the study; 4) Age older than 18 years and under 65 years; 5) Body mass index 18.5–40 kg/m2 [22]. Patients with a history of other cancers, hormone-related diseases, such as polycystic ovary syndrome (PCOS) or endrometriosis, and occurrence of metastasis as well as liquor users and long-term dietary changes were excluded from our study [22].
The benchmark or control group comprised of the people who were hospitalized for a great many non-neoplastic illnesses, with no other way of life measures, like liquor utilization, and long term dietary changes. The control group was selected from patients referred to other different departments of the clinic, like ophthalmology, muscular health, maxillofacial medical procedure, ear, nose, and throat, who were not determined to have BrCa. Also, matching people in the case and control groups based on age variables (± 5 years) and body mass index (BMI) (for three subgroups: people with normal weight BMI = 18.5–24.9, overweight = 25–29.9, with first-degree obesity = 30–34.9, and those with second-grade obesity = 35–40).
In this study, the trained dietitians were the interviewers; thus, all the participants answered all survey questions. Physical activity levels of the participants were estimated by the use of a validated short form of the International Physical Activity Questionnaire (IPAQ short form) [23]. This study was approved by the research council and ethics committee Iran University of Medical Sciences, Tehran, Iran. Written informed consent was obtained from all patients prior to participation. Also, we confirm that all methods were performed in accordance with the relevant guidelines and regulations.
## Dietary assessment
Dietary intake over the previous year was obtained using a validated semi-quantitative food frequency questionnaire (FFQ) which encompassed 168 food items [24]. The FFQ consisted of a list of usual Iranian dietary items with standard serving sizes. For each food item, the average portion size consumed, and frequency of intake were obtained via self-report on the FFQ. Frequency of intake for each food item included: never, 2–3 times/month, 1 time/week, 2–4 times/week, 5–6 times/week, and daily. Completion of the FFQ questionnaire took a maximum of 30 min. Portion sizes were changed to grams by using standard Iranian household measures [25]. Energy and nutrient intake was estimated using Nutritionist IV software. This software uses the USDA food composition table and changes have been made based on the Iranian food composition table [26, 27].
## Assessment of non-dietary exposures
In the current study, all surveys were provided by professional and trained interviewers, which led to $100\%$ completion of the questionnaires. General information and clinical data were gathered via questionnaire, including age, age at first pregnancy (years), education and smoking status, oral contraceptive pills consumption history (months), data related to the history of breast disease (benign and breast cancer) and other cancers in self or family, bra-wearing at night, supplement and non-steroidal anti-inflammatory drugs (NSAIDs) use, and exposure to sunlight during the day. Standardized techniques were used to collect anthropometric data. A digital Seca scale (model 707, Seca, Hamburg, Germany) with a 100 g precision was used to assess body weight while subjects were unshod and wearing light indoor apparel. A tape meter was used to measure height while the subject was standing up without shoes. Weight (kg) divided by the square of height (m2) was used to compute BMI. A non-elastic tap was used to measure waist circumference (WC) at its narrowest point without applying pressure to the body's surface.
## Diet Quality Index-International (DQI-I)
A composite indicator at the level of individual diet was created in 2003 to compare diet quality between different cultures and countries [13]. This index includes 4 parts: diversity, adequacy, moderation, and balance. The diversity part includes the general score between food groups (meat, dairy, fruits, vegetables, and grain) and the score of the diversity of protein intake among protein sources. Adequacy of this index includes adequate intake of fruits, vegetables, protein, fiber, grains, iron, calcium, and vitamin C. In the moderation section, scores related to the groups of total fat, saturated fat, cholesterol, sodium, and energy-boosting foods are considered, and finally, the last part of this index includes the total balance score between macronutrients and fatty acids. Full details on how the DQI-I score is calculated have been published previously [13].
## Mean Adequacy Ratio (MAR)
The construction of the MAR is based on previously published methods [14]. In brief, Nutrient Adequacy Ratios (NAR) were first calculated for individual nutrients. NARs for vitamin A, vitamin D, iron, zinc, calcium, magnesium, and vitamin C were calculated by age based on recommended dietary allowance (RDA) and the NAR of vitamin B1, vitamin B2, and vitamin B12 were calculated by age and based on the estimated average requirement (EAR). To calculate this ratio, the amount of each nutrient consumed was divided by the recommended standard amount. Then, the total score of these ratios was divided by the number of nutrients studied (10 nutrients) and at the end, the average ratio of nutrient adequacy was obtained for each participant [14].
## Dietary Energy Density (DED)
To calculate diet energy density, the reported daily energy intake of each person (Kcal/d) was divided by the total weight of food consumed (g/d). The weight of drinks with no energy content was not calculated because according to previous studies, drinks are not effective in determining energy density [15].
## Statistical analyses
All statistical analyses were conducted using SPSS software (version 19.0; SPSS Inc, Chicago IL). The normality of variables was evaluated by Shapiro–Wilk tests. However, if the variables do not have a normal distribution, we use the following method. First specify Outliers and then delete them. Mean values of more than two groups were assessed using independent sample T-Test for normally distributed variables. Chi-square tests were used to compare categorical variables. The Logistic regression models were used to determine the separate association between the three different DQI’s (DQI-I, MAR and DED) and odds of BrCa, in crude and covariate adjusted models. The overall trend of OR across increasing quartiles was examined by considering the median score in each category as a continuous variable [28]. In the Tables 1, 23 and 4, the data were presented as mean (or N) ± standard deviation (or %) in and, statistical significance was accepted, a priori, at $P \leq 0.05.$Table 1Demographic, anthropometric and lifestyle characteristics of participants in case and control groupsvariablesCase–Control groupP valueaCase ($$n = 253$$)Control ($$n = 267$$)Mean (SD)Mean (SD)Age, y48.91(10.46)47.13(10.08)0.062BMI, kg/m229.61(4.55)29.07(5.39)0.222WC (cm)101.15(96.39)96.39(13.25) < 0.001WHR0.96(0.08)0.89(0.10) < 0.001Physical Activity (Met.h/wk)33.18(6.11)32.70(5.20)0.336Marriage age, y19.43(5.02)18.98(4.48)0.296First pregnancy age, y22.29(5.32)20.35(4.19) < 0.001Child number2.92(1.43)2.54(1.59)0.005N(%)N(%)Abortion history (yes)94 (37.2)78(29.2)0.054Smoking current use (yes)8(3.2)9(3.4)0.894Tobacco current use (yes)8(3.2)18(6.7)0.063Employment status0.532 Housewife203(80.6)206(77.4) Part-time9(3.6)15(5.6) Recruitment22(8.7)30(11.3) Retired14(5.6)10(3.8)Education status0.518 Illiterate29(11.6)24[9] Low education116(46.6)134(50.4) Higher education105[42]108(40.6)Exposure to sunlight during the day (min) Less than 30 min72(28.5)96[36]0.215 60–30 min82(32.4)68(25.5) 120–60 min43[17]46(17.2) More than 120 min56(22.1)57(21.3)A significance level of 0.05 was considered (Pvalue < 0.05)Abbreviations: BMI Body Mass Index, WC Waist- Circumference, WHR Waist-Hip RatioaObtained from independent sample T-Test for continuous variables and Chi-square of independence for Categorical variablesTable 2Medical history of participants in case and control groupsGroups, N (%)Case ($$n = 253$$)Control ($$n = 267$$)P valueaFamily history of breast cancer (yes)14(5.5)12(4.5)0.594Family history of cancer (yes)68(26.9)55(20.7)0.097Benign breast diseases history (yes)20(7.9)14(5.3)0.224Menopausal status (postmenopausal)115(45.5)114(42.7)0.527Inflammatory disease history (yes)32(12.6)35(13.2)0.863Comorbidity (yes)93(36.8)99(37.4)0.888Night bra use (yes)190(75.1)190(71.4)0.345Recent special diet history (yes)54(21.3)61[23]0.647Vitamin D supplement (yes)37(14.6)65(24.3)0.005Herbal drug use (yes)68(26.9)72(27.1)0.961Iron supplement (yes)41(16.2)45(16.9)0.842Multivitamin mineral (yes)14(5.9)19(7.9)0.385Ever use of OCP (yes)126(49.8)149[56]0.156Anti-inflammatory drugs use (yes)26(10.3)47(17.7)0.015A significance level of 0.05 was considered (Pvalue < 0.05)Abbreviations: OCP Oral Contraceptive PillaObtained from independent sample T-TestTable 3Dietary intakes of study participants across case and control groupsGroups, mean (SD)Case ($$n = 253$$)Control ($$n = 267$$)P valueaFood groups (g/day) Dairy479.12 (307.21)533.53 (334.58)0.054 Whole grains90.88 (87.76)92.27(90.03)0.858 Refined grains336.49 (198.70)294.60 (170.09)0.010 Legumes22.63(23.80)33.59 (26.85) < 0.001* Red and processed meat31.64 (24.73)29.59 (20.03)0.297 Fruits415.98 (241.42)513.07 (227.48) < 0.001* Vegetables278.83 (154.72)347.12 (146.46) < 0.001*Nutrients Energy (Kcal/d)2753.45(798.02)2464.1(607.43) < 0.001* Carbohydrate (g/d)56.18(7.47)54.24 (7.04)0.002 Protein (g/d)13.02 (2.15)13.03 (2.13)0.984 Fat (g/d)35.11(6.75)33.14(7.61)0.002 SFA (g/d)32.92 (11.26)29.20(10.53) < 0.001* MUFA (g/d)32.29 (13.29)37.24 (15.97) < 0.001* PUFA (g/d)20.48 (10.35)24.49 (13.29) < 0.001* Cholesterol (mg/d)293.52(135.55)261.88(139.27)0.009 Fibre(g/d)37.96 (19.28)39.89 (18.58)0.247 Sodium (mg/d)4740.74(1811.95)4307.06(1898.50)0.008 Potassium (mg/d)3766.23(1224.29)4297.22 (1261.12) < 0.001* Phosphor (mg/d)1482.87 (492.60)1617.48 (485.35)0.002 Iron (mg/d)20.28 (9.96)16.34 (6.06) < 0.001* Calcium (mg/d)1215.79 (463.90)1335.27 (458.76)0.003 Magnesium (mg/d)370.06(119.89)402.91 (133.15)0.003 Zinc(mg/d)11.76 (3.82)12.95 (4.05)0.001* Vitamin C(mg/d)159.16(89.15)197.87 (78.89) < 0.001* Folate (mcg/d)485.57 (168.28)455.20 (163.07)0.037 Vitamin B12 (mcg/d)5.53 (3.87)6.70 (4.53)0.002 Vitamin E (mg/d)17.64 (13.16)23.59 (17.54) < 0.001* Vitamin D (mcg/d)2.04 (3.44)2.7 (3.06)0.012A significance level of 0.05 was considered (Pvalue < 0.05)Abbreviations: SFA Saturated Fatty Acids, MUFA Mono-Unsaturated Fatty Acids, PUFA Poly-Unsaturated Fatty AcidaObtained from independent sample T-Test*Statistically significant after Bonferroni correction for multiple comparisons (the threshold of statistical significance is $p \leq 0.0017$ when presented 28 parameters are taken into account)Table 4Mean Score of Dietary Quality Indices among Breast Cancer Patients and Control GroupGroups, mean (SD)Case ($$n = 253$$)Control ($$n = 267$$)P valueaDQI indices DQI-I51.21(10.33)53.31 (10.95)0.025 DED1.23(0.20)1.20 (0.23)0.171 MAR1.41(0.46)1.59 (0.51) < 0.001NAR of different nutrients Zinc(mg/d)1.47(0.47)1.61 (0.50)0.001 Iron (mg/d)1.12(0.55)0.90 (0.33) < 0.001 Calcium (mg/d)1.21(0.46)1.33 (0.45)0.003 Vitamin C(mg/d)2.12(1.18)2.63(1.05) < 0.001 Vitamin D (mcg/d)0.13(0.22)0.18 (0.20)0.012 Vitamin B2 (mg/d)1.94(0.69)2.25 (0.70) < 0.001 Vitamin B1 (mcg/d)1.66(0.62)1.54 (0.55)0.027 Vitamin A (mg/d)0.98(0.69)1.34 (0.77) < 0.001 Magnesium (mg/d)1.19(0.38)1.29 (0.42)0.003 Vitamin B12 (mcg/d)2.30(1.61)2.79 (1.88)0.002A significance level of 0.05 was considered (Pvalue < 0.05)Abbreviations: DQI-I Diet Quality Index-International, DQI Diet Quality Index, MAR Mean Adequacy Ratio, DED Dietary Energy Density, NAR Nutrient Adequacy RatiosaObtained from ANOVA We classified all subjects based on the DQI-I, MAR and DED scores into quartile ranges. We adjusted the results in three models using a priori selected potential confounders, which included: model 1- age and BMI, model 2- additional adjustment for waist circumference, early gestational age, number of children, history of abortion, family history of cancer, history of inflammatory diseases, and use of anti-inflammatory drugs and vitamin D supplements, model 3- the latter model plus history of specific diet, family history of breast cancer, history of benign breast disease, and use of contraceptives. In adjusted models, confounders were used from statistical and conceptual approach respectively. In this way, the variables with Pvalue < 0.2 were considered as possible confounders and were entered into the logistic regression and the odds of getting cancer was investigated. Also, in the conceptual approach of adjusting confounders in the model 3, possible confounders were selected based on clinical concepts and based on past articles and added to other confounders.
## Results
The mean ± SD for the age and BMI of the study population were 47.9 ± 10.3 years and 29.43 ± 5.51 kg/m2, respectively. Table 1 shows the distribution of cases and controls according to socio-demographic characteristics, smoking habits, body mass composition, and exposure to sunlight. Compared with control subjects, participants with BrCa had significantly higher waist circumference, WHR, mean age at primiparity, and number of children ($P \leq 0.05$). No significant differences were found for other characteristics among cases and control subjects.
Table 2 shows the medical history between the case and control groups. BrCa patients in compared with control individuals significantly had lower intake of vitamin D supplementation and anti-inflammatory drugs ($P \leq 0.05$). Dietary intakes of patients with BrCa (case group) and control group are shown in Table 3. Compared with control groups, individuals with BrCa had a higher intake of energy, total fats, saturated fatty acids, cholesterol, carbohydrates, sodium, folate, and iron than controls, and had a lower intake of monounsaturated fatty acids, potassium, phosphorus, calcium, and antioxidants such as zinc, magnesium and vitamins E, C and D ($P \leq 0.05$). Also, among food groups, dairy products, legumes, fruits, and vegetables were significantly higher in the control group than the case group ($P \leq 0.05$). However, refined grains were significantly higher in BrCa patients than in control subjects.
Patients with BrCa had significantly lower scores on the DQI-I ($$P \leq 0.025$$) and the MAR ($P \leq 0.001$). Also, in terms of components of the MAR, BrCa patients consumed less zinc, calcium, magnesium and vitamins C, A, D, B2, B1, and B12 compared to the control group ($P \leq 0.05$). However, DED score did not show any significant difference between the two groups (Table 4).
The odds ratio (ORs) and $95\%$ confidence intervals (CIs) of BrCa, according to quartiles of DQI-I, MAR, and DED are presented in Table 5. In the crude and adjusted model 1 there was no evidence of decreased odds of BrCa for subjects the highest compared to the lowest quartile of the DQI-I index (OR = 0.93, $95\%$ CI 0.52 – 1.65; P for trend = 0.074 and OR = 0.93, $95\%$ CI 0.52 – 1.66; P for trend = 0.069, respectively). However, after adjusting for potential confounders in the model 2 and the final model, there was evidence that the odds of BrCa decreased with increasing categories of the DQI-I (p-trend 0.026 and 0.030, respectively). However, there was no evidence of an association between the individual quartiles of the DQI-I and BrCa (OR = 0.91, $95\%$ CI 0.49 – 1.69 for model 2; and OR = 0.91, $95\%$ CI 0.49 – 1.71 for model 3, comparing highest to lowest quartile).Table 5Odds ratio (OR) and $95\%$ confidence interval (CI) for breast cancer based on Quartiles of DQI indicesQuartiles of DQI indicesQ1Q2Q3Q4OR for trendP for trendDQI-I Case/Total (n)$\frac{69}{12866}$/$\frac{13363}{12955}$/130 Crude model1.00 (Ref)1.05(0.59–1.86)1.14(0.64–2.03)0.93(0.52–1.65)0.860.074 Model 1a1.00 (Ref)1.07(0.60–1.91)1.12(0.62–2.00)0.93(0.52–1.66)0.850.069 Model 2 b1.00 (Ref)0.90(0.48–1.66)1.05(0.56–1.99)0.91(0.49–1.69)0.800.026 Model 3 c1.00 (Ref)0.89(0.47–1.67)1.05(0.55–1.99)0.91(0.49–1.71)0.800.030MAR Case/Total (n)$\frac{82}{13062}$/$\frac{13064}{13045}$/130 Crude model1.00 (Ref)0.66(0.38–1.14)0.68(0.39–1.19)0.42(0.24–0.73)0.770.005 Model 1a1.00 (Ref)0.66(0.38–1.15)0.73(0.41–1.28)0.45(0.25–0.79)0.790.012 Model 2b1.00 (Ref)0.77(0.42–1.40)0.66(0.36–1.21)0.44(0.24–0.80)0.760.006 Model 3c1.00 (Ref)0.72(0.39–1.33)0.65(0.35–1.20)0.42(0.23–0.78)0.760.007DED Case/Total (n)$\frac{55}{13063}$/$\frac{13063}{13072}$/130 Crude model1.00 (Ref)1.04(0.60–1.79)1.02(0.58–1.77)1.43(0.83–2.48)1.110.230 Model 1a1.00 (Ref)0.99(0.57–1.71)0.98(0.56–1.72)1.44(0.83–2.52)1.110.216 Model 2b1.00 (Ref)1.11(0.61–2.00)0.87(0.48–1.59)1.62(0.88–2.95)1.120.222 Model 3c1.00 (Ref)1.08(0.59–1.97)0.84(0.45–1.55)1.60(0.87–2.95)1.210.244Abbreviations: DQI Diet Quality Index, MAR Mean Adequacy Ratio, DED Dietary Energy Density** Binary logistic regression was used to obtain OR and $95\%$ CI. The overall trend of OR across increasing quartiles was examined by considering the median score in each category as a continuous variableaModel 1: adjusted for age and BMIbModel 2: waist circumference, early gestational age, number of children, history of abortion, family history of cancer, history of inflammatory diseases, and use of anti-inflammatory drugs and vitamin D supplementscModel 3: adjusted for model 2 and history of recent special diet, menopausal status, family history of breast cancer, history of benign breast disease, and use of contraceptives There was a significant reduced odds of BrCa for those subjects with the highest mean score of adequate dietary index (MAR), when compared to subjects with the lowest score, both in the crude (OR = 0.42, $95\%$CI 0.24 – 0.73; P for trend = 0.005) and the final adjusted (OR = 0.42, $95\%$ CI 0.23 – 0.78; P for trend = 0.007) models, with a trend of approximately $24\%$ reduction in the odds of cancer (OR trend = 0.76). However, no significant association was found between DED index and the odds of BrCa in the crude and fully adjusted model (OR = 1.43, $95\%$ CI 0.83 – 2.48; P for trend = 0.230; OR = 1.60, $95\%$CI 0.87 – 2.95; P for trend = 0.244, respectively) (Table 5).
## Discussion
BrCa is a common disease in the population of different countries and diet is known as a potential risk factor in this disease. Extensive research has focused on the role of lifestyle-related factors, especially nutrition and diet, as preventative measures for BrCa because these factors are potentially modifiable. Also, since there is little research on diet and breast cancer and so they looked at studies of other cancers and diet to see if any similarities could be found. Through a case–control study, we investigated the relationship between DQI’s and the odds of BrCa. Overall, we identified an association between BrCa and DQI-I and the MAR index in a dose-dependent fashion; however, there was no significant difference for the DED index between groups.
More specifically, employing a test for trend, those with a higher DQI-I index had a lower odds of having BrCa in the second in and the third adjusted models. There was a $58\%$ reduction in the odds of having BrCa for those subjects with the highest MAR when compared to subjects with the lowest score after controlling for a large number of confounders, and with evidence of a dose–response relationship. However, no significant association was found between the DED index and the odds of BrCa in the crude and final adjusted model. In one study, the range of the DED index was expressed based on the population under study [18]. This range is between 1.23 and 1.71, which is higher than the score range of this index in our study. In addition, in another study, the DQI index score range was 44 to 53, which is roughly equivalent to our score range for this index [16].
Consistent with our study, in a case–control study by Wang et al., the authors reported an inverse relationship between DQI and odds of oral and laryngeal cancer in women, whereas this relationship was not observed in men [16]. In another study, the results showed an increased risk of pancreatic cancer by following diets higher energy density, such as red meat and potatoes, and a reduction in the risk of pancreatic cancer by following diets with lower energy density, such as fruits and vegetables. In their study, there was a $72\%$ increased risk of pancreatic cancer for subjects in the highest quintile of DED compared to the lower quintile of this index in men [29]. However, according to our findings looking at BrCa, no significant relationship was observed. Also, in a study by Vargas et al., a higher DQI-I score was associated with a reduced risk of colorectal cancer [30], whilst in a 12 year follow-up study in South Korea, the results showed that higher MAR index was associated with a reduction in cancer and cardiovascular disease mortality by $66\%$ and $98\%$, in those under 30 years of age and over 30 years of age, respectively [31]. However, in Arthur et al., inconsistent to our results, the authors reported that higher intake of Western diets and higher energy density (high consumption of red meat, processed meats, refined grains, high-fat dairy and desserts), compared the Mediterranean diet with lower energy density (high consumption of fruits, vegetables, whole grains, poultry, fish and legumes), was associated with an increased odds of hormone-dependent cancers [17]. In addition, in another study of 92,225 postmenopausal women with colorectal, pancreatic, ovarian, endometrial, and laryngeal cancers, contrary to our findings, it was reported that a higher DED index was associated with an increased BMI, WC, and risk of obesity-associated—cancers [32]. Differences in study results may be due to differences in dietary patterns of different populations, study design and s sample size, different methods of measuring and estimating food intake, as well as variability in adjusted confounders. A possible explanation for the lack of association between the DED and BrCa that we observed is that in our study, unlike other studies, fiber intake increased across quartiles of DED. In a weight maintenance trial, with controlled feeding in 48 women, it was found that compared to high-fat ($40\%$ energy) and low fiber diets (12 g per day), low-fat diets (20–$25\%$ energy) with higher fiber (40 g per day) significantly reduced the serum concentration of sex hormones associated with BrCa risk (by 9 to $15\%$) [33]. Studies have also suggested that higher DED scores are linked to lower dietary antioxidant intake and higher insulin concentrations, which may increase the risk of cancer/tumor growth by inhibiting apoptosis, stimulating cell proliferation, and enhancing angiogenesis [29, 34].
Evidence suggests that following a healthy diet includes eating foods rich in antioxidants and phytochemicals known as anti-inflammatory compounds, as well as eating more fruits and vegetables, especially dark green vegetables can indicate a higher score of MAR and DQI-I indices that the balance between the antioxidant and oxidative systems resulting from the intake of these diets can reduce the risk of cancer by regulating cell growth and proliferation [19]. Furthermore, previous studies also suggest that higher scores of DED index, via increasing insulin concentration, can increase the synthesis of insulin-like growth factor IGF-1 and inhibit IGF-1-binding proteins, known to be a predictor of cancer and a factor associated with increasing estrogen in adipose tissue, and promoting tumor growth by inhibiting apoptosis, stimulating cell proliferation, and enhancing angiogenesis [29, 34].
The findings of our study also showed that some micronutrients, including potassium, phosphorus, calcium, zinc, magnesium and vitamins E, C and D, received less in the case group compared to the control group. Studies suggest that lower intake of these micronutrients, which are usually associated with lower fiber intake, can increase and maintain weight and body fat mass. This accumulation and storage of fat in the body is usually associated with an increase and retention of estrogen in the tissues and can increase the risk of chronic diseases, especially hormone-related cancers such as BrCa [35]. Therefore, differences in these nutrients may be clinically impactful. Special attention should be paid to vitamin D, since it plays a key physiological role in the development and function of the mammary gland [36], although the literature remains conflicting regarding vitamin D status and the risk BrCa. For instance, a meta-analysis of 9 prospective studies suggests a $12\%$ decrease in the risk of BrCa in postmenopausal women for each 5 ng/mL increase in 25(OH) D [37]. However, in a RCT including 36,282 postmenopausal women, a reduction in BrCa (in situ) was found for those patients who underwent 400 IU/d of vitamin D3 combined with 1000 mg/d of elemental calcium carbonate [36]. In our study, the control group reported higher use of vitamin D supplements compared to the case group ($24.3\%$ vs $14.6\%$, $$p \leq 0.005$$). Nevertheless, due to the nature of our study design and the lack of control over the dosage across vitamin D supplements, we cannot infer that vitamin D supplements are protective for BrCa. Interestingly however, the Vitamin D and Omega-3 Trial (VITAL) represents ongoing research that may be able to elucidate the clinical magnitude of supplementing vitamin D in preventing cancer by addressing the effect of 2000 IU/d vitamin D3 with or without 1 g of omega-3 fatty acids in 25,871 healthy subjects.
One of the strengths of the current review is the powerful consideration models, which were far reaching. To the best of our knowledge this is the only study which has considered the association between DQI and breast cancer. Also, due to a maximum of 6 months having passed since the diagnosis of the disease in these patients, the likelihood of a change in their habits and eating patterns due to the disease was greatly reduced. The 168 items FFQ used in this study covers most of the foods that our study subjects received. Although this study is innovative, there are certain limitations that should be noted. Some confounders may not have been taken into account despite the fact that this study investigated all potential confounders. Despite finding evidence of a link between DQI and BrCa, the retrospective methodology of this investigation prevented us from establishing causality of the observed correlations. Therefore, this finding has to be verified in further prospective studies and RCTs. Additionally, data were gathered by self-report methods, which are known to be prone to over- or underreporting. However, we aimed to address this by utilizing skilled interviewers and instruments that had undergone thorough validation. Additionally, the statistical method was suitable for reporting at the group level. Another potential disadvantage of the research is the possibility of very modest changes between specific foods consumed during the interview and before to the diagnosis. However, the precise number of participants who altered their diet was not recorded in the research. In addition, we assessed pre-diagnosis consumption for each food item.
## Conclusion
We found that higher DQI-I and MAR indices were associated with decreased odds of BrCa. However, there was no significant association for the DED index between groups. Overall, this case–control study shows an important relationship between different scores of dietary quality indices and the risk of BrCa. The dietary patterns reflected by these scores may serve as possible guidelines for cancer prevention in pre and postmenopausal women. It seems that according to the results of the study on the potential impact of quality and content of diet including total energy intake, micronutrients and macronutrients and other risk factors such as obesity or overweight and lifestyle on the risk of BrCa, we can reducing the risk of BrCa in the community by trying to recommend and teach proper intake of a healthy and nutritious diet by relevant experts and consultants.
## Informed consent
A written informed consent was obtained from all participants.
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|
---
title: Exercise activates Sirt1-mediated Drp1 acetylation and inhibits hepatocyte
apoptosis to improve nonalcoholic fatty liver disease
authors:
- Zongqiang Hu
- Hongyu Zhang
- Yiting Wang
- Boyi Li
- Kaiyu Liu
- Jianghua Ran
- Li Li
journal: Lipids in Health and Disease
year: 2023
pmcid: PMC9990292
doi: 10.1186/s12944-023-01798-z
license: CC BY 4.0
---
# Exercise activates Sirt1-mediated Drp1 acetylation and inhibits hepatocyte apoptosis to improve nonalcoholic fatty liver disease
## Abstract
### Purpose
Aerobic exercise has shown beneficial effects in the prevention and treatment of non-alcoholic fatty liver disease (NAFLD). Nevertheless, the regulatory mechanism is not turely clear. Therefore, we aim to clarify the possible mechanism by investigating the effects of aerobic exercise on NAFLD and its mitochondrial dysfunction.
### Methods
NAFLD rat model was established by feeding high fat diet. and used oleic acid (OA) to treat HepG2 cells. Changes in histopathology, lipid accumulation, apoptosis, body weight, and biochemical parameters were assessed. In addition, antioxidants, mitochondrial biogenesis and mitochondrial fusion and division were assessed.
### Results
The obtained in vivo results showed that aerobic exercise significantly improved lipid accumulation and mitochondrial dysfunction induced by HFD, activated the level of Sirtuins1 (Srit1), and weakened the acetylation and activity of dynamic-related protein 1 (Drp1). In vitro results showed that activation of Srit1 inhibited OA-induced apoptosis in HepG2 cells and alleviated OA-induced mitochondrial dysfunction by inhibiting Drp1 acetylation and reducing Drp1 expression.
### Conclusion
Aerobic exercise alleviates NAFLD and its mitochondrial dysfunction by activating Srit1 to regulate Drp1 acetylation. Our study clarifies the mechanism of aerobic exercise in alleviating NAFLD and its mitochondrial dysfunction and provides a new method for adjuvant treatment of NAFLD.
## Introduction
Chronic liver disease-non-alcoholic fatty liver (NAFLD) occurs worldwide, affecting an estimated 1.95 billion people worldwide [1, 2]. Liver injury, steatohepatitis, cirrhosis and fibrosis all fall under the umbrella of NAFLD and are associated with an increased risk of hepatocellular carcinoma and severe extrahepatic complications [3, 4]. Numerous studies have attested that the pathogenesis of NAFLD is intimately interrelated with mitochondrial dysfunction. Mitochondria play a role in regulating various cellular activities such as oxidative stress. Accumulation of intracellular lipids in NAFLD facilitates oxidative stress to generate superabundant reactive oxygen species (ROS), which gives mitochondrial function is affected and cytotoxic [5]. In contrast, mitochondrial dysfunction impairs fat homeostasis in the liver and results in the overproduction of ROS, which leads to a pernicious cycle that aggravates the evolution of NAFLD [6]. Recent studies have shown that regular physical exercise can improve NAFLD and its mitochondrial dysfunction [7], but its regulatory mechanism is not fully understood.
Sirtuin1 (Srit1) is a histone deacetylase involved in fatty acid synthesis, involved in fatty acid synthesis, oxidation, and adipogenesis [8]. Srit1 has been shown to play a beneficial role in mitigating NAFLD [9]. Activation of Srit1 significantly inhibits adipogenesis, attenuates high-fat diet (HFD)-induced inflammation and protects against hepatic steatosis in obese mice [10, 11]. Furthermore, Srit1 also has a momentous effect on protecting against mitochondrial dysfunction caused by NAFLD [12]. Activation of Srit1 can significantly reduce oxidative stress levels and ROS production in an in vitro-induced NAFLD model and alleviate mitochondrial dysfunction [13, 14]. The latest studies have indicated that exercise can increase the level of Srit1 [15, 16]. However, whether exercise alleviates NAFLD-induced mitochondrial dysfunction by elevating Srit1 expression remains unclear.
The balance of mitochondrial fission and fusion is essential for maintaining mitochondrial function [17]. Mitochondrial fission is normally regulated by dynamic-related protein 1 (Drp1), which functions by binding to receptors on the outer mitochondrial membrane, assembling into larger oligomers, and transporting to the fission site [18, 19]. Drp1 has been shown to promote mitochondrial fission through various posttranslational modifications, including phosphorylation, SUMOylation, and ubiquitination [20–22]. However, the acetylation of Drp1 remains unclear. Recent studies have shown that lipid overload leads to increased acetylation of DRP1 and enhances its activity, which in turn promotes mitochondrial fission and leads to cardiac dysfunction [23]. Srit1 is an inartificial histone deacetylase that exerts its deacetylation effect by removing the acetyl group that the latter adds to lysine residues to counteract the action of protein acetyltransferases [24]. Therefore, we postulate that aerobic exercise-activated Srit1 may alleviate NAFLD-induced mitochondrial dysfunction by regulating the level of Drp1 acetylation.
In this study, it probed the role of aerobic exercise in rats fed an HFD and the latent mechanisms. Our results showed that aerobic exercise alleviates liver injury and mitochondrial dysfunction induced by HFD in rats, and these effects are exerted by activation of Srit1 to inhibit Drp1 acetylation.
## Animal experiment
Three- to four-week-old male SD rats were purchased from the Animal Experimental Center of Kunming Medical University, kept in an immobile temperature and humidity environment, and adapted to a regular diet and drinking water for 7 days. There are normal control (NC) group and HFD group in this study, and the rats were randomly divided. In NC group, the rats were fed common rodent feed, and the proportion ratio of carbohydrate, fat, and protein was 7: 1: 2. The energy distribution proportion ratio of the HFD diet is 2: 6: 2. Rats in the NC group were fed continuously for 11 weeks until the end of the experiment. Obesity and metabolic syndrome characteristics began to develop in rats fed an HFD at the sixth week. After that, HFD rats were divided into three groups according to whether the animals received exercise training. The exercise program was based on a previous study [25]. One group of rats was divided into the HFD followed by aerobic exercise training (HFD + E) group, and the course consisted of a 10-min running warm-up, followed by resistance training, incorporating eight 2-min running sessions (with a 1-min rest interval) during which the rats ran at a slope, gradually increasing from 10 to 25° at an invariable slow speed (20–25 cm/s). Subsequently, persistent aerobic exercise was conducted for 30 min on the treadmill. The treadmill was purchased from Jiangsu SANS Biological Technology Co. Ltd. (product model: SA101). The second group of rats was injected with the Sirt1 inhibitor Tenovin-6 through the tail vein before aerobic exercise and then trained, which was called the HFD + E + T6 group. The third group of rats continued to be fed an HFD and remained sedentary. At week 11, the rats were sacrificed under anesthesia, and carotid blood and liver tissue were collected for subsequent analysis.
## Biochemical analysis of serum and liver tissues
Serum was prepared by centrifuging (4 °C, 5000 × g, 10 min) the collected rat blood and the serum was stored at -80 °C for subsequent analysis. We used PBS to flush the harvested liver tissue and using filter paper to wipe it. Then, we used formalin to immobilize the partial liver tissue at 4 °C, and a part of the liver tissue was freezed in liquid nitrogen and stored at -80 °C for subsequent analysis. Aspartate aminotransferase (AST), triglyceride (TG), and alanine aminotransferase (ALT) concentrations were detected by biochemical kits (Nanjing, China). Biochemical detection of SOD, GSH and MDA in liver tissue was also performed.
## Histopathological examination
For pathological testing, the right lobe of the liver was selected. The formalin-fixed liver tissue was sected, and before dyeing with hematoxylin and eosin (HE), it was cut into 6-μm slices and. Similarly, the liver stored at -80 °C were cut into 6 μm sections by a cryomicrotome. Then, oil red O solution and HE were used to dye the slices. Finally, light microscope was used to observe the histopathological structure.
## RNA extraction and analysis
TRIzol reagent (catalog number: 15596026, Invitrogen) was used to extract total RNA, and it was reversed to cDNA by a reverse transcription kit. Then, the SYBR Green Master Mix was used for RT‒qPCR. The reaction routine was as follows: pre-denaturation at 95℃ for 20 s; Then the amplification cycle was carried out at 95℃ for 1 s and 60℃ for 20 s, and there were 40 cycles in this stage. And then enter a dissolution curve analysis stage, wherein that temperature of the dissolution curve is set to be 60–95 deg C, and each sample is provided with three duplicate hole. Using β-actin as reference control, the level of the target product relative to the internal control was expressed as 2−ΔΔCt.
## Cell culture
In this study, the HepG2 cells (from the Cell Bank of the Chinese Academy of Sciences) were maintained in DMEM medium (containing 4.5 G/L glucose, $8.0\%$ FBS, 100 U/mL penicillin and streptomycin). We used OA (Sigma‒Aldrich, catalog number: O1008) to treat cells. In brief, the parameters of the HepG2 cell incubator were: 37 °C and $5\%$ CO2 for 24 h after seeding, whereas in the model, it was cultured for 48 h after treated with 1.2 mM OA to induce NAFLD. Cells were incubated with 20 μM CAY-10602 (Srit1 activator, MCE, catalog number: HY-104073) for 120 min whereas provoked with OA for 2 days. $12.0\%$ serum was incorporated in the reconciled medium.
## CCK-8 detection of cell proliferation
In 96-well plates, HepG2 cells were treated with OA and CAY-10602 to a density of 5 × 103 cells/mL in triplicate. In each well, 10 μL of CCK-8 was added after incubation for 2 days, and the cells were cultured for 60 min. The OD values detected under a microplate reader at 450 nm.
## HE and Oil Red O staining
For HE staining, we used $10\%$ neutral formaldehyde to immobilize the cells for 20 min after removal of the cell culture medium. After flushed with PBS, the cells were dyed with HE for 1 min. After flushed by PBS, observing under a microscope and taking pictures. For Oil Red O staining, at 25 °C, the cells were dyed with Oil Red O solution (5 mg/mL) for 0.5 h. After flushed by PBS, observing under a microscope and taking pictures.
## Detection of apoptosis by flow cytometry
In this study, to check the apoptosis rate of HepG2 cells, we used the Annexin V-FITC/PI apoptosis kit. In brief, we used PBS to flush the cells, and the cells were digested and centrifuged with 1 × 104 cells/mL. Using Annexin V-FITC and PI to hatch the cells. Finally, collecting the flow cytometry data and using FlowJo software to analyze it.
## Detection of intracellular ROS
In this study, we used an ROS detection kit to detect intracellular ROS production. Cells from each group were pretreated for 2 days, gathered and resuspended in 10 μM DCFH-DA solution (no serum). Then, at 37 °C, the samples were incubated for 20 min in darkness. After flushing with PBS, we used a fluorescence microscope to observe the intracellular fluorescence. Finally, to check the fluorescence intensity, which represents the ROS level, Image-Pro Plus 6.0 was used.
## Determination of adenosine triphosphate (ATP) content
We used an ATP bioluminescence assay kit to detect the cellular ATP levels according to the manufacturer's specifications.
## Mitochondrial membrane potential (MMP) measurement
To check the variation in MMP, we used an MMP assay kit with JC-1 in this study. At 37 °C, each group of cells was pretreated for 48 h, resuspended, and cultured with JC-1 for 20 min. Photographs were taken under a fluorescence microscope, and MMP changes were analyzed in light of the red/green fluorescence intensity ratio by Image-Pro Plus 6.0.
## RT‒qPCR of mitochondrial DNA (mtDNA) content
In this study, we used a QIaamp DNA mini kit to detect the total DNA, which was isolated from cells or frozen liver. Moreover, we used a Pico Green DNA quantification kit to detect the DNA concentration. Primers and FAM-labeled TAMRA quenching probes were purchased from TaKaRa Biotechnology. PCR detection kit was used for amplification and quantification of mtDNA.
## Immunofluorescence
In this study, we used prewarmed PBS to flush HepG2 cells 3 times and $4\%$ paraformaldehyde to immobilize the cells for 20 min. Then, we used PBS (containing $5\%$ BSA) to wash HepG2 cells for 60 min and used primary antibodies against nuclear respiratory factor 1 (NRF1; 1:200; ab200976; Abcam; UK) and Drp1 (1:250; ab184247; Abcam; UK) to incubate for 12 h at 4 °C. Subsequently, we used PBS to flush it 3 times and used secondary antibody goat anti-rabbit IgG (1:200, Abcam; ab150077; Abcam; UK) to hatch for 1 h at indoor temperature, protected from light. Nuclei were dyed with DAPI. Finally, we used Nikon Eclipse 80i microscope to observ sealed slides, and using Image-Pro Plus 6.0 to analyze the fluorescence intensity.
## Western blot
The liver tissue and HepG2 cells were lysed by RIPA lysis solution (Beyotime, China) to extract the protein. The protein concentration was detected by BCA reaction kit. After quantitative analysis, the total protein was denatured in this study. SDS-Page gel was used for electrophoresis, electrophoresis apparatus (Bio-RAD, USA) was adjusted to 120 V for electrophoresis, PVDF membrane (Millipore, USA) was used for membrane transfer, and skim milk (Sigma, USA) for blocking. Primary antibodies (Abcam, UK): Sirt1 (1:1000; ab189494), optic atrophy 1 (Opa1, 1:1000; ab157457), mitofusin 2 (Mfn2, 1:1000; ab124773), Drp1 (1:1000; ab184247), NRF1 (1:1000; ab34682), mitochondrial transcription factor A (TFAM, 1:1000; ab252432; Abcam; UK), Bcl-2 (1:2000; ab182858), cleaved-caspase 3 (1:500; ab2302), BCL2-associated X protein (Bax, 1:1000; ab32503), cleaved-caspase 9 (1:2000; ab32539) and GAPDH (1:2500; ab9485) were then added overnight to incubate. The next day, goat anti-rabbit antibody (1:2000; ab288151) was incubated for 1 h with slow shaking at 25 °C. The immunoreactive bands were visualized by intensive chemiluminescent reagent. The gray value was analyzed by ImageJ software.
## Statistical analysis
For all statistical analyses, statistical analyses GraphPad Prism7 was used. The analysis results are stand for the mean ± SD. One-way analysis of variance and t-test were used, and $P \leq 0.05$ were considered statistically significant.
## Aerobic exercise alleviates liver injury induced by HFD in rats
NAFLD model was established in rats fed with HFD to determine the influence of aerobic exercise on NAFLD-induced liver injury. SD rats aged 3–4 weeks were fed a normal or HFD diet, and after 6 weeks, the rats began to develop early obesity and metabolic syndrome, such as increased body weight and insulin resistance (Fig. 1A-B). Then, the experiment was performed in the following groups: the NC group (normal control diet), HFD group (fed HFD and sedentary), and HFD + E group (fed HFD followed by forced aerobic exercise). Rat body weights were measured weekly during the 11 weeks of feeding. After the last training session, rats were executed under anesthesia for subsequent analysis. The results of continuous weight monitoring for 11 weeks showed that HFD caused significant weight gain in rats compared with the NC group, and aerobic exercise could alleviate the weight gain caused by HFD (Fig. 1C). Citrate synthase activity was evaluated as a certification of notion to validate the effectiveness of the exercise. Therefore, we examined the activity of citrate synthase in rat serum, and the obtained results showed that citrate synthase activity was not arrested in the NC and HFD groups, while citrate synthase activity was prominently enhanced in the HFD + E group (Fig. 1D). Another biochemical test showed that HFD caused a significant increase in blood lipids in rats, and aerobic exercise could prevent HFD-induced dyslipidemia (Fig. 1E). The results of serum ALT and AST tests showed that long-term feeding of HFD led to a prominent enhancement in the content of ALT and AST in rats, while aerobic exercise could reduce the content of ALT and AST in serum (Fig. 1F). In addition, HE staining of rat liver tissue showed that rats fed an HFD for 11 weeks showed a typical appearance of fatty acid infiltration in the liver, indicating that fat metabolism was disrupted, and aerobic exercise effectively alleviated these pathological characteristics. The lipid droplets in HFD + E group were smaller and lower in content, indicating that lipid deposition in liver was alleviated (Fig. 1G).Fig. 1Effects of aerobic exercise on liver injury in HFD rats. A Body weight of rats after 6 weeks of HFD feeding; B Insulin resistance of rats after 6 weeks of HFD feeding; C Body weight monitoring of rats for 11 weeks; D Citrate synthase activity of rats after 11 weeks of HF feeding; E Blood lipid detection of rats after 11 weeks of HF feeding; F ALT and AST content of rat serum after 11 weeks of HF feeding; G HE and oil red O staining of rat liver tissue. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Aerobic exercise improves HFD-induced decreased Sirt1 expression and mitochondrial dysfunction in rats
It has been confirmed that aerobic exercise can alleviate liver injury in rats. Sirt1 levels in liver tissue were detected to explore the mechanism of aerobic exercise to improve liver injury in rats. Obviously, the level of Sirt1 in rat liver tissue decreased after high fat induction, and the expression of Sirt1 was restored after aerobic exercise (Fig. 2A-B). Subsequently, the results of oxidative stress level and antioxidant enzyme activity in rat liver tissue showed that HFD caused a remarkable decrease in SOD and GSH and a prominent increase in MDA, and these were reversed by aerobic exercise (Fig. 2C-E). Mitochondrial fission and fusion are indispensable for the maintenance of form and function. Hence, RT‒qPCR was used to check the influence of aerobic exercise on mitochondrial dysfunction induced by HFD. However, we did not observe significant changes in the gene (Fig. 2F). But, at the protein level, it was found that Drp1 expression increased and Mfn2 and Opa1 expression decreased. Aerobic exercise could reverse the protein changes (Fig. 2G).Fig. 2Aerobic exercise ameliorates HFD-induced reduction of Sirt1 expression and mitochondrial dysfunction in rats. A RT‒qPCR was used to detect the mRNA level of Sirt1; B Western blot for the protein level of Sirt1; C-E Kit for the contents of SOD, GSH and MDA; F RT‒qPCR for the mRNA level of Drp1; G Western blot for the protein level of Drp1, Opa1 and Mfn2. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Aerobic exercise reverses the HFD-induced increase in Drp1 acetylation in rats
Next, we investigated the mechanism by which aerobic exercise downregulates Drp1. Since HFD feeding did not alter the mRNA levels of Drp1 in the liver, it is suggested that transcriptional regulation is not necessarily the latent mechanism. Therefore, we speculated that the posttranslational embellishment of Drp1 may be the cause of the elevated level of Drp1 protein. To test this assumption, we checked the acetylation levels of liver total protein and Drp1 in rats fed an HFD, and high fat induction increased the total protein content and the acetylation level of Drp1 in the liver, and these conditions were also reversed by aerobic exercise (Fig. 3A-B). Subsequently, we examined the acetylation of Drp1 in the cytoplasm and mitochondria. The HFD induced elevated Drp1 acetylation chiefly in the mitochondrial fraction rather than the cytosol, suggesting that acetylation was interrelated with Drp1 translocation to the mitochondria, whereas aerobic exercise decreased the level of Drp1 acetylation in the mitochondria (Fig. 3C).Fig. 3Aerobic exercise reverses the HFD-induced increase in Drp1 acetylation in rats. A Western blot was used to detect the acetylation level of total protein in rat liver; B Western blot for the acetylation level of Drp1 in rat liver; C Western blot for the acetylation levels of Drp1 in cytoplasm and mitochondria. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Mitochondrial dysfunction was alleviated by aerobic exercise
Since Sirt1 is a natural histone deacetylase, aerobic exercise activates the expression of Sirt1. As a consequence, we deduced that aerobic exercise may downregulate the level of Drp1 by activating Sirt1 to reduce Drp1 acetylation. To test this hypothesis, we injected the Sirt1 inhibitor Tenovin-6 into the tail vein of rats before aerobic exercise training to observe its effects on Drp1 acetylation and mitochondrial dysfunction. The acetylation results of liver total protein and Drp1 showed that tenovin-6 reversed the acetylation reduction of total protein and Drp1 by aerobic exercise (Fig. 4A-B). Similarly, inhibitors reverse the high lipid-induced mitochondrial Drp1 acetylation levels (Fig. 4C). In addition, Tenovin-6 also reversed the increase in SOD and GSH levels, as well as the decrease in MDA levels, caused by aerobic exercise (Fig. 4D-F). Next, we found that the expression of Sirt1, Mfn2 and Opa1 proteins decreased and Drp1 protein increased after treatment with the inhibitor on the basis of aerobic exercise (Fig. 4G). In addition, high fat diet can increase the content of TC, ALT and AST in rats, while aerobic exercise could reduce the contents of TC, ALT and AST in serum, and Sirt1 inhibitor reversed the abovementioned results (Fig. 4 H-I). In addition, the HE staining results of rat liver tissuewas infiltrated by fatty acids in the liver, and aerobic exercise effectively alleviated these symptoms. The Sirt1 inhibitor reversed this effect. compared with the HFD group, the lipid droplets in the HFD + E group decreased in size and number, indicating that liver lipid deposition was relieved, and the addition of the Sirt1 inhibitor aggravated liver lipid deposition (Fig. 4J). In short, our results showed that Sirt1 inhibitors reversed the reduced Drp1 acetylation and mitochondrial dysfunction associated with aerobic exercise. Fig. 4Sirt1 inhibitors reverse aerobic exercise-reduced Drp1 acetylation and mitochondrial dysfunction. A Western blot for detecting the acetylation level of total protein; B Western blot for the acetylation level of Drp1; C Western blot for the acetylation levels of Drp1 in cytoplasm and mitochondria; D-F: Kit for the contents of SOD, GSH and MDA; G Western blot for the protein level of Sirt1, Mfn2, Drp1 and Opa1; H Blood lipid detection of rat; I ALT and AST content of rat serum; J HE and oil red O dying of rat liver tissue. ( T6, tenovin-6). (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Sirt1 inhibits OA-induced damage in HepG2 cells
Cell activity and toxicity were detected by CCK8 assay. The obtained results showed that the cell vigor was memorably lessened after OA treatment, while the addition of a Sirt1 activator (CAY-10602, CAY) rescued the low cell vigor caused by OA (Fig. 5A). The results of EdU detection showed that the cell proliferation was markedly lessened after OA remedy, and cell proliferation capacity was observably elevated after adding the Sirt1 activator (Fig. 5B). Subsequently, we validated the influence of Sirt1 activators on apoptosis in HepG2 cells. Flow cytometry results showed that apoptosis was elevated after OA treatment, and the Sirt1 activator rescued OA-induced apoptosis (Fig. 5C). TUNEL and WB showed similar results, with OA treatment increasing apoptosis and the Sirt1 activator reversing this trend (Fig. 5D-E). Taken together, our results indicate that the Sirt1 activator protects against OA-induced injury in HepG2 cells. Fig. 5Sirt1 inhibits OA-induced damage in HepG2 cells. A CCK-8 for checking HepG2 cell viability; B EdU for HepG2 cell proliferation; C Flow cytometry for HepG2 apoptosis; D TUNEL staining for HepG2 apoptosis; E Western blot for the level of proteins Bax, caspase-9, caspase-3 and Bcl-2. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Sirt1 can affect the process of lipid accumulation and mitochondrial damage induced by OA
HepG2 cells were treated with OA to construct NAFLD model in vitro, and the relationship between Sirt1 and lipid accumulation and mitochondrial function was further verified. The results showed that OA treatment led to lipid accumulation in HepG2 cells, which was manifested as intracellular red oil droplets, and the addition of a Sirt1 activator attenuated OA-induced lipid accumulation (Fig. 6A). To assess mitochondrial injury, intracellular ROS levels were visualized by immunofluorescence staining. The obtained results showed that the intracellular ROS level was observably elevated after OA treatment, and the addition of a Sirt1 activator decreased OA-induced ROS production (Fig. 6B). Oxidative phosphorylation (OXPHOS) is a momentous index of mitochondrial function. The Western blot results of OXPHOS showed that the OXPHOS complex was reduced after OA treatment, while OXPHOS enzymatic activity was restored after the addition of the Sirt1 activator (Fig. 6C). Mitochondria are known to be major sites of ATP production, and we checked energy production in OA-treated and decreased ATP production, which was restored by the addition of the Sirt1 activator (Fig. 6D). Similarly, OA treatment decreased the mitochondrial membrane potential (ΔΨm), whereas the addition of a Sirt1 activator restored ΔΨm (Fig. 6E). In addition, OA treatment also decreased the mitochondrial biogenesis-related mtDNA copy number, which was restored by the addition of the Sirt1 activator (Fig. 6F). We also found that OA treatment decreased the protein levels of NRF1 and TFAM in HepG2 cells, and these results were reversed by the addition of a Sirt1 activator (Fig. 6G). Similarly, immunofluorescence showed the same trend, with OA treatment leading to a decrease in NRF1 fluorescence intensity and the addition of a Sirt1 activator leading to an increase in NRF1 expression (Fig. 6H).Fig. 6Sirt1 attenuates OA-induced lipid accumulation and mitochondrial dysfunction in HepG2 cells. A Oil red O staining for observing intracellular lipid accumulation; B immunofluorescence staining for checking intracellular ROS level; C Western blot for intracellular OXPHOS expression; D ATP production in cells was detected by kit; E mitochondrial membrane potential (ΔΨm) changes were detected by kit; F The copy number of mtDNA related to mitochondrial genesis was detected by kit; G Western blot for the level of NRF1 and TFAM; H: Immunofluorescence staining for the level of NRF1. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## OA-induced increase in Drp1 expression in HepG2 cells is caused by increased acetylation levels
Next, we examined the level of Drp1 acetylation in OA-treated HepG2 cells. The obtained results showed that the acetylation level of Drp1 increased with increasing OA remedy concentration and treatment time and further increased after the addition of nicotinamide (NAM, deacetylase inhibitor) (Fig. 7A-B). In addition, Drp1 protein levels were observably elevated after OA treatment and further increased after the addition of NAM (Fig. 7C-D). In summary, our results suggest that the OA-induced elevation of Drp1 expression is caused by increased acetylation levels. Fig. 7OA-induced increased expression of Drp1 in HepG2 cells is caused by increased acetylation levels. A The effect of OA concentration and NAM on the acetylation level of Drp1; B The effect of OA treatment time and NAM on the acetylation level of Drp1; C Western blot for the protein level of Drp1; D Immunofluorescence staining for the level of Drp1. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Sirt1 regulates Drp1 acetylation and affects lipid accumulation and mitochondrial dysfunction
Finally, we added NAM simultaneously with a Sirt1 activator to OA-induced HepG2 cells to test the influence of Drp1 acetylation. Western blot results showed that the Sirt1 activator-induced reduction in Drp1 acetylation was reversed by the addition of NAM (Fig. 8A). The obtained results also showed that the Sirt1 activator-induced decrease in Drp1 protein levels were reversed by the addition of NAM (Fig. 8B). Oil red O staining showed that the addition of NAM inhibited the Sirt1 activator-induced reduction in lipid accumulation (Fig. 8C). Intracellular ROS assays showed that the addition of NAM restored the intracellular ROS decreased by the Sirt1 activator (Fig. 8D). In addition, ATP, mtDNA, OXPHOS, and ΔΨm assays showed that the salutary influence of Sirt1 activators on mitochondrial biogenesis, mitochondrial fusion and division was rescued by the addition of NAM (Fig. 8E-H). The obtained results indicated that the increased NRF1 and TFAM protein expression induced by Sirt1 activator treatment was suppressed by the addition of NAM (Fig. 8I). Immunofluorescence also showed similar results, with NAM treatment reversing the increase in NRF1 fluorescence intensity, and the decrease in Drp1 fluorescence intensity is caused by Sirt1 activators (Fig. 8J). In short, our results suggest that NAM supplementation can regulate the acetylation level of Drp1 to alleviate lipid accumulation and mitochondrial dysfunction induced by Sirt1 on OA.Fig. 8Sirt1 attenuates OA-induced lipid accumulation and mitochondrial dysfunction in HepG2 cells by regulating Drp1 acetylation. A Western blot for Drp1 acetylation; B Western blot for Drp1 protein expression; C Oil red O staining for lipid accumulation; D Immunofluorescence for ROS content; E–H Kit for ATP, mtDNA, OXPHOS and ΔΨm levels; I Western blot for NRF1 and TFAM protein levels; immunofluorescence for NRF1 and TFAM levels. (* $P \leq 0.05$, **$P \leq 0.01$, *** $P \leq 0.001$; ns: no significant difference)
## Discussion
NAFLD is the most hackneyed chronic liver disease, which is characterized by superabundant fat accumulation [26]. The pathological progression of NAFLD tentatively follows a ‘three-hit’ process, namely, steatosis, lipotoxicity and inflammation [27]. The first step in the development of NAFLD is fat accumulation in the liver, and lipid accumulation can promote lipotoxicity and mitochondrial dysfunction, thus triggering hepatocyte death, inflammation and fibrosis in predisposed patients [28]. Aberrant lipid changes in hepatocytes during hepatic steatosis can directly trigger chronic ER stress in the liver. Higher diacylglyceride, phospholipid, free cholesterol (FC), and free fatty acid (FFA) levels activate ER stress. Lipids can directly induce ER stress through IRE1 and PERK, which sense the biophysical modifications of lipid membranes dependent on the ratio of unsaturated/saturated acyl chains. ER stress activates the mitochondrial apoptosis pathway by destroying Ca2+ homeostasis [29, 30]. NAFLD is also linked to chronic inflammation and oxidative stress. Excessive free cholesterol in the livers of diabetic mice with NASH accumulates in mitochondria and the endoplasmic reticulum, leading to an increase in ROS produced by mitochondria and apoptosis in a JNK1-dependent manner [31]. ROS production can also promote hepatic inflammation by increasing the secretion of TNF-α from hepatocytes and KCs, thus upregulating the synthesis of inflammatory cytokines [32].
There is growing evidence that mitochondrial dysfunction is required for the development of NAFLD. Metabolic dysfunction caused by NAFLD leads to mitochondrial dysfunction, which further exacerbates the development of NAFLD. It is well-known that physical exercise (including aerobic exercise and resistance exercise) can reduce liver fat content and effectively alleviate the progression of NAFLD. However, the specific mechanism is unclear and needs to be further explored. In this study, the effect of aerobic exercise on mitochondrial dysfunction induced by NAFLD and its potential mechanism were investigated in rat models of NAFLD. The results show that aerobic exercise can alleviate the liver steatosis and mitochondrial function impairment caused by HFD in rats. The salutary influence of aerobic exercise on mitochondrial dysfunction appears to be associated with activated Sirt1, subsequently decreasing Drp1 acetylation and its activity.
An increasing number of studies have illustrated that exercise can improve the expression of Sirt1 [15, 16]. As a protein deacetylase, Sirt1 is involved in the regulation of multiple cellular pathways [33]. Exercise reduced NAFLD damage caused by HFD by inhibiting lipolysis and enhancing mitochondrial biosynthesis and fatty acid oxidation, and these changes are the result of activation of cellular pathways mediated through Sirt1 [34]. In the constructed zebrafish model of NAFLD, swimming exercise improved hepatic steatosis, inflammation, fibrosis and so on caused by HFD, and these beneficial effects were related to activated Sirt1 signaling [35]. In addition, exercise also alleviates the progression of many diseases by upregulating Sirt1, such as inflammation and metabolic dysfunction of the liver and kidney caused by diabetes [36], myocardial ischemia/reperfusion injury [37] and hypothalamic inflammation [38]. In this study, the level of Sirt1 in rat liver tissue was enhanced by aerobic exercise, and the salutary influence of aerobic exercise on mitochondrial dysfunction caused by HFD was reversed by a Sirt1 inhibitor. At the same time, the same effect of Sirt1 was also shown in the OA cell model. These results are consistent with previous findings that exercise prevents and alleviates NAFLD and its mitochondrial dysfunction by regulating the expression of Sirt1.
Mitochondria are the smallest organelles in cells and the center of energy metabolism and play a momentous role in NAFLD [39]. Mitochondrial biogenesis and fusion/fission are critical in maintaining mitochondrial function [40]. Mitochondrial biogenesis and mitochondrial structural and kinetic alterations have been observed in NAFLD [41]. In NAFLD, the production of superoxide radicals, which are the main source of ROS in mitochondria, is increased. As a result, the production of ROS in mitochondria is increased, which leads to impaired oxidative phosphorylation, reduced ATP and mtDNA production, and ultimately mitochondrial dysfunction [42, 43]. In this study, increased intracellular ROS production, impaired oxidative phosphorylation, reduced ATP and mtDNA production, and decreased mitochondrial membrane potential were observed in a cell model of OA induction. Use of the Sirt1 activator restored the oxidative phosphorylation complex and ATP levels, lessened intracellular ROS generation, and elevated mitochondrial membrane potential and mtDNA copy number. In addition, we examined the protein levels of two factors intimately interrelated to mitochondrial biogenesis, NRF1 and TFAM [44]. The protein levels of NRF1 and TFAM were elevated by the Sirt1 activator. In summary, our results indicated that activation of Sirt1 alleviates mitochondrial dysfunction caused by OA.
Drp1 is a mitochondrial fission-related protein. Abnormal mitochondrial fission mediated by Drp1 leads to ROS overproduction, and inhibition of Drp1 activation can restore mitochondrial function and morphology [45, 46]. Lipid overload has been shown to induce Drp1 acetylation and increase its activity, leading to mitochondrial fission and cardiac dysfunction. Sirt1 is known to be an NAD+-dependent deacetylase. PGC-1α activity is promoted through deacetylation, which in turn regulates mitochondrial biogenesis and energy production [47]. Nevertheless, little is known about Sirt1 regulation of Drp1 acetylation and its effects on mitochondrial function. In this research, Drp1 acetylation and its activity were enhanced in an HFD-induced rat model, and increased Drp1 activity was associated with increased levels of its acetylation. Drp1 acetylation and its activity were reduced with the use of Sirt1 activators, accompanied by recovery of mitochondrial dysfunction. The enhancement of Drp1 acetylation reversed the restorative influence of Sirt1 on mitochondrial dysfunction, suggesting that the alleviative influence of Sirt1 is due to the reduction in Drp1 acetylation.
## Study strengths and limitations
This study studied and discussed the mechanism by which aerobic exercise improves NAFLD, it is hoped to provide a richer theoretical basis for the clinical NAFLD research. Based on animal experiments and cell experiments. However, the mechanism of NALFD in this study is limited to mitochondrial dysfunction, and other mechanisms are not involved in in-depth research, such as ER stress and oxidative stress. In addition, the pathogenesis of NAFLD is complex, which can only be truly clarified by multi-organ joint studies, which may be the focus of our future research.
## Conclusion
This study shows that aerobic exercise alleviates hepatic lipid accumulation and improves mitochondrial dysfunction in HFD-fed rats and OA-treated HepG cells. The salutary effect of aerobic exercise is exerted by activating the expression of Sirt1 and restraining the acetylation and activity of Drp1. These results are helpful to clarify the mechanism of aerobic exercise in alleviating NAFLD and its mitochondrial dysfunction and offer a novel target for the ancillary remedy of clinical NAFLD, which reveals the importance of proper aerobic exercise for NAFLD patient care.
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|
---
title: Unique microRNA expression profiles in plasmic exosomes from intrahepatic cholestasis
of pregnancy
authors:
- Yao Kong
- Yongchi Zhan
- Daijuan Chen
- Xixi Deng
- Xinghui Liu
- Tingting Xu
- Xiaodong Wang
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC9990296
doi: 10.1186/s12884-023-05456-1
license: CC BY 4.0
---
# Unique microRNA expression profiles in plasmic exosomes from intrahepatic cholestasis of pregnancy
## Abstract
### Background
Intrahepatic cholestasis of pregnancy (ICP) is strongly associated with an increased risk of adverse perinatal outcomes. Total bile acid (TBA) levels in the late second or third trimester are a major factor in the diagnosis. Here, we sought to establish the miRNA expression profile of plasm exosomes of ICP and identify possible biomarkers for the diagnosis of ICP.
### Methods
This case–control study involved 14 ICP patients as the experimental group and 14 healthy pregnant women as the control group. Electron microscopy was used to observe the presence of exosomes in plasma. Nanosight and Western blotting of CD63 was used to assess exosome quality. Among them, three ICP patients and three controls were used for isolation plasmic exosome and preliminary miRNA array analysis. The Agilent miRNA array was utilized to dynamically monitor the miRNA expression in plasmic exosomes of included patients in the first trimester(T1), second trimester (T2), third trimester (T3), and delivery (T4). Then, Quantitative real-time Polymerase chain reaction was used to identify and validate differentially expressed miRNAs in plasma-derived exosomes.
### Results
The expression levels of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p in plasma-derived exosomes of ICP patients were significantly higher than those of healthy pregnant women. Besides, these three miRNAs were also significantly up-regulated at the plasma, placental, and cellular levels ($P \leq 0.05$). The diagnostic accuracy of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p was further evaluated by the ROC curve, the area under the curve (AUC) values for each were 0.7591, 0.7727, and 0.8955, respectively.
### Conclusions
We identified three differentially expressed miRNAs in the plasma exosomes of ICP patients. Hence, hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p may be potential biomarkers for enhancing the diagnosis and prognosis of ICP.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-023-05456-1.
## Introduction
Intrahepatic cholestasis of pregnancy (ICP) is the most common pregnancy-specific liver disease, which usually manifests in the middle and late trimesters of pregnancy [1]. Clinical signs include skin pruritus and jaundice, while biochemical manifestations include increased total bile acids (TBA) levels and impaired liver function. The condition progresses such that the aforementioned symptoms and biochemical abnormalities are relieved rapidly after delivery [2]. The etiology and pathogenesis of ICP are relatively complex and have not been fully elucidated at present. Several studies have suggested that it may be related to factors such as inflammation, apoptosis, oxidative stress, lipid metabolism, cell growth, and immune response [3, 4]. Although ICP is reversible in pregnant women, it is associated with an increased risk of perinatal complications including spontaneous preterm birth, meconium-staining amniotic fluid, fetal distress, and sudden intrauterine death [5]. ICP is an exclusionary diagnostic. Currently, the diagnosis of it mainly depends on clinical symptoms and TBA concentration in the second trimester or third trimester. Finding new diagnostic and prognostic ICP biomarkers is crucial since ICP diagnosis has certain limits in clinical work.
MicroRNAs (miRNAs) are non-coding single-stranded RNA molecules encoded by endogenous genes (including animal, plant, some single-celled organisms and viral genomes) with a length of about 22 nucleotides. They do not have an open reading frame (ORF) and do not encode proteins, but can combine with specific complementary sequences in the non-coding region of the 3 '- UTR end of the mRNA of the target gene, mediate the degradation of mRNA or inhibit the translation of protein, and regulate the expression of genes and proteins at the post-transcription level [6, 7]. Due to the wide range of gene regulation capabilities and tissue specificity of miRNAs, researchers assume that they may play important regulatory functions in various systems, tissues, and organs [8]. Studies have found that some pregnancy-related diseases are often accompanied by abnormal expression of miRNAs in placental tissue and even in the peripheral circulation [9–11]. Recent research has shown that the expression of microRNA is closely associated with the occurrence and progression of several pregnancy-related diseases, such as recurrent spontaneous abortion, gestational diabetes mellitus, gestational hypertension, ICP, etc. [ 12–14]. In addition, with the continuous accuracy of detection technology and the accessibility of serum microRNAs, circulating microRNAs have emerged as a kind of disease marker with diagnostic and prognostic potential [15].
MicroRNAs can also be actively released into the circulatory system via exosomes in addition to passive secretion [16]. Exosomes are a class of small membrane transporters with a diameter of about 40–100 nm, which are actively secreted from cells into the extracellular microenvironment, and almost all types of cells can secrete exosomes [17]. Exosomes carry proteins, miRNAs, lncRNAs, circRNAs, mRNAs, and their degradation fragments involved in intracellular signal transduction, and participate in the important regulation of cell activities by transmitting relevant signal molecules to adjacent or distant cells, such as those involved in gene expression regulation, survival and reproduction, reproduction and development, angiogenesis, wound healing and other processes [18]. Among them, exosomes in the blood circulation can be absorbed by recipient cells and affect the biological functions of recipient cells [19]. In addition, the circulating exosomal RNAs are resistant to biochemical degradation by ribonuclease and RNase A in serum under an in vitro condition, so exosomal RNAs are more stable than cellular RNAs [20]. The above characteristics enable circulating exosomes to provide a stable source of RNAs, which can be used as a basis for disease diagnosis, prognosis, and treatment, and it is expected to become an early diagnostic marker of various diseases.
At present, the related research on ICP and exosomes is mainly concentrated in the urine, while the related research on circulating exosomes has not yet been conducted. Furthermore, we know that ICP, in clinical practice, is usually diagnosed in the second and third trimesters of pregnancy, and early screening and prevention cannot be achieved. In the present study, we pursued circulating exosomes, isolated from ICP and the time-matched control group in four different periods: the first trimester, the second trimester, the third trimester, and delivery, trying to find biomarkers that can diagnose ICP early. And quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was used to identify the unique microRNA signals in plasma exosomes from ICP patients after the Agilent miRNA array.
## Participants
In this study, we recruited pregnant women who had regular prenatal care and delivered at West China Second Hospital of Sichuan University from January 2020 to December 2021. All these included population followed up their prenatal examination process, and collected maternal plasma during the first trimester (T1), second trimester (T2), third trimester (T3), and delivery (T4). According to the rules of the Chinese Medical Association of Obstetrics and Gynecology branch [21], ICP is diagnosed by increased maternal serum TBA ≧ 10 µmol/L with or without pruritus and quick disappearance after delivery, as well as the elimination of other causes of liver malfunction or itching. The control group included healthy pregnant women who matched gestational age of the ICP group. All samples were from women with singleton pregnancies. Inclusion criteria of the control group: healthy pregnant women with similar gestational weeks as those in the ICP group, who conceived naturally and with no other pregnancy complications, no medical-related complications, and no history of abortion; Exclusion criteria: got liver disease, biliary tract disease, or autoimmune disease before pregnancy; or pregnant with complications such as gestational hypertension and gestational diabetes mellitus, or medical-related complications.
According to the inclusion and exclusion criteria, a total of 28 pregnant women were enrolled in this case–control study, with 14 ICP patients and 14 healthy pregnant women serving as the experimental group and the control group, respectively. Among them, three ICP patients and three controls were used for isolation plasmic exosome and preliminary miRNA array analysis. Besides, Nanosight was used for nanoparticle tracking analysis and Western blotting of CD63 to assess exosome quality. The plasma samples in the ICP group and control group were 10 and 11, respectively. The remaining subjects, including the 8 patients screened for differential miRNAs, were used for subsequent validation. Written informed consents were obtained from patients before the procedure and manuscript publication. This study was approved by the ethical committees at the West China Second University Hospital of Sichuan University.
## Sample collection
During prenatal care and follow-up, blood samples were collected from participants in the first trimester (8–11 weeks), second trimester (22–24 weeks) and third trimester (34–36 weeks), and finally both blood samples and placental tissue were collected at delivery. All participants were instructed to sit still for half an hour and then take 3 ml of fasting elbow venous blood in a sitting position with an EDTA anticoagulation tube between 8:00 and 8:30 am, and centrifuged at 3000 r/min for 10 min after resting for 3–4 h at 4 °C. Then, the supernatant plasma was divided into sterilized enzyme-free EP tubes and frozen at -80 °C for future use. In addition, after the placenta was isolated, a tissue of about 1 cm × 1 cm × 1 cm was cut from the maternal surface of the placenta, washed with saline and placed in sterile enzyme-free EP tubes, and immediately put into liquid nitrogen for freezing and storage.
## Isolation and examination of exosomes from plasma
Plasma samples were obtained and processed within 2 h. The processing procedure involved centrifugation at 2,000 g for 30 min at 4 °C, then aspiratingthe supernatant and centrifuge at 12,000 g for 45 min at 4 °C, continue to aspirate the supernatant and centrifuge at 110,000 g for 70 min at 4 °C, discard the supernatant and resuspend in 100 μL of 1 × phosphate buffered saline (PBS) and store at -80 °C until analysis. Then, according to the method shown in Bang C, et al. [ 22], the morphology and distribution of the extracted exosomes were measured by Transmission Electron Microscopy and Nanosight, and exosome markers were detected by Western Blot.
## The miRNA microarray analysis
After extraction of total RNA from plasma exosomes, NanoDrop ND-2000 (Thermo Scientific) was used to quantify and Agilent Bioanalyzer 2100 (Agilent Technologies) was applied to detect the integrity of RNA. Then, the total RNA was reverse transcribed into double-stranded cDNA, and cRNA labeled with Cyanine-3-CTP (Cy3) was further synthesized. The labeled cRNA was hybridized with the microarray, and the raw image was obtained by scanning with an Agilent Scanner G2505C (Agilent Technologies) after elution (GEO accession number GSE210764).
## RNA isolation and Quantitative real-time Polymerase chain reaction (qRT-PCR)
Total RNA from tissues and cells was extracted with Trizol reagent (Takara, Code No. 9109) according to the kit instructions. The RNA isolated from each sample was then reverse transcribed into cDNA using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Code No. RR047A). The reaction conditions for reverse transcription were as follows: genomic DNA was removed at 42 °C for 2 min, reverse transcription at 37 °C for 15 min, and reverse transcriptase was inactivated at 85 °C for 5 s. Next, Quantitative real-time PCR was performed using the TB Green® Premix Ex Taq™ II Kit (Takara, Code No. RR820A). The amplification conditions were as follows: pre-denaturation at 95 °C for 30 s, PCR reaction for 40 cycles, denaturation at 95 °C for 5 s, and annealing at 60 °C for 30 s. The relative expression levels were calculated using the 2−ΔΔCTCq method.
## Western blot
Exosome samples were lysed in RIPA lysis buffer (ThermoScientific™, Code No. 89901) and centrifuged at 13,000 rpm for 10 min at 4 °C. Protein concentration was determined and then separated with polyacrylamide gel electrophoresis using $10\%$ SDS-PAGE gels. Proteins were transferred to polyvinylidene difluoride membranes, which were then incubated with $5\%$ bovine serum albumin in Tris-buffered saline for 2 h at room temperature to block nonspecific binding. Membranes were then incubated with the primary antibodies overnight at 4 °C. After three washes, the samples were incubated with goat anti-rabbit secondary antibody for 60 min at room temperature. Finally, the specific proteins were detected using enhanced chemiluminescence (ThermoScientific™, Code No. 34580).
## Cell culture and construction of cholestatic cell model
The human chorionic villus trophoblast cell line (HTR8/SVneo cells) was obtained from the American Type Culture Center (ATCC) and identified by STR Authentication. HTR8/Svneo cells were cultured in $90\%$ RPMI 1640 medium, $1.0\%$ dual antibodies (penicillin and streptomycin), and $10\%$ fetal bovine serum in a complete medium. HTR8/Svneo cells were seeded in sterile 6-well plates and cultured until the cell density reached $70\%$, and then treated with 100 μmol/L sodium taurocholate (TCA) for 24 h to establish a cholestatic cell model [23].
## Statistical analysis
To analyze raw data from Agilent chips, Feature Extraction software (version10.7.1.1, Agilent Technologies) was used to analyze array of images to extract raw data. Then, Genespring software (version 14.8, Agilent Technologies) was applied toanalyze the raw data and identify differentially expressed miRNAs. And the target genes of differentially expressed miRNAs were the predicted intersections with the miRDB and miRWalk databases. Finally, predictive analysis of the miRDB database was applied to predict the roles of these target genes. Hierarchical Clustering was performed to reveal the distinguishable miRNAs expression pattern among samples. Furthermore, statistical analyses were conducted using Microsoft Excel and GraphPad Prism 8 (https://www.graphpad.com/). The results were presented as the mean ± standard deviation (SD). Differences between two groups were compared by the Student’s t test, while differences among multiple groups were compared by one-way or two-way ANOVA analysis of variance. Meanwhile, receiver operating characteristic (ROC) curve analysis was further used to evaluate the diagnostic value of biomarkers for ICP, and the sensitivity and specificity were obtained by referring to the area under the curves (AUCs), where AUC greater than 0.70 was considered as an acceptable level of discrimination.
## Clinical characteristics of the included population
The baseline characteristics and perinatal outcomes of 14 patients with ICP and 14 healthy expectant mothers are summarized in Table 1. There was no significant difference in maternal age, body mass index (BMI), and newborn weight between the two groups ($P \leq 0.05$). However, the levels of TBA, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were significantly higher in ICP patients than in healthy pregnant women. In addition, ICP patients had significantly lower gestational weeks at delivery compared to healthy controls (Table 1).Table 1Baseline characteristics of ICP patients and healthy pregnant womenVariableCon ($$n = 14$$)ICP($$n = 14$$)P-valueAge(years)37.4 ± 1.637.6 ± 2.90.72Body Mass Index(kg/cm2)25.5 ± 1.826.0 ± 1.90.58TBA(μmol/L)1.6 ± 0.836.1 ± 28.30.00*ALT(U/L)20.5 ± 20.0146.9 ± 150.80.00*AST(U/L)20.1 ± 8.7117.5 ± 140.00.00*Gestational weeks39.4 ± 0.437.9 ± 1.40.00*Newborn weight (g)3345 ± 387.23105 ± 462.50.20Con, healthy pregnant women; ICP ICP patients, TBA Total bile acids, ALT Alanine aminotransferase, AST Aspartate aminotransferase; *$P \leq 0.05$
## Analysis and identification of plasma-derived exosomes
We isolated plasma exosomes using ultracentrifugation, which is the current gold standard method for exosome extraction. Electron microscopy was used to examine the morphology of plasma exosomes, as seen in Fig. 1A. It was found that the exosomes in the plasma of ICP patients were intact, spherical, and homogeneous in size. Plasma-derived exosomes all expressed the characteristic tetraglycan protein CD63, as well as the expected Nanosight profile (Fig. 1B, C), indicating that plasma-derived exosomes are a suitable sample to observe the differentiated expression of miRNAs in included patients. Fig. 1Analysis and identification of plasma-derived exosomes. A Electronmicroscopy shows the morphology and particle size distribution of exosomes; B *Nanosight analysis* shows the number of exosomes and the particle size distribution; C Western Blot shows the surface marker CD63 expression of exosomes
## Screening and target gene prediction of differential miRNAs from plasma-derived exosomes
We elected to use Agilent miRNA microarray analysis technology to detect miRNA levels in exosomes after evaluating the sensitivity of next-generation miRNA sequencing technology applied to plasma-derived exosomal miRNAs. First, we inferred the overall distribution of differential miRNAs in plasma-derived exosomes using volcano plots, and screened for differential miRNAs by assessing both the fold change and corrected p-value (qvalue). The default screening condition for differential miRNAs was twofold upregulation or 0.5-fold downregulation, and qvalue < 0.05 when the samples had biological replicates. The results showed that a total of 49 differentially expressed miRNAs were screened, of which 34 miRNAs were up-regulated and 15 miRNAs were down-regulated (Fig. 2A, B). Then, we calculated the number of differential miRNAs obtained by comparing T1, T2, T3, and T4 these four different periods, and made a Venn diagram. The findings revealed thatthe number of miRNAs shared among T1, T2, T3, and T4 was 0, while the numbers of miRNAs unique to T1, T2, T3, and T4 were 6, 73, 5, and 24, respectively (Fig. 2C). Additionally, we further used cluster analysis to compare the number of differential miRNAs in each stage of T1, T2, T3, and T4, respectively, and discovered that there were, respectively, 12, 117, 7, and 73 difference miRNAs at each stage of T1, T2, T3, and T4 (Fig. 2D-F).Fig. 2Bioinformatics analysis of differentially expressed plasma-derived exosomal miRNAs. A Volcano plot of differentially expressed plasma-derived exosomal miRNAs of ICP patients and healthy pregnant women. The scattered dots in the figure represent individual miRNAs. Gray dots indicate non-significantly differentially expressed miRNAs, red dots indicate significantly up-regulated differential miRNAs, and blue dots indicate significantly down-regulated differential miRNAs; B *Cluster analysis* of differentially expressed plasma-derived exosomal miRNAs of ICP patients and healthy pregnant women. Clustering was performed with log10 (TPM + 1) values, with red color indicating highly expressed miRNAs and blue color indicating lowly expressed miRNAs; C Venn diagram of plasma-derived exosomal miRNAs that are differently expressed in ICP patients and healthy pregnant women. Red, blue, green, and purple indicate differentially expressed plasma-derived exosomal miRNAs specific to T1, T2, T3, and T4, respectively; *Cluster analysis* of differentially expressed plasma-derived exosomal miRNAs in ICP patients and healthy pregnant women during T1 (D), T2 (E), T3 (F) and T4 (G). Con, healthy pregnant women; Case, ICP patients; T1, first trimester; T2, second trimester; T3, third trimester; T4, delivery In the meanwhile, we predicted the target genes of exosome-derived differentially expressed hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p. The miRDB database was used to predict all target genes, and Fig. 3 showed the predicted target genes with a miRDB score greater than 90. *Target* genes associated with hsa-miR-940 included SSH2(92.07), KIAA0930 (94.90), PFKFB3 (94.13), HAPLN1 (92.07), RHOA (90.21), XPO7 (94.48), SLC12A6 (90.80), GLG1 (93.81), CDK16 (90.73), CCDC136 (92.84), and HLF (91.37) (Fig. 3A). Additionally, the target genes for hsa-miR-636 included SHTN1 (94.56), TUB (91.88), JADE3 (91.73), PHC3 (93.14), VOPP1 (90.70), ZFYVE26 (91.05), PSEN1 (91.14), and SEMA4A (94.25) (Fig. 3B). Also, the target genes associated with hsa-miR-767-3p include THRAP3 (91.97), PTPRT (96.82), ARID5B (91.05), SET (91.77) and JARID2 (94.69) (Fig. 3C).Fig. 3Target genes of differentially expressed plasma-derived exosomal miRNAs. A *Target* genes associated with hsa-miR-940; B *Target* genes associated with hsa-miR-636; C *Target* genes associated with hsa-miR-767-3p
## Validation of differentially expressed plasma-derived exosomal miRNAs at the plasma level
In plasma, miRNAs are not only found in exosomes but can also be present in other vesicles or bound to plasma non-vesicular proteins [24, 25]. To assess whether differential expression of exosomal miRNAs can be detected using whole plasma samples, we compared the expression levels of eight differentially expressed exosomal miRNAs in whole plasma from healthy pregnant women and ICP patients at each stage of T1, T2, T3, and T4. Candidate miRNAs selected for validation included seven differentially expressed miRNAs and one miRNA with no statistically significant differential expression but with a |logFC| of more than twofold (Table 2).Table 2Differentially expressed serum exosomal miRNAs for validationExosome miRNAFold changeP-valueChange in expression levelhsa-miR-9402.04120.0187Uphsa-miR-30d-5p3.17810.0098Uphsa-miR-18b-3p4.85950.0022Uphsa-miR-767-3p5.86650.0007Uphsa-miR-197-3p6.11490.0024Uphsa-miR-483-3p7.24840.0002Uphsa-miR-664a-3p7.48400.0007Uphsa-miR-6362.24750.0562Up We used real-time PCR to detect the expression of miRNAs at various stages of T1, T2, T3, and T4 in healthy pregnant women and ICP patients in whole plasma. The results showed that only hsa-miR-636 ($p \leq 0.05$) and hsa-miR-767-3p ($p \leq 0.05$) were differentially expressed between the ICP group and the control group, while hsa-miR-940 ($$p \leq 0.46$$) showed an increasing trend in the ICP group compared with the control group (Fig. 4A). During the first trimester (T1), only the expression of hsa-miR-767-3p ($p \leq 0.001$) was significantly increased in the ICP group (Fig. 4B). During the second trimester (T2), only hsa-miR-636 ($p \leq 0.05$) and hsa-miR-767-3p ($p \leq 0.01$) were significantly increased in the ICP group (Fig. 4C). Likewise, only hsa-miR-636 ($p \leq 0.05$) and hsa-miR-767-3p ($p \leq 0.001$) were significantly increased in the ICP group during the third trimester (T3) (Fig. 4D). And during the delivery period (T4), only the expression of hsa-miR-767-3p ($p \leq 0.05$) was significantly increased in the ICP group (Fig. 4E).Fig. 4Validation of differentially expressed plasma-derived exosomal miRNAs at the plasma level. A Dynamicchangesof differential expressed miRNAs in plasma of ICP patients and healthy pregnant women throughout pregnancy; Changes of differential expressed miRNAs in the plasma of ICP patients and healthy pregnant women in thefirst trimester (B), second trimester (C), third trimester (D) and delivery (E). Con, healthy pregnant women; Case, ICP patients. * $P \leq 0.05$; *** $P \leq 0.001$
## Differential expression of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p at the placental level
Subsequently, the expression of these three exosomal miRNAs (hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p) at the placental level was differentially expressedas determined by the real-time quantitative PCR assay. We discoveredthat hsa-miR-940 ($p \leq 0.05$), hsa-miR-636 ($p \leq 0.01$) and hsa-miR-767-3p ($p \leq 0.05$) were significantly upregulated in ICP patients compared with controls (Fig. 5A-C).Fig. 5Differential expression of hsa-miR-940, hsa-miR-636 and hsa-miR-767-3p at the placental level. The levels of hsa-miR-940 (A), hsa-miR-636 (B) and hsa-miR-767-3p (C) in ICP and controls. * $P \leq 0.05$; **$P \leq 0.01$
## Differential expression of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p atthe cellular level
The cells were divided into two groups: HTR8/SVneo cells that had been treated with sodium taurocholate (TCA) and HTR8/SVneo cells withthe blank control group. Real-time quantitative PCR was used to assess the expression of three exosomal miRNAs (hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p) at the cellular level. The results showed that hsa-miR-940 ($p \leq 0.01$), hsa-miR-636 ($p \leq 0.05$), and hsa-miR-767-3p ($p \leq 0.01$) were significantly up-regulated in the TCA-treated group compared with the control group (Fig. 6A-C).Fig. 6Differential expression of hsa-miR-940, hsa-miR-636 and hsa-miR-767-3p at the cellular level. Expression levels of hsa-miR-940 (A), hsa-miR-636 (B) and hsa-miR-767-3p (C) in TCA and control groups. Control, HTR8/SVneo cells; TCA, cholestatic cell model; *$P \leq 0.05$; **$P \leq 0.01$
## Predictive value of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p at the plasma level
To evaluate the likelihood of being diagnosed with ICP, we plotted ROC curves and calculated the AUCs of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p, respectively. As shown in Fig. 7A-C, the AUCs of hsa-miR-940, hsa-miR-636 and hsa-miR-767-3p were, respectively, 0.7591, 0.7727 and 0.8955, while the sensitivity were $63.64\%$, $72.73\%$ and $68.64\%$, and the specificity were $100\%$, $80\%$ and $100\%$, respectively. This indicates that hsa-mir-940, hsa-mir-636, and hsa-mir-767-3p have important diagnostic value in predicting the risk of ICP.Fig. 7Predictive value of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p at the plasma level. A ROC analysis of hsa-miR-940 (B) ROC analysis of hsa-miR-636 (C) ROC analysis of hsa-miR-767-3p
## Discussion
Our study has found that the expression levels of hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p in plasma-derived exosomes of ICP were significantly higher than those of the healthy control group. Similarly, the expression levels of these three miRNAs were significantly up-regulatedat the cellular, placental, and plasma levels. This indicates that the analysis and detection of plasma-derived microRNAs in the context of ICP is feasible, and maternal plasma-derived microRNAs have the potential to become non-invasive biomarkers for the diagnosis of ICP.
There are multiple factors involved in the development of ICP, and its pathogenesis is unclear [26–28]. Even though ICP is a benign disease state for the mother, ICP can lead to many adverse pregnancy outcomes, such as preterm birth, intrauterine fetal distress, stillbirth, and so on [29]. We discovered that the gestational week was significantly shorterin ICP patients than in healthy pregnant womenin the current study, indicating that ICP patients are at potential risk of preterm delivery. Early diagnosis and prognosis of ICP patients are therefore of essential clinical significance. Currently, the clinical diagnosis of ICP is largely based on the corresponding clinical symptoms [30] and laboratory criteria [31]. These standards, however, do not serve as the gold standard for diagnosis because of their flaws. Therefore, further studies of the possibility of early sensitive molecules that might occur in the ICP environment are necessary, as well as screening for sensitive and specific biomarkers. In addition, the diagnosis of ICP is generally in the second and third trimesters of pregnancy, but by analyzing the plasma exosomes of ICP patients, we found that miRNAs were specifically expressed in the first trimester, second trimester, third trimester, and delivery, which may provide new research findings for the diagnosis of ICP.
MicroRNAs are involved in many physiological processes, such as cell growth, energy metabolism, apoptosis, infection, and immunity, and are essential for maintaining the homeostasis of the body [32]. Plasma exosome-derived miRNAs are more stable than plasma-derived miRNAs due to the protection of phospholipid bilayer, and miRNAs in exosomes have higher tissue-derived specificity, thus exosomal miRNAs have greater potential in disease diagnosis [33]. Devor et al. found that exosome-derived miRNA patterns in the first trimester of pregnancy differed between women with preeclampsia and those normal pregnancies and could be used for early preeclampsia diagnosis [34]. Nairet al. used a retrospective case–control study to explore exosome-derived miRNAs and found that the expression of hsa-miR-92a-3p, hsa-miR-16–2-3p, and hsa-miR-1910-5p was significantly upregulated in gestational diabetes and their expression changed with increasing maternal BMI [35]. Besides, it has been reported that the expression levels of some exosomal miRNAs in the urine of ICP patients are significantly higher than those of normal controls, indicating that exosomal miRNAs have potential as non-invasive diagnostic urinary biomarkers for ICP [36]. However, there are few studies on the comprehensive analysis of plasmicexosomal miRNA expression profiles in ICP patients.
In this study, the Agilent miRNA array and RT-qPCR were used to compare the relative expression of plasma exosome-derived miRNAs from ICP patients and healthy pregnant women at different stages of pregnancy (early, middle, late pregnancy, and delivery) to identify the unique microRNA signatures in plasmic exosomes of ICP patients, overcoming the unfavorable conditions by obtaining dynamic plasma and placental tissue specimens during pregnancy. We found that hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p were differentially expressed in both plasma exosomes and plasma. Further validation at the placental tissue level and cellular level revealed that hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p were also significantly upregulated in ICP patients.
Hsa-miR-940 is a non-coding RNA located on chromosome 16p13.3, which mainly affects the stability and translation of target protein-coding genes by binding to the 3' untranslated region, thereby participating inpost-transcriptional regulation of gene expression in multicellular organisms [37]. It has been demonstrated that hsa-miR-940 is expressed unbalanced in many diseases such as ovarian cancer [38], breast cancer [39], gastric cancer [40], etc. Zhang et al. found that miR-940 was highly expressed in the villi of early abortions and was capable of inhibiting the proliferation of trophoblasts by targeting ZNF672, leading to early abortions [41]. Cao et al. found that miR-940 regulates the inflammatory response of osteoarthritic chondrocytes by targeting MyD88 [42]. However, the expression of miR-940 in some cancers remains controversial. Liu et al. discovered that downregulation of plasma miR-940 expression in gastric cancer [40], while Yuan et al. found that high expression of miR-940 inhibited hepatocellular liver cancer growth and was correlated with patients' prognoses [43]. In the present study, our bioinformatics analysis showed that plasma exosome-derived miR-940 was significantly upregulated in ICP patients at early, mid, and late pregnancy as well as at delivery, and this trend was also validated at the plasma, placental, and cellular levels. In addition, the AUC of miR-940 was 0.7591, and its sensitivity and specificity were $63.64\%$ and $100\%$, respectively, suggesting that the upregulation of this exosomal miRNA may become a new biomarker for detecting ICP.
Hsa-miR-636 is a miRNA located on chromosome 17q25.1. It is known to play a role in the occurrence and development of various tumors such as lung cancer [44], prostate cancer [45], etc. Besides, it has been reported that hsa-miR-636 targets cyclin-dependent kinase 6 (CDK6) and B-cell lymphoma factor 2 (Bcl-2), and can inhibit the survival of cervical cancer cells by targeting CDK6/Bcl-2 [46]. In addition, hsa-miR-636 can promote the proliferation of bladder cancer cells by reducing the expression of Kruppel-like factor 9 (KLF9) on the 3’UTR of its mRNA [47]. Although hsa-miR-636 hasshown to play a function in either promoting or inhibiting the formation and progression of various cancer, its expression in ICP and its effect on prognosis have yet to be studied. In our study, we found that plasma exosome-derived hsa-miR-636 was significantly up-regulated in the context of ICP, and its AUC was 0.7727, indicating that hsa-miR-636 has a certain value in the diagnosis of ICP.
Hsa-miR-767 has two types: miR-767-3p and miR-767-5p, among which hsa-miR767-3p is generally considered to be the representative of miRNA-767. Currently, the majority of hsa-miR767-3p-related research is being conducted in the field of cancer research [48]. Wan et al. found that miR-767-3p inhibited lung adenocarcinoma cell proliferation, migration, and invasion by targeting CLDN18 [49]. A study conducted by Zhang et al. showed that miR-767 could function as a tumor promoter in melanoma by targeting CYLD [50]. However, the underlying molecular mechanism of hsa-miR767-3p in the context of ICP has not been thoroughly investigated. Here, we found five genes that hsa-miR767-3p targets. Among them, JARID2 is expressed tissue-specifically and is a regulator of histone methyltransferase complexes that play important roles in embryonic development, including heart and liver development, neural tube fusion process, and hematopoiesis [51]. Furthermore, the AUC of hsa-miR767-3p was 0.8955, which was the largest among the three microRNAs we validated, indicating that hsa-miR767-3p has a high value for diagnosing ICP.
The innovation of this paper is that we are the first one who focused on the comprehensive analysis of the expression profiles of plasmic exosomal miRNAs in ICP patients. Our findings testify that hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p may be potential biomarkers for enhancing the diagnosis of ICP. Besides, we found miRNA specific expression at T1, T2, T3, and T4 different stages through plasma exosome analysis. Given that ICP is now typically diagnosed in the middle and late stages of pregnancy, this finding may provide research findings for the diagnosis of ICP. While the limitation of this study is that this is an exploratory study, the functional roles of these three miRNAs in the emergence of ICP are still unknown. Furthermore, only a small number of samples were evaluated in this study due to difficult specimen collection conditions, and additional research is required to corroborate these findings. Future lines of investigation will build on molecular mechanism studies to look into other diagnostic and/or therapeuticinterventions in ICP disease.
## Conclusions
In conclusion, we identified three differentially expressed miRNAs in theplasmic exosomes of ICP patients and assumed that hsa-miR-940, hsa-miR-636, and hsa-miR-767-3p may be potential biomarkers for enhancing the diagnosis and prognosis of ICP.
## Supplementary Information
Additional file 1: Fig 1C. In order to improve the clarity and conciseness of the presentation, the main paper shows the blot after cutting. The original, uncropped blot have been uploaded to the additional file. Additional file 2: Table S1. Sequences of the reverse transcription quantitative polymerase chain reaction primers used.
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|
---
title: 'Clinical prediction model for prognosis in kidney transplant recipients (KIDMO):
study protocol'
authors:
- Simon Schwab
- Daniel Sidler
- Fadi Haidar
- Christian Kuhn
- Stefan Schaub
- Michael Koller
- Katell Mellac
- Ueli Stürzinger
- Bruno Tischhauser
- Isabelle Binet
- Déla Golshayan
- Thomas Müller
- Andreas Elmer
- Nicola Franscini
- Nathalie Krügel
- Thomas Fehr
- Franz Immer
- Patrizia Amico
- Patrizia Amico
- Patrick Folie
- Monique Gannagé
- Maurice Matter
- Jakob Nilsson
- Andrea Peloso
- Olivier de Rougemont
- Aurelia Schnyder
- Giuseppina Spartà
- Federico Storni
- Jean Villard
- Urs Wirth-müller
- Thomas Wolff
- John-David Aubert
- John-David Aubert
- Vanessa Banz
- Sonja Beckmann
- Guido Beldi
- Christoph Berger
- Ekaterine Berishvili
- Annalisa Berzigotti
- Pierre-Yves Bochud
- Sanda Branca
- Heiner Bucher
- Emmanuelle Catana
- Anne Cairoli
- Yves Chalandon
- Sabina De Geest
- Sophie De Seigneux
- Michael Dickenmann
- Joëlle Lynn Dreifuss
- Michel Duchosal
- Sylvie Ferrari-Lacraz
- Christian Garzoni
- Nicolas Goossens
- Jörg Halter
- Dominik Heim
- Christoph Hess
- Sven Hillinger
- Hans H Hirsch
- Patricia Hirt
- Linard Hoessly
- Günther Hofbauer
- Uyen Huynh-Do
- Bettina Laesser
- Frédéric Lamoth
- Roger Lehmann
- Alexander Leichtle
- Oriol Manuel
- Hans-Peter Marti
- Michele Martinelli
- Valérie McLin
- Aurélia Merçay
- Karin Mettler
- Nicolas J Mueller
- Ulrike Müller-Arndt
- Beat Müllhaupt
- Mirjam Nägeli
- Graziano Oldani
- Manuel Pascual
- Jakob Passweg
- Rosemarie Pazeller
- Klara Posfay-Barbe
- Juliane Rick
- Anne Rosselet
- Simona Rossi
- Silvia Rothlin
- Frank Ruschitzka
- Thomas Schachtner
- Alexandra Scherrer
- Macé Schuurmans
- Thierry Sengstag
- Federico Simonetta
- Susanne Stampf
- Jürg Steiger
- Guido Stirnimann
- Christian Van Delden
- Jean-Pierre Venetz
- Julien Vionnet
- Madeleine Wick
- Markus Wilhelm
- Patrick Yerly
journal: Diagnostic and Prognostic Research
year: 2023
pmcid: PMC9990297
doi: 10.1186/s41512-022-00139-5
license: CC BY 4.0
---
# Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol
## Abstract
### Background
Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.
### Methods
The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.
### Discussion
Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.
### Study registration
Open Science Framework ID: z6mvj
## Introduction
Kidney failure affects between 5 and 7 million people worldwide [1]. Kidney transplantation is considered as the best possible renal replacement therapy. In Switzerland, approximately 240 deceased-donor kidneys are transplanted each year, with approximately 1400 to 1500 patients on the waiting list [2]. Patients receiving a kidney transplant have overall lower mortality compared to patients on dialysis [3, 4].
Nevertheless, despite considerable improvements in the last decades, patients continue to experience late allograft failure. In Switzerland, kidney transplant recipients are enrolled in a nationwide prospective cohort (Swiss Transplant Cohort Study; STCS), with longitudinal follow-up of allograft and patient outcomes after transplantation [5, 6]. However, a validated prognostic model for the risk of allograft failure or patient-reported outcomes such as the quality of life is lacking in Switzerland. Accurately predicting individual patient outcomes would not only be relevant for clinical and therapeutic care, but also enable quality control and the optimization of organ transplantation programs. This can further advance the patients’ gained life-years.
According to a systematic review on risk prediction models for graft survival after kidney transplantation, the most common predicted outcome was graft survival, either with graft failure and death as event (composite endpoint) or with graft failure as event and censored for death [7]. However, with an aging population and an increase in elderly transplant recipients, death with a functioning graft can increasingly bias the results and should rather be considered as a competing risk [8, 9].
Most prediction models for outcomes in kidney transplantation have around 4 to 14 predictor variables [10–14]; often the models consider either donor-only or recipient-only characteristics, but combining both donor and recipient variables in the model may further improve the prognosis [9]. Among the most prominent risk scores is the KDPI (kidney donor profile index) released by the OPTN (US Organ Procurement & Transplantation Network) using 10 donor-only predictors [14, 15]. However, this score would prove suboptimal in clinical practice in Switzerland as some predictors (e.g., ethnicity, hypertension, diabetes, hepatitis C status) do not vary as much when compared to the USA population. Furthermore, the organ transport and related ischemia times are shorter in Switzerland and marginal donor kidneys (lower quality organs with still acceptable medical risks) are transplanted more often compared to the USA. Therefore, due to the numerous differences in donor organ quality, transplant setting, and recipient characteristics a risk score cannot be easily adapted in clinical practice.
Another disadvantage of existing prediction models is that they mainly consider hard endpoints such as patient survival and graft failure; however, these are late events and alternative endpoints are needed that can be assessed early in time. Patient-reported health-related quality of life may be at least as relevant [16, 17] and is closely associated with mortality and survival in end-stage renal disease [18, 19]. Furthermore, new surrogate endpoints for kidney graft survival have recently been suggested such as the slope of the estimated glomerular filtration rate (eGFR slope) for kidney disease progression [20–22].
In summary, a number of prognostic models have been proposed to predict kidney graft survival [9–14]. However, due to variability in donor mixes (donation after brain death [DBD] vs. donation after circulatory death [DCD]), transplant settings (e.g., ischemia times), patient population characteristics, timepoint of risk calculation, and substantial variability in the data available across different countries and the restriction to hard endpoints existing models cannot easily be adapted into clinical practice in Switzerland.
## Objective
The main objective of this study is to develop and validate three clinical kidney prediction models (KIDMO) for graft survival (primary outcome), quality of life, and eGFR slope (secondary outcomes) that included both donor and recipient characteristics as predictors. The study includes model development, internal-external cross-validation and is based on high-quality clinical data from a prospective national multi-centre cohort study and computable at the time of organ allocation. This novel prognostic tool could enable a reliable and valid prognosis at the time of organ allocation compared to existing clinical risk scores. Additionally, existing prognostic models can be assessed with respect to potential bias by means of model recalibration and model revision and subsequently be used as a benchmark for the novel prognostic tool.
## Research design and methods
The study protocol and related materials (expert survey and the a priori selected candidate predictors) are available on the project page [23] (osf.io/35apn) and have been registered on 1 September 2022 on the Open Science Framework (osf.io/z6mvj).
## Data source
In Switzerland, the STCS prospectively enrolls all solid organ transplant recipients since 2008 at six transplantation centres (Basel, Bern, Geneva, Lausanne, St. Gallen, and Zurich) [5, 6]. In order to successfully implement the prediction models, data from two data sources need to be combined:SOAS database (Swiss Organ Allocation System) performs organ allocation according to the Swiss federal law and includes donor, transplant, and recipient characteristics (donor age, sex, cause of death, creatinine, ischemia time, donor-recipient immunological assessments, etc.)STCS database (Swiss Transplant Cohort Study) longitudinally collects patients’ mid and long-term outcomes (status [death, alive, lost to follow-up], graft loss date, quality of life assessment, creatinine, etc.) at baseline, 6 months, and every 12 months after transplantation.
SOAS data is provided by the Federal Office of Public Health, and STCS data by the STCS Data Centre after peer-review of the proposal and approval by the local ethics committee (KEK Bern). Donor-recipient data linkage is ensured via the unique recipient identifier (SOAS RS-number). A schematic overview of the prediction models and the data involved is shown in Fig. 1.Fig. 1Schematic overview of the multivariable prediction models and the data sources involved. Three separate models will be developed to predict the three outcomes by a set of predictors. Predictors are based on SOAS data, while mid- and long-term outcomes were collected by a national multi-centre cohort study
## Target population
We will include all kidney transplant recipients from 2008 onwards that were prospectively enrolled by the STCS and gave informed consent. Exclusion criteria is living-donor transplantation. Multi-organ transplantation, pediatric patients, and pre-emptive transplantations may be considered for exclusion if there were not enough samples and events in these subpopulations (see also “Sensitivity analyses”).
## Study outcomes
Our three outcomes of interest are defined as follows:Primary outcome: death-censored graft survival [24, 25] calculated from the date of transplantation to the date of irreversible graft failure by return to dialysis (or retransplantation). Death with a functioning graft is considered as a competing risk. Secondary outcomes: quality of life (self-reported overall health) at 12 months with the EQ-VAS [26–28] and kidney disease progression with eGFR slope [ml/min per 1.73m2/year] assessed using the first two eGFR follow-up measurements (6 and 12 months) calculated according to the Chronic Kidney Disease Epidemiology Collaboration (2021 CKD-EPI) [29] which is based on serum creatinine, age, and sex.
## Clinical predictor variables
We used expert knowledge to prespecify candidate predictors for graft survival (primary outcome); the same candidate predictors will also be used for the secondary outcomes. For this purpose, a kidney expert group was formed (4 transplant nephrologist from different Swiss transplant centres). Firstly, a comprehensive list of variables was created based on expert interviews and published prediction models. Secondly, a survey (osf.io/gc7jt) was performed ($$n = 8$$, six transplant nephrologists and two kidney transplant recipients) to rate each variable on a 5-point Likert scale (not relevant, low relevance, neutral/not sure, relevant, very relevant). Altogether, 62 variables were assessed for relevance and a list was created with the variables that had the highest average score. Third, the list was presented to the expert group to determine the final candidate predictors (osf.io/f972e), taking into account the score from the survey, clinical considerations, data availability at time of allocation, overlap with other variables, and the number of coefficients to be estimated based on the sample size calculation (as described below). Fourth, the final candidate predictors were presented to the Swisstransplant kidney working group (STAN) that involves kidney experts from all the six transplant centres for final discussion. The final set of candidate predictors are shown in Table 2 and include 16 variables with 19 coefficients to be estimated.
## Sample size
Guidelines regarding the minimum required sample size for the development of a new multivariable prediction model have been proposed [30]. We used the accompanying R package “pmsampsize” [31]: the approach uses a set of criteria to minimize overfitting and ensure precise estimation of key parameters in the prediction model; a R notebook with code is available online (osf.io/35apn).
Based on information from the STCS data centre there were a total of $$n = 2537$$ kidney transplantations between 2008 and 2020 (excluding all living-donor transplantations), see Table 1. Subtracting $20\%$ of the data for cross-validation, adding 240 additional patients enrolled in 2021, and assuming $5\%$ missing data, we arrived at an available sample size of $$n = 2110$$ patients for model development for graft survival and eGFR slope, and $$n = 1126$$ for quality-of-life self-assessments at 1-year follow-up (due to lower return rate). We then calculated the number of parameters that seem feasible to estimate in a multivariable model given the data. Table 1Number of kidney transplantations (deceased donors) per year and transplantation center2008200920102011201220132014201520162017201820192020Sum%BE$1735282518202526192542362934514\%$CHUV$1724261928173338333534343237015\%$HUG$1918231617241727182529261827711\%$KSSG$1116131912121612161891381757\%$USB$4939303724333347334860414251620\%$USZ$5456576861636473668170697285434\%$Sum$1671881771841601691882231852322442192012537100\%$BE Inselspital, Bern University Hospital, CHUV Centre hospitalier universitaire vaudois (Lausanne), HUG Hôpitaux Universitaires de Genève, KSSG Kantonspital St. Gallen, USB University Hospital Basel, USZ University Hospital Zurich For the primary outcome graft survival, we calculated a Cox-Snell adjusted R2 based on the information reported in a study with the same primary outcome and a similar population [13]. The study reported a C statistic of 0.78, 241 events, a sample size of $$n = 2169$$ and 11 parameters. This resulted in a Cox-Snell R2 of 0.08 [32]. We used a graft failure rate of 0.017 and a median follow-up of 4.8 years; this information was derived from Table 2 in [6]. For a model with 19 parameters and a shrinkage factor of 0.9, the resulting minimum sample size was $$n = 2042$.$ The anticipated number of events were 167 which corresponded to 8.8 events per parameter. Table 2Clinical candidate predictors for model development with the number of coefficients to be estimated. The sum of the number of coefficients to be estimated was based on a sample size calculation for clinical prediction modelsNrVariable typeVariable nameLevel of measurementCoefficientsComments1DonorAgeContinuous3Restricted cubic splines with 3 knots2DonorDonor type (DCD, DBD)Binary1Donation after circulatory death (DCD) or brain death (DBD)3DonorHistory of diabetesBinary14DonorHistory of hypertensionBinary15DonoreGFR on admissionContinuous1Estimated glomerular filtration rate6DonoreGFR at allocationContinuous17DonorResuscitationBinary1Donor was reanimated or not8DonorCause of deathCategorial2Using the 2 most common causes9TransplantationAnti-HLA: DSABinary1Presence of donor-specific antibodies10TransplantationHLA mismatchesCount1Human leukocyte antigen mismatches; number between 0 and 611TransplantationRetransplantationBinary1Whether recipient was retransplanted12RecipientAgeContinuous113RecipientCardiovascular diseaseBinary114RecipientHistory of diabetesBinary115RecipientBMIContinuous1Body mass index16RecipientPre-transplant dialysisContinuous1Time on dialysisTotal:19 We also performed sample size calculations for the secondary outcomes. For the quality of life (continuous), we anticipated an adjusted R2 of 0.15 as a lower bound for the model. This estimate is conservative as a study with the same outcome reported an R2 of 0.35, however, in a different population [33]. We know from a previous study that the EQ-VAS score has a mean (SD) of 62.8 (20.73) in the Swiss kidney transplant population [34]. A model with a shrinkage factor of 0.9, an anticipated R2 of 0.15, and 20 parameters required a minimum sample size of $$n = 984$$; for 50 parameters and an R2 of 0.35 the sample size was $$n = 996$.$ For the eGFR slope, we anticipated an R2 of 0.15 and a mean (SD) from two published studies of 1.28 (2.5) and 1.8 (1.9), respectively [22, 35]. In both scenarios, a model with 40 parameters resulted in a minimum sample size of $$n = 2090$.$
Therefore, the following number of parameters can be reliably estimated in a prognostic model:Graft survival: 19 parametersHealth state (EQ-VAS): 20–50 parameterseGFR slope: 40 parameters
## Statistical analysis
Patients’ baseline characteristics will be reported using mean and standard deviation for continuous variables, median and interquartile range in case of non-normality (assessed with histograms), and absolute and relative frequencies for categorial variables. Missing data will be assessed and reported for each variable. As the fraction of missing data is expected to be below $5\%$, a complete case analysis will be carried out. All analyses will be performed with the R software for statistical computing version 4.2.2 [36].
## Prediction model development
For the primary outcome graft survival (time-to-event data), we will use a Fine & Gray model which is an extension to the Cox model to address competing risks using the functions coxph() and finegray() from the survival package [37, 38]. Our research question is focused on the direct assessment of the actual risk. Thus, a regression model that directly acts on cumulative incidence function (CIF) is to be preferred over cause-specific hazards in the context of prediction, the estimation of absolute risks, and clinical decision making [39–42]. For the secondary outcomes, quality of life and eGFR slope (continuous data), we will use two linear mixed models.
Dependencies in the data (kidney allografts from the same donor and retransplanted recipients) will be addressed as follows: as the function coxph() only supports a single cluster term, we will use exploratory analyses to determine which is more important of donor ID or recipient ID. A cluster term in the Fine & Gray model and a random intercept term in the mixed model will then account for dependencies in the data.
After we fitted a model using the a priori selected candidate predictors (Table 2), we perform model reduction with backward elimination using the Akaike information criterion (AIC) [43]. This step is repeatedly done using bootstrap resampling [44, 45], and candidate predictors are required to be retained in > $50\%$ of the bootstrap samples.
Throughout model development, we will perform the following model diagnostics:Investigating potential nonlinear relationship between continuous variables and the outcome with restricted cubic splinesChecking multicollinearity among predictors (using variance inflation factor; VIF)Checking proportional hazards assumption with Schoenfeld residualsInspection of residuals, i.e., residuals vs. fitted values, comparing residual variance across study centres, and Q-Q plots Coefficient estimates of the predictors and $95\%$ CIs will be determined and discussed with the expert group for clinical interpretability.
## Model evaluation and internal-external cross-validation
For the primary outcome, model evaluation includes cumulative incidence curves for different risk groups based on the prognostic index, calibration plots, calibration intercept and slope, Brier score, and Harrell’s c-statistic [8, 46]. For the secondary outcomes, we assess the root-mean-square error (RMSE), the explained variation statistic (adjusted R2), and calibration plots.
For internal validation, we use Monte-Carlo bootstrapping [47]: Models will be developed on 200 bootstrap samples and tested on the same and on the original samples to assess optimism-corrected performance [44]. In a next step, we use internal-external cross-validation [45] with the original data from every transplant center being left out once for validation of the model that was based on data from the remaining centres. This will allow us to assess heterogeneity across transplant centres with random effects meta-analysis [48]. After validation, the final model will be fitted on all the available data and will also include a fixed effect term for the transplant centers.
## Sensitivity analyses
In a sensitivity analysis, we will define an eGFR < 15ml/min/1.73m2 as kidney failure in line with the KDIGO 2012 clinical practice guidelines and use it as surrogate event for kidney survival to investigate the performance of the developed prognostic model with this outcome. We will also assess a potential effect across time (e.g., due to changes in treatment strategies) by including transplantation year as a predictor. Another sensitivity analysis is to assess the prognostic model of the secondary outcome quality of life with quality-of-life data from 24 months after transplantation. Additionally, we can also examine model performance in clinically relevant subgroups. These are paediatric patients, retransplanted patients, multi-organ transplantations, and pre-emptive kidney transplantation [48].
## Model presentation
Reporting will adhere to the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis” (TRIPOD) recommendations [49]. In particular, the final models for the primary and secondary outcomes, respectively, will be presented using the coefficient estimates and $95\%$ confidence intervals, and will include the covariance matrix of the random effects, the error variance, and the regression formula to allow independent application and validation of the model.
## Discussion
We described the study protocol for the planned development and validation of a novel clinical prediction model for kidney graft survival (primary outcome) and quality of life and eGFR slope (secondary outcomes) in the Swiss transplant setting. Our methodological approach will be based on a multivariable Fine and Gray model and two linear mixed models for the primary and secondary outcomes, respectively. Our proposed statistical procedures will take into consideration dependencies in the data (same-donor kidney transplants), between-center heterogeneity, and competing risks.
Previous studies on prognostic models and risk scores related to kidney transplantation outcomes cannot easily be applied into clinical practice in Switzerland; thus, no risk score is currently routinely applied in the deceased-donor organ offer. Among the main reasons are differences in the patient population and transplant setting: for example, in *Switzerland hepatitis* C status and ethnicity, two widely used prognostic factors (e.g., in the OPTN’s KDPI calculator) have too little variability as a prognostic factor in the Swiss population of deceased donors. Also, more marginal donor kidneys are being transplanted compared to the USA. Thus, existing prognostic models and related risk scores need to be carefully assessed and validated before implemented in clinical practice.
The high-quality data collected in Switzerland during organ allocation (demographic data, medical history, immunological data, etc.) and the nationwide multicenter prospective cohort study enrolling all transplant recipients in Switzerland since 2008 is the most ideal setting to develop a novel prediction model. In this process, we will use expert knowledge (four transplant nephrologists and two kidney recipients) to preselect the candidate predictors. Purely data driven methods such as stepwise selection may render too optimistic and therefor underperform with new data [43].
In shared clinical decision-making, adequate communication of the risks tailored to the specific patient is essential. This research project actively involves kidney transplant recipients in the design, the variable selection, and the applicability and interpretation of the novel risk prediction tool.
The potential risk of selection bias in our study is mitigated using a national multi-center cohort that enrolled all transplant recipients in Switzerland and has informed consent of $93\%$ of the population of solid-organ transplanted recipients [6]. The potential risk of model optimism and overfitting is addressed by our sample size calculation that determined the number of parameters that are feasible to fit in our modeling, bootstrap resampling, and by internal-external validation procedure. The approach will enable us to study between center heterogeneity if present.
The long-term goal of this research proposal is to deliver a risk calculator as a tool that is applicable in clinical practice to assist clinicians and their patients in their informed decision-making. In the future, healthcare providers together with patients, for example, can predetermine the risk they are willing to accept from a donor kidney, with graft survival, quality of life, and eGFR slope estimates available for their consideration. Approval from health regulatory authorities need to be considered as well. Furthermore, subsequent studies can perform external validation with data from other countries and assess the clinical impact of the novel prognostic tool.
## Conclusion
The prediction model for the prognosis of kidney graft survival, quality of life (patient-reported overall health), and eGFR slope will use data from a national multi-center cohort study and the Swiss organ allocation system. By adhering to recently developed best practices in model development, validation, and reporting, we will minimize potential risk of bias and provide a reliable risk assessment in deceased-donor kidney transplantation for nephrologists and their patients.
## Revision history
Version Date Change Version 1.01. September 2022Original protocol that was registeredVersion 1.17. September 2022- Added list of current STCS members and STAN members in the section “Acknowledgements”- Added link to study registration in the section “Research Design and Methods”.- Added link to materials in the section “Availability of data and materials”.- Clarification in section “Prediction model development”: “not larger than the maximum number of parameters”. Version 1.22. November 2022- Clarification to use a Fine & Gray subdistribution model to address competing risks- New Table 2 with candidate predictors (was in supplementary material before)- More details for model reduction- More details for sensitivity analyses- Some minor edits to add clarityVersion 1.328. November 2022- Clarification on the sensitivity analysis with poor graft function as outcome- Clarification on how to address dependencies in the data (same donor, and retransplantations)Version 1.48. December 2022- Changed sensitivity analysis outcome to kidney failure < 15 ml/min/1.73m2 in line with KDIGO 2012 clinical practice guidelines.
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|
---
title: 'Maternal and Child Health handbook and under-6 child overweight in greater
Jakarta, Indonesia: a cross-sectional web-based survey'
authors:
- Akiko Saito
- Masahide Kondo
journal: BMC Nutrition
year: 2023
pmcid: PMC9990306
doi: 10.1186/s40795-023-00697-x
license: CC BY 4.0
---
# Maternal and Child Health handbook and under-6 child overweight in greater Jakarta, Indonesia: a cross-sectional web-based survey
## Abstract
### Background
In Indonesia, the double burden of child overnutrition and undernutrition is a public health concern. The nationally distributed Maternal and Child Health (MCH) handbook provides child nutrition information to caregivers. We aimed to find mothers’ information sources regarding child nutrition, including the internet and the MCH handbook, and to explore the association between overweight and use of the MCH handbook.
### Method
A cross-sectional web-based survey was conducted among mothers with children under 6 years old in Greater Jakarta during 2019. Bivariate and multivariate logistic regression examined the association between child nutrition status and use of the MCH handbook.
### Results
Data were collected from 233 children. Overweight, underweight, wasting, and stunting were identified in $36.4\%$, $22.6\%$, $26.8\%$, and $37.6\%$, respectively. $62.5\%$ of mothers used the MCH handbook, and $88.2\%$ used the internet via a mobile phone. Significantly more cases of overweight were observed among children whose mothers used the MCH handbook (adjusted OR [aOR]: 5.829; $95\%$ Confidential Interval [CI]: 1.618–20.999) whereas no relationship was observed between MCH handbook use and child undernutrition. Significant associations with child overweight were found for mother’s education (tertiary) (aOR: 0.294; $95\%$CI: 0.098–0.885), employment type (fulltime) (aOR: 0.185; $95\%$CI: 0.061–0.562), watching television (more than 1 h) (aOR: 4.387; $95\%$CI: 1.648–11.678) and recognition of child overweight by mother (yes) (aOR: 3.405; $95\%$CI: 1.05–11.03).
### Conclusion
These results indicate the need to support mothers of children exhibiting overnutrition and undernutrition. The MCH handbook should be modified to address this issue.
## Background
Increasing prevalence of child overnutrition is observed in developing countries where undernutrition remains a public health problem [1]. Childhood obesity increases the risk of adulthood cardiovascular disease [2, 3]. Globally, 38 million ($5.6\%$) children under 5 years of age were estimated to be overweight or obese in 2019 [1]. Indonesia’s 2018 National Basic Health Research Survey (RISKESDAS) reported that $8.0\%$ of children under 5 years of age were overweight, while stunting, wasting, and underweight were identified in $29.8\%$, $10.3\%$, and $17.7\%$, respectively [4].
To tackle the double burden of overnutrition and undernutrition, it is crucial to deliver the necessary information to mothers or caregivers. However, little is known about the sources of information about child nutrition used by caregivers in Indonesia. In rural Indonesia, health facilities are major sources of information about stunting for mothers [5]. Several previous studies have investigated sources of information for pregnant women regarding nutrition, [6–8] and multiple sources have been identified, including health professionals and the internet [6].
Internet technology is widely used in Indonesia, and $71\%$ of people were found to possess smartphones in urban areas in 2018 [9]. Consequently, it may be expected that many mothers seek information about child nutrition from the internet. Some previous studies have reported that the internet is used as an information source by pregnant women in Indonesia [6–8] and a qualitative study reported that 17 of 23 pregnant women sought nutrition information through the internet [6].
In addition, the nationally distributed Maternal and Child Health (MCH) handbook is also a major source of information about child nutrition in Indonesia. The MCH handbook is a home-based printed booklet that is distributed to all pregnant women at their first antenatal care visit. This handbook functions as a maternal and child health record and information book, and contains nutrition information about various topics, such as exclusive breastfeeding, complementary food, and healthy diets. However, the extent to which mothers use the handbook as a source of information is currently unclear.
Previous studies have investigated the risk factors for child overweight and obesity, [11–17, 17–19] and excess energy intake and low physical activity level have been identified in a number of studies. [ 10, 12, 17, 19]. The reported associations between parents’ education level, [11, 13, 17, 19] economic level [14, 17, 20] and child obesity have been inconsistent among previous studies. In previous studies in Indonesia, being a male child, having parents who are overweight, having a father with a university education, [13, 14] being part of a family in the higher economic quintile, [14, 17] being stunted, [18, 20] being an urban resident, [13, 16, 17] having a low physical activity level, [17] consuming ultra-processed food, [13] and frequent intake of fried foods [19] have been identified as risk factors for child obesity. However, the way in which the MCH handbook relates to child nutrition in current conditions regarding the increasing prevalence of child overnutrition has not been investigated. Osaki et al. [ 2019] reported that the prevalence of stunting and underweight in children were significantly lower in families that were guided and sensitized using the MCH handbook compared with families that were not guided or sensitized [10]. However, it is also necessary to explore the association between the MCH handbook and child overweight.
Thus, in the current study, we sought to clarify the sources of information about child nutrition used by mothers, including the internet and the MCH handbook, and to explore the association between overweight and use of the MCH handbook. Although the main focus of the current study was overweight, undernutrition was also included in the analysis.
## Methods
A cross-sectional survey was conducted among mothers of children under 6 years old in Greater Jakarta from 7 to 10 May 2019.
Mothers who were 16 years old or older and living in Greater Jakarta were recruited through a local web survey agency. No exclusion criteria were set as long as participants meet all inclusion criteria. The web-based survey was purposefully chosen to examine an association between overweight and the MCH handbook as an information source among mothers who have access to internet. Because smartphone possession rate is higher [9] and more child overweight exists in urban than rural area, [13, 16, 17] Greater Jakarta was selected as study site. It is known that child overweight in *Indonesia is* more prevalent in families containing mothers with higher economic level, [14, 17, 20] and a higher level of education, [11, 13, 17, 19] and the participants in the web-based survey were expected to have these characteristics. The target number of participants, 180 mothers, was determined according to sample size calculation and budgetary consideration. An invitation was sent to all mobile panels (30,851 eligible panels out of 963,197 panels as of 2018) who registered to the web survey agency. The first page of the survey contained information, describing the study and asking for their voluntary participation. All participants provided informed consent by reading and responding. Ethics Committee, Faculty of Health Science Technology & Graduate School of Health Care Science, Bunkyo Gakuin University permitted this research (#2018-0034).
In a structured questionnaire, mothers were asked to provide the following information: mother’s sociodemographic information, child’s age, sex, weight (kg), height (cm), hours of watching television, ownership of the MCH handbook, and nutrition practice. Mother’s sociodemographic data included mother’s age, education level, employment type, and household monthly income. Sources of information about child nutrition were collected through multiple choice answers regarding use of the MCH handbook, internet via mobile phone, internet via computer, books or magazines, family members, friends, health professionals, and other sources. Sources of anthropometric data were not identified in the questionnaire.
The World Health Organization 2006 Growth Standard was applied to classify child nutrition status. Child overweight was defined as a weight for height z-score > 2 standard deviations (SD), child stunting was defined as a height/length for age z-score < − 2 SD, child wasting was defined as a weight for height z-score < 2 SD, and child underweight was defined as a weight for age z-score < − 2 SD [22].
Descriptive analysis was performed to present the prevalence of overweight, stunting, underweight, wasting, and normal (not malnourished) as well as the distribution of each variable. Because the analysis was based on the child, a mother’s data were used twice if they provided information about more than one child. Sources of information for child nutrition were also described in percentages (%). The Odds Ratio [OR] and $95\%$ Confidential Interval [CI] for the association of factors related to overweight and each nutrition status were estimated using bivariate and multivariate logistic regression analysis. Appropriate cut-off values were applied to create binary variables for all items. The Statistical Package for Social Science (SPSS) software version 28.0 (IBM, Armonk, NY, USA) as used to perform statistical analysis.
## Results
Data were collected for a total of 233 children from 180 mothers. The distributions of overweight, stunting, underweight, and wasting are presented in Table 1. The high prevalence observed for child overweight ($36.4\%$) was expected, because the study was designed to capture a population at increased risk of child overweight. However, surprisingly, stunting, underweight, and wasting also had relatively high prevalence rates of $22.6\%$, $26.8\%$, and $37.6\%$, respectively.
Table 1Distribution of overweight, stunting, underweight, and wasting ($$n = 233$$)n%OverweightYes8036.4No14063.6Missing13StuntingYes8537.6No14162.4Missing7UnderweightYes5122.6No17577.4Missing7WastingYes3716.8No18383.2Missing13NormalYes7334.8No13765.2Missing13 A description of child sociodemographic characteristics and nutrition practice by nutrition status is provided in Table 2. Nearly half of the children were 0–2 years old and the other half were 3 years old or older. $65.2\%$ of children had mothers that were aged 30 years and older, and $34.8\%$ of children had mothers aged 20–29 years old. None of the mothers were teenagers. Because it has previously been reported that a higher prevalence of child overweight is found in families with mothers who have higher levels of education [11, 13, 17, 19] and economic level [14, 17, 20] in Indonesia, we sought to identify these mothers in the current study. Therefore, most households had a relatively high monthly income, and most mothers had a high level of education. Of the households, $41.6\%$ had a monthly income of Rp. 1.250.001-Rp. 4.000.000, and $58.4\%$ had a monthly income of Rp.4,000,001 or more. All mothers had completed secondary education and $76.8\%$ of them had completed tertiary and higher education. $87.4\%$ of mothers possessed the MCH handbook. $51.8\%$ of the children watched television for less than 1 h a day, and $48.2\%$ watched television for 1 h or more a day.
Table 2Sociodemographic characteristics and nutrition practice of children and mothers by nutrition statusOverallOverweightStuntingUnderweightWastingNormal Child’s age 0–2 years old110 (47.2)39 (48.8)29 (34.1)16 (31.4)16 (43.2)42 (57.5)3 years and over123 (52.8)41 (51.2)56 (65.9)35 (68.6)21 (56.8)31 (42.5) Child's sex Boy114 (48.9)32 [40]47 (55.3)30 (58.8)20 (54.1)36 (49.3)Girl119 (51.1)48 [60]38 (44.7)21 (41.2)17 (45.9)37 (50.7) Mother’s age 20–29 years old82 (35.2)29 (36.3)31 (36.5)24 (47.1)15 (40.5)22 (30.1)30 years old and over151 (64.8)51 (63.7)54 (63.5)27 (52.9)22 (59.5)51 (69.9) Mother’s marital status Married229 (98.3)79 (98.8)85 [100]51 [100]37 [100]71 (97.3)Not married or widowed4 (1.7)1 (1.3)0 [0]0 [0]0 [0]2 (2.7) Mother’s education level Completed secondary school54 (23.2)21 (26.3)19 (22.4)11 (21.6)13 (35.1)9 (12.3)Completed tertiary or higher education179 (76.8)59 (73.8)66 (77.6)40 (78.4)24 (64.9)64 (87.7) Mother’s employed type Not fulltime or housewife145 (62.2)54 (67.5)50 (58.8)29 (56.9)15 (40.5)49 (67.1)Fulltime worker88 (37.8)26 (32.5)35 (41.2)22 (43.1)22 (59.5)24 (32.9) Monthly household income Rp. 1.250.001-Rp. 4.000.00097 (41.6)33 (41.3)33 (38.8)22 (43.1)19 (51.4)28 (38.4)Rp. 4.000.001 or more136 (58.4)47 (58.8)52 (61.2)29 (56.9)18 (48.6)45 (61.6) Owns MCHHB No30 (13.2)11 (13.9)9 (10.6)5 (9.8)6 (16.2)7 (10.1)Yes198 (86.8)68 (86.1)76 (89.4)46 (90.2)31 (83.8)62 (89.9) Hours of television watching a day Less than 1 hour117 (50.2)33 (41.3)47 (55.3)35 (68.6)22 (59.5)36 (49.3)1 hour or more116 (49.8)47 (58.8)38 (44.7)16 (31.4)15 (40.5)37 (50.7) Told child is overweight by medical worker No188 (87.0)51 [75]62 (78.5)45 (91.8)34 (94.4)68 (97.1)Yes28 (13.04)17 [25]17 (21.5)4 (8.2)2 (5.6)2 (2.9) Breastfeeding before 6 months old No23 (9.9)8 (10.1)4 (4.8)2 (3.9)4 (10.8)9 (12.3)Yes209 (90.1)71 (89.9)80 (95.2)49 (96.1)33 (89.2)64 (87.7) Formula milk before 6 months old No123 (53.0)44 (55.7)46 (54.8)33 (64.7)21 (56.8)32 (43.8)Yes109 (47.0)35 (44.3)38 (45.2)18 (35.3)16 (43.2)41 (56.2) Water before 6 months old No210 (90.5)69 (87.3)72 (85.7)49 (96.1)36 (97.3)66 (90.4)Yes22 (9.5)10 (12.7)12 (14.3)2 (3.9)1 (2.7)7 (9.6) Sweetened beverage before 6 months old No209 (90.1)69 (87.3)73 (86.9)50 [98]35 (94.6)66 (90.4)Yes23 (9.9)10 (12.7)11 (13.1)1 [2]2 (5.4)7 (9.6) Complementary food before 6 months old No178 (76.7)57 (72.2)64 (76.2)46 (90.2)35 (94.6)54 [74]Yes54 (23.3)22 (27.8)20 (23.8)5 (9.8)2 (5.4)19 [26] Junk food No216 (93.1)70 (88.6)75 (89.3)51 [100]37 [100]69 (94.5)Yes16 (6.9)9 (11.4)9 (10.7)0 [0]0 [0]4 (5.5)Snacks†Never27 (12.5)12 (16.2)14 (16.9)11 (22.0)5 (13.5)5 (7.8)*Once a* day or more189 (87.5)62 (83.8)69 (83.1)39 (78.0)32 (86.5)59 (92.2)Eat outside‡Less than few times a week111 (51.4)40 (54.1)48 (57.8)36 (72.0)19 (51.4)26 (40.6)Few times a week or more105 (48.6)34 (45.9)35 (42.2)14 (28.0)18 (48.6)38 (59.4)Takeaway food‡Less than few times a week115 (53.2)44 (59.5)47 (56.6)33 (66.0)19 (51.4)29 (45.3)Few times a week or more101 (46.8)30 (40.5)36 (43.4)17 (34.0)18 (48.6)35 (54.7)StuntingNo41 (52.6)Yes37 (47.4)MCH handbook: Maternal and Child Health handbook† 6 months and older children only‡ 1 year and older children only Table 3 shows the sources of information about child nutrition by nutrition status. $65.2\%$ of mothers used the MCH handbook. As expected, accessing the internet via mobile phone was the most prevalent information source of information, used by $88.2\%$ of mothers. Of mothers with an overweight child, $86.6\%$ and $65.1\%$ sought information from the MCH handbook and the internet via mobile phone, respectively.
Table 3Information resource of child nutrition by nutrition status ($$n = 233$$)OverallOverweightStuntingUnderweightWastingNormaln (%)n (%)n (%)n (%)n (%)n (%)Information from MCH handbookNo81(34.8)22 (27.5)23 (27.1)17 (33.3)13 (35.1)29 (39.7)Yes152 (65.2)58 (72.5)62 (72.9)34 (66.7)24 (64.9)44 (60.3)Information from internet via mobile phoneNo26 (11.2)9 (11.25)12 (14.1)7 (13.7)2 (5.4)8 (11.0)Yes207 (88.8)71 (88.8)73 (85.9)44 (86.3)35 (94.6)65 (89.0)Information from internet via computer0No117 (50.2)38 (47.5)43 (50.6)29 (56.9)24 (64.9)33 (45.2)Yes116 (49.8)42 (52.5)42 (49.4)22 (43.1)13 (35.1)40 (54.8)Information from books/magazinesNo109 (46.8)40 [50]44 (51.8)25 (49.0)20 (54.1)33 (45.2)Yes124 (53.2)40 [50]41 (48.2)26 (51.0)17 (45.9)40 (54.8)Information from family membersNo79 (33.9)30 (37.5)24 (28.2)14 (27.5)12 (32.4)26 (35.6)Yes154 (66.1)50 (62.5)61 (71.8)37 (72.5)25 (67.6)47 (64.4)Information from friendsNo110 (47.2)38 (47.5)37 (43.5)22 (43.1)16 (43.2)37 (50.7)Yes123 (52.8)42 (52.5)48 (56.5)29 (56.9)21 (56.8)36 (49.3)Information from medical workersNo117 (50.2)45 (56.25)44 (51.8)20 (39.2)19 (51.4)36 (49.3)Yes116 (49.8)35 (43.8)41 (48.2)31 (60.8)18 (48.6)37 (50.7)Information from othersNo230 (98.7)79 (98.75)84 (98.8)50 (98.0)36 (97.3)72 (98.6)Yes3 (1.3)1 (1.25)1 (1.2)1 (2.0)1 (2.7)1 (1.4)MCH handbook: Maternal and Child Health handbook The results of bivariate and multivariate analysis are presented in Tables 4 and 5, respectively. Multivariate analysis revealed that there was a significantly higher prevalence of overweight children among mothers who used the MCH handbook as an information source (adjusted OR [aOR]: 5.829, $95\%$CI; 1.618–20.999). However, no significant relationship was observed between the MCH handbook and child underweight in the current study. A significant association with child overweight was seen with mother’s education (tertiary) (aOR; 0.294, $95\%$CI; 0.098–0.885), employment type (fulltime) (aOR; 0.185, $95\%$CI; 0.061–0.562) and watching television (more than 1 h) (aOR; 4.387, $95\%$CI; 1.648–11.678). In addition, the results revealed that mother’s recognition of their child’s nutrition status accurately reflected their child’s nutrition status; thus, when mothers thought that their child was overweight, the child tended to be overweight (aOR; 3.405, $95\%$CI; 1.05–11.03). Child’s age (3 years and older) was significantly associated with stunting (aOR; 6.211, $95\%$CI; 2.69–14.34) and underweight (aOR; 5.129, $95\%$CI; 2.012–13.401). Mother’s age (30 years and older) was associated with underweight (aOR; 0.318, $95\%$CI; 0.136–0.741). Complementary food given before 6 months old was associated with wasting (aOR; 0.157, $95\%$CI; 0.029–0.852). In the current study, no association was observed between use of the MCH handbook and undernutrition.
Table 4Results of bivariate logistic regression analysis of nutrition statusOverweightStuntingUnderweightWastingOdds$95\%$CIOdds$95\%$CIOdds$95\%$CIOdds$95\%$CIChild sex (girl)1.7310.9913.0210.6910.4031.1880.6170.3281.1610.770.3791.565Child age (3 years or older)0.9110.5261.5792.6071.4914.5592.5391.314.9211.2160.5962.478Mother's age (30 years or older)0.9770.5511.7310.8710.496.1.530.5020.2660.9490.7890.3831.626Mother’s education (Tertiary)0.7340.3851.3980.9790.5121.871.0430.4892.2220.4680.2181.006Mother’s employment (Fulltime)0.6610.3721.1761.5360.7792.3621.4180.7512.6762.7941.3555.76Monthly household income (Rp. 4,000,001 or more)1.0680.6121.8641.1340.6541.9650.8790.4671.6520.6430.3171.307Television (1 hour or more)1.9551.123.4140.6910.4031.1880.3590.1850.6970.6890.3361.412Owns MCH handbook (yes)0.8240.3621.8781.4560.633.3661.5120.5464.1890.6910.2581.852Information from MCH handbook (yes)1.5580.8562.8351.7240.9593.0981.070.5532.0710.9230.441.938Information from internet via mobile phone (yes)1.090.4622.5740.6180.2681.4250.7210.2831.8352.6420.59611.699Information from internet via computer (yes)1.2750.7362.2110.9360.5461.6040.6840.3651.2830.5130.2461.069Information from books/magazines (yes)0.8670.51.5020.7110.4141.220.8760.4691.6350.7370.3631.498Information from family members (yes)0.8420.4751.4931.4850.8292.6611.450.7282.8871.1480.5412.434Information from friends (yes)1.0440.6021.8091.1750.6842.0181.1620.622.1791.270.6232.589Information from medical workers (yes)0.6740.3881.1710.8930.5211.531.7580.9313.320.9370.4621.9Information from others (yes)0.8730.0789.7860.8270.0749.2651.730.15419.4752.5140.22228.468Breastfed before 6 months old (yes)0.9080.3592.2963.1151.0229.4943.3630.76114.8560.850.2692.689Formula milk given before 6 months old (yes)0.9720.5581.6930.8870.5161.5250.5330.2791.0180.9290.4551.895Water given before 6 months old (yes)2.110.8195.4362.1830.8995.3010.3140.0711.3930.2530.0331.958Sweetened beverage given before 6 months old (yes)2.110.8195.4361.620.6813.8550.1380.0181.0510.5550.1232.51Complementary food given before 6 months old (yes)1.7750.9223.4181.0640.5622.0150.2940.110.7840.1740.040.752Junk food given before 6 months old (yes)4.3711.314.6972.2970.8226.418Snacks given once a day or more (yes) †0.6740.2971.5290.3790.1560.9220.2890.1190.7060.9710.3422.758Eat outside few times a week or more (yes) ‡0.8770.4951.5530.6020.3441.0540.2980.1490.5961.030.5052.101Eat take away food few times a week or more (yes) ‡0.6820.3831.2150.7660.4391.3370.4840.2490.9381.1070.5432.259Mother thinks child is overweight7.1672.67519.202Stunting1.9731.1133.497Overweight1.9731.1133.497MCH Handbook; Maternal and Child Health Handbook† 6 months and older children only‡ 1 year and older children only Table 5Results of multivariate logistic regression analysis of nutrition statusOverweightStuntingUnderweightWastingOdds$95\%$CIOdds$95\%$CIOdds$95\%$CIOdds$95\%$CIChild sex (girl)1.4320.6423.1970.6540.3221.3270.7280.3321.60.6760.31.523Child age (3 years or older)0.5090.2081.2426.2112.6914.345.1922.01213.4010.9890.4092.392Mother's age (30 years or older)0.9740.4192.2640.5560.2621.1780.3180.1360.7410.9640.4122.257Mother’s education (Tertiary)0.2940.0980.8852.0930.7895.5542.550.8847.3560.6550.2361.819Mother’s employment (Fulltime)0.1850.0610.5621.6870.6824.1691.2130.4393.3512.2150.8126.046Monthly household income (Rp. 4,000,001 or more)1.2070.4593.1680.7960.3571.7770.7020.2951.6710.7580.311.851Television (1 hour or more)4.3871.64811.6780.4930.2011.2070.5040.1851.3690.7820.2872.127Owns MCH handbook (yes)0.4690.12.1991.1430.2764.7272.3950.46712.280.6090.1492.487Information from MCH handbook (yes)4.1331.25213.6391.5550.5984.0430.8860.3142.5011.0210.3273.192Information from internet via mobile phone (yes)2.1790.5898.0680.460.1461.4480.7180.1922.6784.0680.73522.507Information from internet via computer (yes)1.9630.7625.0580.9270.42.1480.6570.2591.6610.510.1871.39Information from books/magazines (yes)0.5220.1731.570.5640.2171.4640.5860.21.7180.7680.2722.167Information from family members (yes)0.4250.1311.3761.7490.6484.7231.6690.5195.3641.2960.3874.342Information from friends (yes)2.1920.7596.3341.2150.5232.8231.7960.6614.8761.2770.4373.729Information from medical workers (yes)0.4830.1831.2771.3870.5993.2112.3260.8876.1011.2250.463.257Information from others (yes)2.6970.09675.9840.8630.03919.2822.1440.10244.83911.2380.423298.68Breastfed before 6 months old (yes)2.3230.44712.082.920.63413.4420.9840.1655.8750.5530.1242.467Formula milk given before 6 months old (yes)0.4980.1841.3480.9150.4022.0810.7740.3121.9181.2830.5123.212Water given before 6 months old (yes)1.8780.24714.2593.5950.60621.3041.9080.11531.7511.3080.09218.539Sweetened beverage given before 6 months old (yes)0.2040.0192.2360.6410.1093.7650.2820.0194.1880.7520.1015.623Complementary food given before 6 months old (yes)2.4670.8277.3580.7110.2681.8810.4850.1461.6160.1570.0290.852Junk food given before 6 months old (yes)8.1540.529125.813.4190.50623.084Snacks given once a day or more (yes) †0.4290.1251.4740.3740.1081.2980.2970.0821.0761.5550.445.499Eat outside few times a week or more (yes) ‡0.5810.1931.7530.5520.2081.4680.3220.11.0311.30.4253.976Eat take away food few times a week or more (yes) ‡0.470.1581.3930.9550.3632.5081.4870.4984.4421.2040.423.454Mother thinks child is overweight3.4051.0511.038Stunting2.1840.9145.218Overweight3.2031.4367.141MCH handbook; Maternal and Child Health handbook† 6 months and older children only‡ 1 year and older children only
## Discussion
The current study revealed that $65.2\%$ of mothers used the MCH handbook as a source of information about child nutrition, whereas $88.2\%$ of mothers used the internet via mobile phone as a source of information about child nutrition. In addition, the results revealed that children whose mothers used the MCH handbook as an information source were more likely to be overweight than children of mothers who did not use the MCH handbook.
The observed prevalence ($36.4\%$) of child overweight in the current study was higher than that reported in previous studies [4, 18, 22, 23]. A report published by the UNICEF, the World Health Organization, and the World Bank Group estimated the prevalence of overweight/obesity in children under 5 years old as $11.1\%$ in Indonesia [3]. We sought to target children whose mothers had a high level of education and high economic level to provide an adequate sample of overweight children. Therefore, a high prevalence of child overweight was anticipated. However, surprisingly high prevalence rates of stunting ($37.6\%$), underweight ($22.6\%$) and wasting ($16.8\%$) were also observed. These results indicate the importance of dealing with both overnutrition and undernutrition of children.
The current study revealed that $65.2\%$ of mothers sought information about child nutrition from the MCH handbook, whereas $88.2\%$ of mothers sought information from the internet via mobile phone. We aimed to identify the extent to which mothers use the MCH handbook in the era of internet technology, and expected that the internet would be the most frequently used information source. The current results indicate that mothers still value the MCH handbook as a source of information, although the internet is becoming a popular source. However, while the internet enables mothers to access information in a convenient way, it can be difficult to find reliable information. In contrast, the national MCH handbook is a reliable source of accurate information. The current findings are encouraging for policy makers and health professionals, indicating that the MCH handbook is an effective tool for delivering important information to mothers in Indonesia.
The current study revealed a significant association between child overweight and mothers use of the MCH handbook as a source of information about child nutrition (aOR; 4.133, $95\%$CI; 1.252–13.639) whereas no association was found between undernutrition and the use of the MCH handbook. To the best of our knowledge, this is the first study to investigate the relationship between child overweight and use of the MCH handbook as a source of information in Indonesia. This finding suggests the importance of examining the contents of the MCH handbook. The Indonesian MCH handbook provides information about child nutrition and recommends a healthy diet. However, information regarding the prevention of child overweight/obesity is not clearly described. The handbook was officially approved as a national home-based record in 2004 [25], when undernutrition was the main concern and revisions were made afterwards. Indonesia, like many other developing countries, has experienced a transition in nutritional status because of the introduction of western food and corresponding lifestyle changes. Given this new context, concise and accurate content regarding the prevention of child overweight and obesity should be added to the MCH handbook.
To deliver the necessary information to mothers, it may be useful to create links between the printed MCH handbook and internet websites. Furthermore, an MCH handbook smartphone app could be made available to mothers. Positive outcomes of internet technology use for information delivery in the field of health in Indonesia have previously been reported, including improved women’s knowledge and behavior regarding hygiene [25].
The internet and the MCH handbook each have advantages and disadvantages as sources of information. The MCH handbook is a national service and is distributed free of charge, whereas internet access entails a cost for mothers, including the cost of a device, and wifi/internet access may not be always stable. Regarding information capacity, space is limited in the MCH handbook, whereas the internet has a vast information capacity. Therefore, essential information may be better presented in the printed MCH handbook or via a smartphone app. Further information could then be provided via the internet.
## Study strength and limitations
This study updated existing knowledge regarding mother’s information source of child nutrition. It designed to reflect the current situation of mothers who uses internet daily. Furthermore, studies on the MCH handbook scarce all over the world. The current study adds new findings about the MCH handbook. Then it handled both overnutrition and undernutrition. The double burden of overnutrition and undernutrition is the realistic situation of Indonesia [4] as well as other developing countries [1]. Both at nation level and individual level, coexistence of undernutrition and overnutrition has been recognized [1, 4, 19, 25, 25]. The study adds newer knowledge to tackle such situation.
The current study involved several limitations. First, the study sample was not representative of children in Indonesia, or any other general population, limiting the generalizability of the results. Due to web-based survey, the study sample is limited to internet users that is biased to high education background and monthly household income. Consequently, mothers who uses internet but are not high education background or monthly household income are excluded from this study. However, this was a trade-off with our intended participants that enabled us to analyze relationship between child overweight and the MCH handbook use as information source of child nutrition in the era of internet technology. Second, some important or potentially confounding variables such as father’s education level and parents’ nutritional status were not collected due to budgetary restriction. Third, recall bias might have contributed to the high prevalence observed. Anthropometric data were entered by mothers regardless of the data source. A time lag might have occurred between the age of child at the time of anthropometric measurement and the actual age of the child at the time the survey was completed.
## Conclusion
In the current study, we attempted to determine the prevalence of use of the MCH handbook as a source of information about child nutrition in the era of internet technology, and its relationship with child overweight. The results revealed that many mothers use the MCH handbook despite having access to the internet. However, the current results revealed that use of the MCH handbook was positively associated with child overweight. The content of the handbook should be examined in more depth to determine whether it contains unsuitable information regarding the double burden of child overweight and undernutrition identified in this study. In addition, it would be meaningful to discuss how to deliver information regarding child overweight to mothers and caregivers. Covering related topics in the national MCH handbook and creating a linkage between the MCH handbook and the internet may be useful. Future studies with a larger sample size may be necessary for assessing the situation more accurately.
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---
title: 'Attitudes and experiences of registered diabetes specialists in using health
apps for managing type 2 diabetes: results from a mixed-methods study in Germany
2021/2022'
authors:
- Julian Wangler
- Michael Jansky
journal: Archives of Public Health
year: 2023
pmcid: PMC9990333
doi: 10.1186/s13690-023-01051-0
license: CC BY 4.0
---
# Attitudes and experiences of registered diabetes specialists in using health apps for managing type 2 diabetes: results from a mixed-methods study in Germany 2021/2022
## Abstract
### Background
Hardly any area of application for health apps is seen to be as promising as health and lifestyle support in type 2 diabetes mellitus. Research has emphasised the benefits of such mHealth apps for disease prevention, monitoring, and management, but there is still a lack of empirical data on the role that health apps play in actual type 2 diabetes care. The aim of the present study was to gain an overview of the attitudes and experiences of physicians specialising in diabetes with regard to the benefits of health apps for type 2 diabetes prevention and management.
### Methods
An online survey was conducted amongst all 1746 physicians at practices specialised in diabetes in Germany between September 2021 and April 2022. A total of 538 ($31\%$) of the physicians contacted participated in the survey. In addition, qualitative interviews were conducted with 16 randomly selected resident diabetes specialists. None of the interviewees took part in the quantitative survey.
### Results
Resident diabetes specialists saw a clear benefit in type 2 diabetes-related health apps, primarily citing improvements in empowerment ($73\%$), motivation ($75\%$), and compliance ($71\%$). Respondents rated self-monitoring for risk factors ($88\%$), lifestyle-supporting ($86\%$), and everyday routine features ($82\%$) as especially beneficial. Physicians mainly in urban practice environments were open to apps and their use in patient care despite their potential benefit. Respondents expressed reservations and doubts on app user-friendliness in some patient groups ($66\%$), privacy in existing apps ($57\%$), and the legal conditions of using apps in patient care ($80\%$). Of those surveyed, $39\%$ felt capable of advising patients on diabetes-related apps. Most of the physicians that had already used apps in patient care saw positive effects in increased compliance ($74\%$), earlier detection of or reduction in complications ($60\%$), weight reduction ($48\%$), and decreased HbA1c levels ($37\%$).
### Conclusions
Resident diabetes specialists saw a real-life benefit with added value from health apps for managing type 2 diabetes. Despite the favourable role that health apps may play in disease prevention and management, many physicians expressed reservations regarding usability, transparency, security, and privacy in such apps. These concerns should be addressed more intensively towards bringing about ideal conditions for integrating health apps successfully in diabetes care. This includes uniform standards governing quality, privacy, and legal conditions as binding as possible with regard to apps and their use in a clinical setting.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13690-023-01051-0.
## Background
More than 500 million people contracted diabetes across the world in 2020, including around 8 million in Germany [1–3]. Of these, $95\%$ were suffering from type 2 diabetes mellitus with widespread weight gain and lack of exercise as major reasons [1–3]. Patients with type 2 diabetes also often have other cardiovascular risk factors in addition to chronically high blood sugar and should always agree on individual nutrition, bodyweight, blood sugar, blood pressure and lipid status targets with their physicians [4–9]. As clinical guidelines recommend, the main focus should be placed on adopting a healthy lifestyle.
Health apps for the purpose of assisting in monitoring and management are seen to be especially promising in type 2 diabetes mellitus as a lifestyle-induced disease [10–12]. According to the World Health Organization (WHO), mobile health (mHealth) apps are defined as software programs that, in the sense of personal digital assistants, run on smartphones or tablet platforms, aiming at enabling and improving the delivery of health care. Such tools can promote health and primary disease prevention or support people with chronic illnesses in managing their medical conditions or improve treatment adherence [10, 11]. Depending on the functionality and focus, apps specifically intended for diabetes may fulfil a variety of roles including management data documentation such as vital parameters, logging measured values, diet and nutrition information, calorie and exercise profile creation, and regular blood sugar measurement and medication intake reminders and tracking [13–15].
Various studies have shown diabetes apps to have beneficial effects on disease management [10, 11], especially towards increasing exercise levels, healthy nutrition, weight reduction, and stress management [16–22]. Favourable outcomes with respect to compliance and psychological well-being have also been demonstrated [23, 24]. Kebede et al. reported improvement in self-management, information, and motivation in type 2 diabetes patients using these apps [25]. One meta-analysis reported an improvement in metabolism in type 2 diabetics after app usage [26]. Veaezie examined 15 studies on apps used in diabetes management [27], reporting a significant decrease in HbA1c levels. One meta-analysis on 13 randomised controlled studies showed a significant reduction in HbA1c levels in six of the studies [28]. Offringa et al. reported that using mobile apps led to improved glucose control with less frequent hyperglycaemic episodes [29].
Attitudes amongst physicians specialising in diabetes towards health apps for managing type 2 diabetes plays a decisive role even considering the initial empirical evidence [17]. Depending on attitudes, prior knowledge and experience, physicians may recommend specific apps to their patients and regularly use these apps in their healthcare strategy. Several analyses have implied that medical recommendations may increase usage levels and compliance in using apps focused on diabetes management by between 10 to 30 % [30].
## Study aims and design
The present exploratory study focuses on health professionals, and not patients. It served the purpose of gaining an overview of the attitudes and experiences of physicians in specialist diabetes practices with regard to the benefits of health apps for type 2 diabetes prevention and management. This involved identifying potential added value and issues in clinical settings as well as observations in healthcare, including recommendations for specific apps and their effectiveness. Conclusions from these findings should assist in applying these potential benefits from health apps in type 2 diabetes mellitus diagnosis, management, and prevention.
The present multi-part study combined quantitative and qualitative components for adequate coverage of the topic. First, a comprehensive online survey amongst all physicians in practices specialised in diabetes in the Federal Republic of Germany was performed. After the above survey, a smaller qualitative study was performed in order to obtain additional knowledge by means of interviews with resident diabetes specialists.
## Study participants and ethics approval
During this study, no sensitive patient data was gathered or clinical tests performed. This is a strictly anonymized survey of a total of 538 doctors from specialist diabetology practices. The Ethics Commission of the State of Rhineland-Palatinate, Germany, informed us that approval by an ethics committee was not necessary.
A total of 1746 general practitioners and internists were identified in 1041 practices using the online physician locator provided by the Association of Statutory Health Insurance Physicians in each federal state. This roughly corresponded to the number reported in publicly available healthcare statistics in Germany [31, 32].
All the physicians identified were sent postal invitations to take part in the anonymous survey between September 2021 and April 2022. A once-only letter explained the intention to conduct a survey as well as its anonymisation, giving the physicians to be surveyed password-protected access to the online survey amongst other things. Participants were not given any remuneration or incentives.
The survey saw 543 questionnaires returned from the 1746 sent. The 538 fully completed questionnaires were included in analysis. This yields a response rate of $31\%$. The sample population was as follows:Gender: $50\%$ male, $50\%$ femaleMean age: 55, Median: 55Practice setting: $52\%$ in medium-sized and large towns or cities, $48\%$ in small towns or rural areasPractice model: $36\%$ individual practices, $59\%$ joint practices, $5\%$ otherPatients per quarter: Up to 1000: $14\%$, 1001 to 1500: $28\%$, 1501 to 2000: $25\%$, more than 2000: $33\%$ Sixteen semi-standardised interviews were also performed with randomly selected resident diabetes specialists distributed throughout Germany between April and June 2022. Nine of these interviews were taken by telephone, seven in person. None of the interviewed diabetes specialists took part in the quantitative survey.
## Data collection and questionnaire
The survey questionnaire (see Additional file 1) used was drawn up using extensive literature research on the one hand, and three preliminary studies targeting general practitioners on potential areas of use for health apps on the other [17, 20, 33]. This involved adopting questions on general acceptance, experience, and willingness to use the apps, and usage potential in actual situations in relation to diagnostics, treatment and prevention of chronic diseases then specifically tailored to type 2 diabetes mellitus.
The exploratory questionnaire consists of a total of 23 questions with the above-mentioned focuses. The results of the preliminary studies were primarily incorporated into the creation of the item batteries used (questions 13, 14, 16, 17, 21).
In the questionnaire, mostly closed questions were asked, which could be answered either by single choice (questions 1–4, 8–12, 15, 18–20, 22) or multiple choice (questions 7, 13, 14, 16, 17, 21, 23). Some of these questions included an optional text field for additional answers (questions 7, 16, 17, 21, 23). Besides, two open-ended questions were asked (questions 5, 6).
In order to achieve a good compromise between data quality and intuitive answerability of the questionnaire for the time-critical target group of diabetologists, ordinal scales were widely used (usually with four levels, questions 2–4, 9–12, 15, 18–20).
Sociodemographic data collected included gender, age, practice location population, establishment model and number of patients per quarter. The validity and reliability of the questionnaire was evaluated in the course of a pretest carried out on 20 resident diabetes specialists. Cronbach’s alpha was calculated to assess the reliability and it was 0.82.
The qualitative interview survey instrument (see Additional file 2) was drawn up using the online survey as a basis with the aim of developing on the quantitative findings to greater depth. In essence, the development of the interview guideline was about converting some of the closed questions of the questionnaire into open questions in order to be able to explore the topic additionally. The main focus areas of the interview guideline were: clinical picture of type 2 diabetes and its significance in everyday practice; perception and significance of health apps in general; health apps with regard to type 2 diabetes mellitus; own experiences using type 2 diabetes health apps. Three interviews were conducted in advance with physicians from practices specialising in diabetes towards further specifying and validating the interview guideline used.
## Data analysis
SPSS 23.0 was used for data analysis. Student’s t-test for independent samples was used to detect significant differences between two groups. Values of $p \leq .001$ were considered highly significant.
The team evaluated the resulting transcripts from the qualitative interviews using qualitative content analysis according to Mayring [34] (MAXQDA Software) after data collection. Our focus lay on forming logical categories from the various opinions and experiences. Selected citations are presented to support the quantitative findings.
We used STROBE for reporting statement purposes.
## Usage potential of health apps for managing type 2 diabetes
$51\%$ of the respondents gave health apps a favourable rating as potential aids in healthcare. A quarter of the sample were sceptical ($24\%$) or undecided ($25\%$) on these mHealth applications. Physicians in cities and medium-sized towns viewed apps more favourably than those in small towns and rural communities ($60\%$ vs. $34\%$ favourable ratings, $p \leq .001$). Respondents below the average age of 55 were also more open to apps than older physicians ($55\%$ vs. $41\%$ favourable ratings, $p \leq .001$).
Most physicians anticipated the benefit health apps could mean for prevention, diagnostics, and management in type 2 diabetes mellitus patients as very important ($12\%$) or rather important ($44\%$) as opposed to $42\%$ not so important and $2\%$ no benefit. Most respondents from urban environments rated the benefit of apps significantly more favourably than those in a small-town or rural environments ($64\%$ very/rather important vs. $42\%$ very/rather important, $p \leq .001$).“I think we should be looking at the use of apps in a broader disease management context. Apps could clearly offer added value if they can be integrated into regular medical care, in a fixed disease management programme for example.” ( I-2 m).
$88\%$ the respondents thought health apps would be very or rather useful in prevention (such as in for self-monitoring of risk factors). $86\%$ thought health apps wold be useful in maintaining a health-promoting lifestyle (such as diet and exercise). $82\%$ said they would appreciate apps helping patients in type 2 diabetes patients management (such as by giving reminders to take medication or vaccinations, or to go to check-ups). $65\%$ saw monitoring and treatment in chronic disease as a useful area of application. “No doubt, these eHealth tools are definitely useful for long-term modifications in lifestyle and everyday habits. There is a lot to be said for them in my experience. Even so, selecting the right apps is important, and queries from patients should be expected.” ( I-11f).
Many of the respondents considered reinforcing patient motivation as a potential benefit in these apps (see Fig. 1). Some members in the sample also associated app usage with improvement in patient education and more effective treatment. However, a large share of the sample saw apps as being too complicated for many patient groups. They also expressed concern that incorrect health data could be collected or, in extreme cases, that treatment strategies would fail. Most were dissatisfied with data privacy in most of the health apps, and many respondents were concerned about the additional workload involved. Fig. 1Responses to statements regarding health apps and their use in treating patients with type 2 diabetes mellitus ($$n = 538$$; online survey amongst all physicians at practices specialised in diabetes in Germany, survey period: $\frac{2021}{2022}$)
## Personal experience with regard to the use type 2 diabetes apps
$16\%$ of the respondents stated that they had seen many patients send their health data such as blood sugar logs collected from health apps to the practice in digital form ($17\%$ some, $41\%$ rather few, $26\%$ none). Respondents below the average age saw significantly more of these patients than older physicians (many/some: $48\%$ vs. many/some: $8\%$, $p \leq .001$).
Half the respondents stated that their own patients frequently ($10\%$) or occasionally ($42\%$) asked them about health apps for type 2 diabetes mellitus prevention and/or management ($38\%$ rarely, $10\%$ never). Most diabetes specialists reported raising the subject of these apps to their patients often ($10\%$) or occasionally ($36\%$). Respondents stating that they were more often asked about apps or that they raised the subject themselves were mostly physicians in urban areas. “From my experience, you can’t expect patients to deal with these apps on their own. A coherent healthcare plan plays a crucial role. Accordingly, it makes sense for doctors to make the first move and raise the topic of specific apps with their regular patients. This is the only way of ensuring that the app will be used.” ( I-5 m).
The same applies to recommendations for specific apps. In this respect, $48\%$ stated recommendations for a specific app were frequent ($14\%$) or occasional ($34\%$) compared to rare ($22\%$) or never ($30\%$). The lion’s share of these respondents ($47\%$) stated that they had already used and recommended the mySugr app in their patient care. Other apps named included FreeStyle Libre ($28\%$), Dexcom G6 ($15\%$), Accu-Chek Connect ($9\%$), and DiabetesPlus ($8\%$).
In an open question the respondents were asked to name important criteria as prerequisites for recommending an app. As the results show, the criteria mentioned mainly included simplicity, accessibility, comprehensibility, and intuitive use, but also data privacy and security, exercise motivation, and a sound foundation in guidelines and evidential medicine. “Evidence-based criteria in an app are an important quality indicator for me. This means that the app has been tested and has, for example, been mentioned in recommendations from professional associations or other care-related organisations” (I-16 m).“*There is* probably still a general lack of broad review based on uniform and transparent criteria for apps available in Germany that would warrant recommendation without reservation. [...] The potential for apps to inspire patients towards a change in lifestyle should not be underestimated. So gamification is an important factor in everyday use.” ( I-3f).
A clear preference for certain sources of information emerged amongst physicians recommending apps. Of these, $73\%$ stated that they obtained their information on mHealth apps such as evaluation and recommendation of new apps from the German Diabetes Association (DDG) website. Other sites such as HealthOn ($14\%$), Telemedicine Competence Centres of the federal state ($17\%$), Federal Institute for Drugs and Medical Devices ($13\%$), and the National Health Portal ($7\%$) were used much less frequently for information.
As far as the respondents were aware, their type 2 diabetes patients mainly used health apps for prevention and self-monitoring ($82\%$) and to keep to a healthy lifestyle ($78\%$). Well over half of the respondents at $58\%$ cited monitoring and management (such as documenting parameter trends and symptoms) and $51\%$ for medication and blood sugar measurement reminders.
## Perceived benefit of type 2 diabetes health apps
$34\%$ of those surveyed assume that health apps can play a very important ($10\%$) or rather important ($24\%$) role towards faster disease detection and diagnosis, while $46\%$ saw limited or minor benefit ($15\%$ no benefit, $5\%$ could not say).
One question was aimed at identifying those clinical conditions that could be more effectively identified by health apps in the experience of the respondents. $65\%$ said they expected or observed hypoglycaemia to be detected more quickly by using a health app. $38\%$ named episodes of depression, $35\%$ metabolic syndrome, $19\%$ hyperosmolar coma, and $17\%$ named diabetic foot syndrome and diabetic neuropathy each.
Most respondents observed an increase in compliance and a reduction in complications such as hypoglycaemia as favourable results of successful app use (see Fig. 2). Around half stated that their patients had lost weight as a result of using the app. Every third respondent had already observed a decrease in HbA1c levels to below $7.5\%$. Less frequent improvements included a reduction in the metabolic syndrome, prevention of sequelae and reduction in psychological side effects. Fig. 2Which of the following effects have you already seen from your type 2 diabetes mellitus patients successfully using health apps? ( $$n = 538$$; online survey amongst all physicians at practices specialised in diabetes in Germany, survey period: $\frac{2021}{2022}$)
## Issues and potential for optimisation
Respondents gave a rather reserved rating on their awareness and knowhow regarding the general range of health apps available for type 2 diabetes mellitus prevention and/or management. Under a third at $30\%$ rated their awareness of the range of apps available as (very) extensive compared to $70\%$ as rather or very restricted.
$43\%$ of the respondents saw themselves as capable of distinguishing good from bad health apps for type 2 diabetes mellitus prevention and/or management; significantly more of these physicians were in urban rather than rural practice settings ($55\%$ vs. $27\%$, $p \leq .001$). $39\%$ of the respondents saw themselves as capable of giving patients thorough and competent advice on specific apps in this field of specialisation, again with significantly more of them in urban rather than rural practice settings ($47\%$ vs. $21\%$, $p \leq .001$).“*This is* a highly dynamic and chaotic issue. There are new apps constantly appearing as well as updates and other changes. Physicians can’t be expected to keep up on all the new developments all the time, not even in an isolated specialisation area like type 2 diabetes mellitus. In other words, we need more consistent support from other players. Solid information sources from independent testers, and obviously more involvement from specialist bodies.” ( I-7 m).
A clear majority of the respondents expressed a desire for authoritative data privacy and quality standards to be defined and enforced towards more attractive health apps for use in type 2 diabetes mellitus (see Fig. 3). Most proposed mandatory certification in new apps. The matter of payment for medical services provided in connection with health apps was also raised, such as a separate position in the fee schedule. Apart from that, physicians expressed a need for legal issues to be clarified in including apps in healthcare. “What makes me personally reluctant is that I can’t ever be a hundred percent sure how much I can rely on an app for the patients I treat. This begins with data collection and continues with everyday app use by patients. Could I be held liable for any bugs found in the app? Or for patients using the app incorrectly because of a misunderstanding on how to use it? At what point should I start worrying? How much involvement in app support is legitimate, and where do I start taking dangerous risks? […] I don’t see enough regulation or public communication on this.” ( I-8w).Fig. 3Suggestions for optimising health apps intended for use in diabetes mellitus type 2 ($$n = 538$$; online survey amongst all physicians at practices specialised in diabetes in Germany, survey period: $\frac{2021}{2022}$) Most respondents said they would in principle be willing to include health apps in their patients’ healthcare far more ($26\%$) or rather more ($54\%$) than today if National Healthcare Guideline (NVL) specifically addressed the use of health apps with regard to type 2 diabetes mellitus prevention, monitoring, and management.
## Main findings and comparison with prior work
The results demonstrate that most physicians in practices specialising in diabetes do see potential benefits of health apps for managing type 2 diabetes. Such benefits include effective reinforcement of empowerment, motivation and compliance, and also reminder and lifestyle-supporting features in disease management towards improved type 2 diabetes patients prevention and management [35, 36]. Earlier studies have demonstrated recognition amongst general practitioners and specialists as to specific usage scenarios and added value in apps [17, 20]. Younger physicians, especially in urban environments, show a favourable attitudes towards health apps and use these tools in everyday practice – a result that agrees with other studies on the subject [12, 18–20].
Even so, a substantial share of the respondents expressed scepticism about health apps that included user-friendliness and reliability in existing apps, legal issues such as liability, and the resulting additional workload from additional responsibility in patient counselling. A substantial number of respondents were also concerned about incorrect app use or vital parameter measurement causing risks such as misdiagnosis. The distinct scepticism regarding data privacy in health apps is remarkable considering the strict data protection standards by international comparison due to the requirements set in the European General Data Protection Regulation (GDPR), which applies in Germany. Even so, respondents showed a considerable level of insecurity on this point in particular.
Most have admitted a lack of sufficient general awareness on topic-related health apps owing to the huge and dynamic range of apps available, lack of transparency and basic orientation; this results in limited confidence in advising patients [12, 13, 17, 20, 37].
Respondents that recommended apps named various criteria that would need to be met before making specific recommendations. These criteria mainly included ease of use or usability, reliability, and data privacy guarantees.
Physicians already using apps in patient care had observed positive effects in increased compliance, earlier detection of or reduction in complications, weight reduction, and decreased HbA1c levels. These findings tally with those of other studies [38–41]. Overall, a number of studies have shown digital applications can significantly contribute to a substantial reduction in HbA1c levels, consistent blood sugar management, improved wellbeing, and lifestyle compliance [16–22, 25, 42–49]. Self-monitoring has also been found to result in a decrease in cardiovascular mortality with increased awareness of symptoms [42].
Besides, studies have reported forgetting to take medications and changing doses as reasons for poor compliance in chronic diseases. One such study examined seniors and their medication-taking behaviour using the MyTherapy app for self-monitoring their type 2 diabetes [24]. These seniors used the app most frequently for reminders to measure blood glucose levels and take medication, followed by exercise reminders. Using the app resulted in an improvement in psychological well-being and medication compliance. Other studies have shown the MyTherapy app to increase compliance and strengthen doctor-patient relationships in type 2 diabetes patients [23, 50].
Patient surveys have demonstrated people living in rural areas to be less accepting of health apps in general. However, these populations could benefit even more from telemedical data transmission than urban populations due to the greater distances to physicians and increasing decline in outpatient care providers in some regions [10, 16, 18, 42]. Some respondents were also aware of this by recognising possibilities for easier, low-threshold communication, strengthening the doctor-patient relationship and other benefits.
Most respondents expressed a wish for authoritative data privacy and quality standards as well as certification for new apps, requirements scientifically established in other studies including the CHARISMA study in particular [10, 11]. Many respondents saw the benefit of the national healthcare guideline explicitly addressing evidence-based use of health apps for type 2 diabetes prevention, monitoring, and management. Most respondents stated that they could imagine including health apps in patient care more intensively than before if all these conditions had been met. Other study results have also shown there to be a great desire for evidence-based integration of health apps [50–52].
Physicians in Germany have been permitted to prescribe patients approved digital health applications (so called DiGAs) since the summer of 2020 - a step that was unique in the world at that time and still is [53]. Since then, physicians have been able to prescribe DiGAs to patients with costs covered by the national health system. DiGAs are set to make disease diagnostics and recognition more effective, provide support in treatment, and contribute to prevention while guaranteeing high quality standards. In contrast to ordinary, freely available health apps, these prescription apps are certified as medical devices. DiGAs are reviewed in detail using a standard procedure defined by the German Federal Institute for Drugs and Medical Devices (BfArM). This requires manufacturers to apply for approval in the course of an audit process on a variety of requirements (CE markings for medical devices, data protection, security standards, information quality, usability and robustness in operation, patient safety). They are also required to provide sufficient documentation for the added value of the application in its effect on healthcare.
The range of such approved health apps is still rather limited, but diabetes-related apps in particular have become more strongly represented (apps such as Zanadio, Oviva Direkt, ESYSTA, VIDEAmellitus) [54]. These prescription health apps have been tested for effectiveness based on quality standards with approval for clearly defined specialisations, so they stand to boost willingness amongst not only diabetes patients but also their doctors in the long term. Medical concerns as reflected in the present survey may therefore be addressed far more effectively by prescription apps [55]. Initial surveys of doctors on approved digital health applications show that they are rated as significantly more trustworthy and reliable than conventional health apps. This offers new opportunities and application potential for the future integration of health apps into patient care [56].
## Strengths and limitations
We recruited a heterogeneous sample of physicians from practices specialising in diabetes. Even so, the present study cannot lay any claims to being representative due to the limited number of respondents.
Moreover, it cannot be ruled out that physicians with favourable attitudes and experiences towards health apps took part in the survey to a greater extent than those with negative experiences (possible selection bias).
The present exploratory survey primarily served towards gauging general opinion rather than performing a more nuanced analysis of clinical effectiveness amongst mHealth apps. The breadth and complexity of health apps as a topic limited the present study to an initial exploratory approach. The authors see a need for further, more empirically grounded intervention studies focusing on specific application and use opportunities, but also weaknesses of health apps in healthcare for type 2 diabetes patients.
## Conclusions
The results indicate that diabetes specialists perceive the added value of type 2 diabetes health apps in prevention, diagnostics, and management, and have already had favourable experiences in the selective use of apps in healthcare. Even so, many physicians still harbour concerns about transparency, security and privacy, and user-friendliness in these apps [10, 11, 17, 20]. In addition, general awareness and selection of possible apps pose issues. This has limited willingness to recommend or consistently use these digital applications in everyday practice.
The CHARISMA study brought together a variety of measures towards optimising health apps [11]. These measures included app manufacturers being required to observe authoritative quality criteria, general quality control, and the establishment of clear criteria for intended use in each app. Whether doctors can be held liable for treatment errors caused by app data remains unclear. The German government has made it possible for physicians to prescribe health apps as medical devices, which could make it more of a challenge for manufacturers to achieve quality standards in the future; this could lead to a favourable transformation of the app market in the longer term [53].
General awareness and orientation are a current issue; this means that evidence-based guidance from diabetes networks and relevant specialist associations could help physicians gain a qualified overview of the plethora of apps in this specialisation, keep up to speed on current developments, and assess which app would be best suited to which specialism [16]. The authors also see a need for more training courses explaining the possibilities and limitations of using apps in medical practices and introduce the integration of apps into patient counselling.
A clear framework and larger evidence base will prove crucial in minimising the concerns of diabetes specialists and helping to educate physicians with studies on the benefits, opportunities, and risks of health apps. User acceptance from both patients and physicians will only increase once physicians gain confidence in their application. Under these conditions, high-quality health apps could become a staple in healthcare as they develop their potential in type 2 diabetes patient care.
## Supplementary Information
Additional file 1. Questionnaire. Additional file 2. Interview guideline.
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|
---
title: Identifying behavioural barriers and facilitators to engaging men in a community-based
lifestyle intervention to improve physical and mental health and well-being
authors:
- Oliver J. Bell
- Darren Flynn
- Tom Clifford
- Daniel West
- Emma Stevenson
- Leah Avery
journal: The International Journal of Behavioral Nutrition and Physical Activity
year: 2023
pmcid: PMC9990339
doi: 10.1186/s12966-023-01425-1
license: CC BY 4.0
---
# Identifying behavioural barriers and facilitators to engaging men in a community-based lifestyle intervention to improve physical and mental health and well-being
## Abstract
### Background
There are few community-based lifestyle interventions designed to target physical and mental health of men. We conducted a qualitative focus group study with men to explore their perceived barriers and facilitators to uptake and engagement with interventions designed to improve their physical and mental health and wellbeing.
### Methods
A volunteer sampling approach (advertisements posted on a premier league football club’s social media) was used to recruit men aged 28 to 65 years who were interested in improving their physical and/or mental health and wellbeing. Focus group discussions were conducted at a local premier league football club to 1) explore men’s perceived barriers and facilitators to uptake of community-based interventions; 2) identify health issues considered important to address; 3) obtain participant views on how to best engage men in community-based interventions; and 4) use the findings to inform the development of a multibehavioural complex community-based intervention (called ‘The 12th Man’).
### Results
Six focus group discussions were conducted (duration 27 to 57 min) involving 25 participants (median age 41 years, IQR = 21 years). Thematic analyses generated seven themes: ‘Lifestyle behaviours for both mental health and physical health’; ‘work pressures are barriers to engaging with lifestyle behaviour change’; previous injuries are barriers to engagement in physical activity and exercise’; personal and peer group relationships impact on lifestyle behaviour change’; relationships between body image and self-confidence on mastery of skills for physical activity and exercise’; building motivation and personalised goal setting’; and ‘credible individuals increase uptake and continued engagement with lifestyle behaviour change’.
### Conclusions
Findings suggest that a multibehavioural community-based lifestyle intervention designed for men should promote parity of esteem between physical and mental health. It should also acknowledge individual needs and preferences, emotions in the context of goal setting and planning, and be delivered by a knowledgeable and credible professional. The findings will inform the development of a multibehavioural complex community-based intervention (‘The 12th Man’).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12966-023-01425-1.
## Background
Increased prevalence of long-term physical and mental health conditions has impacted significantly on life expectancy [1]. In 2016, the leading causes of life years lost due to premature mortality were cardiovascular, respiratory and Alzheimer’s disease, with evidence that men are disproportionately affected [1]. Men are twice as likely to develop ischaemic heart disease and report poorer health than women [1]. Multiple factors contribute the elevation of health risks of men compared with women due to biological causes including cellular responses to stress [2] and body fat distribution. Men are more likely to accumulate visceral fat [3] which is associated with conditions including metabolic syndrome [4], coronary heart disease [5] and ischemic heart disease [5]. Furthermore, research has shown that men often underestimate health risks [6] and express concern about being too thin or weak when losing weight [7]. These beliefs, paired with the reduced likelihood of engaging with healthcare services lead to increased health risks. Importantly, physical, and mental health conditions frequently co-exist with multi-factorial associations and robust evidence for bidirectionality [8–10], therefore it is important to address physical and mental health conditions to reduce risk of morbidity and mortality.
Men are less likely than women to be diagnosed with depression and anxiety, although men are more likely to report lower levels of satisfaction with life, use alcohol and other drugs, and avoid strategies to cope with depression. Additionally, they are more likely to report specific symptoms of depression including irritability, increased loss of control and aggression, and are less likely than women to access psychological therapies [11]. Furthermore, men are more likely to die by suicide than women in every country worldwide [12], although women are more likely to attempt suicide – referred to as the gender paradox [13].
Several barriers exist that can impact negatively on uptake and engagement of men with interventions that target behaviours to improve physical and mental health [14–17], particularly interventions delivered within healthcare settings by healthcare professionals [18]. A possible explanation is that few interventions are developed or tailored exclusively for men, and the way in which these interventions are offered, do not appeal [19]. Intervention developers often do not consider sex-specificity or gender sensitivity during the development stage [20]. Therefore, uptake and meaningful engagement of men is often sub-optimal, and they are considered, in some cases ‘hard to reach’ [21].
In the context of improving physical and mental health, a wealth of evidence supports the use of physical activity and exercise interventions [22]. However, it is important to fully understand the specific requirements and support needs of men, and specifically their barriers and enablers to engagement with lifestyle behaviour change interventions. A 2015 systematic review of lifestyle interventions targeting men identified 12 programmes informed by consultation with men from design through to implementation [20]. Four of these programmes utilised interest in sport and the ‘power of the badge’ as a vehicle to engage men in group-based exercise sessions at local sport clubs. “ Banter” in discussions about sensitive health issues including weight gain/loss was considered key to the success of one of these programmes (Football Fans in Training [FFIT]) [23]. The authors of FFIT reported statistically significant increases in self-reported physical activity and weight loss in the intervention group at 12 months follow-up. Participants described how taking part in a programme at a football stadium made them feel “kinda part of it…” and significantly increased acceptability of the programme [24].
The success for FFIT has been replicated worldwide using different sports to engage men [20]. However, there is a paucity of interventions that address both physical and mental health using football as a vehicle for engagement. Given the impact that mental health can have on sedentary behaviour, and the resultant increased risk that sedentary behaviour can have on physical health, there is a pressing need to understand how interventions can be designed to reduce barriers to engaging in physical activity interventions to improve both physical health and mental health in men.
Understanding men’s perceived barriers and facilitators to engaging in community-based lifestyle interventions is critically important to ensure intervention content and mode of delivery is appropriate and adequately addresses their needs and preferences. However, despite the positive impact of the football-informed community-based interventions, there is a dearth of evidence reporting on the active ingredients of these interventions (i.e., key intervention features including mode of delivery, form, content, and duration). As such, the key components of interventions to promote continued engagement following uptake is still unknown. This hinders replicability, faithful delivery, modification to local needs, and optimisation to maximise engagement and outcomes. Furthermore, previously published qualitative studies focus predominantly on physical health rather than mental health, or both. Where studies do report on barriers and facilitators to uptake and ongoing engagement with lifestyle interventions, they often report on practical barriers and facilitators, and place less emphasis on emotional and psychological challenges [25]. Finally, a large proportion of studies focus on the experiences of women, or do not report sex of participants making it difficult to extrapolate the specific barriers and facilitators of men.
To inform the development of a multibehavioural community-based intervention using football as a vehicle for engagement (‘The 12th Man’ Intervention), we conducted a qualitative focus group study with adult men to: 1) explore their perceived barriers and facilitators to uptake of interventions designed to target physical and mental health; 2) identify health issues considered important to address; 3) obtain participant views on how to best engage men in community-based interventions; and 4) use the findings to inform the development of a multibehavioural complex community-based intervention (called ‘The 12th Man’).
## Methods
This study was conducted with reference to the consolidated criteria for reporting qualitative research (COREQ) [26]. Ethical approval was obtained from Newcastle University’s Research Ethics Committee (Ref. $\frac{6228}{2018}$). All participants provided informed written consent prior to participation.
## Design
A qualitative focus group study.
## Participants and setting
A volunteer sampling approach was undertaken to recruit participants from the local community using advertisements placed on social media (Twitter, 14,453 followers, May 2018) and Facebook, 108,229 followers, May 2018) accounts associated with the club community organisation Newcastle United Foundation [NUF]). Potential participants were asked to indicate their interest through the social media channel, or by using the contact email address or telephone number provided. Invitations to participate were also sent directly to men who fulfilled the eligibility criteria who had previously registered their interest in community health and wellbeing programmes with NUF. Those indicating their interest were emailed a copy of the participant information sheet and consent form. Eligible participants were men aged between 28 and 65 years who were interested in improving their physical and/or mental health and wellbeing. This age range was selected because 30 and 65 years are two milestones within a man’s life, according to the health, illness, men and masculinities framework [27]. At age 30, men begin to consider establishing a family which can be socially isolating. At age 65, men consider retirement which, again, brings with it social isolation and a loss of purpose. Recruitment took place between May and August 2018. Informed written consent was obtained from all participants prior to the conduct of the study. All consenting participants were screened for eligibility prior to the conduct of focus groups. No incentives were provided for taking part.
## Data collection
Focus group discussions were conducted in person between July and November 2018 in a meeting room located at a Premier League Football stadium, although participants could request an alternative location (e.g., workplace) if this was their preference. Participants were asked to provide their age only so not to create barriers to participation.
An interview topic guide (see Supplementary document) was developed, informed by research literature to facilitate discussion about the following topics: perceived barriers and facilitators to uptake of interventions aimed to improve physical and mental health and wellbeing, saliant health issues, and views on how to best engage men in interventions. A total of 20 questions were asked. The topics were informed by a scoping review of the literature conducted to identify existing lifestyle interventions targeting at men (unpublished). The articles identified were further screened to identify associated qualitative findings reporting on barriers and facilitators to uptake and engagement with lifestyle interventions. Although the topic guide was not pilot tested prior to initial data collection, research team members met following the conduct of focus group one to discuss the data generated and to identify any potential issues with the topic guide. This process was repeated following the conduct of focus group two.
All focus groups were facilitated by one researcher (OJB) who had no previous contact with study participants. The facilitator was a 25 year old male PhD student with an academic background in Sport, Exercise and Nutrition. He wore a premiership football club tracksuit corresponding to the football club where the focus groups were held where he was also employed at the time. The researcher was supervised to conduct focus groups by two members of the research team (LA and DF) who have expertise in qualitative research methods, including the conduct of focus group discussions. No other individuals from the research team were present during focus group discussions.
## Data Analysis
Focus groups were audio recorded, transcribed verbatim and data thematically analysed using inductive thematic analysis [28]. Following the completion of the first focus group, the transcript was read and re-read by two researchers (OJB and LA) who independently applied preliminary codes to the data. The same two researchers subsequently held a meeting to discuss preliminary coding, agree on a coding framework, and any modifications to the topic guide ahead of the second focus group discussion. This process was repeated by the same two researchers following each focus group until it was agreed that the data generated was meaningful and had reached the point of saturation. MS Excel was used to manage the data.
## Results
Six focus group discussions were conducted. Five took part in a meeting room at the Premier League Football club ($$n = 18$$ participants), and one in a workplace setting ($$n = 7$$ participants). The duration of group discussions ranged from 27 to 57 min (median time 45 min (inter quartile range [IQR] = 14 min) and involved a total of 25 male participants (median age 41 years, IQR = 21 years).
All questions within the topic guide were asked, although, a minority of questions generated limited or closed responses that did not constitute a theme. Thematic analysis generated seven themes. These are presented in Table 1 with supporting direct quotes and key recommendations for intervention development. Each transcript was coded immediately following the conduct of each focus group. Codes were generated and placed into categories, and categories were assigned preliminary labels. Some themes generated more codes than others. For example, theme 1 ‘Lifestyle behaviours for both mental health and physical health’ and theme 5 ‘Relationship between body image and self-confidence on mastery of skills for physical activity and exercise’ generated more codes than other themes across focus groups. Theme 2 ‘Work pressures are barriers to engaging with lifestyle behaviour change’ was generated from codes from across groups, but mostly from the workplace setting focus group. Initial codes included ‘goals’ that were linked to both mental and physical health, ‘enjoyment’ linked to any activity undertaken to make positive lifestyle changes, and ‘stress’ that was most often associated with work, but also other life pressures including family and inability to access free time. Table 1Summary of themes identified from transcripts of focus group discussions and key recommendationsThemeSupporting QuotesKey recommendationsLifestyle behaviours for both mental health and physical health“Good lifestyle, good food, fitness, going to bed at a decent time [all influence physical and mental health]..” (Participant 2, aged 48)“I’ve started going to bed earlier, now, to get the sleep in and I find now I’m getting more sleep and I’m not as stressed” (Participant 5, aged 61)“I think wellbeing is the best word ever because it’s not like you [only] need to be fit, [or that] you need to stop eating bags of chips. It’s wellbeing. I think that is definitely mentally as well as physically. ”(Participant 24, aged 51)• Reinforcement of links between lifestyle behaviours and physical and mental health by a credible individual or credible resourceWork pressures are barriers to engaging with lifestyle behaviour change“I think most of it is work, as with most people nowadays. Where there’s less people working and they’re expecting people to pick up the work and the workload’s getting heavier…” (Participant 5, aged 61)“You want to do exercise, but if you get up at 05:30 in the morning to get here [work place], and you are getting home at 6:30 pm, you just want to have something to eat, have a shower, and go to bed” (Participant 9, aged 33)• Provision of workplace interventions and self-help activities• Engagement with employers• Incorporate barrier identification and problem solving activities• Signpost to facilities close by to reduce time to travel• Tackle workload issues practically and personallyPrevious injuries are barriers to engagement in physical activity and exercise“*About a* year ago I hurt my Achilles and I haven’t been able to run properly since” (Participant 4, aged 38)• Include graded tasks and facilities/activities to enable this process• Incorporate personalised barrier identification and problem solvingPersonal and peer group relationships impact on lifestyle behaviour change“….if I went and trained and stuff and went away, participated in events and stuff, I don't think she'd be too happy” (Participant 25, aged 39)“…if you’ve got a peer group that’s going to keep you motivated, keep you on target” (Participant 4, aged 38)“…my wife has pushed me on any sport I've done or any fitness thing or healthy eating” (Participant 23, aged 57)• Incorporate social support when signposting to lifestyle services/programmes/activities. To include social, practical and emotional supportRelationships between body image and self-confidence on mastery of skills for physical activity and exercise“Whenever you’re big, walking through the door of the gym with all fit and beautiful people is really hard” (Participant 19, aged 45)“There’s no point in me rocking up to a training session that these guys on a Saturday play because I’d just be out of my depth, and I’d look a fool.” ( Participant 25, aged 51)• Generation of rules to address perceived judgement and negativity• Promote group-based activities of similar individualsBuilding motivation and personalised goal setting“But it's having that motivation and having that time when you're not tired to be able to go and take some exercise and say, "*Well this* is doing me the world of good.” ( Participant 16, aged 62)“I think it was small targets that you could see you were achieving, and if you do this you will see results….Setting small targets and seeing small results that help a lot.” ( Participant 19, aged 45)• *Identifying a* range of options to facilitate lifestyle behaviour change• Identify different ways of making meaningful changes in lifestyle behaviours• Incorporate practical, emotional and social support, and treat all three equally• *Offer a* means to self-monitor progressCredible individuals increase uptake and continued engagement with lifestyle behaviour change“I think, good information, given to you… You’ve got to have somebody there who’s accredited or qualified, to actually tell you what you should be doing” (Participant 3, aged 32)“You look like you’re the type of guy that has got knowledge in these areas [referring to facilitator]. Rather than just doing it myself, I would go to the gym. I’ve got no trainer” (Participant 8, aged 52)• Use of qualified/trained and non-judgemental individuals who participants can relate to on a personal level (e.g., professionals with lived experience, peer support workers)• Provide up-to-date, evidence-informed information
## THEME 1: Lifestyle behaviours for both mental health and physical health
The consensus from participants was that being healthy involves having good physical and mental health, and both were equally important. Together these increase wellbeing:“I think wellbeing is the best word ever because it’s not like you [only] need to be fit, [or that] you need to stop eating bags of chips. It’s wellbeing. I think that is definitely mentally as well as physically. ”(Participant 24, aged 51)“I think it’s a healthy mind, so if you have all the stress in the world, that plays a massive part on your health. ”(Participant 7, aged 32) In addition, positive emotions were reported to be key to optimal health and wellbeing. The importance of being happy and enjoying life was commonly reported across focus groups. Participants often paired happiness with accomplishment of goals and setting themselves a new challenge. When participants were asked to describe the healthiest person they know, happiness was a feature of the response:“…they [healthy people] tend to be happier people as well, like [group member] said, they are positive, upbeat and [they] have a target. ”(Participant 17, aged 34) Another participant supported the view that healthy people enjoy “…whatever it is that they do.” ( Participant 4, aged 38).
Maintaining a good social life was reported to be instrumental to being healthy and that this could help to cope with stress:“But I think having a laugh is the best thing for me and family and friends…” (Participant 17, aged 34).
The role of a good social life for maintaining health was consistently highlighted by participants:“It’s celebrating your success and being happier with yourself and getting a good social life which can help you achieve those goals”. ( Participant 6, aged 61).“….sometimes on a Saturday morning we do a health walk.... Sometimes it's not just about the walking, it's the social aspect. It provides health benefits as well. So, it's not just about getting fit, it's about keeping mental awareness.. being healthy, healthy body, healthy brain” (Participant 25, aged 39).
When prompted to discuss the links between lifestyle behaviours, physical and mental health and wellbeing, participants agreed that physical activity and exercise (collectively referred to as exercise by the participants) in particular can have specific benefits for mental health:“I think it [exercise] can definitely impact [positively] on your mental health and help you to face the problems that you are facing every day.” ( Participant 6, aged 61).
Participants described the positive effects that exercise had on their lives:“I’ve found doing more exercise puts me in a better frame of mind, it clears my head”. ( Participant 18, aged 34).“I am getting fitter, again, not just physically but mentally as well”. ( Participant 24, aged 59) One participant explained the importance of his walk home from work and how it allowed him to “…not bring any stress from here [work] home” (Participant 9, aged 33). Others agreed with how physical activity and exercise can impact positively on stress. Some considered it a priority:“Fresh air for me, as well. Just a nice walk through the park. So, if it’s been a busy, stressful day, I’ll just walk through the park. It takes an extra twenty minutes to get home but it’s worth it.” ( Participant 4, aged 38) When asked specifically about strategies used to overcome stress, participants referred to specific lifestyle behaviours including sleep, and how sufficient sleep can help prevent or reduce stress levels. “I’ve started going to bed earlier, now, to get the sleep in and I find now I’m getting more sleep and I’m not as stressed.” ( Participant 5, aged 61) This view was supported by other participants across focus groups. However, there were specific lifestyle behaviours such as alcohol consumption that participants were aware had a negative impact on sleep quality and subsequently stress levels:“…you have a few drinks to try and de-stress which seems to improve your sleep a bit. Then you start suffering from insomnia and then you drink again, and it becomes a cycle.” ( Participant 4, aged 38).
## THEME 2: Work pressures are barriers to engaging with lifestyle behaviour change
This theme was salient throughout all six focus group discussions, however the focus group conducted in the workplace setting generated more frequent work-related barriers and enablers to lifestyle behaviour change. Increasing workloads and ‘doing more and for less’ featured in a large proportion of the participants’ lives. This was linked to long working hours that reduced the time and motivation for engaging in health promoting behaviours:“I think most of it will be work, as with most people nowadays. Where there’s less people working and they’re expecting people to pick up the work and the workload’s getting heavier…” (Participant 5, aged 61).
The increase in workload and lack of time to complete daily tasks was described as ‘frustrating’. “If I plan to do something at a certain time and I don't get to do it, I feel as though I'm playing catch up for the rest of the day…” (Participant 15, aged 35).
The recurrence of this pattern was described to decrease the opportunity to engage in positive health behaviours and reducing or removing this barrier would likely be mutually beneficial to the individual and the workplace.
Participants described time constraints to be a barrier, even outside of work, which left them with little time to engage with physical activity and exercise, and consider other health-related behavioural changes:“You want to do exercise, but if you get up at 5:30 in the morning to get here [work place], and you are getting home at 6:30, you just want to have something to eat, have a shower, and go to bed. ”(Participant 9, aged 33) Other participants described similar situations across focus group discussions:“*It is* pretty hard, I find it anyway, to slot a gym session in somewhere along the line” (Participant 24, aged 51).
Time taken to travel to facilities to exercise was reported as a further barrier:“So it is, it's about time and being able to get there, to the place to do it.” ( Participant 25, aged 39).
Although participants did acknowledge the importance of making time for yourself “…you’ve got to make time, haven't you? That's probably the thing”. ( Participant 8, aged 52).
## THEME 3: Previous injuries are barriers to engagement in physical activity and exercise
Recovering from injuries (acquired recently or in the past) was a common barrier to engaging in physical activity and exercise. Although exercise was considered a ‘good stress reliever’, several participants had discontinued exercise due to injury:“*About a* year ago I hurt my Achilles and I haven’t been able to run properly since” (Participant 4, aged 38).
Exercising after injuries was reported to be a challenge due to lack of motivation following time away from exercise. It was particularly difficult for participants to reclaim the time and effort required to re-engage:“…after about six weeks, after I’d healed, I just couldn’t get myself back into it.” ( Participant 10, aged 37). Furthermore, injuries influenced participant’s physical and mental health:“A mate of mine has broken his foot, obsessed with running, literally meticulous in his eating and everything. Since he's done his foot, he's never been so depressed. He's been hitting rock bottom because he can't get out and do that exercise” (Participant 18, aged 34).
## THEME 4: Personal and peer group relationships impact on lifestyle behaviour change
Participants reported several challenges associated with making lifestyle behaviour changes to improve their physical and mental health, however the barriers reported became more of a challenge if those around them made lifestyle behaviour change difficult.“… having a group of people around you who are not supportive [negatively affects your lifestyle behaviour choices]” (Participant 17, aged 34)“You get home and your lass [female partner/wife] says, ‘Do you want a vodka and coke’” (Participant 12, aged 51)“I lost 24 kilos, something like that. My friends were saying, ‘Oh, you’ve lost enough weight now; you’ll look ill if you lose any more weight.’ And I thought, ‘Alright’. And I stopped and that was the worst thing that I ever did” (Participant 5, aged 61)“…. if I went and trained and stuff and went away…, I don't think she'd [partner be too happy” (Participant 25, aged 39).
Childcare was also an issue when trying to make positive lifestyle changes. Some participants reported feelings of guilt if taking time out for themselves away from their children:“I've got a young daughter. I think it would be quite off for me and my wife both to go to the gym together because we'll have to get my daughter looked after and things.” ( Participant 25, aged 39).
Several participants reported how taking part in a group-based programme would provide important social support:“…team sports like walking football, extensions on that and I think you'd get the social side of that” (Participant 23, aged 57).“I've always known for a fact, if I engage myself in something that was part of a group, that I would be better because I think that's the way I work but I think it's better to work as a team.” ( Participant 17, aged 34) Participants consistently reported the belief that social support would help to maintain motivation over time:“Also, if you’ve got a peer group that’s going to keep you motivated, keep you on target”. ( Participant 4, aged 38)‘‘…I’d love to get involved in a group, with the support of everybody around, to help change my lifestyle and change my mind-set…” (Participant 7, aged 32) Previous participation in group based activities where competition was an important motivator was reported as beneficial: “I think that became a competitive thing with a group of peers and I think that helped” (Participant 21, aged 41).
## THEME 5: Relationships between body image and self-confidence on mastery of skills for physical activity and exercise
Participants consistently reported self-confidence issues when attempting to engage in physical activity and exercise, particularly within community settings:“Whenever you’re big, walking through the door of the gym with all fit and beautiful people is really hard…you're trying to hide your body away even though you can't, so it's like you don't want to expose yourself to ridicule.” ( Participant 19, aged 45)“I think that was the one daunting thing I hated every time I did go to the gym. There would be buckets of sweat pouring off of me and I'd only been there for ten minutes and there'd be guys doing crunches with their legs up in the air and stuff, completely posing and looking at every mirror.” ( Participant 21, aged 41).
Self-consciousness and body image concerns extended to buying clothing to participate in physical activities and sports:“That’s hard. The people are fit. They know what they are doing. And most sports shops don’t cater for big people. So finding clothes that fit is embarrassing and really really difficult.” ( Participant 19, aged 45) Feelings of anxiety relating to ability to master and perform specific skills was also reported:“There’s no point in me rocking up to a training session that these guys on a Saturday play because I’d just be out of my depth, and I’d look a fool.” ( Participant 24, aged 51) There was an expectation that others would be amused by participants inability to master activities: “…see all these blokes but it’s mostly women who are fit and muscly and I’m thinking they’re laughing at me.” ( Participant 19, aged 45).’
## THEME 6: Building motivation and personalised goal setting
A common barrier reported across focus groups was a lack of motivation to even consider making lifestyle changes. This was specifically linked to physical activity and exercise:“taking the first step, that’s the hardest one to take” (Participant 3, aged 32).“But it's having that motivation and time when you're not tired to be able to exercise….” ( Participant 16, aged 62)“I've always tried losing weight, doing exercise but it's just finding the motivation… because I don't particularly like exercise.” ( Participant 15, aged 35)“…can’t be bothered. That’s the top and bottom of it” (Participant 9, aged 33) Although it was acknowledged that taking the first step was the most difficult, and that once a routine was established it became easier: “The first few weeks is always the hardest and then you get into a routine and then after that you do feel the benefits” (Participant 17, aged 34), several participants provided insights into their lack of motivation. For example, enjoyment was frequently reported as an issue. Participants understood the health benefits of increased physical activity and exercise, however the lack of enjoyment experienced during specific exercises was reported to prevent participants from living more active lives:“…I just don’t particularly enjoy that gym side of things, using the machines etc. it just doesn’t motivate me…” (Participant 20, aged 57) There was consensus that setting and reaching behavioural goals (e.g., achieving physical activity targets; reducing alcohol consumption; increasing sleep duration) would lead to positive outcomes (e.g., improved mental health), and that this would be an important motivator to continue. It was emphasised that goals should be graded and achievable:“I think it was small targets that you could see you were achieving, and if you do this you will see results….Setting small targets and seeing small results that help a lot.” ( Participant 19, aged 45)“Rather than just the big picture, small incremental changes. Instead of thinking, ‘Oh, this programme is going to go for six months or twelve months. What can I do?’ just little daily changes.” ( Participant 8, aged 52).
The focus groups also explored how and why participants monitored their health. Although some participants described using body weight scales to monitor health (i.e., a weight within a normal range was an indicator of health), most participants preferred visual cues, specifically clothing, to gauge whether they were becoming unhealthy:“The size of my trousers, that's a big one.” ( Participant 23, aged 57).
Others reflected on a time when they were overweight and expressed a desire to not return to a past state:“It's seeing the benefit. It's seeing the visual and thinking, "I don't want to get back to what I was and what I felt like" (Participant 18, aged 34).
Participants described how feedback from physical activity monitoring devices in relation to their goals impacted positively on their behaviour. Specifically, activity trackers highlighted levels of inactivity and prompted behavioural changes:“And you can be surprised how few steps I’ll actually do if I just drive to work, go to work, and go back home again, and potter around the house” (Participant 14, aged 56) It was also felt that activity trackers can be used positively to create competition with others: “… both me and the missus have got iPhones and we’re both pretty much mapping, step by step, doing the same route” (Participant 1, aged 35). Physical activity monitoring was also described as a prompt to reach a specific goal:“..if you’re near your target, at the end of the day, you just randomly walk around the house trying to just make up the steps? Yes” (Participant 3, aged 32)“I do that in the office, mind. Ten minutes before the hour comes, it tells you how many steps you’ve got to, and you’ve got to do 250 every hour.” ( Participant 5, aged 61) As referred to previously, family members were often considered barriers to making positive lifestyle changes, however they could also be facilitators. Once family members provided their support, they were considered instrumental to success when achieving goals:“Definitely, my wife has pushed me on any sport I've done or any fitness thing or healthy eating.” ( Participant 25, aged 39)“If I get a Tuesday off she'll say, ‘Come on, you go to the gym while I go swimming…so I'd go but that's because she's dragging me along…” (Participant 15, aged 35)
## THEME 7: Credible individuals increase uptake and continued engagement with lifestyle behaviour change
A consistent and salient finding across focus groups was that participation in interventions, designed to improve physical and mental health and wellbeing, would be enhanced if delivered by a credible individual who can provide accurate and evidence-based information. Accreditation or relevant qualifications was considered a marker of credibility:“You’ve got to have somebody there who’s accredited or qualified, to actually tell you what you should be doing” (Participant 3, aged 32)“You look like you’re the type of guy that has got knowledge in these areas..” (Participant 9, aged 33).“I think there are men who like facts – *This is* what you’ve got. This is bad for you. Do this. It will put it right. That’s really what we want.” ( Participant 13, aged 60) Accuracy of health information was a concern, particularly when the information was received from multiple sources, including the media, which can cause confusion:“*The media* complicate things. They don’t know whether you should be eating this or whether you should be eating that, and you get lost at the end.” ( Participant 10, aged 37).
## Discussion
The aim of our study was to identify the behavioural barriers and enablers to engaging men with lifestyle behaviour change interventions targeting physical and mental health.
Three themes identified related specifically to barriers experienced by participants when engaging in lifestyle interventions and have been identified previously by two systematic reviews [29, 30]. For example, work and family pressures; health and physical limitations; a perceived lack of enjoyment, motivation, and time [29, 30]. Similar barriers identified related to forming intentions to make lifestyle changes, particularly in the context of physical activity and exercise when it was perceived as a chore and not enjoyable. As such, the need to provide signposting or access to a range of enjoyable activities is important to promote uptake and continued engagement. This finding is consistent with Self-determination Theory [31] and specifically, intrinsic motivation involving engagement with an activity based on interest, enjoyment, and inherent satisfaction. Teixeira et al., [ 32] reported how seeking an internal goal, that leads to personal enjoyment can satisfy basic psychological needs for motivation, and ultimately lead to success. Findings from our study support the value of goal setting and goal pursuit as a means of maintaining motivation when making lifestyle behaviour changes. Specifically, the need to set small, tangible, and achievable goals and the benefits of receiving positive feedback and social and emotional support was considered important. Behavioural goal setting has been used successfully in previous men’s health interventions [23, 33–35] and should be considered a candidate for inclusion in future interventions.
Burgess et al., [ 29] reported potential gaps in knowledge or a lack of awareness about the importance of physical and mental health when adopting healthy behaviours. Our study did not support this finding; indeed, participants showed a good understanding about the importance of physical and mental health, and how the two impact each other, however knowledge and awareness does not naturally lead to behavioural change [36]. Our findings, and those of Burgess et al., [ 29], suggest that future interventions should continue to highlight the important relationship between lifestyle and optimal physical and mental health, but should place greater emphasis on practical strategies and problem solving to make and sustain behavioural changes (e.g., graded goal setting, social support, problem solving tasks), location of facilities to reduce time travelling and credentials of those delivering them.
In the current study, participants described a lack of self-confidence relating to mastery of skills which prevent engagement in lifestyle behaviours, specifically physical activity and exercise due to fear of embarrassment, and similar findings have been reported by intervention studies [37]. A cross-sectional study investigating barriers to physical activity in men found that those who did not meet physical activity recommendations were more likely lack knowledge, motivation, mastery of skills, and report intimidation or embarrassment. Furthermore, those who reported increased stress levels were also more likely to report intimidation and embarrassment as a key barrier [38]. These behaviours may further explain why men access mental health services following prolonged periods of displaying symptoms and why one in four men will drop out of management regimens [39]. Systematic reviews have also reported negative attitudes of men towards healthcare, that impacts on intention to access services due to traditional beliefs about masculinity and male gender roles (e.g., emotional control, self-reliance, and being successful at any cost) [40]. This highlights the need for interventions that target emotions and aim to support men to develop coping strategies to overcome these challenges. If emotional barriers to lifestyle behaviour change are not addressed alongside other barriers, uptake and long-term engagement will likely remain low. Recommendations based on our findings include the generation of ‘rules’ to address perceived judgement and negativity; promoting group based activities of similar individuals considering perceived ability. These strategies were suggested by participants and have proven successful in previous men’s health interventions [33].
Work related issues and increased stress was a consistent finding of our study. Workplace interventions have emerged to overcome this issue, however there are few interventions that consider the specific barriers faced by men (e.g., pressure to take on overtime at work, and barriers discussed already) [34]. Seaton et al. reported findings from a gender sensitised workplace intervention that aimed to improve self-reported physical activity [35]. The intervention achieved its aims with an increase in walking by 156.5 min/week and was reported to be acceptable by men [35]. A specific finding, consistent with our study was that participants reported that a reduction in workload facilitated physical activity engagement. Interventions to address this issue would likely create opportunities to make and sustain lifestyle behaviour changes.
Our findings also reflect the role of masculinities in the context of health. The issue of work-related issues as barriers to improving lifestyle behaviours could be interpreted as traditional norms of masculinity discouraging men from seeking help for emotional issues [41]. Furthermore, injuries as barriers to re-engaging with previous levels of physical activities / exercise may reflect men’s hyper-competitiveness and a need to strive for physical prowess [42], which men may feel that they can no longer engage with due to injury, which leads to feelings of inadequacy if men identify with these perceptions of masculinity. In addition, issues related to body image and self-confidence with engaging in exercise in community settings was identified in our study, as well as reflecting perceived societal norms about men’s bodies, also reflects the high numbers of men who report experiencing weight stigma. In one study of 1,513 men [43], $40\%$ reported some form weight stigmatisation (most commonly, verbal mistreatment from peers, family members, and strangers).
Our study identified specific facilitators to lifestyle behaviour change to optimise physical and mental health. These included practical support to set personalised and meaningful goals, graded to build confidence, a means to self-monitor progress, and elicit practical and social support from family and peers. A credible individual facilitating and supporting intervention delivery was considered vital, particularly someone who was qualified and could provide evidence-based information and advice in a friendly manner. Furthermore, it was reported to be important that facilitators understood the specific issues related to lifestyle behaviour change for men, were approachable and non-judgemental, and importantly that they were not healthcare professionals. Healthcare professionals often provide information about the benefits of health behaviour change which is widely reported as a mechanism to encourage behaviour change, increase initial uptake of interventions and to promote continued engagement [36–38]. However, for those who do not engage with healthcare services or who feel pressurised by healthcare professionals, it was considered important that information and support is provided by someone who recipients can relate to and get along with.
Our research suggested that social support, including practical support from family members and peers can both positively and negatively influence health-related behaviours, and this finding has been previously reported [29]. However, in our sample this was often linked to work. For example, greater resistance was experienced when trying to find time to take part in leisure time activities and make healthier choices in terms of diet if the working day had been long. Participants reported feeling guilty and explained how partners became resentful. Despite this, participants also described partners and children as being vital when maintaining healthy and physically active lifestyles once they are on-board. Findings also emphasised that participants prefer to be part of a group of similar individuals that creates a sense of identity. This finding corresponds to outcomes reported by the FFIT study that highlighted the role of ‘banter’ and ‘comradery’ as a source of motivation and social support [23].
Social support has also been used effectively as part of interventions to improve the physical activity of men by introducing competition [33, 35, 44–46] and this was an important finding in our study. Participants reported pursuit of goals and meeting personalised targets and referred to healthy competition within group environments. Competition has been used successfully in behaviour change intervention for male participants within workplaces [35, 47], countries outside the UK [46, 48], and within football clubs [23]. As such, social support and competition should be considered key ingredients of interventions targeting lifestyle behaviour change of men.
Longevity as a motivator for behaviour change emerged as a brief discussion point. The importance of longevity to older men (UK retirement age) is highlighted in the Health, Illness, Men and Masculinities (HIMM) framework [49]. Specifically, as men age, they become more aware of their mortality which, when combined with major life events such as retirement, can increase stress [50]. Our findings support this assertion.
Stress was a consistent topic throughout our focus group discussions. Participants regularly talked about a desire to reduce stress and improve quality of sleep to impact positively on daily interactions. However, few intervention studies have considered the impact of sleep on health, specifically those designed for men [51].
Although the findings of the focus groups conducted in a workplace setting versus the football stadium did not differ markedly, data from the workplace setting generated more work-related barriers and enablers, therefore setting in which data is collected should be considered. Furthermore, it was the opinion of the researcher conducting the focus groups that suggestions of enablers were more forthcoming from football stadium participants. A secondary aim of our study was to use the findings to inform the development of a community-based intervention (The 12th Man) targeting improvements in physical and mental health and wellbeing of men. Specifically, identification of target behaviours and key intervention ingredients to engage men and to support them to make personally important changes to their health-related behaviours was a priority. Findings highlighted that physical activity, exercise, alcohol, and sleep to improve mood, function, well-being, and reductions in weight, were important to men, although interestingly diet was not frequently identified or discussed as a target for intervention. With reference to active intervention ingredients, it was agreed that a future intervention should be delivered by a credible individual, or team of individuals who are knowledgeable, can communicate complex and evidence-based health messages simply, and someone who is non-judgemental who participants can relate to and who is considered part of the group. Social pressure and emotional barriers were consistently reported as factors that prevented participants from engaging in health behaviour change. For example, body image and the negative impact it has on mastery of skills and confidence was reported to prevent engagement with physical activity and exercise. Therefore, including strategies within an intervention to reduce or overcome perceived or actual pressure involving social support is important. In terms of optimal duration, there was no data to suggest when an intervention should start and end. Instead participants talked about the need for interventions that provided knowledge, skills, social support, and signposting to local services/activities of interest to enable long-term, sustainable change.
A key strength of this study was the recruitment strategy that was successful in identifying and recruiting male participants who reported a reluctance to engage in health-related interventions offered by healthcare settings. This suggests that the approach taken engaged a different group of men who might be considered as ‘hard to reach’ [21]. This could be attributed to the links with a Premiership football club for the reasons reported previously [24, 27, 52, 53]. Secondly, the interview topic guide and interviewer facilitated discussions around men’s interests initially to understand what prevented them from pursuing their interests rather than something completely new. This approach was successful in identifying barriers and facilitators that could inform the content of a future intervention by incorporating strategies to enable men to participate in activities of choice to improve health and wellbeing.
A potential weakness of the study is that we did not collect health-related data from participants that could characterise the sample. However, a decision was taken not to collect this information so not to dissuade men from participating in a study that is essentially designed to inform intervention development. Lastly, our research focussed on men between the ages of 30 and 65 years in accordance with the rationale provided within our methods, however we acknowledge that younger men also have mental health needs that require further attention in future research.
## Conclusion
The current study was successful in identifying behaviours (specifically, physical activity and exercise and other lifestyle behaviours including sleep and alcohol, albeit to a lesser extent) as targets for intervention that were perceived by men to be important for optimal physical and mental health and well-being, and barriers and facilitators associated with health behaviour change and maintenance. Key active intervention ingredients were also identified as potential mediators to behavioural change (e.g., setting, mode of delivery, intervention content, duration). Findings will be used to inform the development of the 12th Man intervention designed to target lifestyle behaviours to achieve improvements in physical and mental health of men and for delivery through a Club Community Organisation, Newcastle United Foundation.
## Supplementary Information
Additional file 1. Additional file 2.
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|
---
title: 'Relationship between novel anthropometric indices and the incidence of hypertension
in Chinese individuals: a prospective cohort study based on the CHNS from 1993 to
2015'
authors:
- Xin Zhang
- Runyu Ye
- Lirong Sun
- Xueting Liu
- Si Wang
- Qingtao Meng
- Xiaoping Chen
journal: BMC Public Health
year: 2023
pmcid: PMC9990350
doi: 10.1186/s12889-023-15208-7
license: CC BY 4.0
---
# Relationship between novel anthropometric indices and the incidence of hypertension in Chinese individuals: a prospective cohort study based on the CHNS from 1993 to 2015
## Abstract
### Background:
Recently, novel anthropometric indices (AHIs), including the body roundness index (BRI) and a body shape index (ABSI), were proposed to evaluate a subject’s nutritional status and metabolic disorders. In the present study, we mainly analyzed the relationship between AHIs and the incidence of hypertension and preliminarily compared their abilities to discriminate hypertension incidence in the Chinese population from the China Health and Nutrition Survey (CHNS).
### Methods:
A total of 12,154 participants were included in this longitudinal study. The age range of this cohort was 18–94 years old (mean age: 40.73 ± 13.85 years old). 4511 participants developed hypertension during a median of 7.00 years of follow-up. Cox regression analysis, stratified analysis, and interaction tests were used to analyze the relationship between AHIs and the incidence of hypertension. Time-dependent receiver operating characteristic (ROC) curves, integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated to appraise the AHIs’ discrimination value of new-onset hypertension.
### Results:
Kaplan‒Meier curves demonstrated that the participants in higher quartiles of AHIs (ABSI or BRI) at baseline were at greater risk of hypertension incidence during the follow-up. After adjusting for confounding factors, multivariate Cox regression models showed that the quartiles of BRI were significantly associated with an increased risk of hypertension in the whole cohort but were relatively weak for ABSI quartiles (P for trend = 0.387). In addition, ABSI z score (HR = 1.08, $95\%$ CI: 1.04–1.11) and BRI z score (HR = 1.27, $95\%$ CI: 1.23–1.30) were positively associated with increased incident hypertension in the total population. Stratified analysis and interaction tests showed a greater risk of new-onset hypertension in those < 40 years old (HR = 1.43, $95\%$ CI: 1.35–1.50) for each z score increase in BRI and a higher incidence of hypertension in participants who were drinkers (HR = 1.10, $95\%$ CI: 1.04–1.14) for each z score increase in ABSI. In addition, we observed that the area under the curve for identifying hypertension incidence for BRI was significantly higher than that for ABSI at 4, 7, 11, 12, and 15 years (all $P \leq 0.05$). However, the AUC of both indices decreased over time. Furthermore, the addition of BRI improved the differentiation and reclassification of traditional risk factors with a continuous NRI of 0.201 ($95\%$ CI: 0.169–0.228) and an IDI of 0.021 ($95\%$ CI: 0.015–0.028).
### Conclusion:
Increased ABSI and BRI were associated with an increased risk of hypertension in Chinese individuals. BRI performed better than ABSI in identifying the new onset of hypertension, and the discrimination ability of both indices decreased over time.
## Background
Hypertension is one of the largest contributors to morbidity and mortality worldwide and is also the most important modifiable risk factor for cardiovascular disease [1]. According to the newest national hypertension survey, $23.2\%$ of the population aged ≥ 18 years old has hypertension in China, accounting for 240 million patients [2]. It is estimated that high systolic pressure accounted for 2.54 million deaths in 2017 in China [3]. In addition, the direct medical costs related to diagnosis, tests, medication, outpatient visits, and hospitalization therapy for hypertension were 115.7 and 109.0 US dollars per patient per year in urban and rural areas of China [4]. Hence, hypertension causes a huge burden on people’s health and the social economy in our country. Positive prevention and identifying the high risk of hypertension incidence in the normotensive population have great significance.
Anthropometric indices (AHIs) are simple and accessible parameters to evaluate nutritional status. The most commonly used AHIs in clinical practice and epidemiological studies include body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Prospective cohort studies have demonstrated that BMI and WHR could discriminate the incidence of hypertension [5–7]. This suggests that AHIs might help to identify subjects with high risk and to supply a precise prevention strategy for the specific population. Which could further reduce the burden caused by hypertension.
Recently, two novel AHIs have been developed, named the body shape index (ABSI) and body roundness index (BRI) [8, 9]. ABSI, proposed by Krakauer et al. in 2012, is based on WC, height, and BMI and can predict mortality independently from BMI in the United States population [8]. Meanwhile, Thomas et al. suggested BRI as a predictor of visceral adiposity tissue and body fat percentage [9]. BRI has proven to be a good predictor of metabolic syndrome in diverse nationalities and ethnic groups [10]. In recent years, some cross-sectional studies have compared the identification of hypertension by ABSI, BRI, and traditional AHIs in Chinese, Iranian, Peruvian, and European populations [11–14]. Generally, BRI was a significantly better tool to discriminate hypertension than ABSI [15]. To date, only one longitudinal study evaluated the hypertension discrimination value of novel AHIs in a Korean population [16]. Therefore, the relationship between novel AHIs and the incidence of hypertension in the Chinese population is unclear. In the present study, we mainly analyzed the relationship between AHIs and the incidence of hypertension and also preliminarily compared their abilities to discriminate hypertension incidence in the Chinese population from the China Health and Nutrition Survey (CHNS).
## Study population
The longitudinal data of participants in the present study were from the CHNS. The CHNS is a nationwide survey on risk factors, nutrition, and health-related outcomes in the Chinese population from 15 provinces and autonomous districts, including Beijing, Chongqing, Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, Shaanxi, Shandong, Shanghai, Yunnan, and Zhejiang. The CHNS is an ongoing open cohort and an international collaborative project announced in 1989 and subsequently conducted in 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. In this project, a multistage, random cluster process was used to draw the samples surveyed in each of the provinces. Details of the cohort and sampling process have been published elsewhere [17].
To analyze the correlation between AHIs and the risk of hypertension, we used CHNS data from 1993 to 2015. Waist circumference (WC) and hip circumference (HC) were not collected in 1989 and 1991. Adults aged ≥ 18 years at the first wave with integrated data on sex, anthropometric indicators (including height, weight, WC, and WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), ethnicity, and smoking and drinking status were suitable for this analysis. Participants with the following criteria were excluded: those with missing data for the abovementioned indices; those who had just one medical record over the years; those who were pregnant or lactating at the time of the survey; and those with extreme values (e.g., height < 120 cm; weight > 150 kg; WC < 50 cm or > 150 cm; and HC < 50 cm or > 150 cm). In addition, participants with hypertension at baseline (including those who had a self-reported diagnosis of hypertension, antihypertensive medication, and average SBP ≥ 140 mmHg or DBP ≥ 140 mmHg) were also excluded. Stata/SE version 15.1 was used for original data merging, calculation, cleaning and conversion before the statistical analysis, according to the inclusion and exclusion criteria as previously mentioned.
## Demographic parameters
Information on age, gender, race (Chinese Han or other), urban or rural residence, and current smoking and drinking status were gathered from the questionnaires at each follow-up survey. Current smoking was defined by whether participants themselves reported that they had still smoked cigarettes at each survey. Nonsmokers and former smokers were defined as noncurrent smokers. Drinking status was evaluated according to the frequency of alcohol consumption by self-report. In the present study, a participant who did not drink in the past year was defined as nondrinking, and the others (including drinking daily, 3–4 times/week, 1–2 times/week, and less than once a week) were defined as drinking status.
## Blood pressure measurements
The trained staff performed blood pressure measurements following the standard protocol and using appropriately sized cuffs at each follow-up survey. Before measurements, all participants were required to have a 10-min seated rest. Triplicate measurements of blood pressure on the right arm were conducted by using mercury sphygmomanometers, with at least 1 min between recordings [18]. The average of the three blood pressure measurements was calculated for the final analysis.
## Anthropometric measurements
The anthropometric measurements were administered by well-trained research staff in a private and comfortable room. All participants were requested to remove bulky clothing and shoes before measurement. Weight, height, WC, and HC were obtained using the calibrated equipment according to a standard procedure. ABSI was estimated as the WC divided by the BMI raised to two-thirds and by the square root of the height. In addition, the BRI was based on WC and height.
The specific formulas of ABSI and BRI are as follows [8, 9]: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rm{ABSI}} = WC(m)*weight{(kg)^{ - $\frac{2}{3}$}}*height{(m)^{$\frac{5}{6}$}}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{\rm{BRI = 364}}{\rm{.2 - 365}}{\rm{.5}}\, \times \,{\rm{Eccentricity}}\,{\rm{Eccentricity}}\,{\rm{ = }}\\\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\sqrt {1 - \frac{1}{{{\pi ^2}}}{{\left({{\raise0.7ex\hbox{${WC\left(m \right)}$} \!\mathord{\left/{\vphantom {{WC\left(m \right)} {Height\,(m)}}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{${Height\,(m)}$}}} \right)}^2}} \end{array}$$\end{document}
## Study outcome
The outcome of the present study was new-onset hypertension during follow-up. Participants were identified if they had an average SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, self-reported a diagnosis of hypertension, or were currently taking antihypertensive at any one of the follow-up visits [19, 20]. The first time for diagnosis with hypertension was considered the time when the end event occurred. For those free of hypertension in all follow-up surveys, the final survey date was used to calculate the follow-up time.
## Statistical methods
All statistical analyses were performed using R software (Version 4.2.1). Continuous data with a normal distribution were expressed as the mean ± standard deviation (SD). Categorical variables were expressed as the frequency. Continuous data were compared using the independent-samples t test. Differences in categorical variables were compared among the groups using the chi-squared test. The AHIs were converted into z scores and quartiles. In the etiological analysis section, Kaplan-Meier curves were used to evaluate the cumulative incidence for AHIs categories, and the log-rank test was utilized to examine the significance of the differences between groups. Univariate and multivariate Cox regression models were applied to analyze the association between each anthropometric measurement and the incidence of hypertension. The confounders in multivariate Cox regression were selected as those with significance in univariate analysis or reported by previous studies. Stratified analysis and interaction tests were conducted according to age (< 40 and > = 40 years old), sex, ethnicity (Chinese Hans and non-Hans), residence (urban and rural), current smoking status, current drinking status, SBP level (< 120 and > = 120 mmHg), and DBP level (< 80 and > = 80 mmHg). The hazard ratio (HR) of hypertension incidence and the $95\%$ confidence intervals ($95\%$ CI) were calculated. In addition, we preliminarily analyzed the discrimination of hypertension incidence for ABSI and BRI, respectively. First, the ability to discriminate the incidence of hypertension was compared between ABSI and BRI using time-dependent receiver-operating characteristic (ROC) curve analysis. Second, owing to not collecting hypertension family history and serum biochemical indices in the early stage of CHNS, we could not directly use the existing prediction model in the literature. Furthermore, the integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated to appraise the incremental discrimination value of new-onset hypertension beyond the traditional factors based on age, sex, ethnicity, residence, smoking, drinking, SBP and DBP.
## Baseline characteristics
The age range of this cohort was 18–94 years old (mean age: 40.73 ± 13.85 years old). During a median of 7 years (1, 3 quartiles: 4, 15 years) of follow-up, 4511 participants ($\frac{39.21}{1000}$ person-years) developed hypertension, and 7463 were censored. Of them, 2291 incident cases of hypertension in 5484 men ($\frac{43.49}{1000}$ person-years) and 2220 cases in 6490 women ($\frac{35.59}{1000}$ person-years) were reported. The baseline characteristics of those who did and did not develop hypertension are presented in Table 1. There were significant differences between those who did develop hypertension and those who did not in the majority of the parameters except for height ($$P \leq 0.423$$). The participants who developed hypertension had higher age, weight, HC, WC, SBP, DBP, ABSI, and BRI at baseline than those who did not (all $P \leq 0.001$). In addition, the proportion of males, Han ethnicity, rural farmers, current smokers, and drinkers was higher in participants who developed hypertension in the process of follow-up. Furthermore, Kaplan‒Meier curves demonstrated that the participants in higher quartiles of AHIs (ABSI or BRI) at baseline were at greater risk of hypertension incidence during the follow-up (log-rank test, $P \leq 0.001$; Fig. 1).
Table 1The characteristics of the study population at baselineVariablesNot developed hypertensionDeveloped hypertension P value $$n = 7463$$ $$n = 4511$$ Age (years) 37.95 ± 13.2745.34 ± 13.56< 0.001 Height (cm) 161.00 ± 8.17160.76 ± 8.490.423 Weight (kg) 57.23 ± 9.9959.17 ± 10.26< 0.001 HC (cm) 90.90 ± 7.6292.37 ± 7.91< 0.001 WC (cm) 76.43 ± 9.2379.17 ± 9.47< 0.001 SBP (mmHg) 111.38 ± 11.44115.97 ± 11.33< 0.001 DBP (mmHg) 72.81 ± 8.0575.52 ± 7.54< 0.001 ABSI 0.077 ± 0.0060.078 ± 0.006< 0.001 BRI 2.96 ± 1.033.30 ± 1.12< 0.001 Gender < 0.001Male3193 ($42.78\%$)2291 ($50.79\%$)Female4270 ($57.2\%$)2220 ($49.2\%$) Ethnicity 0.025Han6598 ($88.41\%$)4048 ($89.74\%$)non-Hans865 ($11.59\%$)463 ($10.26\%$) Residence < 0.001Urban2995 ($40.13\%$)1423 ($31.55\%$)Rural4468 ($59.87\%$)3088 ($68.45\%$) Current smoke < 0.001No5449 ($73.01\%$)2964 ($65.71\%$)Yes2014 ($26.99\%$)1547 ($34.29\%$) Drinking < 0.001No5054 ($67.72\%$)2761 ($61.21\%$)Yes2409 ($32.28\%$)1750 ($38.79\%$)HC: hip circumstance, WC: waist circumstance, SBP: systolic blood pressure, DBP: diastolic blood pressure, ABSI: a body shape index, BRI: body round index Fig. 1Kaplan-Meier curves of cumulative incidence of new- onset hypertension stratified by AHIs categories. A: for ABSI categories; B: for BRI categories
## Univariate analysis for the incidence of hypertension in the whole cohort
In the univariate analysis (Table 2), the criteria associated with the hypertension occurrence were age (HR = 1.04, $95\%$CI:1.04–1.05)), sex (female vs. male, HR = 0.82, $95\%$CI:0.77–0.87), race (non-Han vs. Han, HR = 0.76, $95\%$CI = 0.69–0.84), smoking (HR = 1.15, $95\%$CI:1.08–1.23), drinking (HR = 1.19, $95\%$CI:1.13–1.27), SBP (HR = 1.04, $95\%$:1.04–1.05), DBP (HR = 1.05, $95\%$CI:1.05–1.06), ABSI z score (HR = 1.25, $95\%$CI:1.22–1.29), and BRI z score (HR = 1.48, $95\%$CI = 1.44–1.52). In addition, the higher quartiles of ABSI and BRI were also correlated with the incidence of hypertension.
Table 2Univariate analysis for incidence of hypertension in the whole cohortVariablesHR ($95\%$CI) P value Age1.04 (1.04, 1.05) < 0.001 Sex = female0.82 (0.77, 0.87) <0.001 Race = non-Han0.76 (0.69, 0.84) < 0.001 Urban residence0.95 (0.89, 1.01) 0.089 Smoking1.15 (1.08, 1.23) <0.001 Drinking1.19 (1.13, 1.27) < 0.001 SBP (mmHg)1.04 (1.04, 1.05) <0.001 DBP (mmHg)1.05 (1.05, 1.06) <0.001 ABSI z score 1.25 (1.22, 1.29) <0.001 ABSI Q1 reference ABSI Q21.13 (1.26–1.52) 0.007 ABSI Q31.47 (1.35–1.60) <0.001 ABSI Q41.89 (1.73–2.95) <0.001 BRI z score 1.48 (1.44, 1.52) <0.001 BRI Q1 reference BRI Q21.39 (1.26–1.52) <0.001 BRI Q31.94 (1.77–2.12) <0.001 BRI Q42.98 (2.73–3.25) <0.001 SBP: systolic blood pressure, DBP: diastolic blood pressure, ABSI: a body shape index, BRI: body round index, HR: hazard ratio, CI: confidence interval
## The association between AHIs and the incidence of hypertension
After adjusting for age, sex, race, residence, smoking, drinking, SBP, and DBP (model 2), the Cox proportional hazard model analysis (Table 3) showed that BRI (Q2 vs. Q1:HR = 1.24, $95\%$CI:1.13–1.36; Q3 vs. Q1:HR = 1.55, $95\%$CI:1.42–1.69; Q4 vs. Q1:HR = 1.99, $95\%$CI:1.82–2.19) were significantly associated with increased risk of hypertension in the whole cohort but were relatively weak for ABSI quartiles (P for trend = 0.387). In addition, ABSI z score and BRI z score were positively associated with an increased incidence of hypertension in the whole cohort. The risk of hypertension increased by $8.0\%$ ($95\%$ CI: 1.04–1.11) and $27\%$ ($95\%$ CI: 1.23–1.30) for every 1 z score increase in ABSI and BRI, respectively.
Table 3The association of anthropometric indices with incidence of hypertension by Cox regressionVariablesQ1Q2Q3Q4 P for trend Z ScoreHR ($95\%$CI)HR ($95\%$CI) ABSI Model 1reference1.01 (0.93, 1.10)1.16 (1.06, 1.26)1.27 (1.16, 1.38) 0.977 1.09 (1.06, 1.13)Model 2reference1.01 (0.93, 1.10)1.15 (1.06, 1.26)1.22 (1.12, 1.33) 0.387 1.08 (1.04, 1.11) BRI Model 1reference1.28 (1.17, 1.40)1.64 (1.50, 1.79)2.32 (2.12, 2.54) < 0.001 1.35 (1.31, 1.39)Model 2reference1.24 (1.13, 1.36)1.55 (1.42, 1.69)1.99 (1.82, 2.19) < 0.001 1.27 (1.23, 1.30)ABSI: a body shape index, BRI: body round index, HR: hazard ratio, CI: confidence interval;Model 1: adjusted for age, sex, race, and residence; Model 2: adjusted for age, sex, race, residence, smoking, drinking, systolic blood pressure, and diastolic blood pressure. The anthropometric measures were converted to a z score using the equation (X-Xmean)/XS
## Stratified analysis on AHIs and the incidence of hypertension
To explore the effect of other variables on the relationship between AHIs and the incidence of hypertension, stratified analysis and interaction tests were conducted. The results (Table 4) showed a greater risk of new-onset hypertension in those < 40 years old (HR = 1.43, $95\%$ CI: 1.35–1.50) for each z score increase in BRI and a higher incidence of hypertension in participants who were drinkers (HR = 1.10, $95\%$ CI: 1.04–1.14) for each z score increase in ABSI. However, the other factors, including sex, race, residence, smoking, and blood pressure levels, did not impact the relationship between the AHIs and the incidence of hypertension.
Table 4Stratified analysis of anthropometric indices and incidence of hypertensionsubgroupsample sizeHR ($95\%$CI)P interactionHR ($95\%$CI)P interactionABSI z scoreBRI z score Baseline age < 4061761.09 (1.04, 1.15)0.2851.43 (1.35, 1.50)<0.001>=4057981.06 (1.02, 1.10)1.20(1.15, 1.24) Sex Male54841.06 (1.01 1.10)0.2821.28 (1.22, 1.34)0.279Female64901.08 (1.04, 1.13)1.25 (1.20, 1.30) Race Hans10,6461.08 (1.04, 1.11)0.9531.27(1.23, 1.30)0.795Non-Hans13281.08 (0.98, 1.19)1.28(1.17, 1.40) Residence Urban44181.05 (1.00, 1.11)0.4441.24 (1.18, 1.30)0.193Rural75561.09 (1.05, 1.13)1.28 (1.24, 1.33) Smoking Yes84131.07 (1.03, 1.11)0.7771.25 (1.20, 1.29)0.104No35611.08 (1.03, 1.14)1.32 (1.25, 1.40) Drinking Yes78151.10 (1.05, 1.14)0.0411.27(1.23, 1.32)0.346No41591.03 (0.98, 1.09)1.25 (1.19, 1.31) Systolic blood pressure < 120 mmHg76341.07 (1.02, 1.11)0.9271.28 (1.22, 1.34)0.367>=120 mmHg43401.08 (1.03, 1.12)1.24 (1.20, 1.30) Diastolic blood pressure < 80 mmHg80211.08 (1.04, 1.03)0.5131.29 (1.23, 1.34)0.182>=80 mmHg39531.07 (1.02, 1.11)1.24 (1.19, 1.30)ABSI: a body shape index, BRI: body round index, HR: hazard ratio, CI: confidence interval;The anthropometric measures were converted to z scores using the equation: (X-Xmean)/XSD;Each stratification adjusted for all the factors (age, sex, race, residence, smoking, drinking, systolic blood pressure, and diastolic blood pressure) except the stratification factor itself
## Comparison of AHIs for identifying the development of hypertension
Time-dependent ROC curve analysis was conducted to analyze the ability of ABSI or BRI to identify new-onset hypertension at different time points. The AUC was 0.660 ($95\%$ CI: 0.636–0.685) at 4 years, 0.647 ($95\%$ CI: 0.632–0.662) at 7 years, 0.620 ($95\%$ CI: 0.606–0.634) at 11 years, 0.610 ($95\%$ CI: 0.597–0.624) at 12 years, and 0.605 ($95\%$ CI: 0.591–0.620) at 15 years for ABSI. In addition, the AUC was 0.704 ($95\%$ CI: 0.680–0.729) at 4 years, 0.702 ($95\%$ CI: 0.688–0.717) at 7 years, 0.683 ($95\%$ CI: 0.670–0.697) at 11 years, 0.673 ($95\%$ CI: 0.660–0.686) at 12 years, and 0.672 ($95\%$ CI: 0.659–0.686) at 15 years for BRI. These abovementioned results were shown in Fig. 2. We observed that the AUC for BRI was significantly higher than that of ABSI at any time point we tested (Table 5). Furthermore, the AUC of both indices decreased over time in this study (Fig. 3).
Fig. 2Time-dependent receiver operating characteristics curve analysis for AHIs to discriminate the incidence of hypertension at different time points. A: for ABSI; B: for BRI Table 5The AUC and corresponding $95\%$ CI of anthropometric indices for hypertension incidence at different time pointsTimeAUC($95\%$CI) P value ABSIBRI4 years0.660(0.636–0.685)0.704(0.680–0.729) < 0.001 7 years0.647(0.632–0.662)0.702(0.688–0.717) < 0.001 11 years0.620(0.606–0.634)0.683(0.670–0.697) < 0.001 12 years0.610(0.597–0.624)0.673(0.660–0.686) < 0.001 15 Years0.605(0.591–0.620)0.672(0.659–0.686) < 0.001 ABSI: body shape index, BRI: body round index, HR: hazard ratio, CI: confidence interval;AUC: area under the curve, CI: confidence interval Fig. 3Comparison of the AUC for AHIs to discriminate the incidence of hypertension over time
## Preliminary exploration on the predictive value of AHIs for hypertension incidence
We further analyzed the incremental effect of both indices on the predictive value for hypertension incidence at 7 years (the median follow-up time of the present study). The results (Table 6) demonstrated that the addition of BRI improved the differentiation and reclassification of traditional risk factors with a continuous NRI of 0.201 ($95\%$ CI: 0.169–0.228) and an IDI of 0.021 ($95\%$ CI: 0.015–0.028). However, the addition of ABSI did not improve discrimination, with an IDI of 0.003 ($95\%$ CI: -0.001-0.008).
Table 6The value of ABSI or BRI improved the risk stratification of hypertension incidence according to continuous-NRI and IDIC-indexIDIcontinuous-NRIEst.($95\%$CI) P value Est.($95\%$CI) P value traditional risk factors0.737 Ref. Ref. traditional risk factors + BRI0.7460.021 (0.015–0.028)<0.0010.201 (0.169–0.228)<0.001traditional risk factors + ABSI0.7380.003 (-0.001-0.008)0.1690.107 (0.063–0.128)<0.001Traditional risk factors: age, sex, race, residence, smoking, drinking, SBP, and DBP;ABSI: a body shape index, BRI: body round index, CI: confidence interval;IDI: integrated discrimination improvement; NRI: net reclassification index
## Discussion
Our study using prospective data from CHNS demonstrated that increased baseline AHIs were associated with an increased incidence of hypertension over a median of 7.0 years of follow-up in Chinese individuals. Of them, BRI performed better than ABSI to discriminate hypertension incidence at different time points. Furthermore, the addition of BRI had a better predictive value for hypertension incidence than ABSI by analysis of IDI and NRI.
To date, four categories with 42 AHIs have been developed, which have been widely used for risk identification of nutritional status, cardiovascular disease, metabolic disorders, and health management [21]. Most of the AHIs are estimated according to 3D human shapes and can be easily obtained by noninvasive, inexpensive measurements in health checks. The most commonly used parameters include traditional indices, namely, weight, height, body mass index (BMI), WC, HC, and waist-to-hip ratio (WHR). The novel AHIs include the ABSI [8], BRI [9], etc. These AHIs were regarded as a suitable screening tool for the early detection of cardiovascular disease and to reduce the associated medical costs [22].
Traditional AHIs, such as BMI and WHR, are usually used to evaluate overweight/obesity and are correlated with the incidence of hypertension. Hu et al. demonstrated that increasing BMI levels were significantly associated with hypertension incidence in Finnish male and female participants when not adjusted for baseline SBP in Cox regression. However, this correlation weakened after a further adjustment for SBP [23]. However, BMI has some deficiencies because it cannot address visceral fat or fat distribution and cannot differentiate between excess fat and high muscle mass [21]. WHR, an indicator of visceral fat, is calculated by WC divided by HC. It might be inaccurate when BMI exceeds 35 kg/m2 [21]. Lee et al. suggested that WHR was correlated with the incidence of hypertension in both sexes of middle-aged Korean people [7]. To date, some studies have compared the ability to identify hypertension using ABSI or BRI by conducting a cross-sectional study [11, 12, 15]. However, only one longitudinal study evaluated the correlation between novel AHIs and the incidence of hypertension and explored their ability to discriminate hypertension incidents in a Korean population [16]. Therefore, the relationship between novel AHIs and the incidence of hypertension in the Chinese population is unclear.
ABSI and BRI are both novel AHIs developed by Krakauer et al. in 2012 and by Thomas et al. in 2013, respectively [8, 9]. ABSI is a parameter based on WC adjusted for weight and height and reflects body shape, abdominal size, and concentration of body volume [21, 24]. ABSI is associated with the onset of diabetes, metabolic syndrome, and carotid atherosclerosis [25–28] and can predict all-cause mortality and cardiovascular death [8, 29]. However, it performed poorly in hypertension prediction. First, Calderón-García et al. demonstrated that the estimated pooled AUC for ABSI (AUC = 0.58, $95\%$ CI: 0.56–0.60) for the discrimination of hypertension was the lowest compared with BRI by conducting a systematic review and meta-analysis that included 13 original studies [15]. Second, Choi et al. also suggested that ABSI showed the lowest discrimination power for hypertension compared to other AHIs in a Korean community-based prospective study [16]. Interestingly, of the two novel parameters we analyzed, ABSI was weak in identifying hypertension incidence at different time points. The poor performance of ABSI for hypertension discrimination can be explained as follows. This formula was initially developed to predict mortality instead of hypertension incidence by using longitudinal data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 [8]. Furthermore, ABSI was developed by the white, black, and Mexican ethnicities of Americans, which might be not suitable for the Chinese population. BRI is based on height and WC, which reflect body shape, visceral adipose tissue, and body fat. The Spearman correlation coefficient between BRI and WHtR was 0.996 in the whole cohort of the present study, which was consistent with Maessen et al. [ 5]. In 2018, Choi and colleagues found that WHtR was a better predictor of incident hypertension than BMI and WHR [30]. In contrast to the WHtR, the BRI can not only assess body fat percentage but also supply a more accurate estimation of health status. Choi’s subsequent study further proved that the hypertension discrimination ability for BRI was basically equal to that for WHtR, with the same AUC value and $95\%$ CI [16]. In the present study, we found that BRI was positively correlated with hypertension incidence and had a higher AUC value than ABSI for hypertension discrimination at different time points in the whole cohort. Furthermore, the addition of BRI improved the differentiation and reclassification of the traditional risk factors. On the basis of Choi’s study, we further found a greater risk of new-onset hypertension in age < 40 years old for each z score increase in BRI. Similarly, Zhang et al. also found that age might influence the relationship between AHIs and hypertension by conducting a cross-sectional survey in 8234 Chinese adults [31]. These results suggested that it is crucial to choose a proper index for hypertension screening according to the specific age. However, the age difference in AHIs to discriminate hypertension is uncertain. Hence, more prospective studies should be conducted to verify this phenomenon. However, it is worth noting that the formula of BRI is complex compared with the simplicity of other indicators, such as BMI or WHR, which might limit its use and promotion in clinical practice. Hence, developing an automatic calculator for AHIs on websites or mobile phone applications is necessary at this moment.
This study has several strengths. To the best of our knowledge, this is the first study to analyze the relationship between novel AHIs and the incidence of hypertension and compare their ability to discriminate hypertension incidence in the Chinese population. Second, the data in the present study were obtained from a large-scale, long-term, multiprovincial, and population-based prospective cohort. This might supply relatively accurate and comprehensive evidence on the relationship between novel AHIs and the incidence of hypertension. However, some limitations of this study should be stated. First, only Chinese individuals were studied. Therefore, our findings may not be generalizable to other ethnic populations. Second, the data on family history of hypertension and parameters of blood biochemical examination were not collected in the early stage of CHNS. Hence, these potential confounders could not be adjusted. Third, the original question for antihypertensive drugs in the CHNS was as follows: “Are you currently taking antihypertensive drugs: 0 for no and 1 for yes”. Therefore, the antihypertensive medication status was defined by participants’ self-report and investigators’ specialized judgment in this investigation. Owing to this situation and not using the anatomical therapeutic chemical classification codes, errors might be caused because of subjects’ recall bias and different comprehensive understanding abilities. In addition, the nutrients and details of diet intake were not corrected in this study.
## Conclusion
Increased ABSI and BRI were associated with an increased risk of hypertension in Chinese individuals. BRI performed better than ABSI in identifying the new onset of hypertension, and the discrimination ability of both indices decreased over time.
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|
---
title: Medicinal plant-derived mtDNA via nanovesicles induces the cGAS-STING pathway
to remold tumor-associated macrophages for tumor regression
authors:
- Jinfeng Liu
- Jiaxin Xiang
- Cuiyuan Jin
- Lusha Ye
- Lei Wang
- Yanan Gao
- Nianyin Lv
- Junfeng Zhang
- Fuping You
- Hongzhi Qiao
- Liyun Shi
journal: Journal of Nanobiotechnology
year: 2023
pmcid: PMC9990354
doi: 10.1186/s12951-023-01835-0
license: CC BY 4.0
---
# Medicinal plant-derived mtDNA via nanovesicles induces the cGAS-STING pathway to remold tumor-associated macrophages for tumor regression
## Abstract
Plant-derived nanovesicles (PDNVs) have been proposed as a major mechanism for the inter-kingdom interaction and communication, but the effector components enclosed in the vesicles and the mechanisms involved are largely unknown. The plant *Artemisia annua* is known as an anti-malaria agent that also exhibits a wide range of biological activities including the immunoregulatory and anti-tumor properties with the mechanisms to be further addressed. Here, we isolated and purified the exosome-like particles from A. annua, which were characterized by nano-scaled and membrane-bound shape and hence termed artemisia-derived nanovesicles (ADNVs). Remarkably, the vesicles demonstrated to inhibit tumor growth and boost anti-tumor immunity in a mouse model of lung cancer, primarily through remolding the tumor microenvironment and reprogramming tumor-associated macrophages (TAMs). We identified plant-derived mitochondrial DNA (mtDNA), upon internalized into TAMs via the vesicles, as a major effector molecule to induce the cGAS-STING pathway driving the shift of pro-tumor macrophages to anti-tumor phenotype. Furthermore, our data showed that administration of ADNVs greatly improved the efficacy of PD-L1 inhibitor, a prototypic immune checkpoint inhibitor, in tumor-bearing mice. Together, the present study, for the first time, to our knowledge, unravels an inter-kingdom interaction wherein the medical plant-derived mtDNA, via the nanovesicles, induces the immunostimulatory signaling in mammalian immune cells for resetting anti-tumor immunity and promoting tumor eradication.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12951-023-01835-0.
## Introduction
Inter-kingdom communication and interactions has been documented to occur between eukaryotic and prokaryotic cells. Accumulating evidences demonstrate that the tissues and cells from humans, plants, animals or even microorganism release chemical substances or cellular components to trigger various signaling pathways, profoundly affecting the phenotype and function of recipient cells. However, current works are mostly focused on the human-microorganism interaction, and little is known about how plant-originated factors are interconnected with mammal cells. Indeed, emerging evidences have indicated that, akin to animal cells, plant cells can automatically or passively release nano-scaled vesicles, known as plant-derived nanovesicles (PDNVs), to induce the specific pathways and impact pathophysiology of targeting cells [1–3]. PDNVs contain a range of cellular components including RNA, DNA, lipids, proteins and secondary metabolites, which may act as the signaling messengers to mediate the signaling transduction and gene expression regulation [4], thereby exerting multiple effects involving anti-inflammatory, anti-viral, anti-fibrotic and anti-tumor activities [5–9]. For instances, the nanovesicles from plants like Ginseng were recently reported to potentially imped cancer growth and improve the immunotherapy efficacy, presumably through increasing the infiltration of immune cells [10]. However, it remains to be further determined about the effector components in the vesicles and the molecular underpinnings of PDNV-mediated immunoregulation.
Artemisinin is a sesquiterpene endoperoxide known as a frontline treatment against *Plasmodium falciparum* malaria. The agent is extracted from the plant *Artemisia annua* by a team of Chinese scientists led by Youyou Tu, who received the Nobel Prize in 2015 for discovering the anti-malaria drug [11]. Notably, recent data demonstrated that, in addition to canonical anti-parasite activity, artemisinin and the relevant medicinal plant could be repurposed to treat the inflammatory diseases, viral infections, fibrosis, autoimmune diseases and cancer [12, 13]. Of particular interest, artemisinin and its derivatives were found to potentially modulate the differentiation, activation and function of immune cells such as T cells, B cells, macrophages and dendritic cells, making it a potential agent to modify tumor microenvironment and anti-tumor immunity [14–16], but the technical restraints such as limited output, low biocompatibility, and little knowledge of the mechanism hamper their further application as potent antitumor or immunoregulatory agents.
Recent study demonstrate that the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway plays a key role in regulating immune and inflammatory response, and may serve as an important mechanism to regulate tumor progression [17]. cGAS is generated upon recognizing intracellular dsDNA and induces the second messenger, cyclic GMP-AMP (cGAMP), which in turn activates the key signaling adaptor STING. The activated STING subsequently triggers the downstream NF-κB and IRF3-driven pathways, leading to the production of inflammatory cytokines, chemokines and IFN-β [18] to induce immune cell infiltration and activation. Considering that progressive tumors are generally associated with impaired immune cell activation, coined as “cold” status, the cGAS-STING pathway may become a promising way to “wake up” the tolerant immune cells such as tumor-associated macrophages (TAMs). Intense research is therefore undertaken to search for natural and synthesized STING ligands/activators to improve the efficacy of cancer therapy [19]. Cyclic dinucleotides (CDNs), for instance, are one of the best characterized STING agonists. Several flaws such as highly negatively charge, difficulty to cross cell membranes, vulnerability to enzymatic degradation preclude the agent however to be an ideal anti-tumor agent. Compared with this, PDNVs are generally wrapped with lipophilic membrane, accessible for target cells, and importantly, capable of delivering nuclear acids to induce the immune-sensing machinery like STING pathway in recipient cells. In this regard, studies from us and other investigators have revealed that exosome-like vesicles harboring the cellular components such as mitochondrial DNA (mtDNA) would be secreted by mammal cells and taken by the recipients to induce STING pathway [20–22]. In addition to animal cells, DNA or cGAMP from prokaryotic cells like viruses or bacteria can also be incorporated into the membrane vesicles, triggering the STING pathway for the inter-kingdom regulation [23, 24]. In plants, the horizontal transfer of mitochondria or mtDNA has been reported to occur between the species [25–27], but whether the DNA-containing vesicles are generated by the medicinal plant like Artemisia annua, and how the encapsulated components signal the immune cells to regulate the anti-tumor immunity have never been explored.
In this study, we isolated and purified the nano-scaled vesicles from Artemisia annua. The newly discovered vesicles, termed ADNVs, displayed a robust anti-tumor activity through reprogramming TAMs from pro-tumor phenotype to pro-inflammatory type. Mechanistically, artemisia-derived mtDNA were taken by TAMs via the vesicles and induced the cGAS-STING pathway to reprogram macrophages, leading to the enhanced cytotoxic T response for tumors regression. We further demonstrate that ADNVs improved the efficacy of αPD-L1-mediated immunotherapy through the activation of STING-driven pathway. To our knowledge, it’s the first time to report that nanovesicle-harboring, plant-derived mtDNA mediates the inter-kingdom communication for resetting the anti-tumor immunity.
## Animals and cell lines
Pathogen-free, 6-to-8-week-old male C57BL/6 mice were obtained from Jiangsu Gempharmatech Co., Ltd (Jiangsu, China). STING knockout mice (STINGgt/gt mouse, 017537) were used and genotyped according to the protocols provided by The Jackson Laboratory. The mice were raised routinely: Temperature 22–25 °C, relative humidity 50–$60\%$, and a 12 h light–dark cycle. All animals were given food and water in a standard laboratory diet. All animal experiments were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Nanjing University of Chinese Medicine (Approval No. 202207A023).
LLC cell line, mouse colon cancer cell line (CT26), human embryonic kidney cell line (HEK293T) and RAW 264.7 were obtained from American Type Culture Collection (ATCC, USA). All cell lines were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) or RPMI 1640 with $10\%$ fetal bovine serum (FBS), supplemented with 100 U/mL penicillin, and 100 mg/mL streptomycin (all from Gibco, Carlsbad, CA, USA). All cells were incubated at 37 °C in a humidified atmosphere with $5\%$ CO2.
## Isolation and purification of ADNVs
For isolation of ADNVs, fresh *Artemisia annua* L. was washed three time with water in a plastic bucket. After the final wash, the plant in phosphate-buffered saline (PBS) (1:2, g/mL) were placed in a blender and chopped at a high speed for 5 min. The obtained juices were sequentially centrifuged at 200 × g for 10 min, 2000 × g for 20 min and 10,000 × g for 30 min to remove the large plant tissues and cell debris. The final supernatant was ultracentrifuged at 150,000 × g for 2 h, and the pellets were re-suspended in PBS. To further purify ADNVs, their suspension was transferred to a gradient sucrose solution ($15\%$, $30\%$, $45\%$ and $60\%$), and ultracentrifuged at 150,000 × g for 2 h. Finally, ADNVs in the $30\%$ layer were harvested and washed 3 times with PBS. The resuspension was filtered (0.45 μm) and used freshly or stored at − 80 °C until further use. The amounts of ADNVs were quantified on the basis of their protein amounts, which were determined using a BCA protein assay kit.
## Tumor growth and treatment
C57BL/6 mice were maintained in an animal facility under pathogen-free conditions. After acclimatization, a total of 2 × 105 LLC cells were subcutaneously injected into the right flank of the mice. Tumor dimensions were measured with calipers every 3 days and the tumor volumes (mm3) were calculated by applying the following formula: (length × width2)/2. ADNVs (25 mg/kg), alone or with αPD-L1 antibody (10 mg/kg) (Bio-XCell, BP0101), were instilled at day 7 after tumor cell inoculation, every 3 days for 2 weeks. To deplete TAMs in tumor, LLC tumor-bearing mice were treated with clodronate liposomes (CL, Yeasen, 40337ES08) at 200 μg per mouse every 4 days by intraperitoneal injection. Mice in the control group were treated with the same dose of liposomes containing PBS (PL, Yeasen, 40338ES05). On the indicated days after inoculation, mice were sacrificed, and the tissues were collected for analysis.
## ADNVs labeling and ex vivo organ imaging
ADNVs labeling with Dil was performed according to the manufacturer’s instructions (ThermoFisher, D282). In brief, 200 mg of ADNVs were suspended in 1 mM Dil staining solution and incubated for 30 min at room temperature. ADNVs were pelleted by ultracentrifuge at 150,000 × g for 2 h to remove free dye. After washing twice in PBS, Dil-labeled ADNVs were ready for use in experiments.
For ex vivo organ imaging, the mice were intraperitoneally injected with Dil-labeled ADNVs (25 mg/kg), and samples were collected for detection 24 h later. The organs were resected and imaged on an Odyssey scanner (LI-COR, USA).
## Tumor digestion and TAMs isolation
To obtain single-cell suspensions, tumors were excised, minced and digested with $5\%$ FBS DMEM containing 2 mg/mL collagenase I (Gibco, 17100017) and 2 mg/mL hyaluronidase (Sigma-Aldrich, H6254) at 37 °C for 45 min with agitation, followed by treatment with ammonium-chloride-potassium buffer for red blood cell (RBC) lysis, and strained through a 70 μm strainer to remove undigested tumor tissues.
For TAMs isolation, the single-cell suspensions were centrifuged at 500 × g for 20 min, with 1 mL of the cell suspension on the top, 5 mL of $45\%$ Percoll (GE Healthcare, 17-0891-09) in the middle and 5 mL of $60\%$ Percoll at the bottom of a 15-mL tube. Mononuclear cells were collected from the cell layer in the interphase between 45 and $60\%$ Percoll. F$\frac{4}{80}$+ TAMs were isolated using anti-F$\frac{4}{80}$ Microbeads (Miltenyi Biotec, 130-110-443) according to the manufacturer’s instructions.
## Mouse bone marrow-derived macrophages (BMDMs) preparation and polarization
Bone marrow was harvested from 8 to 10 weeks old mice by flushing the femurs with PBS. Following RBC lysis, the remaining cells were washed twice with PBS. For induction of macrophage differentiation, the cells were cultured in DMEM supplemented with $10\%$ FBS and 20 ng/mL mouse macrophage colony-stimulating factors (M-CSF) (PeproTech, 315-02). After 3 day of culture, cell media were replenished with the media containing 20 ng/mL M-CSF. On day 7, M2 polarized macrophages were obtained by treatment with mouse 20 ng/mL IL-4 (PeproTech, 214-14) for 48 h. After polarization, cells were phenotyped and used in subsequent assays.
## Flow cytometry and FACS sorting
Tumor cell suspensions, BMDMs, RAW 264.7and TAMs were washed, blocked with Fc Block (anti-mouse CD$\frac{16}{32}$, BD Biosciences) at 4 °C for 15 min, and stained with fluorescence-conjugated antibodies against surface markers CD4 (clone OKT4), CD8 (clone 17D8), F$\frac{4}{80}$ (clone BM8), CD11b (clone VIM12), CD86 (clone BU63), CD206 (clone MR5D3) and PD-L1 (clone MIH5) antibodies purchased from BioLegend or BD Biosciences. Cells were then fixed in a fixation/permeabilization buffer (BD Biosciences, 554715) and stained with antibodies against intracellular proteins, including Granzyme B (clone GB11, BioLegend). The apoptosis of cells were detected using Annexin V-FITC/PI Apoptosis Detection Kit (BD Biosciences, 556547). Single cell suspensions were prepared and analyzed using the FACS Calibur (BD Biosciences). FACS-sorted CD206+ macrophages are from BMDMs for adoptive transfer experiments by using a BD FACS Aria-II SORP flow cytometer (BD Biosciences). Data analysis was performed using FlowJo Version7.6 (BD Biosciences).
## Adoptive transfer experiments
For analysis of the role of TAMs in ADNVs antitumor, 1 × 105 CD206+ macrophages were sorted from BMDMs of mice with or without ADNVs treatment, and adoptively transferred into the tumor-bearing mice by intratumoral injection every 3 days initiating 14 days after tumor cell inoculation. On 21 day after inoculation, tumor-bearing mice were anesthetized, and the tissues were collected for analysis.
For analysis of the effect of ADNVs in promoting TAMs polarization, 1 × 105 CD206+ macrophages sorted from BMDMs were labeled with Dil, and adoptively transferred into tumor-bearing mice by intratumoral injection. Mice were then administered intraperitoneally with or without ADNVs for 24 h, and the tumor tissues were collected for analysis.
## ROS measurement
To measure cellular ROS levels, cells were stained with CellROX Green (ThermoFisher, C10444), which generates fluorescent signals when oxidized by ROS in the cells. The mean fluorescence intensity (MFI) of ROS was detected by flow cytometry and analyzed using FlowJo Version7.6 (BD Biosciences).
## ELISA
Cell culture supernatants were harvested and subjected to centrifugation at 500 × g for 10 min at 4 °C to remove floating cells and debris. The levels of TNF-α and IL-6 were detected via mouse TNF-α ELISA Kit (R&D, MTA00B) and mouse IL-6 ELISA Kit (R&D, M6000B) according to manufacturer’s instructions.
## Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from macrophages or tumor tissues using TRIzol reagent (Invitrogen, 15596026) and reverse transcribed into cDNA using a cDNA Synthesis Kit (Takara, 6210A) according to the manufacturer’s instructions. Then, qRT-PCR was performed using SYBR Green mix (Roche, 4913914001) following the manufacturer’s instructions. Samples were run on an ABI Prism 7500 Sequence Detection System (Applied Biosystems, USA). The primers used for target genes were shown in Additional file 1: Table S1. The 2−ΔΔCt method was used to calculate fold changes in gene expression normalized to β-actin.
## Western blotting
Protein was extracted from the cells using RIPA buffer, resolved by SDS–polyacrylamide gels and then transferred to PVDF membranes. Primary antibodies against STING (1:1000; CST, 13647), Phospho-TBK1 (1:1000; CST, 5483), TBK1 (1:1000; CST, 38066), Phospho-IRF-3 (1:1000; CST, 29047), IRF-3 (1:1000; CST, 4302), Phospho-p65 (1:1000; CST, 3033), p65 (1:1000; CST, 8242), GAPDH (1:1000; CST, 5174) and β-actin (1:1000; CST, 4970) were used. Peroxidase-conjugated secondary antibody (CST, $\frac{7077}{7076}$) was used, and the antigen–antibody reaction was visualized by enhanced chemiluminescence assay (ECL, ThermoFisher, 34580).
## Immunofluorescence and immunohistochemistry
Paraffin-embedded samples were sectioned at 4-mm thickness. Antigen retrieval was performed using a pressure cooker for 3 min in 0.01 M citrate buffer (pH 6.0). Samples were blocked in PBS with $2\%$ BSA for 1 h at room temperature and incubated with antibodies specific for Ki-67 (1:100; Abcam, ab16667), MMP9 (1:100; Abcam, ab283575), CD86 (1:100; Abcam, ab119857), CD206 (1:100; Abcam, ab64693), iNOS (1:100; Abcam, ab178945) and Arg1 (1:100; Abcam, ab96183) overnight at 4 °C. Alexa Fluor or HRP conjugated secondary antibodies was incubated for 1 h at room temperature. DAPI was then used for counterstaining the nuclei, and images were obtained by fluorescence microscope (Axio Observer D1, Zeiss).
## Genomic DNA isolation and PCR
ADNVs were collected and their genomic DNA was isolated using DNeasy Blood & Tissue Kits (Qiagen, 69504). DNA in the nuclear, chloroplast and mitochondria was identified by PCR (Takara, R010A) following the manufacturer’s instructions. The primer used for target genes were shown in Additional file 1: Table S2. The products were separated by electrophoresis on a $1\%$ agarose gel stained with ethidium bromide (EB).
## MtDNA depletion
ADNVs were treated with EtBr (200 ng/mL) for 6 days to deplete mtDNA from the vesicles. The mtDNA depletion efficiency was evaluated by PCR test of total DNA extraction. The primers were provided in Additional file 1: Table S2.
## Statistical analysis
The values are presented as the mean ± standard error of mean (SEM). All data were analyzed using GraphPad Prism 8 (GraphPad Software, USA). The data were examined using the two-tailed Student’s t test, one-way analysis of variance (ANOVA), or two-way ANOVA with post hoc Bonferroni correction. $p \leq 0.05$ was considered statistically significant (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$).
## Purification and characterization of ADNVs
To explore the biological activity of Artemisia-derived micro-vesicles, we isolated and purified exosome-like vesicles from fresh *Artemisia annua* L. by exploiting a method combining differential centrifugation and sucrose density gradient ultracentrifugation. As a result, four bands were observed after the sucrose density gradient ultracentrifugation (Fig. 1a), with the majority accumulated at the $30\%$ interface (band 2). Transmission electron microscopy (TEM) revealed that the vesicles were characterized by spherical morphology and bilayer membrane structure (Fig. 1b). An average hydrodynamic size of ADNVs was 106.8 nm as detected by NanoSight NS 300 system, and the concentration was about 1.25 × 1011 vesicles per mL in solution (Fig. 1c). Zeta potential analysis indicated that ADNVs had a negative zeta potential value of − 22.5 mV (Fig. 1d). In addition, we analyzed the composition of purified ADNVs. The results showed that substantial amounts of nucleic acids and proteins were encapsulated in ADNVs, as examined by agarose gel electrophoresis and SDS-PAGE electrophoresis respectively (Fig. 1e, f).Fig. 1Purification and characterization of ADNVs. A ADNVs were isolated and purified by differential the centrifugation and sucrose gradient ultracentrifugation. B ADNVs harvested from the sucrose density gradient ($30\%$) were characterized by TEM (Scale bar = $\frac{200}{50}$ nm). C Particle size of the ADNVs was measured by NanoSight NS 300 system. D Surface charge was measured by a Zetasizer. E ADNVs-enclosed DNA was electrophoresis on a $1.2\%$ agarose gel, and stained with EtBr. F Proteins of ADNVs were separated by $10\%$ SDS-PAGE and stained with Coomassie blue. Shown are the representative results from at least three independent experiments Next, we examined the impact of ADNVs on cellular viability using cell counting kit-8 (CCK-8) and lactate dehydrogenase (LDH) assays respectively. The results demonstrated that, up to the dose of 160 μg/mL, ADNVs exhibited no significant cytotoxicity on murine macrophages (Additional file 1: Fig. S1A, B). To further evaluate the in vivo safety of ADNVs, we treated mice with ADNVs at the dose up to 25 mg/kg via intraperitoneal (i.p.) injection. Two weeks later, mice were sacrificed for blood biochemistry analysis and histological examinations. The results showed that no apparent damages were observed in the heart, liver, spleen, lung, kidney, or intestines in mice upon ADNVs administration (Additional file 1: Fig. S1C). Lower rather than higher levels of liver enzymes such as alanine transaminase (ALT) and aspartate transaminase (AST) were detected in ADNVs-treated mice relative to control animals, suggesting no significant liver toxicity associated with this treatment (Additional file 1: Fig. S1D, E). Together, our data indicated that the nanovesicles were successfully isolated from Artemisia annua, which exhibited no toxic effects in vitro and in vivo at the doses we applied at later experiments.
## ADNVs treatment impedes cancer growth in a murine lung cancer model
To evaluate the potential role of ADNVs in curbing the common malignancy such as lung cancer, we established a murine lung cancer model by subcutaneously inoculating lewis lung carcinoma (LLC) cells into C57BL/6 mice. After 7 days, mice were treated with ADNVs or vehicle once every 3 days for successive 2 weeks (Fig. 2a). Remarkably, ADNVs administration led to a profound inhibition of tumor growth, as evidenced by smaller sizes, reduced tumor volume and weights in the vesicle-treated group relative to control mice (Fig. 2b–d). Tumors in treated mice exhibited a lower cellular density with nuclear pyknosis, suggestive of cellular apoptosis (Fig. 2e). The levels of Ki67 and MMP9, the molecular marker for cellular proliferation and metastasis respectively, were decreased in ADNVs-treated mice relative to control animals (Fig. 2f).Fig. 2ADNVs inhibit lung cancer growth in mice. A The simplified experimental scheme. C57BL/6 mice ($$n = 5$$) were implanted with LLC cells for 7 d, and then treated with ADNVs (25 mg/kg, i.p.) once every 3 d for a successive 2 week. Mice were sacrificed and tumors were collected at day 21. B Gross photos of tumors at the end of experiments. C Tumor growth profiles in tumor-bearing mice treated PBS or ADNVs. *** $p \leq 0.001$ (Two-way ANOVA and Bonferroni post-tests). D Tumor weights in mice treated with either PBS or ADNVs. *** $p \leq 0.001$ (Student’s t-test). E H & E staining of tumor tissues (Scale bar = 100 μm). F Ki67 and MMP9 staining of tumor tissues (Scale bar = 100 μm). ( G, H) ADNVs were stained with Dil and injected into tumor-bearing mice (25 mg/kg, i.p.). Biodistribution of ADNVs was determined by scanning mice (G), and the quantitative analysis (H). *** $p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). The results are representative data from one of three independent experiments. Shown are representative images, and the data are presented as means ± SEM To further confirm the action of ADNVs in tumors, we next examined the in vivo trafficking of ADNVs. For this, the vesicles were pre-stained with a lipophilic fluorescent dye, Dil, and then administrated into the tumor-bearing mice at 3 weeks post inoculation. The Odyssey imaging analysis revealed that, in addition to liver enrichment, Dil-ADNVs were dominantly concentrated in tumor tissues (Fig. 2g, h). In addition, we compared the efficacy of different routes, such as intraperitoneal (i.p.), intravenous (i.v.), subcutaneous (s.c.) and intratumoral (i.t.) administration to deliver ADNVs to tumors. The results showed that, compared with i.p. injection, the route of both i.t. and i.v. rendered ADNVs to efficiently reach the tumor site, while s.c. injection exhibited negligible effect (Additional file 1: Fig. S2A, B). Moreover, ADNVs instillation via i.t. and i.v. had similar effects in tumor control compared to that via i.p. injection, while s.c. injection had much smaller effect (Additional file 1: Fig. S2C–E). Together, the data demonstrated that ADNVs treatment significantly inhibited tumor growth, impairing the malignant properties involving cellular proliferation, invasion and apoptosis-resistance in tumors.
## ADNVs treatment remolds tumor microenvironment and promotes macrophages shift to pro-inflammatory phenotype
Since the properties of tumor microenvironment (TME), immune-tolerant (“cold”) or immunogenic (“hot”) [28], have a determinative role in cancer initiation and progression, we therefore proceeded to assess the impact of ADNVs on the properties of TME. Critically, ADNVs treatment caused a remarkable shift of TME to an inflamed and tumor-unfavorable status, characterized by highly expression of pro-inflammatory cytokines, leukocyte-recruiting chemokines, type I interferon (IFN) and IFN-stimulated genes (ISGs), as well as the signature genes related with dendritic cell maturation and T-cell priming (Fig. 3a).Fig. 3ADNVs remold tumor microenvironment and reprogram TAM phenotypes. Mice were implanted with LLC and treated with ADNVs or vesicle as described in Fig. 2. Tumor tissues were collected at 21 d post inoculation. A *Ranked analysis* of differential gene expression. B Quantification of M1 (CD86+) and M2 (CD206+) populations by flow cytometry. ** $p \leq 0.01$ (Student’s t-test). C Representative immunofluorescence staining for CD86 and CD206 at tumor sections (Scale bar = 100 μm). D qRT-PCR analysis of M1-marker genes (upper) and M2-related genes. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ (Student’s t-test). E Quantification of CD4+, CD8+ and Granzyme B+ CD8+ cell populations in TME by flow cytometry. ns non-significant, *$p \leq 0.05$, ***$p \leq 0.001$ (Student’s t-test). The results are from one of three independent experiments. Shown are representative images, and the data are presented as means ± SEM *It is* well appreciated that TAMs, predominately populating in TME, would undergo functional reprogramming from pro-inflammatory M1 to pro-tumor M2 phenotype and support cancer progression [29]. We thus investigated the impact of ADNVs on the phenotypic shift of TAMs. Remarkably, ADNVs treatment elevated the ratio of M1-like type macrophages (defined by CD86+CD11b+F$\frac{4}{80}$+) while repressing the portion of M2-like subset (defined by CD206+CD11b+F$\frac{4}{80}$+) (Fig. 3b, Additional file 1: Fig. S3A). Consistently, the percentage of MHC-II+ macrophages was increased while that of PD-L1+ cells was decreased in TAMs from ADNVs-treated mice (Additional file 1: Fig. S3B, C), as compared with those in vehicle-treated animals. The M2/M1-modulating role of ADNVs was further confirmed by immunofluorescence (IF) staining of iNOS (M1) and ARG1 (M2) at tumor tissues (Fig. 3c). Congruently, the transcriptional profiling of TAMs revealed that ADNVs treatment strengthened the expression of M1-related markers but repressed that of M2-associated genes (Fig. 3d). The data thus consistently supported the role of ADNVs in promoting macrophages polarization from pro-tumor type to anti-tumor subset.
Macrophages are known as professional antigen presenting cells (APCs) with the ability to process and present antigens, produce inflammatory cytokines and chemokines for stimulating robust T cell response. In line with augmented M1 polarization induced by ADNVs, we observed that the frequency of intratumoral CD8+ T cells, particularly granzyme B+ T cells, was markedly increased in ADNVs-treated mice relative to control animals (Fig. 3e, Additional file 1: Fig. S3D). No significant change was detected in the ratio of CD4+ T cells between the two groups of mice. Collectively, our data demonstrated that ADNVs treatment profoundly changed the immunological profile of TME, promoting the transition of TAMs to elevate cytotoxic T lymphocytes (CTLs) response, and thereby expediting the eradication of lung cancer.
## TAMs were targeted by ADNVs to mediate the anti-tumor effect
To determine whether TAMs was responsible for ADNVs-mediated tumor growth inhibition, we then applied clodronate liposome (CL) to deplete TAMs from mice that were implanted with LLCs and treated with ADNVs at the first week (Fig. 4a). As expected, TAMs were efficiently cleared upon CL treatment while administration of the control liposome (PL) had slight effect (Additional file 1: Fig. S4A). Depletion of TAMs remarkably decreased tumor burdens in mice with no ADNVs administration, implying the pro-tumor activity of TAMs in this context. By contrast, CL administration significantly increased tumor growth in ADNVs-instilled mice compared with PL treatment, indicating that the vesicle-remolded macrophages exerted the cancer inhibitory effect (Fig. 4b–d).Fig. 4ADNVs are preferentially taken by TAMs and reprogram their phenotypes. A The simplified experimental scheme for B-D. C57BL/6 J mice were implanted with LLC cells for 7 d and then inoculated with clodronate liposomes (CL) or PBS-liposomes (PL) every 4 days to deplete TAMs. The mice were simultaneously administrated with ADNVs, and sacrificed at day 21 post implantation. B Gross photos of tumors at the end of experiments. C Tumor growth profiles. ** $p \leq 0.01$ (Two-way ANOVA and Bonferroni post-tests). D Tumor weights at the end of the experiment. ** $p \leq 0.01$ (One-way ANOVA and Tukey’s significant difference post hoc test). E Schematic outline of adoptive transfer of macrophages for F–H. M2 macrophages were prepared from BMDMs by stimulated with IL-4, and then treated with or without ADNVs (20 μg/mL). M1 macrophages, prepared by stimulation of IFN-ɣ, were used as a positive control. After that, the cells were adoptively transferred to tumor-bearing mice at day 14 post LLCs implantation. Mice were sacrificed at day 21. F Gross photos of tumors at the end of experiments. G Tumor growth profiles. * $p \leq 0.05$, ***$p \leq 0.001$ (Two-way ANOVA and Bonferroni post-tests). H Tumor weights at the end of the experiment. * $p \leq 0.05$, ***$p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). I, J ADNVs were stained with Dil and injected into tumor-bearing mice (25 mg/kg, i.p.). Flow cytometry of Dilpos cells in macrophages and other cell populations in tumors (I). Immunofluorescence (IF) staining of BMDMs and other cell lines taking Dil-labelled ADNVs upon incubation. Scale bar = 200 μm. Nuclear: DAPI (J). ( K-M) BMDMs were incubated with ADNVs or vehicles for 24 h. Quantification of M1 (CD86+) and M2 (CD206+) population by flow cytometry (K); ELISA assay of TNF-α and IL-6 levels (L); Flow cytometry of ROSpos macrophages and quantification of the mean fluorescence intensity (MFI) (M). * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ (Student’s t-test). The results are from one of two or three independent experiments. Shown are representative images, and the data are presented as means ± SEM To further corroborate the relevance of macrophages to ADNVs action in tumor control, we then applied the adoptive transferring methods. To this end, M2-polarized macrophages, which were generally induced by growing tumors, were prepared by incubating murine bone marrow-derived macrophages (BMDMs) with IL-4, the canonical stimulators for type II immune cells. The pre-fabricated cells were then conditioned with ADNVs prior to the delivery to tumor-bearing mice (Fig. 4e). The data showed that the mice taking ADNVs-primed macrophages developed significantly smaller tumors compared with those receiving vehicle-treated macrophages (Fig. 4f–h), further supporting a pivotal role of macrophages in mediating the tumor-inhibiting activity of ADNVs.
As the active contents of natural nanoparticles exert their biological functions mainly within cells, we wondered whether ADNVs could traffic to the tumors and exert biological their regulatory effects on TAMs. Flow cytometry analysis of Dil-ADNVs was thus conducted on TAMs and other remaining cells isolated from tumors. The results indicated that ADNVs trafficked to the tumors and were dominantly taken up by TAMs, when compared with the fluorescent signals detected in other remaining cells (Fig. 4i, Additional file 1: Fig. S3E). In vitro, BMDM, LLC, CT26 and HEK293T cells were incubated with Dil-ADNVs for 12 h. Compared to other cells, we found that ADNVs were taken up more effectively by BMDMs and preferentially localized in the cytoplasm of the cells (Fig. 4j). The results thus indicated the cellular tropism of ADNVs to macrophages in vitro and in vivo.
Next, we examined the impact of ADNVs on macrophages function. Remarkably, ADNVs treatment significantly raised the ratio of CD86+ M1 cells, while decreasing the proportion of CD206+ M2 cells in cell culture (Fig. 4k). Concurrently, production of the proinflammatory cytokines TNF-α and IL-6 was elevated by ADNVs-treated pre-made M2 macrophages compared with that by vehicle-treated cells (Fig. 4l), indicating the functional shift of pre-made M2 cells induced by ADNVs treatment. Given that M1 macrophages are capable of killing tumor cells by producing the effector mediators such as reactive oxygen species (ROS), we thus further measured the impact of ADNVs on macrophages generation of ROS. The data showed that the percentage of ROS-producing macrophages was substantially increased upon ADNVs treatment (Fig. 4m). Associated with this, the apoptotic rate of lung cancer cells, as detected by Annexin V/PI staining or caspase-3 activity, was markedly elevated upon incubation at the culture medium (CM) from ADNVs-treated macrophages relative to that from vehicle-treated cells (Additional file 1: Fig. S4B, C). Collectively, the data indicated that ADNVs trafficked to tumors and preferentially taken up by TAMs, leading to the reprogramming of TAMs into a proinflammatory phenotype for a “hot” (immunostimulatory) tumor microenvironment.
## ADNVs reprogram TAMs through activating the cGAS-STING pathway
We next sought to address the mechanism that was exploited by ADNVs to regulate macrophages phenotypes and functions in tumors. Our and other investigators’ studies have shown that donor cells would be able to transfer cellular components like mtDNA to the recipients and induce the inflammatory signaling involving the GAS-STING pathway [21, 22]. The induction of STING pathway activates downstream IRF3 and TBK1 to drive the inflammatory cytokines and IFN-I production, thereby disrupting the immune tolerant status in tumor niches [30–32]. Based on these premises, we hypothesized that ADNVs might induce the cGAS-STING pathway to promote the polarization of inflammatory macrophages that were pre-conditioned by tumors. Indeed, our initial study showed that the expression of type I IFN and associated ISG genes were specifically induced by ADNVs in TAMs (Fig. 3a). In line with this, the activation of STING and the downstream signaling molecules involving TBK1, IRF3 and p65 in pre-fabricated M2 macrophages was remarkably increased upon ADNVs treatment (Fig. 5a). The regulatory function of ADNVs was largely abrogated in macrophages from STING-knockout goldenticket (gt) mice, and the subsequent M1/M2 macrophage shift was accordingly impeded (Fig. 5b, c). The results thus corroborated the specific activation of STING pathway by ADNVs administration, which consequentially induced the M2 to M1 functional transition. Fig. 5ADNVs promote the functional transition of TAMs through activation of the STING pathway. BMDMs from WT mice and STINGgt/gt mice were polarized into M2 type and then treated with two doses of ADNVs1 (20 μg/mL) or ADNVs2 (30 μg/mL) for 24 h. A Immunoblotting of STING and downstream signaling molecules in macrophages. B, C Flow cytometry analysis and quantification of M1 (CD86+) and M2 (CD206+) populations. ns: non-significant, *$p \leq 0.05$, ***$p \leq 0.001$ (Student’s t-test). D Diagram of workflow for the in vivo experiments. BMDMs from WT mice and STINGgt/gt mice were polarized into M2 type, labeled with Dil, and transferred into tumor-bearing mice. The animals were then treated with ADNVs for 24 h. E Flow cytometry and F quantification of the portion of M1 (CD86+) and M2 (CD206+) subsets respectively. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). The results are from one of three independent experiments. Shown are representative images, and the data are presented as means ± SEM Inspired by the above findings, we further assessed the in vivo relevance of STING pathway to the action mode of ADNVs. To this end, BMDMs from STINGgt/gt and control mice were pre-induced into M2 type, stained with Dil, and adoptively transferred into LLC-bearing mice (Fig. 5d). After administration with ADNVs but not the vehicle, we observed that a portion of Dil-labeled macrophages in tumors shifted from CD206+ M2 to CD86+ M1 subset. This transition however did not occur in STINGgt/gt macrophages regardless of ADNVs administration (Fig. 5e, f). Collectively, the results indicated that ADNVs administration specifically induced the activation of STING pathway to promote the transition of TAMs from M2 to M1 phenotype and hence enhance anti-tumor immunity.
We also investigated the role of other immune pathways such as TLRs in the antitumor activity of ADNVs. We additionally conducted the experiments with macrophages lacking some of TLRs such as TLR2, TLR3 and TLR4 respectively. ( Additional file 1: Fig. S5A–C) The results showed that deletion of TLR2, TLR3 or TLR4 exerted mild or even no impact on the pro-inflammatory and M1-promoting activity of ADNVs, indicating that TLRs pathway are unlikely the major contributors for ADNVs function.
## Medicinal plant-derived mtDNA encapsulated in ADNVs triggers the activation of STING pathway in TAMs
Considering the well-appreciated role of STING pathway in recognizing cytosolic dsDNA and inducing the subsequent immunostimulatory pathway [33], we hypothesized that plant-derived dsDNA might be delivered by vesicles to trigger STING pathway in macrophages. To test it, we firstly analyzed the DNA content in ADNVs by PCR analysis of plant-specific genes including Cox3 (housekeeping gene for mitochondrial DNA), Cox2 (housekeeping gene for nuclear DNA) and Rbcl (housekeeping gene for chloroplast DNA). Intriguingly, the results showed that only Cox3, a representative mtDNA gene, was amplified in ADNVs (Fig. 6a). To directly confirm the transferring of mtDNA via the nanovesicles, we labeled ADNVs with DRAQ5, a membrane-permeable DNA dye [34], and added them onto pre-made M2 macrophages. After complete washing, the intracellular fluoresce intensity of macrophages was measured. Flow cytometry revealed that mtDNA-containing vesicles were prominently taken by macrophages (Fig. 6b), and the internalization of vesicular DNA in macrophages was further confirmed by IF staining (Fig. 6c). To further confirm that mtDNA was effectively internalized by macrophages in tumors, we additionally isolated TAMs and performed the qRT-PCR analysis of Cox3. Similar to the in vitro result, significantly greater amount of Cox3 was detected in TAMs from ADNVs-treated mice, but not in those from vehicle-treated control mice (Fig. 6d). The data thus indicated that medicinal plant-derived mtDNA was encapsulated in the nanovesicles for delivering to macrophages in tumors. Fig. 6Plant-derived mtDNA activates STING pathway to drive macrophage polarization. A PCR assay of the genes Cox3, Cox2 and RbcL in ADNVs. B, C ADNVs were stained by DRAQ5 and incubated with BMDMs for 6 h. Flow cytometry and quantification of the portion of DRAQ5+ cell population (***$p \leq 0.001$, Student’s t-test) B, and immunofluorescence staining showing mtDNA-taking of DRAQ5+ cells. Nuclei: DAPI; Scale bar = 50 μm (C). D qRT-PCR analysis of *Cox3* gene expression in BMDMs treated with ADNVs or vehicle, or in TAMs isolated from mice that were administrated with ADNVs or PBS. *** $p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). E–G ADNVs were treated with or without EtBr (200 ng/mL) for 6 d to deplete mtDNA, and then applied to M2-polarized macrophages. PCR assay of *Cox3* gene in ADNVs (E); Immunoblotting of STING and downstream signaling molecules (F); Flow cytometry and quantification of the percentages of M1 (CD86+) and M2 (CD206+) populations (G). ** $p \leq 0.01$ (One-way ANOVA and Tukey’s significant difference post hoc test). H–J ADNVs were treated with or without EtBr to deplete mtDNA, and then injected into LLC-bearing mice (25 mg/kg, i.p.). H Gross photos of tumors at the end of experiments (21 day post LLCs inoculation. I Tumor growth profiles. *** $p \leq 0.001$ (Two-way ANOVA and Bonferroni post-tests). J Tumor weights evaluated at the end of the experiment. *** $p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). K, L mtDNA was extracted from ADNVs, coated with or without liposomes and then applied to M2-polarized macrophages for 24 h. Immunoblotting of STING and downstream signaling molecules (K); Flow cytometry and quantification of the percentage of M1 (CD86+) populations (L). ns: non-significant, ***$p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). The results are from one of two or three independent experiments. Representative images are shown and the data are presented as means ± SEM To further substantiate the functional significance of mtDNA in the regulatory role of ADNVs, we treated the vesicle with ethidium bromide (EtBr), an agent presumed to selectively deplete mtDNA without affecting genomic DNA [35]. The vesicles were then applied to pre-made M2 macrophages for the analysis of STING activation. As expected, EtBr treatment eliminated mtDNA from the vesicles, as demonstrated by compromised amplification of the representative gene, Cox3 (Fig. 6e). The activation of STING-driven pathway was substantially weakened (Fig. 6f), and the M2 to M1 shift was largely abrogated upon mtDNA depletion (Fig. 6g). As a result, the mice treated with mtDNA-depleted ADNVs developed significantly greater tumors compared with those taking the intact vesicles (Fig. 6h–j).
The results indicated that medicinal plant-derived mtDNA in the nanovesicles played a major role in inducing the anti-tumor immunity, making us to ask whether naked mtDNA or mtDNA enclosed by the vesicle membrane exerted the effect. To address it, we extracted mtDNA from ADNVs and constructed the mtDNA/lipid complex using synthetic liposomes, which were then added onto pre-made M2 macrophages to test their effects. Of interest, the data showed that naked mtDNA failed to induce M1 macrophages polarization, while lipid-encapsulated mtDNA promoted CD86+ macrophages generation, at the level comparable to that of ADNVs treatment (Fig. 6k, l). Together, our data identified plant-derived mtDNA as a critical stimulus for STING activation when internalized into macrophages via nanovesicles, and played an essential role in inducing anti-tumor immunity.
## ADNVs treatment boosts the immunotherapeutic efficacy of PD-L1 blockade in mice
As the above observation demonstrated that ADNVs potentially induced the transition of TAMs from M2 to M1 phenotype, and the expression of PD-L1 was decreased in ADNVs-treated macrophages (Additional file 1: Fig. S3C), we speculated that ADNVs might enhance the therapeutic efficacy of PD-L1 blockade, a frontline immune checkpoint therapy [36]. To test it, combinative treatment of ADNVs and αPD-L1 was applied in a murine lung cancer model. The results demonstrated that, compared with the treatment of αPD-L1 alone, combination of ADNVs and αPD-L1 elicited a more pronounced effect in impeding tumor growth (Fig. 7a–c). In parallel, the activation of STING-driven pathway was enhanced in TAMs upon co-administration of ADNVs and αPD-L1. Macrophage polarization from M2 to M1 was consistently boosted (Fig. 7d, e), and the numbers of CD4+ and CD8+ cells were elevated in mice co-instilled with ADNVs and αPD-L1, when compared with those in mice receiving αPD-L1 monotherapy (Fig. 7f). The data thus indicated that ADNVs administration, through STING-mediated macrophage reprogramming, greatly improved the efficacy of checkpoint blockade therapy. Fig. 7ADNVs strengthen the immunotherapy efficacy of PD-L1 blockade in mice. C57BL/6 mice were implanted with LLC cells for 7 days, and then instilled with ADNVs (25 mg/kg), alone or with αPD-L1 antibody, every 3 days for 2 weeks. Tumors were collected at day 21 post-implantation. A Gross photos of tumors at the end of experiments. B Tumor growth profiles in mice. *** $p \leq 0.001$ (Two-way ANOVA and Bonferroni post-tests). C Tumor weights at the end of the experiment. * $p \leq 0.05$, ***$p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). D Immunoblotting of STING and downstream signaling molecules in TAMs; E Flow cytometry and quantification of the portions of M1 (CD86+) and M2 (CD206+) population in TAMs. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ (One-way ANOVA and Tukey’s significant difference post hoc test). F Immunofluorescence staining of CD4+ and CD8+ cells in tumors. Nuclei: DAPI; Scale bar = 50 μm. The results are from one of two independent experiments. Representative images are shown and the data are presented as means ± SEM
## Discussion
Plant-derived nanovesicles (PDNVs) are membranous vesicles, with lipid bilayers as the basic framework to encapsulate active substances such as proteins, lipids, and nucleic acids [37, 38]. Evidences have demonstrated that PDNVs can serve as a major pathway to mediate material exchange and information transfer between species, thereby playing a pivotal role in regulating cell function, tissue repair, and self-defense [39, 40]. PDNVs, mostly derived from edible plants or herbs, have been reported to exert multiple effects including anti-inflammatory, anti-viral, anti-fibrosis and anti-tumor activity. Known for their well-described features such as safety, biocompatability and few side effects, PDNVs are increasingly recognized a promising therapeutic agents or drug carriers to treat the disorders like cancer, infection and autoimmune diseases [8, 41, 42]. However, the key issues, such as the bioactive contents enclosed in the vesicles, the targeted cells, and the signaling pathway induced, remain to be addressed. In this study, we isolate and purify artemisia-derived nanovesicles and establish them as a potential anti-tumor agent via reprogramming macrophages and hence anti-tumor immunity. Importantly, our data demonstrate that medicinal plant-derive mtDNA, enclosed in the nanovesicles and preferentially taken by TAMs, induces the cGAS-STING pathway to promote the transition of macrophages from the immune-tolerant to proinflammatory phenotypes, and thereby controls tumor progression. It’s the first time, to our knowledge, to describe a cross-kingdom interaction wherein plant-derived mtDNA impacts the phenotype and function of TAMs to rewire the anti-tumor immunity in mammals. Also, we identify an unappreciated role for the cGAS-STING pathway in mediating the immunoregulatory activity of plant-derived nanovesicles, opening an avenue for developing new anti-tumor strategies, alone or combination with αPD-L1 (Fig. 7).
Artemisinin has been recognized as a potent and effective antimalarial drug that has been widely applied in the world [43]. Beyond anti-parasite activity, artemisinin and its derivatives exhibit the therapeutic potentials for viral infections, inflammatory disease, autoimmune diseases and cancer [44–47]. Also, the extracts of Artemisia annua, particularly artemisinin and flavonoids, have been reported to have numerous pharmacological properties [48, 49]. However, we still know little about the action mode of Artemisinin and the relevant components. Recent data have demonstrated that nanovesicle from medicinal or edible plants like grape, grapefruit, ginger, and ginseng, have multiple advantages including rich resources, low immunological risk, low cost efficiency and readiness for mass production, making them a promising therapeutics for relevant diseases [3]. In this study, we purified the nano-scaled vesicles from *Artemisia annua* by the ultracentrifugation combined with density gradient centrifugation. The electron microscopy and zeta potential analyses reveal that the vesicles have the structure similar to mammalian-derived exosomes. These properties may help ADNVs to overcome the biological barriers to effectively deliver the bioactive materials such as protein, lipids and nucleic acids to recipient cells. In particular, our data identify that the enclosed mtDNA served as a major effector molecule to mediate the immunoregulatory function of ADNVs. Since most of current PDNVs studies are focused on microRNAs (miRNAs) [5, 7, 50], our discovery of mtDNA and its biological activities provides a novel insight into the action mode of plant-derived nanovesicles. To confirm the entity and action of mtDNA in vesicles, we carefully screen and exclude the possible sources of contaminations. Firstly, plant-specific mitochondrial DNA, but not genomic and chloroplast DNA, were amplified in the purified vesicles. This amplifi cation was abolished upon mtDNA depletion, further confirming the mtDNA contents in the vesicles; Secondly, mtDNA encapsulated in the vesicle, labeled by the lipophilic dye Dil, can be evidently detected in recipient cells, supporting its origin of donor cells not recipients themselves; Thirdly, enzymatic digestion of nucleic acid or depletion of mtDNA led to the abolishment of STING activation and macrophages polarization; and importantly, pretreatment of mtDNA eliminator largely abrogated the inhibitory effect of ADNVs in tumor growth, bolstering the immunoregulatory role for mtDNA in the nanovesicles. Interestingly, our comparative analysis reveals that naked mtDNA, with no lipid-dominant membrane encapsulated, lost the ability to induce STING pathway when added onto macrophages. The data is reminiscent of previous report that administration of the agents by means of nanovesicles is more efficient, although the action of the enclosed materials is the same, whether they are free or encapsulated [51]. Studies have shown that natural STING ligands such as cyclic dinucleotides (CDNs) are hydrophilic, negatively charged, hard to cross cell membranes, and susceptible to enzymatic degradation. Lipid nanoparticles enhance the stability of CDNs, and allow them to be more efficiently taken up by the phagocytes, and to penetrate deep and accumulate into tumors [52]. For these reasons, lipid nanodisc can be exploited to deliver a range of therapeutic cargos to treat tumors [42, 53]. Future studies might be required to further clarify the lipid components of Artemisia-derived nanovesicles and dissect their roles as the delivery vector or even the immune stimulatory agents.
TAMs are the most abundant immune cells in tumor microenvironment, playing a pro-tumor or anti-tumor role depending on the phenotypes they adopted. Upon exposure of proinflammatory stimuli, macrophages undergo the functional transition to immunogenic M1 phenotype and induce the antitumor immunity by releasing the immunostimulatory cytokines such as IL-1β, IL-12 and IL-23, reactive nitrogen and oxygen inter mediates. By contrast, macrophages in rapidly growing tumors tend to adopt alternatively activated M2 phenotype, with release of IL-10 and expression of mannose and arginase1 (Arg1), resistin-like α (Fizz1), and chitinase-like 3 (Ym1), thereby promoting tumor cell proliferation, metastasis and angiogenesis [54]. The high plasticity of macrophages makes them an attractive target to be manipulated for cancer immunotherapy, and the agents with macrophage-modifying properties have a therapeutic potential for cancers. Our data demonstrate that intraperitoneal instillation of ADNVs efficiently trafficked to tumor tissues, preferentially taken up by intratumoral macrophages. The data are in line with previous studies indicating macrophage-targeting property of plant-derived vesicles [41, 55]. This is likely due to the potential of macrophages to uptake and internalize nanoparticles primarily through receptor-mediated internalization and membrane-fusing machinery. Our recent study revealed the requirement of clathrin and caveolae-mediated endocytosis for internalization of exosome-like vesicles [20]. Future studies might be merited to clarify the mechanism responsible for the internalization of PDNVs and their cellular preference.
Current studies have identified many mechanisms driving macrophage reprogramming and the shift of TME from “cold” to “hot” status. Ligands for the innate immune sensors such as toll-like receptors (TLRs), retinoic acid-inducible gene I (RIG-I), nucleotide-binding oligomerization domain (NOD) have been reported to potentially enhance anti-tumor immunity. The GAS-STING pathway, upon recognizing cytosolic dsDNA, initiates IRF3 and NF-κB-driven pathways to induce proinflammatory macrophages polarization and strengthen anti-tumor immunity [56, 57]. Recent studies have shown that leakage of dsDNA from dying tumor cells would reprogram TAMs from pro-tumor M2 type to an anti-tumor M1 phenotype through activating STING pathway [31, 58]. The pathway was mitigated by phagocytic clearance of apoptotic tumor cells [33], indicating that the stability and persistence of STING ligands are required for optimized anti-tumor response. Our present studies identify plant-derived mtDNA as an unappreciated ligand for STING in rewiring macrophage and subsequent CTL response. Compared with the traditional ligands for STING like cyclic dinucleotides (CDNs), vesicular mtDNA exhibits a variety of merits such as lipophilic property, membrane permeability and resistance to degradation by nucleases. Additionally, as artemisinin and medicinal plants have been widely applied in clinic for years, their safety, biocomparability and in vivo dynamics are guaranteed. All the traits make ADNVs a promising therapy or drug carriers for treating tumors or other related illness [59, 60].
Of particular interest, our data also show that ADNVs treatment significantly enhanced the efficacy of PD-1 blockade in a mice lung cancer model. Although the mechanism for this synergism is to be further addressed, it was recently shown that the STING pathway exerted the regulatory role by transcriptional regulating the production of reactive oxygen species (ROS), which may enhance cellular apoptosis to reduce cancer burden and simultaneously functions as an immunogenic stimuli [61, 62]. In agreement with this concept, our data show that ADNVs treatment markedly elevated the percentage of ROS-generating macrophages, accompanied by increased apoptosis of cancer cells and augmented anti-tumor T cell response. This leads to the speculation that ADNVs may also modulate cellular ROS homeostasis to induce immunogenic cell death, thereby bridging the innate and adaptive immunity to coordinate anti-tumor response. The findings raise a rationale for developing ADNVs-based therapeutics, either alone or combination with other immunotherapy or traditional cancer treatments such as chemotherapy and radiotherapy, which can potentially induce immunogenic cell death [63, 64].
## Conclusions
In summary, we purified and characterized the nanovesicles from *Artemisia annua* and revealed its anti-tumor potential through remolding tumor microenvironment and reprogramming macrophages. The cGAS-STING pathway, induced by plant-derived mtDNA, was identifies as a major mechanism for the immunoregulatory role of ADNVs. We thus uncover an unexplored inter-kingdom interaction between the medicinal plant and mammal immune systems, which might be exploited to develop new treatments for tumors.
## Supplementary Information
Additional file 1: Fig. S1. Analysis of biocompatibility of ADNVs. Fig. S2. Effects of different injection routes on biodistribution and tumor control of ADNVs in vivo. Fig. S3. ADNVs promote TAMs polarization towards M1 phenotype. Fig. S4. ADNVs act through TAMs to exert the anti-tumor effect. Fig. S5. The immunoregulatory function of ADNVs is not through TLR2, TLR3, or TLR4 pathway. Tablse S1. Mouse primers for quantitative RT-PCR analysis. Table S2. Plant primers for PCR analysis.
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|
---
title: 'Effect of probiotic supplementation on gastrointestinal motility, inflammation,
motor, non-motor symptoms and mental health in Parkinson’s disease: a meta-analysis
of randomized controlled trials'
authors:
- Jong Mi Park
- Sang Chul Lee
- Chorom Ham
- Yong Wook Kim
journal: Gut Pathogens
year: 2023
pmcid: PMC9990363
doi: 10.1186/s13099-023-00536-1
license: CC BY 4.0
---
# Effect of probiotic supplementation on gastrointestinal motility, inflammation, motor, non-motor symptoms and mental health in Parkinson’s disease: a meta-analysis of randomized controlled trials
## Abstract
### Background
Parkinson’s disease (PD) is the second most common neurodegenerative disease worldwide. Gut dysbiosis is hypothesized to cause PD; therefore, whether probiotics can be used as adjuvants in the treatment of PD is being actively investigated.
### Aims
We performed a systematic review and meta-analysis to evaluate the effectiveness of probiotic therapy in PD patients.
### Methods
PUBMED/MEDLINE, EMBASE, Cochrane, Scopus, PsycINFO and Web of Science databases were searched till February 20, 2023. The meta-analysis used a random effects model and the effect size was calculated as mean difference or standardized mean difference. We assessed the quality of the evidence using the Grade of Recommendations Assessment, Development and Evaluation (GRADE) approach.
### Results
Eleven studies involving 840 participants were included in the final analysis. This meta-analysis showed high-quality evidence of improvement in Unified PD Rating Scale Part III motor scale (standardized mean difference [$95\%$ confidence interval]) (− 0.65 [− 1.11 to − 0.19]), non-motor symptom (− 0.81 [− 1.12 to − 0.51]), and depression scale (− 0.70 [− 0.93 to -0.46]). Moderate to low quality evidence of significant improvement was observed in gastrointestinal motility (0.83 [0.45–1.10]), quality of life (− 1.02 [− 1.66 to − 0.37]), anxiety scale (− 0.72 [− 1.10 to − 0.35]), serum inflammatory markers (− 5.98 [− 9.20 to − 2.75]), and diabetes risk (− 3.46 [− 4.72 to − 2.20]). However, there were no significant improvements in Bristol Stool Scale scores, constipation, antioxidant capacity, and risk of dyslipidemia. In a subgroup analysis, probiotic capsules improved gastrointestinal motility compared to fermented milk.
### Conclusion
Probiotic supplements may be suitable for improving the motor and non-motor symptoms of PD and reducing depression. Further research is warranted to determine the mechanism of action of probiotics and to determine the optimal treatment protocol.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13099-023-00536-1.
## Introduction
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. According to the Global Burden of Disease study, the incident number of PD in 2019 was 1081.72 × 103, which is an increase of $159.73\%$ since 1990 [1]. The incidence of PD is increasing globally with the aging population, and has become a challenge to global health. PD is characterized by motor symptoms, such as resting tremors, rigidity, bradykinesia, and postural instability, as well as by non-motor symptoms, such as gastrointestinal dysfunction, urinary incontinence, sweating, drooling, and neuropsychiatric problems [2]. Consequently, these features lower the quality of life of patients with PD. Recently, the role of the gut–brain axis—a bidirectional connection between the enteric and central nervous systems—has garnered attention [3, 4]. “ Gut dysbiosis” refers to a change in immunity, inflammation, and neuromodulation caused by microorganisms in the gastrointestinal tract and is considered to play a role in the pathophysiology of PD. Currently, active research on this topic is ongoing [5–7]. Changes in the compositions of important microbes are thought to affect behavior, neurotransmitter synthesis, microglial function, neurogenesis, and the blood–brain barrier; thus, these changes are ultimately implicated in the pathophysiology of PD [4].
According to the World Health Organization, probiotics are defined as “living microorganisms that, when administered in adequate amounts, provide a health benefit to the host” [8]. Given the anti-inflammatory effects of probiotics, they may be an adjuvant treatment option for the management of PD. However, previous clinical results have demonstrated that the effectiveness of probiotics in patients with PD is variable [9–16]. This meta-analysis aimed to analyze the quantitative effects of probiotics on gastrointestinal symptoms, inflammation, metabolic disease risk, and PD symptoms in patients with PD.
## Methods
This meta-analysis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [17, 18]. The PRISMA checklist is provided in Additional file 1. This review was registered in the International Prospective Register of Systematic Reviews (registration number CRD42022356798) on September 9, 2022.
## Search strategy
Relevant studies were systematically searched for in the PUBMED/MEDLINE, EMBASE, Cochrane, Scopus, and PsycINFO databases from inception until December 12, 2022 and we updated our search on February 20 2023. Additional articles were obtained by searching for citations in the list of included research and review articles. The search strategy was as follows: (“probiotic” OR “yeast” OR “yogurt” OR “fermented product” OR “lactobacillus” OR “bifidobacterium” OR “fermented dairy product” OR “symbiotics” OR “cultured milk products”) AND (“Parkinson” OR “Parkinson’s disease” OR “parkinsonism”) (Additional file 2).
## Inclusion and exclusion criteria
The study inclusion criteria followed the Population, Intervention, Comparison, Outcomes and Study design (PICOS) framework. The target population included adult patients with idiopathic Parkinson's disease who were 18 years old or older and diagnosed according to certain criteria (PD UK Social Brain Bank Standard, Queen Square Brain Bank Standard). The consumption/administration of probiotics such as pills or fermented milk over a period of time was considered an “intervention.” For comparison, those in whom probiotics were not administered or in whom a placebo was administered comprised the control group. The included outcomes were: [1] gastrointestinal symptoms with bowel movement, stool type, and constipation symptom data; [2] inflammation with data relating to inflammatory markers (nitric oxide, malondialdehyde, and high-sensitivity C-reactive protein) and antioxidant markers (total glutathione level and antioxidant capacity); [3] risk of metabolic syndrome as determined by fasting plasma glucose, serum insulin, and serum cholesterol levels; and [4] scores of Parkinson's disease-related scales: The Parkinson's Disease Questionnaire (PDQ-39), Non-Motor Symptoms Questionnaire (NMSQ), and Unified Parkinson's Disease Rating Scale (UPDRS). The study design included only randomized controlled trials that reported baseline and post-intervention data or changes in baseline data. Non-human studies, cohort studies, case reports, and studies that did not adhere to the PICOS framework were excluded.
## Data extraction and quality assessment
Two reviewers (JMP and YWK) independently extracted relevant information from studies, including the name of the first author, year of publication, patient demographic data, type of intervention (probiotic strain), intervention protocol, mean age, sex (male, %), sample size, and outcomes.
To identify the risk of bias, the quality of the included studies was evaluated using the Revised Cochrane Risk of Bias Tool for Randomized Trials (RoB2) to identify the risk of bias. The RoB2 assesses the following five components of risk of bias: the randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results. Each part was evaluated as having a low risk of bias, some concerns of bias, high risk of bias, or no information [19]. Discrepancies in data extraction and quality assessment were resolved through discussion between the investigators. Following the recommendations of previous studies [20, 21], we also planned to evaluate publication bias via funnel plots once more than three studies were reviewed.
## Grading of the evidence
The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool was used to assess the certainty of the evidence of the included meta-analyses, consisting of five domains: [1] risk of bias, [2] inconsistency, [3] indirectness, [4] imprecision, and [5] publication bias. The certainty of the evidence was rated as high, moderate, low, or very low [22].
## Statistical analysis
Cohen's kappa statistic was used to calculate the level of agreement between the two reviewers regarding study inclusion. In this meta-analysis, we performed statistical standardization of the effect sizes of probiotics on Parkinson’s. First, the mean and standard deviation (SD) of the change-from-baseline values in the treatment and control groups were calculated. For studies with insufficient data, the mean and SD values of the changes from baseline values were calculated with reference to Chapter 6 of the Cochrane Handbook (version 6.3). In the trials that examined the different effects of probiotics, participants in each category were included in a separate meta-analysis. The effect sizes were expressed as standard mean differences (SMD) in a random effects model, and $95\%$ confidence intervals (CI) between the treatment and control groups were calculated for each study and measure. Due to the relatively small number of heterogeneous studies, we used a random-effects meta-analysis with Hartung–Knapp–Sidik–Jonkman (HKSJ) adjustments [23]. Heterogeneity between studies was assessed using I2 and P-values from the Cochrane Q test. Publication bias was assessed by visual inspection of funnel plots and the Egger bias test. All statistical tests were two-sided, and $P \leq 0.05$ was considered statistically significant using Stata version 17 (Stata Corp LP, College Station, TX, USA).
## Study identification and characteristics
A total of 293 studies were screened, of which 116 duplicate studies were excluded. After reading the titles and abstracts, 114 and 49 papers were excluded, respectively. Of the remaining 14 studies, three were excluded for various reasons (reports not retrieved, insufficient detail in the data, and incomplete study protocol information), and 11 were finally included in the meta-analysis. The degree of agreement of the full-text review phase was calculated using the Kappa score (kappa = 0.831; standard error = 0.160), which showed good agreement among the reviewers. A flowchart depicting the selection of studies is presented in Fig. 1. This meta-analysis included 11 studies and 840 patients with PD.Fig. 1PRISMA flow-chart of the screening and selecting processes of the studies The number of participants in the included studies ranged from 40 [11] to 122 [16] participants, and the average age of the participants was between 66.5 [24] and 75.6 [11] years. All studies included both men and women. The duration of probiotic administration ranged from 4 [9, 13, 15] to 12 [10, 11, 14, 16, 10–11] weeks. Probiotics were administered in the form of fermented milk [9, 13, 16], capsules [10–12, 10–12, 10–12] or powder in sachets [26]. The detailed characteristics of the included studies are summarized in Table 1.Table 1Characteristics of the included studiesStudyStudy designNo. of participants(experimental/control)Age, yearsa(experimental/control groups)ProbioticsControlgroupFrequencyanddurationOutcomemeasureMain findingBarichella et al. [ 9]Randomized double-blindclinical trial$\frac{80}{4071.8}$ ± $\frac{7.7}{69.5}$ ± 10.3Fermented milk containing multiple probiotic strains and prebiotic fiberPasteurized, fermented,fiber-free milkOnce daily for 4 weeks1. Bowel movements2. Bristol Stool Scale score3. Treatment satisfaction4. Treatment continuationProbiotics were superior to placebo in improving constipation in patients with PDBorzabadi et al. [ 10]Randomized double-blindclinical trial$\frac{25}{2566.7}$ ± $\frac{10.7}{66.9}$ ± 7.0Capsule 8 × 109 CFULactobacillus acidophilusBifidobacterium bifidumL. reuteriLactobacillus fermentum(2 × 109 CFU each)Placebo capsuleOnce daily for 12 weeks1. Gene expression(IL-1, IL-8, TNF-α, TGF-β, PPAR-γ, VEGF, and LDLR)2. Biomarkers of oxidative stress(total glutathione and nitric oxide)The probiotics group had significantly improved gene expression levels of IL-1, IL-8, TNF-α, TGF-β, and PPAR-γ; however, the gene expression of VEGF and LDLR and the number of biomarkers of inflammation and oxidative stress were unaffectedDu et al. [ 25]Randomized clinical trial$\frac{23}{2368.39}$ ± $\frac{7.55}{66.65}$ ± 8.66Capsule5 × 109 CFU Bacillus licheniformis1 × 107 CFULactobacillus acidophilus, Bifidobacterium longum,Enterococcus faecalisNoneThree times dailyfor 12 weeks1. Bowel movements2. Bristol Stool Scale scores3. Constipation (PAC-SYM)4. Quality of life (PAC-QOL)The probiotics group demonstrated improved bowel movements and improved scores on symptom scales associated with constipation (PAC-SYM scores and PAC-QOL scores)Georgescu et al. [ 11]Randomized controlled clinical trial$\frac{20}{2075.6}$ ± $\frac{9.7}{69.8}$ ± 5.6Capsule 60 mgLactobacillus acidophilus Bifidobacterium infantisTrimebutine200 mgthree times dailyTwice daily for 12 weeks1. Abdominal pain2. Bloating3. ConstipationProbiotics improved abdominal pain and bloating similar to trimebutine; however, trimebutine was more effective for constipation with incomplete evacuationIbrahim et al. [ 12]Randomized double-blindclinical trial$\frac{22}{2669.0}$ ± $\frac{5.2}{70.5}$ ± 6.1Capsule 3 × 1010 CFULactobacillus sp. Bifidobacterium sp.fructo-oligosaccharides lactoseFermented milkTwice daily for 8 weeks1. Garrigues Questionnaire2. Gut transit time3. Quality of life (PDQ39-SI)4. Motor symptoms (MDS-UPDRS)5. Non-motor symptoms (NMSQ)Probiotics led to improved bowel opening frequencies and whole gut transit times in PDMichela et al. [ 13]Randomized double-blindclinical trial$\frac{80}{40}$N/R/N/RFermented milk containing multiple probiotic strains and prebiotic fiberPasteurized,fermented,fiber-free milkOnce daily for 4 weeks1. Complete bowel movementsProbiotics were superior to placebo in improving numbers of complete bowel movementsMehrabani et al. [ 26]Randomized double-blindclinical trial$\frac{40}{4068.2}$ ± $\frac{7.7}{69.1}$ ± 8.2Powder in sachets5 × 109 CFULactobacillus acidophilusLactobacillus rhamnosus *Lactobacillus plantarum* *Bifidobacterium longum* Streptococcus thermophilusMaltodextrinOnce daily for12 weeks1. Biomarkers of oxidative stress(TAC, TOS, OSI, GSH, MDA)2. Quality of life (PDQ39)3. Mental status(Hospital Anxiety and Depression Scale)4. Fatigue Severity Scale scoresThe probiotics group had increased TAC, reduced MDA levels, and reduced OSI values, and it also had lower levels of depression and displayed improvements in well-being and cognitive impairmentSun et al. [ 24]Randomized double-blindclinical trial$\frac{48}{3466.46}$ ± $\frac{6.98}{68.76}$ ± 6.91Capsule 3 × 1010 CFUBifidobacterium animalis subsp. lactisMaltodextrinOnce daily for 12 weeks1. Number of spontaneous defecation incidents2. Bristol Stool Scale scores3. Motor symptoms (MDS-UPDRS)4. Quality of life (PAC-QOL)5. Hamilton Anxiety Rating Scale scores6. Hamilton Depression Scale scoresProbiotics improved sleep quality and alleviated anxiety and gastrointestinal symptomsTamtaji, O. R., et al. [ 2019][14]Randomized double-blindclinical trial$\frac{30}{3068.2}$ ± $\frac{7.8}{67.7}$ ± 10.2Capsule 8 × 109 CFULactobacillus acidophilusBifidobacterium bifidumLactobacillus reuteriLactobacillus fermentum(2 × 109 CFU each)Placebo capsuleOnce daily for 12 weeks1. Motor symptoms (MDS-UPDRS)2. High-sensitivity CRP3. Biomarkers of oxidative stress(total glutathione,malondialdehyde)4. Diabetes markers(fasting plasma glucose, Insulin, Insulin sensitivity check index)5. Dyslipidemia markers(triglycerides, VLDL, LDL, HDL)Probiotics had useful impacts on MDS-UPDRS scores and some metabolic profilesTan et al. [ 15]Randomized double-blindclinical trial$\frac{34}{3870.9}$ ± $\frac{6.6}{68.6}$ ± 6.7Capsule 109 CFU Lactobacillus AcidophilusLactobacillus reuteriLactobacillus gasseriLactobacillus rhamnosusBifidobacterium bifidum *Bifidobacterium longum* Enterococcus faecalisEnterococcus faeciumPlacebo capsuleOnce daily for 4 weeks1. Bowel movements per week2. Average stool consistency3. Quality of life (PAC-QOL)4. Constipation severity scoresProbiotics were effective for relieving constipation in patients with PDYang et al. [ 16]Randomized double-blindclinical trial$\frac{63}{59}$N/R/N/RFermented milkcontaining 109 CFU Lactobacillus casei strainPlacebo capsuleOnce daily for 12 weeks1. Quality of life (PDQ39)2. Non-motor symptoms (NMSQ)3. Hamilton Anxiety Rating Scale scores4. Hamilton Depression Rating Scale scoresProbiotics may be a useful approach in managing the non-motor symptoms of PDN/R, not recorded; CFU, colony-forming units; IL, interleukin; TNF, tumor necrosis factor; TGF, transforming growth factor; PPAR, peroxisome proliferator-activated receptor; VEFG, vascular endothelial growth factor; LDLR, low-density lipoprotein receptor; PAC-SYM, Patient Assessment of Constipation Symptoms; PAC-QOL, Patient Assessment of Constipation Quality of Life, UPDRS, Unified Parkinson's Disease Rating Scale; NMSQ, Non-Motor Symptoms Questionnaire; PDQ-39, Parkinson's Disease Questionnaire; PDSS, Parkinson’s Disease Sleep Scale; TAC, total antioxidant capacity; TOS, total oxidant status; OSI, oxidative stress index; GSH, glutathione; MDA, malondialdehyde; CRP, C-reactive protein; VLDL, very-low-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoproteinaMean age ± standard deviation
## Risk of bias assessment and assessment of publication bias
All 11 studies were randomized trials, and the majority ($$n = 7$$) described the randomization methods employed. However, only the abstract was available for two [13, 16] studies, which did not provide sufficient information, while two other studies [11, 25] did not sufficiently explain the allocation concealment method employed. The types of probiotics utilized in the intervention and placebo groups were identical and blinded in most studies; therefore, the risk of bias due to deviations from the intended intervention was low. However, two studies [16, 25] did not comment on the details of the placebo intervention protocol. A missing outcome bias was also reported in one study [16], and this was low risk in seven studies. Although most studies did not mention detection biases, gastrointestinal symptoms, inflammation, metabolic disease risk, and PD scores were measured quantitatively using various scales. All studies were rated as having a low-risk bias in outcome measures as the influence of knowledge was small.
All studies except one [13] were conducted according to a pre-randomized study protocol; therefore, the reported results were rated as having low selection bias. A traffic light plot for the assessment of each included study is shown in Fig. 2. The publication bias for each meta-analysis is shown in Additional file 3 as a funnel plot. In addition, publication bias was confirmed using Egger's test. Statistically significant publication biases were identified for gastrointestinal motility ($$P \leq 0.02$$), inflammation markers ($$P \leq 0.02$$), diabetes risk ($$P \leq 0.001$$), dyslipidemia risk ($$P \leq 0.01$$), and subgroup analyses ($$P \leq 0.01$$). Factors such as Bristol Stool Scale scores ($$P \leq 0.82$$), constipation symptom reduction levels ($$P \leq 0.16$$), antioxidant marker levels ($$P \leq 0.05$$), UPDRS Part III scores ($$P \leq 0.19$$), quality of life measures ($$P \leq 0.23$$), anxiety scale scores ($$P \leq 0.62$$), and depression scale scores ($$P \leq 0.62$$) showed no significant publication biases. Fig. 2Risk of bias of the included studies. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 Tool
## Effects of probiotics on gastrointestinal symptoms
Gastrointestinal motility per week were reported in six studies [9, 12, 13, 15, 12–13] that included eight comparisons. The meta-analysis showed significant improvement in the probiotics groups (SMD, 0.83; $95\%$ CI 0.63 to 1.04). Subgroup analyzes were performed according to the follow-up period, as the length of follow-up varied between studies and ranged from 4 to 12 weeks. Three studies [9, 15, 24] that followed up after 4 weeks showed significant improvement in the probiotics groups (SMD, 0.78; $95\%$ CI 0.35 to 1.12). Two studies [12, 13] that followed up after 8 weeks showed significant improvement in the probiotics groups (SMD, 0.74; $95\%$ CI 0.35 to 1.12). Two studies [24, 25] that followed up after 12 weeks showed significant improvement in the probiotics groups (SMD, 1.12; $95\%$ CI 0.75 to 1.50) (Fig. 3A). The gastrointestinal motility quality of evidence was estimated as moderate performing the GRADE system (based on the publication bias) (Table 2).Fig. 3Meta-analysis of the effects of probiotics on gastrointestinal symptoms in Parkinson’s disease. Table 2Summary of the findings and quality of evidence assessment using the GRADE approachOutcome measureSummary of findingsQuality of evidence assessment (GRADE)No. of patients (trials)Effect size ($95\%$ CI)Risk of biasaInconsistencybIndirectnesscImprecisiondPublication biaseQuality of evidencefGastrointestinal motility210 [8]0.83 (0.63, 1.04)Not seriousNot seriousNot seriousNot seriousSeriousModerateStool type: Bristol Stool Scale210 [5]0.46 (− 0.50, 1.42)Not seriousSeriousNot seriousSeriousNot seriousLowConstipation symptom reduction258 [7]− 0.63 (− 1.72, 0.46)SeriousSeriousSeriousSeriousNot seriousVery lowInflammation markers250 [4]− 5.98 (− 9.20, − 2.75)Not seriousSeriousSeriousNot seriousSeriousVery lowAntioxidant markers330 [5]0.92 (− 0.28, 2.13)Not seriousSeriousSeriousSeriousNot SeriousVery lowDiabetes risk240 [4]− 3.46 (− 4.72, − 2.20)Not seriousSeriousSeriousNot SeriousSeriousVery lowDyslipidemia risk240 [5]− 1.18 (− 2.48, 0.12)Not seriousSeriousSeriousSeriousSeriousVery lowUPDRS Part III219 [3]− 0.65 (− 1.11, − 0.19)Not seriousNot seriousNot seriousNot seriousnot seriousHighNMSQ177 [2]− 0.81 (− 1.12, − 0.51)Not seriousNot seriousNot seriousNot seriousNot seriousHighQuality of life509 [7]− 1.02 (− 1.66, − 0.37)Not seriousSeriousNot seriousNot seriousNot seriousModerateAnxiety Scale366 [4]− 0.72 (− 1.10, − 0.35)Not seriousSeriousNot seriousNot seriousNot seriousModerateDepression Scale366 [4]− 0.70 (− 0.93, − 0.46)Not seriousNot SeriousNot seriousNot seriousNot seriousHighGastrointestinal motility: fermented milk360 [3]0.55 (0.33, 0.77)Not seriousNot seriousNot seriousNot seriousSeriousModerateGastrointestinal motility: capsules392 [6]1.06 (0.85, 1.28)Not seriousNot seriousNot seriousNot seriousSeriousModerateUPDRS, Unified Parkinson's Disease Rating Scale; NMSQ, Non-Motor Symptoms Questionnaire.aRisk of bias based on the Revised Cochrane Risk of Bias Tool for Randomized Trials (RoB2)bDowngraded if a significant and unexplained heterogeneity (I2 > $50\%$, $P \leq 0.10$) was not explained by meta-regression or subgroup analysis resultscDowngraded if there were any factors related to the participants, interventions, or results that limited the generalizability of the resultsdDowngraded if the $95\%$ confidence interval ($95\%$ CI) crossed the benefit-or-harm boundaryeDowngraded if there was evidence of publication bias using Egger's testfBecause all included studies were meta-analyses of randomized clinical trials, the certainty of the evidence was graded as high for all outcomes by default and then downgraded according to prespecified criteria. Quality was graded as high, medium, low, or very low.
Bristol stool scale were reported in four studies included five comparisons. The meta-analysis showed no significant difference between the experimental and control groups (SMD, 0.46; $95\%$ CI − 0.50 to 1.42). Three studies [9, 15, 24] that followed up after 4 weeks showed no significant difference (SMD, 0.15; $95\%$ CI − 0.65 to 0.95). Two studies [24, 25] that followed up after 12 weeks showed no significant difference (SMD, 0.95; $95\%$ CI − 1.92 to 3.82) (Fig. 3B). The bristol stool scale quality of evidence was estimated as low performing the GRADE system (based on the inconsistency and imprecision) (Table 2).
Symptoms of constipation were reported in four studies [11, 15, 24, 25] that included seven comparisons. There were no significant differences in the summary effect size between the groups (SMD, − 0.63; $95\%$ CI − 1.72 to 0.46). Two studies [15, 24] that followed up after 4 weeks showed no significant difference (SMD, − 1.30; $95\%$ CI − 2.78 to 0.18). Three studies [11, 24, 25] that followed up after 12 weeks showed no significant difference (SMD, − 0.33; $95\%$ CI − 1.93 to 1.27) (Fig. 3C). The constipation symptom quality of evidence was estimated as very low performing the GRADE system (based on the risk of bias, inconsistency, indirectness and imprecision) (Table 2).
## Effects of probiotics on inflammation
Three studies [10, 14, 26] assessed serum inflammatory markers such as nitric oxide (μmol/L), malondialdehyde (mmol/L), and high-sensitivity C-reactive protein. The meta-analysis revealed a significant reduction in the serum levels of inflammatory markers in the probiotic groups (SMD, − 5.98; $95\%$ CI − 9.20 to − 2.75) (Fig. 4A). The quality of evidence for inflammatory markers was estimated to be very low according to the GRADE system (based on inconsistency, indirectness, and publication bias) (Table 2).Fig. 4Meta-analysis of the effects of probiotics on inflammation in Parkinson’s disease.
Three studies [10, 14, 26] assessed total glutathione (μmol/L) and total antioxidant capacity (mmol/L) levels. The meta-analysis showed no significant difference (SMD, 0.92; $95\%$ CI − 0.28 to 2.13) in antioxidant markers (Fig. 4B). The quality of the antioxidant marker data was estimated to be very low according the GRADE system (based on inconsistency, indirectness, and imprecision) (Table 2).
## Effect of probiotics on metabolic syndrome risk
Metabolic syndrome risk was identified by assessing the risk of diabetes and dyslipidemia. One study [14] included four comparisons pertaining to the risk of diabetes (fasting plasma glucose [mg/dL], serum insulin [mIU/mL], homeostasis model of assessment-estimated insulin resistance, and quantitative insulin sensitivity check index). The meta-analysis revealed a significant reduction in the risk of diabetes in the probiotic groups (SMD, − 3.46; $95\%$ CI − 4.72 to − 2.20) (Fig. 5A). The quality of evidence for the risk of diabetes was estimated to be very low according to the GRADE system (based on inconsistency, indirectness, and publication bias) (Table 2).Fig. 5Meta-analysis of the effects of probiotics on metabolic syndrome risk in Parkinson’s disease.
The aforementioned study above [14] also included five comparisons of the risk of dyslipidemia (serum levels of triglycerides [mg/dL], very-low-density lipoprotein (VLDL)-cholesterol [mg/dL], total cholesterol [mg/dL], low-density lipoprotein (LDL)-cholesterol [mg/dL], and HDL-cholesterol [mg/dL]). There were no significant differences between the groups (SMD, − 1.18; $95\%$ CI − 2.48 0.12) in the risk of dyslipidemia (Fig. 5B). The quality of evidence for the risk of dyslipidemia was estimated to be very low according to the GRADE system (based on inconsistency, indirectness, imprecision, and publication bias) (Table 2).
## Effects of probiotics on PD scale scores
Two studies [12, 24] included UPDRS Part III motor scores, and there were significant improvements in the probiotic groups (SMD, − 0.65; $95\%$ CI − 1.11 to − 0.19) (Fig. 6A). The UPDRS Part III quality of evidence was estimated to be high according to the GRADE system (Table 2).Fig. 6Meta-analysis of the effects of probiotics on Parkinson’s disease-related scales.
Two studies [12, 16] reported Non-Motor Symptoms Questionnaire (NMSQ) scores. The meta-analysis revealed a significant reduction in NMSQ scores in the probiotic groups (SMD, − 0.81; $95\%$ CI − 1.12 to − 0.51) (Fig. 6B). The NMSQ quality of evidence was estimated to be high according to the GRADE system (Table 2).
Quality of life was reported in six studies [12, 15, 16, 15–16] that included seven comparisons. There were significant improvements in the probiotic groups (SMD, − 1.02; $95\%$ CI − 1.66 to − 0.37). Two studies [15, 24] that followed up after four weeks showed significant improvements in the probiotic groups (SMD, − 1.32; $95\%$ CI − 2.36 to − 0.28). Four studies [16, 24–26] that followed up after 12 weeks also showed significant improvements in the probiotic groups (SMD, − 1.11; $95\%$ CI − 2.09 to − 0.13) (Fig. 6C). The quality of evidence for quality of life was estimated to be moderate according to the GRADE system (this was reduced due to inconsistency) (Table 2).
## Effects of probiotics on patients' mental health
Two studies [16, 24] used the Hamilton Anxiety Rating Scale (HAMA) and one study [26] used the Hospital Anxiety and Depression Scale (HADS) Anxiety subscale. There were significant improvements in the probiotic groups (SMD, − 0.72; $95\%$ CI − 1.10 to − 0.35). In addition, three studies [16, 24, 26] that followed up after 12 weeks showed significant improvements in the probiotic groups (SMD, − 0.79; $95\%$ CI − 1.29 to − 0.28) (Fig. 7A). The quality of evidence for the anxiety scales was estimated to be moderate according to the GRADE system (reduced due to inconsistency) (Table 2).Fig. 7Meta-analysis of the effects of probiotics on mental health in Parkinson’s disease. Anxiety Scale.
Two studies [16, 24] used the Hamilton Depression Rating Scale (HAMD) and one study [26] used the Hospital Anxiety and Depression Scale (HADS) Depression subscale. There were significant improvements in the probiotic groups (SMD, − 0.70; $95\%$ CI, − 1.04 to − 0.43). In addition, three studies [16, 24, 26] that followed up after 12 weeks showed significant improvements in the probiotic groups (SMD, − 0.73; $95\%$ CI, − 1.04 to − 0.43) (Fig. 7B). The quality of evidence for the depression scales was estimated to be high according to the GRADE system (Table 2).
## Subgroup analysis
A subgroup analysis was performed according to the probiotic intake method. Both fermented milk (SMD, 0.55; $95\%$ CI 0.33 to 0.77) and capsules (SMD, 1.06; $95\%$ CI 0.85 to 1.28) demonstrated significant improvements in gastrointestinal motility (Fig. 8). The effect was greater when probiotics were ingested as capsules compared to when consumed as fermented milk. The quality of evidence was estimated as moderate according to the GRADE system (reduced due to publication bias) (Table 2).Fig. 8Subgroup analysis of the effects of probiotics on gastrointestinal motility by administration method
## Safety/adverse events
Five [11, 14, 24–26] of the ten included studies reported no adverse effects. Two studies [9, 12] reported abdominal bloating, whereas one study [12] reported dizziness. Lethargy was reported in one patient, and this improved a week after discontinuation of the probiotics [15]. There were no reports of safety in any of the included studies, and no adverse events were reported in three studies [10, 13, 16].
## Discussion
This study performed a comprehensive and quantitative evaluation of randomized controlled trials on the effects of probiotics in patients with PD. We found high-quality evidence that probiotics significantly improve motor, non-motor PD symptoms, and depression. Moderate quality of evidence suggests that probiotics significantly increase gastrointestinal motility and quality of life and reduce anxiety. Low or very low quality evidence showed a significant reduction in serum levels of inflammatory markers and risk of diabetes. However, stool quality, constipation, antioxidant marker level, and risk of dyslipidemia were low or very low quality evidence, and there was no significant difference between the probiotic group and the control group. The improvement in gastrointestinal motility was greater when the probiotics were administered in capsules compared to when they were consumed in fermented milk forms. In addition, the longer the follow-up period, the better the gastrointestinal motility and mental health scores. These results suggest that probiotics can be considered as an adjuvant treatment option based on the pathophysiology of “gut dysbiosis” in patients with PD.
. Studies from 1957 and 1960 revealed a loss of neurons in the substantia nigra and decreased dopamine levels in the striatum in patients with PD [27, 28]. In addition, degeneration of dopaminergic neurons in the substantia nigra compacta and projections to the striatum can induce PD. This denaturation appears as toxic aggregation of alpha-synuclein (α-syn), a major component of Lewy bodies [29]. Due to neuroinflammation through the blood–brain barrier and vagus nerve, α-syn, a pathological marker of PD, accumulates in the central nervous system, peripheral nervous system, and enteric nervous system [30, 31] (Fig. 9). Short-chain fatty acids (SCFAs) are major metabolites produced by microorganisms in the large intestine through anaerobic fermentation of undigested polysaccharides. SCFAs play an important role in maintaining intestinal barrier integrity, preventing microbial migration, and preventing inflammation by regulating the expression of tight junction proteins [32]. An imbalance in the gut flora due to increased harmful bacteria creates endotoxins (e.g., lipopolysaccharides [LPS]) and damages the intestinal barrier, thus triggering the migration of microorganisms and bacterial metabolites and consequently inducing pro-inflammatory pathways. Fig. 9An overview of gut dysbiosis and the effects of probiotics in Parkinson’s disease. “ Gut dysbiosis” decreases the levels of short-chain fatty acids (SCFAs) and increases the levels of lipopolysaccharides (LPS). In addition, it impairs intestinal epithelial barrier integrity (“leaky gut”) and triggers an inflammatory response, leading to the crossing of metabolites, chemokines, and cytokines through the intestinal wall into the bloodstream as well as through the blood–brain barrier. This inflammatory pathway also triggers misfolding of α-synuclein into enteric glial cells via the vagus nerve and into the brain. Probiotics alter the composition of microorganisms to increase SCFA levels, decrease LPS levels to reduce inflammation, and strengthen the intestinal barrier to prevent microbial migration. This figure was created using Medical Illustration & Design LPS interact with immune cells to induce cytokines, such as tumor necrosis factor and interleukins to induce systemic inflammatory responses [33]. LPS also interact with intestinal glial cells and brain microglia cells to activate inflammatory cytokines (nuclear factor kappa-light-chain-enhancer of activated B cells and pro-inflammatory cytokine interleukin-1β via cluster of differentiation 14 + and Toll-like receptor 4 receptors, which in turn produce neuroinflammatory α-syn aggregation in the vagus nerve and brain [34–38]. The vagus nerve directly innervates the myenteric plexus, where neurons run through the prevertebral ganglia within the spinal cord and finally to the brain. According to Braak’s hypothesis, pathogens enter the oral and nasal cavities and initiate the formation of Lewy bodies, resulting in sporadic PD [39]. Aggregation of α-syn initiates in the gut and olfactory sensory nerves, passes through the nasal olfactory lobe to the midbrain, and then the vagus nerve propagates α-syn to the brainstem and cortex [31, 40].
Bacteria that aid in SCFAs production include Butyricicoccus, *Clostridium sensu* stricto, Roseburia, and Faecalibacterium prausnitzii, whereas bacteria that decrease SCFAs and increase LPS include Akkermansia, Escherichia/Shigella, Flavonifractor, Intestinimonas, Phascolarctobacterium, and Sporobacter [41–47]. The effectiveness of probiotics on gut dysbiosis has been confirmed in several papers. In a C. elegans model of synucleinopathy, *Bacillus subtilis* was effective in eliminating α-syn aggregates and preventing their further aggregation [48]. In an in vitro model of the human colon microbiome, *Bifidobacterium longum* subsp. infantis (B. infantis) reduced the levels of LPS [49]. In addition, fermented milk containing lactic acid bacteria reduced LPS-induced neuroinflammation in a rat model [50]. Furthermore, in both in vitro and in vivo models of PD, probiotics reversed “gut dysbiosis” by altering the composition of the gut microbiome, thus disrupting pathways associated with inflammation [51, 52]. The above studies may explain the results that taking probiotics improves motor and non-motor symptoms in PD patients.
In addition to their effects on PD, the effects of probiotics on metabolic disorders are well known. The effects of probiotics on intestinal hormone and short-chain production affect glucose and lipid metabolism [53]. Supplementation with Lactobacillus casei has been shown to improve the glycemic response in diabetic patients by increasing levels of sirtuin 1, which is a key regulator of homeostasis and improves glucose metabolism by affecting gene expression. Lactobacillus rhamnosus has been reported to decrease blood glucose levels in diabetic mice by suppressing gluconeogenesis-associated gene expression, whereas *Lactobacillus acidophilus* has been shown to function as an antidiabetic agent by regulating the expression levels of genes related to glucose and lipid metabolism as well as inflammatory cytokines, such as glycogen synthase kinase 3β and sterol regulatory element-binding transcription factor 1c [54]. Certain strains of probiotics are capable of breaking down bile salts, resulting in a reduction in blood cholesterol levels. Probiotics also affect the expression of fasting-induced adipose factor (FIAF), which can limit the activity of lipoprotein lipase (LPL) and fat storage [55, 56]. However, unlike previous studies, this study found that taking probiotics reduced the risk of diabetes but did not make a significant difference to the risk of hyperlipidemia in PD patients. The evidence quality of this analysis is considered very low, most likely due to indirectness and imprecision resulting from merging markers from multiple blood tests from a single study.
## Strengths and limitations
The core strength of this meta-analysis is that we used quantitative statistical methods to determine the most comprehensive effects of probiotics in patients with PD.
Although there have been few clinical trials in humans, our meta-analysis provides insights into the effects of probiotics on PD. Additionally, we compared the effects of probiotics according to type and duration of administration.
This meta-analysis has several limitations. First, the number of studies and participants was small. To reduce heterogeneity between studies, we only included studies of participants diagnosed with idiopathic PD and excluded studies of patients with secondary parkinsonism, including a relatively small number of 840 patients. Second, we found significant heterogeneity among studies. This was confirmed not only through the I2 values but also through the $95\%$ confidence interval [57]. We attempted to account for this heterogeneity by utilizing a random effects model and performing subgroup analyzes according to dosing method and follow-up period. However, heterogeneity remained high even after subgroup analysis was performed, suggesting additional sources of heterogeneity. In previous studies examining the different effects of probiotics reported that higher doses (≥ 1010 CFU), longer duration [58], and various strains of probiotics [59] were more effective. We therefore attribute the considerable heterogeneity of our study to the diversity of study protocols, such as probiotic strains, dosage and duration of intervention, and method of administration. These differences should be considered while interpreting the results. Third, the intervention durations of the included studies (ranging from 4 to 12 weeks) were short and insufficient to understand the long-term effects of probiotics. Fourth, some of the studies included in the meta-analysis may have been from the same center, which could have led to data overlap.
## Conclusions
Our study shows high-quality evidence that probiotics improve motor function, non-motor symptoms, and reduce depression in PD patients. Probiotic supplementation may be an affordable and safe adjuvant treatment option for PD management. To establish more trustworthy evidence on the potential benefits of probiotics for PD, it is necessary to conduct larger randomized controlled trials and long-term follow-up studies. The studies should be subdivided based on factors such as the severity of the disease, type and dosage of probiotics, duration of intervention, and they should include assessments of motor and cognitive function as well as other predictors of disease.
## Supplementary Information
Additional file 1. PRISMA 2020 checklist. Additional file 2. Search date: 2023.02.20.Additional file 3. Funnel plot to detect publication bias.
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|
---
title: Rumen microbial-driven metabolite from grazing lambs potentially regulates
body fatty acid metabolism by lipid-related genes in liver
authors:
- Zhen Li
- Xingang Zhao
- Luyang Jian
- Bing Wang
- Hailing Luo
journal: Journal of Animal Science and Biotechnology
year: 2023
pmcid: PMC9990365
doi: 10.1186/s40104-022-00823-y
license: CC BY 4.0
---
# Rumen microbial-driven metabolite from grazing lambs potentially regulates body fatty acid metabolism by lipid-related genes in liver
## Abstract
### Background
Lipid metabolism differs significantly between grazing and stall-feeding lambs, affecting the quality of livestock products. As two critical organs of lipid metabolism, the differences between feeding patterns on rumen and liver metabolism remain unclear. In this study, 16S rRNA, metagenomics, transcriptomics, and untargeted metabolomics were utilized to investigate the key rumen microorganisms and metabolites, as well as liver genes and metabolites associated with fatty acid metabolism under indoor feeding (F) and grazing (G).
### Results
Compared with grazing, indoor feeding increased ruminal propionate content. Using metagenome sequencing in combination with 16S rRNA amplicon sequencing, the results showed that the abundance of propionate-producing Succiniclasticum and hydrogenating bacteria Tenericutes was enriched in the F group. For rumen metabolism, grazing caused up-regulation of EPA, DHA and oleic acid and down-regulation of decanoic acid, as well as, screening for 2-ketobutyric acid as a vital differential metabolite, which was enriched in the propionate metabolism pathway. In the liver, indoor feeding increased 3-hydroxypropanoate and citric acid content, causing changes in propionate metabolism and citrate cycle, while decreasing the ETA content. Then, the liver transcriptome revealed that 11 lipid-related genes were differentially expressed in the two feeding patterns. Correlation analysis showed that the expression of CYP4A6, FADS1, FADS2, ALDH6A1 and CYP2C23 was significantly associated with the propionate metabolism process, suggesting that propionate metabolism may be an important factor mediating the hepatic lipid metabolism. Besides, the unsaturated fatty acids in muscle, rumen and liver also had a close correlation.
### Conclusions
Overall, our data demonstrated that rumen microbial-driven metabolite from grazing lambs potentially regulates multiple hepatic lipid-related genes, ultimately affecting body fatty acid metabolism.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40104-022-00823-y.
## Background
Ruminants rely on natural pastures for nutrients to grow and reproduce, with the attendant negative effects of lower growth and reduced industrial efficiency [1]. Moreover, the demand for increased husbandry output has led to a series of overgrazing problems such as the continued deterioration of grasslands. To improve the growth performance of these ruminants and alleviate the pressure of overgrazing, indoor feeding is an alternative to natural grazing [2]. However, the results of numerous studies confirmed that indoor feeding, while improving production efficiency, leads to poor flavor and fatty acids (FAs), which do not meet consumer demand for healthy green foods [3, 4]. The unique natural climatic conditions and high-quality grazing resources in Ningxia Hui Autonomous Region of China, have resulted in an excellent breed of Tan lamb with very good eating quality having a unique flavor, and relatively low saturated fatty acid (SFAs) and cholesterol content, and has appeared on the G20 Table in 2016. With the emergence of environmental issues and the quest for productivity, the feeding model of Tan lambs is gradually shifting to indoor feeding, with the consequent decline in lamb quality, especially in terms of FAs [5, 6]. The current study is thus aimed at investigating the mechanisms of differences in lipid metabolism between grazing and indoor feeding sheep.
In ruminants, the role of the rumen is crucial for the metabolism of FAs. Ruminal microorganisms synthesize odd-carbon fatty acids from propionate and valerate, as well as branched-chain fatty acids from isobutyrate, isovalerate and branched-chain amino acids, which are deposited directly or indirectly in muscle, adipose and milk [7]. Along with the synthesis of FAs, rumen microbes also break down lipids and hydrogenate unsaturated fatty acids (UFAs). Thus, both synthesis and digestion processes together have an impact on the content and composition of the final FAs flowing from the rumen [8]. A previous study assessed adipose tissue fatty acid profiles by rumen fluid, which further suggests an important association between rumen and fatty acid deposition [9]. Microbial fermentation ensures normal physiological activity, while changes in microbial community structure may lead to differences in productivity and product quality. Notably, rumen community composition is extremely sensitive to diets or feeding patterns, thereby causing changes in lipid metabolism [10, 11]. However, studies on the regulation of fatty acid metabolism by rumen microbes in sheep are scarce and need to be further developed. The short-chain fatty acids produced in the rumen are absorbed through the rumen wall, while the rest of the lipid digestion products are absorbed in the posterior part of the small intestine and then enter the peripheral circulation to reach the liver and other tissues. As the ultimate distributor of nutrients for the growth of peripheral tissues and organs, the liver is also the central organ of lipid metabolism in the body [12]. The hepatic lipid machinery is complex and highly coordinated, and is extremely susceptible to the effects of diet, environment and other factors [13, 14].
Based on the sensitivity of microbial community composition and liver biology to diets or feeding methods, it is valuable to investigate the influence of rumen microorganisms and liver metabolism by changing feeding patterns to regulate the FAs metabolism of the organism. To the best of our knowledge, there are few researches on variations in rumen and liver metabolism between grazing and indoor feeding lambs. The relationship between rumen microorganisms and FAs metabolism in sheep is also lacking. Thus, this study investigated the changes in specific microorganisms in the rumen ecosystem and liver lipid metabolism under grazing and indoor feeding practices and their potential effects on fatty acid deposition in lamb from a multi-omics perspective.
## Grassland preparation
The experiment was conducted in the desert and semi-desert steppes of Dashuikeng, Yanchi County, Ningxia, China (106°58′E, 37°26′N; elevation 1400 m). The area's average annual temperature was 8.3 °C, and the average yearly precipitation was 282.3 mm, with most of it falling between June and September. The experimental pasture was about 1900 m long from east to west and 250 m wide from north to south, and divided into eight grazing plots with the same area. Grazing was carried out in one plot each day and rotated in the eight plots. The vegetation composition of the eight plots was basically the same.
## Experimental design and sampling
Twenty-six male Tan lambs (Ovis aries) from the same flock, approximately 120 days of age with similar body weight (25.06 ± 0.32 kg) were selected and randomly divided into one of the two feeding systems ($$n = 13$$ per group): the indoor feeding group (F; feed twice a day at 8:00 and 17:00) and the natural pasture grazing group (G; graze from 7:00 to 19:00). Lambs in the F group were fed pellets supplemented with hay separately (Table S1) in individual pens (size: 1.5 m × 3 m). The experiment lasted for 83 d, including a 10-d adaptation period. During the adaptation period, the daily feeding amount was adjusted based on the actual intake of the previous day to ensure a $15\%$ surplus. Clean water was available for the animals all the time. Serum samples were collected from the jugular vein through a 10-mL vacuum tube in the morning before feeding on the last day of the experiment. The serum samples were separated by centrifugation of blood and stored at −20 °C for the determination of serum variables. At the end of the feeding experiment, Tan lambs were fasted for 24 h and prevented from drinking for 12 h before slaughter. The liver and rumen samples were collected after slaughter and stored in liquid nitrogen for subsequent analysis.
## Analysis of muscle and herbage fatty acid composition
After intramuscular fat was extracted from longissimus dorsi (LD) muscle and herbage by using a chloroform/methanol mixture, FAs in intramuscular fat were quantified using an Agilent 6890 gas chromatograph coupled with a mass spectrometer (GC/MS, Agilent Technologies Inc., Santa Clara, CA, USA). More details can be found in Guo’s research [6]. We selected the 16 lambs that underwent rumen and liver metabolism measurements, and then obtained the FAs content in their LD muscle, which are as follows: C18:2n6 (2.517 ± 0.147), C18:3n3 (0.217 ± 0.022), C20:3n6 (0.081 ± 0.005), C20:4n6 (1.054 ± 0.069), C20:5n3 (0.078 ± 0.009), C22:6n3 (0.039 ± 0.005), total FA (29.818 ± 1.654), n-3 polyunsaturated fatty acids (PUFAs) (0.334 ± 0.035), n-6 PUFAs (3.652 ± 0.208), n-6/n-3 PUFAs (12.889 ± 1.326). FA content was expressed as an mg/100 g of fresh meat. Based on the fatty acid content in the herbage and the estimated forage intake, we calculated the daily fatty acid intake of grazing sheep as follows: C12:0 (50.14 mg), C14:0 (99.48 mg), C16:0 (1589.48 mg), C16:1 (51.64 mg), C17:0 (33.36 mg), C18:0 (277.71 mg), C18:1n9c (1160.59 mg), C18:2n6 (4331.75 mg), C18:3n3 (1248.74 mg), C20:0 (196.46 mg), C20:1 (28.61 mg), C21:0 (98.35 mg), C20:2 (28.57 mg), C22:0 (292.76 mg), C22:1n9 (26.62 mg), C23:0 (119.74 mg), C24:0 (212.77 mg), total FAs (9846.78 mg).
## Rumen fermentation characteristics
After opening the sheep's abdominal cavity, the internal organs were immediately dissected and the rumen was separated. Rumen fluid samples were collected by straining the ruminal content through a four-layer gauze. The pH value of rumen fluid was immediately measured using an electric pH meter (PHS-3C, Shanghai Leijun Experimental Instrument Co., Ltd., Shanghai, China). Then rumen fluid samples were stored in liquid nitrogen for subsequent analysis. The content of volatile fatty acid (VFA) in rumen fluid was determined by using the Trace 1300 gas chromatograph model (Thermo Fisher Scientific, Waltham, MA, USA). The content of ammonia nitrogen was determined by phenol-sodium hypochlorite colorimetry [15].
## Serum and liver biochemical parameters
Serum and liver tissue samples were stored at −20 ℃. We added 1 g of liver into 9 mL normal saline, and then ground into $10\%$ liver tissue homogenate. The total antioxidant capacity (T-AOC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), blood urea nitrogen (BUN), triglyceride, cholesterol, nonestesterified fatty acid (NEFA), fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC) in the serum and liver were measured using the corresponding colorimetric assay kit (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).
In the biochemical parameters of serum (Fig. 2a–c), the contents of LDL and BUN were higher in the F group ($P \leq 0.05$), whereas T-AOC was lower in the F group ($P \leq 0.05$). In the liver (Fig. 2d–e), the grazing pattern significantly reduced the contents of triglyceride, cholesterol, FAS, and ACC ($P \leq 0.05$).Fig. 2Comparisons of the biochemical parameters in the serum (a–c) and liver (d–e) between the F and G groups. * $P \leq 0.05$; **$P \leq 0.01.$ “ F” means indoor feeding group; “G” means grazing group; the bar in each column means standard error. T-AOC, total antioxidant capacity. LDL, low-density lipoprotein. HDL, high-density lipoprotein. BUN, blood urea nitrogen. NEFA, nonestesterified fatty acid. FAS, fatty acid synthase. ACC, acetyl-CoA carboxylase
## Ruminal 16S rRNA sequencing
Genomic DNA was extracted from rumen content using HiPure Stool DNA Kits (Magen, Guangzhou, China). The V3 + V4 region of 16S rRNA was amplified with specific primers with barcode: 341F: 5'-CCTACGGGNGGCWGCAG-3' and 806R: 5'-GGACTACHVGGGTATCTAAT-3'. The PCR products after amplification were purified using AMPure XP Beads. Then, ABI StepOnePlusTM Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) was used for quantification. Machine pooling and sequencing were performed according to the PE250 mode of Novaseq 6000. According to the standard protocol, purified amplicons were collected on the Illumina platform at equal mole-concentration and sequenced in pairs. The raw reads obtained by sequencing were filtered and corrected by DADA2, and the non-redundant reads and their corresponding abundance information were output. Then the reads were spliced into tags, and the chimeric tags were removed to obtain the Tag sequence and abundance information for subsequent analysis, namely ASV sequence and ASV abundance information. The representative ASV sequences were classified into organisms by a naive Bayesian model using RDP classifier (version 2.2) based on the SILVA database (version 132), with a confidence threshold value of 0.8 [16]. Based on the ASV sequence and abundance data, species annotation, species composition analysis and community function prediction were carried out, and we compared the differences between the two groups. Good’s coverage, Chao1, Simpson, and other alpha indexes were calculated in QIIME (version 1.9.1) and statistical analysis of Anosim (analysis of similarities) test and Welch’s t-test were calculated in R project Vegan package (version 2.5.3).
The raw reads of 16S rRNA sequence were deposited into the NCBI Sequence Read Archive (SRA) database (project number, PRJNA859697, accession number, SRP386848).
## Ruminal metagenomics analysis and data processing
Genomic DNA was extracted using HiPure Bacterial DNA Kits (Magen, Guangzhou, China) according to the manufacturer’s instructions and subsequently DNA quality was tested. Qualified genomic DNA was firstly fragmented by sonication to a size of 350 bp, and then end-repaired, A-tailed, and adaptor ligated using NEBNext® ULtra™ DNA Library Prep Kit for Illumina (New England BioLabs, Ipswich, MA, USA) according to the preparation protocol. DNA fragments with a length of 300–400 bp were enriched by PCR. At last, PCR products were purified using the AMPure XP system (Beckman Coulter, Brea, CA, USA) and libraries were analyzed for size distribution by 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) and quantified using real-time PCR. Genome sequencing was performed on the Illumina Novaseq 6000 sequencer using pair-end technology (PE 150). The raw data from the Illumina platform was filtered using FASTP (version 0.18.0) according to the following criteria: 1) removal of reads with ≥ $10\%$ unidentified nucleotides (N); 2) removal of reads with ≥ $50\%$ bases having phred quality scores ≤ 20; and 3) removal of reads aligned to barcode adapters [17]. After filtering, the resulting clean reads were used for genome assembly. Clean reads of each sample were assembled individually using MEGAHIT (version 1.1.2) to generate sample-derived assembly. We used MetaGeneMark (version 3.38) to predict genes based on the final assembly contigs. All predicted genes of length > 300 bp were merged according to $95\%$ identity and $90\%$ coverage of reads using CD-HIT (version 4.6) to reduce the number of redundant genes in downstream assembly steps. Using Bowtie (version 2.2.5) to count reads numbers, the reads were realigned to the predicted genes [18]. The final gene catalog was obtained from non-redundant genes with gene reads count greater than 2.
Several complementary methods were used to annotate the assembled sequences. The unigenes were annotated using DIAMOND (version 0.9.24) by aligning with the deposited ones in the database of Kyoto Encyclopedia of Genes and Genomes (KEGG). Additional annotation was carried out based on the Carbohydrate-Active enZYmes (CAZy). Statistical analysis of Welch’s t-test was calculated using R project Vegan package. Biomarker features in each group were screened by LEfSe software (version 1.0).
The raw reads of rumen metagenome sequences were deposited into the NCBI SRA database (project number, PRJNA860332, and accession number, SRP387213).
## Metabolome analysis in rumen fluid and liver tissue
The rumen fluid and liver samples were lyophilized, dissolved in methanol solution (−20 °C), and vortexed for 1 min. Centrifuged at 12,000 r/min for 10 min at 4 °C, and 450 μL of supernatant was taken for vacuum concentration. The samples were dissolved in 150 μL 2-chlorobenzalanine (4 μL/L), and the supernatant was filtered through a 0.22 µm membrane to obtain the prepared samples for Liquid Chromatography-Mass Spectrometry (LC–MS). Chromatographic separation was accomplished in a Thermo Ultimate 3000 system equipped with an Acquity UPLC® HSS T3 (150 mm × 2.1 mm, 1.8 µm, Waters, Milford, MA, USA) column maintained at 40 ℃. The temperature of the autosampler was 8 ℃. Gradient elution of analytes was carried out with $0.1\%$ formic acid in water and $0.1\%$ formic acid in acetonitrile (positive model) or 5 mmol/L ammonium formate in water and acetonitrile (negative model) at a flow rate of 0.25 mL/min. Injection of 2 μL of each sample was done for gradient elution after equilibration. Electrospray ionization positive-ion and negative-ion modes were used for detection. The experiments were executed on the Thermo Q Exactive mass spectrometer with a spray voltage of 3.5 kV (positive model) and 2.5 kV (negative model) with the 325 ℃ of capillary temperature. Sheath gas and auxiliary gas were set at 30 and 10 arbitrary units, respectively. The analyzer scanned over a mass range of m/z 81–1000 for full scan at a mass resolution of 70,000. Proteowizard (version 3.0.8789) was used to transform the raw data files into mzXML format. Peak identification, peak filtering, and peak alignment for each metabolite were performed using the R (version 3.3.2) package XCMS [19]. The following were the major parameters: bw = 5, quality deviation = 15, peakwidth = c [5, 30], mzwid = 0.01, mzdiff = 0.01, method = "centWave". For further examination, the mass-to-charge ratio (m/z), retention duration and intensity, and positive and negative precursor molecules were employed. Batch normalization was used to convert peak intensities to overall spectral intensity. The precise molecular formula (molecular formula error < 20) was used to identify metabolites. To validate metabolite annotations, peaks were matched using Metlin (http://metlin.scripps.edu) and MoNA (https://mona.fiehnlab.ucdavis.edu).
To extract the most useful information, the collected multidimensional data were reduced and classified, including unsupervised principal component analysis (PCA) and discriminant analysis of squares (PLS-DA) with minimal supervision. The first principal component of the variable importance in the projection (VIP) was obtained from PLS-DA to refine this analysis. Metabolites with a VIP value exceeding 1 were further applied to t-test at the univariate level to measure the significance of metabolite in two groups, and the P value less than 0.05 was deemed as statistically significant. Receiver operating characteristic (ROC) curve analysis by R pROC package to evaluate the predictive power of each of the discriminant metabolites. The area under the curve (AUC) was computed via numerical integration of the ROC curves. The metabolite signature that has the largest AUC was identified as having the strongest predictive power for discriminating the two groups. The fold-change value of each metabolite was calculated by comparing the mean value between G and F. The differential metabolites (DFMs) were further identified and validated by KEGG. The KEGG database was applied to the enrichment analysis of the KEGG metabolic pathway based on the DFMs. The calculated P-value was gone through false discovery rate (FDR) correction, taking FDR ≤ 0.05 as a threshold. Pathways that satisfied this condition were defined as significantly enriched in DFMs.
## Transcriptome sequencing and quantitative real-time PCR validation of liver
Total RNA was extracted according to the manufacturer’s protocol using a Trizol reagent kit (Invitrogen, Carlsbad, CA, USA). RNase-free agarose gel electrophoresis was used to verify RNA quality using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Following total RNA extraction, eukaryotic mRNA was isolated using Oligo (dT) beads, whereas prokaryotic mRNA was enriched using the Ribo-ZeroTM Magnetic Kit (Epicentre, Madison, WI, USA) to remove rRNA. The enriched mRNA was then fragmented into small fragments with fragmentation buffer before being reverse transcribed into cDNA with random primers. DNA polymerase I, RNase H, dNTP, and buffer were used to make second-strand cDNA. The cDNA fragments were then purified using a QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), end repaired, poly (A) added, and ligated to the Illumina sequencing platform. The ligation products were sized by using agarose gel electrophoresis, then PCR amplified and sequenced on an Illumina HiSeq2500.
To obtain high-quality clean reads, raw reads obtained from the sequencing machines were further filtered by fastp (version 0.18.0). Reads were mapped to the ribosome RNA (rRNA) database by using the short reads alignment tool Bowtie2 (version 2.2.8) to eliminate the rRNA mapped reads [18]. The remaining clean reads were further used in assembly and gene abundance calculation. An index of the reference genome was built, and paired-end clean reads were mapped to the reference genome using HISAT2. 2.4. For each transcriptional region, FPKM values (fragment per kilobase of transcript per million mapped reads) were calculated using StringTie (version 1.3.1) software to quantify expression abundance and variation [20].
RNA differential expression analysis was performed by DESeq2 software between two groups [21]. The transcripts with the parameter of FDR below 0.05 and absolute fold change ≥ 2 were considered as differentially expressed transcripts. Differential expression genes (DEGs) in two groups were functionally annotated by gene ontology (GO) enrichment analysis. Physiological metabolism events and signal pathways of the DEGs were assessed using KOBAS software to test the statistical enrichments of the DEGs in KEGG pathways. The calculated P-value was gone through FDR correction, taking FDR ≤ 0.05 as a threshold. Pathways of GO and KEGG analysis meeting this condition were defined as significantly enriched pathways in DEGs. After selecting the eleven genes that were enriched in the lipid-related pathway, we conducted quantitative real-time PCR (qPCR) to validate the expression.
The raw reads of transcriptome sequences were deposited into the NCBI SRA database (project number, PRJNA859628 and accession number, SRP386837).
## Statistical analyses
The rumen fermentation characteristics, serum and liver biochemical parameters were compared using t-test by IBM SPSS Statistics for Windows (version 22.0, IBM Corp, Armonk, NY, USA). Statistical significance was defined at $P \leq 0.05.$ Correlation analyses were conducted by Pearson correlation analysis. Statistical significance was defined at $P \leq 0.05.$
## Ruminal fermentation parameters
Compared with the F group, grazing enhanced the pH of rumen fluid ($P \leq 0.05$, Fig. 1a). The ammonia-N concentration was greater in the stall-feeding lambs than in the G groups ($P \leq 0.01$, Fig. 1b). As for the fermentation indicators (Fig. 1c–e), the total VFA, propionate, valerate, isovalerate, and valerate proportion were increased in stall feeding sheep, while acetate proportion was decreased ($P \leq 0.05$).Fig. 1Effects of feeding patterns on the rumen fermentation parameters: including rumen pH (a), ammonia-N (b) and the concentration of total VFA (c). Comparisons of the concentrations (d) and proportion (e) of VFA in the rumen between the F and G groups ($$n = 6$$ per group). * $P \leq 0.05$; **$P \leq 0.01.$ “ F” means indoor feeding group; “G” means grazing group; the bar in each column means standard error. VFA, volatile fatty acid
## Ruminal microbial community characteristics
There was an average of 103,855 ± 1195 clean reads (mean ± standard error of the mean [SEM]) per sample via 16S rRNA sequencing. The Good’s coverage of all samples was greater than 0.99, indicating that the sequencing data of 16S rRNA was sufficient (Table S2). As shown in Fig. 3, the Shannon, Simpson, Ace, Chao1 and Sob indexes of bacterial richness and diversity were significantly higher in G group than F group ($P \leq 0.01$). The Anosim based on Bray–Curtis distances showed significant differences between the two groups (Fig. 3, $P \leq 0.01$). At the phylum level (Fig. 4a), most sequences were assigned to Bacteroidetes ($53.90\%$ ± $3.25\%$) and Firmicutes ($36.09\%$ ± $2.76\%$). At the genus level (Fig. 4b), the most predominant genus was Prevotella_1 ($17.68\%$ ± $2.24\%$) in the rumen. Based on the bacterial diversity and Bray–Curtis metric, eight samples (4 lambs per group) were selected and used for shotgun metagenome sequencing. Metagenome sequencing generated 69.17 ± 0.72 million raw reads. After quality control and removing host genes, 68.93 ± 0.72 million clean reads were retained. After assembly, a total of 1,361,185 and 1,576,316 contigs were generated in F and G groups, respectively. We then performed gene prediction and clustering for contigs larger than 500 bp and obtained 432,423.75 ± 29,037.86 non-redundant genes. Through the sequencing, there were four specific phyla in the G group, which were Euryarchaeota, Nanoarchaeota, Thaumarchaeota and Abditibacteriota (Fig. S1a). Based on the Welch’s t-test, with P-value less than 0.05 as the threshold, a total of 35 differential phyla were screened, all of which showed higher abundance in the F group, including Tenericutes and Candidatus_Saccharibacteria ($P \leq 0.05$, Fig. 4c). Linear discriminant analysis effect size (LEfSe) was used to screen the main specific microorganisms between the two groups, and we found that the abundance of Butyrivibrio_sp_AC2005 and Clostridium_sp_CAG_1024 in the G group was higher than that in the F group. However, the abundance of Succiniclasticum_ruminis, Acidaminococcales, Acidaminacoccaceae, Succiniclasticum, Coprobacillus and Candidatus_Saccharimonas showed opposite changes (LDA > 2.8, Fig. 4d).Fig. 3Analysis of rumen microbial diversity of sheep in the F and G groups by 16S rRNA sequencing. Alpha diversity analysis (a–e). Anosim (analysis of similarities) (f) based on Bray–Curtis distances between the F ($$n = 7$$) and G ($$n = 8$$) group. * $P \leq 0.05$, **$P \leq 0.01.$ “ F” means indoor feeding group; “G” means grazing groupFig. 4Relative abundance of bacterial community proportions at the phylum (a) and genus (b) levels are compared between the two groups based on the 16S rRNA data (as a percentage of total sequences). The most ten abundant differential phyla screened by metagenomic sequencing based on the Welch's t-test ($$n = 4$$ per group) (c). The significantly differential microorganisms based on the linear discriminant analysis effect size (LEfSe) cladogram in metagenomic sequencing (d), and the differences are represented by the color of the group. “ F” means indoor feeding group; “G” means grazing group
## Functions of the rumen microbiome
The Tax4Fun- and PICRUSt2-based functional predictions revealed two important functions of the rumen microbiota (Fig. 5a–b). These functions were “carbohydrate metabolism” and “amino acid metabolism”. Based on Tax4Fun predictions of microbial functional differences, three lipid metabolism pathways were screened, including primary bile acid synthesis, secondary bile acid synthesis, and etheric lipid metabolism, all of which were significantly higher in the grazing group than stall-feeding group (Fig. 5c). Through metagenomic sequencing, among the unique genes derived from the rumen microflora, $71.92\%$ genes were classified into KEGG pathways, and $11.96\%$ genes were classified into CAZymes. Based on the KEGG database for functional annotation of metagenome data, both groups shared 118 pathways. According to Welch's t-test ($P \leq 0.05$), two pathways, ascorbate and aldarate metabolism and penicillin and cephalosporin biosynthesis, were significantly different between the two groups (Fig. S1b). CAZy function can be used to explore the contribution of microorganisms to carbohydrate metabolism, and we found the highest percentage of two major classes of glycoside hydrolases (GH) and glycosyltransferases (GTs) at level A. At level B, based on Welch's t-test, the following differential enzyme families: GH45, GT14, GT20, GT25 and GT26 were found to be higher in the stall-feeding group than in the G group (Fig. 5d). Also, the reporter score analysis showed that three pathways, namely beta-alanine metabolism, alpha-linolenic acid metabolism, and biosynthesis of unsaturated fatty acid, were significantly different between the two groups (Fig. S1c). To explore the potential microbial functions, Pearson correlations were constructed between the microorganisms and the VFA in the rumen. As shown in Table S3, the abundance of Succiniclasticum and Acidaminococcales was significantly and positively correlated with propionate and total VFA concentrations. Also, the abundance of Candidatus-Saccharimonas showed a positive correlation with the concentration of valerate and total VFA.Fig. 5Functional prediction of the rumen microbiota based on PICRUSt2 (a) and Tax4Fun (b) is performed on 16S rDNA data. Prediction of microbial functional differences based on Tax4Fun by the Welch's t-test in 16S rDNA sequencing (c). Comparisons of the abundance of CAZymes genes of rumen microbiomes in the F and G groups by the Welch's t-test in metagenomic sequencing (d). “ F” means indoor feeding group; “G” means grazing group
## Rumen and liver metabolome
In the rumen metabolome, 20,058 positive ion peaks and 13,031 negative ion peaks were detected by positive ion mode and negative ion mode detection, respectively. Good separation of the rumen metabolites among the two groups was achieved in the PLS-DA score plots of the negative ion mode (Fig. S2). By t-test and VIP filtering of relative concentrations of rumen metabolites, 31 DFMs were identified between the two groups in the positive ionization mode; 15 of them were up-regulated and 16 were down-regulated; 52 differential peaks were identified between the two groups in the negative ionization mode, 27 of them were up-regulated and 25 were down-regulated. After finding the metabolites, the pathway enrichment analysis by KEGG was performed for the DFMs. As shown in Fig. S3a, the top 20 enriched pathways related to lipid metabolism included biosynthesis of unsaturated fatty acids, propanoate metabolism and fatty acid biosynthesis, among which biosynthesis of unsaturated fatty acids was the significantly enriched pathway. Among the DFMs enriched in this pathway, UFAs including icosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and oleic acid were shown to be upregulated in the G group. And the SFAs decanoic acid showed down-regulation in the fatty acid biosynthesis pathway. By random forest analysis, the top 15 items screened out by Mean Decrease Accuracy could be used as high contributing DFMs with annotated names including 3,4-dihydroxymandelic acid, 2-ketobutyric acid and 12-hydroxydodecanoic acid (Fig. 6a). And we noted that 2-ketobutyric acid was enriched in the propionate metabolism pathway. Fig. 6The top 15 high-contribution differential metabolites in the rumen are screened out by random forest analysis in the positive (a) and negative (b) ion mode. The top 10 high-contribution differential metabolites in the liver are screened out by AUC area calculation in the positive (c) and negative (d) ion mode. The annotated metabolites are named in the figure ($$n = 8$$ per group) For liver metabolism, 7125 positive ion peaks and 4629 negative ion peaks were detected by positive and negative ion mode detection, respectively. Good separation of the rumen metabolites among the two groups was achieved in the PLS-DA score plots of the positive and negative ion modes (Fig. S2). By t-test and VIP filtering of relative concentrations of rumen metabolites, 430 DFMs were identified between the two groups in the positive ionization mode; 225 of them were up-regulated and 205 were down-regulated; 199 differential peaks were identified between the two groups in the negative ionization mode, 99 of them were up-regulated and 100 were down-regulated. The KEGG pathway enrichment analysis shown in Fig. S3b revealed the top 20 pathways that were affected by each of the two different feed regimes. Among them, the pathways related to lipid metabolism include primary bile acid biosynthesis, glycerophospholipid metabolism, and sphingolipid metabolism. Moreover, we observed that icosatrienoic acid (ETA), which was rich in unsaturated fatty acids metabolism pathway, was upregulated in the G group. Subsequently, we plotted ROC curves and calculated AUC areas. The top 10 AUC areas were screened for annotated DFMs, including fomepizole and 3-hydroxypropanoate, with 3-hydroxypropanoate being down-regulated in the G group and also enriched in the propionate metabolism pathway (Fig. 6b). Finally, citric acid enriched in the citric acid metabolic pathway was also found to be downregulated in the grazing lambs.
## Liver transcriptome analysis
To investigate the differences in the hepatic gene’s transcriptional level between the two groups, we performed transcriptome sequencing on total RNA samples from 16 lambs (8 lambs per group). In total, 60.33 ± 2.35 million clean sequence reads were obtained from the liver transcriptome. Among the encoded genes, 245 DEGs were identified from the comparison of the two groups. Among these DEGs, there were 102 upregulated genes and 143 downregulated genes. DEGs in each pair of the two groups of different feeding strategies were functionally annotated by GO analysis. Of the 192 significantly changed GO terms (FDR < 0.05), lipid metabolism that we mainly pay attention to belongs to biological processes, with 54 items, including lipid metabolic process, steroid metabolic process, lipid biosynthetic process, steroid biosynthetic process, cellular lipid metabolic process, lipid catabolic process. As shown in Fig. 7, multiple pathways related to lipid metabolism were identified in the top 20 pathways enriched by KEGG, including steroid hormone biosynthesis, PPAR signaling pathway, steroid biosynthesis, arachidonic acid metabolism, fatty acid metabolism, cholesterol metabolism, fatty acid degradation, biosynthesis of unsaturated fatty acids, glycolysis/gluconeogenesis and propanoate metabolism. We found that AKR1C1, SCD, FADS1, FADS2, CYP7A1, CYP4A6, ACADM, ALDH6A1, and ACSS2 were significantly upregulated in the G group; inversely, grazing significantly downregulated CYP2C23 and PLB1. The expression of these 11 genes was validated using qPCR, and the expression trends remained consistent (Fig. 7c).Fig. 7The top 20 enriched pathways of differential expression genes (DEGs) between the F and G groups lambs’ liver by KEGG analysis (a). The ordinate is the number of DEGs enriched into the pathway; *Q-value < 0.05; **Q-value < 0.01. Expression of differential genes involved in lipid metabolism in the F and G groups (b). Heatmap colors indicate FPKM values. The arrow represents the gene enrichment pathway, in which the pink arrow represents the down-regulated genes and the blue arrow represents the up-regulated genes. The verification of candidate genes expression in RNA-seq ($$n = 8$$ per group) by quantitative real-time PCR test ($$n = 4$$ per group) (c). “ F” means indoor feeding group; “G” means grazing group
## Correlation of genes and metabolites in the liver
On the basis of our biological processes of interest, important metabolites enriched to the unsaturated fatty acid metabolic pathway and the propionate metabolic pathway were subjected to Pearson correlation analysis with differential genes related to lipid metabolism (Fig. 8a). In detail, ETA was negatively correlated with PLB1. DHA was found to have a negative correlation with PLB1 and CYP2C23, and a positive correlation with CYP4A6, FADS1, ACADM, and ALDH6A1. The 3-hydroxypropanoate was negatively correlated with DHA, CYP4A6, FADS1, FADS2, ALDH6A1, and positively correlated with CYP2C23.Fig. 8Pearson correlation analysis of lipid-related metabolites and genes in liver (a). Pearson correlation analysis of lipid-related metabolites in muscle, rumen and liver (b) ($$n = 8$$ per group). * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ ETA, icosatrienoic acid. DHA, docosahexaenoic acid. EPA, icosapentaenoic acid
## Correlations of lipid metabolism in the rumen, liver and muscle
A Pearson correlation analysis was performed to analyze the effect of rumen and liver lipid metabolism on muscle fatty acid deposition (Fig. 8b). We found that EPA and n-3 PUFAs in muscle were significantly positively correlated with EPA, DHA, oleic acid in the rumen and DHA in the liver, and conversely significantly negatively correlated with rumen decanoic acid and hepatic 3-hydroxypropanoate. And there was a significant positive correlation between DHA in muscle and EPA and oleic acid in rumen. In the communication between rumen and liver, EPA in the rumen was positively correlated with ETA in the liver.
## Discussion
For sheep, the rumen and liver are two important organs involved in metabolism and production. Rumen, as a unique and vital digestive organ of ruminants, converts indigestible forage into nutrients, the main energy source of host animals, through its symbiotic microbiota. As the core site of lipid metabolism, the function of liver determines the decomposition, synthesis and deposition of lipids in ruminants. Currently, there is limited knowledge of the difference of rumen microbes and liver metabolism under different feeding patterns. By integrating the rumen metagenome, rumen and liver metabolome and liver transcriptome, we investigated the contribution of rumen microbiome-dependent and host hepatic metabolome-dependent mechanisms to lipid metabolism of the body.
The feeding pattern is a complex system containing various factors, including the level of fiber [22], type of fiber [23], rumen pH [24], etc. Previous researches have determined that various fiber quantities, sources, and pH could change the molar ratio of VFA in the rumen, which then affect the whole FAs metabolism after entering the circulation via the portal vein. Although understanding the variability of these factors is important for a deeper investigation of FAs metabolism, resolving these individual factors separately is difficult to achieve in practical generation. Taken together, it is relevant to analyze the effects on FAs brought about by feeding patterns. The ruminal VFA concentration is the crucial factor that can reflect the impact of different feeding treatments on ruminal fermentation [25]. Similar to a previous study, rumen fermentation characteristics were significantly affected due to the differences in feeding pattern, which represented the increased carbohydrate fermentation in the rumen of indoor feeding lambs [26]. Rumen fermentation is affected by the combination of pH and dietary substrate, and then regulates lipid metabolism. A previous study found that rumen propionate concentration increased with decreasing pH [27], which was consistent with this experiment. In current work, the significantly increased propionate, isovalerate, and valerate in the rumen represented the increased utilization of energy, which indicated more nutrients could be available for growth due to the enhanced rumen energy intake when propionate could be absorbed and converted to glucose, amino acids and lipids [28]. Concisely, these evidences indicated that the change in feeding regime would accordingly alter the rumen fermentation, thereby affecting nutrient absorption and metabolism.
Rumen microbial community structure could be affected by environmental, host, physiological state, and even behavioral characteristics that have evolved together with the varied feeding strategies in ruminants [29]. Recently, a study focused on the gut microbiome of herbivores, suggesting that the richness of the microbiota was increased in animals from the wild environment than in captive animals [30]. In this present study, the richness and evenness of microorganisms were significantly higher in G group than in F group, which was consistent with the results of Xue et al. [ 26]. A previous paper has shown that the high-grain diet hurts the microbial diversity index of rumen microorganisms [31], which may partly explain this result. In addition, we obtained four unique phyla in G group through metagenomic analysis, which further confirmed that grazing model was more conducive to the diversity of rumen microecology.
The functional differences of microorganisms in bacterial communities were predicted based on Tax4Fun using 16S rRNA sequencing data, and the 3 pathways screened were related to lipid metabolism. These preliminary results indicated that there were differences in lipid metabolism between the two groups of microorganisms. The key differential microorganisms Butyrivibrio_sp_AC2005 and Acidaminococcales, both screened by LEfSe analysis, were found to be closely associated with lipid metabolism, providing further confirmation of the above speculation [32, 33]. Interestingly, Succiniclasticum, as a core component of the differential rumen microbiome, has the ability to convert succinate to propionate [34, 35], hence its enhancement in the F group could be a logical explanation for the augment in the propionate in the rumen liquid of the stall-feeding lambs. Besides, the significant positive correlation of Succiniclasticum abundance with ruminal propionate concentration further corroborated our point. In addition, although there is no direct evidence, some studies have found that the synergistic reduction of Coprobacillus and rumen succinate improved dyslipidemia caused by a high-fat diet, and Candidatus_Saccharimonas was positively correlated with the proportion of propionate in rumen [35, 36]. Both suggested that Coprobacillus and Candidatus_Saccharimonas were directly or indirectly involved in the rumen production of succinate and propionate, respectively, and further confirmed the vital function of propionate in lipid metabolism between the two groups. Meanwhile, it was obvious that the dietary composition of lambs under the two feeding modes was significantly different, especially in terms of carbohydrates, due to the concentrate supplement in the F group. So, we used the CAZyme database for comparison, and found that the abundance of typical endoglucanase GH45 and four GTs family enzymes genes were higher in the F group. GHs cleave bonds by the insertion of a water molecule to hydrolyze complex carbohydrates, while GTs assemble complex carbohydrates from activated sugar donors [37, 38]. A recent study reported that the increased abundance of GTs gene in rumen of dairy cows promoted the production of volatile acids, which further promoted the production activities [39]. Similarly, compared to the G group, the stall-feeding lambs had higher abundances of genes encoding CAZymes involved in carbohydrate degradation (GH45) and synthesis (GTs), as well as higher concentration of major VFAs in the rumen, indicating that the rumen microbiomes of F group might be more efficient to generate VFAs, and therefore provide more energy for growth in host sheep.
After entering the rumen, dietary lipids are hydrolyzed to release FAs, which are metabolized by rumen microorganisms, including degradation, synthesis and hydrogenation. Nevertheless, rumen microbial degradation of FAs is less than $1\%$ of the total fatty acid [40]. Moreover, the synthesis of FAs by rumen microorganisms is also very small, because rumen microbes prefer to directly utilize dietary FAs rather than synthesize them themselves [41]. Therefore, due to the above characteristics of the metabolism of FAs by rumen microorganisms, the hydrogenation of rumen microorganisms is the main factor that ultimately affects FAs composition in ruminant products. The effects of different FAs on human health are diverse, and SFAs can increase the risk of cardiovascular disease. The FAs that have positive effects on human health are mainly UFAs, especially PUFAs, which play a role in regulating cell cycle, reducing body fat, and preventing cardiovascular diseases. However, the biohydrogenation of PUFAs in rumen will generate a large number of SFAs, which will reduce the deposition of PUFAs in ruminant products [42]. According to rumen metabolome analysis, EPA, DHA and oleic acid were down-regulated and decanoic acid was up-regulated in the F group. Moreover, Tenericutes, which significantly increased in abundance under stall-feeding condition, was found to have a role in biohydrogenation to convert PUFA to SFA [43]. Therefore, it could be speculated that indoor feeding to some extent enhanced the ruminal biohydrogenation by increasing Tenericutes, leading to the accumulation of SFA. It has been found that biohydrogenation of PUFAs in the rumen was lower in hay-fed animals than that in concentrate-fed animals, with a greater percentage of PUFAs bypassing the rumen in the former than that in concentrate-fed animals [44]. Based on the sensitivity of rumen microorganisms to diets, this phenomenon may be caused by the difference in diet components. For PUFAs, the balance of n-3 PUFAs (including EPA and DHA) and n-6 PUFAs is an important index used to evaluate the nutritional value of meat quality for humans [45]. Previous studies of our team have found that grazing model increased the content of n-3 PUFAs and n-6 PUFAs and reduced the ratio of n-6/n-3 in LD muscle, which is more beneficial to human health [6]. The substantial positive association between EPA and DHA in rumen and EPA and n-3 PUFAs content in muscle indicated that the rumen of grazing lambs produced more EPA and DHA and thus potentially up-regulated the n-3 PUFAs content in muscle, contributing to better lamb meat quality. Besides, the accumulation of n-3 PUFAs could inhibit lipid synthesis in liver and reduce cholesterol, triglycerides, and LDL in plasma [46]. These results suggested that the decrease of LDL in plasma and triglyceride, cholesterol, FAS and ACC in liver by grazing may be related to the up-regulation of n-3 PUFAs, leading to the decrease of lipid metabolic activity in the liver.
In ruminants, some UFAs are hydrogenated in the rumen and further metabolized in various tissues, including the liver, with important roles in lipid and lipoprotein metabolism. Therefore, manipulation of muscle fatty acid composition should take into account liver metabolism, especially in the biosynthesis of n-3 PUFAs [47]. Through liver metabolomics, we found that ETA enriched in the biosynthesis of unsaturated fatty acids pathway was up-regulated in the G group, which belongs to n-3 PUFAs. During the synthesis of long-chain PUFAs, ETA can be desaturated by Δ5-desaturase to produce EPA, which provides a precursor for more deposition of EPA in muscle [48]. In the liver, the mechanism of formation of 3-hydroxypropanoate involves the conversion of propionate to propionyl-CoA, which is reduced to acrylyl-CoA, followed by hydration of acrylyl-CoA to 3-hydroxypropanoate-CoA, and hydrolysis to 3-hydroxypropanoate [49]. Combined with the positive correlation between ruminal propionate and the biomarker 3-hydroxypropanoate in the liver in Table S4, it followed that the increased ruminal propionate was absorbed by the liver, resulting in changes in hepatic propionate metabolism in the F group. At the same time, the citric acid cycle would also be regulated by the metabolism of propionate in the liver. It has been reported that the increase of citric acid stimulated adipogenesis and gluconeogenesis by activating ACC, which was compatible with the results of increased ACC in liver enzyme activity in the F group [50]. Meanwhile, due to gluconeogenesis in liver, propionate can indirectly modulate adipose tissue lipogenesis through increased glucose availability [47]. Furthermore, the role of propionate in reducing the synthesis of long-chain PUFAs in the liver has also been previously found [51]. Therefore, the negative correlation between hepatic 3-hydroxypropanoate and n-3 PUFAs in the muscle suggested that the downregulation of muscle PUFAs in the F group may be related to the changes in propionate metabolism.
Based on the concerning biology processes, we focused on the expression profile of eleven lipid metabolic-related genes involved in the signal pathway of liver. Among them, AKR1C1, SCD, FADS1, FADS2, CYP7A1, CYP4A6, ACADM, ALDH6A1 and ACSS2 were significantly up-regulated in the G group; inversely, grazing significantly down-regulated CYP2C23 and PLB1 which were essential factors for the variations in liver lipid metabolism under different feeding patterns. Further correlation analysis revealed the major roles of FADS1, FADS2, CYP4A6, ACADM, ALDH6A1, PLB1, and CYP2C23 in the metabolism of UFAs. FADS1 and FADS2, encode enzymes involved in the conversion of α-linolenic acid to EPA, DPA, and DHA and linoleic acid to γ-linolenic acid and arachidonic acid, respectively [52]. A study in bovine mammary epithelial cells found a significant positive correlation between the contents of EPA and the expression of FADS1 [53]. Another previous study has reported that reduced activity of the desaturase enzymes mediated by FADS1 and FADS2 leads to a reduction of PUFAs in plasma [54]. Combined with the significant positive correlation between DHA and FADS1, it was speculated that PUFAs deposition in the G group was related to the up-regulation of FADS1 gene. It is well known that meat quality is strongly related to fatty acid composition, especially the proportion of PUFAs. Our previous results found that while indoor feeding increased daily gain, it enhanced the proportion of n-6/n-3 PUFAs, which negatively affected meat quality [6]. Similarly, it has been reported that the transcription of FADS2 could affect the endogenous transformation of long-chain PUFAs, reducing the bioavailability of n-3 PUFAs and promoting the accumulation of n-6 PUFAs [55]. Though the exact mechanism is not clear, it further confirms the important role of FADS2 gene in the conversion of long-chain PUFAs. CYP4A6 is a member of the cytochrome P450 IVA gene subfamily, which encodes several enzymes that catalyze the metabolic process of SFAs and UFAs, including arachidonic acid, and plays an essential role in the metabolism of FAs [56]. Medium-chain acyl-CoA dehydrogenase, encoded by the ACADM gene, catalyzes the first step of β-oxidation. Previous studies showed that ACADM gene knockdown remarkably enhanced lipid accumulation in vitro, implying that the decrease of triglyceride and cholesterol in liver of grazing group may be related to ACADM upregulation [57]. According to a study, ALDH6A1 was identified as a new adipose tissue marker associated with obese people through its involvement in propionate metabolism, further suggesting a major function for propionate in lipid metabolism [58]. A series of n-3 PUFAs such as EPA can act as an efficient substrate of CYP2C23 enzyme, so downregulation of the CYP2C23 gene may lead to the accumulation of n-3 PUFAs in the liver [59]. The significant negative correlation between CYP2C23 and DHA also supports this conjecture. PLB1 is a secreted enzyme with lysophospholipase hydrolase and lysophospholipase transacylase activities, which is required for the release of arachidonic acid from phospholipids [60]. EPA can be synthesized by both ETA and arachidonic acid pathways, respectively [48]. Owing to the negative relationship between PLB1 and ETA, it was hypothesized that the deletion of the arachidonic acid pathway caused by the down-regulation of PLB1 might be compensated by the up-regulation of ETA, which finally did not affect the synthesis of EPA in the liver.
Notably, correlation analysis also revealed that 3-hydroxypropanoate, the important DFMs in the propionate metabolism pathway were significantly correlated with CYP4A6, FADS1, FADS2, ALDH6A1 and CYP2C23. Previous reports on the influence of propionate metabolism on liver lipid metabolism, suggested that the above changes of lipid metabolic-related genes may be mediated by propionate metabolism [61, 62]. Combined with the result in Table S4 that ruminal propionate content had a significant positive correlation with 3-hydroxypropanoate in the liver, it further demonstrated that microbial-driven propionate production may mediate changes in hepatic propionate metabolism and subsequently participate in the regulation of multiple fatty acid metabolism-related signaling pathways. Moreover, based on the close correlation between UFAs in muscle and liver, it is implied that changes in hepatic lipid metabolism potentially may lead to differences in fatty acid deposition in muscle.
## Conclutions
In summary, our findings addressed the variations in lipid metabolism between grazing and stall-feeding lambs, from the rumen to the liver. By reducing the abundance of Succiniclasticum, the grazing pattern decreased the propionate content and changed the propionate metabolism of rumen. The rumen wall absorbed more propionate from stall-feeding sheep, which reached the liver and changed the propionate metabolism and citrate cycle in the liver. Meanwhile, 3-hydroxypropanoate, the key DFM enriched in the propionate metabolism pathway, was significantly correlated with lipid-related genes CYP4A6, FADS1, FADS2, ALDH6A1, and CYP2C23, suggesting that propionate metabolism may regulate hepatic lipid metabolism. In addition, the decreased abundance of Tenericutes in grazing sheep weakened the hydrogenation of UFAs, leading to the accumulation of EPA, DHA and oleic acid and the reduction of decanoic acid in rumen, which also became a potential reason for the up-regulation of UFAs in muscle. Overall, microbial-mediated metabolic changes in the rumen of grazing sheep were important in affecting ruminal and hepatic lipid metabolism, and may further contribute to the deposition of FAs in muscle, but the specific mechanisms in meat need to be more explored.
## Supplementary Information
Additional file 1: Table S1. Composition and nutrient level of the experimental diet. Table S2. Good's coverage of rumen microbial diversity of sheep under different feeding regimes. Table S3. Pearson correlation analysis between microorganisms and VFA in rumen. Table S4. Pearson correlation analysis between ruminal VFAs and lipid-related metabolites in liver. Fig. S1. Venn diagram illustrating the overlap of microbial phyla between the two groups in metagenomic sequencing (a). Comparisons of the pathway annotation based on the KEGG database of rumen microbiomes in the two groups by the Welch's t-test (b). Metabolic pathway enrichment score in metagenomic sequencing (c). Fig. S2. Score plot of discriminant analysis of squares (PLS-DA) model obtained in positive (a) and negative mode (b) of rumen metabolism. Score plot of PLS-DA model obtained in positive (c) and negative mode (d) of liver metabolism. Fig. S3. Functional enrichment analysis of differential metabolites in rumen (a) and liver (b).
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|
---
title: Deficits in mitochondrial TCA cycle and OXPHOS precede rod photoreceptor degeneration
during chronic HIF activation
authors:
- Vyara Todorova
- Mia Fee Stauffacher
- Luca Ravotto
- Sarah Nötzli
- Duygu Karademir
- Lynn J. A. Ebner
- Cornelia Imsand
- Luca Merolla
- Stefanie M. Hauck
- Marijana Samardzija
- Aiman S. Saab
- L. Felipe Barros
- Bruno Weber
- Christian Grimm
journal: Molecular Neurodegeneration
year: 2023
pmcid: PMC9990367
doi: 10.1186/s13024-023-00602-x
license: CC BY 4.0
---
# Deficits in mitochondrial TCA cycle and OXPHOS precede rod photoreceptor degeneration during chronic HIF activation
## Abstract
### Background
Major retinal degenerative diseases, including age-related macular degeneration, diabetic retinopathy and retinal detachment, are associated with a local decrease in oxygen availability causing the formation of hypoxic areas affecting the photoreceptor (PR) cells. Here, we addressed the underlying pathological mechanisms of PR degeneration by focusing on energy metabolism during chronic activation of hypoxia-inducible factors (HIFs) in rod PR.
### Methods
We used two-photon laser scanning microscopy (TPLSM) of genetically encoded biosensors delivered by adeno-associated viruses (AAV) to determine lactate and glucose dynamics in PR and inner retinal cells. Retinal layer-specific proteomics, in situ enzymatic assays and immunofluorescence studies were used to analyse mitochondrial metabolism in rod PRs during chronic HIF activation.
### Results
PRs exhibited remarkably higher glycolytic flux through the hexokinases than neurons of the inner retina. Chronic HIF activation in rods did not cause overt change in glucose dynamics but an increase in lactate production nonetheless. Furthermore, dysregulation of the oxidative phosphorylation pathway (OXPHOS) and tricarboxylic acid (TCA) cycle in rods with an activated hypoxic response decelerated cellular anabolism causing shortening of rod photoreceptor outer segments (OS) before onset of cell degeneration. Interestingly, rods with deficient OXPHOS but an intact TCA cycle did not exhibit these early signs of anabolic dysregulation and showed a slower course of degeneration.
### Conclusion
Together, these data indicate an exceeding high glycolytic flux in rods and highlight the importance of mitochondrial metabolism and especially of the TCA cycle for PR survival in conditions of increased HIF activity.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13024-023-00602-x.
## Background
The retina is an energetically highly active tissue and relies on a well-functioning blood supply for the delivery of sufficient oxygen and nutrients to meet its metabolic needs [1]. The photoreceptor (PR) cells, residing in the outer retina, are especially energy-demanding and have high oxygen and glucose consumption levels [2–5]. Oxygen reaching the outer retina is mainly used in the photoreceptor inner segments (IS), a specialized cell compartment tightly packed with mitochondria [6]. Here, ATP is produced providing the required energy to maintain ion gradients across the cell membrane by Na+/K+-ATPase and to sustain vision [7, 8]. Despite the high energy demand, isolated retina produces lactate in the presence of oxygen [9–12]. This incomplete oxidation of glucose under aerobic conditions, or aerobic glycolysis, and the associated high glucose consumption are metabolic adaptations characteristic of anabolic and proliferating cells facilitating the incorporation of nutrients into biomass [13]. Thus, retinal lactate production has been attributed to the highly anabolic PRs possibly supporting the diurnal renewal of the photoreceptor outer segments (OS) [12, 14–16]. A growing body of evidence suggests also metabolic coupling of PRs and the underlying retinal pigmented epithelium (RPE), in which the RPE uses lactate (produced by the PRs) as fuel, sparing glucose for the sake of PRs [16–18].
In the aging human eye, the RPE and the neighbouring choroid undergo a series of morphological changes, including accumulation of intracellular and extracellular deposits, thickening of the Bruch’s membrane, and reduction of choroidal blood flow [19–22]. These morphological alterations may hinder oxygen delivery to the RPE and the outer retina generating a tissue environment where oxygen demand may exceed oxygen availability (hypoxia). Thus, hypoxia is a factor in the development of retinal dysfunction and degeneration during aging [23, 24], as well as in other pathologies including diabetic retinopathy and retinal detachment [25, 26].
An evolutionarily conserved pathway mediated by the hypoxia-inducible transcription factors (HIFs) is crucial for the cellular adaptation to hypoxia [27]. HIFs have been associated with the development of age-related macular degeneration (AMD), providing potential therapeutic targets [28]. Central to the HIF pathway is the von Hippel-Lindau tumor suppressor protein (VHL), that forms an E3 ubiquitin ligase complex together with elongins B and C, cullin 2, and RING box protein 1 [29] and ubiquitinates hydroxylated HIF-alpha (HIFA) subunits leading to their proteasomal degradation in normoxia. Inactivation of VHL prevents HIFA degradation activating the heterodimeric transcription factors HIF1 and HIF2, and, thus, enables the activation of the cellular response to hypoxia even in normoxic conditions. We previously generated rod-specific Vhl knockout mice (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl) where chronic HIF1 (but not HIF2) activation led to a late-onset and age-dependent PR and RPE degeneration, mimicking aspects of retinal degenerative diseases associated with aging in humans [30, 31].
In this study, we addressed the relationship between energy metabolism and PR degeneration using chronic HIF activation as a model of age-related retinal degeneration. Using two-photon laser scanning microscopy (TPLSM) of genetically encoded biosensors, layer-specific proteomics and in situ enzymatic assays, we analyzed lactate and glucose dynamics at the cellular level, investigated oxidative phosphorylation pathway (OXPHOS) and the tricarboxylic acid (TCA) cycle, and addressed their contribution to PR survival.
## Chronic HIF activation in rods reduces outer segment length and increases lactate production
Age-related changes may impair oxygen flux from the choroid to the RPE and outer retina, generating a hypoxic environment in the retina. Hypoxia activates an intracellular molecular response that includes activation of HIF transcription factors and production of vascular endothelial growth factor (VEGF), the main driver of neovascularization in wet AMD [32]. However, chronic hypoxia not only drives neovascularization but also profoundly affects various cellular metabolic processes that may contribute to the development of dry AMD. *To* generate a mouse model that mimics the situation of the retina in the aged eye, we inactivated Vhl specifically in rods using the Vhlflox/flox;OpsinCre (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl) mice [30, 31] (Fig. 1a). Vhl inactivation led to stabilization and chronic activation of HIF transcription factors in rods resulting in a late onset and slowly progressing HIF1-dependent PR degeneration [30, 31] (Fig. 1b).Fig. 1Reduced OS length in rods with chronically active HIFs. a Deletion of Vhl leads to chronic activation of HIF transcriptional factors and serves as a model for the aged human retina, in which reduced O2 delivery may cause tissue hypoxia contributing to development of AMD. b Representative micrographs of the retinal morphology of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control (ctrl) mice at 2.5 and 6 months of age. Scale bar, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm. c,d Spidergrams (left) and bar graphs (right) of OS length (top), IS length (middle) and ONL thickness (bottom) in ctrl (black) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (orange) mice at 2.5 (c, $$n = 4$$) and 6 months (d, $$n = 3$$ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl, $$n = 4$$ ctrl) of age. N: number of mice (one eye per mouse in the analysis). All measurements from the spidergrams are included in the bar graphs. Data represent mean ± SD. Statistics: two-way ANOVA and Bonferroni’s multiple comparison test (spidergrams) or nested t-test (bar graphs). *: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.05. **: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.01. ***: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.001. ****: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.0001. e Chronic HIF activation may affect anabolism through altered glycolysis and mitochondrial metabolism leading to reduced OS biogenesis preceding PR degeneration. OS: outer segments. IS: inner segments. ONL: outer nuclear layer To identify mechanisms leading to the degeneration in this disease model, we focused on early alterations. Already at 2.5 months of age, a time point before PR degeneration started, rod OS were significantly shorter in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice while outer nuclear layer (ONL) thickness and IS length were similar to controls (Fig. 1c). OS shortening progressed with time and was accompanied by reduced IS length and ONL thickness at 6 months of age (Fig. 1d). We hypothesized that chronic HIF activation led to early anabolic deficits affecting OS biosynthesis preceding the onset of PR degeneration. Since anabolism is closely linked to energy metabolism, we focused on glycolysis and mitochondrial metabolism, including OXPHOS and the TCA cycle, to unravel the mechanistic basis of the anabolic deficit (Fig. 1e).
To investigate glycolytic metabolites, we delivered FRET biosensors to PR using subretinal injections of adeno-associated viruses (AAV) and imaged the sensors in acute retinal slices by TPLSM (Fig. 2a,b). Since PRs are light sensitive cells and their metabolism may differ in light and darkness [7], we first investigated whether TPLSM activates phototransduction by monitoring Ca2+ levels during imaging using rod-specific expression of GCaMP6s (Fig. S1a,b). GCaMP6s is a genetically encoded single fluorophore calcium sensor consisting of a circularly permuted green fluorescent protein (cpGFP), the calcium-binding protein calmodulin (CaM), and the CaM-interacting M13 peptide [33]. Ca2+ binding to the CaM-M13 complex induces a conformational change in the protein leading to an increase in the brightness of cpGFP that is detectable by microscopy (Fig S1b).Fig. 2Increased lactate production in rods with chronically active HIFs. a Expression of the lactate sensor Laconic in photoreceptors after subretinal AAV application. Scale bar, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm. b Preparation of acute retinal slices from flat mounted half retinas and imaging of Laconic by TPLSM. c Schematic representation of monocarboxylate transporter (MCT) trans-acceleration with high extracellular levels of oxamate causing export of lactate. d Laconic raw FRET signal (individual values and means ± SD) in PRs at baseline (BL) and at minimum (MIN) obtained after exposure to oxamate (orange; 6 retinal slices from 2 mice) or to aglycemic medium (blue; 11 retinal slices from 4 mice). e Laconic traces (mean ± SD) in PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (orange; 19 retinal slices from 10 mice) and ctrl mice (black; 21 retinal slices from 11 mice) during MCT trans-acceleration with oxamate. Traces were normalized to “zero” lactate (values at the end of the experiment). f Basal lactate levels (individual values and means ± SD) in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl PRs calculated from the laconic traces shown in (e). g Schematic representation of lactate transport inhibition by pharmacologically blocking MCT activity with AR-C155858. h Laconic traces (mean ± SD) in PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (orange; 24 retinal slices from 11 mice) and ctrl mice (black; 23 retinal slices from 8 mice) during MCT block with AR-C155858. Traces were normalized to baseline (values before MCT inhibition). i Intracellular lactate accumulation (amplitude, left) and production rate (slope, right) calculated from the laconic traces shown in (h). Shown are individual values and means ± SD. Statistics: Mann-Whitney nonparametric test. PS: photoreceptor segments. ONL: outer nuclear layer. OPL: outer plexiform layer. INL: inner nuclear layer TPLSM induced a drop in the fluorescence signal intensity in photoreceptor segments (PS) and synaptic terminals in the outer plexiform layer (OPL) of wild-type rods (Fig. S1c,d), indicating a decrease in intracellular Ca2+ concentrations during imaging. The stable GCaMP6s fluorescence signal in Gnat1a-/- mice with their light-insensitive rods [34] (Fig. S1e) verified that the drop observed in wild-type mice was due to the activation of the phototransduction cascade by the laser light and/or by the fluorescence emitted from the sensor. Considering that the emission spectra of all used biosensors overlap, our imaging data for lactate and glucose relate to light-exposed rods.
We used the FRET biosensor Laconic [35] to analyze lactate, the end product of glycolysis. Laconic is a fusion protein composed of the lactate binding bacterial transcription regulator LldR and the FRET pair mTFP and Venus. Binding of lactate decreases FRET efficiency leading to reduced Venus signal (Fig. 2b). To compare basal lactate levels in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice, we depleted intracellular lactate by using the trans-acceleration property of the monocarboxylate transporters (MCTs) upon application of the non-metabolized MCT substrate oxamate (Fig. 2c) [36]. Due to the symporter type carrier characteristics of the MCTs, occupancy of the transporters in trans by oxamate induces import of oxamate into and extrusion of lactate out of the cell. Successful depletion of intracellular lactate by oxamate was confirmed by assessing lactate levels in aglycemic conditions, which reduced the Laconic FRET signal to a similar level (Fig. 2d). When normalized to this nominal zero lactate, there was no significant difference between rod lactate levels in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice (Fig. 2e,f). To test whether chronic HIF activation affected lactate production rather than lactate levels, we inhibited MCT mediated lactate transport across the cellular membrane with AR-C155858 [37] (Fig. 2g). This resulted in an increase of intracellular lactate (Fig. 2h) demonstrating that rods are net lactate producers in the presence of glucose as the exclusive fuel. Higher amplitude and higher rate of lactate accumulation suggested that PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice are faster lactate producers than those of control mice (Fig. 2h,i).
Increased lactate production may be a consequence of increased glycolysis or decreased mitochondrial pyruvate consumption. We tested the first possibility by analysing intracellular glucose dynamics in retinal cells by TPLSM of the FRET glucose sensor FLIIP (Fig. 3a). FLIIP is composed of the glucose/galactose binding protein of *Escherichia coli* (MglB) and the FRET pair eCFP and Citrine and exhibits an increase in FRET efficiency upon glucose binding [38]. The sensor was delivered to PRs and to inner retinal cells by subretinal and intravitreal AAV injections, respectively (Fig. 3b). The rate of glycolysis at its entry point was estimated by pharmacological inhibition of glucose import using cytochalasin B, a competitive inhibitor of glucose transporters (Fig. 3c-e) [39–41]. Basal glucose levels were assessed by normalizing the FRET traces to nominal zero glucose using medium without glucose (aglycemia) at the end of the experiment (Fig. 3d,e). Surprisingly, baseline glucose levels and glucose consumption rate in PRs did not differ between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice (Fig. 3f,g), suggesting that chronic HIF activation did not accelerate glycolysis in PRs and that increased intracellular lactate levels may have resulted from reduced mitochondrial pyruvate utilization. The very sharp drop of the FRET signal after blocking glucose import indicated that PRs have a remarkably high glucose consumption rate that was significantly higher than that observed in cells of the inner nuclear layer (INL) and ganglion cell layer (GCL) (Fig. 3d,e,g). Similarly, basal glucose levels were higher in PRs than in cells of the inner retina (Fig. 3d-f). Although FRET sensors may be affected by the cellular environment [42], raw FRET signals from FLIIP in aglycemic conditions were comparable between PRs, and cells of the INL and GCL (Fig. 3h), suggesting a similar sensor behavior in the different retinal cell types. Fig. 3Glucose dynamics in photoreceptors with chronically active HIFs. a Preparation of acute retinal slices from flat mounted half retinas and imaging of the glucose sensor FLIIP by TPLSM. b Expression of FLIIP in PRs after subretinal AAV application (left) or in cells of the inner retina after intravitreal AAV application (right). Scale bar, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm. c Schematic representation of glucose transport inhibition by cytochalasin B allowing to monitor glycolytic rate through the hexokinases. d FLIIP traces (mean ± SD) in PRs from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (orange; 19 retinal slices from 5 mice) and ctrl mice (black, 16 retinal slices from 4 mice) during inhibition of glucose transport with cytochalasin B followed by aglycemia (zero glucose). Traces were normalized to zero glucose (values at the end of experiment). e FLIIP traces (mean ± SD) in cells of the INL (blue; 10 retinal flatmounts from 7 mice) and GCL (green; 16 retinal flatmounts from 7 mice) during inhibition of glucose transport with cytochalasin B followed by aglycemia (zero glucose). Traces were normalized to zero glucose (values at the end of experiment). f Basal glucose levels in PRs and cells of the INL and GCL in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice as calculated from the FLIIP traces shown in (d) and (e). Shown are individual values and means ± SD. g Glucose consumption rate in PRs and cells of the INL and GCL in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice as calculated from the FLIIP traces shown in (d) and (e). Shown are individual values and means ± SD. h Comparison of FLIIP raw FRET signals in PRs and cells of the INL and GCL in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (orange) and ctrl (black) mice at zero intracellular glucose. Statistics: Mann-Whitney nonparametric test for comparison of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice. Kruskal-Wallis nonparametric test and Dunnett’s multiple comparison test for comparison of PRs, GCL and INL cells. PS: photoreceptor segments. ONL: outer nuclear layer. OPL: outer plexiform layer. INL: inner nuclear layer. GCL: ganglion cell layer Together, our data showed that even though glucose dynamics were not altered, lactate production was enhanced in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice, suggesting that pyruvate is redirected to lactate upon chronic HIF activation. Since pyruvate is the main mitochondrial substrate and most of the mitochondria within PRs are localized in the IS, we established the retinal layer separation (ReLayS) method [43] and used proteomics (Fig. 4a) to investigate the protein landscapes in PS and soma (ONL) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice. Principal component analysis (PCA) of PS and ONL proteomes showed a strong separation between the two groups (Fig. 4b), indicating their markedly different protein compositions. The PS samples were strongly enriched in proteins belonging to the phototransduction cascade, the cilium and mitochondria, while many of the most abundant proteins in the ONL samples were nuclear proteins (Fig. 4c and Table S1) validating the separation procedure. Several proteins involved in glycolysis were significantly upregulated in the ONL (Fig. 4d and Table S2) and PS (Fig. 4e and Table S3) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. Of particular interest were the increased levels of the glucose transporter GLUT3 (encoded by Slc2a3), since GLUT3 is not present in PRs under physiological conditions [44, 45]. This finding was verified by immunofluorescence studies showing a strong but patchy GLUT3 signal in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl but not control mice (Fig. 4f), which resembled the expression of Cre in the ONL of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. S2). Importantly, we also detected increased levels of the lactate transporter MCT4 (encoded by Slc16a3) in both the ONL (Fig. 4d and Table S5) and PS (Fig. 4e and Table S3) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. This is of significance since MCT4 is not blocked by AR-C155858 [37], the inhibitor used to determine lactate production by TPLSM (Fig. 2g-i). Thus, our imaging results may have even somewhat underestimated the magnitude of the lactate production in PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice, because lactate could still have been exported through the MCT4.Fig. 4Proteomic landscape of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl PS and ONL. a Photoreceptor segments (PS) and the outer nuclear layer (ONL) were isolated from six \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and six ctrl mice and their proteomic landscape analysed by LC-MS/MS. b Principal component analyses of the PS and ONL proteomes from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice. c Heatmap of the top 25 up- and downregulated proteins in the ONL compared to the PS samples of all 12 mice. Proteins belonging to the phototransduction cascade, cilium, mitochondria or nucleus are indicated. d,e Volcano plots showing differentially up- (red) and downregulated (blue) proteins in the ONL (d) and PS (e) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice compared to ctrl. Total number of differentially regulated proteins as indicated. Threshhold: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.05. f Representative immunofluorescence labeling for GLUT3 in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl retina. Scale bar, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm. $$n = 3$$ mice per genotype. g,h Gene set enrichment analyses using the MSigDB hallmark dataset. Enrichment of proteins of the hallmark HYPOXIA (g) and OXPHOS (h) in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice as indicated. The top of each panel shows the running enrichment score (green) for the gene set. The lower part of each panel shows the position of identified members of the gene set (black lines: hits, red to blue gradient: strength of enrichment in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control (ctrl) mice) in the ranked list of proteins. For statistical analysis on proteomics data see Methods. NES: normalized enrichment score. PS: photoreceptor segments. ONL: outer nuclear layer. INL: inner nuclear layer. IPL: inner plexiform layer. GCL: ganglion cell layer Finally, gene set enrichment analysis (GSEA) yielded an increased enrichment score of proteins belonging to the hallmark hypoxia and a reduced score of proteins involved in OXPHOS in both PS and ONL of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 4g,h). This indicated that the chronic activation of HIFs in rods caused a proteomic landscape characteristic for hypoxia and suggested that this intracellular environment profoundly affected OXPHOS.
## Chronic HIF activation in rods reduces OXPHOS
Our TPLSM and proteomics data suggested a preferential conversion of pyruvate to lactate pointing to downregulation of OXPHOS in PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. To address OXPHOS in PRs directly, we examined the activity of respiratory complexes IV (cytochrome c oxidase (COX)) and II (succinate dehydrogenase (SDH)) by in situ enzymatic assays. Prominent COX and SDH signals in the IS, the OPL, and the inner plexiform layer (IPL) of control mice indicated strong OXPHOS activity in the retinal regions known to be enriched in mitochondria (Fig. 5a,b). In \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl retinas, however, areas of reduced COX and SDH activities were evident in the IS already at 1.5 months of age, several weeks before the onset of degeneration. This phenotype deteriorated with age and depended on HIF1A but not HIF2A (black arrows in Fig. 5a,b) and, thus, on the same HIF isoform that drives retinal degeneration in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice [31]. A HIF1 dependent impairment of COX was further evidenced by the absence of COX4i1 (a catalytic subunit of COX) in parts of the IS in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif2\alpha }$$\end{document}rodΔVhl;Hif2α (white arrows in Fig. 5c) but not \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif1\alpha }$$\end{document}rodΔVhl;Hif1αor control retinas. The patchy pattern of COX4i1 expression was very similar to the pattern of COX and SDH activity (Fig. 5a,b) and was likely due to the mosaic Cre expression in OpsinCre mice [30, 46] (Fig. S2b). Indeed, only Cre-positive areas of the ONL were associated with reduced COX4i1 levels in the adjacent IS (white arrows in Fig. 5d). It is unlikely that this effect was specific for the mitochondria in the rod IS, however, mitochondria localized in the photoreceptor synaptic terminals could not be assessed due to their close proximity to dendritic mitochondria belonging to 2nd order neurons in the retina. Fig. 5Dysregulation of OXPHOS in rods with chronically active HIFs. a,b Representative in situ enzymatic staining for COX (a) and SDH (b) activity in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif1\alpha }$$\end{document}rodΔVhl;Hif1α, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif2\alpha }$$\end{document}rodΔVhl;Hif2α and ctrl mice at time points as indicated. Shown are all retinal layers (top panels), a magnification of the photoreceptor segments (PS; boxed area, middle panels), and pixel intensity (PI) profiles through the PS (bottom panels). Black arrows indicate regions with reduced COX or SDH activity. $$n = 3$$ mice per genotype. c Representative immunofluorescence labeling for COX4i1 in the PS region of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif1\alpha }$$\end{document}rodΔVhl;Hif1α, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif2\alpha }$$\end{document}rodΔVhl;Hif2α and ctrl mice at 2.5 months of age (top panels). PI profiles through the PS (bottom panels). White arrows indicate regions with reduced COX4i1 labeling. $$n = 3$$ mice per genotype. d Representative immunofluorescence labeling for Cre (green) and COX4i1 (red) in the ONL and PS regions of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. White arrows indicate regions with reduced COX4i1 labeling in the IS of Cre-positive cells. $$n = 3$$ mice. e Significantly downregulated OXPHOS complex subunits and assembly factors and upregulated complex V inhibitory factor ATPIF1 in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. $$n = 6$$ mice per genotype. For statistical analysis on proteomics data see Methods. ONL: outer nuclear layer. OPL: outer plexiform layer. INL: inner nuclear layer. IPL: inner plexiform layer. GCL: ganglion cell layer. Scale bars, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm Since reduced activity of the OXPHOS complexes II and IV in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice corroborated the GSEA analyses of the layer-specific proteomic data (Fig. 4h), we further examined proteins directly related to OXPHOS, such as mitochondrial complex subunits and assembly factors. The mouse MitoCarta3.0 (http://www.broadinstitute.org/mitocarta) lists 165 such proteins, 81 of which were identified with high confidence in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice. 28 of these proteins, belonging to complexes I, III, IV or V, including COX4i1, were significantly downregulated in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl PS (Fig. 5e). The only upregulated OXPHOS-related protein was the ATPase inhibitory factor 1 (ATPIF1), suggesting complex V inhibition in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. Interestingly, BNIP3 was detected in three of the six PS samples from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl but in none of the control mice (Table S5). Although its presence in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl was inconsistent, BNIP3 may be an interesting protein for further investigations due to its connection to cell death [47] and mitochondrial energetics [48]. Such future experiments will address the potential of BNIP3 to reduce rod survival in conditions of activated HIF proteins. Collectively, these data indicated that chronic HIF1 activation in rods caused OXPHOS dysregulation preceding the onset of cell degeneration.
## Chronic HIF activation in rods affects mitochondrial protein composition
Downregulation of OXPHOS-related proteins in PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice was unlikely due to loss of mitochondria. Only $14\%$ of the mitochondrial proteins identified in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice were significantly downregulated (Fig. 6a and Table S4) and well-known mitochondrial marker proteins, such as mitochondrial apoptosis-inducing factor 1 (AIFM1), mitochondrial import receptor subunit TOM70 (TOM70) and dihydrolipoamide dehydrogenase (DLD) had comparable levels in PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice (Fig. 6b). Immunofluorescence labeling for voltage dependent anion channel 1 (VDAC1) also did not indicate loss of mitochondria in rods lacking COX4i1 in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl retinas (white arrows point to areas with reduced COX4i1 staining in Fig. 6c). Vdac1 expression was even significantly upregulated in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. S3a). To address a possible increase in transcriptional regulation of mitochondrial proteins, we assessed the relative expression levels of translocase of outer mitochondrial membrane 20 (Tomm20), another outer mitochondrial membrane protein. However, Tomm20 expression was significantly downregulated in ONL and PS samples from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. S3a) suggesting that there was no general increase in transcription of mitochondrial genes. Together, these data indicate that chronic HIF activation in rods inhibited OXPHOS without affecting the overall mitochondrial biomass. Fig. 6Mitochondria in rods with chronically active HIFs. a Pie chart of all identified and differentially regulated mitochondrial proteins in the photoreceptor segments (PS) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. $$n = 6$$ mice per genotype. b Protein levels of the mitochondrial markers AIFM1, TOM70, and DLD in PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice and ctrl mice at 2.5 months of age. Shown are boxplots with the median and $25\%$ and $75\%$ percentiles of $$n = 6$$ mice per genotype. The samples with the highest (red) and lowest (blue) Cre levels are indicated. c Representative immunofluorescence labeling for VDAC1 (green, top panels) and COX4i1 (red, middle panels) in the PS region of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice at the indicated time points. Bottom panels: merge. White arrows: regions with reduced COX4i1 labeling. $$n = 3$$ mice per genotype. d MT-CO2 levels in outer nuclear layer (ONL) and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice at 2.5 months of age. Shown are boxplots with the median and $25\%$ and $75\%$ percentiles of $$n = 6$$ mice per genotype. The samples with the highest (red) and lowest (blue) Cre levels are indicated. e Relative quantification of mtDNA copy number in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice at 2.5 months of age. Shown are boxplots with the median and $25\%$ and $75\%$ percentiles of $$n = 6$$ mice per genotype. The samples with the highest (red) and lowest (blue) Cre levels are indicated. Statistics: Student’s t-test. f Representative immunofluorescence labeling for TFAM in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice at time points as indicated. $$n = 3$$ mice per genotype. g Significantly regulated proteins involved in mtDNA maintenance (left), mtRNA metabolism (middle), and mitochondrial translation (right) in the PS and ONL of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. $$n = 6$$ mice per genotype. ND, not detected. For statistical analysis on proteomics data see Methods. *: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.05. OPL: outer plexiform layer. Scale bars, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm Although only one (cytochrome c oxidase subunit 2 (MT-CO2)) of the five mtDNA-encoded proteins identified in our proteomics data was downregulated in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 5e, Fig. 6d and Table S5), we examined whether OXPHOS downregulation was a result of compromised mtDNA integrity. Relative quantification of mtDNA copy number showed no difference between PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and control mice (Fig. 6e), indicating that mtDNA integrity was not strongly affected by chronic HIF activation at 2.5 months of age.
Nevertheless, immunofluorescence labeling for the nuclear-encoded mitochondrial transcription factor A (TFAM) showed decreased immunoreactivity in patchy areas in the ONL of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice as early as at 2 months of age, a phenotype that became even more pronounced with increasing age (Fig. 6f). Consistently, proteomics data showed a significant downregulation of TFAM and three other proteins involved in mtDNA maintenance (EXOG, PolG-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upbeta$$\end{document}β, ATAD3) in PRs of 2.5 months old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 6g). In addition, two proteins involved in mtRNA metabolism (LACTB2 and REXO2) and three in mitochondrial translation (MRP-L4, MRP-S2, and TrpRS) were differentially and mostly downregulated in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 6g). Similarly, expression of two mtDNA-encoded genes (mt-Co2 and NADH-ubiquinone oxidoreductase chain 1 (mt-Nd1)) was significantly downregulated in ONL and PS samples of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. S3b). Together, our data suggest that chronic HIF1 activation in rods led to an early severe downregulation of OXPHOS-related proteins without a significant loss of mitochondria despite an affected mtDNA maintenance machinery. Furthermore, rods seemed to survive despite OXPHOS dysfunction, at least for some time.
## Inhibition of OXPHOS in rods does not lead to early anabolic deficiency
To further investigate the dependency of rod OS biogenesis and survival on OXPHOS, we generated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice that have impaired OXPHOS activity due to compromised complex IV assembly [49] in rods (Fig. 7a). Similar to chronic HIF activation, Cox10 deletion (indicated by the presence of Cre) led to the loss of the complex IV subunit COX4i1 (Fig. 7b) but not the outer mitochondrial membrane protein VDAC1 (Fig. 7c; white arrows point to areas with reduced COX4i1 staining) in rod IS. Furthermore, rods showed remarkable resilience against impaired OXPHOS activity and survived for several months (Fig. 7b,d-f) as indicated by the presence of Cre-positive rods in 6-month-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice (Fig. 7b). At this time point, ONL thickness was only moderately affected (Fig. 7d,f) and scotopic and photopic electroretinograms were in the normal range (Fig. S4). Significant loss of rods and reduced scotopic function were observed only at 12 months of age (Fig. 7d,g and Fig. S4). In strong contrast to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice, OS length in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice was not affected at the early age (Fig. 7d,e), indicating that OS biogenesis did not depend on OXPHOS activity. Fig. 7Rods survival in the absence of Cox10. a Mouse model for OXPHOS deficiency in rods (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10). Cox10 deletion impairs assembly and function of the mitochondrial complex IV. b Representative immunofluorescence labeling for Cre (green) and COX4i1 (red) in the ONL and PS regions of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice at time points as indicated. White arrows: regions with reduced COX4i1 labeling in the PS of Cre-positive cells. $$n = 3$$ mice per time point. c Representative immunofluorescence labeling for VDAC1 (green, top panels) and COX4i1 (red, middle panels) in the PS region of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 and ctrl mice at 3 months of age. Bottom panels: merge. White arrows: regions with reduced COX4i1 labeling. $$n = 3$$ mice per genotype. d Representative micrographs of the retinal morphology of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 and ctrl mice at time points as indicated. e-g Spidergrams (left) and bar graphs (right) of OS length (top), IS length (middle) and ONL thickness (bottom) in ctrl (black) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 (orange) mice at 3 (e, $$n = 3$$ mice per genotype), 6 (f, $$n = 3$$ mice per genotype), and 12 months (g, $$n = 4$$ mice per genotype) of age. All measurements from the spidergrams are included in the bar graphs. Data represent mean ± SD. Statistics: two-way ANOVA and Bonferroni’s multiple comparison test (spidergrams) or nested t-test (bar graphs). *: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.05. **: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.01. ***: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.001. ****: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.0001. PS: photoreceptor segments. OS: outer segments. IS: inner segments. ONL: outer nuclear layer. OPL: outer platform layer. Scale bars, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm
## Chronic HIF activation in rods downregulates the citric acid cycle independently of OXPHOS
Since enzymatic staining showed a downregulation of SDH in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice, an enzyme complex serving not only OXPHOS but also the TCA cycle, we hypothesised that chronic HIF activation affects the TCA cycle and, thus, generation of TCA metabolites important for anabolism and OS biogenesis. Detailed analysis of the proteomics data revealed a significant downregulation not only of SDH subunit A (SDHA) but also of six additional TCA associated enzymes in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 8a-c). Immunofluorescence labeling for SDHA, citrate synthase (CS), and aconitase 2 (ACO2) validated the proteomics data and indicated that the TCA cycle was severely affected in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl but not \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice (Fig. 8d-f). Together, these results suggest that chronic HIF activation concomitantly affected the TCA cycle and OXPHOS in rods, leading to anabolic dysfunction and ultimately to PR degeneration (Fig. 8g).Fig. 8Dysregulation of the citric acid cycle in rods with chronically active HIFs. a,b Significantly downregulated citric acid cycle associated enzymes in photoreceptors segments (PS) or outer nuclear layer (ONL) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. Protein levels are shown as boxplots with the median and $25\%$ and $75\%$ percentiles (a) or bar graphs representing normalized abundance ratios of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl/ctrl (b). $$n = 6$$ mice per genotype. The samples with the highest (red) and lowest (blue) Cre levels are indicated. For statistical analysis on proteomics data see Methods. nd, not detected. *: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.05. **: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.01. c Representation of the citric acid cycle with red arrows marking significantly downregulated proteins in the PRs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. d-f Representative immunofluorescence labeling for SDH subunit A (SDHA; d), citrate synthase (CS; e), and aconitate hydratase (ACO2; f) in the PS regions of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 and ctrl mice at time points as indicated (top panels). Pixel intensity (PI) profiles through the PS (bottom panels). White arrows: regions with reduced labeling. Scale bars, 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm. $$n = 3$$ mice per genotype. g Dysregulation of OXPHOS and the TCA cycle in rods with chronic HIF activation is associated with impaired of OS biogenesis and leads to PRs degeneration
## Discussion
We investigated PR energy metabolism in a mouse model of slow progressing retinal degeneration [30, 31]. Our results show that PRs have a remarkably high glucose consumption rate, and that chronic HIF activation induces an increase in lactate production while severely affecting OXPHOS and the TCA cycle in rods. We also show that OXPHOS deficiency on its own is surprisingly well tolerated by rods, while additional impairment of the TCA cycle leads to an early dysregulation of rod OS biogenesis and retinal degeneration.
Since metabolism is fundamental factor for cell survival, tissue integrity and disease development, the investigation of glucose, the main metabolic precursor, becomes central for the understanding of pathophysiological processes. Using TPLSM and FRET biosensors, we provide, to our knowledge, the first data on lactate (Fig. 2) and glucose (Fig. 3) dynamics at cellular resolution in the intact retinal tissue. Results indicate that PRs are not only net lactate producers in the presence of glucose as the only fuel (Fig. 2h) but that they also have markedly higher basal glucose levels and glucose consumption rate than cells of the inner retina (Fig. 3). These results confirm the long-standing hypothesis of PRs being the main site of aerobic glycolysis in the retina [4, 12, 16, 18], a phenomenon that has been observed on tissue level already a century ago by Otto Warburg [9].
It is of interest that rods express GLUT3 upon chronic HIF activation (Fig. 4f), a glucose transporter with higher glucose affinity and greater transport capacity than GLUT1 [50]. Since GLUT3 is not present in rods under normoxic conditions [44, 45, 51], it is conceivable that rods may attempt to increase glucose uptake as a response to reduced OXPHOS activity during HIF activation. However, there was no measurable increase in basal glucose levels or glucose consumption rate in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 3). The absence of glycolysis stimulation in the face of OXPHOS failure was unexpected. One possibility is that in these cells the glycolytic rate is already near a maximum under normoxic conditions. Alternatively, chronic hypoxia may redirect anabolic branches like the pentose phosphate pathway towards the main glycolytic pathway for the production of ATP and lactate release, as observed in stressed neurons [52]. This redeployment of glucose would explain the combination of higher lactate production with conserved glucose consumption.
The increase of lactate production in rods upon chronic HIF activation (Fig. 2i) is in accordance to previous studies reporting oxygen level dependent lactate production in isolated retinas [53]. Notably, our data rather underestimated the magnitude of this increase for two reasons: First, the mosaic Cre expression (Fig. S2b) inevitably led to the inclusion of both Vhl knockout and wild type cells in our TPLSM measurements. Second, AR-C155858 potently inhibits MCT1 and MCT2 but not MCT4 [37] that is even significantly upregulated in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice (Fig. 4d,e) and, thus, lactate could be exported from the cell through the high affinity MCT4 [54], escaping detection by the biosensor.
Since the glycolytic flux through the hexokinases was not altered, increased lactate production in rods of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice was likely the result of a reduced pyruvate utilization by the mitochondria. In addition, feedback mechanisms and allosteric regulation of TCA cycle enzymes [55, 56] may also have affected function of mitochondrial metabolic pathways and thus contributed to the effect. Proteomics data from isolated PS and ONL (Fig. 4h, Fig. 5e, Fig. 8a,b) as well as results from enzymatic in situ experiments (Fig. 5a,b) and immunofluorescence studies (Fig. 5e, Fig. 8d-f) showed that chronic HIF1 (but not HIF2) activation in rods severely downregulated protein levels of several key enzymes involved not only in OXPHOS but also in the TCA cycle (Fig. 5, Fig. 8). Despite the downregulation of these proteins and the severely reduced activity of SDH and COX in photoreceptor segments, mitochondria remained present (Fig. 6c) and photoreceptors survived for several weeks (Fig. 1b,c). Survival of rods with OXPHOS deficiency was further corroborated in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice, suggesting that the high glucose consumption rate of rods may generate sufficient energy for the cells to function and survive in such conditions. This extends the list of cells (including astrocytes [57], oligodendrocytes [58], brain neurons [59, 60], and skeletal muscle cells [49]) that are reported to survive as glycolytic cells in the absence of Cox10. Additionally, it has been suggested that cells in the retina can also use fumarate as electron acceptor instead of oxygen by reversing the succinate dehydrogenase reaction [61]. The resulting succinate can then be transported to the neighbouring RPE, oxidised to malate, and shuttled back to the retina. The existence of such malate-succinate shuttle could also help to explain the resilience of COX deficient rod PRs. Although PRs eventually die in both \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (Fig. 1b-d) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice (Fig. 7d-g), the degenerative phenotype was more severe and appeared earlier in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. For survival, rods may thus not so much depend on OXPHOS but more heavily so on functional TCA cycle that is seemingly unaffected in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 but not \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl mice. This underlines the significance of the TCA cycle with its pivotal role in anabolic and homeostatic processes that are essential, among others, for OS biogenesis. Reduced TCA cycle activity as well as shorter rod OS length have been reported in mouse models of retinitis pigmentosa (RP), an inherited retinal degenerative disorder with high genetic heterogeneity [62–65]. Shorter rod OS length was also described in RP patients [66, 67] as well as in the context of hypoxia in mice [68] and human [69]. Thus, TCA cycle dysregulation might represent a common molecular feature preceding PR degeneration. As suggested by others [64], supporting the TCA cycle may thus constitute a promising strategy to improve PR survival in a variety of patients.
## Conclusions
Our study demonstrates the consequences of an activated hypoxic response for glucose flux and lactate levels in rods and highlights the importance of the TCA cycle for rod survival. Despite the need for high ATP levels for maintenance of the visual function [7], rods survive in the absence of OXPHOS, indicating that they can satisfy their energy needs by alternative pathways such as aerobic glycolysis and/or beta-oxidation of fatty acids. In fact, it has been proposed that only about $20\%$ of glucose is completely oxidized in the outer retina while the rest is used for lactate production [4] and that the long-chain fatty acid palmitate may serve as a fuel for mitochondrial respiration in the isolated retina [70, 71]. However, no sufficiently effective alternative pathway to the TCA cycle may exist to provide satisfactory amounts of intermediate metabolites needed for the constant biosynthesis of complex cellular structures such as the outer segments of PRs.
## Animals
All experimental procedures were approved by the local veterinary authorities, conforming to the guidelines of the Swiss Animal Protection Law, Veterinary Office, Canton Zurich (Act of Animal Protection 16 December 2005 and Animal Protection Ordinance 23 April 2008) and were performed in accordance with the respective national, federal and institutional regulations. Mice were maintained as breeding colonies at the Laboratory Animal Services Center of the University of Zurich in a 14h:10h light-dark cycle. Mice had access to food and water ad libitum. The average light intensity at cage levels was 60-150 lux, depending on the position in the rack.
Vhlflox/flox [72], Hif1\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 flox/flox [73], Hif2\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 flox/flox [74], Cox10flox/flox [49], and OpsinCre (LMOPC1 [46]) mice were intercrossed to obtain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl (Vhlflox/flox;OpsinCre), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif1\alpha }$$\end{document}rodΔVhl;Hif1α(Vhlflox/flox;Hif1\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 flox/flox;OpsinCre), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl;Hif2\alpha }$$\end{document}rodΔVhl;Hif2α (Vhlflox/flox;Hif2\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 flox/flox; OpsinCre), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 (Cox10flox/flox;OpsinCre) mice. All breeding pairs were heterozygous for OpsinCre and littermates without OpsinCre served as controls. Rod-specific Cre expression in OpsinCre mice starts around postnatal day 7 and increases up to 6 weeks of age [46]. Homozygous rod transducin \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-subunit knockout mice [34] (Gnat1a-/-) were used as controls in TPLSM calcium imaging. All mice were homozygous for the Rpe65450Leu variant [75, 76]. For tissue sample preparations, mice were euthanized with CO2 followed by decapitation.
## Retinal morphology analysis
Eyes were enucleated, fixed in glutaraldehyde ($2.5\%$ in cacodylate buffer) for 12-24 h at 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C, trimmed, post-fixed in $1\%$ osmium tetroxide, and embedded in Epon 812 as described [77]. Retinal cross-sections of 0.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm were cut through the optic nerve head, stained with toluidine blue, and analyzed by light microscopy (Zeiss, Axioplan). OS and IS length, and the thickness of the ONL were measured at indicated distances from the optic nerve head using the Adobe Photoshop CS6 ruler tool (Adobe). Mann-Whitney nonparametric test was used to compare the overall OS and IS length, as well as ONL thickness of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Cox10}$$\end{document}rodΔCox10 mice to their respective controls. P-values < 0.05 were considered to show significant differences.
## Cloning and virus production
The GCaMP6s gene [78] was cloned into an AAV plasmid backbone containing a rod PR-specific mouse opsin promoter (mOP) (a gift from Sanford L. Boye) [79]. The laconic AAV plasmid was a gift from Luis Felipe Barros (Addgene plasmid #44238) [35]. The FLIIP AAV plasmid [38] was codon-diversified to avoid recombination during AAV production [80, 81]. All AAVs were produced by the Viral Vector Facility of the Neuroscience Center Zurich and packaged in the AAV2(QuadYF+TV; 7m8)/2 capsid [82].
## Intraocular injections of AAVs carrying fluorescent sensors
For intraocular AAV injections, the pupils of mice were dilated with Cyclogyl $1\%$ (Alcon Pharmaceuticals) and Neosynephrine $5\%$ (Ursapharm Schweiz). Mice were anesthetized by a subcutaneous injection of ketamine (85 mg/kg, Parke-Davis) and xylazine (10 mg/kg, Bayer AG). Viscotears (Bausch & Lomb Swiss AG) were applied to keep the eyes moist. 1 x 109 total viral genome in 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μl total volume were injected either intravitreally or subretinally to transduce the inner or the outer retinal cells, respectively. Anesthesia was reversed by a subcutaneous injection of antisedan (2 mg/kg, Atipazemole). Two to three weeks after injection, mice were subjected to fluorescent funduscopy and OCT to test for transgene expression and detect potential injection-inflicted tissue damage including bleeding or persistent retinal detachment. Such eyes were excluded from the study. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice were injected at 5-6 weeks of age and TPLSM experiments was performed at 10-14 weeks of age.
## Acute retinal preparations for TPLSM ex vivo imaging
Mice were housed in a facility with a reversed $\frac{12}{12}$ h light/dark cycle for at least 5 days before the experiments. Prior to dissection, mice were dark adapted for at least 2 h, and all steps of the dissection procedure were carried out in dim red light. Retinas were rapidly dissected through a slit in the cornea and placed in freshly prepared artificial cerebrospinal fluid (ACSF) containing: 126 mM NaCl, 3 mM KCl, 2 mM CaCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, 2 mM MgSO4, 6 mM D-glucose), bubbled with oxycarbon ($95\%$ O2/ $5\%$ CO2, PanGas, 820, and kept at RT. PRs were imaged in the cross-section of acute retinal slices as previously described [83]. In brief, half retinas were flatmounted with the PRs side facing up on a nitrocellulose filter membrane and cut in 250 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm-thick slices using a razor blade attached to a custom-made tissue chopper. For imaging inner retinal cells, flatmounted retinas with the ganglion cells facing up were used without slicing the tissue. The tissue was left to rest while slowly warming the ACSF to 35\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C for 30 min before imaging.
## Two-photon excitation laser scanning microscopy
A custom-made two-photon laser scanning microscope equipped with a two-photon laser with < 120 fs temporal pulse width (InSight DeepSee Dual; Spectra-Physics) and a 16x water immersion objective (Nikon N16XLWD-PF, 0.8 NA, 3 mm WD) was used for image acquisition [84]. Excitation and emission beam paths were separated by a dichroic mirror (F73-825; AHF Analysentechnik). Dichroic mirrors at 506nm (F38-506; AHF Analysentechnik) and 560 nm (F38-560; AHF Analysentechnik) further separated the emission light into colored components, that were then focused on GaAsP photomultipliers (H10770PA-40sel; Hamamatsu Photonics) equipped with filters for cyan ($\frac{475}{50}$; AHF Analysentechnik), and green/yellow ($\frac{542}{50}$; AHF Analysentechnik) wavelenghts. ScanImage 3.8 [85] and custom-written LabVIEW software (Version 2012; National Instruments) were used for image control and data acquisition.
For imaging, constantly oxygenated ACSF was gravity-fed into the recording chamber at a flow rate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx$$\end{document}≈ 2 ml/min and temperature was maintained at 35\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C using a temperature controller (V TC05, Luigs & Neumann GmbH) with an in-bath temperature sensor. All solutions used during imaging were infused using the same gravity flow system and osmolarity was adjusted at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx$$\end{document}≈ 300 mOsm. Data was acquired from acute retinal slices at 50-70 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm below the cross-section surface, from cells in the GCL at 0-10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm and from cells in the INL at 50-60 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm below the surface.
## Calcium imaging parameters and analysis
GCaMP6s was excited at 940 nm wavelength and images were acquired at 11.84 Hz with a 128x256-pixel resolution. Analyses were performed with ImageJ (National Institutes of Health) and regions of interest were selected manually. Prism software (GraphPad) was used for data visualization.
## Lactate and glucose imaging parameters and analysis
Laconic and FLIIP were excited at 870 nm wavelength and images were acquired at 5.94 Hz with a 256x256-pixel resolution. ACSF containing 20 mM oxamate was used in the trans-acceleration imaging protocol. ACSF containing 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μM AR-C155858 (BioTechne Tocris) was used in the lactate transport inhibition imaging protocol. ACSF containing 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μM cytochalasin B (BioTechne Tocris, 5474) was used in the glucose transport inhibition imaging protocol. ACSF containing 0 mM glucose was used in the aglycemia imaging protocol.
Whole-frame image analysis was carried out using custom-made code for MATLAB 2017b (MathWorks) and ImageJ (National Institutes of Health). For each experiment, images were aligned using a 2D convolution engine to account for xy drift in time. To optimize signal to noise ratio, time smoothing of 11 frames for Laconic and 5 frames for FLIIP was applied, as well as automatic thresholding using the Li’s minimum cross entropy method [86]. Ratiometric data were visualized with Prism software (GraphPad). Mann-Whitney nonparametric test was used to compare \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice. Kuskal-Wallis nonparametric test and Dunnett’s multiple comparison test were used to compare PRs, GCL and INL cells. Because AAVs were injected subretinally and biosensor expression was CMV driven, both rod and cone PRs may have been transduced. However, given that rods outnumber cone PRs by a factor of 30 in the mouse retina, all imaging data represent primarily rods.
## Sample preparation with the ReLayS method
Samples were prepared using the ReLayS method [43]. In brief, retinas from 2.5-months-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl ($$n = 6$$) and control ($$n = 6$$) mice were rapidly dissected through a slit in the cornea and placed in PBS on ice. Vitreous was carefully removed using a pair of forceps and retinas were halved through the optic nerve head. The half-retinas were flattened on a small piece of nitrocellulose filter membrane with the photoreceptor side facing up and frozen on a metal platform placed on dry ice. Frozen retinal flatmounts were stored at -80\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C until further use. PS and ONL samples were consecutively separated from frozen half-retinal flatmounts using the adherence of the cellular structures to a nitrocellulose filter membrane placed on top of the photoreceptors or the ONL, respectively. The PS and ONL membranes were frozen on a metal platform placed on dry ice and transferred to 1.5 ml Protein LoBind\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circledR }$$\end{document}® Tubes (Eppendorf; 0030108116). For protein isolation, samples were homogenized by sonication with an ultrasonic homogenizer in ice-cold Tris-HCl (100 mM, pH 8.0) containing protease inhibitors (Sigma-Aldrich, P2714). After sonication, $10\%$ SDS (final concentration $1\%$ SDS in Tris-HCl, pH 8.0) was added and samples were incubated at 75\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C for 10 min. Protein samples were stored at -20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C until further use.
## Mass spectrometry
The mass spectrometry proteomic data have been deposited to the ProteomeXchange *Consortium via* the PRIDE [87] partner repository with the dataset identifier PXD034057.
From each sample, total proteins were proteolyzed with LysC (Wako Chemicals, Neuss, Germany) and trypsin (Promega) using a suspension trapping protocol (S-Trap, Protifi) to remove SDS according to the manufacturer’s instructions. Briefly, samples were reduced and carbamidomethylated, followed by acidification with phosphoric acid and addition of methanol to a final concentration of >$70\%$ before loading to the trap columns. Proteins were washed while trapped on column, then digested on column with LysC (2 hours at RT) followed by trypsin (overnight, at 37\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C). Peptides were collected by centrifugation and acidified. Eluted peptides were analyzed on a Q Exactive HF mass spectrometer (Thermo Fisher Scientific) in the data dependent mode. Approximately 0.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μg peptides per sample were automatically loaded to the online coupled ultra-high-performance liquid chromatography (UHPLC) system (Ultimate 3000, Thermo Fisher Scientific). A nano trap column was used (300-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm ID X 5mm, packed with Acclaim PepMap100 C18, 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm, 100 Å; LC Packings) before separation by reversed phase chromatography (Acquity UHPLC M-Class HSS T3 Column 75 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm ID X 250 mm, 1.8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm; Waters) at 40\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. Peptides were eluted from the column at 250 nL/min using increasing ACN concentrations (in $0.1\%$ formic acid) from $3\%$ to $41\%$ over a linear 95-min gradient. MS spectra were recorded at a resolution of 60 000 with an AGC target of 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 106 and a maximum injection time of 50 ms from 300 to 1’500 m/z. From the MS scan, the 10 most abundant peptide ions were selected for fragmentation via HCD with a normalized collision energy of 28, an isolation window of 1.6 m/z, and a dynamic exclusion of 30 s. MS/MS spectra were recorded at a resolution of 15’000 with an AGC target of 105 and a maximum injection time of 50 ms. Unassigned charges and charges of +1 and above +8 were excluded from precursor selection.
## Data processing - protein identification and label-free quantification
Proteome Discoverer 2.4 software (Thermo Fisher Scientific; version 2.4.1.15) was used for peptide and protein identification via a database search (Sequest HT search engine) against SwissProt mouse database (release 2020_02, 17061 sequences), considering full tryptic specificity, allowing for up to two missed tryptic cleavage sites, precursor mass tolerance 10 ppm, and fragment mass tolerance 0.02 Da. Carbamidomethylation of Cys was set as a static modification. Dynamic modifications included deamidation of Asn and Gln, oxidation of Met; and a combination of Met loss with acetylation on protein N-terminus. Percolator [88] was used for validating peptide spectrum matches and peptides, accepting only the top-scoring hit for each spectrum, and satisfying the cut-off values for FDR < $1\%$, and posterior error probability < 0.01. The final list of proteins complied with the strict parsimony principle.
The quantification of proteins, after precursor recalibration, was based on abundance values (intensity) for unique peptides. Abundance values were normalized to the total peptide amount to account for sample load errors. The protein abundances were calculated by summing the abundance values for admissible peptides. The final protein ratio was calculated using median abundance values of six replicate analyses each. The statistical significance of the ratio change was ascertained employing the approach described in [89], which is based on the presumption that we look for expression changes for proteins that are just a few in comparison to the number of total proteins being quantified. The quantification variability of the non-changing “background” proteins can be used to infer which proteins change their expression in a statistically significant manner.
## Data analysis and visualization
PCA was performed on normalized abundance protein levels using ClustVis [90] (http://biit.cs.ut.ee/clustvis/). Heatmap of the top 25 proteins in ONL and PS was generated from the scaled normalized abundance values using the ComplexHeatmap package + [91] and the viridis color palette with the R package circlize [92]. The volcano plots were generated using VolcaNoseR [93] (https://huygens.science.uva.nl/VolcaNoseR2/). GSEA was performed using GSEA (Version 4.1.0) software provided by Broad Institute of Massachusetts Institute of Technology and Harvard University [94, 95]. Analysis was conducted on a pre-ranked protein list based on their relative abundance in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl against control samples for the PS and ONL, respectively. The hallmark gene set from Molecular Signatures Database [96, 97] (MSigDB, Version 7.2) was used for comparison. Normalized protein abundances and abundance ratios were visualized with Prism software (GraphPad).
## mtDNA copy number assay
PS and ONL samples were prepared from 2.5-months-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl ($$n = 6$$) and control ($$n = 6$$) mice using the ReLayS method [43] as described above. Tissue was lysed by proteinase K (Sigma-Aldrich, 03115879001) treatment at 56\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C for 45 min with shacking at 300 rpm and RNase A (100 mg/ml; Thermo Fisher Scientific, 12091021) was added to the samples. DNA isolation was performed with QIAamp DNA Blood Mini Kit (Qiagen, 51104) according to manufacturer instructions. DNA from PS and ONL samples was pooled together to obtain DNA samples from photoreceptors. Relative quantification of mtDNA levels was determined by the ratio of the mitochondrial ND1 (mt-Nd1) gene to the nuclear-encoded 18S rRNA gene using real-time PCR. 16 ng DNA was used as template and the genes of interest amplified using the PowerUp SYBR Green Master Mix (ThermoFisher Scientific) in the ABI QuantStudio 3 system (ThermoFisher Scientific) and specific primer pairs: ND1 fwd 5’-3’: CTAGCAGAAACAAACCGGGC and ND1 rev 5’-3’: CCGGCTGCGTATTCTACGTT; 18S rRNA fwd 5’-3’: CGCGGTTCTATTTTGTTGGT and 18S rRNA rev 5’-3’: AGTCGGCATCGTTTATGGTC. Data analysis was carried out using the 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\wedge }$$\end{document}∧-ddCt method [98].
## Immunohistochemistry
Eyes were marked at the nasal limbus, enucleated, and fixed in $4\%$ PFA in PBS for a total of 4h at 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. For cryosectioning, eyes were placed in $30\%$ sucrose in PBS for 2h at 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C and embedded with tissue freezing medium (Leica Biosystems, 81-0771-00). 12-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm-thick sections were prepared using a Leica cryostat (Biosystems Switzerland AG, CM1860). For paraffin sections, PFA fixed eyes were dehydrated in a series of ethanol solutions of increasing concentrations up to $100\%$ and immersed in xylene prior to paraffin embedding. 5-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm-thick sections were prepared using a Zeiss microtome (Microm HM 440E). Paraffin sections were deparaffinized, rehydrated, and epitopes were unmasked by heat-induced antigen retrieval at pH 6. For immunofluorescence staining, paraffin- and cryosections were blocked with PBS containing $3\%$ normal goat serum and $1\%$ Triton X-100. The following primary antibodies were incubated overnight at 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C: rabbit anti-GLUT3 (1:250; Sigma-Aldrich, ab1344), mouse anti-COX4i1 (1:100; Abcam, ab14744), rabbit anti-CRE (1:300; Merck Millipore, 69050-3), rabbit anti-VDAC1 (1:250; Abcam, ab15895), rabbit anti-TFAM (1:250; Abcam, ab131607), rabbit anti-CS (1:250; GeneTex, GTX110624), rabbit anti-ACO2 (1:250; Cell Signaling Technology, 6571), rabbit anti-SDHA (1:250; Cell Signaling Technology, 11998) or rabbit anti-cARR (1:1000; Merck Millipore Chemicals, AB15282). Secondary antibodies (Alexa Fluor 488- or 568-conjugated, 1:500) were applied for 1 h at RT. Fluorescence images were acquired with a Zeiss microscope (Axioplan 2, Zeiss), processed, and analyzed with ImageJ (National Institutes of Health). Pixel intensity profiles through the PS were obtained using the profile plots tool controlled by the line tool in ImageJ.
## COX and SDH enzymatic histochemistry
Eyes were marked at the nasal limbus, enucleated, briefly washed in ice cold PBS, and immediately embedded in tissue freezing medium (Leica Biosystems, 81-0771-00). 12-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm-thick sections were prepared using a Leica cryostat. For COX enzymatic histochemistry, sections were incubated with COX detection solution (1.6 mM DAB, 1.25 mg/ml cytochrom C, 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μl catalase in PBS, pH 7.0) for 45 min at 37\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. For SDH enzymatic histochemistry, sections were incubated with SDH detection solution (1.55 mM nitrotetrazolium blue chloride, 0.13 mM sodium succinate, 0.2 mM phenazine methosulfate, 0.1 mM sodium azide in PBS, pH 7.0) for 25 minutes at 37\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. Sections were dehydrated in a series of ethanol solutions of increasing concentrations up to $100\%$ and immersed in xylene. Coverslips were mounted with Cytoseal (Thermo Scientific, 8312-4). Bright-field microscopy images were acquired with a Zeiss microscope (Axioplan 2), processed and analyzed with ImageJ (National Institutes of Health). Pixel intensity profiles through the PS were obtained using the profile plots tool controlled by the line tool in ImageJ.
## Scotopic and photopic electroretinography (ERG)
Mice were processed for ERG as described [99]. Briefly, after overnight dark-adaptation and pupil dilation with $1\%$ Cyclogyl (Alcon) and $5\%$ Neosynephrin-POS, mice were anesthetized with ketamin (85 mg/kg) and xylazine (10 mg/kg). Electroretinograms were recorded simultaneously from both eyes using a Diagnosys Celeris rodent ERG device (Diagnosys). Ten flash intensities ranging from 8x10-6 cd*s/m2 to 3 cd*s/m2 and six flash intensities ranging from 1 cd*s/m2 to 200 cd*s/m2 were used for dark- (scotopic) and light-adapted (photopic) single-flash intensity ERG series, respectively. Five sweeps per intensity were averaged for the scotopic and ten sweeps per intensity for the photopic ERGs. The standard rod-suppressive background light (30 cd/m2) was used prior (5 min) and during recordings in photopic conditions. Statistical analysis was performed using two-way ANOVA and Bonferroni’s multiple comparison test (spidergrams) or nested t-test (bar graphs) with Prism software (GraphPad).
## Semi-quantitative real-time PCR
PS and ONL samples were prepared from 2.5-months-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl ($$n = 6$$) and control ($$n = 6$$) mice using the ReLayS method [43] as described above. Total RNA was isolated from the tissue on the membrane with an RNA isolation kit (Thermo Fisher PicoPure RNA Isolation Kit, KIT0204) including an on-column DNaseI treatment. cDNA synthesis was carried out with oligo-(d)T primers and M-MLV reverse transcriptase (Promega). For semi-quantitative real-time PCR, 10 ng cDNA was amplified using the PowerUp SYBR Green Master Mix (Thermo Fisher Scientific) and specific primer pairs (Actb fwd 5’-3’: CAACGGCTCCGGCATGTGC and rev 5’-3’: CTCTTGCTCTGGGCCTCG; Vdac1 fwd 5’-3’: CAAGGTCACACTGAACATGG and rev 5’-3’: TCACTTTGGTGGTTTCCGT; Tomm20 fwd 5’-3’: TGCATCTACTTCGACCGCAAA and rev 5’-3’: GTCCACACCCTTCTCGTAGTC; mt-Co2 fwd 5’-3’: CCTCCACTCATGAGCAGTCC and rev 5’-3’: AATAACCCTGGTCGGTTTG; mt-Nd1 fwd 5’-3’: CTAGCAGAAACAAACCGGGC and rev 5’-3’: CCGGCTGCGTATTCTACGTT) in the ABI QuantStudio 3 system (Thermo Fisher Scientific). Actin-beta (Actb) was used as a reference housekeeping gene. Data analysis was carried out using the 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\wedge }$$\end{document}∧-ddCt method [98]. Data were visualized with Prism software (GraphPad) and Mann-Whitney nonparametric test was used to compare \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta \ Vhl}$$\end{document}rodΔVhl and ctrl mice.
## Supplementary information
Additional file 1: Figure S1. Light-induced calcium response in rods during TPLSM. a, Expression of the calcium sensor GCaMP6s (green) in cone arrestin (cARR, red) negative photoreceptors after subretinal AAV application. White arrows: cARR-positive but GCaMP6s negative cells. b, Preparation of acute retinal slices from flat mounted half retinas and imaging of GCaMP6s by TPLSM. c, Representative TPLSM micrographs illustrating the drop of intracellular calcium levels during imaging. Left: Max intensity of whole image series. Middle and right: intensity weighted images at 0 s (middle) and 4 s (right) of imaging. White arrows indicate regions with reduced GCaMP6s signal after 4 s of imaging. Scale bars, 50 μm. d, Representative GCaMP6s traces in the outer plexiform layer (OPL), outer nuclear layer (ONL), and photoreceptor segments (PS) of wild type mice during 6 s of TPLSM. e, GCaMP6s signal (mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± SD) in the OPL of Gnat1a-/- mice (12 retinal slices from 4 mice) at 2, 4, and 6 s during TPLSM.Additional file 2: Figure S2. Variability and mosaicism of Cre levels in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice. a, Relative normalized abundance of Cre, SAG, and GAPDH in the outer nuclear layer (ONL) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. Shown are means (normalized to 1) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± SD and individual data points. The samples with the highest (red) and lowest (blue) Cre level are indicated. Statistics: Bartlett's test for homoscedasticity. b, Immunofluorescence labeling for Cre in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice at 2.5 months of age. White box: magnification of the ONL region. Scale bar, 50 μm. Panorama: Scale bar, 500 μm. Additional file 3: Figure S3. Expression of mitochondrial genes in the ONL and PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice. a, Relative expression of Vdac1 and Tomm20 in ONL and PS samples from 2.5 months old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl and ctrl mice. b, Relative expression of the mt-DNA encoded genes mt-Co2 and mt-Nd1 in ONL and PS samples from 2.5 months old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl and ctrl mice. $$n = 6$$ mice per genotype. Statistics: Mann-Whitney nonparametric test. Additional file 4: Figure S4. Retinal function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Cox10}$$\end{document}rodΔCox10 mice. a, Scotopic single flash ERG responses to light stimuli with increasing intensities (top to bottom) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Cox10}$$\end{document}rodΔCox10 (orange) and ctrl (black) mice at 6 (left) and 12 (right) months of age. Shown are averaged traces. b,c, Scotopic a-wave (b) and b-wave (c) amplitudes as a function of stimulus intensity derived from (a). Shown are means \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± SD. **: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\leq$$\end{document}≤ 0.01. ****: p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\leq$$\end{document}≤ 0.0001. d, Photopic single flash ERG responses to light stimuli with increasing intensities (top to bottom) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Cox10}$$\end{document}rodΔCox10 (orange) and ctrl (black) mice at 6 (left) and 12 (right) months of age. Shown are averaged traces. e, Photopic b-wave amplitudes as a function of stimulus intensity derived from (d). Shown are means \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± SD. $$n = 5$$ (6-month-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Cox10}$$\end{document}rodΔCox10 mice); $$n = 6$$ (6- and 12-month-old ctrl mice; 12-month-old \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Cox10}$$\end{document}rodΔCox10 mice).Additional file 5: Table S1. Top 25 upregulated proteins in the PS and ONL.Additional file 6: Table S2. Top 50 differentially regulated proteins in the ONL of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice. Additional file 7: Table S3. Top 50 differentially regulated proteins in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice. Additional file 8: Table S4. Differentially regulated mitochondrial proteins in the PS of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl mice. Additional file 9: Table S5. Proteomic analysis of ONL and PS from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$rod^{\varDelta\ Vhl}$$\end{document}rodΔVhl and ctrl mice; includes UniProt IDs, number of unique peptides, ensembl IDs, gene symbols and short description, abundance ratios, adj-p values and raw data.
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|
---
title: Machine learning-based prediction of clinical outcomes after first-ever ischemic
stroke
authors:
- Lea Fast
- Uchralt Temuulen
- Kersten Villringer
- Anna Kufner
- Huma Fatima Ali
- Eberhard Siebert
- Shufan Huo
- Sophie K. Piper
- Pia Sophie Sperber
- Thomas Liman
- Matthias Endres
- Kerstin Ritter
journal: Frontiers in Neurology
year: 2023
pmcid: PMC9990416
doi: 10.3389/fneur.2023.1114360
license: CC BY 4.0
---
# Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke
## Abstract
### Background
Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors.
### Methods
We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations.
### Results
The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression.
### Conclusion
Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.
## 1. Introduction
Stroke is the second most common cause of death and a major cause of disability on a worldwide scale [1]. It occurs when the blood supply to brain tissue is interrupted by either blockage (ischaemic stroke) or bleeding caused by rupture of cerebral blood vessels (haemorrhagic stroke) ultimately resulting in irreversible neuronal death [2]. The incidence of stroke is set to rise due to the demographic shift affecting populations across the globe [3]. Thus, it is paramount to identify parameters that can aid in accurate prediction of long-term clinical outcome post-stroke.
In recent years the move toward electronic health records and the application of machine learning (ML) techniques in the medical research field have opened new frontiers of personalized medicine and decision support. The key advantage is that—in contrast to traditional statistical analyses—not only can predictors and biomarkers be identified on a group level, but ML techniques also enable prediction on an individual patient level. In other words, the outcome for a single patients can be predicted by considering a vast array of variables [4]. Numerous studies have successfully demonstrated the ability of ML models to predict specific clinical outcomes after stroke with remarkable accuracy and identified leading baseline factors that carry high prognostic value (5–8). Most studies so far have focused on the prediction of the modified Rankin Scale (mRS) [9] as it is the gold standard for determining functional recovery after stroke. While there are some studies investigating the ML-based prediction of the Barthel Index (BI) [10] and Modified Telephone Interview for Cognitive Status (TICS-M) [11], research regarding the Center for Epidemiologic Studies Depression Scale (CES-D) [12] and Mini-Mental State Examination (MMSE) [13] is sparse. In addition, the heterogeneity of ML techniques, clinical outcomes and datasets used in these studies makes it difficult to assess the broader implications of their findings [4].
The primary aim of the present study was therefore to conduct a systematic comparison of ML-based outcome prediction after first-ever ischemic stroke featuring measures of functional recovery (mRS, BI), cognitive function (MMSE, TICS-M), depression (CES-D), and mortality. The analysis was based on three powerful ML models and an array of baseline features including demographic, clinical, serological and MRI variables. As a secondary aim, we set out to identify to the key prognostic markers for each outcome using state-of-the-art visualization techniques.
## 2.1. Dataset and feature selection
The patients included in these analyses were selected from the PROSpective Cohort with Incident Stroke Berlin (PROSCIS-B) study. Recruitment for this prospective cohort study was conducted over a three-year period starting in March 2010 at the Center for Stroke Research Berlin and Charité University Hospital with a consecutive three-year follow-up period. The study population consists of patients aged 18 years and over with acute first-ever stroke according to the WHO stroke criteria [14]. The complete inclusion and exclusion criteria are described in detail on https://clinicaltrials.gov (NTC01363856). The study was approved by the ethics committee of the Charité - Universitätsmedizin Berlin (EA$\frac{1}{218}$/09) and was conducted in accordance with the Declaration of Helsinki. For the purposes of this exploratory analysis only patients with ischemic stroke and input features with no more than $15\%$ missing values were included.
MRI data was collected after study completion from clinical routine data. In order to quantify the characteristics of the imaging data all acute and chronic stroke lesions were delineated on Diffusion-weighted imaging (DWI) and Fluid-attenuated inversion recovery (FLAIR) sequences, respectively, using MRIcron [15] from the Center for Advanced Brain Imaging (University of South Carolina, Chris Rordan, USA). The delineation and volume extraction for acute and chronic stroke lesions were performed by medical students supervised by two independent expert neuroradiologists while all further MRI parameters were obtained by expert neuroradiologists.
Due to significant differences in the number and mean age of female and male patients, we balanced the dataset by separating all patients into groups according to sex and age and then randomly selecting patients within these groups until there were no more significant differences (up to p ≤ 0.1). This was necessary to ensure the predictions of our models were not based on an inherent bias in the training data (e.g., women being older on average and thus having worse outcomes) [16]. The patient selection process is shown in Figure 1 and the characteristics of the dataset are described in Table 1.
**Figure 1:** *Flowchart depicting patient selection process. PROSCIS, PROSpective Cohort with Incident Stroke; MRI, Magnetic resonance imaging.* TABLE_PLACEHOLDER:Table 1
## 2.2. Input data and outcomes
This study includes a total of 43 stroke-related baseline variables in four input subdomains. They consisted of 6 demographic and 16 clinical variables, 10 serological markers and 11 MRI parameters as listed in Table 1. Procalcitonin serum levels, which have previously been identified as a prognostic marker for 30-day mortality after stroke [18], had to be excluded since this variable had more than $15\%$ missing values. The outcomes included measures of functional recovery (mRS and BI), cognitive function (MMSE and TICS-M), depression (CES-D) and survival. The mRS and BI were assessed at patient discharge, and 1 year post-stroke. Cognitive impairment was evaluated using the MMSE at discharge and later with the TICS-M at 1 and 3 years. CES-D and survival were also assessed 1 and 3 years after the index event. The follow-up process included an initial telephone assessment of cognitive function, followed by a structured interview conducted either by phone or mail. Table 2 shows the distribution of outcomes in the dataset, their respective follow-up time points, and the cut-off points for good vs. poor clinical outcome as defined by clinical scoring gold standards.
**Table 2**
| Distribution of outcomes in patient population | Distribution of outcomes in patient population.1 | Distribution of outcomes in patient population.2 | Distribution of outcomes in patient population.3 |
| --- | --- | --- | --- |
| Outcome | Time points | Good outcome, n (total/female/male) | Poor outcome, n (total/female/male) |
| mRS | PD | 221/110/111 | 86/41/45 |
| mRS | Year 1 | 193/89/104 | 40/27/13 |
| BI | PD | 263/125/138 | 44/26/18 |
| BI | Year 1 | 195/90/10 | 7/6/1 |
| MMSE | PD | 271/126/145 | 29/21/8 |
| TICS-M | Year 1 | 147/69/78 | 48/32/16 |
| TICS-M | Year 3 | 125/60/65 | 19/8/11 |
| CES-D | Year 1 | 163/79/93 | 48/35/13 |
| CES-D | Year 3 | 132/53/79 | 30/19/11 |
| Mortality | Year 1 | 271/132/139 | 36/19/17 |
| Year 3 | 142/78/64 | 165/73/92 | |
| Cut-off points for good vs. poor outcome | Cut-off points for good vs. poor outcome | Cut-off points for good vs. poor outcome | Cut-off points for good vs. poor outcome |
| Outcome | Total points | Good outcome | Poor outcome |
| mRS | 0–6 | 0–2 | 3–6 |
| BI | 0–100 | 61–100 | 0–60 |
| MMSE | 0–30 | 24–30 | 0–23 |
| TICS-M | 0–50 | 30–50 | 0–29 |
| CES-D | 0–60 | 0–15 | 16–60 |
## 2.3. Machine learning analysis
The aim of this study was to conduct a systematic comparison of ML-based outcome prediction models after first-ever ischemic stroke. To accomplish this, a linear model, a non-linear model, and a tree-based model were selected for comparison (see Figure 2). To reduce complexity and potential problems brought on by multiple comparisons, a small set of three ML algorithms were selected. A Support Vector Machine (SVM) with linear kernel (SVM-lin) [19] and a SVM with radial basis function kernel (SVM-rbf) [20] were chosen as linear and non-linear models due to their strong performance in previous studies and the ability to directly compare them [6, 16, 21]. Similarly, Gradient Boosting (GB) [22] was chosen as the tree-based classifier due to its superior performance and when compared to other tree-based models [23, 24]. We compensated for missing data in the training and validation set with Multiple Imputation using Chained Equations (MICE) [25]. The outcome class imbalances in the training set were counteracted with the Synthetic Minority Over-sampling Technique (SMOTE) [26] and random oversampling [27]. Categorical input features were transformed using one-hot encoding. Then, models were carefully evaluated using ten times repeated 5-fold nested cross-validation with fixed seed to increase robustness [28]. Here the data is split into five training ($80\%$) and test sets ($20\%$). Each of these training sets is then subdivided into further five training ($80\%$) and validation sets ($20\%$). The hyperparameters of the ML models (listed in Supplementary Table S1) have been optimized on these training and validation sets via grid search before finally being evaluated on the unseen data of the test sets.
**Figure 2:** *Process flow of input data, machine learning analysis and outcome prediction. mRS, modified Rankin Scale; BI, Barthel Index; MMSE, Mini-Mental State Examination; TICS-M, Modified Telephone Interview for Cognitive Status; CES-D, Epidemiologic Studies Depression Scale; SVM-lin, Support Vector Machine with linear kernel; SVM-rbf, Support Vector Machine with radial basis function kernel; GB, Gradient Bossting Classifier; MRI, Magnetic resonance imaging.*
Performance of each model was evaluated using balanced accuracy (BA), area under the receiver operating characteristic curve, sensitivity, specificity, likelihood ratio (LR) and Integrated Discrimination Improvement index (IDI). BA is the arithmetic mean of sensitivity and specificity while the receiver operating characteristics curve (ROC) plots the true positive rate in relation to the false positive rate of the ML models. The area under the curve (AUC) of the ROC is routinely used as a measure of performance in ML. For each outcome, we reported the mean BA and AUC along with their standard deviation (SD) for ten iterations of 5-fold nested cross-validation. The LR compares the fit of two models by taking the ratio of their likelihoods [29] while the IDI ranks the model according to the change of the discrimination slopes [30]. To test for statistical significance, we performed non-parametric permutation testing [31]. Here, the exact same ML analysis and nested cross-validation procedure was performed a hundred times on randomly permuted ground truth labels before being compared to the original results. Results were considered statistically significant below p ≤ 0.05 and p ≤ 0.01 after Bonferroni correction for multiple comparisons (3 ML algorithms × 5 feature subsets). We used the Python 3.6 programming language with the scikit-learn, pandas, statsmodel, matplotlib and seaborn packages for all analyses and visualizations.
## 2.4. Feature importance and Shapley values
In order to discern feature importance we implemented Shapley values using the SHAP (SHapley Additive exPlanations) framework [32]. This statistic is a solution concept originating from cooperative game theory which calculates the relative importance of an input feature for the final prediction result and has already demonstrated convincing results in biomedical and clinical research applications [33, 34]. Shapley values are calculated by determining the average marginal contribution of each feature over all possible combinations of input features. This is done by analyzing the effect of each feature on the prediction when it is included or excluded, while also taking into account the dependencies between features. For the purposes of this study, we implemented the Kernel SHAPexplainer which acts as a specially-weighted local linear regression [32].
## 3. Results
Out of the 621 PROSCIS-B patients 125 had no MRI associated with their study ID and in 5 further cases we were unable to locate the MRI data. This resulted in 491 patients with imaging data out of which 255 had received a 3T scan at the Center of Stroke Research Berlin (CSB) and 236 had been processed on scanners at Charité - Universitätsmedizin Berlin ranging from 1 to 1.5T, all of which were Siemens MRI units. In 56 cases the imaging data could not be delineated due to missing sequences or motion artifacts and in 8 cases participants had retracted their consent for the study which resulted in a total of 427 fully delineated cases. The final balanced dataset consisted of 307 patients. There was a loss to follow-up of 74 patients ($24.1\%$) in mRS, 105 patients ($34.2\%$) in BI, 51 patients ($26.2\%$) in TICS-M, and 49 patients ($23.2\%$) in CES-D from the initial sample size. No loss was observed for mortality.
We evaluated and ranked the performance of the ML models using the metrics of BA and AUC. The results of these analyses can be found in Supplementary Tables S2–S6. In Figure 3, we show the performance in BA for all outcomes (mRS, BI, MMSE, TICS-M, CES-D, and survival), time points, and ML models (SVM-lin, SVM-rbf and GB). Additionally, we calculated the Integrated IDI and LR to provide further insight into the models' performance. The detailed results are reported in Supplementary Tables S7–S11. While the LR revealed no significant differences between the ML models it is important to note that the results obtained from the BA, AUC and the LR should be viewed independently, as they are based on different methods of evaluating the models' performance. Although in many cases the performance of the three ML models was at a comparable level the strongest predictive performance overall was achieved by SVM-rbf for TICS-M after 3 years (BA ± SD = 0.7 ± 0.13; AUC ± SD = 0.76 ± 0.13; p ≤ 0.05) using the demographic input subdomain. Table 3 states the most important predictors according to the Shapley values. The following paragraphs will list significant results (p ≤ 0.05 or p ≤ 0.01 Bonferroni corrected) according to the permutation test for each outcome per input subdomain.
**Figure 3:** *Prediction performance in balanced accuracy (BA) for all outcomes, time points and input subdomains. In (A) all input parameters were considered while (B–E) show the results of the (B) demographic, (C) clinical, (D) serological and (E) MRI input subdomain. Results for BI after 1 year were unreliable due to the extreme class imbalance in the dataset (see Table 2). mRS, modified Rankin Scale; BI, Barthel Index; MMSE, Mini-Mental State Examination; TICS-M, Modified Telephone Interview for Cognitive Status; CES-D, Epidemiologic Studies Depression Scale; SVM-lin, Support Vector Machine with linear kernel; SVM-rbf, Support Vector Machine with radial basis function kernel; GB, Gradient Bossting Classifier; MRI, Magnetic resonance imaging.* TABLE_PLACEHOLDER:Table 3
## 3.1. Modified Rankin Scale
The highest prediction score for mRS at patient discharge was achieved by GB (BA ± SD = 0.69 ± 0.07; AUC ± SD = 0.77 ± 0.06; p ≤ 0.01) followed by SVM-lin (BA ± SD = 0.67 ± 0.07; AUC ± SD = 0.74 ± 0.07; p ≤ 0.01) and SVM-rfb (BA ± SD = 0.65 ± 0.06; AUC ± SD = 0.77 ± 0.06; p ≤ 0.01) using all input parameters. In the serological input subdomain GB (BA ± SD = 0.63 ± 0.07; AUC ± SD = 0.68 ± 0.08; p ≤ 0.01) and SVM-rbf (BA ± SD = 0.57 ± 0.06; AUC ± SD = 0.63 ± 0.07; p ≤ 0.05) attained significant prediction results. The top five predictors using all input parameters were National Institutes of Health Stroke Scale (NIHSS), hsCRP, glucose, cholesterol and supra-/infratentorial infarct location.
The mRS after 1 year could best be predicted using the demographic input subdomain by SVM-rbf (BA ± SD = 0.68 ± 0.09; AUC ± SD = 0.73 ± 0.01; p ≤ 0.01) followed by SVM-lin (BA ± SD = 0.67 ± 0.08; AUC ± SD = 0.73 ± 0.01; p ≤ 0.01) and GB (BA ± SD = 0.61 ± 0.08; AUC ± SD = 0.66 ± 0.09; p ≤ 0.05). In the serological input subdomain, SVM-rbf (BA ± SD = 0.63 ± 0.1; AUC ± SD = 0.64 ± 0.12; p ≤ 0.01) led in prediction results. Waist circumference, sex, age, education, and BMI were the leading predictors in the demographic input subdomain.
## 3.2. Barthel Index
For BI at patient discharge, SVM-lin (BA ± SD = 0.65 ± 0.08; AUC ± SD = 0.73 ± 0.11; p ≤ 0.05) and GB (BA ± SD = 0.63 ± 0.08; AUC ± SD = 0.74 ± 0.07; p ≤ 0.05) achieved significant prediction results using all input parameters. The strongest predictors were NIHSS, smoking, the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, infarct pattern and infarct origin. However, BI after 1 year could not be predicted by any model.
## 3.3. Mini-Mental State Examination
The leading ML models for predicting MMSE at patient discharge were SVM-rbf (BA ± SD = 0.67 ± 0.09; AUC ± SD = 0.71 ± 0.11; p ≤ 0.01) and SVM-lin (BA ± SD = 0.65 ± 0.1; AUC ± SD = 0.7 ± 0.1; p ≤ 0.05) using the demographic input subdomain with education, sex, age, waist circumference and BMI being the most important predictors.
## 3.4. Modified Telephone Interview for Cognitive Status
The best predictions for TICS-M after 1 year were by SVM-lin (BA ± SD = 0.67 ± 0.09; AUC ± SD = 0.73 ± 0.09; p ≤ 0.01), SVM-rbf (BA ± SD = 0.65 ± 0.09; AUC ± SD = 0.72 ± 0.09; p ≤ 0.01) and GB (BA ± SD = 0.63 ± 0.08; AUC ± SD = 0.69 ± 0.11; p ≤ 0.01) using the demographic input subdomain. Further significant prediction results were achieved by GB (BA ± SD = 0.6 ± 0.08; AUC ± SD = 0.66 ± 0.1; p ≤ 0.01) using the clinical input subdomain. The top five predictors in the demographic input subdomain were education, age, BMI, sex, and hip circumference. TICS-M after 3 years was most successfully predicted by SVM-rbf (BA ± SD = 0.7 ± 0.13; AUC ± SD = 0.76 ± 0.13; p ≤ 0.05), SVM-lin (BA ± SD = 0.69 ± 0.14; AUC ± SD = 0.77 ± 0.13; p ≤ 0.05) and GB (BA ± SD = 0.68 ± 0.12; AUC ± SD = 0.74 ± 0.13; p ≤ 0.01) using the demographic input subdomain. Education, age, sex, waist circumference, and hip circumference were the leading variables.
## 3.5. Center for epidemiologic studies depression scale
For the prediction of CES-D after 1 year the use of the demographic input subdomain led to a significant prediction performance by GB (BA ± SD = 0.63 ± 0.09; AUC ± SD = 0.7 ± 0.1; p ≤ 0.05), SVM-lin (BA ± SD = 0.63 ± 0.08; AUC ± SD = 0.68 ± 0.1; p ≤ 0.05) and SVM-rbf (BA ± SD = 0.62 ± 0.07; AUC ± SD = 0.7 ± 0.09; p ≤ 0.01). The strongest predictors were education, sex, BMI as well as hip and waist circumference. No ML model achieved significant prediction results for CES-D after 3 years.
## 3.6. Survival
Survival within 1 or 3 years could not be predicted reliably by any model.
## 4. Discussion
To the best of our knowledge, this is the first study to apply highly comparable standardized ML models to predict a wide range of long-term patient outcomes including functional recovery, cognitive impairment, depression, and mortality from a single, homogenous patient collective. While functional recovery scores like mRS and BI are often used as primary outcome endpoints in most major stroke cohorts, cognitive impairment and depression play a vital role in terms of long-term patient outcome. Up to $80\%$ of patients are affected by cognitive impairment post-stroke and up to $30\%$ will develop a clinically relevant depression within 2 years after the index event [35, 36]. These factors not only negatively affect functional recovery by decreasing a patient's capability for actively participating in rehabilitation measures but also disrupt their social integration. Although numerous previous studies have used similar ML models to predict functional recovery after stroke [5], here we demonstrate the accuracy of ML models to predict post-stroke cognitive status and depression up to 3 years post-stroke, as well as functional recovery.
Our results are in line with previous studies in identifying NIHSS as the leading predictor for mRS at patient discharge amongst all input variables [37, 38]. Increased levels of hsCRP were correlated with poor clinical outcome which supports findings reported by den Hertog et al. [ 39] in acute stroke. Interestingly, waist circumference was the leading predictor for mRS after 1 year. Being underweight (BMI < 18.5 kg/m2) has previously been associated with unfavorable outcomes in terms of mortality and functional recovery in previous studies [40]. Figure 4 illustrates the decision-making process of GB for mRS at patient discharge on a single-subject level.
**Figure 4:** *Decision-making process by the Gradient Boosting Classifier for the modified Rankin Scale (mRS) at patient discharge on the level of individual patients depicted via Shapley values. The relative importance of an input variable can be quantified by its Shapley value and represented by the length of a bar. In this example, features in red counted toward a good outcome while blue features signified poor outcome for mRS at patient discharge. In (A) a patient with a mRS score of 1 point was correctly classified as having a good outcome with variables such as low National Institutes of Health Stroke Scale (NIHSS), high-sensitivity C-reactive protein (hsCRP), cholesterol and acute infarct volume in Diffusion-weighted imaging (DWI) outweighing a high Wahlund Score. In (B) a patient with a mRS score of 4 points was correctly predicted as having poor outcome due to high NIHSS, hsCRP and cholesterol whilst offsetting a low acute infarct volume in DWI. In both instances the decision was made by considering the total impact of all features.*
In a study by Monteiro et al. [ 6] various ML models were applied to predict mRS after 3 months from 425 patients using 152 input variables. The best performance using baseline variables was achieved using a Random Forest (RF) classifier with an AUC of 0.808 ± 0.085. In a separate study by Heo et al. [ 7] a DNN was used on 3,522 patients and achieved a classification accuracy of AUC = 0.888 with no reported SD. However, the authors did not mention whether cross-validation or repetition were used, which are important for developing a robust ML model and avoiding over-fitting. In a study by Li et al. [ 21] predicting mRS after 6 months a SVM (AUC = 0.865; $95\%$ CI 0.823–0.907) performed comparably well with six other models, including a RF classifier (AUC = 0.874; $95\%$ CI 0.835–0.912) and a DNN (AUC 0.867; $95\%$ CI 0.827–0.908). In contrast, in our study, for mRS at patient discharge the SVM-lin (AUC ± SD = 0.74 ± 0.07) was outperformed by GB (AUC ± SD = 0.77 ± 0.06). However, comparing the results of these studies is challenging due to variations in follow-up time points, input variables, methodology, and performance measures. Nevertheless, it appears that SVMs tend to perform similarly to, or worse than, tree-based classifiers or DNNs for predicting mRS outcomes.
Considerable overlap exists between mRS and BI in the development of functional recovery post stroke [41]. This is reflected in NIHSS being the leading predictor for BI at patient discharge. Our results also confirm the relative importance of stroke origin for this outcome [42]. The BI after one year could not be predicted—this may be due to the extreme class imbalance of this outcome (see Table 2). In contrast, in a study by den Hertog et al. [ 39] a ML model for identifying prognostic factors for motor and cognitive improvement after post-stroke rehabilitative training was developed based on a SVM-lin. The model included 55 patients and the results of the ischemic test set reported performance scores of correlation = 0.75, MADP = 87,$03\%$ and RMSE = 21,74 for BI. The most important parameters for the prediction were identified as the Functional Independence Measure and BI at patient discharge as well as serological markers such as Platelet-to-lymphocyte ratio, Red Cell Distribution Width and Lymphocytes.
Amongst the leading predictors for cognitive function post-stroke were demographic factors such as education, age and BMI which confirms previously published results [43, 44]. While our findings are in line with the results by Casanova et al. [ 45] and Aschwanden et al. [ 46] their studies additionally identified the importance of socioeconomic status and ethnicity in terms of cognitive function post-stroke. Unfortunately, in the current study, these variables could not be accounted for.
Education being the top predictor for levels of depression after 1 year is in accordance with several studies linking low education level to an increased risk of post-stroke depression [47]. Previous studies have found a significant association between higher waist circumference with an elevated rate of depression [48]. In the current analysis, female sex was also identified as an important predictor of depression [49]. A study by Hama et al. [ 50] achieved an impressive AUC above 0.90 for the prediction of post-stroke depression using a probabilistic artificial neural network on 274 stroke inpatients at the Hibino Hospital. The predicted clinical score was the Hospital Anxiety and Depression Scale and its lead predictors were the Japanese Perceived Stress Scale, the Symbol Digit Modalities Test, tapping span backward, visual cancellation Kana time and the Continuous Performance Test. This jump in prediction accuracy may be explained in part by the inclusion of these very specific test scores.
## 4.1. Methodological considerations
While many previous ML-based studies achieved noteworthy results, there are some potentially problematic methodological factors to consider: ideally, a ML model is trained and tested on numerous different samples in order to create a robust predictor for new, unseen data [51]. In face of limited clinical data, it is crucial to include a re-sampling procedure to ensure effective training [52]. Additionally, few studies performed more than one iteration of their analyses which negatively impacts robustness [28]. In our study, we accounted for these factors by using a repeated 5-fold nested cross-validation. Furthermore, many studies use datasets and ML methods specific to the purpose of predicting an individual outcome. This impedes comparability as it remains unclear whether differences in performance are based on variations in input data or technical aspects of the ML analysis [5]. Neglecting to balance these datasets regarding age and sex may also lead to biased results [53]. We therefore balanced the dataset according to age and sex and predicted a range of clinical outcomes from the same dataset using three classical ML models while ensuring independence between training and test data. In addition, and in contrast to previous ML studies, we estimated the relative importance of features using Shapley values allowing to assess the impact of different input features for clinical outcome prediction in individual patients (see Figure 4).
## 4.2. Clinical implications
In the coming years, the advancement of big data analytics based on collaboration networks and electronic health records is set to drive a paradigm shift in clinical research [54]. Novel automated and computer-based methods will play a key role in making use of increasing datasets and processing power. Therefore, we take a crucial step forward in the application of ML-based research methods to one of the most common and severe diseases around the globe and show that established as well as less traditional risk predictors can be identified and reproduced with ML techniques even in a limited sample size.
There is currently no established prediction score for depression outcomes following ischemic stroke. However, there are already a variety of scores available in the scientific literature for predicting functional outcomes (such as the Wang et al. [ 55] and ASTRAL [56] scores), cognitive outcomes (such as the CHANGE [57] and SIGNAL2 [58] scores), and mortality outcomes (such as the iScore [59] and PLAN [60] scores). In future studies, the aim should be to develop a universal model that can predict multiple outcomes-including functional recovery, cognitive impairment, depression, and mortality outcomes-using a basic set of variables such as NIHSS, education, sex, age, or BMI. This model would ideally be an easy-to-use tool for clinicians in real-world medical practice and act as an AI-based clinical decision support system (CDSS). The implementation of CDSS has been shown to be a cost-effective and efficient method for enhancing clinical workflow and decision-making [61]. CDSSs have the potential to enhance patient safety by mitigating the occurrence of oversights and treatment errors. In the case of stroke, functional recovery is heavily dependent on rehabilitation measures which in turn requires adequate cognitive function and management of post-stroke depression [62, 63]. The ability of CDSSs to alert providers to potential challenges in the management process can provide valuable guidance for more personalized rehabilitation programs and patient-tailored secondary prevention strategies, ultimately improving post-stroke outcomes.
## 4.3. Limitations
This study has several limitations that warrant discussion. First and foremost, this study had a limited sample size, the outcome classes were imbalanced, and an external control dataset was lacking. The application of 5-fold nested cross-validation, SMOTE and random oversampling partially counteract these limitations. To avoid shortcut learning and develop a model representative of the general population, we balanced our dataset by age and sex. Shortcut learning occurs when the model relies heavily on easily observable features like age rather than underlying causes, leading to potential biases and inaccuracies when applied to individuals outside the trained age range. However, this approach does not account for the natural incidence variation within the population, which may impact the ML model's predictions. Additionally, most of the patients included in this study had relatively mild to moderate strokes (NIHSS median of 2 (1–4)); this may have negatively affected prediction performance and limits generalizability to more severely affected stroke cohorts. There was also no data available on whether patients entered a rehabilitation program post-stroke, or which secondary prevention strategies were initiated. Therefore, these factors could not be accounted for in terms of post-stroke outcome endpoints in this analysis.
## 5. Conclusion
Based on a systematic comparison, the results of this study demonstrated the viability of ML-based outcome prediction after first-ever ischemic stroke for functional recovery, cognitive function, depression, and mortality. Compared to group-based statistical analyses, the advantage of ML-techniques is their ability to make predictions on a single-subject level by considering a multitude of variables which is key for future application in clinical routine. Furthermore, we extracted the most important prognostic variables for each outcome. On the one hand, the results confirmed several already established prognostic markers and on the other identified novel candidates such as education, hsCRP and waist circumference as relevant predictors of important clinical endpoints. However, further studies are needed to confirm these findings and to establish their clinical viability.
## Data availability statement
The PROCIS-B data is available upon request from TL. The code and results data are available upon request from KR.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the Charité - Universitätsmedizin Berlin (EA$\frac{1}{218}$/09). The patients/participants or their legal representative provided their written informed consent to participate in this study.
## Author contributions
LF, KV, AK, and KR: conceptualization. LF, UT, KV, AK, HA, SP, and KR: data curation. LF, UT, and KR: formal analysis, methodology, visualization, and software. LF, TL, and KR: project administration. LF: writing–original draft. KV, AK, ES, SH, SP, PS, TL, ME, and KR: writing–review and editing. KV, TL, and KR: resources. KV and KR: supervision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
ME reports grants from Bayer and fees paid to the Charité from Abbot, Amgen, AstraZeneca, Bayer, 296 Boehringer Ingelheim, BMS, Daiishi Sankyo, Sanofi, Novartis, Pfizer, all outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1114360/full#supplementary-material
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---
title: Efficient knock-in method enabling lineage tracing in zebrafish
authors:
- Jiarui Mi
- Olov Andersson
journal: Life Science Alliance
year: 2023
pmcid: PMC9990459
doi: 10.26508/lsa.202301944
license: CC BY 4.0
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# Efficient knock-in method enabling lineage tracing in zebrafish
## Abstract
This study introduces a knock-in method in zebrafish that is cloning-free, preserves the endogenous gene, achieves high germline transmission, and useful for both cell labelling and lineage tracing.
Here, we devised a cloning-free 3′ knock-in strategy for zebrafish using PCR amplified dsDNA donors that avoids disrupting the targeted genes. The dsDNA donors carry genetic cassettes coding for fluorescent proteins and Cre recombinase in frame with the endogenous gene but separated from it by self-cleavable peptides. Primers with 5′ AmC6 end-protections generated PCR amplicons with increased integration efficiency that were coinjected with preassembled Cas9/gRNA ribonucleoprotein complexes for early integration. We targeted four genetic loci (krt92, nkx6.1, krt4, and id2a) and generated 10 knock-in lines, which function as reporters for the endogenous gene expression. The knocked-in iCre or CreERT2 lines were used for lineage tracing, which suggested that nkx6.1+ cells are multipotent pancreatic progenitors that gradually restrict to the bipotent duct, whereas id2a+ cells are multipotent in both liver and pancreas and gradually restrict to ductal cells. In addition, the hepatic id2a+ duct show progenitor properties upon extreme hepatocyte loss. Thus, we present an efficient and straightforward knock-in technique with widespread use for cellular labelling and lineage tracing.
## Graphical Abstract
## Introduction
With the advent of genome editing tools, the CRISPR-Cas9 system has become the most popular method for the generation of genetically engineered animal models (Wang et al, 2013; Yang et al, 2013), where we refer to “knock-in” as the insertion of exogenous DNA at a specific locus in the host genome. Multiple knock-in strategies have been introduced and optimized for the construction of nonhuman primate (Zuo et al, 2017; Yao et al, 2018a), mouse (Zuo et al, 2017), medaka (Gutierrez-Triana et al, 2018; Seleit et al, 2021; Tan & Winkler, 2022), and zebrafish models (Irion et al, 2014; Hoshijima et al, 2016; Wierson et al, 2020; Almeida et al, 2021). In zebrafish, the knock-in methods vary in terms of the targeting regions (such as 5′ noncoding region, exon, intron, or 3′ end) (Han et al, 2021; Levic et al, 2021), DNA double-stranded break repair mechanisms (homology-directed repair [HDR] or nonhomologous end joining [NHEJ]) (Auer et al, 2014), the type of donor templates (Wierson et al, 2020), the injection of Cas9 protein or Cas9 mRNA, and the use of drugs in promoting HDR (Albadri et al, 2017; Aksoy et al, 2019).
In zebrafish, 5′ knock-in methods have been intensively investigated in the locus upstream of the start codon ATG using donor plasmids containing in vivo linearization site(s) (Auer et al, 2014; Hisano et al, 2015). The single linearization site upstream of the insertion sequence can facilitate NHEJ-mediated integration (Irion et al, 2014; Kimura et al, 2014; Kesavan et al, 2017); researchers also tried to introduce long homologous arms (HAs) flanked by two I-SceI/gRNA recognition sites to induce HDR (Hoshijima et al, 2016). Although fluorescent reporter lines, and even CreERT2 lines, have been generated by such methods (Kesavan et al, 2018), the wide applications of these methods are still hampered by the disruption of one allele of the endogenous gene and the multiple molecular cloning steps. The 3′ knock-in method has also been applied using circular plasmids as the donor, with either long or short HAs flanked by two in vivo linearization sites (Eschstruth et al, 2020; Gillotay et al, 2020). The advantage of 3′ knock-in is that it keeps the knock-in cassettes in-frame and maintains the functionality of the endogenous gene. However, in certain cases, a few amino acids in the C-terminus may be deleted when using the NHEJ strategy (Cronan & Tobin, 2019). Several studies reported that the HDR-mediated 3′ knock-in efficiency is highly dependent on the length of the HAs (usually with higher efficiency using >500 bp HAs) (Irion et al, 2014; Hoshijima et al, 2016). Nevertheless, one recent study showed that the introduction of short HAs in the donor plasmids flanked by two linearization sites can enhance microhomology-mediated end joining (MMEJ) with good efficiency (Luo et al, 2018). However, such methods are still somewhat limited in use because of the low scalability and the complex construct preparation steps. Recently, intron-based and exon-based knock-in approaches have remarkably expanded the knock-in toolbox by targeting genetic loci beyond the 5′ or 3′ end (Li et al, 2015; Li et al, 2019; Welker et al, 2021). These methods mostly rely on the NHEJ method, and the endogenous genes can be either destroyed or rescued (depending on whether the exon sequences downstream of the insertion site are added into the donor or not). Given that all these methods are limited in scalability and involve multiple molecular cloning steps in construct preparation, the development of a straightforward and efficient knock-in methodology is still warranted in the zebrafish field.
Recent studies in mouse and in vitro systems have demonstrated several approaches that improve HDR. The Tild-CRISPR (targeted integration with linearized dsDNA-CRISPR) strategy that used PCR-amplified or enzymatic-cut donors with 800-base pair HAs have been successfully applied in generating knock-in mouse lines (Yao et al, 2018b), indicating that nude double-stranded DNA (dsDNA) can serve as an effective donor in eukaryote embryos. Furthermore, 5′ modified dsDNA with short HAs (roughly 50 base pairs) demonstrated impressive knock-in efficiency in an in vitro culture system (Yu et al, 2020). In this study, the researchers systematically compared 13 modifications to dsDNA with gRNA targeting the 3′ UTR of the GAPDH gene in HCT116 cells. The dsDNAs were synthesized by PCR amplification with the modifications incorporated in the primers. It showed that C6 linker (AmC6) or C12 linker (AmC12) and moieties by adding on secondary modifications outperformed no C6/C12 linked modifications with a substantial increase of knock-in efficiency of more than fivefold. Although the mechanism is still undetermined, it is postulated that the 5′ modification can help prevent degradation and multimerization of the donor and circumvent stochastic NHEJ, indicated by less NHEJ events and random insertions.
Inspired by these previous efforts to improve knock-in efficiency (Yu et al, 2020), here, we introduce a straightforward CRISPR-Cas9-guided 3′ knock-in approach to generate zebrafish lines for cellular labelling and lineage tracing. We synthesized 5′ modified dsDNA with either short or long HAs as the donor by a simple PCR step with 5′ modified primers (AmC6). The donor templates code for two kinds of 2A peptides linking the endogenous gene product with a fluorescent protein and then with iCre/CreERT2. By coinjecting this type of donor with in vitro preassembled Cas9/gRNA ribonucleoprotein complexes (RNPs), we generate mosaic F0 with very high probability of giving rise to germline transmission. Ultimately, we generated 10 knock-in fish lines, demonstrating high scalability. Our knock-in lines can precisely reflect the endogenous gene expression, as visualized by optional fluorescent proteins. Importantly, we also performed lineage-tracing experiments using the knock-in iCre and CreERT2 lines to delineate cell differentiation paths in pancreas and liver development and injury models.
## A 3′ knock-in pipeline and the characterization of TgKI(krt92-p2A-EGFP-t2A-CreERT2)
With the aim to generate knock-in zebrafish lines for both cellular labelling and lineage tracing, we designed our vector templates encompassing a fluorescent protein and different Cre recombinases linked by two self-cleavable 2A peptide sequences (p2A and t2A) (Fig 1A). The insertion sequences were flanked by left and right long HAs (nearly 900 base pairs) on the basis of the GRCz11 reference genome archived in the Ensembl database. Next, we used pairs of 5′ AmC6 modified primers to amplify dsDNA with the insertion sequence flanked by either long or short HAs by PCR. Subsequently, we coinjected the PCR product, which serves as the direct donor, together with in vitro preassembled Cas9/gRNA RNPs into the one-cell stage zebrafish embryos (of the TL strain). Whereas many of the injected embryos can display some fluorescence/integration, we aim to sort out the F0 with high mosaicism (estimated as >$30\%$) based on the fluorescence in the expected cell types and raise them up. A handful of adult F0 fish were then to be outcrossed with WT fish to screen for founders. To test the validity and efficiency of this pipeline, we first selected an epithelial marker, krt92, as it facilitated the identification of mosaic F0 by a simple detection of fluorescence in the skin.
**Figure 1.:** *Knock-in pipeline and characterization of knock-in line at the krt92 locus.(A) Schematic representation of the 3′ knock-in pipeline with 5′ modified dsDNA as the donor. The iCre indicates improved Cre. (B) The design of the template vector, PCR-amplified dsDNA with 5′ modifications, and the gRNA sequence for the construction of TgKI(krt92-p2A-EGFP-t2A-CreERT2). The gRNA1 and gRNA2 indicate in vivo linearization sites (sequences listed in Table S2). The pink lollipops in the middle of long LHA indicate synonymous point mutations on the left HAs. The orange lollipops at the end of dsDNAs indicate 5′ AmC6 modifications. The nucleotide sequence in blue indicates gRNA; whereas the nucleotide sequence in red indicates the PAM sequence. (C, D, E) Summary statistics of krt92 knock-in efficiency using different donors, including the percentage of injected F0 with at least 30% fluorescence labelling in the skin (C), the percentage of adult F0 giving rise to germline transmission (D), and the percentage of F1 siblings from four different founders (Fish 1–4) carrying the knock-in cassette (E). (F) The scheme for the lineage-tracing strategy for the iCre or tamoxifen-inducible Cre knock-in lines. There are two alternatives for the color switch using the Cre responder lines, option 1 contains ubb:loxp-CFP-stop-loxp-H2BmCherry transgene (abbreviated as ubi:CSHm), whereas option two contains ubb:loxp-EGFP-stop-loxp-mCherry (abbreviated as ubi:Switch). Cells with Cre recombination will ubiquitously express H2BmCherry or mCherry. (G, H, I, J) Temporal labelling with 20 μM 4-OHT treatment at 6–26 hpf in TgKI(krt92-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) line (G); and representative confocal images at 2 dpf (H–H’’), 3 dpf (I–I’’), and 6 dpf (J–J’’). Skin cells with Cre recombination after 4-OHT treatment were labelled with H2BmCherry. The insets are magnified views showing the expression pattern of two fluorescent proteins. Scale bars = 200 μm.*
For the krt92 locus, the dsDNA donor and gRNA sequence are shown in Figs 1B and S1. We selected one gRNA 20 base pairs upstream of the stop codon. To circumvent the cleavage of the donor, and keep the endogenous amino acid sequence intact, we incorporated several synonymous point mutations in the left HA (Fig S1). We used long HAs on both sides to enhance the annealing of the sequences. With 5′ modified dsDNA injection, we observed that 8 out of 158 ($5.1\%$) injected embryos showed fluorescence in approximately a third of the skin, suggesting early integration (Fig 1C). Among them, four fish were identified as founders by screening more than 200 F1 embryos from each fish (Fig 1D). The proportion of the F1 generation that carried the knock-in cassette ranged from $11.5\%$ to $20\%$ (Fig 1E). Sanger sequencing confirmed the correct in-frame integration at the junction between the endogenous gene and the 5′ end of the integrated sequence (Fig S2 and data uploaded to a public repository: https://osf.io/tdkvh/).
**Figure S1.:** *Sequence information of the construct used for krt92 knock-in.The sequences of different cassettes are shown using different colors. The mutated sequences in the left homologous arms are highlighted with a dark green background color. The introduction of these point mutations keeps the amino acid sequence intact without cleavage by the Cas9/gRNA complex meant to only cleave the genomic insertion site.* **Figure S2.:** *Chromatogram of Sanger sequencing of the genomic integrations.Sanger sequencing of the integrations at the junction of the last exon to the integration for each of the 10 generated knock-in lines. The chromatograms display the sequences, the reverse direction, and show integrations in-frame with the endogenous gene without indels at any of the junctions.*
To validate the functionality of Cre recombinase, we crossed TgKI(krt92-p2A-EGFP-t2A-CreERT2) with the responder line Tg(ubb:loxP-CFP-STOP-Terminator-loxP-hmgb1-mCherry) (abbreviated as Tg(ubi:CSHm)) (Fig 1F). The cells with krt92 expression during the 4-hydroxytamoxifen (4-OHT) treatment were expected to have Cre recombinase translocated to the nucleus to conduct recombination. After recombination, all the krt92+ cell progeny would express H2BmCherry as it is directly driven by the ubiquitin B promoter (Mosimann et al, 2011). The H2BmCherry signal was detected in skin cells after 4-OHT administered at 6 hours postfertilization (hpf) (Figs 1G–J and S3). We also observed that various proportions of intestinal cells were fluorescently labelled after 4-OHT treatments at different timepoints (Fig S4). Although in situ hybridization for krt92 is not feasible because of the high sequence similarity with other keratins, the fluorescence pattern matched the recent single-cell RNA-seq data of the zebrafish intestine, showing a widespread expression across different intestinal epithelial cell types (Wen et al, 2021; Willms et al, 2022). Lastly, we noticed that neither the circular plasmid with in vivo linearization sites (indicated by gRNA1 and gRNA2) nor the dsDNA without 5′ end protection could achieve successful integration (Fig 1B and C). In summary, the dsDNA with 5′ modifications is an efficient donor for generating HDR-dependent knock-in zebrafish lines.
**Figure S3.:** *TgKI(krt92-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish larvae without 4-OHT treatment.(A, B, C) Representative confocal images of live zebrafish larvae at 2 dpf (A, A’, A’’), 3 dpf (B, B’, B’’), and 6 dpf (C, C’, C’’). (A’’, B’’, C’’) are merged channels with EGFP and mCherry. The magenta signal is background appearing in pigmented cells and yolk during live imaging. There is no overlap of H2BmCherry with EGFP in the skin, indicating no leakage from residual recombination. (D) Fluorescence microscopic image of live zebrafish embryos at 1 dpf showing the appearance of green fluorescence in the skin. The yellow arrows point to fluorescence-positive embryos. (A, B, C, D) Scale bars = 200 μm (A, B, C) and 500 μm (D).* **Figure S4.:** *TgKI(krt92-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish larvae with 4-OHT temporal labelling at different timepoints.(A, B, C) Representative confocal images of zebrafish larvae at 6 dpf showing krt92 lineage-traced cells in the intestinal bulb with 4-OHT treatment from 6–26 hpf (A, A’, A’’), 26–50 hpf (B, B’, B’’) or 56–80 hpf (C, C’, C’’). The white dashed lines outline the intestinal bulb. (D, E, F) Representative confocal images of zebrafish larvae at 6 dpf showing krt92 lineage-traced cells in the hindgut and pronephros with 4-OHT treatment from 6–26 hpf (D, D’, D’’), 26–50 hpf (E, E’, E’’) or 56–80 hpf (F, F’, F’’). The white dashed lines outline the hindgut; the cyan dashed lines outline the pronephros. The signal from the fluorescent protein was enhanced by immunostaining. Scale bars = 80 μm.*
## The generation of nkx6.1 knock-in lines using short or long HAs
Next, we aimed to knock-in donors at the 3′ end of nkx6.1, which is a transcription factor essential in the development of the pancreas and motor neurons (Cheesman et al, 2004). We selected a gRNA spanning over the stop codon region and used 5′ modified dsDNA with long HAs to generate TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2) (Fig 2A). 2 out of 1,000 ($0.2\%$) nkx6.1-p2A-EGFP-t2A-CreERT2-injected F0 embryos using long HAs showed detectable fluorescent signals in at least $30\%$ of the spinal cord (Fig 2B). Overall, it was harder to estimate the percentage mosaicism when the expression was low and present in tissues with cells overlaying each other, although this did not affect the establishment of the line.
**Figure 2.:** *The design and characterization of knock-in lines at the nkx6.1 locus.(A) The design of donor dsDNA template and gRNA sequence for the construction of knock-in lines at the 3′ end of the nkx6.1 locus. The nucleotide sequence in blue indicates gRNA; whereas the nucleotide sequence in red indicates the PAM sequence. The bottom panel shows the sequences of LHA and RHA. (B, C, D) Summary statistics of nkx6.1 knock-in efficiency, including the percentage of injected F0 with observable fluorescence labelling in the hindbrain and spinal cord (B), the percentage of adult F0 giving rise to germline transmission (out of those that bred) (C) and the percentage of F1 siblings inheriting each allele (D). (E, F, G) Representative confocal images of TgKI(nkx6.1-p2A-mNeonGreen-t2A-iCre);Tg(ubi:CSHm) at 1 (E), 2 (F), and 3 dpf (G). Cells expressing mNeonGreen indicate nkx6.1+ cells; all progenies of nkx6.1+ cells were labelled with H2BmCherry. The insets are magnified views showing the expression pattern of two fluorescent proteins. (H) Representative confocal image of lineage-tracing results in the principal islet of the pancreas in the TgKI(nkx6.1-p2A-mNeonGreen-t2A-iCre);Tg(ubi:CSHm) line at 6 dpf. Cells in white shown in H are β-cells with Insulin staining, whereas cells in green in H’’ are α-cells with Glucagon staining. (E, F, G, H) Scale bars = 200 μm (E, F, G) or 20 μm (H).*
Because the donor plasmids with short HAs flanked by in vivo linearization sites were also applicable in zebrafish knock-ins by inducing MMEJ (Nakade et al, 2014; Sakuma et al, 2016; Luo et al, 2018), we then converted to dsDNA with short HAs generated by a simple PCR step with primers containing 41 and 33 base pairs flanking the insertion sequence. We injected the dsDNA donor carrying p2A-mNeonGreen and p2A-mNeonGreen-t2A-iCre cassettes and, strikingly, we noted a dramatic increase in the frequency of embryos showing mosaic fluorescence reporter expression in the spinal cord, that is, nkx6.1-p2A-mNeonGreen-t2A-iCre (in 5 out of 355 injected embryos) or nkx6.1-p2A-mNeonGreen (in 4 out of 229 injected embryos) (Fig 2B). The percentages of founders among these mosaic F0 were between $75\%$ and $100\%$ (Fig 2C); 2.5–$15.5\%$ of the F1 siblings carried the knock-in cassettes (Fig 2D). For further comparison, we also injected p2A-EGFP-t2A-CreERT2 dsDNA with short HAs and could identify 5 out of 155 ($3.2\%$) injected embryos with detectable fluorescence. This indicated that short HAs are superior to (more than 10-fold) long HAs at this locus (Fig 2B) when comparing different donors that all carried the 5′ AmC6 modification. Sanger sequencing confirmed the correct in-frame integration at the junction between the endogenous gene and the 5′ end of the integrated sequences in all recovered nkx6.1 lines (Fig S2 and data uploaded to a public repository: https://osf.io/tdkvh/).
The iCre and CreERT2 functions were characterized by the color switch in the offspring when crossed with Tg(ubi:CSHm). We noticed that cells expressing nkx6.1 (displayed by the green fluorescence) were located on the ventral side of the spinal cord; whereas H2BmCherry positive cells, which include all the progenies of nkx6.1+ cells after the iCre recombination, resided in both the ventral and dorsal parts of spinal cord, suggesting a progenitor cell population of nkx6.1+ cells in zebrafish spinal cord (Figs 2E–G and S5A–C). In addition, a preceding immunostaining experiment using the TgBAC(nkx6.1:EGFP) reporter showed that nkx6.1+ cells exist in both the dorsal and ventral buds of the pancreas at 17–48 hpf, indicating that they might be multipotent pancreatic progenitor cells (Binot et al, 2010; Ghaye et al, 2015); however, definitive evidence from lineage tracing experiments is still lacking. To further determine the nkx6.1+ cell lineage in the pancreas, we firstly performed immunostaining for the fluorescent proteins in the knock-in lines and showed that nkx6.1 specifically labelled the intrapancreatic duct in zebrafish larvae (Fig S5D–F). Secondly, using the nkx6.1 knock-in iCre line, we could trace back all three major cell types in the pancreas (acinar, ductal, and endocrine cells) to nkx6.1 lineage (Figs 2H–H’’’ and S5G–I), suggesting that all these different cell types were indeed derived from nkx6.1+ cells.
**Figure S5.:** *Lineage tracing and cell labelling of nkx6.1+ cells in the CNS and pancreas.(A, B, C) Representative confocal images of nkx6.1-expressing and lineage-traced cells in the CNS (A, A’, A’’, B, B’, B’’) and pancreas (C, C’, C’’) in zebrafish larvae at 6 dpf. (C) The white dashed lines outline the whole pancreata (C, C’, C’’). (D, E, F) Representative confocal images of nkx6.1-expressing cells in the intrapancreatic duct in 6 dpf zebrafish larvae. (D, E, F) The pancreata are outlined by white dashed lines (D, E, F). (E, F) Arrowhead points to the principal islet depicted by Insulin staining displayed in grey (E, F). (G, H, I) Representative confocal images of pancreata showing the contribution of nkx6.1-traced cells in the pancreas with acinar cells labelled with ptf1a:EGFP (G, G’, G’’, G’’’) or ela3l:H2BGFP (H, H’, H’’, H’’’) with magnifications for improved visualization (I, I’, I’’, I’’’), in 6 dpf zebrafish larvae. Low laser power was used to not visualize mNeonGreen while observing EGFP/H2BGFP. The white dashed lines outline the whole pancreata. (A, B, C, D, E, F, G, H) Scale bars = 200 μm (A, B, C) or 80 μm (D, E, F, G, H).*
## Short-term and long-term lineage tracing depicting the nkx6.1 lineage
To further explore cell fate determination in the zebrafish pancreas, we did a lineage-tracing experiment using TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) with 4-OHT treatments at multiple timepoints (Fig 3A). The immunostaining at 6 dpf showed that both intrapancreatic ductal cells and a portion of acinar cells can be lineage traced when the 4-OHT treatment started at the 6-somite stage (Fig 3B and B’). In contrast, nkx6.1 lineage-traced cells were mostly restricted to the intrapancreatic duct when the 4-OHT treatment started at the eight-somite stage (Fig 3C, C’, and D). These results pinpoint the exact timing of the early cell fate divergence between acinar cells and ductal/endocrine lineages.
**Figure 3.:** *nkx6.1+ cells were gradually restricted to the duct and gave rise to secondary islets in the zebrafish pancreas.(A) Experimental timeline for temporal labelling using short-term and long-term lineage tracing. 4-OHT (20 μM) was added at the 6 or 8 somite stage and the treatment continued until 36 hpf for short-term lineage tracing. (B, C, E, F, G, H) (B, B’, C, C’) 4-OHT (20 μM) was added from 1–2 dpf and confocal imaging was performed at 60 dpf for the long-term lineage tracing (E, F, G, H). Representative confocal images of the pancreas at 6 dpf in TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2; ptf1α:EGFP);Tg(ubi:CSHm). The progenies of nkx6.1+ cells after 4-OHT treatment were H2BmCherry positive. The EGFP signals indicate acinar cells. Intrapancreatic ductal cells were demonstrated by membrane staining using the anti-Vasnb antibody (shown in white). The region within the yellow dashed line in B indicates H2BmCherry+/EGFP+ enrichment. Arrowheads in (B’, C, C’) point to H2BmCherry+/EGFP+/Vasnb− cells, both indicating acinar cells from nkx6.1+ cell origin. For each condition, we scanned five samples with 16–24 single-planes for larval pancreata and 18–30 single planes for juvenile pancreata. (B, C) The Z-stacked images were displayed (B, C) demonstrating a large number of mCherry+/EGFP+ cells with 4-OHT treatment starting from six-somite stage, whereas the number of double positive cells are decreased with statistical significance. (D) The quantification and statistical results of EGFP/mCherry double positive cells with 4-OHT treatment starting at 6 and 8 somite stages. Two-tailed t test was used for statistical analysis, with P-value < 0.05 considered as statistically significant. (E, F, G, H) Projection images of lineage-traced secondary islets in the TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish pancreas at 60 dpf. The progenies of nkx6.1+ cells after the 4-OHT 1–2 dpf treatment were H2BmCherry positive. (E) The selected area in the white dashed square in (E) was magnified in a single plane (E’, E’’). (E) The cyan dashed lines outline the pancreas (E). (E, F, G, H) Split channels of (E) are displayed for clarity (F, G, H). Arrowheads point to lineage-traced β-cells in the secondary islet co-stained with an anti-Insulin antibody. (B, C, E, F, G, H) Scale bars = 80 μm (B, B’, C, C’), 20 μm (E, F, G, H), or 10 μm (E’, E’’), respectively.*
Previous studies using transgenic lines based on tp1 promoter, which is a Notch-responsive element from the Epstein–*Barr virus* mediating expression in the intrapancreatic duct (including Tg(tp1:H2Bmcherry), Tg(tp1:venusPEST), and Tg(tp1:CreERT2)), suggested that Notch-responsive intrapancreatic ductal cells can give rise to endocrine cells. These endocrine cells are mainly located in secondary islets and appear during growth or upon Notch inhibition (Parsons et al, 2009; Ninov et al, 2013; Delaspre et al, 2015). In addition, previous immunostaining results in TgBAC(nkx6.1:EGFP) showed insulin/EGFP colocalization in adult zebrafish pancreatic tail region after β-cell ablation, suggesting latent duct-to-β-cell neogenesis is maintained in adulthood (Ghaye et al, 2015; Carril Pardo et al, 2022). However, lineage tracing using tp1:CreERT2 can only label at maximum $75\%$ of the Notch-responsive ductal cells and traced a very limited amount of endocrine cells, indicating that tp1 was not an efficient tracer for ductal neogenesis (Delaspre et al, 2015; Singh et al, 2017). To have a better understanding of duct-to-β-cell neogenesis, we performed a 2-month long-term lineage-tracing experiment using TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) with 4-OHT treatment at 1–2 dpf. We observed that nearly $65\%$ of ins+ cells in the secondary islets (residing along the large ducts) can be lineage traced (Figs 3E–H and S7D). Furthermore, we performed short-term lineage-tracing experiments in the presence of a Notch inhibitor (LY411575) or a REST inhibitor (X5050), as previous studies described (Ninov et al, 2012; Rovira et al, 2021), to demonstrate the neogenic potential of nkx6.1+ intrapancreatic ductal cells in zebrafish larvae (Fig S6). We found that more than $80\%$ of ins+ cells and around $75\%$ of glucagon positive (gcg+) cells in the pancreatic tail can be lineage traced after 3-d treatment (3–6 dpf) with LY411575 and X5050, respectively. These results, all together, suggested a latent progenitor property of the nkx6.1+ duct, one that can serve as the origin for endocrine cells.
**Figure S6.:** *Chemically induced neogenesis of pancreatic endocrine cells from the nkx6.1+ lineage.(A, B, C, D) Representative confocal images of nkx6.1 lineage-traced cells in the whole pancreas (A, B, C, D) and principal islet (A’, B’, C’, D’) after DMSO treatment from 3 to 6 dpf. (E, F, G, H) Representative confocal images of nkx6.1 lineage-traced cells in the whole pancreas (E, F, G, H) and secondary islets (E’, F’, G’, H’) after NOTCH inhibitor (LY-411575, 1 μM) treatment from 3–6 dpf. Arrowheads point to β-cells depicted by insulin staining in the secondary islet. (I, J, K, L) Representative confocal images of nkx6.1 lineage-traced cells in the whole pancreas (I, J, K, L) and secondary islets (I’, J’, K’, L’) after REST inhibitor (X5050, 5 μM) treatment from 3–6 dpf. Arrowheads point to α-cells depicted by glucagon staining in the secondary islet. (A, B, C, D, E, F, G, H, I, J, K, L) The white dashed lines outline the whole pancreas (A, B, C, D, E, F, G, H, I, J, K, L). (A, B, C, D, E, F, G, H, I, J, K, L) The selected areas in cyan dashed squares in (D, H, L) were magnified in split channels (A’, B’, C’, D’, E’, F’, G’, H’, I’, J’, K’, L’). (M, N, O) Quantification of the number of secondary islet (M), β-cells in the secondary islets (N), and α-cells in the secondary islets (O) after DMSO, LY-411575 (1 μM) or X5050 (5 μM) treatment from 3–6 dpf. Low laser power was used to not visualize EGFP while observing gcg. (A, B, C, D, E, F, G, H, I, J, K, L) Scale bars = 80 μm (A, B, C, D, E, F, G, H, I, J, K, L) or 10 μm (A’, B’, C’, D’, E’, F’, G’, H’, I’, J’, K’, L’).*
Lastly, regarding the CNS, the temporal-controlled experiments showed a similar labelling pattern to the noninducible Cre nkx6.1 knock-in line, confirming that the nkx6.1+ cells in the spinal cord are also progenitors (Fig S7A–C). Control experiments without 4-OHT treatment indicated no leakage problem with the nkx6.1 knock-in CreERT2 line (Fig S8).
**Figure S7.:** *Further characterization of nkx6.1+ lineage-traced cells.(A, B, C) Representative lateral and dorsal confocal images of nkx6.1+ lineage-traced cells in the CNS in zebrafish larvae at 6 dpf after treatment with 4-OHT at 20 μM from 1–2 dpf. (D) Quantification of the long-term lineage-traced β-cells displayed in Fig 3E–H. We randomly selected three secondary islets from each juvenile zebrafish and pooled the results of five juveniles together; the total number of counted mCherry/ins double-positive cells was 367, whereas the total number of ins-positive cells was 562.* **Figure S8.:** *TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish larvae without 4-OHT treatment.(A, B, C) Representative confocal images of live zebrafish larvae at 2 dpf (A, A’, A’’), 3 dpf (B, B’, B’’), and 6 dpf (C, C’, C’’). (A’’, B’’, C’’) are merged channels with EGFP and mCherry. The magenta signal is background appearing in pigmented cells and yolk during live imaging. There is no overlap of H2BmCherry with EGFP in the CNS, indicating no leakage from residual recombination. Scale bars = 200 μm.*
## Generation of krt4 knock-in lines using 5′ modified dsDNA with short HAs
Similar to nkx6.1, we identified one gRNA target spanning over the stop codon in krt4 (Fig 4A). To assess the knock-in efficiency of a dsDNA donor using short HAs with 5’ modifications, we amplified three different insertion sequences (p2A-mNeonGreen, p2A-mNeonGreen-t2A-iCre, and p2A-EGFP-t2A-CreERT2) with pairs of primers containing 32 and 33 base pairs homologous overhang at the 5′ ends by PCR (Fig 4A). Around 2.0–$7.4\%$ of injected F0 displayed green fluorescence labelling in at least $30\%$ of the skin at 1 dpf (Fig 4B). In addition, more than $75\%$ of these F0 were characterized as founders (Fig 4C). The proportion of the F1 generation that carried the knock-in cassettes ranged from 1.2–$22.0\%$ (Fig 4D). The iCre function was confirmed by crossing the TgKI(krt4-p2A-mNeonGreen-t2A-iCre) with Tg(ubi:CSHm). We observed H2BmCherry labelled cells in the skin and intestine (Fig 4E–H). However, TgKI(krt4-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) embryos showed mosaic leakage in the skin and intestine without 4-OHT treatment (Fig S9A–C). For systematic comparison, we also injected a p2A-EGFP-t2A-CreERT2 PCR-amplified donor without end protection and observed that only $0.3\%$ (1 out of 299) injected F0 achieved early integration based on the above criteria (Fig 4B), indicating that the 5′ modification can achieve around fivefold more efficient integration of dsDNA donors when using short HAs at this locus. Lastly, to compare the knock-in efficiency of different modifications, that is, AmC6 versus biotin, which has previous been used as dsDNA end protection for making knock-in lines in medaka, we injected the same batch of embryos with each 5′ modified donors. In the F0, we observed 35 out of 428 embryos displayed >$30\%$ skin area with green fluorescence using the AmC6 modification, whereas only 3 out of 526 embryos displayed visible green using the biotin modification, indicating that at least in certain genomic locus, the AmC6 modification results in better integration efficiency than biotin. We also confirmed the correct in-frame integration at the 5′ end of the integrated sequences in all the recovered krt4 lines using Sanger sequencing (Fig S2 and data uploaded to a public repository: https://osf.io/tdkvh/).
**Figure 4.:** *The design and characterization of knock-in lines at the krt4 locus.(A) The design of donor dsDNA templates and gRNA sequence for the construction of knock-in lines at the 3′ end of the krt4 locus. The nucleotide sequence in blue indicates gRNA, whereas the nucleotide sequence in red indicates the PAM sequence. The bottom panel shows the sequences of LHA and RHA. (B, C, D) Summary statistics of knock-in efficiency at the krt4 locus, including the percentage of injected F0 with fluorescence labelling on approximately one-third of their skin. (B, C, D) Knock-in mosaicism (B), the percentage of adult F0 giving rise to germline transmission (C), and the percentage of F1 siblings carrying the knock-in cassettes (D). (E, F, G, H) Representative confocal images of TgKI(krt4-p2A-mNeonGreen-t2A-iCre);Tg(ubiCSHm) at 1 dpf (E, E’, E’’), 2 dpf (F, F’, F”), 3 dpf (G, G’, G’’), and 6 dpf (H, H’, H’’). Skin and intestinal epithelial cells (at 3 and 6 dpf) were broadly recombined and labelled with H2BmCherry. (H) The white dashed lines outline the intestinal bulb (H, H’, H’’). Scale bars = 80 μm.* **Figure S9.:** *TgKI(krt4-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish larvae without 4-OHT treatment.(A, B, C) Representative confocal images of live zebrafish larvae at 1 dpf (A, A’, A’’), 2 dpf (B, B’, B’’) and 3 dpf (C, C’, C’’). (A’’, B’’, C’’) are merged channels with EGFP and mCherry. The magenta signal is nuclear (H2BmCherry) and overlapping with EGFP in the skin, indicating leakage from residual recombination in a tissue with very high krt4 expression. The magenta signal in pigmented cells and yolk is background appearing during live imaging. (D) Fluorescence microscopic image of live zebrafish embryos at 2 dpf showing the appearance of green fluorescence in the skin. The yellow arrows point to fluorescence-positive embryos. (A, B, C, D) Scale bars = 200 μm (A, B, C) and 500 μm (D).*
As the krt4 transgenics have been widely used for labelling skin epithelial cells (Lam et al, 2013; Fischer et al, 2014) (Fig S9D), we performed a further characterization of krt4 expression in the gut using HCR3.0 in situ hybridization and EGFP immunofluorescence in both Tg(krt4-p2a-EGFP-t2a-CreERT2) and Tg(krt4:EGFP-Mmu. Rpl10a) zebrafish larvae (Fig S10). Notably, the krt4 in situ result showed very strong signals in both the intestinal bulb and the hindgut. The green fluorescence in the krt4 knock-in line fully recapitulates endogenous krt4 expression (Fig S10C and D), whereas the krt4 transgenics displayed no signals in the gut (Fig S10A and B), indicating that the cis-regulatory element cloned in the krt4 transgenic line is unable to mimic endogenous krt4 expression and insufficient to drive EGFP expression in the gut.
**Figure S10.:** *The comparisons of Tg(krt4:EGFP-Mmu.Rpl10a) and TgKI(krt4-p2A-EGFP-t2A-CreERT2) in intestinal bulb and hindgut.(A, B, C, D) Representative confocal images of krt4-positive cells in the intestinal bulb and the hindgut (shown in green) together with krt4 HCR3.0 in situ hybridization (shown in magenta) in Tg(krt4:EGFP-Mmu.Rpl10a) (A, B) and TgKI(krt4-p2A-EGFP-t2A-CreERT2) (C, D). (A, B, C, D) The white dashed lines indicate the intestinal bulb (A, C) and hindgut (B, D). Scale bars = 80 μm.*
## Generation of id2a knock-in lines using short HAs
Next, we used similar strategies to knock-in p2A-mNeonGreen, p2A-mNeonGreen-t2A-iCre, and p2A-EGFP-t2A-CreERT2 fragments into the 3′ end of the id2a locus using short HAs (Fig 5A). We found that $1.5\%$ (2 out of 137), $0.8\%$ (2 out of 252), and $3.0\%$ (7 out of 240) injected embryos displayed strong fluorescence in the hindbrain, spinal cord, and olfactory organs and were raised up to adulthood (Fig 5B). We observed high percentage of mosaic F0 with germline transmission ($100\%$, $100\%$, and $71.4\%$, respectively) (Fig 5C) and the percentage of the F1 generation carrying the cassettes ranged from 3.2–$29.0\%$ (Fig 5D). We crossed the iCre and CreERT2 knock-in lines with Tg(ubi:CSHm) and observed a similar pattern of mCherry signal as mNeonGreen in various tissues (hindbrain, dorsal side of spinal cord, pronephros, olfactory organ, and muscles), verifying the functionality of the Cre recombinases (Figs 5E–G and S11A–C). Control experiments suggested that there is no leakage problem with the id2a knock-in CreERT2 line (Fig S12). Sanger sequencing confirmed the correct in-frame integration at the junction between the endogenous gene and the 5′ end of the integrated sequences in all recovered id2a lines (Fig S2 and data uploaded to a public repository: https://osf.io/tdkvh/).
**Figure 5.:** *The design and characterization of knock-in lines at the id2a locus.(A) The design of the donor dsDNA template and gRNA sequence for the construction of knock-in lines at the 3′ end of the id2a locus. The nucleotide sequence in blue indicates gRNA, whereas the nucleotide sequence in red indicates the PAM sequence. The bottom panel shows the sequences of LHA and RHA. (B, C, D) Summary statistics of id2a knock-in efficiency, including the percentage of injected F0 with observable fluorescence labelling in the hindbrain, spinal cord, and olfactory organs (B), the percentage of adult F0 giving rise to germline transmission (C), and the percentage of F1 siblings carrying the knock-in cassettes (D). (E, F, G) Representative confocal images of TgKI(id2a-p2A-mNeonGreen-t2A-iCre); Tg(ubi:CSHm) at 2 dpf (E, E’, E’’), 3 dpf (F, F’, F’’), and 6 dpf (G, G’, G’’). Cells that are mNeonGreen positive indicate id2a-expressing cells, whereas the progenies of the id2a lineage were mCherry labelled. (H, I) Representative confocal images of lineage-tracing experiments in the zebrafish larval pancreata (H, H’, H’’, H’’’), intestine (H, H’, H’’, H’’’), and liver (I, I’) in the TgKI(id2a-p2A-mNeonGreen-t2A-iCre);Tg(ubi:CSHm) line. We scanned five pancreata and livers, with 26–34 single planes imaged. Cells in cyan are α-cells based on anti-Glucagon antibody staining. (E, F, G, H, I) Scale bars = 200 μm (E, F, G) or 80 μm (H, I).* **Figure S11.:** *Characterization of id2a+ cells in CNS, pronephros, intestine, hepatic biliary system, and retina.(A, B, C) Representative lateral and dorsal confocal images of id2a lineage-traced cells in the CNS and pronephros, with 4-OHT treatment from 1–2 dpf. (D, D’, D’’) Representative confocal images of immunostained id2a:mNeonGreen-positive and tp1:H2BmCherry-positive cells in the intestinal bulb. Arrowheads point to double-positive cells. (E, E’, E’’) Representative confocal images of immunostained id2a:mNeonGreen-positive and tp1:H2BmCherry-positive cells in the hindgut. Arrowheads point to double-positive cells. (F, F’, F’’) Representative and magnified images of id2a-p2amNeonGreen, tp1:H2BmCherry double-positive cells in the intestinal bulb. (F) The selected area in the white dashed square in (F) was magnified in split channels (F’, F’’). The white dashed lines outline the intestinal luminal structure. (G, H, I) Representative confocal images showing the id2a-positive cells in the intrapancreatic duct (G, G’, G’’), intrahepatic duct (H, H’, H’’), and extrahepatic duct (I, I’, I’’). (J) Quantification of the percentage id2a+ cells in intrahepatic duct, intermediate hepatic duct, and extrahepatic duct, respectively. (K, L) Representative confocal images of id2a-positive cells in the whole retina (K, K’, K’’) and magnified images in the retinal epithelial cells (L, L’, L’’). We scanned 3–5 zebrafish larvae for each tissue with 18–32 single-planes. (A, B, C, D, E, F, G, H, I, K, L) Scale bars = 200 μm (A, B, C, D, E) or 10 μm (F, K, L) or 40 μm (G, H, I).* **Figure S12.:** *TgKI(id2a-p2A-EGFP-t2A-CreERT2);Tg(ubi:CSHm) zebrafish larvae without 4-OHT treatment.(A, B, C) Representative confocal images of live zebrafish larvae at 2 dpf (A, A’, A’’), 3 dpf (B, B’, B’’), and 6 dpf (C, C’, C’’). (A’’, B’’, C’’) are merged channels with EGFP and mCherry. The magenta signal is background appearing in pigmented cells and yolk during live imaging. There is no overlap of H2BmCherry with EGFP in the CNS, liver, intestine, and pancreas, indicating no leakage from residual recombination. Scale bars = 200 μm.*
Previous studies have shown that id2a is important in the development of endodermal organs (e.g., liver and pancreas) (Khaliq et al, 2015; Choi et al, 2017; Tarifeno-Saldivia et al, 2017) and retina (Uribe & Gross, 2010). Therefore, we performed immunostaining of the knock-in EGFP line to visualize id2a expression in these tissues. In the zebrafish gut (intestinal bulb and hindgut), we observed a large number of id2a+ cells showing overlapping fluorescence signals with tp1:H2BmCherry, suggesting that these cells are id2a+ and have active Notch signaling (Fig S11D and E). These double positive cells had apical membranes oriented towards the gut lumen, indicating that they were either sensory cells or secretory cells (Fig S11F). This result was in line with two recent single-cell RNA-seq studies of the larval gut, proposing the subpopulation of Notch-responsive cells were best4/otop2+ ionocytes (Fig S13E–H) (Wen et al, 2021), which regulate ion concentrations.
**Figure S13.:** *t-SNE plots showing id2a expression in zebrafish pancreas and intestine.(A, B, C, D) UMAP plots representing id2a, cftr, sox9b, and onecut1 in adult zebrafish pancreas single-cell RNA-seq dataset. (E, F, G, H) UMAP plots representing id2a, cftr, best4, and otop2 in larval zebrafish intestine single-cell RNA-seq dataset.*
In the zebrafish pancreas and liver, the id2a knock-in reporter showed fluorescence in the intrapancreatic duct and a subset of extrahepatic and intrahepatic ductal cells, and in what we term the intermediate duct that branches out in between the extrahepatic and intrahepatic ducts (Figs S11G–J and S13A–D). In the retina, the id2a reporter labelled a substantial number of retinal epithelial cells (Fig S11K and L). Altogether, the id2a knock-in reporter closely recapitulated known expression domains of id2a.
## id2a tracing delineates developmental paths in the liver and pancreas
Previous fate mapping studies suggested that the pancreas and liver originate from common progenitors in the endodermal sheet, in which cells close to the midline are prone to differentiate into pancreatic endocrine cells and intestine; whereas cells located two cells away from the midline are inclined to develop into liver and exocrine pancreas (Chung et al, 2008; Yang et al, 2021). The mediolateral patterning of cell fate decision relies on mesoderm-derived Bmp2b (Chung et al, 2008). Higher levels of Bmp2b promote liver versus pancreas development, whereas notochord-derived Nog2, a Bmp antagonist, increases the number of pancreatic progenitors (Amorim et al, 2020). Given that id2a belongs to the inhibitor of a differentiation protein family and is a downstream gene of Bmp signaling, we use both id2a iCre and CreERT2 knock-in lines for lineage tracing to have a better understanding of the hepatic–pancreatic development. The results from TgKI(id2a-p2A-mNeonGreen-t2A-iCre);Tg(ubi:CSHm) demonstrated a universal labelling in the liver and pancreas, suggesting that id2a+ cells are common hepatic-pancreatic multipotent progenitors (Fig 5H and I). Furthermore, we did temporal labelling by treating with 4-OHT at several timepoints (20, 24, 32, and 48 hpf) for 24 h (Fig 6A–D). Both hepatocytes and hepatic ducts were H2BmCherry positive when labelled at 20 hpf (Fig 6A). However, the H2BmCherry positive cells in the liver were gradually restricted to the hepatic duct at 24 hpf (Fig 6B) and demonstrated specific hepatic duct labelling when the treatment started at 32 or 48 hpf (Figs 6C and D and S14). In the zebrafish pancreas, similar to nkx6.1, temporal labelling of id2a+ cells at 1 dpf marked mainly ductal cells (intra- and extra- pancreatic ductal cells) and a subpopulation of endocrine cells (Fig 6E and F). These results suggest that Bmp signaling is active in the liver, dorsal bud-derived pancreas, and ventral bud-derived pancreas specification at early developmental stages, whereas its activity is preferentially maintained in hepatic and pancreatic duct.
**Figure 6.:** *Temporally controlled id2a lineage-tracing experiments in the zebrafish liver and pancreas.(A, B, C, D) Representative confocal images of the liver in TgKI(id2a-p2A-EGFP-t2A-CreERT2) treated with 20 μM 4-OHT at 20 hpf (A, A’, A’’), 24 hpf (B, B’, B’’), 32 hpf (C, C’, C’’), and 38 hpf (D, D’, D’’) for 24 h. For each condition, we scanned five embryos; for each embryo, 20–32 single planes were imaged. The progenies of id2a+ cells were labelled with H2BmCherry. The white dashed lines indicate the liver. (E, F) Representative confocal images of zebrafish pancreas and principal islet at 6 dpf treated with 4-OHT 20 μM at 1–2 dpf. (F’, F’’) Magnified confocal images of (F) showing zebrafish principal islets. Extrapancreatic ductal cells and the principal islet are defined by anti-Vasnb antibody (in white) and anti-Glucagon antibody (in green), respectively. The white dashed lines indicate the pancreas; the cyan dashed lines indicate the extrapancreatic duct. (A, B, C, D, E, F) Scale bars = 35 μm (A, B, C, D), 40 μm (E) or 20 μm (F).* **Figure S14.:** *The quantification of lineage-traced hepatic ductal cells.(A, B, C) The percentage of lineage-traced hepatic ductal subtypes in TgKI(id2a-p2A-EGFP-t2A-CreERT2) treated with 4-OHT at 24 hpf (A), 32 hpf (B), and 48 hpf (C) for 24 h.*
Lastly, we examined whether there is ductal-to-hepatocyte conversion in two liver injury models, as previous studies showed that tp1+ intrahepatic ductal cells can convert to hepatocytes in an extreme hepatocyte ablation condition (He et al, 2014). Here, we changed the responder line to Tg(−3.5ubb:loxP-EGFP-loxP-mCherry) (abbreviated as Tg(ubi:Switch)) for the lineage-tracing experiment, as the cytoplasmic mCherry can allow us to distinguish the cell type based on morphology (Choi et al, 2014). The id2a+ hepatic ductal cells were first temporally labelled from 2–3 dpf, with subsequent metronidazole (MTZ) treatment using larvae carrying the fabp10:CFP-NTR transgene or with chemical-induced severe liver injury (acetaminophen 10 mM + $0.5\%$ ethanol for 48 h, resulting in near $90\%$ hepatocyte ablation) from 3–5 dpf followed by 2 d of regeneration (North et al, 2010). Based on the morphology of the mCherry positive cells and the Vasnb co-staining, we confirmed that a large number of hepatocytes were derived from an id2a+ duct origin after the MTZ/NTR-induced liver injury (Fig 7). All lineage-traced cells, however, retained their ductal identity after the chemical-induced injury (Fig S15), indicating that id2a+ duct-to-hepatocyte conversion mainly occurs after extreme hepatocyte loss, whereas this phenomenon is very sparse in the severe liver injury model. Thus, the Notch- and BMP-responsive hepatic ductal cells maintain progenitor potential in zebrafish.
**Figure 7.:** *id2a lineage-traced cells in an extreme liver injury model.(A) Experimental timeline of id2a lineage tracing in an MTZ/NTR-induced extreme liver injury model. (B) Representative confocal images of id2a lineage-traced cells in the extreme liver injury model. In total, we scanned five samples with 21–35 single planes in each zebrafish larvae. The white dashed lines indicate the liver, the yellow dashed lines indicate the pancreas, and the cyan dashed lines indicate the intestinal bulb. (C) Magnified image of the liver showing large numbers of regenerated hepatocytes lineage-traced back to an id2a+ cellular origin. The arrows point to clusters of regenerated hepatocytes. (B, C) Scale bars = 40 μm (B) and 20 μm (C).* **Figure S15.:** *id2a lineage-traced cells in chemically induced liver injury model.(A) Experimental timeline of id2a lineage-tracing in a chemically induced liver injury model. (B, C, D) Representative confocal images of id2a lineage-traced cells in severe chemically induced liver injury model, with acetaminophen 10 mM and 0.5% ethanol treatment for 2 d followed by 2 d of recovery. The white dashed lines indicate the liver. Scale bars = 40 μm.*
## Discussion
Here, we introduce a straightforward 3′ knock-in pipeline to generate zebrafish lines for both cellular labelling and lineage tracing. This method combines a one-step PCR amplification for 5′ modified dsDNA donor with short or long HAs and coinjection with Cas9 protein/gRNA RNPs. Notably, we observed high F0 mosaic integration (often half of the injected embryos displayed some fluorescence/integration, but it varied depending on the target) and a very high germline transmission rate of those with >$30\%$ mosaicism, indicating that this method can achieve early genetic integration. By systematic comparisons with different donors, we proposed that 5′ modified dsDNAs with short HAs had the best performance when gRNAs spanning over stop codons are available. Lastly, we managed to knock in large DNA fragments with multiple cassettes linked by 2A peptides that generated zebrafish lines useful for multiple applications. In all, this straightforward and highly efficient knock-in pipeline is versatile and amenable to a wide range of users, allowing researchers to carry out knock-in projects in a scalable fashion.
The CRISPR-Cas9 mediated knock-in method for experimental animals was first introduced in mouse models and, afterwards, optimizations have been developed in various organisms. However, each optimization approach needs to be rigorously tested in each individual model because of their huge differences in early embryonic development. The NHEJ-guided 5′ knock-in upstream of ATG, NHEJ-guided intron-based knock-in, exon-based knock-in (such as the Geneweld toolbox), and 3′ knock-in with circular plasmids have been reported to generate several reporter lines and floxed lines (Auer et al, 2014). In addition, several recent studies used dsDNA as the direct donor. The Tild-CRISPR method, which is based on in vitro vector linearization by PCR amplification or enzyme cutting, can dramatically increase the knock-in efficiency in mice (Yao et al, 2018b). In zebrafish, however, there are conflicting results: one former study showed that such in vitro linearization was inefficient to mediate HDR compared with circular plasmids, whereas another study reported that coinjection of synthetic gRNA and linear dsDNA together showed promising results (Hoshijima et al, 2016; DiNapoli et al, 2020). In addition, PCR amplicons containing fluorescent tagging with several hundred base pairs flanking sequences can be successfully knocked in to noncoding regions at N- or C-terminal regions, indicating that PCR amplicons can be useful donors in facilitating zebrafish HDR-based knock-in (Levic et al, 2021). Interestingly, one study systematically compared 13 modifications on dsDNA donors with short HAs in generating knocked-in human cell lines and showed several modifications (especially 5′ C6-PEG10, amine group with a C6 linker [5′ AmC6] or C12 linker [5′ AmC12]) can exceptionally enhance the 3′ knock-in rate (with up to $500\%$) (Yu et al, 2020). Although the underlying mechanism is not fully understood, it is proposed that the 5′ end protection can prevent the NHEJ event and donor multimerization (Gutierrez-Triana et al, 2018).
Here, we focused on 3′ knock-in because this method, theoretically, can keep the endogenous gene functional and intact. We observed that short HAs were sufficient when knocking in long DNA fragments, that is, when there is a good gRNA spanning over the stop codon region. Furthermore, our data targeting the krt92 and krt4 loci suggesting that AmC6-end protection greatly improves the efficiency of HDR integration in zebrafish are consistent with the idea that end protection of dsDNA limits exonuclease degradation and concatenation. Given that the dsDNAs with short HAs are smaller in size (compared with circular plasmid or dsDNA with long HAs), we assume that they can more efficiently translocate into the nucleus. Altogether, the 5′ end protection and short HAs may lead to a high concentration of dsDNA in the local integration region for the promotion of HDR. In krt4 locus, we also observed that the AmC6-end protection showed much higher efficiency than biotin-end protection. Although it is hard to conclude that the AmC6 modification is generally better than biotin in zebrafish, we showed that different modifications can impact the integration efficiency and converting to alternative modifications could be a simple way to improve knock-in efficiency in certain genetic loci rather than switching to a different gRNA. However, we believe it is difficult to make comparisons regarding efficiency between various knock-in methods because different loci were chosen for testing (Irion et al, 2014; Hoshijima et al, 2016; DiNapoli et al, 2020; Almeida et al, 2021).
The main advantages of our method become apparent in several aspects. First, we are targeting the 3′ end without the disruption of the endogenous gene products. In addition, the 3′ in-frame integration did not show any effects on the mRNA expression of the endogenous genes (Fig S16). This is of great importance in developmental and regenerative studies as in certain cases, the loss of one gene allele (i.e., in transcription factors) can generate detectable phenotypes (Delous et al, 2012). Second, we found that dsDNAs with 5′ modifications were efficient donors in zebrafish. Moreover, the short HAs were at least as equally good as long HAs in the locus we tested, although that could depend on the intrinsic features of these particular long HAs (e.g., structure, repeats, etc.). However, the main point is that short HAs are sufficient and the preparation of donors can be dramatically simplified by one-step PCR using primers harboring 29–41 base pairs overhangs, which is in line with previous work (Luo et al, 2018). Third, the design of 2A peptides linking different functional cassettes enables us to generate knock-in lines for multiple uses. Fourth, it is much easier to identify the mosaic founders for knock-in of nonfluorescent protein (e.g., Cre) when combined with a fluorescent protein as an in-frame marker (which also indicates correct integration). Fifth, although previous studies supported the use of Cas9 mRNA in medaka (Gutierrez-Triana et al, 2018; Seleit et al, 2021), here, we instead recommend injecting Cas9 protein in zebrafish embryos to enable early integration. Medaka has a much slower cell cycle during early development (16-cell stage at 3 hpf) (Iwamatsu, 2004), whereas zebrafish embryos display very rapid cell division (over 1,000 cells at 3 hpf) (Kimmel et al, 1995). *The* generation of dsDNA breaks need to be concordant with a sufficient amount of donor templates ready for HDR, otherwise, cells are inclined to use error-prone NHEJ, which would introduce indels. As Cas9 protein can be transported into the nucleus rapidly, it can cleave genomic DNA soon after injection, which is particularly important for obtaining high germline transmission in zebrafish. Given that dsDNAs are smaller in size and can diffuse faster than whole plasmids, the cleaved genomic DNA has a higher chance to be precisely repaired by MMEJ rather than NHEJ. Sixth, we chose the strongest green fluorescence monomer, mNeonGreen, as an alternative to EGFP for cellular labelling. Strong fluorophores are helpful for the identification of mosaic embryos in case the targeted gene has a low expression. Moreover, the mNeonGreen is not recognized by the anti-GFP antibody, allowing the users to combine it with transgenic lines expressing GFP or its derivatives.
**Figure S16.:** *Expression of the endogenous genes in loci targeted for knock-in.(A, B, C, D, E, F, G, H, I, J) qRT-PCR results of the relative expression of krt92 (A), nkx6.1 (B, C, D), krt4 (E, F, G), and id2a (H, I, J) in knock-in lines compared with the WT.*
We shall also note that the use of short HAs is highly dependent on whether there is a good gRNA spanning over the stop codon region. If there is an absence of such gRNA, long HAs might be a better choice as mutations need to be introduced in the left (for gRNA sites upstream of the stop codon) or right (for gRNA sites downstream of the stop codon) HAs, such that the gRNA does not also cleave the donor dsDNA. However, the use of short HAs provides an easy and efficient way to knock in a specific donor into several candidate genes in parallel, that is, without needing to perform complex molecular cloning steps.
We also employed our knock-in lines to investigate cell fate determination in the liver and pancreas. We, for the first time, used a lineage-tracing method to make a definitive conclusion that the nkx6.1+ intrapancreatic ductal cells can serve as progenitor cells that can differentiate into endocrine cells in secondary islets. Also, by combining the lineage-tracing results from noninducible and inducible Cre lines, we depicted a temporal cell differentiation map for the specification between the acinar fate and ductal/endocrine fate in the pancreas, and between hepatocyte fate and hepatic ductal fate in the liver. Lastly, we consolidated and extended the previous findings indicating that Notch- and BMP-responsive liver ductal cells are progenitors and able to convert to hepatocytes only upon extreme liver injury (Choi et al, 2014; He et al, 2014). Further experiments using lineage tracing, single-cell RNA sequencing, and tissue-specific gain-and-loss of function are warranted to investigate detailed cellular and molecular mechanisms in the development and regeneration of these tissues.
Through systematic comparisons of the pattern of krt4 gene expression in both widely used transgenics and our knock-in lines, we found that our knock-ins can fully recapitulate the endogenous gene expression, whereas the krt4 cis-regulatory element cloned for skin cell labelling is incapable of driving the reporter gene expression in the intestine (intestinal bulb and hindgut). This is of particular importance for lineage-related research as most previous cell lineage discoveries in zebrafish are based upon cloning promoters for transgenics, which may fail to label certain cell types, display ectopic expression or exhibit leakage issues. This might be because of the fact that different tissues/cell types tend to use different cis-regulatory elements with different chromatin structures, and that the enhancer–promoter loops might vary greatly in different cell types (Heinz et al, 2015). It is difficult to precisely predict the exact region of the regulatory sequences sufficient to activate the gene expression in each cell type without a comprehensive profiling of the epigenetic landscape in a single-cell resolution. Therefore, we believe that our method, together with other zebrafish knock-in Cre/CreERT2 methods, might divert the standard toward knock-in-based genetic fate mapping for both confirming old and making new discoveries.
We should note that our knock-in strategy is a good alternative to the current zebrafish genome editing toolbox and can complement other methodologies depending on the purpose of the research. Our method is useful for generating knock-in tracers to delineate natural occurring events without disruption of the endogenous gene product. However, other knock-in strategies, especially the generation of knock-in/knock-out lines can greatly help to trace cells in loss-of-function conditions. We also suggest users to target genes with strong and widespread expression patterns with our method as genes with low or very restricted expression patterns might not be visible even if you achieve integration at the single-cell stage. Further studies combining our method with methods involving a sorting marker (with fluorescence in the eye or heart) could be a promising strategy to target such genes (Wierson et al, 2020; Tan & Winkler, 2022).
In summary, we described a novel 3′ knock-in pipeline for the construction of zebrafish lines with multiple cassettes. This method is easy to implement as it only includes a one-step PCR reaction and coinjection with Cas9/gRNA RNPs. The application of dsDNA with short HAs allows us to knock-in specific donors in multiple genes in a scalable fashion. This method is highly efficient as it can achieve a desirable percentage of germline transmission from mosaic F0, which is helpful for small-to-medium sized zebrafish labs with limited space to screen for founders. The design using 2A peptides for linkage makes it possible to knock-in multiple cassettes at the same genetic locus, further expanding the utility of the knock-in lines. Therefore, we anticipate that this efficient and straightforward knock-in method will be of widespread use in the zebrafish field.
## Zebrafish lines used in the study
Males and females ranging from 3 mo to 2 yr were used for breeding. Zebrafish larvae were incubated in 28.5°C until 7 dpf. The following published transgenic zebrafish (Danio rerio) lines were used in this study: Tg(ptf1a:GFP)jh1 (Godinho et al, 2005), Tg(Tp1bglob:H2BmCherry)S939 (Ninov et al, 2012) abbreviated Tg(Tp1:H2BmCherry), Tg(−3.5ubb:loxP-EGFP-loxP-mCherry)cz1701 (Mosimann et al, 2011) abbreviated as Tg(ubi:Switch), Tg(UBB:loxP-CFP-STOP-Terminator-loxP-hmgb1-mCherry)jh63 (Zhang et al, 2017) abbreviated as Tg(ubi:CSHm), Tg(ela3l:H2BGFP) (Schmitner et al, 2017), Tg(krt4:EGFP-Mmu. Rpl10a) (Lam et al, 2013), and Tg(fabp10a:CFP-NTR)S931 (Choi et al, 2014).
The following lines were newly generated by the CRISPR-Cas9 3′ knock-in strategy: TgKI(krt92-p2A-EGFP-t2A-CreERT2)KI126, TgKI(krt4-p2A-mNeonGreen)KI127, TgKI(krt4-p2A-mNeonGreen-t2A-iCre)KI128, TgKI(krt4-p2A-EGFP-t2A-CreERT2)KI129, TgKI(nkx6.1-p2A-mNeonGreen)KI130, TgKI(nkx6.1-p2A-mNeonGreen-t2A-iCre)KI131, TgKI(nkx6.1-p2A-EGFP-t2A-CreERT2)KI132, TgKI(id2a-p2A-mNeonGreen)KI133, TgKI(id2a-p2A-mNeonGreen-t2A-iCre)KI134, and TgKI(id2a-p2A-EGFP-t2A-CreERT2)KI135.
Adult fish were maintained on a 14:10 light/dark cycle at 28°C. All studies involving zebrafish were performed in accordance with local guidelines and regulations and were approved by the ethical committee (called Stockholms djurförsöksetiska nämnd).
## The vector design for 3′ knock-in
The vector templates were manually designed for krt92 and nkx6.1 3′ end loci. The vectors include a left long HA of 900 base pairs genomic sequence upstream of the stop codon of the endogenous gene product followed by a GSG linker (glycine–serine–glycine, GGAAGCGGA), p2A sequence (GCTACTAACTTCAGCCTGCTGAAGCAGGCTGGAGACGTGGAGGAGAACCCTGGACCT), zebrafish codon optimized EGFP or mNeonGreen (without stop codons), GSG linker, t2A sequence (GAGGGCAGAGGCAGTCTGCTGACATGCGGTGATGTGGAAGAGAATCCCGGCCCT), zebrafish codon optimized iCre or CreERT2 (with stop codons), and 950 base pairs right long HA downstream of endogenous stop codon flanked by in vivo linearization sites (GAGCTCGGTACCCGGGGATC[AGG] on the left; ATCCTCTAGAGTCGACCTGC[AGG] on the right).
## PCR amplification and gel purification
The 5′ modified primers were ordered with AmC6 5′ modification from Integrated DNA Technologies. The primer powders were diluted with distilled water into 100 mM as stock solution. The 50 μl PCR mixture includes:Forward primer: 2.5 μlReverse primer: 2.5 μlTemplate plasmid: 1 μlDistilled water: 19 μlQ5 Hot Star High-Fidelity (Hifi) 2× Master Mix: 25 μlWe use the following PCR cycle setting:Pre-denaturing: 98°C, 30 sDenaturing: 98°C, 10 sAnnealing: 58–60°C, 20 sExtension: 72°C, 90 sFinal extension: 72°C, 2 min, and hold at 4°C Next, we ran the PCR products in $1\%$ agarose gel with 100 V for 45–60 min. The corresponding bands were cut out and purified using the wizard SV gel and PCR clean-up system (A9282; Promega). The concentrations of purified PCR products were measured by NanoDrop (2000c) and then diluted with distilled water to 70–100 ng/µl. The purified PCR products were stored at −20°C before injection.
## The selection of gRNA
We used the CHOPCHOP web-based tool (http://chopchop.cbu.uib.no/) and set the reference genome as “danRer11/GRCz11.” We selected “CRISPR/Cas9” and the “knock-in” module after determining the targeted gene. This tool would scan through the gene exon regions and rank the gRNA based on efficiency score, self-complementarity, and the number of mismatches. Apart from the in silico prediction, we also manually examined the 3′ end in the Ensembl database (https://www.ensembl.org/Danio_rerio/Info/Index) to avoid polymorphisms in the targeting site. The following gRNAs were used in this study (with PAM sequence in the brackets):krt92 (on reverse strand): 5′-AACCTCGCTCGAGATTGGG(AGG)-3′krt4 (on forward strand): 5′-GTCAGCAGTAAACGCTATT(AGG)-3′nkx6.1 (on forward strand): 5′-AGAGCTCGTAAAAAGGAAAC(GGG)-3′id2a (on forward strand): 5′-AGGACACTTTACCGTTAATC(AGG)-3′ For the control experiment using plasmids with linearization sites, we used the following gRNAs:Left: 5′-GAGCTCGGTACCCGGGGATC(AGG)-3′Right: 5′-ATCCTCTAGAGTCGACCTGC(AGG)-3′.
## In vitro preassembly of gRNA, Cas9 protein, and donor dsDNA
The chemically synthesized Alt-R-modified crRNA, tracrRNA, Hifi Cas9 protein, and nuclease-free duplex buffer were ordered from Integrated DNA Technologies. The crRNA and tracrRNA powders were diluted to 100 μM with nuclease-free duplex buffer. The 10 μl Hifi Cas9 protein was aliquoted into 10 tubes followed by 1:5 dilution with Opti-MEM (31985062; Thermo Fisher Scientific) solution before use.
Next, we prepared a 10 μM crRNA: tracrRNA duplex solution by mixing 1 μl crRNA stock solution, 1 μl tracrRNA stock solution, and 8 μl nuclease-free duplex buffer and then incubating at 95°C for 3 min in a thermocycler followed by natural cooling at room temperature for 15 min. Afterwards, we prepared the Cas9/gRNA RNP by mixing 2 μl Cas9 protein solution and 2 μl crRNA:tracrRNA duplex solution in 37°C for 10 min. Lastly, we mixed 2 μl Cas9/gRNA RNP, 5 μl donor dsDNA, and 0.8 μl phenol red (P0290; Sigma-Aldrich) and stored it at 4°C. We recommend performing this preassembly step the day before injection.
## Microinjection and sorting for mosaic F0
We injected 1–2 nl Cas9 RNP and donor dsDNA (50–70 pg/nl) into zebrafish embryos at the early one-cell stage. The overall mortality rate was around $50\%$ and we sorted out all dead embryos in the following days. We selected mosaic F0 at 2 dpf based on the fluorescence of the skin (krt92 and krt4), hindbrain and spinal cord (nkx6.1), and hindbrain, spinal cord, and olfactory organ (id2a) under a wide-field fluorescence microscope LEICA M165 FC (Leica Microsystems) using either the GFP (EGFP or mNeonGreen) or YFP (mNeonGreen) channel. Positive mosaic F0 were put into the fish facility at 6 dpf.
## Genotyping of F1
The clipped zebrafish fins were added to a lysis buffer (10 mM Tris–HCl pH 7.5, 1 mM EDTA, 50 mM KCL, $0.3\%$ TWEEN 20) and boiled at 95°C for 10 min. Next, we added $10\%$ volume proteinase K (10 mg/ml) and incubated it at 55°C overnight. On the following day, we heat-inactivated proteinase K by boiling at 95°C for 10 min and used 1 μl as the template for PCR reactions. The following primers were used to amplify the fragments over the insertion site, and the reverse primers were used as the sequencing primer:krt92 (forward primer): 5′-CAAGCTCAAGCTCAAGTTCC-3′krt4 (forward primer): 5′-GTTATGGTGGTAGCGGCTCTGG-3′nkx6.1 (forward primer): 5′-CGACGACGACTACAATAAACC-3′id2a (forward primer): 5′-CTCGACTCCAATTCGGCG-3′EGFP (reverse primer): 5′-CATGTGGTCGGGGTAGCG-3′mNeonGreen (reverse primer): 5′-ACTGATGGAAGCCATACCCG-3′.
## Quantitative RT–PCR
qRT-PCR was performed using SYBR Green on a ViiA 7 Real-time PCR machine. *The* gene encoding b-actin was used as the control for normalization. The primer sequences are as follows:krt92 (forward primer): 5′-CCGAAACCCTCACCAAGGAA-3′krt92 (reverse primer): 5′-CCTCGCTCGTAGATTGGGAG-3′krt4 (forward primer): 5′-AACAAGCGTGCTTCCGTAGA-3′krt4 (reverse primer): 5′-GCGATCATGCGGTTGAGTTC-3′nkx6.1 (forward primer): 5′-CGTGCTCACATCAAAAC-3′nkx6.1 (reverse primer): 5′-CGGTTTTGAAACCACACCTT-3′id2a (forward primer): 5′-CAGATCGCGCTCGACTCCAA-3′id2a (reverse primer): 5′-CAGGGGTGTTCTGGATGTCCC-3′b-actin (forward primer): 5′-CGAGCAGGAGATGGGAACC-3′b-actin (reverse primer): 5′-CAACGGAAACGCTCATTGC-3′ qRT-PCR data are expressed as relative fold change (ΔΔCt). Four zebrafish larvae were pooled as biological replicates. We have four biological replicates for each knock-in and WT. Two-tailed t test was used with P-value = 0.05 as statistically significant.
## Lineage tracing by tamoxifen-inducible Cre recombinase
We used both iCre and CreERT2 lines for the testing of Cre and the genetic lineage-tracing experiment. The knock-in iCre lines were crossed with either Tg(ubi:Switch) or Tg(ubi:CSHm). For temporal labelling, we treated the zebrafish larvae carrying knock-in CreERT2 and ubi:CSHm or ubi:Switch transgenes with 20 μM 4-OHT (Sigma-Aldrich) in an E3 medium in 24-well plates, four to eight embryos/larvae per well, for 24–36 h without refreshment. Upon induction by 4-OHT, cytoplasmic CreERT2 would be translocated into the nucleus to excise the DNA in between the two loxp sites to enable downstream mCherry or H2BmCherry expression.
## Sample fixation for immunostaining
Before fixing the zebrafish larvae/juveniles, we confirmed the presence of the knock-in cassettes or the sorting markers by examining the corresponding fluorescence signals using a LEICA M165 FC fluorescence microscope. We euthanized the zebrafish juveniles with 250 mg/l tricaine (Sigma-Aldrich) in the E3 medium. Before fixation, we washed the zebrafish larvae/juveniles with distilled water three times. We fixed the samples in $4\%$ formaldehyde (Sigma-Aldrich) in PBS (Thermo Fisher Scientific) at 4°C for at least 24 h. After three washes with PBS, we removed the skin and crystallized yolk (in larvae) by forceps under the microscope to expose the pancreas and liver for immunostaining.
## Hybridization chain reaction
Hybridization chain reaction v3.0 was performed following the protocol described by Choi et al [2018]. In brief, the larvae were fixed in $4\%$ PFA at 4°C overnight, with subsequent perforation in $100\%$ methanol at −20°C for at least 24 h. After that, we performed gradient rehydration steps using $75\%$, $50\%$, and $25\%$ methanol and five washes using $100\%$ phosphate-buffered saline solution with $0.1\%$ TWEEN 20 (PBST). The larvae were then permeabilized using 30 μg/ml proteinase K for 45 min at room temperature, followed by 20 min post-fixation using $4\%$ PFA and five washes with PBST. The larvae were incubated overnight at 37°C in hybridization buffer containing 2 pmol of probe set. On the next day, the solution was washed off four times with a washing buffer at 37°C, followed by 5× saline-sodium citrate with $0.1\%$ TWEEN 20 (SSCT) incubation at room temperature for 15 min each. Next, the larvae were incubated in an amplification buffer with 15 pmol of fluorescently labelled hairpin amplifier overnight at room temperature. On the next day, the larvae were incubated with DAPI (1:1,000, in 5× SSCT) for 30 min at room temperature followed by four washes with 5× SSCT. Probe sequences were designed by the manufacturer (Molecular Instruments). The probe set used was: dr_krt4-B1. The conjugated hairpin amplifier is B1, with fluorophore 647.
## Confocal imaging of live zebrafish larvae
We prepared $1\%$ low-melting agarose gel by dissolving 0.01 g agarose (A9414; Sigma-Aldrich) in 1 ml E3 solution with 250 mg/l tricaine followed by 65°C heating for 10 min and cooling on a wedged zebrafish mold. Zebrafish larvae were euthanized in the E3 medium with 250 mg/l tricaine and then repositioned in the gel groove to a suitable position. The confocal imaging was performed using the laser scanning microscopy platform Leica TCS SP8 (Leica Microsystems) with a 10× objective.
## Immunostaining and confocal imaging
We performed immunostaining similarly to our previous report (Liu et al, 2021). In brief, we firstly incubated the zebrafish samples in blocking solution ($0.3\%$ Triton X-100, $4\%$ BSA in PBS) at room temperature for at least 1 h. We then incubated the samples in the blocking solution with primary antibodies at 4°C overnight. After removing the primary antibodies, we washed the samples with washing buffer ($0.3\%$ Triton X-100 in PBS) 10 times at room temperature for at least 4 h in total. Next, we incubated the samples in the blocking solution with fluorescent dye-conjugated secondary antibodies and the nuclear counterstain DAPI (Thermo Fisher Scientific) at 4°C overnight. Afterwards, we removed the secondary antibodies and DAPI and washed the samples with washing buffer 10 times at room temperature for at least 4 h. The following primary antibodies were used: chicken anti-GFP (1:500, GFP-1020; Aves Labs), goat anti-tdTomato (1:500, MBS448092; MyBioSource), mouse anti-mNeonGreen (1:50, 32F6; ChromoTek), rabbit anti-Insulin (1:100, customised; Cambridge Research Biochemicals), mouse anti-Glucagon (1:50, G2654; Sigma-Aldrich), rabbit anti-Cdh17 (1:1,000; customised sera, gift from Prof. Ying Cao, Tongji University), and rabbit anti-Vasnb (1:1,000; customised sera, gift from Dr. Paolo Panza).
Before confocal imaging, we mounted the stained samples in VECTASHIELD Antifade Mounting Medium (Vector Laboratories) on microscope slides with the pancreas or liver facing the cover slips. We imaged the pancreas and liver with the Leica TCS SP8 platform.
## Hepatocyte ablation by chemo-genetic and pharmacological approaches
The extreme hepatocyte injury model was induced based on the metronidazole/nitroreductase (MTZ/NTR) system (Curado et al, 2008). In brief, the TgKI(id2a-EGFP-t2a-CreERT2); Tg(ubi:Switch); Tg(fabp10a:CFP-NTR) larva were treated with 10 mM MTZ (final concentration) in E3 medium (Choi et al, 2014). The severe hepatocyte injury model included incubating the zebrafish larvae in E3 medium supplemented with 10 mM acetaminophen (A7085; Sigma-Aldrich) and $0.5\%$ ethanol for 48 h from 3–5 dpf, followed by three washes with the E3 solution and then 2 d of recovery, as previously reported (North et al, 2010).
## Statistical analysis and data visualization
Similar experiments were performed at least two times independently. The number of cells in the confocal microscopy images was all quantified manually with the aid of the Multipoint Tool from ImageJ. The knock-in strategy scheme was illustrated by using “IBS” software (Liu et al, 2015). Statistical analyses were carried out by two-tailed Mann–Whitney U tests (comparing two groups) or by Kruskal-Wallis tests (comparing three groups) unless otherwise stated. The results are presented as the mean values ± SEM and P-values ≤ 0.05 are considered as statistically significant. The n number represents the number of zebrafish in each group for each experiment, and all raw numbers for quantifications can be found in the Table S1. All statistical analyses and data visualizations were performed on R platform (version 4.0.2) using “ggplot2” and “ggpubr” packages.
Table S1. Raw data for figures. Raw numerical values used for plots presented in figures.
## Data Availability
The specifics of the key resources are available (Table S2), the construct maps and the Sanger sequencing files that support the correct in-frame integration at the 5′ end of the integrated sequences are available in the public depository (https://osf.io/tdkvh/).
Table S2. Key resources. Specifics regarding zebrafish lines, antibodies, recombinant DNA reagents, oligonucleotide sequences, kits, recombinant proteins, chemicals, and software used in this study.
## Author Contributions
J Mi: conceptualization, data curation, formal analysis, investigation, visualization, methodology, and writing—original draft, review, and editing. O Andersson: conceptualization, formal analysis, supervision, funding acquisition, validation, investigation, visualization, project administration, and writing—review and editing.
## Conflict of Interest Statement
The authors declare that they have no conflict of interest.
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|
---
title: A multiplexed siRNA screen identifies key kinase signaling networks of brain
glia
authors:
- Jong-Heon Kim
- Jin Han
- Ruqayya Afridi
- Jae-Hong Kim
- Md Habibur Rahman
- Dong Ho Park
- Won Suk Lee
- Gyun Jee Song
- Kyoungho Suk
journal: Life Science Alliance
year: 2023
pmcid: PMC9990460
doi: 10.26508/lsa.202201605
license: CC BY 4.0
---
# A multiplexed siRNA screen identifies key kinase signaling networks of brain glia
## Abstract
This study reports a multiplexed brain glial kinase screen, providing insight into the largely unknown glial intracellular signaling networks controlling neuroinflammation and highlights the therapeutic potential of targeting glial kinases to modulate glial phenotypes and treat neuroinflammatory disorders.
The dynamic behaviors of brain glial cells in various neuroinflammatory conditions and neurological disorders have been reported; however, little is known about the underlying intracellular signaling pathways. Here, we developed a multiplexed kinome-wide siRNA screen to identify the kinases regulating several inflammatory phenotypes of mouse glial cells in culture, including inflammatory activation, migration, and phagocytosis of glia. Subsequent proof-of-concept experiments involving genetic and pharmacological inhibitions indicated the importance of T-cell receptor signaling components in microglial activation and a metabolic shift from glycolysis to oxidative phosphorylation in astrocyte migration. This time- and cost-effective multiplexed kinome siRNA screen efficiently provides exploitable drug targets and novel insight into the mechanisms underlying the phenotypic regulation of glial cells and neuroinflammation. Moreover, the kinases identified in this screen may be relevant in other inflammatory diseases and cancer, wherein kinases play a critical role in disease signaling pathways.
## Introduction
The central nervous system (CNS) includes a sophisticated immune system that actively participates in maintaining homeostasis and resolving infections. Glial cells, such as astrocytes and microglia, are nonneuronal cells that play key roles in homeostasis and immune surveillance (Engelhardt et al, 2017). Because these cells undergo extensive morphological and functional changes during neuroinflammation, they are commonly termed as reactive or activated glia (Liddelow & Barres, 2017). However, such terminology is currently controversial (Escartin et al, 2019); thus, the neuroinflammatory aspects of glial responses require further investigation and a better definition. Nevertheless, there is a consensus on the significant changes in glial phenotypes in response to neuroinflammatory or pathological stimuli. In fact, accumulating evidence has revealed a spectrum of glial phenotypes, including cellular (re)activation, proliferation, migration, and phagocytosis (Burda & Sofroniew, 2014). These cellular phenotypes are primarily involved in resolving encountered challenges and promote tissue remodeling. For instance, after an acute focal injury, activated microglia and astrocytes produce cytokines to propagate inflammatory signals. The signals released by activated glia attract other glial cells to the injury site, leading to the clearance of cellular debris or pathogenic molecules, reconstruction of tissues, and reestablishment of homeostasis. However, in cases of severe or chronic neuroinflammation, these phenotypes can cause detrimental effects, such as neurodegeneration and CNS tissue damage. Indeed, activated glial cells release neurotoxic molecules, such as reactive oxygen and nitrogen species (Kempuraj et al, 2017), interfere with neuronal activities (Robel & Sontheimer, 2016) and cause nonspecific phagocytosis (Jung & Chung, 2018). The response of activated glia to CNS damage involves a complex combination of events that should ideally be balanced between beneficial and detrimental effects. Inappropriate glial cell activity is a “common denominator” in a wide spectrum of neurological disorders (Kempuraj et al, 2017); thus, elucidating the molecular mechanisms underlying glial activation could facilitate the discovery and development of viable therapeutic targets. However, little is known about the intracellular signaling pathways underlying the diverse phenotypes and associated behaviors of glial cells.
Kinases comprise a large group of enzymes that not only regulate protein function by phosphorylating serine, threonine or tyrosine residues on target proteins but also contain a regulatory domain for other kinases. Kinases play a predominant regulatory role in nearly all cellular processes, including cell proliferation, differentiation, growth, metabolism, cytoskeletal rearrangements, and survival. Therefore, exploring the role of kinases is essential for understanding the modulation of glial phenotypes or their hyperactivity. Aberrant kinase activity has been the focus of molecular research on various diseases, particularly those involving inflammation or malignant proliferation, such as rheumatoid arthritis or cancer. As such, the kinase-targeting studies published to date have primarily focused on nonCNS diseases (Roskoski, 2019). Specific kinases, including neuronal leucine-rich repeat kinase 2 and glycogen synthase kinase-3β in Parkinson’s disease (Li et al, 2014), and τ tubulin kinase 1 in Alzheimer’s disease and other neurodegenerative disorders (Nozal & Martinez, 2019), were targeted in such studies. However, to date, research on glial kinases has been limited. Therefore, in this study, we present a novel approach for identifying kinases with important roles in the regulation of neuroinflammatory glial phenotypes and behaviors, such as glial activation, death/survival, migration, and phagocytosis. To simultaneously address various glial cell behaviors, we designed and performed a multiplexed cell-based screen of an siRNA library targeting 623 kinases in LPS-stimulated mixed glial cell (MGC) cultures prepared using the brains of neonatal mice. Four different phenotypic assays were performed in parallel or tandem sequence. Using stringent criteria, we identified hit kinases and selected some for further validation and characterization. Our findings provide insight into the largely unknown intracellular signaling networks of glia controlling neuroinflammation and highlight the therapeutic potential of targeting glial kinases to modulate glial phenotypes and treat neuroinflammatory disorders.
## Multiplexed kinase siRNA screen in brain glial cells
To identify kinases that modulate the inflammatory activation of glia and their function during neuroinflammation, we performed a multiplexed kinome-wide siRNA screen in primary MGC cultures stimulated with the proinflammatory endotoxin LPS (Fig 1). The kinome-wide siRNA screen was performed in a 96-well plate format. We used an siRNA library containing a pool of three siRNAs targeting each of the 623 kinases across seven plates. The seven library plates were reformatted into 24 plates for the assay to obtain data in triplicate (by splitting a single pool of kinase siRNAs into three wells). MGCs, obtained from the brains of 20 pups for a single screening round, were seeded into the assay plates at a density of 2,500 cells per well and transfected with the siRNA library, nontargeting siRNA or transfection reagent alone. MGCs were then incubated with or without LPS (1 μg/ml) for 48 h (to a total of 48 plates; 24 LPS-treated and 24 LPS-untreated plates). After LPS stimulation, four different assays were performed using either the culture medium or the remaining cells in the assay plates. All measurements were performed in triplicate. The whole multiplexed siRNA screen was repeated twice (96 plates in total). The LPS-untreated condition was used as screening control. Quality checks across different plates or culture conditions were performed by including multiple controls, such as LPS-untreated MGCs, nontargeting siRNA-transfected cells, and transfection reagent alone, in every assay plate. To determine the cellular proportions in different MGC preparations, we performed immunocytochemical staining with glia-specific markers (Fig S1).
**Figure 1.:** *Overview of the multiplexed kinase siRNA screening strategy.For the kinome-wide siRNA screening, we used the siRNA mouse kinase library in a 96-well plate format. Each well contained a single pool of three different siRNA sequences targeting each of the 623 kinases (seven plates). The seven library plates (master plates) were reformatted to 24 plates (daughter plates) to conduct the assay and obtain data in triplicate. Mixed glial cells (MGCs) were seeded into 24 plates (assay plates) at a density of 2,500 cells per well and transfected with the siRNA library, nontargeting siRNA or the transfection reagent alone. MGCs were then incubated with or without LPS (1 μg/ml) for 48 h (to a total of 48 plates with 24 LPS-treated plates and 24 LPS-untreated plates). After LPS stimulation, four different assays were performed using either the culture medium or the remaining cells in the assay plates. All measurements were performed in triplicate. The whole multiplexed siRNA screen was repeated twice (96 plates in total). For a single screening round, MGCs were prepared from the brains of 20 pups. The LPS-untreated condition was used as screening control. See the “Materials and Methods” section for detailed descriptions of individual assays.* **Figure S1.:** *Identification of astrocytes, microglia, and oligodendrocytes in mixed glial culture.MGC were isolated from whole brains of mouse pups at postnatal day 3. At 14 d in vitro, MGC were transferred to 96-well plates (2,500 cells per well). After 48 h, MGC were immunostained with anti-GFAP (1:1,000; green), anti-Iba-1 (1:1,000; gray), and anti-Olig2 (1:500; red) antibodies for astrocytes, microglia, and oligodendrocytes, respectively. DAPI (blue) was used for counterstaining. Scale bar = 200 μm.*
Four different phenotypes were examined in parallel or tandem sequence in a multiplexed manner: (i) inflammatory glial activation (via a nitric oxide [NO] assay); (ii) glial cell death/survival (via a cytotoxicity assay); (iii) glial migration (via a wound healing assay); and (iv) glial phagocytosis (via a Zymosan uptake assay; Fig 1). The sequence in the multiplexed screen was simulated in vivo conditions, in which, glia are first activated, then they migrate, and finally perform phagocytosis at the site of brain inflammation. Moreover, the multiplexed screen allowed multiple assays to be conducted under the same conditions, thereby reducing experimental variation and saving time and reagents. The reproducibility of all assays was confirmed by at least duplicate siRNA screens. A Pearson’s correlation coefficients for correlations between replicate screens were >0.8, considered to be a strong correlation as previously described (Mukaka, 2012): NO assay ($r = 0.8134$), wound healing assay ($r = 0.8872$), cytotoxicity assay ($r = 0.8893$), and Zymosan uptake assay ($r = 0.8744$). To identify the kinases engaged in glial phenotype regulation in an LPS-dependent manner, we compared the hits identified in the presence and absence of LPS stimulation (Fig S2). In the wound healing and Zymosan uptake assays, 45 and 31 hit kinases were LPS-dependent, respectively (Table S1). These data were used as baseline reference for determining the most relevant hits.
**Figure S2.:** *Identification of LPS-dependent or -independent kinase hits.(A) Scatterplot analysis showing LPS-dependent or LPS-independent kinase hits. Fold change values were obtained after kinase knockdown in both LPS-treated and LPS-untreated conditions. The ratio of fold changes (LPS-treated/LPS-untreated condition) was calculated to determine the difference in the outcome measures between the two alternative conditions. LPS-dependent or LPS-independent kinase hits were defined by the LPS-treated/LPS-untreated condition ratio: hits for which the ratio was >1.5 (upper left side of red dotted lines) were considered LPS-dependent. (B, C, D) Representative Kyoto Encyclopedia of Genes and Genomes pathways and functional group networks (Gene Ontology [GO] term analysis) of LPS-dependent kinase hits in the (B) nitric oxide (NO) assay (glial activators), (C) wound healing assay, and (D) Zymosan uptake assay. Kyoto Encyclopedia of Genes and Genomes pathways were analyzed using the DAVID bioinformatics tool. GO terms were generated with ClueGO. Terms are functionally grouped based on shared genes (κ score) and indicated with different colors.*
Table S1. LPS-dependent kinases in the assays.
The efficiency of the siRNA-mediated knockdown of kinase gene expression was validated using real-time PCR and immunoblotting for the selected kinases. For validation at the mRNA level, 12 kinases were selected based on phenotypic screening of the siRNA library (Table S2); four from the activation screen; four from the migration screen; three from the phagocytosis screen; and four that did not show significant effects in any screens (Fig S3A). For validation at the protein level, six kinases were selected based on inhibition efficiency at the mRNA level (>$50\%$, three kinases; <$50\%$, three kinases; Fig S3B). Our data showed similar siRNA down-regulation of specific kinases (mRNA/protein) in PBS and LPS conditions: all reduction rates (%) had a P-value >0.1 in comparisons between PBS and LPS conditions.
Table S2. Criteria for selecting a subset of kinases used in the validation experiments of siRNA knockdown efficiency.
**Figure S3.:** *Determination of siRNA knockdown efficiency in MGCs.(A) Knockdown efficiency of siRNAs targeting 12 kinases at mRNA level determined using real-time PCR. Data are presented as mean ± SEM (n = 3 replicate wells per group). Unpaired t test. *P < 0.05 versus (Ctrl, without siRNAs) in the PBS-treated group. #P < 0.05 versus Ctrl without siRNAs in the LPS-treated group. The kinases were selected based on phenotypic screening of siRNA hits (Table S2). (B) Confirmation of the knockdown at the protein level. The kinases were selected based on inhibitory efficiency at the mRNA level (<50%, three kinases; >50%, three kinases). Data are presented as mean ± SEM (n = 3 replicate wells per group). Unpaired t test. *P < 0.05 versus Ctrl without siRNA. #P < 0.05 versus Ctrl without siRNAs in the LPS-treated group. Assays (A, B) were repeated at least twice with similar results and the interassay coefficients of variation were <20%, indicating a high level of confidence in the result. (C) Fluorescence-based evaluation of siRNA transfection efficiency in the MGC culture. Upper panel: MGC were isolated from whole brains of mice pups at postnatal day 3. At 14 d in vitro, MGC were transfected with Silencer Cy3-labeled GAPDH siRNA (20 μM) by Lipofectamine RNAiMAX Reagent overnight. After 48 h, MGC were immunostained with anti-GFAP (1:1,000; green) and anti-Iba-1 (1:1,000; gray) antibodies. DAPI (blue) was used for counterstaining. Scale bar = 500 μm. Lower panel: High magnification images showing colocalization of GFAP or Iba-1 with Cy3-labeled GAPDH siRNA. Colocalizations (yellow) are indicated by arrows. Scale bar = 100 μm.*
After immunocytochemical staining, the analysis of fluorescent images revealed the cellular composition of cultured MGCs (Fig S1 and Table S3). MGCs comprised approximately $66\%$ astrocytes, $15\%$ microglia, and $15\%$ oligodendrocytes in the LPS-untreated group and $64\%$ astrocytes, $23\%$ microglia, and $13\%$ oligodendrocytes in the LPS-treated group. The interassay variation was <$10\%$ across different preparations or <$20\%$ between LPS-untreated and LPS-treated wells, indicating statistical consistency (Li et al, 2017). In this study, we focused on the phenotypes of astrocytes and microglia—the main players in neuroinflammation. The efficiency of siRNA transfection was determined using a Cy3-labeled siRNA transfection control (siRNA of GAPDH) followed by immunocytochemical staining with antibodies against glial cell markers, such as anti-glial fibrillary acidic protein (GFAP) or anti-ionized calcium binding protein (Iba-1; Fig S3C). Transfection rates of astrocytes and microglia ($70.1\%$ and $54.8\%$, respectively) in the LPS-untreated group and of those ($67.1\%$ and $56.3\%$, respectively) in the LPS-treated group were determined, and the intergroup variation in transfection rate was found to be <$20\%$ (Table S3). Therefore, both glial cell types were efficiently transfected with siRNAs; similarly, the kinase expression in astrocytes and microglia was successfully knocked down in the MGC model; thus, the observed phenotypic changes are likely relevant to both glial cell types.
Table S3. Percentage of cell subsets and transfection efficiency in MGCs cultures.
## The screen identifies glial kinases that regulate inflammatory activation of glia
After siRNA-mediated knockdown and LPS treatment of MGC in 96-well plates, culture media were transferred into two 96-well plates. One plate was used for a NO assay to assess glial cell activation and the other for a cell toxicity assay (lactate dehydrogenase [LDH] assay). The remaining MGCs in the original plates were sequentially subjected to wound healing and phagocytosis assays using Zymosan particles (Fig 1).
NO production was used as an indicator of inflammatory activation of glia in this study, as in previous studies (Calabrese et al, 2007). In the first round of assays, NO production was assessed using the Griess assay, which measures nitrite (NO2−), a derivative of NO. Hits were selected using a dual-flashlight plot in which both the average fold change (FC) and the strictly standardized mean difference (SSMD) were considered simultaneously (Fig 2A). The following criteria were used to select siRNA hits: for siRNAs that increased NO production (kinases inhibiting glial activation), an average FC ≥ 0.5 (on the log2 scale) and SSMD ≥1.65 were used; conversely, for siRNAs that decreased NO production (kinases enhancing glial activation), an average FC ≤ −0.5 (on the log2 scale) and SSMD ≤ −1.65 were used. Overall, we identified 16 and 86 kinases that inhibited (glial inhibitors) or enhanced glial activation (glial activators), respectively (Fig 2A and Table S4). Using the Reactome database (https://reactome.org/), kinase hits were compared with the “TLR4 signal pathway” including NF-kB, MAP kinase, and interferon signaling, which were expected hits in the screen (Fig S4A). Several TLR4 signaling-associated kinases, including Ikbkb (log2FC = −0.8), Ikbke (log2FC = −0.58), Mapk11 (log2FC = −0.5), Rps6ka5 (log2FC = −0.6), and Tbk1 (log2FC = −0.8), were identified as significant regulators of NO production.
**Figure 2.:** *Kinases regulating glial activation.(A) Dual-flashlight plot for strictly standardized mean difference versus fold change (log2 scale) in the nitric oxide assay. Increased or decreased phenotypes were defined by fold changes ≥0.5 or ≤−0.5, respectively (log2 scale). Values on the x-axis indicate average fold change of three siRNAs for each target kinase. Final hit kinases were selected by strictly standardized mean difference values ≥1.65 or ≤−1.65. (B) Representative KEGG pathway analysis using the DAVID bioinformatics tool. (C) Representative functional group network views for Gene Ontology (GO) terms generated using ClueGO. Terms are functionally grouped based on shared genes (κ score) and shown in different colors.*
Table S4. Kinases identified in nitric oxide assay.
**Figure S4.:** *Validation of the kinase hits identified in the nitric oxide assay.(A) The toll-like receptor four signaling pathway and kinases identified as nitric oxide (NO) production regulators in this study. Asterisks and numbers indicate log2-fold changes in the NO assay. (B) Pharmacological validation of the kinase hits from the siRNA screen (NO assay). Mixed glial cell cultures were treated with LPS (1 μg/ml) and commercially available small-molecule kinase inhibitors as indicated. After 48 h, the NO and MTT assays were performed. The results were compared with those obtained from the siRNA screen dataset. The NO assay results are presented as fold change (log2 FC); ns indicates no statistical difference relative to each control (without siRNA or vehicle). Blue indicates a decrease compared with each control. The effect sizes (Hedges’ g values) are shown as well. Data are presented as mean ± SEM (n = 3 replicate wells per group). Two-way ANOVA was followed by Tukey’s post-hoc test. *P < 0.05. Veh, vehicle. Gray, PBS-treated group; black, LPS-treated group. The assay was repeated at least twice with similar results and the interassay coefficients of variation were <20%, indicating a high level of confidence in the results.*
Next, the effects of siRNAs on NO production were pharmacologically confirmed by reassaying with commercially available small-molecule inhibitors for 24 kinases (Fig S4B). LPS-induced NO production was reduced by the inhibitors of glial activation-enhancing kinases such as Itk, Mlkl, Irak4, Ulk1, Pkc, Dgk, Ephb4, Src, Ikbkb, and Mapk14. Because of the limited number of NO-decreasing kinases identified in the screen ($15.7\%$), the selected hits were coincidentally either NO-increasing kinases or kinases without significant effects. These results were consistent with those obtained in the siRNA screen. The overall false positive rate was $25\%$, relatively low according to previous research (Mukaka, 2012). The effect size (Hedges’ g) was also compared between the siRNA screen and pharmacological validation experiments; a g value ≥0.8 reflects a large effect size, whereas 0.5 and 0.2 can be considered moderate and small effect sizes, respectively (Jacob, 1998). Here, the average effect sizes for the original siRNA screen and pharmacological validation experiments were 2.96 and 4.42, respectively.
The 16 glia-inhibiting and 86 glia-activating kinases were subjected to enrichment analyses using Gene Ontology (GO) through the Database for Annotation, Visualization and Integrated Discovery (DAVID) (i.e., gene function classification) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and ClueGO (GO integration; Fig 2B and C). Among glia-inhibiting kinases, the most significantly enriched KEGG pathways were related to “phosphatidylinositol signaling system,” and “inositol phosphate metabolism”; among the glia-activating kinases, the “HIF-1 signaling pathway,” “TLR signaling pathway,” and “chemokine signaling pathway,” were the most significantly enriched (Fig 2B and Table S5). ClueGO analysis showed a significant enrichment of reactome pathways such as “effects of PIP2 hydrolysis” and “synthesis of PIPs at the plasma membrane” for glia-inhibiting kinases. However, “interferon γ signaling,” “NOD$\frac{1}{2}$ signaling pathway,” “semaphoring interactions,” and “regulation of TP53 activity through phosphorylation” were highly enriched for glia-activating kinases (Fig 2C).
Table S5. KEGG pathway of the kinases identified in the nitric oxide assay.
## Kinases associated with glial cell death/survival regulation
The second round of assays (cell toxicity assay) identified 15 kinases essential to glial cell viability (Fig 3A and Table S6). Hits were selected using a dual-flashlight plot in which both the average FC and SSMD were considered simultaneously (Fig 3A). To select siRNA hits that increase cell death (kinases essential for survival), we used the following criteria: an average FC ≥ 0.5 (on the log2 scale) and SSMD ≥ 1.65. To select siRNA hits decreasing cell death (kinases enhancing glial cell death), we used an average FC ≥ −0.5 (on the log2 scale) and SSMD ≥ −1.65. Then, pharmacological validation was performed using commercially available small-molecule inhibitors for three kinases (Fig S5). Among them, Src kinase inhibitor (PP2) at 10 μM enhanced cell death (Fig S5). Furthermore, ClueGO analysis indicated that “neurotrophin TRK receptor signaling pathway” and “peptidyl-tyrosine autophosphorylation” were significantly enriched in the 15 kinases (Fig 3B). The most significant KEGG pathways enriched for glial survival kinases were related to the “thyroid hormone signaling pathway,” “inflammatory mediator regulation of TRP channels,” “MAPK signaling pathway,” and “Hh signaling pathway” (Table S7).
**Figure 3.:** *Kinases regulating glial cell death.(A) Dual-flashlight plot for strictly standardized mean difference versus fold change (log2 scale) in the cytotoxicity assay. Increased or decreased phenotypes were defined by fold changes ≥0.5 or ≤−0.5, respectively (log2 scale). Values on the x-axis indicate the average fold change of the three siRNAs for each target kinase. The selected hit kinases had strictly standardized mean difference values ≥1.65 or ≤−1.65. Top kinases are indicated as well. (B) Representative functional group network view for Gene Ontology (GO) terms generated using ClueGO. Terms were functionally grouped based on shared genes (κ score) and shown in different colors.*
Table S6. Kinases identified in the cell death assay.
**Figure S5.:** *Validation of the kinase hits identified in the cytotoxicity assay.Pharmacological validation of the kinase hits from the siRNA screen (cytotoxicity assay). Mixed glial cell cultures were treated with LPS (1 μg/ml) and commercially available small-molecule kinase inhibitors as indicated. After 48 h, the MTT assay was conducted. The results were compared with those obtained from the siRNA screen dataset. Cytotoxicity is represented as fold change (log2 FC) or not significantly (ns) different relative to each control (without siRNA or vehicle). The red color indicates an increase compared with each control. The effect sizes (Hedges’ g values) are shown as well. Data are presented as mean ± SEM (n = 3 replicate wells per group). One-way ANOVA followed by Tukey’s post-hoc test. *P < 0.05 versus vehicle (Veh). The assay was repeated at least twice with similar results and the interassay coefficients of variation were <20%, indicating a high level of confidence in the results.*
Table S7. KEGG pathway of the kinases identified in the cell death assay.
## Kinases controlling glial migration
In the third round of assays, we identified novel kinases regulating glial migration. Hits were selected using a dual-flashlight plot in which both the average FC and SSMD were considered simultaneously (Fig 4A). To select siRNA hits that accelerated migration (kinases that decelerated glial migration), we used the following criteria: an average FC ≥ 0.5 (on the log2 scale) and SSMD ≥ 1.65; conversely, to select siRNA hits that decelerated migration (kinases that accelerated glial migration), we used an average FC ≤ −0.5 (on the log2 scale) and SSMD ≤ −1.65. In the assay, 75 and 22 kinases that respectively decelerated (glial migration decelerators) or accelerated (glial migration accelerators) glial migration were identified (Fig 4A and Table S8). Subsequently, 24 kinase hits were pharmacologically validated by reassaying with small-molecule kinase inhibitors (Fig S6). Specific kinase inhibitors for Ulk1, Src, Ikbkb, Akt1, and Mapk14 decelerated glial migration, whereas inhibitors for Pdk2, Irak4, Frap1, Pkc, Pkam Pkm2, Ephb4, and Map2k5 accelerated glial migration. These data were generally in agreement with the siRNA screen (false-positive rate = $16\%$). The average effect sizes for the original siRNA screen and pharmacological validation experiments were 1.44 and 1.55, respectively.
**Figure 4.:** *Kinases regulating glial migration.(A) Dual-flashlight plot for strictly standardized mean difference versus fold change (log2 scale) in the wound healing assay. Increased or decreased phenotypes were defined by fold changes ≥0.5 or ≤−0.5, respectively (log2 scale). Values on the x-axis indicate the average fold change of three siRNAs for each target kinase. Final hit kinases were selected by strictly standardized mean difference values ≥1.65 or ≤−1.65. (B) Representative KEGG pathway analysis using the DAVID bioinformatics tool. (C) Representative functional group network views for Gene Ontology (GO) terms generated using ClueGO. Terms are functionally grouped based on shared genes (κ score) and shown in different colors.*
Table S8. Kinases identified in the wound healing assay.
**Figure S6.:** *Validation of the kinase hits identified in the wound healing assay.Pharmacological validation of the kinase hits from siRNA screening (migration assay). Mixed glial cells were treated with LPS (1 μg/ml) and commercially available small-molecule kinase inhibitors as indicated. After 48 h, a wound healing assay was conducted. The results were compared with those obtained from the siRNA screen dataset. Glial migration is presented as fold change (log2 FC); ns indicates no statistical difference relative to each control (without siRNA or vehicle). Red and blue indicate an increase or decrease, respectively, compared with each control. The effect sizes (Hedges’ g values) are shown as well. Data are presented as mean ± SEM (n = 3 replicate wells per group). One-way ANOVA was followed by Tukey’s post-hoc test. *P < 0.05 versus Veh (vehicle). The assay was repeated at least twice with similar results and the interassay coefficients of variation were <20%, indicating a high level of confidence in the results.*
According to KEGG pathway and GO enrichment analyses, the glial migration decelerators were related to “central carbon metabolism in cancer,” “insulin signaling pathway,” “mTOR signaling pathway,” “PI3K-Akt signaling pathway,” “Rap1 signaling pathway,” and “HIF-1 signaling pathway,” whereas the glial migration accelerators were related to “TCR signaling pathway,” “adherens junction,” and “axon guidance” (Fig 4B and Table S9). ClueGO analysis revealed that “regulation of TP53 activity,” “negative regulation of the PI3K/AKT network,” “synthesis of PIPs at the plasma membrane,” and “cellular response to heat stress” were significant reactome pathways for the glial migration decelerators; reactome pathways such as “VAV exchanges GTP for GDP on RAC1, activating it” and “CD28 dependent PI3/AKT signaling” were associated with glial migration accelerators (Fig 4C).
Table S9. KEGG pathway of the kinases identified in the wound healing assay.
## Kinases regulating glial phagocytosis
In the final round of assays, we identified 57 kinases that inhibited phagocytosis (siRNAs increased phagocytosis) and 124 kinases that enhanced phagocytosis (siRNAs decreased phagocytosis; Fig 5A and Table S10). Hit selection was performed using a dual-flashlight plot in which both average FC and SSMD were considered simultaneously. To select siRNA hits that increased phagocytosis (kinases that inhibited phagocytosis), we used the following criteria: an average FC ≥ 0.5 (on the log2 scale) and SSMD ≥ 1.65; conversely, to select siRNA hits that decreased phagocytosis (kinases that enhanced phagocytosis), we used an average FC ≤ −0.5 (on the log2 scale) and SSMD ≤ −1.65.
**Figure 5.:** *Kinases regulating glial phagocytosis.(A) Dual-flashlight plot for strictly standardized mean difference versus fold change (log2 scale) in the phagocytosis assay. Increased or decreased phenotypes were defined by fold changes ≥0.5 or ≤−0.5, respectively (log2 scale). Values on the x-axis indicate average fold change of three siRNAs for each target kinase. Final hit kinases were selected by strictly standardized mean difference values ≥1.65 or ≤−1.65. (B) Representative KEGG pathway analysis using the DAVID bioinformatics tool. (C) Representative functional group network views for Gene Ontology (GO) terms generated using ClueGO. Terms are functionally grouped based on shared genes (κ score) and shown in different colors. (D) Simplified “Fcγ receptor-mediated phagocytosis” pathway. Asterisks indicate the kinases associated with the signaling pathway, which were identified in the siRNA screen.*
Table S10. Kinases identified in the Zymosan uptake assay.
According to KEGG pathway and GO enrichment analyses, the “MAPK signaling pathway,” “TCR signaling pathway,” “TNF signaling pathway,” and “ErbB signaling pathway” were the most significant KEGG pathways enriched for kinases inhibiting phagocytosis; in contrast, “mTOR signaling,” “insulin signaling,” “Fc γ R-mediated phagocytosis,” “VEGF signaling,” “central carbon metabolism in cancer,” “HIF-1 signaling,” “regulation of actin cytoskeleton,” and “NF-κ B signaling” were significant pathways for kinases enhancing glial phagocytosis (Fig 5B and Table S11). ClueGO analysis showed that several reactome pathways, including “PAK1,2,3 phosphorylates MAPK6,4,” “FCERI mediated MAPK activation,” “PI3K/AKT signaling network,” “FCGR activation,” “FCGR dependent phagocytosis,” “AMPK signaling,” and “signaling by interleukins” were enriched for phagocytosis-regulating kinases (Fig 5C). Moreover, several phagocytosis-enhancing kinases identified in our screen were included in canonical Fc γ R-mediated phagocytosis signaling pathways (Fig 5D).
Table S11. KEGG pathway of the kinases identified in the Zymosan uptake assay.
## Role of the identified kinase hits in other aspects of neuroinflammation
In the present study, MGCs were stimulated with the bacterial product LPS. To assess the phenotypes of glia activated by endogenous stimuli, we conducted additional experiments (NO and migration assays) using either high-mobility group box 1 (HMGB1) protein (Pedrazzi et al, 2007) or cell lysates (Eppensteiner et al, 2019) as representative sources of damage-associated molecular patterns (DAMPs). The siRNAs of the 12 kinases used in a previous validation experiment (Fig S3A) were added to MGC cultures, which were then incubated with HMGB1 (20 μg/ml) or cell lysates (1 μg/ml) for measuring NO release and glial migration. As a result, LPS and DAMPs showed similar overall response patterns after kinase knockdown in the MGC culture (Fig S7A).
**Figure S7.:** *Comparison of glial phenotypes upon stimulation with damage-associated molecular patterns and different neuroinflammatory aspects altered by kinase knockdown.(A) Comparison of glial nitric oxide (NO) production and migration after treatment with LPS or damage-associated molecular patterns. Mixed glial cell (MGC) cultures were transfected with 12 kinase siRNAs overnight. After 48 h, LPS (1 μg/ml), HMGB1 protein (20 μg/ml) or MGC lysates (1 μg/ml) were applied. (a) Comparison of NO production. Data are presented as mean ± SEM (n = 3 replicate wells per group). (b) Comparison of migration. Effects of kinase siRNA transfection in each assay compared with an untransfected control. Data are presented as mean ± SEM (n = 3 replicate wells per group). Abbreviation: ns, not significant. (B) Comparison of different neuroinflammatory aspects altered by kinase knockdown. At 14 d in vitro, MGC cultures were transfected with the 12 kinase siRNAs also used in the validation experiments in our study, overnight. After 48 h, LPS (1 μg/ml) was applied. (a) Screen dataset for the NO assay. Data are presented as mean ± SEM (n = 3 replicate wells per group). (b) Superoxide production assay. Transfected MGC cultures were stimulated with LPS (1 μg/ml) for 30 min and incubated with 10 μM dihydroethidium. The cells were then washed with ice-cold PBS and superoxide production was evaluated with a microreader (excitation: 534 nm; emission: 580 nm). Data are presented as mean ± SEM (n = 3 replicate wells per group). (c, d) TNF-α or MMP-9 protein release in the cultured media of transfected MGC cultures. The transfected cells were stimulated with LPS (1 μg/ml) for 48 h; then, culture media were harvested. Levels of (c) TNF-α or (d) MMP-9 protein were measured using a mouse TNF-alpha DuoSet ELISA Kit or mouse Total MMP-9 Quantikine ELISA Kit. Data are presented as mean ± SEM (n = 3 replicate wells per group). (e) Summary of the complete data. Abbreviations: ns, not significant. Values (A, B) are represented as fold change (log2 FC) or not significantly different (ns) relative to the control (without siRNA). Red and blue indicate an increase or decrease, respectively, compared with each control. Unpaired t test. *P < 0.05 versus untransfected control. All assays were repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result.*
Our multiplex screen was composed of four different assays for distinct glial phenotypes related to neuroinflammation. However, to further evaluate the role of the identified kinase hits in other aspects of neuroinflammation, we conducted a superoxide assay and ELISA measurement of TNF-α or matrix metalloproteinase (MMP)-9 as representative cytokines or proteases, respectively, using these 12 previously-selected kinase siRNAs (Fig S7B). The results were then compared with those of the NO assay in our screen. The data consistency among the four different assays was as follows: NO assay versus superoxide assay, $83.3\%$; NO assay versus TNF-α ELISA, $91.7\%$; and NO assay versus MMP-9 ELISA, $91.7\%$. Thus, we observed relatively high consistency between the NO assay and the other assays measuring different aspects of neuroinflammation.
## Comparison of glial phenotype screens between MGCs and single cell type culture
To test whether our screening system can be extended to single glial cell types, we compared glial phenotype screens in cultures of a single cell type with that of MGCs after introducing the siRNAs of the 12 representative kinases previously used (Fig S3A). The changes in NO production in MGC culture after the knockdown of kinases were similar to those of microglia (consistency = $50\%$) rather than astrocytes (consistency = $33.3\%$). These results suggest that the NO production of MGCs may be more closely related to microglia and their responses than to astrocytes (Fig S8A). The effects of kinase knockdown on MGC migration were also similar to those for microglia (consistency = $75\%$) and astrocytes (consistency = $91\%$; Fig S8B). Furthermore, we assessed cell death in cultures of a single cell type (microglia vs. astrocytes). Then, the top five kinases identified in MGC cytotoxicity assay (∼$50\%$ cell death) were selected for validation experiments with cultures of a single cell type (Fig S8C). Our data revealed that both microglia and astrocytes were similarly sensitive to cytotoxicity after kinase knockdown.
**Figure S8.:** *Comparison of glial phenotypes between MGC and single cell type culture.(A) Comparison of LPS-induced NO production among mixed glial cell, microglia, and astrocyte cultures after transfection with kinase siRNAs. At 14 d in vitro, astrocytes were isolated from MGC by shaking overnight to remove other cell types. Microglia were isolated by mild trypsin treatment. Cells were transfected with 12 kinase siRNAs overnight. After 48 h, LPS (1 μg/ml) was applied. NO production was measured at 48 h after LPS treatment and compared with that of an untransfected control. (a) Screen dataset (NO production in MGC), (b) NO production in microglia, (c) NO production in astrocytes, and (d) summary of all data in the NO assay. Data are presented as mean ± SEM (n = 3 replicate wells per group). (B) Comparison of LPS-induced migration among mixed glial cell, microglia, and astrocyte cultures after transfection with kinase siRNAs. At 14 d in vitro, astrocytes were isolated from MGCs by shaking overnight to remove other cell types. Microglia were isolated by mild-trypsin treatment. Cells were transfected with 12 kinase siRNAs overnight. After 48 h, LPS (1 μg/ml) was applied. (a) Screen dataset (migration in MGCs), (b) migration in microglia, (c) migration in astrocytes, and (d) summary of all data in the migration assay. Abbreviations: ns, not significant. Values (A, B) are represented as fold change (log2 FC) or not significantly different (ns) relative to the control (without siRNA). Red and blue indicate an increase or decrease, respectively, compared with each control. Data are presented as mean ± SEM (n = 3 replicate wells per group). (C) Comparison of LPS-induced cell death among MGCs, microglia, and astrocyte cultures after transfection with kinase siRNAs. At 14 d in vitro, astrocytes were isolated from MGCs by shaking overnight to remove other cell types. Microglia were isolated by a mild trypsin treatment. Cells were transfected with the kinase siRNAs overnight. After 48 h, LPS (1 μg/ml) was applied. Cytotoxicity was measured by lactate dehydrogenase assay at 48 h after LPS treatment. Data are presented as mean ± SEM (n = 3 replicate wells per group). Unpaired t test. *P < 0.05 versus untransfected control. All assays were repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result.*
## Role of microglial interleukin-2-inducible T-cell kinase (ITK) in neuroinflammation
In the NO assay, the “TCR signaling pathway” was enriched in glia-activating kinases (Fig 2B). Indeed, our siRNA screen identified several TCR signaling-associated kinases, such as tyrosine-protein kinase Akt1, Ikbkb, Itk, Pik3r3, MapK11, and Mapk13 as glial activators (Fig 6A). Among them, the knockdown of interleukin-2-inducible T-cell kinase (Itk) produced the most potent effects in the NO assay. Because *Itk is* a well-known component of TCR signaling and its CNS expression has not been reported, we first investigated whether *Itk is* expressed in glia and the CNS in vivo. After treatment with LPS for 6 h, Itk mRNA expression was increased in MGCs and the inflamed mouse brain 24 h after LPS injection (i.p.; Fig 6B and C). Immunohistochemical analysis identified Itk expression and phosphorylation in microglia, but not astrocytes, in the hippocampal region of the inflamed brain after LPS injection (i.p.; Fig 6D). Furthermore, phosphorylation of PLC-γ and Vav-1, major components of the TCR signaling pathway, was increased in mouse primary microglial cells stimulated with LPS (Fig 6E).
**Figure 6.:** *Itk expression and activity in the brain.(A) Simplified “T cell receptor signaling” pathway. Asterisks indicate the kinases identified in the siRNA screen. (B)
Itk mRNA levels in MGC stimulated with LPS (1 μg/ml) for 6 h. Data are presented as mean ± SEM (n = 3 replicate wells per group). The assay was repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result. (C)
Itk mRNA levels in the inflamed brain 24 h after intraperitoneal LPS injection (5 mg/kg). Itk mRNA levels were measured by qRT-PCR in the whole brain. Data are presented as mean ± SEM (n = 3 mice per group). (D) Immunofluorescence analysis of phosphorylated Itk (p-Itk) and total-Itk (Itk) in the hippocampus of LPS-induced inflamed brains. Scale bar = 25 μm. Mice were injected with LPS (5 mg/kg) i.p. At 24 h after LPS injection, brains were prepared for immunofluorescence analysis. Glial cells were stained with anti-Iba-1 (microglia) and anti-GFAP (astrocytes) antibodies. A quantification of microglial cells (Iba-1+) co-stained with p-Itk (Iba-1+ p-Itk+) or Itk (Iba-1+ Itk+) is also shown (right). Data are presented as mean ± SEM (PBS, n = 3 mice; LPS, n = 5 mice; each data point indicates the average values of six fields of view per mouse). (E) Immunoblots of kinases related to the T cell receptor signaling pathway. Primary microglia were treated with LPS (1 μg/ml). Total protein was harvested at the indicated time points and subjected to immunoblot analysis (upper) and quantification (lower). p-PLC-γ, phosphorylated PLC-γ; PLC-γ, total PLC-γ; p-Vav, phosphorylated Vav1; Vav1, total Vav1. Data are presented as mean ± SEM (n = 3 replicate wells per group). (B, C, D, E) Unpaired t test (B, C, D), One-way ANOVA followed by Tukey’s post hoc test (E), *P < 0.05. The assay was repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result.Source data are available for this figure.*
To further investigate the role of Itk in microglial activation, commercially available Itk inhibitors (BMS509744 and GSK2250665A) were applied; these inhibitors significantly reduced LPS-induced NO production in a dose-dependent manner in primary microglia (Fig 7A). The effective concentration was 0.01 μg/ml (6 nM) for BMS509744 and 0.8 μg/ml (1.6 μM) for GSK2250665 in primary microglial cells. Similarly, BMS509744 blocked LPS-induced PLC-γ phosphorylation (Fig 7B), implying that Itk-mediated PLC-γ phosphorylation is involved in microglial activation. To determine the IC50 of the Itk inhibitor, we conducted two different cell-based Itk activity assays (Fig S9A). In the first, Itk inhibition by BMS509744 was quantified in microglial cell lysates using a commercially available Itk kinase activity assay kit. In the second, Itk activity was measured in a Western blot of phospho-PLC-γ (Tyr783), a direct substrate of Itk in microglia. In both assays, the IC50 of BMS509744 in microglial cells was around 100–400 nM, substantially higher than the reported IC50 (∼19 nM) in the cell-free system. Accordingly, we speculate that the effective concentrations of the Itk inhibitor may differ between cell-free conditions and microglial cells, supporting the observed relationship between Itk inhibition and its effects on NO production in microglia.
**Figure 7.:** *Inhibition of microglial Itk alleviates LPS-induced neuroinflammation.(A) Interleukin-2-inducible T-cell kinase (Itk) inhibitors (BMS509744 and GSK2250665A) diminished LPS-induced NO production in primary microglial cells. The cells were stimulated with LPS (1 μg/ml) for 24 h. NO production was measured by nitrite concentration in the cultured media and cell viability was assessed using an MTT assay. Data are presented as mean ± SEM (n = 3 replicate wells per group). (B) Immunoblots of PLC-γ (an Itk substrate) after treatment of primary microglia with LPS (1 μg/ml) in the presence or absence of the Itk inhibitor BMS509744 (1 μg/ml). The quantification is shown in the adjacent graph. Data are presented as mean ± SEM (n = 3 replicate wells per group). (C) Immunohistochemistry of glial cells in the hippocampal region. Mice were i.p. injected with LPS (5 mg/kg) with or without BMS509744 (10 mg/kg, i.p.). Astrocytes and microglia in the hippocampus were immunostained with anti-GFAP and anti-Iba-1 antibodies, respectively, at 24 h after LPS treatment. Scale bar = 400 μm. The quantification of glial activation is shown in the adjacent graph. Data are presented as mean ± SEM (n = 5 mice per group; each data point indicates the average value of six fields of view per mouse). (D) Expression of proinflammatory genes (Tnf-α, Il-1β, and Nos2) in mouse brains. After LPS injection (i.p., 24 h), hippocampal tissues were subjected to RT–PCR analysis. Data are presented as mean ± SEM (n = 5 mice per group). (E) PLC-γ immunoblots in hippocampal tissues at 24 h after LPS injection (5 mg/kg, i.p.) with or without the Itk inhibitor BMS509744 (10 mg/kg, i.p.). The quantification is shown in the adjacent graph. Data are presented as mean ± SEM (n = 3 mice per group). (F) Effect of Itk inhibition on LPS-induced behavioral impairments. After 3 d of LPS treatment (5 mg/kg, i.p. injection) with/without the Itk inhibitor BMS509744 (10 mg/kg, i.p.), spatial working memory (Y-maze test) and depression-like behavior (forced swim test) were measured. Data are presented as mean ± SEM (n = 6 mice per group). The Y-maze spontaneous alternation test showed that impaired spatial memory after administration of LPS was reversed by BMS509744 injection (left panel). The forced swim test revealed that the significant increase in immobility observed in LPS-injected mice was alleviated by BMS509744 injection (right panel). (A, B, C, D, E, F) Two-way ANOVA followed by Tukey’s post hoc test (A), one-way ANOVA followed by Tukey’s post hoc test (B, E, F), unpaired t test (C, D). *P < 0.05. The assays (A, B) were repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result.Source data are available for this figure.* **Figure S9.:** *Itk activity in the microglial cells.(A) Itk activity assay in BV-2 microglial cells. The microglial cells were incubated with LPS (100 ng/ml) and a serially diluted Itk inhibitor (BMS509744). Cell lysates were harvested in the presence of protease and phosphatase inhibitors. (a) Itk activity assay 1. We first determined the optimal amounts of cell lysate (containing Itk and other proteins) for measuring Itk activity based on signal-to-background ratio. A substrate (poly E4Y1)/ATP mix was added to the cell lysate and incubated for 1 h. After depleting the remaining ATP, Itk activity was visualized by kinase detection solution. The IC50 of BMS509744 was around 109 nM in this assay. Data are presented as mean ± SEM (n = 3 replicate wells per group). (b) Itk activity assay 2. The cell lysate was subjected to Western blotting for the detection of phospho-PLC-gamma (Tyr783), a direct substrate of Itk. Based on the inhibition of PLC-gamma phosphorylation, the IC50 of BMS509744 was around 407 nM in this assay. Data are presented as mean ± SEM (n = 3 replicate wells per group). All assays were repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result. (B) Schematic drawing depicting the putative role of Itk (asterisk) in microglial activation signaling.*
Next, we sought to determine the role of Itk in microglia-mediated neuroinflammation. In the systemic LPS injection-induced neuroinflammation model, co-administration of the Itk inhibitor BMS509744 (10 mg/kg) significantly reduced the activation of microglia and astrocytes. Glial activation characterized by hypertrophy or “gliosis” (Silver & Miller, 2004; Pekny & Nilsson, 2005) was measured based on the immunofluorescence intensity of Iba-1-positive microglia and GFAP-positive astrocytes (Fig 7C). These data suggest that the Itk inhibitor inhibited not only microglial activation but also microglia-mediated astrocyte activation. Moreover, the inhibitor decreased the mRNA expression of the proinflammatory cytokines Tnf-α, Il-1β, and Nos-2 in the neuroinflammation model (Fig 7D). LPS-induced PLC-γ phosphorylation in the brain was significantly blocked by the Itk inhibitor (Fig 7E). Because Itk has previously been associated with NF-κB signaling (Wang et al, 2017), Itk inhibition or knockdown may suppress NF-κB-dependent expression of inducible nitric oxide synthase (iNOS) and proinflammatory cytokines in microglia (Fig S9B). Next, we tested whether neuroinflammation-associated cognitive impairment and depressive-like behavior can be relieved by Itk inhibition in mice (Fig 7F). In a Y-maze test, LPS-injected mice showed a significantly lower percentage of spontaneous alternation compared with control mice (PBS and vehicle-injected mice), indicating that the former were cognitively impaired. However, Itk inhibitor injection alleviated this cognitive deficit. Furthermore, in a forced swim-induced behavioral despair test, the Itk inhibitor significantly reduced the immobility of LPS-treated mice. These results suggest that microglial *Itk is* involved in the behavioral deficits observed under the neuroinflammatory condition.
## Pdk2 modulates astrocyte migration by regulating mitochondrial metabolism: Pdk2 favors glycolysis and inhibits astrocyte migration
Among the kinase hits and associated biological pathways identified in the wound healing assay, we focused on carbon (glucose) metabolism, as it may be potentially involved in the regulation of cell migration (Turner & Adamson, 2011; Rash et al, 2018; Kaushik et al, 2019; Qiao et al, 2019). Hexokinase, pyruvate kinase M, and pyruvate dehydrogenase kinase isoform 2 (Pdk2) were identified in our screen as some of the most potent kinases for deceleration of glial migration (Figs 4B and 8A). All these kinases are closely associated with the glycolytic metabolism. Hexokinase and PKM may indirectly regulate the phosphorylation of other proteins (Roberts & Miyamoto, 2015; Zhang et al, 2019), although these enzymes do not directly phosphorylate proteins as regulatory protein kinases. In particular, Pdk2 has been implicated in neurological diseases (Rahman et al, 2016) and plays a crucial role in macrophage polarization to the M1 phenotype (Min et al, 2019).
**Figure 8.:** *Role of pyruvate dehydrogenase kinase isoform 2 (Pdk2) in glial cell migration and glycolytic metabolism.(A) Simplified “Central carbon metabolism in cancer” pathway. Asterisks indicate the kinases associated with specific signaling pathways, identified in the siRNA screen. (B) Representative images from the wound healing assay with mixed glial cells from WT mice. The cells were incubated with a Pdk2 inhibitor (AZD7545, 1 μM), glycolysis inhibitor (2-DG, 2 mM) or an OXPHOS inhibitor (oligomycin or FCCP, 1 μg/ml) for 48 h. Scale bar = 100 μm. (C) Representative images from the wound healing assay of mixed glial cells from Pdk2 KO mice. (B) The cells were incubated with the glycolysis inhibitor or OXPHOS inhibitor for 48 h as described in (B). Scale bar = 100 μm. (B, C, D) Quantification of migration velocity (μm/h) in (B, C). Data are presented mean ± SEM (n = 3 replicate wells per group). One-way ANOVA followed by Tukey’s post hoc test, *P < 0.05 versus control (Ctrl). The assay was repeated at least twice with similar results and the interassay coefficients of variation was <20%, indicating a high level of confidence in the result. (E) Effect of Pdk2 inhibition on astrocyte migration in vivo. Male C57BL/6 mice were intracortically injected with the vehicle or AZD7545 (1 μM). Pdk2 KO mice were injured by needle stabbing. BrdU was administered i.p. every 6 h after stab-wounding, as indicated. After 24 h, the mice were euthanized. Brain sections were obtained in the transverse plane (tangential to the direction of the needle injury). The quantification of GFAP-positive astrocytes around the injection site in the prefrontal cortex is shown in the right panel. The cell numbers in each concentric circle (radius step size = 500 μm) were analyzed. Data are presented mean ± SEM (n = 3 mice per group). Two-way ANOVA followed by Tukey’s post hoc test, *P < 0.05 versus Ctrl in the same area (area 2 or 4); #P < 0.05 versus Ctrl in area 1. (F) Effect of Pdk2-deficiency on oxygen consumption rate (OCR) in cultured astrocytes after LPS treatment. Primary astrocyte cultures were prepared from WT or Pdk2 KO mice and stimulated with LPS (1 μg/ml). A seahorse assay was conducted after 16 h of LPS treatment. Oligomycin was used to inhibit ATP synthase and reduce OCR. FCCP was applied to raise OCR to a maximal value by uncoupling oxygen consumption from ATP production. Rotenone, a complex I inhibitor, was used to completely inhibit the mitochondrial respiration. (a) OCR at different time points. (b) Basal OCR level in the astrocytes of WT or Pdk2 KO mice. Data are presented mean ± SEM (n = 5 replicate wells per group). Unpaired t test, *P < 0.05 versus WT.*
For further investigation of the regulatory role of Pdk2 in glial migration, we used a pharmacological inhibitor of Pdk2 (AZD7545) and *Pdk2* gene-deficient glial cells. In the wound healing assay, AZD7545 treatment or *Pdk2* gene KO significantly increased MGC migration after 48 h of LPS-priming (Fig 8B–D and Video 1). As the glucose metabolism has been suggested to play a role in cell migration (Urra et al, 2018), we compared glial cell migration after treatment with a glycolysis inhibitor (2-DG) or oxidative phosphorylation (OXPHOS) inhibitors (FCCP). As shown in Fig 8B–D, the up-regulation of glial migration caused by AZD7545 or Pdk2 KO was further enhanced by 2-DG but abrogated by oligomycin or FCCP.
Next, we evaluated the comparative movement of glial cells in vivo using a cortical needle injury model. The cortical injury was concurrently generated by local injections of vehicle or AZD7545 into the prefrontal cortex of WT mice. Pdk2 KO mice were injured by needle insertion alone in the same area of the brain (Fig 8E). Pdk2 expression was mainly observed in reactive astrocytes, as revealed by a high level of GFAP expression, which was used as a standard marker for reactive astrocytes (Liddelow & Barres, 2017) in this model (Fig S10A). The number of glia was immunohistochemically evaluated by counting GFAP- or Iba-1-positive cells in concentric circles from the injection site. BrdU staining was performed to detect proliferative cells. At 24 h after the cortical needle injury, the accumulation of astrocytes around the injury site was significantly increased in AZD7545-injected or Pdk2 KO mice compared with WT animals (Fig 8E). Moreover, BrdU staining indicated that glial cell migration was not greatly affected by cell proliferation (Fig S10B). Equivalent results were not observed in microglia (Fig S10C). Therefore, Pdk2 inhibition apparently promotes astrocyte migration toward the injury site, which is consistent with the data obtained from cultured glial cells.
**Figure S10.:** *Expression and role of Pdk2 in microglia migration in vivo.(A) Immunofluorescence analysis of Pdk2 in the prefrontal cortex of a stab wound injury model. At 24 h after the stab wound injury, male C57BL/6 mice were euthanized. Brain sections were obtained in the transverse plane. Astrocytes were stained by a GFAP antibody. Scale bars = 200 μm in the upper panels and 25 μm in the high magnification lower panels. The yellow asterisk indicates the injury site. (B) Representative immunofluorescence images of BrdU staining in the stab wound injury model. Ctrl, control; Scale bars = 500 μm. Asterisks indicate the injury sites. (C) Pdk2 inhibition does not significantly affect microglial migration in vivo. Male C57BL/6 mice were intracortically injected with the vehicle (control, Ctrl) or AZD7545 (1 μM). Pdk2 KO mice were injured through needle stabbing. BrdU was administered i.p. every 6 h, as indicated. At the end of the experiment, the mice were euthanized, and brain sections were obtained in the transverse plane. The quantification of Iba-1-positive microglia around the injection site in the prefrontal cortex is shown in an adjacent graph. The cell numbers in each concentric circle (radius step size = 500 μm) were analyzed. Data are presented as mean ± SEM (n = 3 mice per group). One-way ANOVA was followed by Tukey’s post-hoc test. *P < 0.05 versus control (Ctrl) in area 1. Abbreviations: ns, not significant.*
We also sought to determine the mechanism underlying the effects of Pdk2 on astrocyte mobility by assessing the mitochondrial metabolic state of astrocytes using a Seahorse XF 24 extracellular flux analyzer (Fig 8F). After 16 h of LPS treatment, the oxygen consumption rate (OCR) was significantly higher in Pdk2 KO astrocytes than in the WT (Fig 8F). These data imply that Pdk2 KO astrocytic metabolism is potentially dominated by OXPHOS, with reduced glycolysis-based lactate production. Collectively, these results suggest that Pdk2 modulates astrocyte migration by regulating the mitochondrial metabolism; Pdk2 favors glycolysis and inhibits astrocyte migration.
## Discussion
In this study, we provide an example of an effective method for identifying key kinases regulating the glial phenotypes underlying neuroinflammation. Specifically, we used a novel multiplex screen in which four different assays were performed in parallel or tandem sequence. Through it, we identified a large number of kinases that positively or negatively regulate glial phenotypes.
We used a systematic and stringent screening approach and identified the essential kinases among the 623 kinases ($100\%$) acting as glial activators ($13.8\%$) or inhibitors ($2.6\%$), key regulators of glial cell death/survival ($2.4\%$), glial migration accelerators ($12\%$) or inhibitors ($3.5\%$), and phagocytosis enhancers ($9.1\%$) or suppressors ($19.9\%$). Validation experiments using pharmacological inhibitors of the representative kinases revealed phenotypic changes similar to those identified in the multiplex kinase siRNA screen. Our multiparametric screening strategy is faster and more efficient than conventional assays that have until now been the workhorses for measuring neuroinflammatory indicators. Conventional assays individually measure single parameters and can be laborious and inefficient in terms of time and cost. The merit of our multiplex screen is its ability to fill the gap between in vitro and in vivo experiments as it runs across multiple stages of in vivo events, such as cell activation, migration, and phagocytosis, which generally occur sequentially in vivo. The multiplex screen could also help alleviate the limitation of data variation stemming from and depending on experimental batch conditions such as cellular population or culture environment. In summary, our multiplex screen enables analyses of several consecutive in vivo events in cultured cells in a time- and cost-efficient manner with minimal experimental variation.
In our screen, the first round of assays (the NO assay) identified crucial regulators of glial cell activation. NO plays a critical role in several physiological and pathological processes in glial cells. It is synthesized mainly by iNOS through TLR4 signaling in response to LPS exposure. Glia-derived NO has significant pathophysiological implications (Calabrese et al, 2007). In the present study, functional network analysis of the glia-inhibiting kinases identified in the NO assay revealed a high enrichment of the phosphatidylinositol signaling pathway. Previous reports demonstrated that LPS triggers a transient activation of PI3-kinase in macrophages (Diaz-Guerra et al, 1999) and microglia (Saponaro et al, 2012); conversely, using specific inhibitors of PI3-kinase, namely, wortmannin or LY294002, results in up-regulation of iNOS expression (Diaz-Guerra et al, 1999). This indicates that PI3-kinase acts as a negative regulator in NO production. Similarly, our screen identified PI3-kinases and related kinases in the same signaling pathway as major inhibitory regulators of glial activation. Nevertheless, both the positive and negative roles of the PI3-kinase/AKT cascade in microglial activation have been previously reported (Calabrese et al, 2007). We also found that significant proinflammatory pathways, such as the HIF-1, TLR, and chemokine signaling pathways, were enriched KEGG pathways for glial activators (Fig 2B). Moreover, we identified several TLR4 signaling-associated kinases, including Ikbkb, Ikbke, Mapk11, Rps6ka5, and Tbk1, as significant regulators of NO production (Fig S4A). These data imply that our screen successfully identified several kinases that regulate glial activation.
Herein, we also provided insight into Itk signaling pathways in glial cells, particularly microglia. Itk, also known as Emt and Tsk, is a member of the Tec family nonreceptor tyrosine kinases. This kinase is expressed primarily in hematopoietic cells and serves as an important mediator of antigen receptor signaling in lymphocytes (Berg et al, 2005). Itk activation after TCR stimulation has been previously demonstrated (Andreotti et al, 2010). T-cell activation after the binding of antigen to the TCR involves organization of the Vav1–SLP76–Itk complex. This, in turn, activates PLC-γ1, generating IP3 and DAG, which trigger calcium release and PKC activation, respectively. The mobilization of intracellular calcium activates key transcription factors, such as the nuclear factor of activated T lymphocytes, whereas activation of a specific PKC isoform, PKCθ, is associated with distal events leading to NF-κB activation (Paul & Schaefer, 2013). NF-κB plays a critical role in regulating the survival, activation, and differentiation of innate immune cells, such as microglia, and T cells. NF-κB comprises a family of inducible transcription factors regulating a large set of genes involved in various immune and inflammatory response processes. In T cells, the TCR-to-NF-κB pathway involves a series of complex events in which TCR engagement evokes a cytoplasmic cascade of protein−protein interactions and posttranslational modifications, leading to the nuclear translocation of NF-κB. NF-κB then promotes the differentiation of T helper type 1 cells (Oh & Ghosh, 2013). In microglia, NF-κB is a critical mediator of M1-type inflammatory responses triggered by both myeloid differentiation (MYD)88-dependent and toll/IL-1 receptor domain-containing adaptor-inducing IFN-β-dependent TLR signaling pathways (Lin et al, 2012). The MYD88-dependent TLR pathway is crucial for M1 macrophage polarization and is required to induce several inflammatory genes, including those encoding TNF-α, IL-1β, IL-6, IL-12p40, and cyclooxygenase-2. Although the activation and functional mechanisms of NF-κB differ between T cells and microglia, NF-κB is undoubtedly involved in the immune and inflammatory activation of both cell types. In this study, we found that *Itk is* another common signaling component in both these cell types. Specifically, we demonstrated Itk expression and activation in microglia for the first time. Immunohistochemical analysis revealed that Itk was phosphorylated in activated microglia but not in astrocytes after a systemic LPS challenge. Moreover, PLC-γ (Y783) and Vav1 were phosphorylated in microglial cells after LPS treatment. Furthermore, Itk inhibition mitigated LPS-induced PLC-γ phosphorylation and NO production, mRNA expression of proinflammatory cytokines, and microglial activation. These observations indicate the potential role of the Vav1–SLP76–Itk complex and PLC-γ1 in microglial activation, although the detailed mechanism underlying this observation remains unknown and should therefore be investigated further.
In the multiplexed screen, the wound healing assay showed that knocking down kinases involved in cellular metabolism alters the glial migration phenotype (Fig 4B). Of the kinases identified in the screen, we demonstrated the novel role of Pdk2 in glial migration (Fig 8). Genetic or pharmacological inhibition of Pdk2 resulted in accelerated glial migration, which was associated with a metabolic shift toward OXPHOS (Fig 8F). Pdk2 is one of the various PDK isoforms, which are key regulators of the mitochondrial gatekeeping enzyme pyruvate dehydrogenase (PDH) complex. The PDH complex is composed of three catalytic components, PDH (E1), dihydrolipoamide transacetylase (E2), and dihydrolipoamide dehydrogenase (E3). The basic core of the E1 PDH component is a heterotetramer of two α and two β subunits (α2β2) that catalyzes the first step of pyruvate decarboxylation, converting pyruvate to acetyl-CoA to be used in the tricarboxylic acid cycle and OXPHOS. Pdks inhibit the PDH complex by catalyzing the phosphorylation of serine residues in the E1 α subunit. Whereas Pdk-mediated phosphorylation and subsequent inactivation of the PDH complex result in a metabolic shift toward glycolysis, PDH dephosphorylation by PDH phosphatase induces entry into OXPHOS. Thus, as demonstrated in the current study, either pharmacological Pdk2 inhibition via siRNA or gene KO may promote the up-regulation of PDH activity, thereby inducing a metabolic shift toward OXPHOS, a process associated with accelerated glial migration.
Recently, the metabolic plasticity of glia has been suggested. Multiple lines of evidence have elucidated the metabolic switch in the neurotoxic activation of microglia and astrocytes. ( Ghosh et al, 2018; Morita et al, 2019). Under neuroinflammatory conditions, activated microglia and astrocytes prefer aerobic glycolysis over OXPHOS. Recently, it has been suggested that reestablishing OXPHOS in microglia may improve their migratory activity and their phagocytic potential in Alzheimer’s disease (Pan et al, 2019). Cell migration is an energetically expensive process that requires considerable amounts of ATP for cytoskeletal rearrangement. Glial cell migration is crucial to clearing cellular debris through phagocytosis and reestablishing homeostasis in the microenvironment of injured CNS tissue (Saadoun et al, 2005; Morizawa et al, 2017). In line with these reports, we showed that Pdk2 inhibition increases astrocytic migration toward the site of brain injury, indicating a likely role of OXPHOS and ATP in alternative or beneficial astrocyte activation.
Using MGC cultures can be a limitation in screening experiments, as MGCs are not a homogeneous single cell type. Nevertheless, the complexity of this culture may be useful for interpreting in vivo glial responses. Previous studies have used the MGC culture to incorporate complex cell behaviors in vivo into in vitro models and to mimic the key properties of CNS responses (Chen et al, 2013). Thus, MGCs can be used to investigate integrated glial functions and cell−cell communication, given that they mimic the heterogeneous nature of CNS glial cell types, and may therefore help improve current understanding of the mechanistic basis of glial responses (when used in combination with in vivo approaches). Moreover, in vitro culture of single glial cell types can potentially involve contamination with other glial cell types; thus, the robustness of the MGC culture as an in vitro model is another of its advantages. Although the MGC culture model may have some limitations, evidence suggests that it is a reasonable model for in vitro screening for the discovery of initial candidates, provided that the MGC-based screen must be combined with subsequent validation studies involving single glia cell types and in vivo experiments.
In our multiplexed screen results, it is not clear whether different kinase hits act in the same cell, making it difficult to interpret them as “pathways.” Different types of glia, particularly microglia and astrocytes, not only play distinct roles in neuroinflammation but also signal to each other; thus, their contribution to neuroinflammation is non-cell autonomous. The clustered functional pathways in each assay reported in this study potentially provide insight into the overall glial function, including the collective dynamics of a multicellular system (Bich et al, 2019). In addition, the data could indicate the multicellularity of glia, showing cell−cell communication and other non-cell autonomous properties, among others. Interglial crosstalk is crucial for brain development, function, and disease (Jha et al, 2019). Microglia determine the functions of reactive astrocytes, from neuroprotective to neurotoxic (Jha et al, 2018). Conversely, astrocytes regulate microglial phenotypes and functions, such as motility and phagocytosis, through their secreted molecules (Jha et al, 2018). Therefore, the data from the present study improve current understanding on the participation of glial cells in neuroinflammation and related diseases in vivo.
Glial cells show extensive heterogeneity and phenotypic plasticity in neuroinflammation. In addition, diverse glial phenotypes, including activation, migration, proliferation, death/survival, and phagocytosis, all are involved in the regulation of neuroinflammation. In the present study, our multiplexed screen identified a number of kinases that modulate these diverse glial phenotypes. Some kinases uniquely regulate a single glial phenotype, whereas others simultaneously control multiple glial phenotypes (Fig S11 and Table S12). As kinases are the single most important component of intracellular signaling pathways, the glia-regulating kinases identified in this study will improve our understanding of the regulatory signaling mechanisms underlying specific glial phenotypes. Moreover, these kinases could be a valuable source of druggable targets for a glia-based therapy of brain disorders. A search for human diseases associated with these glial kinases in DisGeNET, a database of gene-disease association, revealed a high relevance to common neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, schizophrenia, and depressive disorders (Fig S12 and Table S13). The disease associations of our kinase hits provide further insight into the role of glial phenotypes and neuroinflammation in neurodegeneration and other common neurological disorders.
**Figure S11.:** *Venn diagrams representing the number of kinase siRNA hits unique or common to each glial phenotype.Venn diagrams representing the number of kinase siRNA hits unique or common to each glial phenotype assay. Venn diagrams depict the number of kinase hits identified as (upper) glial activators or (lower) glial inhibitors along with modulators of other glial phenotypes.*
Table S12. Phenotypic clustering of the kinases identified in the multiplex screen.
**Figure S12.:** *CNS diseases linked to glial kinases.Pie charts represent the percentage of kinases associated with CNS diseases, identified in our glial phenotype assays.*
Table S13. List of kinases associated with CNS disease.
Our findings suggest that Itk and Pdk2 are potential kinase targets for therapeutic modulation of the neuroinflammatory phenotypes of glia (Fig 9). Itk inhibition may suppress neurotoxic microglial activation, whereas Pdk2 inhibition may promote astrocytic migration, aiding neurorepair (Renault-Mihara et al, 2011; Chiareli et al, 2021). Combination therapies targeting these two kinases may exhibit enhanced therapeutic effects. Therefore, the present multiplexed glial kinase screen not only provides a therapeutic roadmap for targeting glial kinases to halt neuroinflammation and treat the related CNS diseases but also constitutes a more efficient approach to the cell-based phenotypic screen.
**Figure 9.:** *Schematic summary showing the relevance of microglial Itk and astrocytic Pdk2 in neuroinflammation and neurotoxicity.Brain injury results in the release or leakage of inflammatory molecules from the damaged brain tissue, activating the microglia (through Itk, asterisk). Aberrant microglial activation and phagocytosis can also induce neurotoxicity. This process is followed by astrocytic migration toward the injury site (through Pdk2 inhibition, asterisk). Reactive astrocytes can further cause glial scar formation. The astrocytic scar separates healthy tissue from the damaged tissue, exhibiting neuroprotective effects in brain injury. Thus, Itk inhibition may suppress neurotoxic microglial activation whereas Pdk2 inhibition may promote astrocytic migration and might assist with neurorepair.*
## Reagents
LPS from *Escherichia coli* 0111:B4, oligomycin, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), rotenone, mitomycin C, HMGB1, and BrdU were purchased from Sigma-Aldrich. The following kinase inhibitors were purchased from Tocris: leucine-rich repeat kinase 2-IN-1, BMS509744, GSK650394, and GSK2250665A. IRAK-$\frac{1}{4}$ inhibitor 1 was purchased from Sigma-Aldrich, SBI-0206965 and GNF-2 were obtained from Selleckchem, P-M2tide was obtained from Enzo Life Sciences, and MlkL inhibitor was obtained from Calbiochem. AZD7545 was kindly provided by Professor In-Kyu Lee at Kyungpook National University.
## Glial cell cultures
Whole brains from 3-d-old C57BL/6 mice were minced and mechanically disrupted using a nylon mesh. The cells obtained were seeded in culture flasks containing DMEM supplemented with $10\%$ heat-inactivated fetal bovine serum, 100 U/ml penicillin, and 100 μg/ml streptomycin and grown at 37°C in a $5\%$ CO2 atmosphere. The culture medium was changed initially after 5 d and then every 3 d. Cells were used after 14–21 d of culture. To check the cell-specific population, MGC were immunostained with anti-GFAP (mouse IgG, 1:1,000; BD Biosciences), anti-Iba-1 (rabbit IgG, 1:1,000; Wako) or anti-Olig2 (goat IgG, 1:500; R&D systems) antibodies for astrocytes, microglia, and oligodendrocytes, respectively. This was followed by incubation for 2 h at room temperature with the following fluorescence-conjugated secondary antibodies: FITC-conjugated anti-mouse (donkey IgG, 1:500; Jackson Immuno Research Laboratories), Cy3-conjugated anti-goat (donkey IgG, 1:500; Jackson Immuno Research Laboratories), or Cy5-conjugated anti-goat (donkey IgG, 1:500; Jackson Immuno Research Laboratories). DAPI was used for counterstaining (blue). Pure astrocyte cultures were prepared from MGCs by shaking overnight. Pure microglia cultures were obtained from MGCs by mild trypsinization (Saura et al, 2003). The BV-2 immortalized mouse microglial cell line was maintained in DMEM supplemented with $10\%$ FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin.
## Kinase siRNA libraries
The siRNA libraries of 623 mouse kinases (MISSION siRNA Mouse Kinase Panel, SI42050) containing three different sequences of siRNAs targeting each kinase gene were purchased from Sigma-Aldrich. The siRNAs were supplied in 96-well plates, diluted to 10 μM working stocks upon arrival, according to the manufacturer’s instructions, and stored at −20°C until use.
## Evaluation of siRNA transfection efficiency
The control siRNA of GAPDH (Life Technologies) was labeled with Cy3 according to the manufacturer’s instructions. MGCs were transfected with Silencer Cy3-labeled GAPDH siRNA (20 μM) using Lipofectamine RNAiMAX reagent (Life Technologies) overnight. After 48 h, MGCs were immunostained with anti-GFAP (rabbit IgG, 1:1,000; Dako) or anti-Iba-1 (goat IgG, 1:1,000; Wako) antibodies. This was followed by incubation for 1 h at room temperature with the following fluorescence-conjugated secondary antibodies: FITC-conjugated anti-rabbit (donkey IgG, 1:500; Jackson Immuno Research Laboratories) and FITC-conjugated anti-goat (donkey IgG, 1:500; Jackson Immuno Research Laboratories). DAPI was used for counterstaining (blue).
## High-throughput siRNA screen in a multiplexed format
Before the high-throughput screen, the concentration of siRNAs, transfection reagents, and transfection efficiency was optimized for MGCs. For the screen, MGCs at 14 d in vitro were seeded (2,500 cells per well) into each well of 96-well flat, clear-bottomed and black-walled, polystyrene-treated tissue-culture microplates (Corning). After 48 h, the cells were transfected with siRNAs using Lipofectamine RNAiMAX reagent (Invitrogen), according to the manufacturer’s instructions. Afterwards, the following four assays were performed in sequence (Fig 1).
## NO assay
The NO2− concentration in culture media was measured to assess NO production in MGCs using Griess reagent. For each sample, 50 μl aliquots were mixed with 50 μl of the Griess reagent ($1\%$ sulfanilamide/$0.1\%$ naphthylethylene diamine dihydrochloride/$2\%$ phosphoric acid) in a 96-well plate. The absorbance at 550 nm was then measured on a microplate reader (SpectraMax M5; Molecular Devices). NaNO2 was used as the standard to calculate NO2 concentrations.
## Cytotoxicity assay
Cytotoxicity was evaluated by measuring the amount of released LDH using the CytoTox 96 Non-Radioactive Cytotoxicity Assay Kit (Promega), according to the manufacturer’s instructions. Culture media (50 μl) from the MGCs were incubated with an LDH substrate solution (50 μl) for 20 min in a dark room. After adding the stop solution, the absorbance was measured at 490 nm using a microplate reader (SpectraMax M5; Molecular Devices). The data were calculated using the following formula: % cytotoxicity = 100 × (experimental/maximum LDH release), according to the manufacturer’s instructions. Lysis solution ($0.8\%$ Triton X-100) was used to generate the maximum LDH release with $100\%$ cell death control.
## Wound healing assay
The in vitro wound healing assay was performed using the IncuCyte ZOOM Live-Cell Imaging system (Essen Bioscience). MGCs were treated with mitomycin C (5 μg/ml) for 2 h before performing a wound to inhibit cell proliferation. Wounds were made with an IncuCyte WoundMaker and plates were automatically analyzed for wound closure using the IncuCyte ZOOM Live-Cell Imaging system. Real-time images were acquired every 2–3 h for 48 h. Cell confluence was quantified using time-lapse curves generated by the IncuCyte ZOOM software.
## Phagocytosis assay
The phagocytosis assay was performed according to the manufacturer’s instructions using pH-sensitive pHrodo Red Zymosan BioParticles (Life Technologies). The pHrodo Red conjugates do not fluoresce outside the cell at neutral pH but do fluoresce at acidic pH values such as those in phagosomes; this enables an accurate measurement of phagocytosis. Briefly, MGCs were incubated with Zymosan conjugate particles (10 μg/ml) diluted in a live-cell imaging solution (Thermo Fisher Scientific) for 2 h in the dark. Nuclei were counterstained using Hoechst (1:1,000; Life Technologies). After incubation, the cells were thoroughly washed and fixed with $2\%$ paraformaldehyde for 10 min at room temperature. Cell fluorescence was then measured with a microplate reader (SpectraMax M5; Molecular Devices).
## Quality control, hit selection, and pathway enrichment analysis
For quality control purposes, the data quality of individual plates was controlled with the SSMD method, and applied to each positive control within a plate (Birmingham et al, 2009). The criteria for quality control were fixed according to the SSMD definition for a “moderate effect.” The SSMD method, combined with FC values, was used to rank genes in each screen and performed in triplicate. SSMD cutoffs <−1.65 and >1.65 with FC values of 0.7fold and 1.5fold were used to define inhibitors and enhancers, respectively. The reproducibility of all assays was confirmed by duplicate trials and Pearson’s correlation. Pathway enrichment analyses based on GO (Immune systems, Reactome pathway, and Reactome) or KEGG pathways were performed using the DAVID bioinformatics resource (*Huang da* et al, 2008) and the ClueGO plug-in for Cytoscape software (Bindea et al, 2009).
## Mice and animal care procedures
Male C57BL/6 mice (8 wk old) were obtained from Samtaco. Male pyruvate dehydrogenase kinase isoform 2 (Pdk2) KO mice aged 8–10 wk were used after generation as previously described (Go et al, 2016). Age-matched WT mice were produced from C57BL/6J mice (The Jackson Laboratory), used to stabilize the genetic background of the Pdk2 KO. Genotypes were confirmed by PCR of the genomic DNA as previously described (Go et al, 2016). Animals were housed under a 12-h light/dark cycle (lights on from 07:00–19:00) at a constant ambient temperature of 23°C ± 2°C with food and water ad libitum. Each individual animal was used for a single experimental purpose. All animal experiments were performed in accordance with the animal protocols and guidelines approved by the Animal Care Committee at Kyungpook National University (No. KNU 2018-0084).
## LPS injection and stab-wound injury model
A systemic LPS injection was performed to evoke neuroinflammation in mice, as previously described (Jo et al, 2017). Mice received an i.p. injection of vehicle or LPS (5 mg/kg). Animals in the vehicle control group received the same volume of saline solution. Animals were killed 24 h after injection under deep ether-induced anesthesia. For the stab-wound injury model, mice were anesthetized using inhaled isoflurane ($3\%$) and placed in a stereotaxic device. A stab-wound injury was performed using a needle (30 G) injection. Pdk2 inhibitor (AZD7545) or vehicle was then stereotactically injected (flow rate: 0.1 μl/min) at a volume of 0.5 μl into the prefrontal cortical area (anteroposterior: −2.5 mm; mediolateral: 1.5 mm; dorsoventral: −1.0 mm) through a small burr hole. In Pdk2 KO mice, the injury was generated by inserting the needle only. The skin was sutured after mounting the burr hole using sterile bone wax (Ethicon). To assess glial cell proliferation, BrdU (200 mg/kg) was injected (i.p.) three times every 6 h after surgery. The mice were killed at 24 h after inflicting the stab-wound injury.
## Behavioral tests
For the Y-maze test, the Y-maze comprised of a horizontal maze with three arms (length: 40 cm; width: 3 cm; and wall height: 12 cm). Tested animals were initially placed in the center of the maze and the order (e.g., ABCCAB) and number of arm entries were manually recorded over a period of 7 min for each animal. Voluntary shifts were defined as trials with entries into all three arms in sequence (i.e., ABC, CAB, or BCA, but not BAB). The maze was thoroughly cleaned with water after each test to remove the residual animal odor. The ratio of alternatives was calculated according to the following equation: % alternation = ([number of alternations]/[total arm entries]) × 100. The total number of arm entries was used as an indicator of locomotor activity. All recordings and calculations were automatically obtained by SMART video tracking software version 3.0 (Harvard Apparatus).
For a forced swim test, mice were placed individually in a vertical acrylic cylinder (height: 60 cm; diameter: 20 cm) lled with tap water (26°C) to a depth that did not allow the mice to touch the bottom with their hind paws (20 cm). The animals were removed from the water after 6 min and dried quickly before being returned to their cages. Each session was video-recorded and analyzed blindly. The three following behaviors were considered according to Porsolt’s criteria (Can et al, 2012): (i) immobility: mice were considered immobile when they oated passively, making only small movements to keep their nose above the surface; (ii) climbing (or thrashing): defined as upward-directed movements with the forepaws, in and out of the water, and/or along the side of the swim chamber; and (iii) swimming: including active movements (usually horizontal) more than necessary to merely maintain their head above the water. Diving and face-shaking behaviors were not scored. The time spent immobile was measured as well.
## Double-staining immunohistochemistry
For the immunofluorescence analysis of animal tissues, frozen brain sections (20 μm) were permeabilized in $0.1\%$ Triton X-100 and blocked using $1\%$ bovine serum albumin and $5\%$ normal donkey serum for 1 h at room temperature. Brain sections were incubated with the following primary antibodies at 4°C overnight: anti-GFAP (rabbit IgG, 1:500; Dako), anti-Iba-1 (goat IgG, 1:500; Wako), anti-phosphorylated Itk (mouse IgG, 1:200; Thermo Fisher Scientific), anti-Itk (mouse IgG, 1:200; Santa Cruz Biotechnology) or anti-Pdk2 (rabbit IgG, 1:100; Abcepta). This was followed by a 2-h incubation at room temperature with the following fluorescence-conjugated secondary antibodies: FITC-conjugated anti-rabbit (donkey IgG, 1:500; Jackson Immuno Research Laboratories), FITC-conjugated anti-goat (donkey IgG, 1:500; Jackson Immuno Research Laboratories) or Cy3-conjugated anti-mouse (donkey IgG, 1:500; Jackson Immuno Research Laboratories). Finally, the sections were mounted and counterstained using DAPI-containing gelatin.
## Immunohistochemistry of astrocytes and microglia
Brain tissues were processed and sections subjected to immunohistochemical analysis using anti-GFAP (rabbit IgG, 1:500; Dako), anti-Iba-1 (rabbit IgG, 1:500; Wako) or anti-BrdU antibodies (rat IgG, 1:200; Bio-Rad) overnight. This was followed by incubation for 2 h at room temperature with the following fluorescence-conjugated secondary antibodies: FITC-conjugated anti-rabbit (1:200; Jackson Immuno Research Laboratories) and Cy3-conjugated anti-rat (1:200; Jackson Immuno Research Laboratories). Data acquisition and immunohistological intensity measurements were performed using ImageJ, as previously described (Jo et al, 2017). In brief, tiled images of each section were captured using a CCD color video camera (Ximea). An image composite was then constructed for each section using Adobe Photoshop version CS3. The images were binary threshold at $50\%$ of the background level and the particles were then converted to a subthreshold image. Areas <300 pixels and >5 pixels were considered GFAP- or Iba-1-positive cells. The quantification of cells around the needle injection site was performed using an adapted version of Sholl analysis, as previously described, with slight modifications (Jo et al, 2017). Briefly, the number of cells was counted in concentric circles starting from the center of the injury site with a radius step size of 500 μm. The final radius was set where the cell density reached the normal cell distribution. Proliferating cells were identified by merging GFAP- or Iba-1 staining with BrdU staining.
## Real-time PCR
Real-time PCR was performed using a One-Step SYBR PrimeScript RT–PCR Kit (Perfect Real-Time; Takara Bio), followed by detection using an ABI Prism 7000 Sequence Detection System (Applied Biosystems). Normalization was performed using two internal controls, ribosomal protein lateral stalk subunit P0 (Rplp0) and tubulin α 1A (Tub1a), and a model-based variance and stability calculation (Vandesompele et al, 2002; Andersen et al, 2004). The nucleotide sequences of the primers were based on published cDNA sequences (Table S14).
Table S14. DNA sequences of the primers used in the RT–PCR analysis.
## Immunoblotting analysis
Cells were lysed in ice-cold RIPA lysis buffer (Thermo Fisher Scientific) and the protein concentration in cell lysates was determined using a Bradford protein assay kit (Bio-Rad). An equal amount of protein (30 μg per sample) was separated using $12\%$ SDS–PAGE and transferred to polyvinylidene fluoride filter membranes (GE Healthcare). The membranes were blocked using $5\%$ skim milk and incubated sequentially with the following primary antibodies: anti-phosphorylated-Vav1 (Y174) antibody (mouse IgG, 1:1,000; Santa Cruz Biotechnology), anti-Vav1 antibody (rabbit IgG, 1:1,000; Cell Signaling Technology), anti-PLC-γ (Y783) antibody (rabbit IgG, 1:1,000; Cell Signaling Technology); anti-PLC-γ antibody (rabbit IgG, 1:1,000; Cell Signaling Technology), anti-Epha2 (rabbit IgG, 1:1,000; Cell Signaling Technology), anti-Akt2 (rabbit IgG, 1:1,000; Cell Signaling Technology), anti-Itk (mouse IgG, 1:1,000; Santa Cruz Biotechnology), anti-Pdk2 (rabbit IgG, 1:1,000; Abcepta), anti-Irak4 (rabbit IgG, 1:1,000; Cell Signaling Technology), anti-Mlkl (mouse IgG, 1:1,000; Cell Signaling Technology), and anti-β-actin antibody (mouse IgG, 1:2,000; Thermo Fisher Scientific). This was followed by a 2-h incubation at room temperature with horseradish peroxidase-conjugated secondary antibodies: anti-rabbit IgG antibody (1:2,000; Cell Signaling Technology) or anti-mouse IgG antibody (1:2,000; Cell Signaling Technology). Immunoblots were developed using a SuperSignal WestPico chemiluminescent substrate (Thermo Fisher Scientific).
## Assessment of extracellular metabolic flux
An XF24 Extracellular Flux Analyzer (Seahorse Bioscience Inc.) was used to determine the OCR, as previously described. Briefly, astrocytes from WT and Pdk2 KO mice were plated at a density of 40,000 cells per well with LPS in a Seahorse XF24 plate and cultured for 16 h. 1 h before the assay, the medium was exchanged for Seahorse XF basal DMEM containing 1.5 mM sodium pyruvate, 1 mM glutamine, and 25 mM glucose. Rotenone or 2-DG, FCCP, and oligomycin were diluted in XF24 medium with LPS and loaded into the accompanying cartridge to achieve final concentrations of 2, 1, and 1 μg/ml, respectively. Reagent injections into the medium occurred at the time points specified before the analysis start point; subsequently, OCR was monitored. Each cycle was set to mix for 3 min, delay for 2 min, and measure for 3 min. Total protein was extracted from the cells immediately after OCR readings, and results were normalized to the total protein concentration.
## Superoxide assay
Transfected MGC cultures were stimulated with LPS (1 μg/ml) for 30 min and incubated with 10 μM dihydroethidium (Molecular Probes). The cells were then washed with ice-cold PBS and examined with a microreader (excitation: 534 nm; emission: 580 nm) to evaluate superoxide production.
## ELISA
The transfected MGC cells were stimulated with LPS (1 μg/ml) for 48 h and then culture media were harvested. Levels of TNF-α or MMP-9 protein were measured using a mouse TNF-α DuoSet ELISA Kit (R&D Systems) or mouse Total MMP-9 Quantikine ELISA Kit (R&D Systems).
## Assay 1
BV-2 microglial cells were incubated with LPS (100 ng/ml) and a serially diluted Itk inhibitor (BMS509744). Cell lysates were then harvested in the presence of protease and phosphatase inhibitors. After determination of the optimal amounts of cell lysate (containing Itk and other proteins) necessary to measure Itk activity based on the signal-to-background ratio, the substrate (poly E4Y1)/ATP mix was added to the cell lysate and incubated for 1 h. After depleting the remaining ATP, Itk activity was visualized by a kinase detection solution.
## Assay 2
The cell lysate was subjected to Western blotting for detecting phosphorylated-PLC-γ (Tyr783) (rabbit IgG, 1:1,000; Cell Signaling Technology), a direct substrate of Itk. The IC50 of BMS509744 was calculated based on the inhibition of PLC-γ phosphorylation.
## Quantification and statistical analysis
Data are presented as means ± SEM or ± SD. Data were compared using an unpaired two-tailed t test or ordinary one-way ANOVA followed by Tukey’s post hoc test. All statistical analyses were performed using Prism software version 8.0 (GraphPad Software). P-values <0.05 were considered statistically significant.
## Author Contributions
J-H Kim: conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, and writing—original draft. J Han: data curation, formal analysis, validation, investigation, visualization, and methodology. R Afridi: data curation, formal analysis, validation, investigation, visualization, methodology, and writing—review and editing. J-H Kim: data curation, formal analysis, validation, investigation, visualization, and methodology. MH Rahman: data curation, formal analysis, validation, investigation, visualization, and methodology. DH Park: data curation, formal analysis, validation, investigation, visualization, and methodology. WS Lee: data curation, formal analysis, validation, investigation, visualization, and methodology. GJ Song: data curation, formal analysis, validation, investigation, visualization, and methodology. K Suk: conceptualization, data curation, supervision, funding acquisition, project administration, and writing—original draft, review, and editing.
## Conflict of Interest Statement
The authors declare that they have no conflict of interest.
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|
---
title: Value of neutrophil-to-lymphocyte ratio for diagnosing sarcopenia in patients
undergoing maintenance hemodialysis and efficacy of Baduanjin exercise combined
with nutritional support
authors:
- Jun Wang
- Mei-chang Xu
- Li-juan Huang
- Bei Li
- Lei Yang
- Xu Deng
journal: Frontiers in Neurology
year: 2023
pmcid: PMC9990467
doi: 10.3389/fneur.2023.1072986
license: CC BY 4.0
---
# Value of neutrophil-to-lymphocyte ratio for diagnosing sarcopenia in patients undergoing maintenance hemodialysis and efficacy of Baduanjin exercise combined with nutritional support
## Abstract
### Objective
To investigate the value of neutrophil-to-lymphocyte ratio (NLR) for diagnosing sarcopenia in patients undergoing maintenance hemodialysis (MHD) and efficacy of Baduanjin exercise combined with nutritional support on MHD patients with sarcopenia.
### Methods
A total of 220 patients undergoing MHD in MHD centers were selected, among which 84 had occurred with sarcopenia confirmed by measurements from the Asian Working Group for Sarcopenia. Data were collected for analyzing the influencing factors that lead to the onset of sarcopenia in MHD patients with the use of one-way analysis of variance and multivariate logistic regression. The role of NLR in the diagnosis of sarcopenia was explored, and its correlation with relevant diagnostic measurement performance such as grip strength, gait speed and skeletal muscle mass index was analyzed. In the end, some 74 patients with sarcopenia that qualify for further intervention and observation standards were divided into observation group (Baduanjin exercise plus nutritional support) and control group (nutritional support only), which were both intervened for 12 weeks. A total of 68 patients finished all interventions, with 33 patients in the observation group and 35 in the control group. The grip strength, gait speed, skeletal muscle mass index as well as the NLR were compared between the two groups.
### Results
With the employment of multivariate logistic regression analysis, it was found that age, hemodialysis duration and NLR were risk factors for the onset of sarcopenia in MHD patients ($P \leq 0.05$). The area under ROC curve for NLR of MHD patients with sarcopenia was 0.695, and NLR was negatively correlated with a biochemical indicator—human blood albumin ($P \leq 0.05$). NLR was also negatively correlated with patient's grip strength, gait speed and skeletal muscle mass index, with the same correlation found in sarcopenia patients (all $P \leq 0.05$). After intervention, patient's grip strength and gait speed were both higher, and the NLR lower in the observation group than those in the control group ($P \leq 0.05$).
### Conclusion
The occurrence of sarcopenia in MHD patients is associated with patient's age, hemodialysis duration and NLR. Therefore, it has been concluded that NLR has certain values in the diagnosis of sarcopenia in patients undergoing MHD. Moreover, the muscular strength can be enhanced and inflammation decreased in sarcopenia patients through nutritional support and physical exercise, i.e., Bajinduan exercise.
## Introduction
Maintenance hemodialysis (MHD) is one of the essential alternative treatments for end stage renal disease [1]. Previous reports have shown that micro-inflammatory state, loss or insufficient intake of proteins as well as metabolic acidosis in patients undergoing MHD could lead to changes in muscle structure and decrease in muscular strength, ultimately resulting in sarcopenia [2]. It has been reported that the prevalence rate of sarcopenia in MHD patients reaches as high as 3.9–$63.3\%$ [3, 4]. Studies in China and some other countries around the world have suggested that sarcopenia could elevate the occurrence of several adverse events, thus impacting the prognosis of patients undergoing MHD [5, 6]. According to some reports, the occurrence rate of sarcopenia in MHD patients and factors that contribute to the disease vary in different regions [7, 8]. A study in recent years suggested that inflammation was closely associated with the onset of sarcopenia, as pro-inflammatory factors were significantly higher in patients with sarcopenia than those without [9]. Neutrophil-to-lymphocyte ratio (NLR) is a novel inflammatory indicator that reveals the presence or absence of systemic inflammation in human bodies. Studies have shown that NLR plays a significant role in the occurrence and development of arteriosclerosis and tumors [10, 11]. A previous report also demonstrated that NLR in MHD patients was related to Protein Energy Wasting, an important factor contributing to the onset of sarcopenia [12]. One study reported that systemic inflammation could lead to decreased protein turnover and imbalance of cell growth, which further led to injury in skeletal muscles [13]. It was also reported that the occurrence of sarcopenia was in close association with malnutrition and muscular hypoactivity [14]. Studies outside China have indicated that treatments for sarcopenia could be realized through improving muscular mass and functions. Nutritional support as well as exercises are two effective approaches in treating this disease in patients undergoing MHD [15, 16]. Therefore, based on the conclusions mentioned above, we investigated the diagnostic value of NLR in MHD patients, and the efficacy of Baduanjin exercise plus nutritional support on the treatment of sarcopenia in patients undergoing MHD.
## General data
A total of 220 patients who received MHD in the MHD center of Nanjing Hospital of Integrated Traditional Chinese and Western Medicine and Nanjing Hospital of Traditional Chinese Medicine from October 2021 to May 2022 were selected. According to the diagnostic standards of Asian Working Group for Sarcopenia, 84 patients were confirmed with sarcopenia, among which only 74 were qualified for further clinical interventions. The 74 patients were randomized into different cohorts to receive nutritional support and Baduanjin exercise, namely, observation group for nutritional support plus Baduanjin exercise and control group for nutritional support only. During the intervention process, two patients from the control group and two from the observation group discontinued the trial because they were transferred to the other hospital, 2 from the observation group were not followed up any more for they were intolerant to the treatment. At last, the observation group included 33 patients and the control group 35. This trial was approved by the Ethics Committee of Nanjing Hospital of Integrated Traditional Chinese and Western Medicine and Nanjing Hospital of Traditional Chinese Medicine (No.: 2021035), with written informed consent obtained from all enrolled patients.
## Inclusion and exclusion criteria
Patients were eligible for the study if they conformed to the diagnostics standards of end-stage renal disease, their hemodialysis duration lasted for over 6 months, aged not < 18 years old and conformed to the diagnostic standards of sarcopenia by Asian Working Group for Sarcopenia [14], which were as follows: i. grip strength assessment: male should have grip strength no < 28 kg and female no < 18 kg, otherwise their muscular strength is considered as weakened; ii. gait speed assessment: if the walking speed of patients is no >1 m/s for 6-meter walking distance, their physical capacity is considered as decreased; iii. assessment of skeletal muscle mass in the four extremities by Bioelectrical impedance analysis (BIA); if the Appendicular Skeletal Muscle Index of male patients is < 7.0 kg/m2 and that of female < 5.7 kg/m2, then the skeletal muscle mass of patients is deemed as reduced. Patients who conformed to i and iii or to ii and iii are confirmed with sarcopenia; those who met all three criteria above are considered as having severe sarcopenia.
Patients were ineligible for the study if they had infectious disease in the past 3 months, could not be able to follow treatment instructions due to cerebrovascular sequela or mental illness, had severe cardiopulmonary disease, liver disease or malignant tumor, or if they had received oral administration of corticosteroids or immunosuppressants in the last 6 months.
## Methods
*The* general and clinical data of patients including age, gender, hemodialysis duration, BIA-measured body mass and some lab indexes for the blood.
Specific interventions: for nutritional support, firstly a nutritional support team (NST), including one attending doctor or with higher titles, two specialty nurses in charge or in higher position, one pharmacist and one nutritionist, was organized. All team members ensured to be in possession of sufficient knowledge of their own specialty and also received additional NST training. During the treatment, the general and clinical data of patients were collected and assessed for their nutritional state, and factors might contribute to malnutrition were corrected. The doctors and pharmacist mainly accounted for assessing patients and making or adjusting treatment plans. The nutritionist guided each patient with their diet after their nutritional state evaluation. The two specialty nurses gave healthcare education to patients in accordance with their disease progression and physical condition, and supervised patients to record their daily food intake and stick to the nutritional plan made by the nutritionist. Patients were evaluated every 4 weeks for their nutritional state and to adjust their treatment plan accordingly, for a total of 12 weeks; for Baduanjin exercise, all enrolled patients were required to watch the video made by the State Sport General Administration of China, Baduanjin: A Health-care Qigong, twice to get familiar with the exercise. The video is about 20–30 min long. After that, patients were taught to do the exercise by a specialist to correct their moves for 2 weeks. Then they were instructed to do the exercise at their own home on non-hemodialysis days. The time and frequency of the exercise were designed according to previous reports [16], as 3 times a week, 2 h after each meal to do the exercise 2–3 times for 30–60min, for a total of 12 weeks. Patient 's vital signs were monitored closely during exercise. The exercise should be stopped immediately if any of the following circumstances, such as hypoglycemia, abnormal blood pressure, chest tightness, dizziness occurs. See Tables 1, 2 for specific exercise plans.
## Outcome measures
One-way analysis of variance and multivariate logistic regression were used to analyze the influencing factors of sarcopenia in patients undergoing MHD and the correlation of NLR with biochemical indicators (normal NLR is between 1 and 3, above 3 is deemed as increased). The value of NLR for diagnosing sarcopenia, and its correlation with patient's grip strength, gait speed and skeletal muscle mass indexes were explored, as well as influences of different interventions on the treatment of sarcopenia and on NLR.
## Statistical analysis
Data were analyzed using SPSS 22.0 software. Continuous variables were expressed as mean ± standard deviation (x ± sd). Data that did not conform to normal distribution were expressed as M (P25, P75), and those conforming to normal distribution and homogeneity of variance were analyzed by t-test and expressed as t. Data that did not conform to normal distribution and homogeneity of variance were analyzed with rank sum test and expressed as Z. Enumeration data were analyzed by Pearson chi-square test and expressed as chi-square. One-way analysis of variance was used for different variants, and binary logistic regression for detecting the risk factors of sarcopenia in MHD patients. ROC curve was used to analyze the value of NLR for diagnosing sarcopenia. Person test was used to analyze the correlation between two variables. $P \leq 0.05$ was considered statistically significant.
## Comparison among influencing factors for the occurrence of sarcopenia in MHD patients
Among the primarily enrolled 220 patients, 84 of them were diagnosed with sarcopenia ($38.18\%$). The age, hemodialysis duration, the occurrence rate of coronary heart disease as well as the NLR of MHD patients with sarcopenia (sarcopenia group) were all higher than those without (non-sarcopenia group); and their serum albumin, hemoglobin, body mass index, grip strength, gait speed and skeletal muscle mass index were lower than those of MHD patients without sarcopenia ($P \leq 0.05$), as shown in Table 3.
**Table 3**
| Item | Sarcopenia group (n = 84) | Non-sarcopenia group (n = 136) | χ2/Z value | P |
| --- | --- | --- | --- | --- |
| Male cases (%) | 60 (71.43) | 88 (64.71) | 1.066 | 0.302 |
| Age (year) | 66.0 (58.0, 74.0) | 59.0 (51.3, 68.0) | −3.783 | < 0.001 |
| Hemodialysis duration (month) | 78.0 (68.0, 88.0) | 64.0 (34, 5, 80.0) | −4.125 | < 0.001 |
| Education duration (year) | 10.0 (5, 0, 11.0) | 10.0 (7, 0, 11.0) | −1.457 | 0.145 |
| Type 2 diabetes complication | 32 (38.09) | 46 (33.82) | 0.414 | 0.520 |
| Coronary heart disease complication | 19 (22.62) | 14 (10.29) | 5.338 | 0.021 |
| Hemodialysis thoroughness | 1.67 (1, 43, 1.87) | 1.61 (1, 44, 1.79) | −0.541 | 0.588 |
| Serum albumin (g/L) | 38.05 (34.43, 41.93) | 41.50 (38.30, 42.50) | −3.501 | < 0.001 |
| Hemoglobin (g/L) | 116.0 (89.0, 121.0) | 118.0 (100.0, 122.0) | −1.972 | 0.044 |
| Urea nitrogen before hemodialysis (mmol/L) | 29.30 (19.80, 31.50) | 29.55 (20.98, 31.50) | −0.423 | 0.672 |
| Serum creatinine before hemodialysis (umol/L) | 719.61 (373.31, 810.24) | 728.62 (409.72, 810.24) | −0.443 | 0.665 |
| Uric acid (umol/L) | 478.50 (386.25, 497.00) | 480.00 (392.50, 497.00) | −0.201 | 0.841 |
| Triglycerides (mmol/L) | 2.02 (1.59, 2.36) | 2.19 (1.61, 2.36) | −0.588 | 0.556 |
| Total cholesterol (mmol/L) | 4.18 (3.23, 4.39) | 4.22 (3.34, 4.39) | −0.418 | 0.676 |
| High-density lipoprotein (mmol/L) | 1.19 (0.75, 1.32) | 1.22 (0.80, 1.32) | −0.513 | 0.608 |
| Low-density lipoprotein (mmol/L) | 1.67 (2.75, 3.88) | 3.72 (2.87, 3.87) | −0.418 | 0.676 |
| Serum potassium (mmol/L) | 4.85 (3.89, 5.04) | 4.88 (3.96, 5.05) | −0.128 | 0.898 |
| Blood calcium (mmol/l) | 1.85 (2.15, 2.22) | 1.90 (2.16, 2.22) | −0.459 | 0.646 |
| Serum phosphate (mmol/l) | 2.09 (1.36, 2.23) | 2.07 (1.46, 2.20) | −0.415 | 0.678 |
| NLR | 4.13 (2.57, 4.79) | 2.57 (2.34, 3.30) | −4.849 | < 0.001 |
| PLR | 116.87 (111.25, 136.21) | 113.36 (110.54, 134.27) | −1.705 | 0.088 |
| Body Mass Index (kg/m2) | 23.38 (19.12, 24.56) | 23.92 (21.50, 24.76) | −1.978 | 0.035 |
| Grip strength (kg) | 16.73 (12.84, 22.75) | 31.64 (24.76, 33.58) | −9.914 | < 0.001 |
| Gait speed (m/s) | 0.77 (0.43, 1.00) | 0.94 (0.84, 1.02) | −3.731 | < 0.001 |
| Skeletal muscle mass index (kg/m2) | 5.30 (5.10, 6.20) | 7.25 (6.72, 7.40) | 11.607 | < 0.001 |
## Multivariate regression analysis of sarcopenia in MHD patients
Grip strength, gait speed and skeletal muscle mass index were excluded from the multivariate regression analysis because they were established diagnostic measurements for sarcopenia. However, by multivariate regression analysis, it was indicated that age, hemodialysis duration and NLR were independent factors contributing to the incidence of sarcopenia in MHD patients (see Table 4).
**Table 4**
| Variables | β | SE | Wald value | OR value (95% CI) | P |
| --- | --- | --- | --- | --- | --- |
| Constants | −0.482 | 0.139 | 12.056 | – | 0.001 |
| Age (year) | 0.042 | 0.015 | 7.381 | 1.043 (1.012–1.075) | 0.007 |
| Hemodialysis duration | 0.022 | 0.006 | 11.833 | 1.022 (1.009–1.035) | 0.001 |
| NLR | 0.441 | 0.142 | 9.621 | 1.555 (1.176–2.054) | 0.002 |
## Correlation of NLR and albumin
NLR is negatively correlated with serum albumin (see Figure 1).
**Figure 1:** *Correlation of NLR with albumin.*
## Diagnostic values of NLR for sarcopenia prediction in MHD patients
The area under ROC curve of NLR for the diagnosis of sarcopenia in MHD patients was 0.695, and when NLR was at the 3.28 cut-off value, its Youden index was 0.369, specificity was 0.750 and sensitivity was 0.619, as shown in Figure 2.
**Figure 2:** *ROC curve of NLR for the diagnosis of sarcopenia in MHD patients.*
## Correlation of NLR with grip strength, gait speed and skeletal muscle mass index
NLR was negatively correlated with grip strength, gait speed and skeletal muscle mass index of enrolled patients (all $P \leq 0.05$), as shown in Figures 3A–C. No significant correlation was found between NLR and grip strength, gait speed and skeletal muscle mass index of patients without sarcopenia (all $P \leq 0.05$), as shown in Figures 3D–F. However, negative correlation was observed between NLR and grip strength, gait speed and skeletal muscle mass index of sarcopenia patients (all $P \leq 0.05$), as shown in Figures 3G–I.
**Figure 3:** *Correlations of NLR with grip strength, gait speed and skeletal muscle mass index, among which 3 (A–C) stand for correlations of NLR with grip strength, gait speed and skeletal muscle mass index of enrolled patients; (D–F) for correlations of NLR with grip strength, gait speed and skeletal muscle mass index of sarcopenia-free patients; and (G–I) for correlations of NLR with grip strength, gait speed and skeletal muscle mass index of sarcopenia patients. (A, D, G) show correlation of NLR with grip strength, (B, E, H) show that with gait speed, and (C, F, I) show that with skeletal muscle mass index.*
## Comparison of baseline data between the two groups
No statistical differences were found in baseline data between the two groups ($P \leq 0.05$) (see Table 5).
**Table 5**
| Item | Observation group (n = 33) | Control group (n = 35) | χ2/t/Z value | P |
| --- | --- | --- | --- | --- |
| Male cases (%) | 20 (60.61) | 27 (77.14) | 2.176 | 0.14 |
| Age (year) | 62.2 ± 10.0 | 62.1 ± 9.8 | 0.042 | 0.967 |
| Hemodialysis duration (month) | 78.0 (48.5, 99.2) | 77.0 (70.0, 86.0) | −0.080 | 0.936 |
| Education duration (year) | 10.0 (5.0, 11.0) | 9.0 (4.0, 11.0) | −0.285 | 0.775 |
| Type 2 diabetes complication | 13 (39.40) | 14 (35.00) | 0.003 | 0.959 |
| Coronary heart disease complication | 9 (27.27) | 9 (25.71) | 0.021 | 0.884 |
| Hemodialysis adequacy | 1.75 (1.35, 1.84) | 1.71 (1.45, 1.83) | −0.443 | 0.658 |
| Serum albumin (g/L) | 40.4 (35.3, 42.5) | 38.3 (34.8, 42.3) | −0.596 | 0.551 |
| Hemoglobin (g/L) | 118.0 (87.5, 121.0) | 114 (86.0, 122.0) | −0.283 | 0.778 |
| Urea nitrogen before hemodialysis (mmol/L) | 27.3 (23.0, 31.2) | 29.3 (19.8, 31.6) | −0.387 | 0.699 |
| Serum creatinine before hemodialysis (umol/L) | 637.5 (499.2, 785.5) | 719.6 (361.5, 810.2) | −0.129 | 0.897 |
| Uric acid (umol/L) | 480.0 (419.5, 497.0) | 486.0 (380.5, 505.0) | −0.129 | 0.897 |
| Triglycerides (mmol/L) | 2.01 (1.67, 2.28) | 1.98 (1.57, 2.37) | −0.131 | 0.883 |
| Total cholesterol (mmol/L) | 4.01 (3.54, 4.37) | 4.18 (3.23, 4.44) | −0.362 | 0.717 |
| High-density lipoprotein (mmol/L) | 1.09 (0.90, 1.29) | 1.19 (0.75, 1.32) | −0.436 | 0.663 |
| Low-density lipoprotein (mmol/L) | 3.54 (3.06, 3.87) | 3.67 (2.75, 3.92) | −0.424 | 0.672 |
| Serum potassium (mmol/L) | 4.88 (4.25, 5.04) | 4.94 (3.82, 5.18) | −0.006 | 0.995 |
| Blood calcium (mmol/l) | 2.09 (1.95, 2.20) | 2.15 (1.85, 2.24) | −0.393 | 0.694 |
| Serum phosphate (mmol/l) | 2.03 (1.45, 2.21) | 2.09 (1.21, 2.23) | −0.227 | 0.82 |
| NLR | 3.48 ± 1.31 | 3.61 ± 1.21 | 0.427 | 0.67 |
| PLR | 116.8 (111.6, 136.8) | 116.9 (111.2, 135.2) | −0.553 | 0.58 |
| Body Mass Index (kg/m2) | 23.92 (18.82, 24.56) | 23.05 (18.43, 24.76) | −0.295 | 0.768 |
| Grip strength (kg) | 17.37 ± 7.60 | 19.29 ± 6.98 | 1.086 | 0.283 |
| Gait speed (m/s) | 0.75 ± 0.32 | 0.70 ± 0.30 | 0.685 | 0.497 |
| Skeletal muscle mass index (kg/m2) | 5.54 ± 0.62 | 5.53 ± 0.61 | 0.075 | 0.941 |
## Comparison of relevant indexes between the two groups before and after intervention
After interventions, grip strength and gait speed of patients in the observation group were higher than those in the control group, while the NLR was lower in the observation group than that in the control group ($P \leq 0.05$) (see Table 6).
**Table 6**
| Group | Cases | Grip strength (kg) | Grip strength (kg).1 | Gait speed (m/s) | Gait speed (m/s).1 | Skeletal muscle mass index (kg/m 2 ) | Skeletal muscle mass index (kg/m 2 ).1 | NLR | NLR.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Before intervention | After intervention | Before intervention | After intervention | Before intervention | After intervention | Before intervention | After intervention |
| Observation group | 33 | 17.37 ± 7.60 | 21.38 ± 4.47 | 0.75 ± 0.32 | 0.91 ± 0.20 | 5.54 ± 0.62 | 5.62 ± 0.58 | 3.48 ± 1.31 | 2.96 ± 1.26 |
| Control group | 35 | 19.29 ± 6.98 | 18.37 ± 5.25 | 0.70 ± 0.30 | 0.71 ± 0.24 | 5.53 ± 0.61 | 5.58 ± 0.73 | 3.61 ± 1.21 | 3.59 ± 1.25 |
| t value | - | 1.086 | 2.539 | 0.685 | 3.654 | 0.075 | 0.312 | 0.427 | 2.093 |
| P | - | 0.283 | 0.013 | 0.497 | 0.001 | 0.941 | 0.756 | 0.670 | 0.040 |
## Discussion
As services and insurance policies in medical filed improve, an increasing number of patients have been able to receive MHD in China. However, in view of the specialty of MHD treatment and in consideration of the severeness of the disease requiring the treatment, patients have a high risk in getting sarcopenia, which in turn results in higher mortality and hospitalization rates, and increases burdens on hospitals as well as on patient's families [17]. According to previous reports, the occurrence of sarcopenia in MHD patients reaches as high as 3.9–$63.3\%$ [18]. In this study, 84 out of 220 ($38.18\%$) patients were diagnosed with sarcopenia, conforming to the result mentioned above. Excluding the established diagnostic indexes for sarcopenia, it was found that age, hemodialysis duration and NLR were all independent risk factors contributing to sarcopenia in MHD patients. Why age is a contributing factor? Because the synthetic ability of testosterone in the body weakens with the increase of age, thus leading to decreased muscle mass because of reduced proteins in the muscles [19]. One study suggested that patient's muscular strength and mass reduced year on year at a rate of 1–$2\%$ after the age of 50 years old. Also, the function of each organ declines as age increases, especially the function of stomach that hampers the absorption of nutrition, therefore increasing the risk of muscle loss [20]. It has been shown that motor neuron loss is the main cause for sarcopenia. The motor neurons could be reduced with the growth of age, resulting in muscle fiber atrophy and degeneration, ultimately leading to muscle mass decline [21]. A previous study suggested that hemodialysis duration was a major cause of sarcopenia [22]. With the hemodialysis duration of patients getting longer, more patients tend to have nutrient loss, acid-base imbalance and hormone level changes, leading to an increase in the incidence of sarcopenia [4]. Inflammation plays an important role in the development of sarcopenia as well. Several studies have shown that elevated levels of inflammatory factors could very likely contribute to decreased muscle mass and strength. Of these inflammatory factors, C-reactive protein (CPR), interleukin-6 (IL-6) and tumor necrosis factor-a (TNF-a) are critical for predicting the incidence of sarcopenia [23, 24]. Micro-inflammation is a prevalent status in MHD patients due to the disease they have [25]. NLR is an indicator reflecting systemic inflammatory state, which imbalances protein turnover and cell growth, thereby impairing skeletal muscle mass [13]. Moreover, systemic inflammatory responses allow the body to produce more inflammatory factors, which could accelerate the breakdown of muscle proteins, leading to the occurrence of sarcopenia [26]. Previous studies suggest that muscular strength in MHD patients could be decreased as contributions of many factors, among which malnutrition is critical one, leading to the onset of sarcopenia [27]. Our one-way analysis of variance suggested that serum albumin was markedly decreased in MHD patients with sarcopenia, and multivariate regression analysis indicated that NLR was an independent risk factor for the occurrence of sarcopenia. And we further demonstrated that NLR was negatively correlated with albumin levels. Increased NLR might reduce albumin levels, resulting in sarcopenia caused by weakened skeletal muscles.
Our study showed that the area under ROC curve of NLR for predicting sarcopenia in MHD patients was 0.695. The specificity of NLR was 0.750 and the sensitivity was 0.619 at the cut-off value of 3.28. A study outside China that had included over 40,000 patients demonstrated that NLR was more advantageous and easier in assessing systemic inflammation in comparison with CRP [28]. And a study in China suggested that high NLR might contribute to the occurrence of sarcopenia in middle age and elderly patients [29]. Another cross-sectional study in Turkey showed that for every unit increase in NLR, elderly adults had a 1.31-fold increased risk of sarcopenia [30]. Higher NLR was found to be associated with amyotrophy incidence in gastric cancer patients. The medium of NLR of gastric cancer patients diagnosed with amyotrophy was 3.15, with significantly lower 5-year survival rate of patients with high NLR and amyotrophy than those without [31]. NLR is an abbreviation for neutrophil-to-lymphocyte ratio. It was reported that low lymphocyte level was related to malnutrition in elderly patients [32], and higher NLR is also related to malnutrition, leading to the occurrence of sarcopenia.
Currently, the occurrence of sarcopenia in MHD patients has gained attention in both China and other countries. However, the correlation of NLR with sarcopenia occurrence hasn't been studied, yet. In our study, it was concluded that for every unit increase in NLR, MHD patients had a 1.55-fold increased risk of sarcopenia. Grip strength, gait speed and skeletal muscle mass index are all clinical indicators for sarcopenia diagnosis, and also important measures reflecting muscle mass and strength. Our results showed that NLR was negatively correlated with grip strength, gait speed and skeletal muscle mass index, indicating NLR has certain value in predicting the incidence of sarcopenia.
Clinical interventions for MHD patients with sarcopenia have great significance for the prognosis and quality of life of patients, among which nutritional support and exercises are both important for the treatment of sarcopenia [33]. At present, the nutritional support for MHD patients, i.e., oral administration of nutritional supplements, can effectively improve the synthetic and metabolic capacity of skeletal muscle proteins of patients [34]. A study outside China showed that the nutritional support in addition to standard treatment for MHD patients could significantly improve their nutritional condition, and another study also suggested that the nutrition of patients who were inconvenient to have food regularly could be supplemented through additional nutritional support [35]. Although nutritional support is effective in treating MHD patients with sarcopenia, such support should be customized for patients after thorough evaluation of their disease progression, body conditions and food-consuming habits. The nutritional support team is in charge of individualized supply of nutrition for patients who had malnutrition. It was reported that the team ought to make customized nutritional therapy for different patients according to their disease progression, diet habit and food intake amount, in order to enhance the compliance of patients [36]. Exercises are also significant for MHD patients. The study of Tentori et al., which included 20,920 patients from 12 countries, observed the relation between physical exercise and the efficacy of hemodialysis, and found that doing physical exercise at least once a week could reduce the risk of death [37].
Baduanjin is an aerobic exercise with moderate intensity and easy operation, an exercise you can anytime in anywhere. A study suggested that doing Baduanjin exercise can improve the cardiopulmonary function in patients [38]. It was reported that the physical conditions as well as quality of life of patients undergoing peritoneal dialysis had been improved after doing Baduanjin exercise for 12 weeks [39]. It was also reported that for patients with chronic heart failure, their muscular strength in four extremities became prominently better after doing Baduanjin exercise for 24 weeks [40]. As far as we know, no studies have yet reported the efficacy of Baduanjin exercise combined with nutritional support on sarcopenia treatment for MHD patients. And it is concluded in this study that the combined treatment can improve the muscular function of patients probably through alleviating the systemic inflammation in their bodies.
However, there are some limitations to this study. Firstly, the sample size is relatively small, a larger simple size should be designed for further exploration. Secondly, the intensity of Baduanjin can't be assessed effectively due to the lack of related tools. Thirdly, longer Baduanjin intervention period should be suggested to observe the prognosis of MHD patients with sarcopenia.
In summary, high NLR is a risk factor for the occurrence of sarcopenia, which has some certain value in predicting the incidence of the disease. However, sarcopenia in MHD patients can be treated through the employment of Baduanjin exercise in combination with nutritional support to enhance patient's muscular strength and reduce their systemic inflammation.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The study was approved by the Ethics Committee of Nanjing Integrated Traditional Chinese and Western Medicine Hospital and Nanjing Hospital of Chinese Medicine (Ethics Approval Number 2021043). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JW, L-jH, and BL: played a critical role in conceptualizing this study, data analysis and interpretation, and review and revision of the manuscript. M-cX, LY, and XD: data collection. XD: draft manuscript. M-cX and LY: statistical analyses. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1072986/full#supplementary-material
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|
---
title: Integrated analysis of the microbiota-gut-brain axis in response to sleep deprivation
and diet-induced obesity
authors:
- Jibeom Lee
- Jiseung Kang
- Yumin Kim
- Sunjae Lee
- Chang-Myung Oh
- Tae Kim
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9990496
doi: 10.3389/fendo.2023.1117259
license: CC BY 4.0
---
# Integrated analysis of the microbiota-gut-brain axis in response to sleep deprivation and diet-induced obesity
## Abstract
### Introduction
Sleep deprivation (SD) and obesity are common in modern societies. SD and obesity frequently coexist, but research on the combined consequences of SD and obesity has been limited. In this study, we investigated the gut microbiota and host responses to SD and high-fat diet (HFD)-induced obesity. In addition, we attempted to identify key mediators of the microbiota-gut-brain axis.
### Methods
C57BL/6J mice were divided into four groups based on whether they were sleep deprived and whether they were fed a standard chow diet (SCD) or HFD. We then performed fecal microbiome shotgun sequencing, gut transcriptome analysis using RNA sequencing, and brain mRNA expression analysis using the nanoString nCounter Mouse Neuroinflammation Panel.
### Results
The gut microbiota was significantly altered by the HFD, whereas the gut transcriptome was primarily influenced by SD. Sleep and diet are both important in the inflammatory system of the brain. When SD and the HFD were combined, the inflammatory system of the brain was severely disrupted. In addition, inosine-5' phosphate may be the gut microbial metabolite that mediates microbiota-gut-brain interactions. To identify the major drivers of this interaction, we analyzed the multi-omics data. Integrative analysis revealed two driver factors that were mostly composed of the gut microbiota. We discovered that the gut microbiota may be the primary driver of microbiota-gut-brain interactions.
### Discussion
These findings imply that healing gut dysbiosis may be a viable therapeutic target for enhancing sleep quality and curing obesity-related dysfunction.
## Introduction
Sleep deprivation (SD) and obesity are common in modern society. In the United States, only 65 percent of individuals report sleeping for 7 hours or more per day. The average prevalence of obesity in adults was 19.5 percent across OECD nations in 2015 [1]. These two clinical conditions are now recognized as serious problems, because sleep and diet are both important for maintaining physical and mental health. Insufficient sleep leads to metabolic imbalance and an increased risk of metabolic diseases, including cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), hypertension, obesity, and depression [2, 3]. Even acute SD can cause altered glucose metabolism, changes in hormone production, and weight gain (4–6). Similarly, obesity has been linked to a variety of modern diseases, including T2DM, CVD, and other metabolic illnesses [7].
Over the past decade, the microbiome has emerged as a significant component of human health [8]. Many studies have revealed that the gut microbiome communicates with the host and participates in the regulation of the systemic immune, metabolic, and nervous systems, as well as gut metabolism [9]. The gut-brain axis (GBA) is a bidirectional communication pathway between the gut and the brain. Recent research has revealed that gut microbiota may play a role in mediating these interactions, which have been dubbed microbiota-gut-brain interactions [9, 10].
Diet and obesity are well-recognized contributors to the gut microbiome [11]. Obesity is a major risk factor for gut dysbiosis, and the gut microbiota contribute to metabolic dysfunction in obese individuals [12]. SD also induces dysbiosis in the gut microbiome. Sleep disturbance and loss are linked to systemic inflammation and oxidative stress in the gut [13, 14]. Insomnia leads to significant structural and functional changes in the gut microbiome [15], which fluctuates in response to disturbances in sleep and circadian rhythm [16]. The gut microbiota can also influence sleep quality through the GBA [17].
In modern society, there are concurrent epidemics of SD and obesity with a potential bidirectional relationship [18]. While SD is a well-known risk factor for obesity [19], obesity can also cause sleep disorders, such as insomnia, obstructive sleep apnea, and obesity hypoventilation syndrome [20]. Although SD and obesity frequently coexist, research on the combined consequences of SD and obesity have been limited. Furthermore, to the best of our knowledge, there have been no studies on the key regulators of microbiota-gut-brain interactions in the gut and brain. In this study, we investigated the shotgun metagenomic sequencing and host gene expression profiles in response to SD and obesity in a mouse model. In addition, we performed a multi-omics factor analysis to identify a major driver of these profile alterations in response to metabolic stress caused by SD and obesity.
## Mouse experimental model
Eight-week-old C57BL6/J male mice were randomly divided into four cages and fed one of two diets: a standard chow diet or high-fat diet (D12492; Research Diets, Inc., New Brunswick, NJ, USA). Sleep conditions were either SD or exercise control (EC) after eight weeks, with one day of habituation inside the wheel and five days in the sleep environment, as stated in the graphical abstract (Figure 1A; Supplementary Figure 1). C57BL/6J mice were then divided into four groups based on whether they were deprived of sleep and whether they were fed a standard chow diet (SCD) or high-fat diet (HFD): EC+SCD, EC+HFD, SD+SCD, and SD+HFD (number of mice per group = 3, the minimum requirement for statistics). These mice were fed SD or HFD for 8 weeks as previously described [21, 22]. All mice were maintained in a 12 h dark-light cycle, with the lights turning on at 7 AM and off at 7 PM. There are no inclusion/exclusion criteria for mice selection for the analysis. Because C57BL/6J mouse is genetically homogeneous inbred strain.
**Figure 1:** *Gut flora depending on sleep conditions and diet type. The experimental design is described graphically here by the following two factors: diet type and sleep (A). The sample distance for β-diversity was calculated using the Bray-Curtis method (B). Permutational multivariate analysis of variance was applied to compare the means of samples for a single factor, either diet or sleep type, and two factors (C). The α-diversity was measured using the Shannon index, and the median was analyzed using the Wilcoxon test, depending on two factors (left) and specifically diets for each sleep condition (right) (D). The relative abundance of 29 genera in the 12 samples indicated a different proposition at the genus level (E). The mean proportion and 95% confidence interval (P < 0.05) explained the main genus contributors related to each factor (F). *P < 0.05, **P < 0.01 and ***P < 0.001*
## Mouse sleep deprivation
SD was achieved by placing mice individually in activity wheels (Lafeyette Instruments, Lafayette, IN, USA) that had a motorized wheel with a diameter of 6.985 cm and an internal wheel with a diameter of 5.715 cm, with free access to food and water, as previously described [23]. First, the mice were habituated to the activity wheel conditions for 24 h. We conducted experiments on the SD mice, including the SD+SCD and SD+HFD groups, over the course of five days using a slow rotational movement of the activity wheel (programmed on a schedule of 3 s ‘on’ and 12 s ‘off’) for 18 h (from ZT 06 to ZT 24) as previously described [24]. SD in this study involved forcing the mice to move to interrupt their sleep. Exercise control is necessary to avoid confusing the interpretation of experimental results because of the nonspecific effects of movement itself. For five days, mice were provided with the same walking distance (EC+SCD and EC+HFD groups) but with a wheel on/off schedule of 15 min on/15 min off at 3 m/min speed for 7.2 h, enabling them to sleep uninterrupted for longer periods.
## Tissue harvest and RNA precipitation
Mouse tissues were all harvested at 7 AM, and the harvest time in each mouse sample did not exceed 3 min, limiting variations and stress environments between the groups. After cervical dislocation, whole blood was collected from the left ventricle of the heart tissue and perfused with $4\%$ paraformaldehyde (PFA). Whole blood was centrifuged for 10 min at 6000 rpm, and floating serum was collected in a clean container. Blood sera were collected and stored at -80°C. Overnight storage of brain tissue was performed in a $4\%$ PFA solution.
RNA from colon samples was extracted using TRIzol (Invitrogen, Waltham, MA, USA) and followed up according to the manufacturer’s protocol. The extracted RNA was then stored at < -80°C. The brain was divided vertically, and the right hemisphere was used for RNA extraction with a PureLink FFPE RNA Isolation Kit (Invitrogen).
## Colon RNA sequencing
The quality of RNA from colon tissue was checked using the Agilent 2100 bioanalyzer. Only samples with a high-quality score (RNA Integrity Number) of 6 or higher were used for making a library. The quality of the sequencing was also checked using a Phred quality score (Q score). More than $92\%$ of the sequencing had a high score of Q30, which means the accuracy of the sequencing was at $99.9\%$. Colon RNA FASTQ was automatically counted as the transcript amount using Kallisto (version 0.45.0). DESeq2 packages were used to extract the list of differentially expressed genes (DEGs) with a design matrix for diet, sleep, and interaction terms. Volcanoplot was used to compare DEG analysis results without interaction terms, and with an adjusted P-value cutoff of 0.01 and absolute value of log2 transformed fold-change 2. For pathway enrichment analysis using clusterprofiler packages (version 4.0.5), such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database-based pathway enrichment, the input list had significant values that were below the adjusted P-value of 0.05 for the DEseq2 results. Input data were based on the Entrez ID labeling, which indicates that non-available gene names in the Entrez ID are automatically removed. During the analysis, the number of genes enriched in KEGG function was 499 and 557 in SD vs. EC (Ref. SCD) and SD vs. EC (Ref. HFD), respectively; however, the filtered genes were automatically recognized at 479 and 541, respectively. The results of the pathway enrichment analysis based on GO biological processes were 199 and 371 in SD vs. EC (Ref. SCD) and SD vs. EC (Ref. HFD), respectively. Gene set enrichment analysis (GSEA) was performed using the GSEA software (version 4.1.0) on 3895 gene sets. The enrichment map of GSEA was programmed in Cytoscape (version 3.8.2) using the enrichmentMap tool (version 3.3.3).
## Shotgun metagenomic sequencing
Using sterilized forceps, internal feces from the colon were collected, and colon tissue was washed with phosphate-buffered saline to remove any remaining feces and then stored at -80°C. Shotgun sequencing was used to sequence precipitated fecal DNA. The total number of reads produced from 12 samples averaged 76,004,118 ± 636,745.9436, with at least $92\%$ of the nucleotides having a Phred quality score ≥30. Overall, a total of 6 different phyla, 22 families, 29 genera, and 53 species were detected using Metaphlan (version 3.0). The HUMAnN program (version 3.0) was used to measured estimated genes, as referred by uniref90, which were quantified as copies per million, and the total sum of all genes ranged from 1,294,101 to 1,467,302, depending on the samples. STAMP (version 2.1.3) and Mofapy2 (version 1.2.2) were used to examine the metagenomic data from the relative abundance results (version 0.6.4). α- and β-diversities were calculated from species-level taxa using the vegan package (version 2.5-7). In particular, for Mofapy2, significant gene sets of positive and negative weights from colon RNA sequencing and brain nCounter were manually extracted from the GO database.
## Brain nanoString nCounter
Brain RNA samples were loaded onto the nCounter Inflammation Panel (nanoString, Seattle, WA, USA). The cartridge counted the probe-tagged fluorescent barcode with a digital analyzer equipped with a microscope. One molecule was quantified as the count value. Gene expression levels between samples were confirmed using 13 housekeeping genes: Aars, Asb10, Ccdc127, Cnot10, Csnk2a2, Fam104a, Gusb, Lars, Mto1, Supt7l, Tada2b, Tbp, and Spnpep1. Counting data from the nanoString nCounter analysis were analyzed using R software (versions 4.1.0 and 4.1.1). The DEseq2 package was used to analyze the DEGs.
## Ethical statement
All experiments were reviewed and approved by the Institutional Animal Care and Use Committee of Gwangju Institute of Science and Technology (Approval number: GIST-2021-064).
## High-fat diet mainly loaded gut microbiome diversity
To determine the impact of SD and HFD-induced obesity on the baseline composition of the gut microbiome, fecal samples were collected, and shotgun metagenome sequencing was performed for taxonomic profiling and functional analysis. To calculate inter-group dissimilarity (beta-diversity), we computed the Bray-Curtis dissimilarity, and unweighted and weighted Unifrac. Beta-diversity-based principal coordinate analysis plots (Figure 1B; Supplementary Figure 2) showed a strong distinction by diet. This means that diets had a greater impact on gut microbial diversity than that of sleep variables. In the permutational multivariate analysis of variance test (Figure 2C), diet showed a significant effect, but sleep did not cause significant differences between the groups (Figure 1C).
**Figure 2:** *Difference for genetic expression and clade distribution of the gut microbiome. The clade distribution pattern in a group (n=3) is referred to as a tree cladogram (A). The top 10 significantly variable gut microbiota species were measured using an analysis of variance test depending on sleep and diet conditions (P < 0.05) and expressed as z-scores of relative abundance values (B). Eleven environmental factors affecting Bray–Curtis β-diversity and its ordination are shown as arrows (P < 0.005) (C). The inosine-5’-phosphate biosynthesis I and II were estimated from DNA reads, and species attribution was identified (D).*
At the phylum level, the Firmicutes/Bacteroidetes (F/B) ratio is significantly associated with intestinal homeostasis [25]. The HFD significantly increased the microbiota F/B ratio by increasing the content of Firmicutes; in contrast, SD decreased the F/B ratio, but the difference was not statistically significant (Tables 1, 2). Figure 1D shows the intragroup diversity (alpha diversity) expressed using the Shannon index. The HFD decreased alpha diversity while SD increased alpha diversity, but these changes were not statistically significant. At the genus level, the HFD decreased the relative abundance of the genera Prevotella and Muribaculum and increased that of Lactococcus. SD increased the abundance of Firmicutes (Figures 1E, F).
## Predictive metabolomic profiling of gut microbiota following sleep deprivation and consumption of a high-fat diet
Gut microbiota composition was then examined at the species level. Figure 2A shows the diverse compositions of the bacterial species within each group. The HFD reduced the relative abundance of Muribaculum intestinale and Prevotella MGM1 and MGM2 and increased the abundance of *Anaerotruncus colihominis* and Lactococcus lactis. When compared to that in the control (EC+SCD group), *Muribaculaceae bacteria* DSM 103720 abundance increased in the SD+SCD group but not in the SD+HFD group. When SD and an HFD were combined, Ileibacterium valens was a key player (Figure 2B). Principal component analysis (PCA) ordination plots of the relative abundance of species indicated the main drivers of each principal component (Figure 2C).
The gut microbiota is an important part of host digestion, and this process results in hundreds of microbial metabolites [26]. Recent findings suggest that there is a bidirectional link between the brain and intestine, the so-called GBA; microbial metabolites are major mediators of this communication (26–28). To understand the metabolic effects of sleep and diet, we performed strain-level functional pathway-enriched pathway analysis (Figure 2D). Among the enriched pathways, inosin-5’-phosphate (5’-IMP) biosynthesis-related strains were significantly increased in the SD+HFD group compared to those in the other groups.
## Transcriptome analysis of mouse large intestine after sleep deprivation and consumption of a high-fat diet
Next, to compare the effects of sleep and diet on the gut transcriptome, we performed RNA-seq analysis of the large intestine of mice from the EC+SCD, EC+HFD, SD+SCD, and SD+HFD groups. PCA was performed to reveal the major stress on the gut transcriptome between sleep and diet. Figure 3A depicts the results. Unlike the gut microbiota, which was mostly affected by diet, the gut transcriptome was primarily affected by sleep. In a DEG analysis (Figure 3B), 90 genes were upregulated, and 26 genes were downregulated in the SD+SCD group compared to those in the EC+SCD group. Gasdermin C-like 2 (Gsdmcl2), chymase 1 (Cma1), solute carrier family 37 member 2 (Slc37a2), Alpha-2,8-sialyltransferase 8E (St8sia5), and gasdermin C4 (Gsdmc4) genes were the top five upregulated DEGs (based on P-value). Stress-associated endoplasmic reticulum protein 1 (Serp1), death-associated protein 1(Dap), transmembrane protein 35A (Tmem35a), ubiquitin-like modifier enzyme 5 (Uba5), and bone gamma-carboxyglutamate protein 3 (Bglap3) were the top five downregulated DEGs (based on P-value). In the HFD group, only 11 genes were upregulated, and 23 genes were downregulated (SD+HFD vs. EC+HFD). The *Serp1* gene was also one of the top five downregulated genes in the SD+HFD group compared to that in the EC+HFD group (Figure 3B).
**Figure 3:** *Colon RNA sequencing. Principal coordinate analysis plot (A). Volcanoplot of three differentially expressed genes (DEGs) with cut-off conditions of adjusted P-value < 0.01 and |Log2 (fold change)| > 2 (B). Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed for a group of genes (adjusted P-value < 0.05 and |Log2 (fold change)| > 1) (C). Gene set enrichment analysis based on Gene ontology (GO) biological processes was plotted as an enrichment map; a total of 2487 and 1408 gene sets were upregulated in the sleep deprivation (SD) + high-fat diet (HFD) and exercise control (EC) + HFD groups, respectively (D). The intersected 32 GO pathways between the two DEGs are described similarly (E). In the case of normalized count levels, 613 genes were significantly different depending on the diet and sleep conditions, and two clusters were summarized in the GO BP pathway enrichment analysis (F).*
Gsdmcl2, Cma1, Slc37a2, and St8sia5 were downregulated in the SD+HFD group compared to those in the EC+SCD group but were upregulated in the SD+SCD group compared to those in the EC+SCD group (Figure 3B). After SD, the tumor protein D52-like 1 (Tpd52l1), cellular communication network factor 3 (Ccn3), and UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransfera 9 (B3gnt9) genes were downregulated in both SCD- and HFD-fed mice (Supplementary Figure 3).
The KEGG enrichment analysis indicated that DEGs between the SD+SCD and EC+SCD groups were enriched in digestive system-related pathways, including “Pancreatic secretion” and “Protein digestion and absorption” (Figure 3C). The DEGs between the SD+HFD and EC+HFD groups were enriched in nucleic acid metabolism-related and lipid metabolism-related pathways (Figures 3C, D). Figure 3E shows the common GO terms related to SD. The nutrient metabolism-related terms, including “carbohydrate biosynthetic process,” “fatty acid metabolic process,” and “glycoprotein biosynthetic process,” and immune system-related terms, including “cytokine-mediated signaling pathway,” “leukocyte migration,” and “macrophage-derived foam cell differentiation,” were highly enriched in both the SCD and HFD conditions after SD (Figure 3E).
Using DEGs among the four groups, we performed heatmap clustering analysis. Figure 3F shows a heatmap of GO terms based on the DEGs in each cluster. Cluster A, which was composed of DEGs in the EC+HFD group, included immune system-related GO terms such as “leukocyte proliferation,” “lymphocyte proliferation,” and “regulation of mononuclear cell proliferation.” Cluster D, which was composed of DEGs in the EC+SCD group, included DNA replication-related genes (Figure 3F).
## Neuroinflammatory changes in the brain after sleep deprivation and consumption of a high-fat diet
To identify neuroinflammatory changes associated with SD or an HFD in the brain, we employed a nanoString neuroinflammation panel [29], which covers 770 genes related to neuroinflammation in the brain. Figure 4A shows volcano plots of the DEGs between each group. Under the SCD, cathepsin S (Ctss), endothelial cell adhesion molecule (Esam), and minichromosome maintenance complex component 6 (Mcm6) were the top three upregulated genes, and aspartate beta-hydroxylase (Asph), ribosomal protein S (Rps21), and BRCA-associated RING domain 1 (Bard1) were the top three downregulated genes after SD. The mcm6, C-C motif chemokine ligand 4 (CCl4), and interleukin 1 receptor kinase 3 (Irak3) genes were upregulated in the EC+HFD group compared to those in the EC+SCD group (Figure 4A).
**Figure 4:** *Brain Nanostring nCount analysis. Volcanoplot of the four differentially expressed gene (DEG) results (P-value < 0.05) (A). A total of 33 significantly expressed genes are listed by z-score in the heatmap (B). The table includes details regarding four DEGs in sleep deprivation (SD) + standard chow diet (SCD) vs. exercise control (EC) + SCD, SD + high-fat diet (HFD) vs. EC + HFD, EC + HFD vs. EC + SCD, and SD + HFD vs. SD + HFD, including the cell types to which they are particularly linked and the biological processes to which they are applied (C). The detailed DEGs for EC + SCD vs. SD + HFD are described (D).*
Using DEGs from the four groups, we compared the expression patterns in each group. In the SD+HFD group, Fc epsilon receptor 1 g (Fcer1g), growth arrest and DNA damage inducible alpha (Gadd45a), and RAS like proto-oncogene B (Ralb) gene expression was significantly increased compared to that in the other groups (Figure 4B). Figure 4C shows the DEGs between each group and their roles in neuroinflammation. Under the SCD, five genes related to adaptive immune response, three genes related to microglial function, and three genes related to the cell cycle were differentially expressed after SD. Under the HFD, two genes related to cytokine signaling, two genes related to the innate immune response, and three genes related to microglial function were differentially expressed after SD. Adaptive immune response-related genes did not show significant differences between the SD+HFD and EC+HFD groups. Sialic acid-binding Ig-like lectin 1 (Siglec1), a marker for active neuroinflammation [30], was highly expressed in the SD+HFD group compared to that in the SD+SCD group. Compared with that in the EC+SCD group, the SD+HFD group had the highest number of DEGs (Figure 4A). *These* genes were related to inflammation, neuropathology, and microglial function (Figure 4D).
## Integration analysis of gut microbiome and host gene expression
To identify the main factors that mediate the microbiota-gut-brain interactions, we performed multi-omics factor analysis (MOFA) by integrating microbiome and gene expression data [31, 32]. Figure 5A displays the four determinants discovered by the factor analysis. Among the four factors, factors 1 and 2 showed effective discriminating values (Figure 5A; Supplementary Figure 4). The variable with the largest weight in factors 1 and 2 was from the microbiome layer. Prevotella sp. MGM1 in factor 1 and Bacteroides satori in factor 2 showed the highest weight in this analysis (Figure 5B; Supplementary Figure 5).
**Figure 5:** *Multi-omics factor analysis from the gut microbiome to the colon and brain axis. The sample variation was represented by four captured factors of the multi-omics factor analysis (MOFA)2 and the distribution of the sample was plotted for factors 1 and 2 (A). The weighted gut microbiome species in factor 1 are described following the ranks, and strongly associated species are described (B). The seven significant gene sets related to Factor 1 are listed with P-values (C). The top 10 features of factor 1 weights, which are estimated to provide a strong effect in specific variation, in colon RNA sequencing (left) and brain nanostring (right) are shown as weight values, and the correlation analysis (D).*
Figure 5C shows the GO terms with a high weight of factor 1 in the DEGs for the gene expression data. In the colon RNA sequencing data, “regulation of vasoconstriction,” “oxidation and reduction process,” “small molecule metabolic process,” and “Termination of RNA polymerase II transcription” were the top enriched GO terms. Figure 5D shows the absolute loadings of the top features of factor 1. Factor 1 was positively correlated with the P21(RAC1) activated kinase 1 (Pak1) gene in the brain and negatively aligned with Neuroglin 1 (Nlgn1) in the brain, as well as with serin/arginine-rich splicing factor 3 (Srsf3) and H2A.Z variant histone 1 (H2az1) genes in the colon (Figure 5D). Factor 2 was positively correlated with the Apoprotein E (APOE) and Calreticulin (CALR) genes in the brain and negatively aligned with the Srsf3 and H2az1 genes in the colon (Supplementary Figure 5).
## Discussion
Here, we examined the effects of SD and diet-induced obesity on the gut microbiota, gut transcriptome, and brain gene expression. In addition, we integrated these data to reveal the main drivers of microbiota-gut-brain interactions. In the present study, we revealed the pleiotropic effects of SD and a HFD on the gut and brain. Previous studies have shown that SD can affect bodyweight by suppressing appetite [33] and decreasing energy expenditure [34]. In both human and animal studies, SD have positive associations with obesity and weight gain [35, 36]. However, our results did not show an effect of SD on body weight in either the standard chow diet (SCD) group or the HFD group, which may be due to the relatively short duration of SD in our experiment.
The HFD reduced gut microbiota biodiversity in terms of both alpha and beta diversity (Figures 1C, D). The gut transcriptome was primarily influenced by SD (Figures 3A, B), whereas brain gene expression associated with neuroinflammation was significantly altered following exposure to a HFD with SD (Figures 4A, D). Gut microbiota analysis revealed that the HFD caused dramatic changes in the physiology of the gut microbiota (Figures 1, 2). SD did not induce robust changes in the gut microbiota, as in other previous studies [37, 38]. Our results showed that the HFD both alone and with SD increased Firmicutes (Table 2), which has already shown significant correlations with obesity and sleep quality [39, 40].
Interestingly, the HFD alone increased the F/B ratio, whereas the HFD with SD decreased the F/B ratio by increasing Bacteroidetes (Table 2). Recent research by Gregory et al. has revealed a link between poor sleep and a higher body mass index (BMI), as well as a positive relationship between sleep quality and the F/B ratio [41]. This is consistent with our findings, which show that sleep deprivation leads to a decrease in the F/B ratio. Recent findings also suggest that an increased F/B ratio leads to more effective glucose fermentation and higher nutritional absorption [42]. This means that the increased F/B ratio in the HFD mice may be a compensatory reaction to overeating, and sleep disrupts this compensation. Prevotella species have shown negative associations with a HFD in mice [43] and significant relationships with weight change in a human randomized controlled trial [44]. In this study, Prevotella sp. MGM1 and MGM2 levels were decreased in both the EC+HFD and SD+HFD groups compared to those in the other groups (Figure 2B).
The abundance of Prevotella and Muribaculum was higher in the SD+SCD group than that in the EC+SCD group but did not increase in the SD+HFD and EC+HFP groups. Recently, Badran et al. [ 45] reported that fecal microbiota transplantation using a fecal slurry, which has abundant levels of Prevotella and Muribaculaceae, improves sleep disturbances in mice. The composition of gut microbiota may adapt to defend against the stressors associated with sleep deprivation, while a high-fat diet (HFD) may complicate these protective responses. The gut microbiota is a critical factor in the body’s ability to adapt to stress, affecting neuroendocrine substances such as ghrelin and serotonin [46]. This suggests that the gut microbiota plays a crucial role in regulating the body’s stress response. There is increasing evidence that gut microbiota may have a role in mitigating the effects of sleep disturbance. Studies in both humans and animals have shown that probiotic intervention can improve sleep quality [47, 48]. To identify the possible mediators of microbiota-GBA interactions, we performed a microbial pathway analysis and found that the abundance of 5’-IMP synthesis-related species was highly increased in the SD+HFD group compared to that in the other groups (Figure 2D). 5’-IMP plays a key role in purine nucleotide synthesis and regulates various immune responses [49]. In addition, 5’-IMP is important for hypnotic action in the brain [50]. The adenosine and its metabolite inosine have been shown to be closely linked to the regulation of the circadian rhythm [51]. Studies have shown that inosine activates the adenosine A2A receptor [52, 53], which plays a role in regulating sleep. Furthermore, research in animal models has demonstrated that inosine administration can increase neuronal proliferation in the brain and prevent depression-like behavior. Additionally, inosine has been found to prevent memory impairment in a rat model of Alzheimer’s disease [54].Therefore, increased 5’-IMP synthesis might be a protective response in the gut against metabolic stresses induced by SD and an HFD.
Gut transcriptome analysis revealed that SD caused robust changes in the gut transcriptome (Figure 3). In this study, the most significantly enriched pathways were related to ribosome biogenesis and nucleic acid metabolic processes (Figure 3C), which are critical for gut mucosal defense [55] and colorectal cancer progression [56]. This result elucidates the current association between sleep, obesity, and colon cancer. Both SD and obesity are high-risk factors for colorectal cancer development [57, 58]. Recent studies have shown deleterious effects of SD on the gut [14, 59]. For example, SD causes premature death by increasing reactive oxygen species in the gut [14]. We also found that SD enhanced the renin-angiotensin system (RAS)-associated pathways in the gut (Figure 3C). The gut RAS interacts bidirectionally with the gut microbiota and can promote intestinal inflammation and fibrosis [60, 61].
In the brain, genes related to neuroinflammation were altered by both SD and the HFD. The *Mcm6* gene was highly upregulated after SD under the SCD and HFD conditions (Figure 4A). *This* gene encodes a protein that is a component of the MCM complex, which is required for the initiation of eukaryotic genome replication [62]. Furthermore, this gene has shown positive correlations with poor prognosis in brain and gastrointestinal tumors [63, 64]. The most severe neuroinflammatory changes were observed in the SD+HFD group compared to those in the EC+SCD group. CXC motif chemokine ligand 10 (Cxcl10), insulin-like growth factor-1 (Igf1), and cluster of differentiation 70 (Cd70) were the top three highly elevated genes (Log2FC). *These* genes are well-known markers of proinflammatory signals (65–67).
To determine the main driver of the microbiota-gut-brain interactions, we performed factor analysis. MOFA2 revealed that the major feature of factors 1 and 2 was gut bacteria (Figure 5; Supplementary Figure 5). This suggests that the main driver of microbiota-gut-brain interactions with SD and an HFD is the gut microbiome. Notably, the SRSF3 genes in the gut showed significant negative correlations with factors 1 and 2. According to recent studies, SRSF3 suppresses tumorigenesis [68] and inhibits cellular senescence [69]. Thus, reduced SRSF3 expression might be an important contributor to gastrointestinal dysfunction caused by SD and an HFD.
Our study had several limitations. First, we performed an MOFA based on microbiome and transcriptome data. Further evidence, such as the blood metabolome and gut proteome, is required to substantiate our conclusions. Second, we only tested adult male mice. As a result, sex differences and aging were not reflected in our study.
In summary, our study revealed novel associations between the gut microbiota and host responses after SD and diet-induced obesity. Obesity with SD has deleterious effects on gut and brain health. We discovered that the gut microbiota may be the primary driver of microbiota-gut-brain interactions, and 5’-IMP may be an essential microbial metabolite that facilitates gut-brain communication. These findings imply that healing gut dysbiosis may be a viable therapeutic target for enhancing sleep quality and curing obesity-related dysfunction.
## 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 animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Gwangju Institute of Science and Technology (Approval number: GIST-2021-064).
## Author contributions
SL, C-MO, and TK contributed to the conceptual design of the project and the experiments described in the manuscript. The experiments were performed by JL and JK. The data were analyzed by JL, JK, and YK. The manuscript was written by JL, JK, and C-MO. Then, the manuscript was edited and critically evaluated by SL, C-MO, and TK. 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.2023.1117259/full#supplementary-material
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|
---
title: Association Between Recreational Physical Activity and mTOR Signaling Pathway
Protein Expression in Breast Tumor Tissue
authors:
- Ting-Yuan David Cheng
- Runzhi Zhang
- Zhihong Gong
- Bo Qin
- Rikki A. Cannioto
- Susmita Datta
- Weizhou Zhang
- Angela R. Omilian
- Song Yao
- Thaer Khoury
- Chi-Chen Hong
- Elisa V. Bandera
- Christine B. Ambrosone
journal: Cancer Research Communications
year: 2023
pmcid: PMC9990525
doi: 10.1158/2767-9764.CRC-22-0405
license: CC BY 4.0
---
# Association Between Recreational Physical Activity and mTOR Signaling Pathway Protein Expression in Breast Tumor Tissue
## Abstract
Physical activity (PA) is associated with decreased signaling in the mTOR pathway in animal models of mammary cancer, which may indicate favorable outcomes. We examined the association between PA and protein expression in the mTOR signaling pathway in breast tumor tissue. Data on 739 patients with breast cancer, among which 125 patients had adjacent-normal tissue, with tumor expression for mTOR, phosphorylated (p)-mTOR, p-AKT, and p-P70S6K were analyzed. Self-reported recreational PA levels during the year prior to diagnosis were classified using the Centers for Disease Control and Prevention guideline as sufficient (for moderate or vigorous) PA or insufficient PA (any PA but not meeting the guideline) or no PA. We performed linear models for mTOR protein and two-part gamma hurdle models for phosphorylated proteins. Overall, $34.8\%$ of women reported sufficient PA; $14.2\%$, insufficient PA; $51.0\%$, no PA. Sufficient (vs. no) PA was associated with higher expression for p-P70S6K [$35.8\%$ increase; $95\%$ confidence interval (CI), 2.6–80.2] and total phosphoprotein ($28.5\%$ increase; $95\%$ CI, 5.8–56.3) among tumors with positive expression. In analyses stratified by PA intensity, sufficient versus no vigorous PA was also associated with higher expression levels of mTOR (beta = 17.7; $95\%$ CI, 1.1–34.3) and total phosphoprotein ($28.6\%$ higher; $95\%$ CI, 1.4–65.0 among women with positive expression) in tumors. The study found that guideline-concordant PA levels were associated with increased mTOR signaling pathway activity in breast tumors. Studying PA in relation to mTOR signaling in humans may need to consider the complexity of the behavioral and biological factors.
### Significance:
PA increases energy expenditure and limits energy utilization in the cell, which can influence the mTOR pathway that is central to sensing energy influx and regulating cell growth. We studied exercise-mediated mTOR pathway activities in breast tumor and adjacent-normal tissue. Despite the discrepancies between animal and human data and the limitations of our approach, the findings provide a foundation to study the mechanisms of PA and their clinical implications.
## Introduction
Physical activity (PA) is associated with lower breast cancer risk among women and associated with lower risk of recurrence and mortality among patients with breast cancer (1–3). Given that the epidemiologic evidence regarding PA is well documented, mechanisms, including decreasing levels of sex hormone, insulin, glucose, leptin, inflammation, and adiposity, as well as improving immune function, are being hypothesized as potential pathways in the association between PA and improved survival rates among people with cancer [1]. Although there are emerging data showing changes in blood biomarkers associated with PA among patients with cancer or survivors (4–10), data on changes in tumor markers due to PA largely found no significant exercise-mediated effects [11, 12]. Randomized trials of exercise did not observe changes in Ki-67, an important marker for cell proliferation in breast cancer [13, 14]. The number of participants was small (≤100) in these trials and observational studies examining tumor tissue. Thus, studies on potential pathways in breast tumor tissue or tumor microenvironments are needed to improve our understanding of the direct impact of PA on patients with cancer. Evidence that PA is associated with tumor markers would provide insights into developing precision, mechanism-based interventions [15], such as promoting exercise among patients with specific tumor markers to provide a larger benefit of PA in these patients.
A mechanism of PA for reducing body weight is increasing energy expenditure as well as limiting the availability of energy utilized in the cell. The mTOR signaling pathway is a sensor of energy influx and plays a key role in regulating protein synthesis, cell growth, and cell survival [16]. Overactivation of the mTOR signaling pathway due to obesity, a result of positive energy balance, is associated with poor outcomes in patients with breast cancer [17, 18]. Data from animal models have shown that PA decreases the protein expression levels of activated mTOR, AKT, and phosphorylated P70S6K (p-P70S6K)—important factors in the mTOR signaling pathway—in mammary carcinomas [19, 20]. However, the relationship between physical activity and mTOR activities in the tumors of patients with breast cancer has not been reported.
Here, we investigated the association between recreational PA levels and mTOR pathway activation as measured by a panel of protein and phosphoprotein expression levels in tumors from participants enrolled in the Women's Circle of Health Study (WCHS). Previous analysis of a consortium of Black women including WCHS showed vigorous PA is associated with a lower risk of breast cancer [21]. We hypothesized that women who had “sufficient” recreational PA levels would have lower mTOR pathway activity in breast tumors compared with women who had “insufficient” or no PA.
## Study Participants
Study participants were women with breast cancer recruited between 2001 and 2015 for the WCHS, a multisite case–control study conducted in New York City and 10 counties in eastern New Jersey. Details on study recruitment have been described elsewhere [22, 23]. The design of the case–control study recruited Black women and White women in 1:1 ratio; in a later stage, the tissue collection effort was more focused on the enrolled Black women than White women. All participants provided written informed consent. The protocol was approved by all relevant Institutional Review Boards and conducted in accordance with the Declaration of Helsinki. In brief, the cases included patients who self-identified as Black women or as White women between 20 and 75 years of age, able to speak English, with no previous history of cancer other than nonmelanoma skin cancer and who were within 9 months of having received a diagnosis of primary, histologically confirmed, invasive breast cancer or carcinoma in situ. Of patients eligible for inclusion, >$95\%$ allowed for the use of their tumor tissue as part of the informed consent form. Clinical and tumor characteristics were obtained from pathology reports. Formalin-fixed paraffin-embedded tissue specimens were used for tissue microarray (TMA) construction that included at least two tumor tissue cores and an adjacent-normal tissue core when available per patient. In total, samples from 865 cases included in TMAs were available for laboratory assays. After immunostaining, tumor tissue cores <25 cells for scoring were excluded. Subsequently, 739 cases (668 invasive breast cancer and 71 carcinoma in situ) who had at least one tumor tissue core scored with any of the mTOR pathway proteins assayed and data on PA variables were retained for statistical analyses. A subset of cases ($$n = 125$$) who had adjacent-normal tissue paired with the tumor tissue were also analyzed.
## IHC and Image Analysis
TMAs consisting of both tumor and adjacent-normal tissue cores were sectioned at 5 μm and stained by IHC methods for mTOR (clone 7C10), phosphorylated mTOR (p-mTOR, Ser2448), phosphorylated AKT (p-AKT, Ser473), and p-P70S6K (T389). Detailed methods for staining are given elsewhere [24]. Stained slides were digitally imaged at a magnification of × 20 using the Aperio ScanScope XT (Leica Biosystems) digital slide scanner system, and images were manually annotated to identify tumors for analysis. Automated image analysis was performed on the annotated regions using validated algorithms with minor adjustments for cell shape and intensity thresholds. Specific locations (cytoplasm for mTOR and p-mTOR expression, and both cytoplasm and nuclei for p-AKT and p-P70S6K) were scored for staining intensity (0, none; 1+, partial or weak; 2+, moderate; or 3+, strong) and for the percentage of positive cells in each category. A histologic score (H-score) at the core level was calculated by the formula [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)] × 100 [25]. The core-level data were collapsed into case-level data using a cellularity-weighted approach [26]. In addition, the p-mTOR/mTOR ratio, defined as the H-score of p-mTOR divided by the H-score of mTOR, and total phosphoprotein, derived as the summation of H-scores from p-mTOR, p-AKT, and p-P70S6K, were also examined.
## PA and Covariate Measurements
PA levels during the year prior to diagnosis were obtained via a structured home interview. During the period of participant recruitment, two versions of PA questionnaires were used. In WCHS 1 (2002–2012), participants reported exercise, sport, and leisure-time activities they had participated in for ≥1 hour per week for at least 3 months, the average duration (hours per week), and the age of engagement. A metabolic equivalent of task (MET) value was then assigned to each reported activity according to the Compendium of Physical Activity [27, 28]. In WCHS 2 (2012–2017), participants reported weekly duration of moderate PA (MET 2.5 to <6) and vigorous PA (MET ≥6) during the year before diagnosis [29]. We combined the data collected from the two versions of questionnaires and classified individuals as having sufficient (150 minutes/week for moderate PA or 75 minutes/week for vigorous PA), insufficient (any PA but not meeting the level of sufficient PA), or no PA, based on the aerobic component of the 2008 Physical Activity Guidelines for Americans by the Centers for Disease Control and Prevention (CDC; ref. 30).
Information on age, race, educational level, menopausal status, and medication use was obtained during the in-person interviews. A food frequency questionnaire was self-administered for usual dietary intake during the year before diagnosis [31]. Anthropometric measurements were obtained by trained staff using a standardized protocol described elsewhere [32]. Standing height was measured to the nearest 0.1 cm; weight was measured using a Tanita TBF-300A scale. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared [33]. Clinical and tumor characteristics, including the expression status of hormone receptors [HR; i.e., estrogen receptors (ER) and progesterone receptors] and HER2, were obtained from pathology reports.
## Statistical Analysis
We assessed protein expression levels in tumor and adjacent-normal tissue according to the three PA levels using one-way ANOVA. Regression models were only performed for protein expression in tumor tissue because the number of patients with the adjacent-normal tissue would not provide sufficient statistical power. Linear regression was performed to estimate the difference in H-scores for mTOR between the categories of PA levels because it was normally distributed. For the phosphoproteins, because a high proportion of tumors were negative for expression (H-score = 0 for $12\%$ of p-mTOR staining, $27\%$ of p-AKT, and $21\%$ of p-P70S6K; Supplementary Table S1), gamma hurdle models were used to model the positive (nonzero) versus negative (zero) data with a logistic model and then the positive data with a gamma model with a log-link. ORs and percentage differences were converted from the regression coefficients of the respective parts of the gamma hurdle models. The covariates included age (continuous), race (Black or White), educational level (≤high school, some college, or ≥college graduate), menopausal status (premenopausal or postmenopausal), BMI (continuous), history of diabetes (yes or no; defined as using any oral or injection medication for diabetes), breast cancer molecular subtype (HR+/HER2−, HR+/HER2+, HR−/HER2+, or HR−/HER2−), tumor grade (low, intermediate, or high), tumor size (<1.0, 1.0–1.9, or ≥2.0 cm), and disease stage (American Joint Committee on Cancer staging system stage 0/I, II, or III/IV). Because BMI and history of diabetes can be intermediate variables on the causal pathway between PA and mTOR signaling, we also performed models without each of these two variables as a sensitivity analysis. Because dietary caloric intake is associated with PA levels and can modulate p-mTOR and p-AKT activities [34], total energy intake was additionally adjusted in a sensitivity analysis. Furthermore, to examine specific PA intensity in association with mTOR signaling pathway activity, we estimated the associations for (i) moderate PA (3 to <6 METs of exercise for at least 150 minutes/week defined as sufficient) among patients without vigorous PA and (ii) vigorous PA (≥6 METs of exercise for at least 75 minutes/week defined as sufficient) regardless the level of moderate PA. Exploratory stratification analyses were performed by race, BMI, menopausal status, history of diabetes, total energy intake levels, breast cancer stage, tumor grade, ER status, tumor size, and lymph node status because these variables were associated with energy balance or the mTOR pathway signaling. All tests of statistical significance were two sided; a P value less than 0.05 was considered statistically significant. All analyses were a priori, and the results were not adjusted for multiplicity.
## Data Availability
The data supporting the findings of this study are not publicly available to protect patient privacy. The data will be made available to authorized researchers with the approval of the WCHS committee and relevant Institutional Review Boards.
## Ethics Approval and Consent to Participate
All participants provided written informed consent and a release for access to medical records, pathology data, and tumor tissues prior to study participation. The protocol was approved by the Institutional Review Boards of all participating institutions, including Roswell Park Comprehensive Cancer Center and Rutgers Cancer Institute of New Jersey.
## Results
Table 1 gives the characteristics of participants overall and according to PA levels based on the CDC Guideline. Eighty percent of participants were Black women and $20\%$ were White women. Overall, $14.2\%$ and $34.8\%$ of women reported insufficient and sufficient PA during the year before diagnosis, respectively. Approximately half ($51.0\%$) of women reported no regular PA. White women ($47.3\%$) were more likely than Black women ($31.6\%$) to report sufficient PA. Premenopausal women (vs. postmenopausal), with lower (vs. higher) BMI, and without (vs. with) history of diabetes were more likely to meet the CDC guideline for PA. PA levels did not differ by the clinical and tumor characteristics.
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | Physical activity levels | Physical activity levels.1 | Physical activity levels.2 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| | All cases | No | Insufficient | Sufficient | P value, X2 tests |
| Characteristic | n (column %) | n (row %) | n (row %) | n (row %) | |
| All participants | 739 (100.0) | 377 (51.0) | 105 (14.2) | 257 (34.8) | |
| Race | | | | | 0.001 |
| Black | 591 (80.0) | 319 (54.0) | 85 (14.4) | 187 (31.6) | |
| White | 148 (20.0) | 58 (39.2) | 20 (13.5) | 70 (47.3) | |
| Age (years) | | | | | 0.007 |
| <40 | 80 (10.8) | 41 (51.2) | 7 (8.8) | 32 (40.0) | |
| 40–49 | 212 (28.7) | 99 (46.7) | 26 (12.3) | 87 (41.0) | |
| 50–59 | 232 (31.4) | 112 (48.3) | 34 (14.7) | 86 (37.1) | |
| ≥60 | 215 (29.1) | 125 (58.1) | 38 (17.7) | 52 (24.2) | |
| Educational level attainment | | | | | <0.001 |
| ≤High school | 317 (42.9) | 187 (59.0) | 46 (14.5) | 84 (26.5) | |
| Some college | 187 (25.3) | 94 (50.3) | 22 (11.8) | 71 (38.0) | |
| ≥College graduate | 235 (31.8) | 96 (40.9) | 37 (15.7) | 102 (43.4) | |
| Menopausal status | | | | | 0.011 |
| Premenopausal | 334 (45.2) | 164 (49.1) | 37 (11.1) | 133 (39.8) | |
| Postmenopausal | 405 (54.8) | 213 (52.6) | 68 (16.8) | 124 (30.6) | |
| BMI | | | | | 0.001 |
| <25.0 | 159 (21.5) | 63 (39.6) | 22 (13.8) | 74 (46.5) | |
| 25.0–29.9 | 205 (27.7) | 102 (49.8) | 27 (13.2) | 76 (37.1) | |
| 30.0–34.9 | 191 (25.8) | 96 (50.3) | 32 (16.8) | 63 (33.0) | |
| ≥35.0 | 183 (24.8) | 115 (62.8) | 24 (13.1) | 44 (24.0) | |
| Missing | 1 (0.1) | 1 (100) | 0 (0) | 0 (0) | |
| History of diabetes | | | | | 0.007 |
| Never | 632 (85.5) | 313 (49.5) | 85 (13.4) | 234 (37.0) | |
| Ever | 107 (14.5) | 64 (59.8) | 20 (18.7) | 23 (21.5) | |
| Tumor grade | | | | | 0.73 |
| Low | 103 (13.9) | 50 (48.5) | 12 (11.7) | 41 (39.8) | |
| Intermediate | 232 (31.4) | 124 (53.4) | 30 (12.9) | 78 (33.6) | |
| High | 311 (42.1) | 166 (53.4) | 44 (14.1) | 101 (32.5) | |
| Missing | 93 (12.6) | 37 (39.8) | 19 (20.4) | 37 (39.8) | |
| Tumor size (cm) | | | | | 0.30 |
| <1.0 | 93 (12.6) | 41 (44.1) | 17 (18.3) | 35 (37.6) | |
| 1.0–1.9 | 248 (33.6) | 127 (51.2) | 30 (12.1) | 91 (36.7) | |
| ≥2.0 | 328 (44.4) | 180 (54.9) | 43 (13.1) | 105 (32.0) | |
| Missing | 70 (9.5) | 29 (41.4) | 15 (21.4) | 26 (37.1) | |
| AJCC stage | | | | | 0.39 |
| 0/I | 350 (47.4) | 167 (47.7) | 54 (15.4) | 129 (36.9) | |
| II | 280 (37.9) | 154 (55.0) | 35 (12.5) | 91 (32.5) | |
| III, IV | 98 (13.3) | 54 (55.1) | 14 (14.3) | 30 (30.6) | |
| Missing | 11 (1.5) | 2 (18.2) | 2 (18.2) | 7 (63.6) | |
| Lymph node status | | | | | 0.45 |
| Negative | 373 (50.5) | 182 (48.8) | 59 (15.8) | 132 (35.4) | |
| Positive | 253 (34.2) | 134 (53.0) | 32 (12.6) | 87 (34.4) | |
| Missing | 113 (15.3) | 61 (54.0) | 14 (12.4) | 38 (33.6) | |
| Molecular subtype | | | | | 0.84 |
| HR+/HER2− | 437 (59.1) | 230 (52.6) | 56 (12.8) | 151 (34.6) | |
| HR+/HER2+ | 90 (12.2) | 43 (47.8) | 16 (17.8) | 31 (34.4) | |
| HR−/HER2+ | 40 (5.4) | 19 (47.5) | 6 (15.0) | 15 (37.5) | |
| HR−/HER2− | 120 (16.2) | 64 (53.3) | 13 (10.8) | 43 (35.8) | |
| Missing | 52 (7.0) | 21 (40.4) | 14 (26.9) | 17 (32.7) | |
| Invasiveness | | | | | 0.06 |
| No | 71 (9.6) | 29 (40.8) | 16 (22.5) | 26 (36.6) | |
| Yes | 668 (90.4) | 348 (52.1) | 89 (13.3) | 231 (34.6) | |
| PA intensity | | | | | |
| Moderate PA | 739 (100) | 433 (58.6) | 111 (15) | 195 (26.4) | |
| Vigorous PA | 739 (100) | 599 (81.1) | 30 (4.1) | 110 (14.9) | |
Examination of the mTOR pathway protein expression by PA levels (Table 2) suggested that women with sufficient versus no PA had significantly higher expression of p-AKT, p-P70S6K, and total phosphoprotein in tumors (all $P \leq 0.05$). Comparing protein expression in a subset of tumors with paired adjacent-normal tissue (Supplementary Table S2), there was an indication that mTOR pathway proteins had higher expression, that is, higher proportions of positive (H-score >0) expression and higher mean and median H-scores among those with positive expression, in tumors than in adjacent-normal tissue. Women with sufficient PA compared with those with no PA had higher mean and median protein expression levels in the adjacent-normal tissue although the proportions of positive expression were generally lower in the sufficient PA group (Supplementary Table S3).
**TABLE 2**
| Unnamed: 0 | Unnamed: 1 | Physical activity levels | Physical activity levels.1 | Physical activity levels.2 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| | | No | Insufficient | Sufficient | P value, |
| Protein | No. | Median (IQR) | Median (IQR) | Median (IQR) | ANOVA |
| mTOR | 720 | 138.34 (75.6–194.0) | 140.5 (93.9–195.4) | 141.2 (87.3–205.3) | 0.29 |
| p-mTOR | 717 | 20.3 (1.3–87.1) | 36.6 (5.3–89.9) | 33.3 (2.7–82.0) | 0.33 |
| p-AKT | 722 | 10.2 (0–58.0) | 19.1 (0.3–92.3) | 19.1 (0.2–79.8) | 0.010 |
| p-P70S6K | 721 | 4.4 (0–66.7) | 27.0 (1.3–144.5) | 26.1 (0.7–115.5) | <0.001 |
| Total phosphoprotein | 705 | 94.6 (16.8–211.1) | 172.0 (57.2–282.0) | 129.5 (46.4–256.3) | <0.001 |
| p-mTOR/mTOR | 705 | 0.15 (0.02–0.59) | 0.27 (0.07–0.54) | 0.23 (0.03–0.60) | 0.27 |
In the regression analyses adjusting for covariates (Table 3), there was no association between PA and mTOR expression. However, sufficient PA versus no PA was associated with p-P70S6K expression (OR = 1.60; $95\%$ CI, 1.00–2.59, positive vs. negative protein expression) with a borderline significance. Among women with positive expression of p-P70S6K, sufficient PA was associated with $35.8\%$ ($95\%$ CI, 2.6–80.2) higher expression in breast tumor tissue. In addition, sufficient (vs. no) PA was associated with $28.5\%$ ($95\%$ CI, 5.8–56.3) higher expression of total phosphoproteins among women with positive expression. After additionally adjusting for total energy intake (Supplementary Table S4), the associations for p-P70S6K and total phosphoprotein remained unchanged. We also performed models without two intermediate variables—BMI and history of diabetes (Supplementary Tables S5 and S6). The analysis showed that the association of sufficient PA with p-P70S6K and total phosphoprotein expression in breast tumors was essentially the same as the main model that included both variables.
**TABLE 3**
| Unnamed: 0 | Logistic modelb | Logistic modelb.1 | Logistic modelb.2 | Logistic modelb.3 | Logistic modelb.4 | Gamma or linear modelc | Gamma or linear modelc.1 | Gamma or linear modelc.2 | Gamma or linear modelc.3 | Gamma or linear modelc.4 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Protein expression (Outcome)a | No. | Insufficient PA vs. no PAOR (95% CI) | P | Sufficient PA vs. no PAOR (95% CI) | P | No. | Insufficient PA vs. no PABeta (95% CI) | P | Sufficient PA vs. no PABeta (95% CI) | P |
| mTOR | | | | | | 599 | 0.48 (−17.29 to 18.25) | 0.96 | 9.01 (−3.96 to 21.98) | 0.17 |
| p-mTOR | 593 | 1.53 (0.69–3.77) | 0.32 | 1.55 (0.87–2.86) | 0.14 | 523 | 9.0 (−20.6 to 52.3) | 0.59 | 8.0 (−14.0 to 36.1) | 0.51 |
| p-AKT | 598 | 1.62 (0.9–3.01) | 0.11 | 1.36 (0.9–2.07) | 0.14 | 421 | 10.8 (−22.0 to 60.6) | 0.57 | 12.7 (−14.1 to 48.4) | 0.38 |
| p-P70S6K | 595 | 1.33 (0.73–2.55) | 0.37 | 1.60 (1.00–2.59) | 0.052 | 467 | 9.4 (−24.6 to 62.1) | 0.63 | 35.8 (2.6–80.2) | 0.027 |
| Total phosphoprotein | 585 | | | 1.55 (0.56–4.77) | 0.42 | 566 | 18.1 (−8.8 to 54.8) | 0.21 | 28.5 (5.8–56.3) | 0.010 |
| p-mTOR/mTOR | 587 | 1.48 (0.67–3.65) | 0.36 | 1.75 (0.95–3.33) | 0.08 | 490 | 13.9 (−16.0 to 57.3) | 0.41 | 4.5 (−16.4 to 31.1) | 0.70 |
The exploratory stratified analysis results are shown in Supplementary Tables S7–S16. Sufficient PA was associated with total phosphoprotein among Black women only ($30.8\%$ increase, $$P \leq 0.019$$). When stratified by biological factors, the sufficient PA-phosphoprotein expression association was more apparent in women with overweight or normal weight (vs. those with obesity), being menopause (vs. premenopausal), without diabetes (vs. with diabetes), with higher (vs. lower) total energy intake, and having earlier [0/I; vs. more advanced (II–IV)] stage of breast cancer, smaller (vs. larger) tumors in size, ER+ (vs. ER−) tumors, and lymph node-negative (vs. lymph node-positive) disease.
In the analysis by PA intensity, sufficient versus no moderate PA (Table 4) was not significantly associated with the protein expression levels. However, sufficient versus no vigorous PA (Table 5) was associated with higher expression of mTOR (beta = 17.7; $95\%$ CI, 1.1–34.3, all women). There was a nonlinear association that insufficient versus no vigorous PA was associated with $57.0\%$ higher ($95\%$ CI, 4.1–148.7, gamma model) and sufficient versus no vigorous PA was associated $28.6\%$ higher ($95\%$ CI, 1.4–65.0, gamma model) total phosphoprotein expression.
## Discussion
In this study population of women with newly diagnosed breast cancer, sufficient PA that met the aerobic portion of the CDC guideline recommendation a year before the diagnosis was associated with higher levels of p-P70S6K and total phosphoprotein expression in breast tumor tissue. The associations were also observed for vigorous PA, but not for moderate PA among patients who did not report any vigorous PA.
PA involves a broad range of dimensions, including frequency, intensity, duration, purpose (long-term training vs. acute exercise), and type (aerobic, resistance, and flexibility). *In* general, PA reduces circulating growth factors and hormones that can trigger the mTOR signaling pathway. However, little is known about the extent to which those PA dimensions are related to the mTOR signaling pathway in human tumor tissue. Resistance training leads to increased mTOR activation, with mTOR being a key protein for increasing muscle size and strength (35–37). In rats, the activity of P70S6K in skeleton muscle increases 6 hours after exercise and is tightly associated with changes in muscle mass after 6 weeks of training [36]. A similar observation has also been made in humans, with p-P70S6K and p-mTOR expression increasing after resistance training in training-accustomed young healthy men [37]. No other type of PA (besides resistance training) has been reported to activate the mTOR signaling pathway in muscle. Interestingly, preclinical evidence suggests that mTOR activation by resistance PA is not through classical growth factor/PI3K/Akt signaling but likely through the activation of tuberous sclerosis complex 2, an mTOR regulator [38]. This mechanism may explain why PA reduces circulating growth factors, but in our study, PA was associated with higher mTOR activation in breast tumors. Because our study did not examine mTOR expression in skeleton muscle, a study examining mTOR in both skeletal and tumor tissue would provide important data on whether changes associated with PA for mTOR signaling in tumors are similar to those in muscle.
The findings of our study were inconsistent with those of preclinical studies focusing on breast cancer. Jiang and colleagues compared the effects of PA (low-intensity, consistent, self-determined wheel running that mimicked the national recommendation of 10,000 steps), restricted energy intake, and no PA on tumor incidence and mTOR pathway activation in tumors using a well-designed mouse model [19]. The study found that the mammary gland cancer incidence rate was lower in the PA group than in the sedentary group. In mammary tumors, levels of p-Akt (Ser473), p-P70S6K (Thr398), and p-4E-BP1 (Thr$\frac{37}{46}$) detected by Western blotting were downregulated in the PA group as well as in the mildly restricted energy group albeit to a lesser extent, both compared with sedentary controls. Those findings support the hypothesis that mTOR may be a mechanism through which PA is associated with reduced breast cancer risk. However, our study showed the opposite result, that is, PA was associated with higher mTOR signaling pathway activity in breast tumors. The mechanism underlying our finding is unclear, but our results suggested that how PA influences mTOR pathway activation in human breast cancer may be more complex than in preclinical models. For example, the animal model controlled energy intake in the PA group to be the same as the sedentary controls, resulting in a “negative” energy balance ($92\%$ of the controls) and weight reduction in the PA group [19]. In our study, however, we were unable to control participants’ energy intake, and women with a high level of PA might have higher energy intake than those with a lower level of PA. Also, the validity of energy intake assessment from a single food frequency questionnaire is limited. Although our analysis adjusting total energy intake using regression methods did not materially change our findings, the adjustment may not be sufficient to reduce the influence of energy intake on our findings. In addition, total energy intake in humans varies widely between individuals, and cellular amino acids, glucose, and ATP/AMP concentrations from food can promote mTOR activation. We also observed relatively low mTOR pathway activities among the non-PA group. The group had a sedentary lifestyle and a higher proportion of being obese, an indication of a positive energy balance. However, the observation is inconsistent with our previous finding that higher versus lower BMI was associated with higher mTOR activities in breast tumors [24]. In that study, however, PA levels were not considered. Thus, it is important to confirm whether PA has a dominant effect compared with BMI or total energy intake on the mTOR pathway activities, as our modeling results showed that these two variables had limited contribution to the main effect of PA. Also, studying how mTOR signaling factors, such as insulin-like growth factors (IGF), affect the pathway activities in tumors among patients with a sedentary versus active lifestyle is needed. Our findings highlight the challenge of comparing exposures related to energy intake and expenditure in animal models with human observational studies.
Epidemiologic evidence assessing PA levels and tumor mTOR and IGF pathway-related changes is not consistent across different cancer types and tissue markers. Among patients with colorectal cancer, there was no association between PA assessed after diagnosis and insulin receptor substrate 1, a mediator of insulin and IGF [39]. In a prospective study that assessed PA at baseline before cancer diagnosis, higher (vs. lower) levels of nonoccupational PA were associated with lower expression levels of proteins involved in the Warburg effect, that is, upregulation of aerobic glycolysis in cancer cells signaled by PI3K/Akt, in rectal tumors among women [40]. However, the association was opposite for colon cancer among women and rectal cancer among men. For breast cancer, in a preoperative exercise intervention trial to increase PA to 200 minutes of exercise per week, including 40 minutes of strength training and 180 minutes of moderate-intensity aerobic exercise, the intervention did not affect tumor gene expression [13]. However, in a randomized trial of 32 overweight/obese patients with breast cancer, expression of PI3Kinase genes, upstream signaling of mTOR, significantly increased after aerobic exercise plus caloric restriction [14]. More data are needed to conclude whether different types, doses, and schedules of PA can affect IGF or mTOR signaling in tumors.
Our results from adjacent-normal tissue and stratified analyses provide additional support for the main finding. Among the adjacent-normal tissue, we also observed higher mTOR pathway activities among participants with sufficient PA compared with no PA. However, the interpretation is limited by the fact that not all cases had analyzable adjacent-normal tissue despite our best effort to collect and assay these TMA cores. Normal breast tissue from healthy women without breast cancer would provide ideal samples for testing our hypothesis. The stratified analysis results are plausible because the PA-mTOR association was more apparent among participants with less influence from obesity and diabetes, factors that can affect the mTOR pathway activities. Also, previous research has shown that mTOR activities may be higher among patients with breast cancer with earlier (vs. more advanced) stage, lower (vs. higher) grade, smaller (vs. larger) tumors, and ER+ (vs. ER−) tumors [24]. These clinical and pathologic factors should be considered when studying and interpreting the hypothesized associations.
Other limitations of our study include potential reversed causality, recall error from self-report, and confounding. The information on prediagnostic PA was collected after the breast cancer diagnosis, and women may have changed their PA levels due to preclinical symptoms although the questionnaire inquired about PA levels in the year before cancer diagnosis. Our questionnaire may not have covered all types and modes of PA, such as resistance or muscle-strengthening exercise as well as nonrecreational activities, and the accuracy of self-report PA is likely affected by recall errors. A prospective study with objective measures of PA is warranted to confirm the findings. Our analysis according to the moderate or vigorous PA levels might have been influenced by each other because of the high degree of overlap among individuals engaging in both levels of PA. For example, among those who reported any amount of vigorous PA, $60.0\%$ reported moderate PA, and this can be a reason that we observed a higher tumor total phosphoprotein expression among the insufficient versus no vigorous PA group. Also, we are unable to adjust all possible confounders. For example, women with a higher PA level may be more engaged in screening behavior (i.e., receiving a mammogram) than women with lower PA levels or no PA. Adjusting for tumor size and stage in our analysis may have reduced confounding due to screening because screening is more likely to identify smaller tumors and earlier stages of disease compared with nonscreening-detected breast cancer. In addition, the number of proteins assessed in this study may not be large enough. It may be necessary to measure a larger number of proteins in assessing mTOR signaling pathway activity to further understand associations with PA.
It is important to note that patients with breast cancer and breast cancer survivors should engage in any PA and increase their activity levels [41]. Exercise during breast cancer treatment may provide benefits, such as improving quality of life and managing adverse treatment effects [42]. There is clear evidence that PA assessed before as well as after diagnosis is associated with reduced risk of mortality in women with long-term disease-free living or stable breast cancer [41, 43].
In conclusion, we found indications of higher mTOR signaling pathway activity in breast tumor tissue among women with sufficient PA levels compared with women with no PA. These findings are inconsistent with evidence from animal models indicating that exercise may be associated with lower mTOR signaling pathway activity in mammary cancer. Validation is needed in future research that uses a prospective study design and a more objective assessment of PA and considers the complexity of behavioral and biological variations of energy balance.
## Authors’ Disclosures
B. Qin reports grants from NIH during the conduct of the study. T. Khoury reports personal fees from Daiichi Sankyo, Inc. and AstraZeneca outside the submitted work. E.V. Bandera have served in Pfizer Advisory Board for Clinical Trials Diversity Initiative, which is unrelated to this work and does not represent a conflict of interest. No disclosures were reported by the other authors.
## Authors’ Contributions
T.-Y.D. Cheng: Conceptualization, resources, supervision, funding acquisition, investigation, writing-original draft, project administration, writing-review and editing. R. Zhang: Formal analysis, investigation, writing-review and editing. Z. Gong: Data curation, methodology, writing-review and editing. B. Qin: Data curation, writing-review and editing. R.A. Cannioto: Writing-review and editing. S. Datta: Formal analysis, methodology, writing-review and editing. W. Zhang: Conceptualization, writing-review and editing. A.R. Omilian: Investigation, writing-review and editing. S. Yao: Resources, investigation, writing-review and editing. T. Khoury: Investigation, writing-review and editing. C.-C. Hong: Resources, writing-review and editing. E.V. Bandera: Resources, data curation, investigation, writing-review and editing. C.B. Ambrosone: Conceptualization, resources, data curation, supervision, methodology, writing-review and editing.
## Financial Support
This work was supported by grants from the US National Institutes of Health grants P01 CA151135 (C.B. Ambrosone), R01 CA100598 (C.B. Ambrosone), R01 CA185623 (C-C. Hong and E.V. Bandera), P30 CA016056 (C.B. Ambrosone), P30 CA072720 (E.V. Bandera), K07 CA201334 (T-Y.D. Cheng), R37 CA248371 (T-Y.D. Cheng), US Army Medical Research and Materiel Command grant DAMD-17-01-1-0334 (C-C. Hong), and the Breast Cancer Research Foundation (C.B. Ambrosone and C-C. Hong).
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---
title: 'Basic psychological needs satisfaction of stroke patients: a qualitative study'
authors:
- Huiqi Lu
- Xiyi Tan
- Xiangmin Wang
- Qinger Lin
- Simin Huang
- Jinjun Li
- Hongzhen Zhou
journal: BMC Psychology
year: 2023
pmcid: PMC9990554
doi: 10.1186/s40359-023-01107-4
license: CC BY 4.0
---
# Basic psychological needs satisfaction of stroke patients: a qualitative study
## Abstract
### Background
Previous studies have shown that the satisfaction of basic psychological needs is related to psychological well-being. Improving satisfaction will increase personal well-being, promote positive health outcomes, and improve disease recovery. However, no research has focused on the basic psychological needs of stroke patients. Therefore, this study aims to determine the basic psychological needs experience, satisfaction, and its influencing factors of stroke patients.
### Methods
12 males and 6 females in the non-acute phase with stroke were recruited in the Department of Neurology, Nanfang Hospital. The individual, semi-structured interviews were conducted in a separate room. The data were imported to Nvivo 12 and analyzed using the directed content analysis approach.
### Results
Three main themes consisting of 9 sub-themes were derived from the analysis. These three main themes focused on the needs for autonomy, competence, and relatedness of stroke patients.
### Conclusion
Participants have different degrees of satisfaction of their basic psychological needs, which may be related to their family environment, work environment, stroke symptoms, or other factors. Stroke symptoms can significantly reduce the patients’ needs for autonomy and competence. However, the stroke seems to increase the patients’ satisfaction of the need for relatedness.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40359-023-01107-4.
## Background
Stroke is the second leading cause of death and disability worldwide, especially in low- and middle-income countries [1], with the characteristics of high morbidity, mortality, disability rate, and many complications. With the highest disability-adjusted life-years (DALYs) of any other disease in China [2], the disability of stroke mainly manifests in the changes of appearance caused by facial paralysis, dyskinesia, dysphagia, speech disorder, and visual and hearing impairment. Disability and dysfunction seriously affect the ability of patients to take care of themselves and social interaction, which increases the burden on their families, and leads patients to negative emotions. Due to the psychological burden and pathophysiological factors of stroke, approximately one-third of patients suffer from depression after stroke [3]. And patients with post-stroke depression (PSD) have poor treatment adherence, prognosis, and low quality of life, compared to patients without PSD. Therefore, it’s necessary to carry out psychological interventions according to patients’ psychological burdens and needs.
Basic psychological needs theory (BPNT), developed by American psychologists Deci and Ryan, is the core theory of self-determination theory (SDT), including three parts: the need for autonomy, competence, and relatedness. Deci and Ryan point out that there are specifiable psychological and social nutrients that, when satisfied within the interpersonal and cultural contexts of an individual’s development, can facilitate one’s growth, integrity, and well-being. And they refer to these necessary satisfactions for personality and cognitive growth as basic psychological needs (BPN) [4]. Autonomy refers to feeling willingness and volition for one’s behaviors. Competence refers to feeling effective in one’s interactions with the social environment—that is, experiencing opportunities and supports for the exercise, expansion, and expression of one’s capacities and talents. Relatedness refers to both experiencing others as responsive and sensitive and being able to be responsive and sensitive to them—that is, feeling connected and involved with others and having a sense of belonging [5].
Based on empirical studies, the satisfaction of basic psychological needs will increase an individual’s well-being, promote positive health outcomes and facilitate the rehabilitation of diseases. Research on the smoker’s health project has shown that the satisfaction of BPN could facilitate long-term tobacco abstinence [6]. A diabetes management plan based on BPNT was proven effective in lowering the patient’s glucose level [7]. However, few studies focused on the BPN of stroke patients, so there is a gap in knowledge about what they need and how to satisfy their needs. Therefore, the current study aimed to determine the performance, satisfaction, and influencing factors of BPN in stroke patients.
## Study design and setting
We conducted a qualitative study using individual, semi-structured interviews to examine participants’ perceived needs satisfaction after stroke. In the interview, we could explore the participants’ experiences with autonomy, competence, and relatedness to create a comprehensive, deep understanding of the influencing factors that are relevant to the satisfaction of BPN. The interviews were conducted in a separate room in the Department of Neurology, Nanfang Hospital, when participants were free from any treatment and therapy.
## Participants
In this study, the selection criteria of the participants were the following: [1] diagnosed with stroke, regardless of the type; [2] confirmed to be in the non-acute phase by the doctor; and [3] without speech disorder. The researcher checked the information and files of potential participants and asked if they would like to join the study.
## Ethics
This study was reviewed and approved by the Medical Ethics Committee of Nanfang Hospital of Southern Medical University. All participants signed the informed consent after receiving the purpose and procedures of the study, especially the audio and handwriting recording throughout the interview.
## Data collection
The individual, semi-structured interviews were conducted by the first author, who had been trained for the qualitative interview. Before the interview, the interviewer would create a private, respectful, and relaxing atmosphere to ensure that the participant felt cared for and would like to speak freely without any hesitation, to improve the quality of the interview. An interview guide was developed to conduct the interview based on BPNT and previous researches. It included six main questions. [ 1] Talk about your rehabilitation plan. Do you find it challenging for you? Are you willing to persist in completing your rehabilitation training? What supported you to persist in completing rehabilitation training? [ 2] Did you do any work before you got sick? ( If yes) Would you consider returning to work later? What makes you feel you can/can’t continue working? [ 3] Talk about your hobbies and have you continued to carry out your hobbies after stroke? [ 4] Talk about your autonomous choices during treatment, rehabilitation, and daily life. [ 5] How well do you get along with the people around you after stroke? Do you feel that the way you get along with the people around you is different from before you got stroke? How do you feel about this change? [ 6] what do you think is the most significant impact that the stroke has on you? During the interview, we wished to explore the basic psychological needs of patients after stroke. For example, their autonomous choices in various aspects of their lives, and changes in their hobbies that were considered autonomous behaviors. And if there were any changes in the patient’s abilities in daily life, work, treatment, and rehabilitation. Finally, we wanted to talk about the relationship between patients and the people around them. These phenomena that we pay attention to also became the units of analysis in the next step of data analysis. The interviewer used a mobile phone to record the entire interview and noted some key phrases and participants’ facial expressions. Both the audio records and written notes were informed to the participants before the interview. Data collection and data analysis were carried out simultaneously. Participant recruitment was stopped when the codes were repeated and no new themes appeared in the data analysis [8]. For example, the code “competence” refers to the ability that the participant demonstrated in a particular task or job. And themes are identified as the manifestation, satisfaction, or frustration of a psychological need of the participants in a particular environment, for example, the satisfaction of the need for autonomy in daily life. In other words, when exploring the basic psychological needs of patients, no new manifestations of BPN or new needs emerged, and the research sample reached saturation.
## Data analysis
After each interview, 2 researchers (LH and TX) listened to the audio record, checked the handwritten note, and transcribed them into written transcripts within 12 h. Then 2 researchers checked their transcripts, and if any discrepancies were found, they would listen to the audio record again until they reached a consensus. All transcripts were imported to Nvivo 12, a software used to analyze qualitative data. Two researchers (WX and LQ), who had an experience in qualitative research and learned about BPNT, participated in the data analysis. The directed content analysis approach was used to analyze data, which is appropriate where an existing theory can guide research questions and initial theory-based themes and can be supported, challenged, or extended [9]. This analysis approach has the following steps. [ 1] Identify key concepts as initial coding framework based on existing theory and published researches. [ 2] Carefully read and record all transcripts in detail and encode the them; [3] Induce and abstract all codes, and classify them according to the initial coding framework; [4] Codes that cannot be classified using the initial coding framework will be given a new theme. In this study, WX and LQ started with the first three transcripts. They read the transcripts carefully, discussed the initial codes, and determined the initial coding framework based on the BPNT. That is, the need for autonomy, the need for competence, and the need for relatedness in BPNT were identified as three main themes in the analysis. The units of analysis have been described above, and among them, the three basic psychological needs were the most basic and critical units of analysis. All transcripts were divided equally to two researchers to be analyzed and coded. All codes were incorporated into three main themes in the initial coding framework, and new main themes can be determined if needed. And the codes were induced and abstracted into themes. As the coding level progressed, three levels of themes emerged and their representative quotes were determined. Challenges and disagreements in the structures and meanings associated with the codebook, were resolved through consensus in regular group meetings including all co-authors. Leading to the final directed content analysis presented in the results.
## Results
Eighteen participants participated in the semi-structured interviews, including 12 males ($66.67\%$) and 6 females ($33.33\%$) (Table 1). The mean age of participants was 57.56 ± 10.67 years. Almost all participants ($88.89\%$) were diagnosed with ischemic stroke. And they had a median course of stroke of 5 months (range 0.5 to 48 months). Only 5 participants ($27.78\%$) were still working after stroke. And almost all participants ($94.44\%$) lived with their family. The median time duration of the interviews is 14 min (rage 10 to 55 min). All participants were named with English letters as their code names to protect their privacy.
Table 1Participant characteristicsParticipantAge (years)GenderCourse of stroke (months)Type of strokeOccupationResidential statusA45Male5.0aWorkingWith familyB54Male1.0aNot workingWith familyC80Male1.0aNot workingWith familyD62Female6.0aWorkingAloneE68Female18.0bNot workingWith familyF43Female0.5aNot workingWith familyG59Male1.0aNot workingWith familyH64Female1.0aWorkingWith familyI58Female9.0aWorkingWith familyJ65Male11.0aNot workingWith familyK34Male10.0aNot workingWith familyL65Male48.0aNot workingWith familyM48Male10.0aNot workingWith familyN57Male4.0aWorkingWith familyO55Female5.0aNot workingWith familyP61Male10.0bNot workingWith familyQ52Male2.0aNot workingWith familyR66Male5.0aNot workingWith familya refers to ‘Cerebral ischemia’, b refers to ‘Intracerebral hemorrhage’.
Based on basic psychological needs theory, participants talked about their needs for autonomy, competence, and relatedness during treatment and rehabilitation after stroke. According to the interview guide, the information in Question 1 and 2 are particularly associated with the need for competence. And the information in Question 3 presents 3 basic psychological needs of stroke patients. The information obtained from question 4 mainly describes the patients’ needs for autonomy. And the information in Question 5 focuses primarily on their needs for relatedness. As an open question, the information obtained from question 6 is considered to be associated with 3 basic psychological needs. As components of BPN, these three needs were identified as the main themes, according to directed content analysis. And data analysis yielded 115 codes dispersed in 31 sub-subthemes belonging to 9 sub-themes (Table 2). There were 10 sub-subthemes in main theme 1, 13 in main theme 2, and 8 in main theme 3. Since each sub-subtheme contains fewer quotes and implications, the Results and Discussion sections would mainly revolve around the sub-themes with more quotes and implications. All themes and their corresponding quotes are presented in the appendix. The result is presented with their quotes along with code name, gender, age, and course of stroke.
Table 2Main themes and sub-themesMain themeSub-themenumber of narrators (%)the need for autonomythe need for autonomy in daily life11 (61.11)the need for autonomy during treatment and rehabilitation11 (61.11)making lifestyle changes consciously10 (55.56)the need for competencethe impact of stroke on the ability of daily living activities11 (61.11)the impact of stroke on the ability to work10 (55.56)the need for competence during treatment and rehabilitation11 (61.11)the need for relatednessthe need for relatedness during hospitalization7 (38.89)the need for relatedness in the rehabilitation stage9 (50.00)the impact of stroke on relationships6 (33.33)
## Main theme 1: the need for autonomy
The participants’ need for autonomy is reflected in various aspects, such as work, medical treatment, and activities in daily life. Although they have a mean age of 57.56 years, and most have retired or left the working environment, they still have tasks that can be called work in their daily life, like, doing housework and raising grandchildren. After abstracting and summarizing, this main theme includes three sub-themes, [1] the need for autonomy in daily life, [2] the need for autonomy during treatment and rehabilitation, and [3] making lifestyle changes consciously. There are 10 sub-subthemes included which are described in each sub-theme.
## Sub-subthemes 1.1.1 at a high degree of autonomy satisfaction in daily life
Most participants said that there were few things they had to do in daily life, and most of the things they did were out of their own will, that is to say, that participants were at a high degree of autonomy need satisfaction in daily life.
When it comes to food, my son said: “Mom, you can buy whatever you want. It’s not that we don’t have money, so don’t worry about it.” … And I can do what I want to do. Even though my son told me not to (do something), as long as I want to or like to, then I can do it in the end. ( H, female, 64, a month after stroke diagnosis)
## Sub-subthemes 1.1.2 an increased sense of autonomy in housework after stroke
After the stroke, most things changed in the participants’ life. A participant who had done housework for many years felt so tired of the overwhelming housework before stroke. And after stroke, she felt an increased sense of autonomy in housework, which made her feel relaxed and happy.
Before I got sick, I had to do housework even if I didn’t want to. When I saw things dirty and thrown everywhere, I could do nothing but clean them up…However, it comes different now. They do the housework, and I could do something if I want to and vice versa. ( D, female, 62, 6 months after stroke diagnosis)
## Sub-subthemes 1.1.3 do not feeling free during hospitalization
However, the disease also has adverse effects on the participants’ need for autonomy. Another participant felt sad about this and was reluctant to talk more about autonomy.
This… (a long silence) is that I don’t feel free and can’t do many things I want to do. ( Researcher: “what do you want to do?“ The participant looked left and right repeatedly and did not answer the question.) ( G, male, 59, a month after stroke diagnosis).
## Sub-subthemes 1.1.4 the impact of stroke on interests
When it comes to autonomy, interest is seen as a primitive form of intrinsic motivation, which promotes the satisfaction of the need for autonomy. Different people have different interests. Participants who enjoy high-intensity activities such as running and dancing said they didn’t dare to continue doing these. But as for reading books or watching TY, stroke has little impact on these activities.
I usually just play TikTok and watch TV. Because I feel dizzy sometimes after stroke, I don’t dare to dance, which I used to. ( E, female, 68, 18 months after stroke diagnosis)
## Sub-subthemes 1.2.1 the sense of autonomy during treatment and rehabilitation
During treatment and rehabilitation, participants also have a sense of autonomy in making medical decisions. Especially in physiotherapy and rehabilitation training, they have more choices, which satisfies the need for autonomy.
Just like doing this (medium frequency physiotherapy), I’ll tell you where I feel uncomfortable and where I decide to do it. ( D, female, 62, 6 months after stroke diagnosis)
## Sub-subthemes 1.2.2 willing to follow the advice of a medical professional
However, it needs to be clear that the term autonomy does not refer to independence. People can do something independently and act volitionally. Yet, people can also depend on others as they would like to, which represents autonomous dependence. It means that participants willingly chose to follow medical professionals’ advice, which is also a manifestation of a sense of autonomy.
I have a good friend who is a doctor and a professor. I would always listen to his opinions. For example, I have been taking this medicine for a long time, which he introduced me to take. ( C, male, 80, a month after stroke diagnosis)
## Sub-subthemes 1.2.3 less self-determination due to the lack of knowledge
Unfortunately, there are few choices for patients to make in the medical decision. Perhaps one of the reasons is that they lack enough knowledge to make autonomous decisions.
I have been in the hospital for many months, and now I just want to go home, but I am not sure about that. Because I need to do rehabilitation training, staying in the hospital will have a better effect. However, I would feel better at home so that I may recover well. Therefore, I don’t know how to make a decision. ( R, male, 66, 5 months after stroke diagnosis)
## Sub-theme 1.3: making lifestyle changes consciously
In the interviews, we found an interesting phenomenon that almost all participants mentioned that they did think that they had engaging in some behaviors that are known as risk factors for stroke. Not only that, but some also said that they would change or had changed these unhealthy lifestyles consciously.
## Sub-subthemes 1.3.1 correctness of unhealthy behavior in daily life
A participant who had drunk and smoked for many years said that, although many people told him it was not good for his health in these years, he never thought about making changes until this stroke.
I have been smoking and drinking since I was 20 years old. Now I would not smoke and drink anymore since I got stroke. I always told them to stop showing me alcohol and cigarettes. ( G, male, 59, a month after stroke diagnosis) One participant noted that her personality might have something to do with her stroke.
I feel like I’m being too impatient. I would get everything done at once and not stop until it was finished. ( D, female, 62, 6 months)
## Sub-subthemes 1.3.2 correctness of unhealthy eating habits
Besides the unhealthy behavior in daily life, many participants had unhealthy eating habits, such as high-fat and high-salt diets.
I eat less than before, especially rice, but more vegetables. And I am on a low-fat and low-salt diet. ( P, male, 61, 10 months after stroke diagnosis)
## Sub-subthemes 1.3.3 development of an exercising habit
Due to the rehabilitation training, some participants developed a habit of exercise. Some would insist on doing rehabilitation training learned from the hospital, and some would go for general sports like walking and running.
I emphasize exercising now…Every day I would go for a walk after dinner and do some exercises or something else. ( L, male, 65, 48 months after stroke diagnosis)
## Main theme 2: the need for competence
This theme focused on the impact of stroke on participants’ ability and the need for competence. The stroke can negatively affect the patient’s physical function and impair the patient’s ability to perform various activities, which reduces the satisfaction of the need for competence. This main theme includes three sub-themes, [1] the impact of stroke on the ability of daily living activities, [2] the impact of stroke on the ability to work, and [3] the need for competence during treatment and rehabilitation. There are 13 sub-subthemes included which are described in each sub-theme.
## Sub-subthemes 2.1.1 difficulty in performing some daily activities
Most participants said that stroke did produce physical symptoms that could not be ignored and impaired their daily activities, such as dizziness and numbness of limbs. Therefore, they had difficulty in performing some daily activities.
I used to drive, but now I don’t dare. I tried for one time, but I felt that my reaction was not so responsive, such as when turning and decelerating. ( M, male, 48, 10 months after stroke diagnosis)
## Sub-subthemes 2.1.2 difficulty in doing housework
Participants even had difficulty in doing housework due to the reduced physical mobility.
I didn’t do any heavy work either, just do some sanitation at home… My left hand is not so neat, but my right hand can work. ( O, female, 55, 5 months)
## Sub-subthemes 2.1.3 difficulty in caring for the family
As mentioned above, some participants usually did housework and took care of family at home, however, it was difficult for them to continue after stroke.
I have been caring and cooking for my son because he is not married yet and no one can do this, which is very tiring. Now it’s such a hassle since I got sick, I can’t take care of him anymore. ( C, male, 80, a month after stroke diagnosis)
## Sub-subthemes 2.1.4 part of the work is undertaken by others
Even so, some participants still continued doing the same things they did before got stroke while recovering at home. Certainly, their family members would help or undertake part of the work.
I used to grow some vegetables at home. But now I cannot do much hard work, so my husband would do most of the work and I just have to do the easy part. ( I, female, 58, 9 months after stroke diagnosis)
## Sub-subthemes 2.1.5 recuperating at home and no need to work
One participant said she didn’t need to do anything at home, even though it had been a long time since she was diagnosed with stroke and she was capable to do something. Thanks to her family, she is indeed felt relieved from the hectic housework finally.
I used to do housework at home and take care of my grandchildren… And I stopped working since the cerebral hemorrhage last year. ( E, female, 68, 18 months after stroke diagnosis)
## Sub-theme 2.2: the impact of stroke on the ability to work
This sub-theme focused on work, or job, which is paid and in a specific environment. Because the medical treatment and rehabilitation would last a long time, most participants quit their jobs or asked for some days off.
## Sub-subthemes 2.2.1 return to work
Coincidentally, two participants owned a store and worked there. They all said they would go back to work in the store after being discharged from the hospital. One participant was confident and thought she was capable of continuing to work.
I will definitely return to work after I am discharged from the hospital. Because this disease does not affect me, I don’t need to do heavy work in the store. Ah, I work there because it’s better to have someone in the store to supervise the employees. It’s just that. It’s nothing. ( H, female, 64, a month after stroke diagnosis)
## Sub-subthemes 2.2.2 reducing intensity and content of the work
However, the other participant said she would reduce the intensity and content of the work due to her physical condition after stroke.
…I can only walk around, do what I can, and instruct employees to do what I can’t do. Because of dizziness, I dare not walk for too long. Maybe I should just sit there and look at the monitoring system. ( D, female, 62, 6 months after stroke diagnosis)
## Sub-subthemes 2.2.3 planning to do something new after being discharged
Due to retirement age, one participant planned to retire and do something new after being discharged from the hospital.
…Now I plan to renovate the house, um, the house I am living in. ( G, male, 59, a month after stroke diagnosis)
## Sub-subthemes 2.2.4 not able to return to work yet
However, some participants failed to return to work, even though they had been in rehabilitation for quite some time.
I don’t know how to talk about it. Anyway, I haven’t gone to work. And I just do housework and take care of my children at home. ( K, male, 34, 10 months after stroke diagnosis)
## Sub-theme 2.3: the need for competence during treatment and rehabilitation
Stroke can be a considerable challenge and change many things in participants’ life. Changes in participants’ abilities, environment, and what they need to do will affect the satisfaction of the need for competence.
## Sub-subthemes 2.3.1 knowledge of treatment and rehabilitation
In sub-theme 1.2, it was found that lack of knowledge would impair the satisfaction of the need for autonomy, and so did it in this theme. Inadequate knowledge reduces their ability to deal with events and circumstances, making them appear somewhat helpless and have no idea what to do and how to do it during treatment and rehabilitation [10]. Some participants didn’t know how to adjust their daily diet and whether they should take medicines for a long time. And they didn’t know where and how to do rehabilitation training.
I don’t know. ( laughs) I don’t understand those things. …( hesitation) Then where can I go to do the rehabilitation training? … Oh… Is the community hospital where can do it? OK, let me ask. I often feel pain, and it’s not good, so I’ll ask the workers there. ( I, female, 58, 9 months) Some had a misunderstanding about rehabilitation, which may slow down the recovery process.
I guess that the effect of rehabilitation training is not significant, and I feel much better now, so I didn’t ask for the training. ( Q, male, 52, 2 months after stroke diagnosis) Fortunately, part of the participants could obtain relevant knowledge from the Internet.
Just when I was sleeping, I felt numb in my feet, which I had never met before. But I know that, um, sometimes I see videos in TikTok saying that because of high blood pressure, the numbness in my feet may imply a stroke. ( H, female, 64, a month after stroke diagnosis)
## Sub-subthemes 2.3.2 rehabilitation training is within the ability
Most participants indicated that the rehabilitation training was within their ability, so they could keep doing it.
The rehabilitation training is not difficult, and I can do it. ( K, male, 34, 10 months after stroke diagnosis)
## Sub-subthemes 2.3.3 creating his own rehabilitation plan
And a participant was so proud to tell the researcher that he had created his own rehabilitation training plan.
Now I do rehabilitation exercises several times a day, every day, which was designed by ourselves. ( laughs) (N, male, 57, 4 months after stroke diagnosis).
## Sub-subthemes 2.3.4 rehabilitation training ended for various reasons
However, some participants ended their rehabilitation for various reasons. The main reason was that they did not get an obvious effect in rehabilitation training. He may have no idea that rehabilitation training needs to last for a long time to get obvious results.
I had been doing rehabilitation training for about half a year. Because I felt that the effect was not obvious, I stopped doing it. ( E, female, 68, 18 months after stroke diagnosis) The other reason was that their poor physical condition resulted in poor mobility does not allow further training.
Running sometimes makes me feel comfortable, um, sometimes it’s not. Because I have arthritis, sometimes running makes me feel painful, so I stop it. ( I, female, 58, 9 months after stroke diagnosis)
## Main theme 3: the need for relatedness
The theme focused on the impact of stroke on participants’ relationships and their need for relatedness during treatment and rehabilitation. Stroke might not only change the relationship with family and friends, but it could also develop new relationships between participants, medical workers, and wardmates. This main theme includes three sub-themes, [1] the need for relatedness during hospitalization, [2] the need for relatedness in the rehabilitation stage, and [3] the impact of stroke on relationships. There are 8 sub-subthemes included which are described in each sub-theme.
## Sub-subthemes 3.1.1 longing for the company of family during hospitalization
Almost all of the participants indicated they were very eager for the company of their family during hospitalization.
My sons and daughters-in-law all have busy jobs, so they don’t have time to come here and take care of me. And I don’t think I need someone to care for. However, sometimes when they call me, tears run down my face. ( Laughs) Actually, I want someone to be here with me. ( E, female, 68, 18 months after stroke diagnosis) In addition, a participant said that he felt happy when his family visited him and believed that being with his family would help him recover.
I’ve recovered well now, and so do my language function. Since I got sick, I have had difficulty in speaking. For example, sometimes I didn’t know how to say something. When my grandchildren came to visit me, I felt so happy and could call their names and talk to them about anything. But after that, I couldn’t speak well again. ( R, male, 66, 5 months after stroke diagnosis)
## Sub-subthemes 3.1.2 relatives and friends were unable to visit the patient in hospital
Although it may be difficult for relatives and friends to visit patients in hospitals, they often contact patients by phone or WeChat.
Last night my worker sent a WeChat message asking how I was in the hospital. I said it was all good. ( Laughs)… My son often calls me, asks if the food is good, and tells his wife to buy some delicious food for me. ( H, female, 64, a month after stroke diagnosis) However, some participants chose not to inform relatives and friends of the illness, so as not to increase their burden.
I haven’t officially told my friends about my illness, but I will definitely tell them after I am discharged…And I also wanted to tell my wife after I was cured and discharged, because I was afraid that if I told her, it would make her feel stressed and sad. ( C, male, 80, a month after stroke diagnosis)
## Sub-subthemes 3.1.3 geting along well with healthcare workers and other patients
Although participants didn’t get enough company from family and friends, they still had healthcare workers and other patients with whom they got along well. Most participants felt that they got enough care from healthcare workers.
I have a lot of confidence in you all. You are all professional and care much about (me), so I trust you. In a word, you doctors and nurses are all very responsible in all aspects. ( A, male, 45, 5 months after stroke diagnosis) But he didn’t talk too much with other patients.
When it comes to other patients in the ward, I don’t talk to them too much. Because I don’t know exactly how they are, I dare not laugh and act too happy if they feel so bad. ( A, male, 45, 5 months after stroke diagnosis)
## Sub-subthemes 3.2.1 rehabilitation training accompanied by family members
After being discharged from the hospital, participants moved into a rehabilitation stage. Most participants stay at home or go to specific institutions for rehabilitation training at this stage. And the training takes a long time. Therefore, most family members would accompany the participants to do the exercise or training.
I do rehabilitation exercises every day, (laughs), and my wife also exercises with me at least three times a day. After the exercises, she also massages my hands and feet. ( N, male, 57, 4 months after stroke diagnosis)
## Sub-subthemes 3.2.2 receiving help from other patients
Those who go to specific institutions for rehabilitation training can often meet other patients and get their help.
On the past day, the older adults in the rehabilitation institution said that the training was helpful, so I went there and tried. And I also bought a physiotherapy device they recommended, and I use it every day. ( E, female, 68, 18 months after stroke diagnosis)
## Sub-subthemes 3.2.3 socializing with friends
As participants’ physical function recovered, their lives went back on track. They usually socialized with friends in their spare time, such as playing cards, traveling, etc.
I live in the countryside and don’t have much to do. I usually play cards and chat with the people next door, which makes me quite happy. ( O, female, 55, 5 months after stroke diagnosis)
## Sub-subthemes 3.3.1 feeling like family cares more about him
Through interviews, almost all the participants said their relationships with their families improved after stroke. Because of the illness, they felt that family cared much more about them than before, and so did friends.
The way that my family treats me must be different. For example, the time when they come home has been changed. In the past, when they came back, I had already been asleep…And nowadays, when I felt a little bit not good, they became so nervous and wanted to send me to the hospital. ( N, male, 57, 4 months after stroke diagnosis)
## Sub-subthemes 3.3.2 the response of company staff to the illness
However, the relationship between participants, and their workmates or boss is much more complicated. The response of company staff to his illness can be very different. A participant said he had a considerate boss, so he successfully got sick leave.
My boss was so concerned about me and he just gave me a sick leave. And he told me not to return to work until I recovered. ( A, male, 45, 5 months after stroke diagnosis) But unfortunately, another participant lost his job. He appeared a little sad and angry.
I worked before but would not return to work after being discharged. They said I didn’t need to go back, and how do I know why! Definitely, they thought I needed to have a long break! ( B, male, 54, a month after stroke diagnosis)
## Discussion
This study was the first qualitative study that examined the basic psychological need of stroke patients and analyzed the influencing factors and satisfaction of the needs. Based on BPNT, the initial coding framework was determined to be 3 themes that were, [1] the need for autonomy, [2] the need for competence, and [3] the need for relatedness. And this framework was also validated by the analysis result, because no code could not be classified into these three themes. Below, we reflect on each of these main themes.
## The need for autonomy in daily life
The majority of stroke patients are older adults. And in this study, $44.44\%$ of the participants are older than 60. Most of them did not participate in work or had retired, so they had much leisure time in their daily life to do what they liked. In addition, they had stable and sufficient financial resources, such as pensions. Therefore, when being asked, most of the participants said without hesitation that they could do what they wanted and not do what they didn’t want to do in daily life, which means that they had a high satisfaction degree of the need for autonomy in their daily life [11]. Of course, there are exceptions. In most Chinese families, due to being idle at home, the older adults need to undertake the tasks of doing housework and taking care of grandchildren to reduce the burden on their children [12], which may also be a heavy burden and occupying almost all the time of the older adults, weakening their sense of autonomy. It was found in the interviews that the sense of autonomy has a greater relationship with their home environment and relationship with their families. They get more negative emotions when they are overburdened with tasks and don’t get any understanding or help from their families, which may usually lead to depression [13]. However, things changed after stroke. Due to recuperating from the disease, the patients are relieved of all the things they had to do in the past and have more time to make their own arrangements. Therefore, they have a higher satisfaction degree of the need for autonomy after stroke. But beyond that, the disease may impair patients’ physical functions, reducing their sense of autonomy [14]. Hobbies are purely enjoyable activities that manifest a sense of autonomy and can also be affected by the disease [4]. For example, patients can’t run or dance because of limb numbness or decreased muscle tone, and they can’t watch TV or read a book due to dizziness or impaired visual function. Especially during hospitalization, patients can feel trapped and lose freedom. Decreased satisfaction of the need for autonomy can cause negative emotions, which are detrimental to mental health and recovery from stroke [15].
## The need for autonomy during treatment and rehabilitation
Regarding treatment and rehabilitation, especially during hospitalization, obedience and dependence are the first things that come to mind, instead of autonomy. It is true that the main things for stroke patients to do during hospitalization are to follow the doctor’s instructions for infusion, taking medicine, doing examinations, and doing rehabilitation. It seems that there is no room for patients to make their own decisions. However, as we have emphasized, autonomy does not equal independence [4]. There may be a patient whose self-care ability has decreased due to a stroke and needs the care of others temporarily. If the caregiver is a close and trusted person to this patient, he can rely on the caregiver, which is also a manifestation of autonomy [16]. Most participants said that all they had to do in the hospital was listen to the doctor and follow the treatment plan, because healthcare workers were professional in helping patients recover from the disease. At the same time, hospitals are paying more attention to shared decision-making [17]. When there are different treatment plans, doctors will invite patients and their families to participate in medical decision-making, which can meet the needs of patients’ autonomy [18]. The essential thing in shared decision-making is that the details, advantages, and disadvantages of different treatment plans should be correctly and unreservedly informed to patients, so that they have a comprehensive understanding of the plans, which helps them make a decision.
## Making lifestyle changes consciously
As we all know, it is so difficult to change a habit or a lifestyle. A study showed that initiating and maintaining lifestyle changes is a long and complex process [19]. It is not so difficult to initiate the lifestyle change, and controlled motivations can play a role in the initiation. For example, patients are told that this lifestyle is unhealthy and needs to be changed. On the contrary, maintenance of lifestyle changes is the harder and more critical part [20]. In this stage, controlled motivation is not enough, and autonomous motivation is required. In other words, lifestyle changes need patients to have a greater sense of autonomy. It was so nice to see that in the interviews, most of the participants shortly after the onset of stroke were able to understand and accept that they needed to change their unhealthy lifestyles and confidently reassure the researchers that they would change in the future. Some participants with a longer course of disease said they had successfully changed their previous unhealthy lifestyles and could consciously continue to maintain them. And what can we do to help them initiate and maintain lifestyle changes? The first thing is to provide correct, detailed, and accessible health education to patients, so that they realize that they have an unhealthy lifestyle [21]. For example, hospitals and communities regularly conduct health classes, produce brochures, and disseminate health knowledge through the Internet. And the most important is how to help them maintain their lifestyle changes. Web applications are widely used today and can also be used for that. For example, patients can set a goal in advance, then self-check or be checked every day to determine whether the goal has been met. Rewards can also be incorporated into the application to provide some motivation. An experiment showed that the application offered instructions for a healthy lifestyle effectively promoted a lifestyle change [22].
## The impact of stroke on the ability of daily living activities
Unlike the need for autonomy, the need for competence is undermined by stroke. Patients experience different degrees of functional impairment after stroke. According to surveys, about $70\%$ of patients have dysfunction and disability, which seriously affects their activities in daily life, including Activities of Daily Living (ADLs) and Instrumental Activities of Daily Living (IADLs) [23]. ADLs refers to daily household-based activities such as eating, going to the toilet, and dressing. IADLs refers to driving and taking transportation. If these self-care activities, which are regarded as easy things, cannot be completed, the patient may feel that he has lost control of his own life, which may lead to negative emotions, that may be closely related to post-stroke depression [3]. rehabilitation medicine is booming now, and different types of rehabilitation training are provided to patients with various functional disorders. Occupational therapy, for example, aims to improve the patient’s ability to perform activities of daily living, so that the patient can complete self-care activities independently without relying on others [24]. Similar to autonomous dependence mentioned above, is there independent in-competence? That is, whether the patients would choose to demonstrate ineffective interactions with the environment. However, this study gave a negative answer. Unlike being unable to take care of themselves, patients seem to be a little anxious and guilty when they can no longer take care of their family members. Influenced by the unique traditional culture, most families in China are stem families. That is, parents live with a pair of married children and usually grandchildren [25]. In such families, the older adults are usually responsible for housework and caring for the family, especially grandchildren. They are more inclined to make contributions to the family in order to reduce the burden on their family members, which is contrary to “independent in-competence”. Under such circumstances, patients are transformed from caregivers to care recipients, and their work is taken on by family members, which reduces the sense of competence they feel in life and impair the satisfaction of need for competence.
## The impact of stroke on the ability to work
A study showed that the satisfaction of the need for competence in the older adults has the lowest contribution to basic psychological needs [26], which may be related to the fact that the older adults do not have many activities in their daily lives that demonstrate capacity and talent. For young stroke patients who are still engaged in work, the stroke significantly affects their need for competence. Almost all patients suspend work or even quit their jobs after stroke. And the time to return to work varies, mainly depending on the patient’s recovery and prognosis [27]. Although most young patients recover quickly from stroke symptoms, other functional impairments, such as cognitive impairment, may persist and impede return to work [28]. And completing the work tasks is the best to meet the need for competence. Most patients look forward to returning to work rather than doing nothing at home. They will think that if they are not involved in work and idle at home, they will look useless and unhelpful. Therefore, many patients said they knew their work ability and energy were not as good as before the stroke, but they still hoped to return to work, even if the work intensity and content were reduced. Moreover, returning to work also appears to enhance patients’ self-perceived participation and autonomy [29]. As a result, it is important to provide work-oriented information and rehabilitation support for working-age patients.
## The need for competence during treatment and rehabilitation
As mentioned above, the stroke changes the patient’s life, and their daily activities and work are forced to stop. However, stroke also brings new challenges for patients, that is, insisting on rehabilitation training and learning disease-related knowledge to help recovery and prevent a recurrence. Patients with functional impairment may be hospitalized in a specific rehabilitation hospital, go to a rehabilitation institution every day or a few days a week, or perform rehabilitation training at home according to the doctor’s guidance [30]. Either option can be challenging for patients. Nonetheless, patients indicated that rehabilitation training was within their ability, regardless of the rehabilitation method chosen. Even a patient who did exercises at home proudly said that she designed the rehabilitation training program by herself, and could insist on completing it every day, which made her experience a strong sense of competence and greatly satisfied her need for competence. However, not all patients can adhere to rehabilitation training. The financial burden and poor physical condition force patients to stop rehabilitation training [31, 32], frustrating the rehabilitation training is useless or not so effective, so they stop training [32]. When it comes to disease-related knowledge, this is a more complex issue. Thanks to the Internet, patients can easily obtain a variety of information, allowing them to have a certain degree of understanding and mastery of their physical conditions, and they will feel that they are effectively dealing with disease-related matters. However, not all information on the *Internet is* correct, and it is difficult for patients to judge the quality of online information [33, 34]. Likewise, hospitals often produce brochures, web articles, and videos to educate patients about stroke, medication, recovery, etc. [ 35] But it is difficult to guarantee that patients can actually acquire knowledge from it. Therefore, it is essential to create an environment where patients can easily acquire the correct knowledge, improving their disease-related knowledge and satisfying their need for competence.
## The need for relatedness during hospitalization
It’s a whole new environment for a patient to be hospitalized after stroke. And according to the coronavirus disease 2019 (COVID-19) protection policy, almost all hospitals only allow patients to have one chaperone in the hospital. Having only one familiar person in a new environment can be a threat to the satisfaction of the patient’s need for relatedness [36]. To make matters worse, for various reasons, some patients are hospitalized alone, without anyone to accompany them. Feelings of loneliness and lack of support make patients feel sad or other negative emotions, which may be detrimental to their treatment and recovery from the stroke [37]. Especially when patients need to go to different wards for examination, the company of family instead of the hospital staff will make them feel more secure. In addition, the company of family may allow patients to quickly adapt to the new environment of the hospital and gain a sense of belonging. Studies have shown that family companionship, especially long-term companionship, improves patients’ functional impairment and promotes recovery [38]. Due to the widespread use of smartphones, even if relatives and friends cannot visit and accompany patients in the hospital, they will frequently contact patients to express their concerns. Even though they may be far apart in space, they will feel connected in spirit and satisfy patients’ need for relatedness. The good news is that most of the patients get along well with healthcare workers and get enough care from the workers, especially the nurses who get along day and night. A good doctor-patient relationship helps to promote treatment progress and improve their physical and mental condition [39].
## The need for relatedness in the rehabilitation stage
During the rehabilitation stage, in addition to the patients who remain in the hospital, patients can reach more people and get support and help from them. For patients who recuperate at home, their family members will accompany them to do rehabilitation training, such as exercises, walking, and massage, making them feel closer to their families [40]. Meanwhile, most patients reported that their families gave more attention to their physical and mental condition to find the problem and provide help at the first time. Patients in a rehabilitation institution can meet fellow patients with the same condition. They usually communicate about the disease, medication, rehabilitation training, etc., from which they exchange experiences and help each other, which is very important for a chronic illness with a long recovery period such as stroke [41]. In conclusion, during the rehabilitation stage, patients may form new relationships and strengthen relationships with family members, especially caregivers, both of which satisfy the need for competence.
## The impact of stroke on relationships
As mentioned above, the stroke strengthens the patient’s connection with his family and friends and gives the patient an opportunity to form new relationships, which greatly satisfies the patient’s need for relatedness. Another point that cannot be ignored is that the reaction and attitude of colleagues and bosses of patients who participated in work before stroke can have a great impact. If the patient is cared for, understood, and given enough vacation time for treatment and rehabilitation, he can devote himself to treatment and rehabilitation wholeheartedly and without burden [42]. And the patient can have a good expectation of returning to work after his physical condition recoveries, which may improve the patient’s physical and mental health. However, if the patient only gets complaints or even dismissal, that certainly has a negative impact on the patient’s recovery.
## Strengths and limitations
Although more and more scholars pay attention to the basic psychological needs of patients, there is still no research focusing on the satisfaction of BPN of stroke patients. This study is the first qualitative study to explore their psychological experience, satisfaction, and influencing factors in stroke patients. The qualitative data provide a rich first-person resource that lays the foundation for subsequent quantitative research of BPN in stroke patients. Based on the results of the qualitative study, we verified that BPNT is also applicable to stroke patients. Therefore, future research can focus on using BPN scales to determine the satisfaction of patients’ BPN, and to make interventions for them. When recruiting subjects, we followed the principle of information saturation and included 18 participants. And based on the widely applied theory of BPN, this study aimed to explore the BPN of stroke patients. Moreover, the researchers have been trained for qualitative interviews, and most of the participants can clearly answer the questions in the interview guide. Therefore, the narrow study aim, the applied established theory and the strong quality of dialogue indicate a high information power in this study [43]. In addition, we followed the principle of maximum variation [9] and thoroughly considered the variety of participants’ age, gender, occupation, and disease course, which enhanced the credibility and representativeness of the results. However, limited by the clinical status of the patients, only a small number of patients with intracerebral hemorrhage were recruited. The quotes were fully displayed in the results section, and all the results and their corresponding quotes were presented in an additional table [see Supplementary Information], which improved the reliability of the results [44]. Although the researchers have systematically learned qualitative interviews and content analysis methods, they are all members of the same research group and not from multiple backgrounds, which may have greater limitations in data analysis. As Hsieh said, there are certain limitations in the directed content analysis approach based on existing theory [9]. Researchers may analyze data so biased that they are hard to find evidence that doesn’t support the theory. When determining the research plan, our researchers did consider this issue and set a more open question in the interview guide, that is, question 6, to explore whether stroke patients have psychological needs beyond BPN. However, all codes could be classified into the initial coding framework, and no unclassifiable codes were generated in the data analysis. On the one hand, this result may verify the foundation and universality of BPNT. The psychological needs of stroke patients can be described by three basic psychological needs. On the other hand, perhaps researchers are so immersed in the theory that during the process of data collection and analysis, it is easier to find evidence for the theory than evidence against it.
A number of post-stroke patients lives with a speech or language disorder. However, limited by interviews, this study excluded these patients with speech disorder, which is also a limitation of this study. Obviously, speech disorder will greatly affect the satisfaction of basic psychological needs of patients. Difficulty in expressing one’s own opinions can impair his autonomous decisions and reduce his sense of autonomy [45]. At the same time, speech disorder can delay a patient’s return to work, frustrating his need for competence. The impact of speech disorder on the need for relatedness can be complex. As this study found, patients during the rehabilitation stage have more time to spend with family and friends, as well as the opportunities to form new relationships with healthcare workers and other patients. Due to speech disorder, patients need more help from caregivers, get more care from them, which may promote the satisfaction of the need for relatedness. However, due to difficulty in expressing themselves accurately, patients may be misunderstood by others, resulting in disagreements and conflicts, which may frustrate the need for relatedness. Therefore, more research is needed to focus on the basic psychological needs of patients with speech impairment after stroke.
## Conclusion
Improving the satisfaction of patients’ basic psychological needs can improve the patient’s physical and psychological status and promote the recovery and prognosis of the disease. In this study, it was found that the impact of stroke on the satisfaction of patients’ BPN is various, and the satisfaction of patients also has a large discrepancy, which may be related to their family environment, work environment, stroke symptoms, or other factors. In particular, a symptom like limb numbness can significantly reduce the satisfaction of patients’ needs for autonomy and competence. We recommend developing individualized rehabilitation training for patients to facilitate recovery and return to work. However, the stroke seems to increase the patients’ satisfaction of the need for relatedness. Hospitals and healthcare workers can improve patient satisfaction through many measures, such as listening to patients’ opinions and doing health education to improve patients’ knowledge. The hospital can create a warm atmosphere, improve the doctor-patient relationship, and enhance the patient’s sense of belonging during hospitalization. After discharge, we encourage the family to accompany patients through rehabilitation training. And patients are encouraged to go out to meet new people or socialize with friends.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: 'Stéatose hépatique non alcoolique : diagnostic et traitement en 2022'
authors:
- Nikoletta Maria Tagkou
- Nicolas Goossens
journal: Schweizer Gastroenterologie
year: 2023
pmcid: PMC9990575
doi: 10.1007/s43472-023-00091-9
license: CC BY 4.0
---
# Stéatose hépatique non alcoolique : diagnostic et traitement en 2022
## Résumé
La NAFLD (Non Alcoholic Fatty Liver Disease) est la manifestation hépatique d’un trouble métabolique multisystémique. Elle est la principale cause de maladie hépatique au niveau mondial, avec une prévalence croissante. Bien qu’il s’agisse principalement d’une maladie silencieuse à évolution lente, certains patients présentent un risque élevé de progression de la maladie et d’issues plus graves telles que la cirrhose, le carcinome hépatocellulaire et la transplantation hépatique. Malgré les multiples études menées et les nombreux essais cliniques en cours, il n’existe pas de médicaments approuvés pour la NAFLD/NASH (Non Alcoholic Steato-Hepatitis), et le traitement doit donc se fonder sur des stratégies de modification du mode de vie. Cette revue explorera la définition et l’épidémiologie courantes de la NAFLD et de la NASH ainsi que les facteurs de risque et les conséquences de la maladie, tout en résumant les recommandations existantes pour le diagnostic, la stratification du risque et la prise en charge de la maladie.
## Introduction
Au cours des dernières décennies, la stéatose hépatique non alcoolique (non-alcoholic fatty liver disease en anglais, NAFLD) est devenue la maladie hépatique la plus fréquemment rencontrée dans le monde, touchant environ un quart de la population adulte [1]. L’épidémiologie mondiale de la NAFLD est directement liée à l’épidémie d’obésité et il est reconnu qu’elle est étroitement associée au syndrome métabolique et à ses composants individuels, tels que le diabète de type 2 (DT2), l’hyperlipidémie et l’hypertension [2]. En raison de l’augmentation rapide de la prévalence de ces comorbidités métaboliques, la NAFLD est en train de devenir une cause majeure de morbidité (cirrhose, cancer primaire du foie, transplantation du foie) et de mortalité liée aux maladies du foie [1]. Il est important de noter que le fardeau économique de la maladie va probablement augmenter au cours des prochaines années, et devrait dépasser la maladie du foie liée à l’alcool (alcohol-related liver disease, ALD) et devenir la principale indication de transplantation hépatique (TH) dans les pays occidentaux [3]. La prise en charge de cette maladie dans la pratique clinique présente quelques défis majeurs que nous allons aborder ici. L’établissement d’un système de classification distinct, la mise en place d’une stratégie de diagnostic et de surveillance efficace, l’identification des individus présentant un risque plus élevé de développer une maladie hépatique avancée et des complications et la mise en œuvre de politiques de prévention à l’échelle nationale en sont quelques-uns. Cette revue vise donc à explorer les tendances actuelles en matière d’épidémiologie, de diagnostic et de gestion de la NAFLD, tout en résumant les recommandations existantes pour la pratique clinique quotidienne.
## Définition de la NAFLD et de la NASH
La NAFLD est considérée comme la manifestation hépatique d’un syndrome métabolique multisystémique et la stéatohépatite non alcoolique (non-alcoholic steatohepatitis, NASH) est la composante inflammatoire de la NAFLD [4]. Selon la définition actuelle, la NAFLD est un diagnostic d’exclusion, défini par la présence d’une accumulation de stéatose dans plus de 5 % des hépatocytes en l’absence d’autres étiologies de maladies hépatiques (par exemple, hépatite virale, maladie hépatique auto-immune, etc.) ou de causes secondaires de stéatose hépatique (médicaments stéatogènes, consommation significative d’alcool défini comme ≥30 g par jour pour les hommes et ≥20 g par jour pour les femmes, etc.) [ 4, 5]. Sur le plan histologique, la NAFLD est un terme générique qui englobe un large spectre de maladies allant de la stéatose isolée (stéatose hépatique simple ou NAFL) à la NASH, cette dernière pouvant potentiellement conduire à une fibrose hépatique, une cirrhose ou un carcinome hépatocellulaire (CHC) [4, 5]. Au niveau cellulaire, outre la stéatose hépatique, la NASH est définie par une inflammation lobulaire et des signes de lésions hépatocytaires (caractérisés par une ballonisation des hépatocytes) avec différents degrés de fibrose [6].
## Nomenclature MAFLD contre NAFLD
En 2020, un groupe d’experts internationaux a réévalué la définition actuelle de la maladie stéatosique du foie et est parvenu à un consensus en faveur d’un changement de nomenclature, de la stéatose hépatique non alcoolique (NAFLD) à la stéatose hépatique associée à un dysfonctionnement métabolique (metabolic (dysfunction)-associated fatty liver disease, MAFLD) [7, 8]. En outre, ils ont établi un ensemble de critères diagnostiques “positifs” complets pour les patients adultes et pédiatriques [9]. Par conséquent, selon les nouveaux critères proposés, la MAFLD est définie par la présence d’une stéatose hépatique, en plus de l’un des trois critères suivants, à savoir le surpoids/l’obésité, la présence d’un DT2 ou la présence d’un dérèglement métabolique [7, 8].
Cette initiative est plus qu’un changement de nomenclature car elle a souligné la relation étroite entre les conditions métaboliques et la stéatose hépatique et elle a fait de la MAFLD un diagnostic d’inclusion qui n’exclut plus les patients atteints de maladies hépatiques concomitantes [8]. Bien que cet appel ait initialement suscité un débat dans le domaine, il a reçu un soutien important de la part des professionnels de la santé, des sociétés scientifiques, des infirmières ainsi que de représentants des patients, pharmaceutiques et réglementaires [10]. Il reste toutefois à déterminer si l’utilisation de la nouvelle nomenclature de MAFLD va intégrer la pratique clinique.
## Incidence et prévalence de la NAFLD et de la NASH
Il est impératif de comprendre l’incidence et la prévalence de la NAFLD et de la NASH, car elles sont directement liées à l’évolution des tendances en matière de TH et à l’augmentation des coûts des soins de santé associés. La NAFLD est sans aucun doute la principale étiologie des maladies chroniques du foie dans le monde, mais l’absence de critères de diagnostic cohérents rend sa véritable prévalence difficile à déterminer [2]. De plus, alors que la NAFLD peut être diagnostiquée par des modalités d’imagerie telles que l’échographie abdominale, le diagnostic de la NASH nécessite une histologie. Selon une méta-analyse récente, la prévalence mondiale de la NAFLD, lorsqu’elle est diagnostiquée par imagerie, est estimée à 32,4 % (intervalle de confiance [IC] 95 % 29,9 à 34,9) et elle a considérablement augmenté au fil du temps [1].
En raison de son association étroite avec un dérèglement métabolique, la NAFLD est plus fréquemment rencontrée chez les personnes présentant une composante du syndrome métabolique. Par exemple, la prévalence globale de la NAFLD et de la NASH chez les patients atteints de DT2 est estimée à 55,5 % (IC 95 %, 47,3 à 63,7) et 37,3 % (IC 95 %, 24,7-50,0 %), respectivement [11]. La NAFLD se retrouve également jusqu’à 80 % chez les patients obèses et chez plus de 90 % des patients subissant une chirurgie bariatrique [12].
L’incidence de la NAFLD dans la population générale est difficile à estimer et les données sont limitées. Une méta-analyse récente qui a utilisé le code CIM-10-CM pour la NAFLD a démontré une incidence allant de 28,0 pour 1000 personnes-années (IC 95 %, 19,3 à 40,6) en Israël à 50,9 pour 1000 personnes-années (IC 95 %, 44,8 à 57,4) en Asie, mais les taux sont probablement sous-estimés [1, 13]. Selon une étude de modélisation, la population NAFLD devrait augmenter de 30 % d’ici 2030, la Chine étant la plus touchée. Dans le même temps, la prévalence de la NASH devrait augmenter de 56 % et les maladies hépatiques avancées ainsi que la mortalité liée au foie devraient doubler [14].
La Suisse ne pouvait pas faire exception à la pandémie mondiale de NAFLD. Une étude de modélisation a estimé que d’ici 2030, il y aura 2.234.000 (1.918.000 à 2.553.000) cas de NAFLD, soit 24,3 % (20,9 à 27,8 %) de la population suisse totale. Alors que l’incidence de la maladie hépatique avancée est prévue d’augmenter de 40 % au cours de la même période [15].
## Conséquences de la NAFLD et de la NASH
L’histoire naturelle et les conséquences de la NAFLD ont fait l’objet de nombreuses études au cours des dernières décennies. Dans le passé, des études ont associé la NAFLD à une mortalité globale et liée au foie plus élevée que dans la population générale [16]. Cependant, la relation entre la NAFLD et la mortalité de toutes causes confondues reste débattue, certaines études ne rapportant aucune association [17].
Il est intéressant de noter que la NAFLD se caractérise par des degrés variables de progression de la maladie et de résultats cliniques, la majorité des patients présentant une maladie stable ou lentement progressive, tandis qu’une petite partie d’entre eux développe une fibrose avancée, une cirrhose et même un CHC [18]. Il est impératif de reconnaître les facteurs de risque de la progression de la maladie et des résultats indésirables pour élaborer des conseils efficaces en matière de soins aux patients.
Il a été démontré que le DT2 double le risque de fibrose avancée, de complications liées à la cirrhose et de mortalité liée au foie [19], tandis que la présence d’une obésité, d’une hyperlipidémie et d’une hypertension est également associée à un risque accru de maladie hépatique progressive [19]. L’âge avancé (> 60 ans), qui est lié à une plus longue durée de la maladie, joue également un rôle [20]. Il est de plus en plus évident que les patients atteints de NASH histologique, comparés à ceux atteints de NAFL ou de NAFLD, présentent un risque plus élevé de conséquences négatives telles que la progression de la maladie hépatique, la décompensation cirrhotique et le développement d’un CHC, et ont des taux de mortalité liés au foie plus élevés [21, 22]. Selon une méta-analyse d’études de biopsies hépatiques appariées de la NAFLD, la fibrose a progressé d’un stade en moyenne pendant 7,1 ans pour les patients NASH contre 14,3 ans pour les patients NAFLD, soit presque le double [21].
Il est important de noter que les patients atteints de NASH développent un CHC à un taux annuel 12 fois plus élevé que les patients atteints de NAFLD (5,77 contre 0,44 événements pour 1000 personnes-années) et ont un taux de mortalité annuel 1,7 fois plus élevé que les patients atteints de NAFLD (25,56 contre 15,44 événements pour 1000 personnes-années) [1]. Même les patients atteints de NASH non cirrhotique sont exposés à un risque accru d’hépato-carcinogenèse, qui peut être induit par la nécro-inflammation [23]. Malgré l’augmentation de la mortalité liée au foie chez les patients présentant à la fois un diagnostic de la NAFLD et de la NASH, les maladies cardiovasculaires semblent être la principale cause de décès, suivies par les tumeurs malignes extra-hépatiques (par exemple, le cancer colorectal et le cancer du sein) [5]. Enfin, les patients atteints de la NAFLD, en particulier ceux qui présentent une fibrose avancée, sont reconnus comme présentant un risque plus élevé de maladie grave due au SARS-Cov‑2 [24].
## Diagnostic de la NAFLD
Dans la pratique quotidienne, la NAFLD est le plus souvent diagnostiquée par imagerie, bien qu’elle puisse également être identifiée histologiquement ou à partir de scores de risque cliniques. La modalité la plus couramment utilisée pour le diagnostic de la NAFLD est l’échographie abdominale, où la stéatose hépatique est caractérisée par une hyperéchogénicité hépatique et un flou de la vascularisation hépatique [25]. Une fibrose coexistante peut compliquer l’évaluation par échographie, car elle peut rendre l’écho-texture hépatique plus grossière. Une autre limite de l’échographie est sa faible sensibilité (<30 %) en cas de stéatose légère [26]. Les mesures basées sur l’imagerie par résonance magnétique (IRM) sont très sensibles pour l’évaluation de la stéatose hépatique (avec une sensibilité de 92 à 100 % et une spécificité de 92 à 97 %) et peuvent détecter une stéatose de 5 %. Par contre, l’IRM est réservée au cadre de la recherche, car elle est comparativement coûteuse et peu disponible dans la pratique courante. Aucune des modalités non invasives mentionnées ci-dessus ne permet de différencier la NAFLD de la NASH [27].
## Rôle de la biopsie du foie
La biopsie hépatique est la méthode actuellement acceptée pour différencier de manière fiable la NASH de la NAFLD et la méthode de référence pour l’évaluation de la fibrose hépatique [28, 29]. En outre, la biopsie hépatique est une exigence standard pour l’inscription aux essais cliniques pour les traitements de la NASH et de la NAFLD et la méthode la plus acceptée pour l’évaluation des progrès du traitement [30]. Cependant, étant donné que des modifications du mode de vie sont généralement recommandées pour tous les patients atteints de NAFLD et qu’aucun traitement spécifique de la NASH n’est actuellement approuvé, la nécessité d’une biopsie dans le cadre de la NAFLD reste controversée [30]. La biopsie hépatique présente certaines limites qui rendent sa mise en œuvre chez tous les patients atteints de NAFLD/NASH pour le diagnostic et l’évaluation de la sévérité et de la progression de la maladie impossible. Bien qu’elle soit généralement bien tolérée, il s’agit d’une procédure invasive qui comporte un risque de complications telles que des saignements, des infections, des fuites biliaires, des lésions d’autres organes et un risque de mortalité rare [31]. Il semble également exister une certaine variation dans l’échantillonnage et l’interprétation par les observateurs qui peut affecter l’intégrité du diagnostic [32].
## Tests non invasifs pour l’évaluation de la gravité de la maladie
Au cours des dernières décennies, les limites de la biopsie hépatique, combinées à l’épidémie croissante de NAFLD, ont stimulé le développement de stratégies alternatives non invasives afin de servir d’outils de diagnostic et de pronostic. Les stratégies non invasives s’appuient soit sur des scores et des biomarqueurs sériques, soit sur des mesures de l’élasticité du foie par imagerie, à l’aide de techniques basées sur l’échographie ou l’IRM.
## Scores sériques et biomarqueurs non invasifs
Les concentrations d’enzymes hépatiques ont traditionnellement été utilisées par les cliniciens pour évaluer les patients atteints de maladies du foie. Cependant, les enzymes hépatiques sont souvent normales chez ces patients et peuvent ne pas refléter la gravité histologique [33]. Le degré de fibrose étant le facteur prédictif le plus fort de la morbidité et de la mortalité liées aux maladies du foie, de nombreux efforts de recherche ont été déployés pour mettre au point des scores sériques simples et non invasifs permettant d’estimer le degré de fibrose d’un patient [22].
Les plus couramment utilisés sont le score de fibrose de la NAFLD (NFS), l’indice de fibrose FIB‑4 et l’indice du rapport entre l’aspartate aminotransférase et les plaquettes (APRI), qui sont calculés à partir de paramètres de laboratoire, cliniques et démographiques couramment disponibles ([34]; Tab. 1). Bien que ces scores aient une précision modérée, leur valeur prédictive négative élevée en font des outils utiles pour exclure une fibrose avancée [35, 36]. Le FIB‑4 a été particulièrement proposé dans le cadre d’un algorithme de soins aux patients en tant qu’outil de dépistage afin de sélectionner les patients présentant un risque plus élevé de fibrose avancée qui pourraient nécessiter une évaluation spécialisée avec une mesure de l’élasticité du foie ou éventuellement une biopsie du foie ([37]; Fig. 1). Le FIB‑4 a également été identifié comme un prédicteur indépendant de la mortalité et des résultats liés au foie dans la NAFLD [38].Test non invasif de la fibroseParamètresAlgorithme de calculTests indirects de la fibroseFIB‑4Age, ASAT, ALAT, plaquettesAge (years) x ASAT (U/L) / [plaquettes (109/L) x ALAT$\frac{1}{2}$ (U/L)]NFSAge, BMI, diabète, ASAT, ALAT, plaquettes, albumine−1,675 + 0,037 x age (years) + 0,094 x BMI (kg/m2) + 1,13 x diabète (oui = 1, non = 0) + 0,99 x ASAT/ALAT − 0,013 x plaquettes (109/L) − 0,66 x albumine (g/dL)APRIASAT, ALAT, plaquettes[ASAT/limite supérieure de l’intervalle normal de l’ASAT] x 100 / plaquettes (109/L)Tests directs de la fibroseELFHA, PIIINP, TIMP‑12,494 + 0,846 In(C HA) + 0,735 In(C PIIINP) + 0,391 In(C TIPM-1)ASAT aspartate transaminase, ALAT alanine transaminase, BMI body mass index, HA hyaluronic acid, PIIINP procollagen III amino terminal peptide, TIMP‑1 tissue inhibitor of metalloproteinase 1 Parmi les divers biomarqueurs sanguins directs de la fibrose, le score ELF (Enhanced Liver Fibrosis) a été le plus étudié et son utilisation est recommandée par le UK National Institute for Health and Care Excellence avant l’orientation vers un spécialiste en hépatologie [39, 40]. Cependant, selon une récente méta-analyse, le test a montré une performance diagnostique limitée dans des contextes de faible prévalence [41].
## Imagerie
L’évaluation de la fibrose par imagerie non invasive repose principalement sur les techniques d’élastographie qui mesurent l’élasticité du foie en quantifiant la vitesse de propagation dans le parenchyme hépatique d’une onde mécanique. Les tissus fibrotiques plus rigides propagent les ondes plus rapidement, ce qui entraîne des valeurs plus élevées (mesurées en kilopascals, kPa) [42]. Les techniques d’élastographie peuvent être basées sur l’échographie, comme l’élastographie impulsionnelle à vibration contrôlée (VCTE) ou FibroScan, l’élastographie point Shear Wave ou l’élastographie ultrasonore impulsionnelle 2D en mode Shear Wave Elastography, ou sur l’IRM, comme l’élastographie par résonance magnétique [43].
En ce qui concerne le VCTE/Fibroscan, afin d’exclure une fibrose significative (stade de fibrose F2–F3), une faible valeur seuil de 8,0 kPa est recommandée selon le guide des soins cliniques de l’Association Américaine de Gastroentérologie (AGA) [37]. En effet, une grande étude européenne qui a recruté 1073 patients atteints de NAFLD dans 10 centres hépatiques a démontré qu’une faible valeur seuil de 8.0 kPa a une sensibilité de $93\%$ pour l’exclusion de la fibrose avancée (stade de fibrose ≥F3), ce qui a également été soutenu par une revue systématique récente [35, 44]. Il a été constaté qu’une valeur de 12,1 kPa sur le VCTE a une valeur prédictive positive de $88\%$ pour le diagnostic d’une fibrose significative chez les patients d’une clinique d’hépatologie [41]; c’est pourquoi une valeur arrondie de 12,0 kPa est recommandée comme seuil supérieur [37]. Les mesures de l’élasticité du foie sont également corrélées au risque de CHC et aux complications de la cirrhose [45], tandis que les critères de Baveno VI combinent la VCTE et la valeur des thrombocytes dans le sang pour identifier les patients à risque de varices œsophagiennes qui doivent être traités [46].
## Prise en charge de la NAFLD et de la NASH
La NAFLD et la NASH sont des affections multifactorielles avec des dérèglements métaboliques coexistants variables. Par conséquent, la prise en charge initiale, que ce soit dans le cadre de soins primaires ou secondaires, doit commencer par la recherche d’autres étiologies possibles de la maladie hépatique et de la consommation concomitante d’alcool, mais aussi par le contrôle des éventuelles comorbidités métaboliques comme le DT2 et, surtout, par le calcul du risque d’événements cardiovasculaires. Pour les raisons susmentionnées, la prise en charge de la NAFLD/NASH est difficile à standardiser et requiert une approche plus personnalisée et holistique qui fait appel à des cliniciens de différentes spécialités, tels que des gastroentérologues, des hépatologues, des endocrinologues, des chirurgiens viscéraux, des spécialistes de la médecine interne et des médecins généralistes. Le Tab. 2 présente une comparaison de toutes les recommandations actuelles des associations de gastroentérologie et d’hépatologie en Europe, aux États-Unis et en Asie (Tab. 2).EASL, 2016 [4]AASLD, 2018 [5]APASL, 2020 [81]Dépistage1. Les personnes présentant des enzymes hépatiques anormales de manière persistante.2. Les patients présentant une résistance à l’insuline et/ou une obésité ou un MetS1. Le dépistage de routine de la NAFLD dans les groupes à haut risque n’est pas conseillé1. Le dépistage du MAFLD par échographie doit être envisagé dans les patients en surpoids/obésité, DT2 et MetSEvaluation de la fibrose1. NFS et FIB-4 peuvent être utilisés pour la stratification du risque afin d’exclure une maladie grave.2. Les patients présentant un risque moyen à élevé doivent être orientés vers un hépatologue afin de subir une élastographie et/ou biopsie du foie.3. L’identification d’une fibrose avancée ou d’une cirrhose par des outils non invasifs est moins précise et doit être confirmée par une biopsie du foie.4. La NASH doit être diagnostiquée par une biopsie du foie1. NFS, FIB-4 ou VCTE ou MRE pour identifier les personnes présentant un risque de fibrose avancée (F3 ou F4).2. Les patients atteints du MetS sont à risque de développer une NASH et doivent être ciblés pour une biopsie du foie1. VCTE ou SWE et les biomarqueurs sanguins et scores de fibrose ou des combinaisons peuvent exclure le risque élevé de fibrose significative ou avancée (F2–F4).2. La confirmation d’une fibrose significative ou avancée par des outils non invasifs est moins précise et doit être confirmée par une biopsie du foie.3. La biopsie du foie est le test de choix pour la NASHIntervention sur le mode de vie1. Objectif de perte de poids totale de 7 à 10 %.2. Défaut énergétique de 500–1000 kcal/jour, pour une perte de poids de 500–1000 g/semaine.3. Consommer l’alcool en dessous du seuil de risque et éviter les boissons et aliments contenant du fructose.4. 150–200 min/semaine d’activité physique aérobique d’intensité modérée en 3–5 séances1. Objectif de perte de poids de 7 à 10 %.2. Combinaison d’un régime hypocalorique (réduction de 500 à 1000 kcal/jour) et d’un programme d’activité physique d’intensité modérée.3. Pas de consommation de grandes quantités d’alcool1. Objectif de perte de poids de 7 à 10 %.2. Restriction énergétique et exclusion des aliments industriels et riches en fructose. Le régime méditerranéen est conseillé.3. La combinaison du régime alimentaire et de l’exercice physique est plus efficace.4. Exercice aérobique ou entraînement en résistance, selon la condition physiquePharmacothérapie1. Réservée aux patients atteints de la NASH, en particulier à ceux qui présentent une fibrose significative (≥ F2) ou avec une maladie moins grave, mais a risque élevé de progression (diabète, MetS, augmentation persistante de l’ALT, nécro-inflammation).2. La pioglitazone (hors indication en dehors de DT2) et la vitamine E ou une combinaison peut être utilisée dans la NASH.3. Les statines peuvent être utilisées pour prévenir le risque cardiovasculaire1. Réservée aux personnes atteintes de NASH avec une fibrose prouvée par biopsie.2. La pioglitazone peut être utilisée chez les patients avec ou sans DT2 avec une NASH prouvée par biopsie.3. La vitamine E (800 UI/jour) peut être utilisée chez les patients sans DT2 dont la NASH a été prouvée par biopsie.4. Les statines peuvent être utilisées pour traiter la dyslipidémie chez les patients atteints de la NAFLD et de la NASH1. Aucune recommandation spécifique de pharmacothérapie pour la MAFLD.2. Les statines doivent être envisagées chez tous les patients atteints de MAFLD présentant une hyperlipidémieAASLD Association Américaine pour l’Étude du Foie, APASL Association Asie-Pacifique pour l’Étude du Foie, EASL Association Européenne pour l’Étude du Foie, MetS syndrome métabolique
## Investigation et gestion de la consommation d’alcool
Au niveau mondial, la consommation d’alcool est associée à une augmentation de pathologies multiples [47]. Dans le domaine de l’hépatologie, l’impact de l’alcool chez les patients atteints de NAFLD reste un sujet de débat. Selon le récent consensus suggérant un changement de nomenclature de la NAFLD à la MAFLD, l’alcool ne doit plus être exclu pour le diagnostic de MAFLD [7, 8]. Cependant, l’évaluation du niveau de consommation d’alcool dans la pratique clinique quotidienne est un défi car il n’existe ni de questionnaire standard largement reconnu ni de biomarqueurs sériques précis [48]. Actuellement, selon les directives de l’Association Européenne pour l’Étude du Foie (EASL), la consommation excessive d’alcool est définie comme une consommation régulière de > 20 g pour les femmes et > 30 g pour les hommes par jour [48]. Des études récentes ont montré que la consommation d’alcool dans les limites actuellement acceptées chez les patients atteints de NAFLD peut être nuisible, tandis que d’autres études ont conclu à un effet protecteur [49, 50]. Une vaste étude coréenne qui a recruté des patients atteints de NAFLD avec de faibles scores sériques non invasifs de fibrose (NFS et FIB-4) a suggéré qu’une consommation légère (1–9,9 g/jour) ou modérée (10–29,9 g/jour pour les hommes ou 10–19,9 g/jour pour les femmes) était indépendamment associée à la progression de la fibrose [49]. Une autre étude récente qui a évalué l’alcool à l’aide d’un biomarqueur sérique a révélé qu’une consommation modérée d’alcool est associée à une fibrose avancée et que les patients atteints de DT2 sont plus à risque [51]. Compte tenu des preuves croissantes de l’effet négatif d’une consommation modérée d’alcool dans la progression de la fibrose, la consommation d’alcool devrait être découragée chez ces patients [4]. Chez les sujets avec fibrose avancée, voire une cirrhose, la consommation d’alcool doit être proscrite afin d’éviter la progression de l’hépatopathie ou développement de complications.
## Recherche et gestion des comorbidités
Même avant le consensus sur la définition de la MAFLD, le diagnostic et la gestion des comorbidités métaboliques faisaient partie de la pratique quotidienne dans le domaine de la NAFLD. Dans un premier temps, le diagnostic d’une autre hépatopathie coexistente doit être effectué, tel qu’une hépatite virale, une hépatopathie médicamenteuse, une hémochromatose, une maladie auto-immune et des étiologies moins courantes comme la maladie de Wilson [52]. En ce qui concerne l’hépatite virale et la NAFLD, des études ont montré que la coexistence des deux est associée à une hépatopathie plus avancée. Par exemple, les patients atteints d’hépatite C et de stéatose présentent un risque plus élevé d’évènements plus graves, tels que des événements cardiovasculaires [53], et les patients atteints d’hépatite B et de syndrome métabolique traités par des analogues nucléosidiques présentent un risque plus élevé de résistance virale, de développement de CHC et de progression de la maladie [54]. Par conséquent, un dépistage et une prise en charge appropriés doivent être effectués.
## Gestion des maladies cardiovasculaires
Chez les patients atteints de NAFLD, un dépistage systématique des maladies cardiovasculaires devrait être mis en place, car les événements cardiovasculaires augmentent la morbidité et la mortalité. L’une des cibles du traitement doit être la dyslipidémie. Le traitement par statine s’est avéré sûr pour les patients atteints de maladies hépatiques, même ceux qui présentent des taux élevés de transaminases, et doit être prescrit conformément aux directives [55, 56]. Par conséquent, l’utilisation de statines et/ou l’ézétimibe devrait être administrée chez les patients NAFLD présentant un risque cardiovasculaire élevé [57].
## Mesures hygiéno-diététiques
Le mode de vie représente le facteur le plus pertinent pour la NAFLD, en tant que composante hépatique du syndrome métabolique. Bien que de nombreuses données aient démontré l’effet bénéfique de la modification du mode de vie dans la NAFLD, la complexité du sujet rend difficile la formulation de recommandations cliniques fondées sur des preuves [58].
## Régime alimentaire
On sait que la composition en macronutriments de l’alimentation influe sur le dépôt de graisse hépatique, mais aucun régime spécifique en macronutriments ne s’est avéré bénéfique pour la NASH (régime riche en protéines, riche en glucides/faible en graisses, faible en glucides/riche en graisses etc.) [ 58].
Une étude qui visait à étudier l’effet d’un régime riche en protéines sur le contenu lipidique intrahépatique a montré que les sujets ayant suivi un régime riche en protéines ont obtenu une diminution plus importante du contenu lipidique intrahépatique (43 %) par rapport à ceux ayant suivi un régime normal ou faible en protéines (37 % et aucune réduction, respectivement), mais les différences ont été attribuées à la différence de concentration en glucides dans chaque régime [59]. Un régime riche en glucides et pauvre en graisses peut être bénéfique à moyen terme s’il entraîne un déficit calorique global [60], mais la qualité des graisses doit toujours être prise en compte [61]. En outre, on s’inquiète de plus en plus des effets indésirables du suivi à long terme d’un régime riche en glucides [62].
Il a notamment été démontré que le régime méditerranéen a des effets bénéfiques sur la NAFLD. De récents essais de contrôle randomisés (ECR) ont montré que l’adhésion à un régime méditerranéen conduit indépendamment à une diminution de la quantité de la stéatose intrahépatique [63–65], tandis qu’une grande étude prospective observationnelle a rapporté une association inverse entre le régime méditerranéen et la NAFLD [66]. En outre, l’exclusion des facteurs favorisant la NAFLD, comme les aliments industriels et les aliments et boissons à haute concentration de fructose ajouté, est recommandé [4].
D’autre part, la perte de poids par simple restriction calorique est considérée comme la stratégie la plus appropriée et la plus efficace pour les patients atteints de NAFLD [5]. Un régime hypocalorique, quelle que soit sa composition, peut entraîner une diminution du poids corporel, des taux de transaminases, de la graisse corporelle totale et de la graisse viscérale [67]. Une étude récente a montré que le degré de perte de poids après un régime hypocalorique et le degré de régression histologique de la NAFLD sont fortement associés [60] et cette corrélation a été récemment confirmée par une méta-analyse [68]. Des études ont montré qu’une perte de poids d’au moins 5 % est nécessaire pour l’amélioration de la stéatose hépatique dans la NASH [67, 69], tandis qu’une perte de poids de plus de 10 % peut conduire à une résolution complète de la NASH dans 90 % des cas et à une régression de la fibrose dans 45 % des cas [60]. Cependant, le pourcentage de patients capables d’atteindre cet objectif est faible, il faut donc mettre l’accent sur l’objectif d’une perte de poids de 7 à 10 %, en utilisant une approche personnalisée, tenant compte de la cuisine, du coût et des habitudes locales [60].
## Activité physique
Il a été démontré que l’activité physique réduit la proportion de graisse intrahépatique et les marqueurs de lésions hépatocellulaires, indépendamment de la perte de poids [70]. Cependant, lorsque l’activité physique est associée à un régime alimentaire, les bénéfices sont considérablement accrus [70]. Bien que les études évaluant l’effet de l’exercice sur l’histologie dans la NASH manquent, une méta-analyse a montré que l’exercice améliore les scores histologiques mais pas les niveaux de l’alanine transaminase (ALAT) [71]. La durée et le type d’exercice optimaux restent également indéterminés. Une vaste étude coréenne a démontré que l’exercice modéré cinq fois par semaine est bénéfique pour la prévention ou l’amélioration de la NAFLD, indépendamment du poids corporel [52]. Selon les directives de pratique clinique de l’EASL, de l’Association Européenne pour l’Étude du Diabète et de l’Obésité et les directives de l’Association Américaine pour l’Étude du Foie, il est recommandé aux patients atteints de la NAFLD de pratiquer une activité physique aérobie d’intensité modérée pendant 150 à 200 min par semaine, en trois à cinq séances [4, 72]. Dans l’ensemble, tous les patients atteints de la NAFLD, indépendamment de leur poids et de leur adiposité, devraient recevoir une éducation sur la nutrition, l’activité physique et la restriction d’alcool [4].
## Pharmacothérapie
L’EASL propose de traiter les patients présentant une fibrose significative ou une maladie moins sévère mais présentant un risque élevé de progression de la maladie (par exemple, avec un DT2, un syndrome métabolique, une augmentation persistante de l’ALAT, une nécro-inflammation élevée à l’histologie) [4]. À l’heure actuelle, aucun médicament n’est approuvé pour la NASH en particulier, avec seulement quelques médicaments entrant dans la phase III des essais cliniques. Par conséquent, tout traitement médicamenteux recommandé serait hors indication ou dans le cadre d’essais cliniques. La pathophysiologie complexe de la maladie se traduit par une pléthore de cibles thérapeutiques potentielles, comprenant certains médicaments ciblant l’homéostasie métabolique, comme le lanifibranor, d’autres agissant directement sur le foie, comme le resmetirom et l’acide obéticholique et les analogues du récepteur du glucagon-like peptide‑1 (GLP-1) comme le semaglutide [73].
Lanifibranor est un agoniste des récepteurs activés par les proliférateurs de peroxysomes (PPAR) qui a terminé les essais de phase II. Agissant sur de multiples mécanismes métaboliques dont l’adipogenèse, l’inflammation et la fibrose, lanifibranor a atteint son critère principal de diminution du score histologique stéatose – activité – fibrose de > 2 points sans aggraver la fibrose chez 49 % des patients [74].
Les résultats de l’étude de phase II du resmetirom sont encourageants, montrant une réduction de la stéatose hépatique de 30 % à 12 semaines et une résolution significative de la NASH, tandis que la phase III présente déjà des résultats positifs en termes de stéatose et de rigidité du foie [75, 76]. D’autre part, une grande étude de phase III sur l’acide obéticholique a montré une amélioration d’au moins un stade de fibrose mais pas de résolution de la NASH [77].
Les agonistes des récepteurs du GLP‑1 ont de multiples modes d’action et sont connus pour induire une perte de poids significative et une amélioration des paramètres cardiométaboliques [78]. L’essai de phase II du semaglutide a montré une résolution de la NASH sans aggravation de la fibrose chez 56 % des patients par rapport à 20 % pour le placebo, sans toutefois réussir à améliorer la fibrose [79].
Finalement, selon un grand ECR, la vitamine E, qui a un mode d’action antioxydant important, peut améliorer la stéatose, l’inflammation et le ballonisation et a résolu la NASH dans 36 % des cas, contre 21 % dans le bras placebo [52]. Une méta-analyse de 5 ECR a démontré que la vitamine E améliore les transaminases hépatiques chez les patients adultes atteints de NAFLD sans DT2 [80].
Enfin, il est probable que dans un avenir proche, ces médicaments joueront un rôle important dans la prise en charge des patients atteints de la NAFLD/NASH avec ou sans DT2, mais des études prospectives et des ECR supplémentaires sont nécessaires.
## Message à retenir
La NAFLD est la composante hépatique d’un trouble métabolique multi systémique qui est la principale cause de maladie du foie dans le monde, avec une prévalence qui ne cesse d’augmenter. Bien qu’il s’agisse principalement d’une maladie silencieuse et à progression lente, certains patients présentent un risque élevé de progression de la maladie et de résultats plus graves tels que la cirrhose, le carcinome hépatocellulaire et la transplantation hépatique. En étudiant et en comprenant mieux l’histoire naturelle et les facteurs de risque, il est impératif de développer des parcours de soins cliniques efficaces pour le diagnostic et la stratification du risque pour les patients. Bien que la biopsie hépatique reste la méthode de référence pour l’évaluation de la fibrose, il convient d’accorder une plus grande attention aux stratégies non invasives. Enfin, la prise en charge de la NAFLD et de la NASH nécessite la collaboration d’une équipe multidisciplinaire qui doit mettre l’accent sur l’identification d’autres maladies hépatiques possibles et de comorbidités métaboliques et cardiovasculaires susceptibles de modifier sensiblement le pronostic. Actuellement, il n’existe aucun médicament approuvé pour la NAFLD; le traitement doit donc se concentrer sur des stratégies de modification du mode de vie.
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|
---
title: 'Shared decision-making practices and patient values in pharmacist outpatient
care for rheumatic disease: A multiple correspondence analysis'
authors:
- Ikkou Hirata
- Shunsuke Hanaoka
- Ryo Rokutanda
- Ryohkan Funakoshi
- Hiroyuki Hayashi
journal: Journal of Pharmacy & Pharmaceutical Sciences
year: 2023
pmcid: PMC9990622
doi: 10.3389/jpps.2023.11135
license: CC BY 4.0
---
# Shared decision-making practices and patient values in pharmacist outpatient care for rheumatic disease: A multiple correspondence analysis
## Abstract
Purpose: To investigate the value-to-value relationships, relationship between values and patient background, continuation rate of treatment after shared decision-making (SDM), and disease status in order to clarify the values involved in drug therapy decisions for patients with rheumatic disease.
Methods: We investigated patient values (efficacy of drug therapy [effectiveness], safety, economics, daily life, and other) and the continuance rate and disease status of treatment after 6 months in 94 patients with rheumatic disease aged ≥18 years who made decisions with pharmacists and physicians in the pharmacy outpatient clinic between September 2019 and April 2021. Multiple correspondence and K-means cluster analyses were performed to show the relationship between values and basic patient information.
Results: Among the selected patients, $87\%$ and $47\%$ selected effectiveness for multiple selections and single selection, respectively. Effectiveness was at the center of the graph; three clusters containing other values were placed around it. History of allergy or side effects caused by biologics or Janus kinase inhibitors were in the safety cluster. The non-usage history of biologics or Janus kinase inhibitors was in the economic cluster.
Conclusion: Effectiveness was the most important factor for patients with rheumatic disease; the values that patients consider important may shift from effectiveness to other values based on each patient’s subjective experience with the treatment and/or the stage of life in which they were treated. It is important to positively link patient values and information about the treatment plan in shared decision-making while establishing rapport with the patient.
## Introduction
In recent years, shared decision-making (SDM) practices are regarded as important when initiating or changing treatment [1, 2]. SDM is a communication process between a patient and a healthcare provider that integrates evidence-based medicine with patient values and emotions. Patients and physicians are required to implement SDM as the minimum combination (1–5). However, there are reports of pharmacists participating in SDM for treatment of psychiatric, diabetic, and cardiovascular diseases (6–9).
Rheumatic diseases, such as rheumatoid arthritis (RA) and other connective tissue diseases, are mostly treated by drug therapy. Therefore, adherence is important, and patient participation in drug therapy is essential [2, 10]. Physicians’ drug decisions are based on their expectations of therapeutic efficacy. Pharmacists, in contrast, determine the suitability of drugs for patients by considering various factors, such as adverse events and side effects. Implementing SDM with patients involving collaboration between physicians and pharmacists with different perspectives on drugs leads to concordance. The patient participates as a member of the medical care team, which may improve patient adherence and solve the effectiveness, safety, economic, and daily life issues in drug therapy.
In clinical practice, favorable decision-making for patients and high-quality drug therapies are achieved when pharmacists are involved in SDM. However, there is no report on the analysis of the values that patients use as criteria for the selection of drug therapies when SDM involves pharmacists. Therefore, we investigated the value-to-value relationships, relationship between values and patient background, continuation rate of treatment after SDM, and disease status in order to clarify the values involved in drug therapy decisions for patients.
## Study design
This was a single-center, cross-sectional, retrospective observational study. This study complied with the standards of the Declaration of Helsinki and the current ethical guidelines. The design and methodology, including the opt-out method of consent available to all patients, were approved by the Kameda General Hospital Clinical Research Review Committee (approval number: 21-010).
## SDM process
The process and flow for SDM are shown in Figure 1 [3, 4]. In 2015, Kameda General Hospital started a pharmacist outpatient clinic in the inflammatory bowel disease specialty outpatient clinic and now also offers this service in the outpatient clinic of the rheumatism, collagen disease, allergy, internal medicine department. In this pharmacist outpatient clinic, pharmacists participate in SDM and provide support in situations in which drug therapy needs to be intensified or changed. All patients with rheumatic disease received medical consultations and were treated according to the guidelines from Phase 1 onwards. The pharmacist evaluated the current medications of all patients prior to the physician’s consultation during their regular clinical practice regardless of the duration of the disease. Patients whose condition was stable proceeded directly to the physician’s consultation. For patients considered by the pharmacist to be likely to have difficulty continuing drug treatment, or patients who requested a change in drug therapy, an SDM was made between the pharmacist and patient according to SDM-Q-9 [11].
**FIGURE 1:** *Shared decision-making process and flow.*
To present patients with new treatment options that are consistent with their values in SDM, pharmacists confirmed the values that the patient considered important in selecting drug treatment with the patients. The pharmacist presented five values to the patient: the efficacy of drug therapy (effectiveness), the safety of drug therapy (safety), the economic burden (economical), the impact of drug treatment on the quality of life (daily life), and other physical and mental burdens caused by drug treatment (other). The “economical” value was defined as a patient’s comprehensive view of the cost of drugs relative to their income at a specific stage of their life. “ Daily life” refers to the impact of treatment on daily life (routine living and habits), such as the frequency of hospital visits. “ Other” factors included the physical and mental burden of drug treatment on the patient, such as the type of device used, route of administration, and duration of intravenous infusion. Patients initially made several selections from the five values influencing their treatment choice (multiple selections) and subsequently chose one value as the most important to prioritize during treatment selection (single selection). The pharmacist suggested that the value selected by the patient as most important be resolved first, and also provided information to help resolve the other multiple-selected values. After the pharmacist and patient shared a new treatment decision, the pharmacist proposed a prescription based on the results of the SDM to the physician. The physician examined the patient, and they were given the opportunity to reject the SDM results, which was agreed upon in advance by the pharmacist and patient. The physician, pharmacist, and patient consulted again to confirm no difficulties with the previously determined SDM, and the current medications were changed.
## Study population
We retrospectively enrolled all 94 patients with rheumatic disease aged ≥18 years who were involved in decision-making with a pharmacist and physician in the pharmacist outpatient clinic between September 2019 and April 2021. If one patient attends more than one SDM session, each SDM session was enrolled as one patient because the values that patients select for each SDM session can change with disease status, life stage changes, and other factors. Rheumatic diseases included RA, adult-onset Still’s disease, ankylosing spondylitis, Behcet’s disease, systemic lupus erythematosus, spondylarthritis, psoriasis, psoriatic arthritis, Sjogren’s syndrome, and palmoplantar pustulosis.
## Survey items
Patient information at the time of SDM implementation was retrospectively obtained from the medical records. The surveyed items were sex, age (years), age (<65 years/≥65 years), RA, multiple rheumatic diseases, disease duration (years), disease duration (<10/≥10 years), disease activity (active/inactive), number of drugs used (number), number of drugs used (<5/≥5), history of allergy or side effects, history of allergy or side effects due to biologics or Janus kinase inhibitor, biologics, or Janus kinase inhibitor usage history before SDM. Age was classified into two categories according to the World Health Organization (WHO). Disease duration was classified into two categories based on a previous report on RA [12]. Disease activity was classified into two categories; patients with RA were categorized as inactive if they exhibited low disease activity or remission based on their DAS28-CRP value and as active if they exhibited moderate or high disease activity. Those with other rheumatic diseases were classified as inactive if clinically judged by a physician to be in remission or have low disease activity and as active if they were otherwise judged to have moderate or higher-intensity symptoms. The number of drugs used was classified into two categories based on a previous report on polypharmacy [13].
The influential values of patients regarding drug treatment were compiled from subjective data describing values chosen by patients during conversations between pharmacists and patients during SDM implementation. The values compiled were multiple selected and single selected from the five values: effectiveness, safety, economic, daily life (drug treatment burden on life), and others (such as route of administration, type of device).
The continuance rate of treatment 6 months after SDM and disease status (improvement, aggravation, and no change) were evaluated, with reasons including inadequate effectiveness, side effects, economic issues, daily life issues, and other issues among patients.
The details of the drug treatment changes selected after the implementation of SDM were tabulated. The results included changing the drugs, tight control, no change, drug use cessation, increased dosage, change in the route of administration, addition of an oral drug, oral drug cessation, reduced dosage, changes in oral drug, and shortening of the interval between doses.
Because SDM was performed in the usual clinical setting, the pharmacists were not blinded to the outcomes, such as the continuance rate of treatment.
## Statistical analysis
Regarding basic information on patient characteristics, patient selection of SDM values, treatment continuity, and disease activity after SDM, continuous variables were expressed as the median (interquartile range), and categorical variables were expressed as numbers (%). Fisher’s exact tests were performed to compare categorical variables, and Mann–Whitney U tests were performed to compare continuous variables between the two groups. $p \leq 0.05$ was considered statistically significant.
Multivariate analysis was performed using multiple correspondence analysis (MCA) to show the relationship between values and basic patient characteristics. MCA is an extension of correspondence analysis when multiple variables are considered, and a method of analyzing categorical/categorized data and presenting the results in a graph (map). The categorical variables that were fed into MCA and transformed into a cross table are listed below [14].
## Patient values involved in SDM
Influential multiple and most important single selections values with yes/no response consisted of effectiveness, safety economy, daily life and others.
## Basic information on patient characteristics
Sex (female or male), age_group (<65/≥65 years), disease duration_group (<10/≥10 years), disease activity (active/inactive), number of drugs used_group (<5/≥5), history of allergy or side effects caused by drugs in general (yes/no), history of allergy or side effects caused by biologics or Janus kinase inhibitor (yes/no), biologics or Janus kinase inhibitor usage history_before SDM (yes/no).
The information described in each dimension was evaluated using the *Greenacre inertia* adjustment, and the categorical variables were plotted in two dimensions with the highest inertia [15]. A K-means cluster analysis, a non-hierarchical cluster analysis identifying mutually exclusive clusters by calculating the quadratic Euclidean distance (coefficient of similarity) of the point categories [16, 17], was necessary to objectively ascertain which values each of the patient background items related to or belonged to. The coordinates (object scores) of each of the dimensions 1 and 2 of each variable calculated by MCA were input to K-means cluster analysis and the categorical variables, including the values and the basic information of patient characteristics, were grouped [18]. The cubic clustering criterion (CCC) was calculated with statistical software and used to determine the optimal number of clusters [19, 20]. After clustering each variable, density ellipses (α = $95\%$) for each cluster were shown to indicate the overlap between clusters and were overlaid with the MCA plot. Statistical analyses were performed using JMP Pro 16 software (SAS Institute Inc., Cary, NC, United States).
## Patient characteristics
The patient characteristics are shown in Table 1. Ninety-four patients underwent SDM with a pharmacist during the study period. The median patient age was 66 [52–71] years, and $70\%$ were women. Among these patients, $89\%$ had RA and $11\%$ had other rheumatic diseases as primary rheumatic disease. Nine percent of all patients were affected by multiple rheumatic diseases. Patient acceptance of the results of SDM in collaboration with physicians and pharmacists was $98\%$, with only two refusals (one patient and one physician).
**TABLE 1**
| Unnamed: 0 | n or median | (%) or [range] |
| --- | --- | --- |
| Overall | 94 | (100) |
| Age (years) | 66 | [52–71] |
| Age (≥65 years) | 48 | (51) |
| Sex (female) | 66 | (70) |
| Primary rheumatic disease | Primary rheumatic disease | Primary rheumatic disease |
| RA | 84 | (89) |
| Other rheumatic diseases | 10 | (11) |
| Patients affected by multiple rheumatic diseases | 8 | (9) |
| Disease duration (years) | 8 | [3–6] |
| Disease duration (≥10 years) | 40 | (43) |
| Disease activity (active) | 66 | (70) |
| Number of drugs used | 6 | [4–8] |
| Number of drugs used (≥5) | 64 | (68) |
| Number of BIO or JAK usage history before SDM | 1 | [1–2] |
| BIO or JAK usage history before SDM | 72 | (77) |
| History of allergy or side effects | 45 | (48) |
| History of allergy or side effects (BIO or JAK) | 24 | (26) |
| Patient rejection | 1 | (1) |
| Physician rejection | 1 | (1) |
## Influential values involved in SDM
The values that the patients selected as important in their decision-making regarding drug therapy are shown in Table 2. Among eligible patients, $87\%$ and $47\%$ selected effectiveness for multiple selections and single selections, respectively: most patients selected effectiveness. Therefore, effectiveness was the most important influential factor. Out of 25 patients who selected “other” for multiple selections, 20 marked the route of administration as a factor (data not shown).
**TABLE 2**
| Values | n | (%) |
| --- | --- | --- |
| Overall | 94 | (100) |
| Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) |
| Effectiveness_multiple_yes | 82 | (87) |
| Safety_multiple_yes | 54 | (57) |
| Economical_multiple_yes | 30 | (32) |
| Daily life_multiple_yes | 23 | (24) |
| Other_multiple_yes | 25 | (27) |
| Most important influential values (single selection) | Most important influential values (single selection) | Most important influential values (single selection) |
| Effectiveness_most | 44 | (47) |
| Safety_most | 24 | (26) |
| Economical_most | 14 | (15) |
| Daily life_most | 3 | (3) |
| Other_most | 9 | (10) |
## Analysis of influential values (multiple selections) compared with the most important values (single selection)
The patient characteristics and values selected in multiple selection were compared for each value selected in a single selection (Table 3). Age (years) ($$p \leq 0.001$$), age (group) ($$p \leq 0.008$$), and disease activity (active) ($$p \leq 0.001$$) showed significant differences. In the multiple selections of values, significant differences were observed. Moreover, a comparison of patient characteristics between the two disease activity groups revealed that the Active group had a significantly had a lower history of biologics or Janus kinase inhibitor use prior to SMD than did the Inactive group ($68\%$ vs. $96\%$, $$p \leq 0.003$$), although there were no significant between-group differences in the remaining items (data not shown).
**TABLE 3**
| Patient characteristics and influential values (multiple selections) | Overall | Overall.1 | Most important influential values (single selection) | Most important influential values (single selection).1 | Most important influential values (single selection).2 | Most important influential values (single selection).3 | Most important influential values (single selection).4 | Most important influential values (single selection).5 | Most important influential values (single selection).6 | Most important influential values (single selection).7 | Most important influential values (single selection).8 | Most important influential values (single selection).9 | p |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Patient characteristics and influential values (multiple selections) | Overall | Overall | Effectiveness_most | Effectiveness_most | Safety_most | Safety_most | Economical_most | Economical_most | Daily life_most | Daily life_most | Other_most | Other_most | p |
| Patient characteristics and influential values (multiple selections) | n or median | (%) or [range] | n or median | (%) or [range] | n or median | (%) or [range] | n or median | (%) or [range] | n or median | (%) or [range] | n or median | (%) or [range] | p |
| Overall | 94 | (100) | 44 | (100) | 24 | (100) | 14 | (100) | 3 | (100) | 9 | (100) | – |
| Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics | Patient characteristics |
| Age (years) | 66 | [52–71] | 66 | [53–71] | 66 | [53–71] | 52 | [42–57] | 78 | [78–78] | 73 | [63–78] | 0.001 |
| Age (≥65 years) | 48 | (51) | 23 | (52) | 13 | (54) | 2 | (14) | 3 | (100) | 7 | (78) | 0.008 |
| Sex (female) | 66 | (70) | 32 | (73) | 18 | (75) | 9 | (64) | 3 | (100) | 4 | (44) | 0.360 |
| Disease duration (years) | 8 | [3–16] | 7 | [2–14] | 8 | [3–16] | 7 | [4–12] | 55 | [9–55] | 14 | [6–37] | 0.094 |
| Disease duration (≥10 years) | 40 | (43) | 19 | (43) | 11 | (46) | 3 | (21) | 2 | (67) | 5 | (56) | 0.399 |
| Disease activity (active) | 66 | (70) | 38 | (86) | 14 | (58) | 9 | (64) | 3 | (100) | 2 | (22) | 0.001 |
| Number of drugs used | 6 | [4–8] | 7 | [4–9] | 5 | [4–8] | 5 | [4–7] | 5 | [5–13] | 7 | [6–9] | 0.239 |
| Number of drugs used (≥5) | 64 | (68) | 30 | (68) | 16 | (67) | 7 | (50) | 3 | (100) | 8 | (89) | 0.307 |
| BIO or JAK usage history before SDM | 72 | (77) | 34 | (77) | 19 | (79) | 8 | (57) | 3 | (100) | 8 | (89) | 0.429 |
| History of allergy or side effects (drugs in general) | 45 | (48) | 19 | (43) | 15 | (63) | 5 | (36) | 2 | (67) | 4 | (44) | 0.463 |
| History of allergy or side effects (BIO or JAK) | 24 | (26) | 9 | (20) | 11 | (46) | 2 | (14) | 0 | (0) | 2 | (22) | 0.133 |
| Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) | Influential values (multiple selections) |
| Effectiveness_multiple_yes | 82 | (87) | 44 | (100) | 14 | (58) | 14 | (100) | 3 | (100) | 7 | (78) | <0.0001 |
| Safety_multiple_yes | 54 | (57) | 22 | (50) | 24 | (100) | 4 | (29) | 0 | (0) | 4 | (44) | <0.0001 |
| Economical_multiple_yes | 30 | (32) | 11 | (25) | 3 | (13) | 14 | (100) | 0 | (0) | 2 | (22) | <0.0001 |
| Daily life_multiple_yes | 23 | (24) | 11 | (25) | 0 | (0) | 4 | (29) | 3 | (100) | 5 | (56) | <0.0001 |
| Other_multiple_yes | 25 | (27) | 11 | (25) | 5 | (21) | 0 | (0) | 0 | (0) | 9 | (100) | <0.0001 |
## Analysis of patient characteristics compared with each influential value (multiple selections)
For each of the five values marked on multiple selections, the patient characteristics were compared between the selected (yes) and non-selected (no) groups (Table 4). In terms of effectiveness, the “yes” group had significantly fewer patients aged ≥65 years than the “no” group ($$p \leq 0.028$$). With respect to safety, female sex ($$p \leq 0.024$$), inactive disease ($$p \leq 0.039$$), biologics or Janus kinase inhibitor usage history before SDM ($$p \leq 0.028$$), history of allergy or side effects caused by drugs in general ($$p \leq 0.004$$), and history of allergy or side effects caused by biologics or Janus kinase inhibitor ($p \leq 0.0001$) were significantly more frequent in the “yes” group than in the “no” group. Regarding the economics, the “yes” group was significantly younger ($$p \leq 0.007$$) and had significantly fewer patients aged ≥65 years ($$p \leq 0.002$$). History of biologics or Janus kinase inhibitor use before SDM was significantly lower in the “yes” group ($$p \leq 0.017$$). No significant differences were found in any of the items in daily life. The “yes” group was significantly older ($$p \leq 0.003$$) and had more patients aged ≥65 years ($$p \leq 0.001$$). The disease duration was significantly longer in the “yes” group ($$p \leq 0.046$$). Disease activity was significantly higher in the “no” group than in the “yes” group ($$p \leq 0.039$$).
**TABLE 4**
| Patient characteristics | Influential values (multiple selections) | Influential values (multiple selections).1 | p |
| --- | --- | --- | --- |
| Patient characteristics | n (%) or median [range] | n (%) or median [range] | p |
| | Effectiveness_multiple | Effectiveness_multiple | |
| | Yes | No | |
| Overall | 82 (100) | 12 (100) | — |
| Age (years) | 63 [52–71] | 68 [66–75] | 0.085 |
| Age (≥65 years) | 38 (46) | 10 (83) | 0.028 |
| Sex (female) | 56 (68) | 10 (83) | 0.500 |
| Disease duration (years) | 8 [3–17] | 6 [3–15] | 0.790 |
| Disease duration (≥10 years) | 36 (44) | 4 (33) | 0.548 |
| Disease activity (active) | 60 (73) | 6 (50) | 0.173 |
| Number of drugs used | 6 [4–8] | 5 [4–7] | 0.346 |
| Number of drugs used (≥5) | 56 (68) | 8 (67) | 1.000 |
| BIO or JAK usage history before SDM | 19 (23) | 3 (25) | 1.000 |
| History of allergy or side effects | 44 (54) | 5 (42) | 0.542 |
| History of allergy or side effects of BIO or JAK | 64 (78) | 6 (50) | 0.070 |
| | Safety_multiple | Safety_multiple | |
| | Yes | No | |
| Overall | 54 (100) | 40 (100) | — |
| Age (years) | 66 [54–70] | 64 [48–72] | 0.731 |
| Age (≥65 years) | 29 (54) | 19 (48) | 0.677 |
| Sex (female) | 43 (80) | 23 (58) | 0.024 |
| Disease duration (years) | 10 [3–16] | 5.5 [1–19] | 0.257 |
| Disease duration (≥10 years) | 27 (50.0) | 13 (33) | 0.098 |
| Disease activity (active) | 33 (61) | 33 (83) | 0.039 |
| Number of drugs used | 7 [4–8] | 5 [4–9] | 0.535 |
| Number of drugs used (≥5) | 39 (72) | 25 (63) | 0.374 |
| BIO or JAK usage history before SDM | 46 (85) | 26 (65) | 0.028 |
| History of allergy or side effects | 33 (61) | 12 (30) | 0.004 |
| History of allergy or side effects of BIO or JAK | 22 (41) | 2 (5) | <0.0001 |
| | Economical_multiple | Economical_multiple | |
| | Yes | No | |
| Overall | 30 (100) | 64 (100) | — |
| Age (years) | 57 [48–67] | 67 [53–73] | 0.007 |
| Age (≥65 years) | 8 (27) | 40 (63) | 0.002 |
| Sex (female) | 21 (70) | 45 (70) | 1.000 |
| Disease duration (years) | 6 [2–12] | 9 [3–18] | 0.129 |
| Disease duration (≥10 years) | 9 (30) | 31 (48) | 0.119 |
| Disease activity (active) | 19 (63) | 47 (73) | 0.341 |
| Number of drugs used | 6 [4–8] | 7 [4–8] | 0.467 |
| Number of drugs used (≥5) | 17 (57) | 47 (73) | 0.154 |
| BIO or JAK usage history before SDM | 18 (60) | 54 (84) | 0.017 |
| History of allergy or side effects | 11 (37) | 34 (53) | 0.184 |
| History of allergy or side effects of BIO or JAK | 5 (17) | 19 (29) | 0.212 |
| | Daily life_multiple | Daily life_multiple | |
| | Yes | No | |
| Overall | 23 (100) | 71 (100) | — |
| Age (years) | 67 [56–76] | 64 [52–71] | 0.191 |
| Age (≥65 years) | 13 (57) | 35 (49) | 0.634 |
| Sex (female) | 15 (65) | 51 (72) | 0.604 |
| Disease duration (years) | 6 [1–17] | 8 [3–16] | 0.348 |
| Disease duration (≥10 years) | 8 (345) | 32 (45) | 0.470 |
| Disease activity (active) | 17 (74) | 49 (69) | 0.795 |
| Number of drugs used | 8 [5–9] | 5 [4–8] | 0.070 |
| Number of drugs used (≥5) | 18 (78) | 46 (65) | 0.306 |
| BIO or JAK usage history before SDM | 18 (78) | 54 (76) | 1.000 |
| History of allergy or side effects | 12 (52) | 33 (47) | 0.811 |
| History of allergy or side effects of BIO or JAK | 4 (17) | 20 (28) | 0.413 |
| | Other_multiple | Other_multiple | |
| | Yes | No | |
| Overall | 25 (100) | 69 (100) | — |
| Age (years) | 68 [66–75] | 60 [49–70] | 0.003 |
| Age (≥65 years) | 20 (80) | 28 (41) | 0.001 |
| Sex (female) | 16 (64) | 50 (73) | 0.452 |
| Disease duration (years) | 11 [5–18] | 7 [3–14] | 0.046 |
| Disease duration (≥10 years) | 15 (60) | 25 (36) | 0.058 |
| Disease activity (active) | 13 (52) | 53 (77) | 0.039 |
| Number of drugs used | 8 [6–9] | 5 [4–8] | 0.076 |
| Number of drugs used (≥5) | 20 (80) | 44 (64) | 0.210 |
| BIO or JAK usage history before SDM | 23 (92) | 49 (71) | 0.051 |
| History of allergy or side effects | 14 (56) | 31 (45) | 0.361 |
| History of allergy or side effects of BIO or JAK | 5 (20) | 19 (28) | 0.595 |
## Continuity of treatment and disease status 6 months after SDM
The treatment continuance rate, reasons for treatment cessation, and disease status 6 months after SDM are shown in Table 5. Among the patients, $78\%$ continued with treatment 6 months after SDM, and $90\%$ had either improved or reported no change in disease status. There were no cases of non-adherence or abrupt cessation of treatment. Regardless of the outcome, all patients who had participated in SDM were satisfied with the process.
**TABLE 5**
| Continuation of treatment and disease status | n | (%) |
| --- | --- | --- |
| Overall | 94 | (100) |
| Continuity of treatment | 73 | (78) |
| Treatment cessation | 21 | (22) |
| Inadequate effectiveness | 11 | (12) |
| Side effects | 10 | (11) |
| Economic issues | 2 | (2) |
| Daily life issues | 0 | (0) |
| Other issues | 0 | (0) |
| Disease status | Disease status | Disease status |
| Improvement | 55 | (59) |
| No change | 30 | (32) |
| Aggravation | 9 | (10) |
## Details of change and disease status after SDM
The details of the treatment changes due to SDM are presented in Table 6. Among the patients, $35\%$ changed the route of administration and $52\%$ changed the drugs.
**TABLE 6**
| Route of administration and details of change | n | (%) |
| --- | --- | --- |
| Overall | 94 | (100) |
| Route of administration before SDM | Route of administration before SDM | Route of administration before SDM |
| Oral | 42 | (45) |
| Subcutaneous self-injection | 28 | (30) |
| Intravenous | 15 | (16) |
| Subcutaneous injection by a nurse | 9 | (10) |
| Route of administration after SDM | Route of administration after SDM | Route of administration after SDM |
| Oral | 32 | (34) |
| Subcutaneous self-injection | 31 | (33) |
| Subcutaneous injection by a nurse | 13 | (14) |
| Intravenous | 6 | (6) |
| | 12 | (13) |
| Details of change of administration route | Details of change of administration route | Details of change of administration route |
| No change | 49 | (52) |
| Change of administration route | 33 | (35) |
| Subcutaneous self-injection | 14 | (15) |
| Subcutaneous injection by a nurse | 9 | (10) |
| Oral | 9 | (10) |
| Intravenous | 1 | (1) |
| Drug use cessation | 12 | (13) |
| Details of treatment change | Details of treatment change | Details of treatment change |
| Change of drugs a | 49 | (52) |
| Change of drug | 22 | (23) |
| Tight control | 22 | (23) |
| Addition of oral drug | 2 | (2) |
| Oral drug cessation | 2 | (2) |
| Change of oral drug | 1 | (1) |
| No change b | 17 | (18) |
| Drug use cessation | 12 | (13) |
| Increased dosage | 10 | (11) |
| Change of administration route c | 3 | (3) |
| Reduced dosage | 2 | (2) |
| Shortening of interval between doses | 1 | (1) |
## MCA and K-means cluster analysis outcome
To conduct MCA, a multidimensional contingency table of all two-way cross-tabulations across all variables, called the Burt matrix, was analyzed (data not shown) [14]. MCA was conducted, and the first two dimensions accounted for $64\%$ (dimension 1 was $46\%$ and dimension 2 was $18\%$) of Greenacre-adjusted inertia in the first two dimensions. The coordinates of the categorical variables in Figure 2 depicts the relationship between the values and categorical data, including the patient information. The position on the map of each categorical variable in Figure 2 shows the relationship between each variable, including the values and characteristics of the patients.
**FIGURE 2:** *Outcome of MCA and K-means cluster analysis of patients with rheumatic disease. Adjusted λ, Greenacre’s adjusted inertia; BIO, biologics; JAK, Janus kinase inhibitor; most, single selection; multiple, multiple selections; SE, side effects; SE history, history of allergy or side effects.*
K-means cluster analysis was performed using object scores (data not shown) for dimensions 1 and 2, respectively, to which each category was assigned. The clustering results identified four clusters (CCC = −2.3). The $95\%$ probability ellipses for each cluster were calculated from the object scores of the categorical variables included in each cluster and overlaid on the MCA map (Figure 2).
The cluster containing effectiveness_most was placed at the center of the map, and the three clusters safety_most, economical_most, and daily life_most and other_most were placed around it. The cluster safety_most included safety_multiple_yes, effectiveness_multiple_no, history of allergy or side effects caused by biologics or Janus kinase inhibitor_yes, history of allergy or side effects caused by drugs in general_yes, and inactive. The cluster economical_most included economic_multiple_yes, age_<65, drugs_<5, and biologics or Janus kinase inhibitor usage history_before_no. daily life_most and other_most were in the same cluster and included other_multiple_yes.
## Discussion
In this study, pharmacists and physicians collaborated to conduct SDM in patients with rheumatic disease, and we articulated the relationship of each value that patients considered important, as well as the relationship between the values and patient background. “ Effectiveness” was the most important value, while “Safety,” “Economical,” “Daily life,” and “Other” were selected based on the background and experience of each patient. Therefore, drugs are not solely prescribed as per the evidence or the values of healthcare providers; however, patient values are important in the selection of therapeutic agents. The acceptance of SDM by the patients in our study was good, and patients were satisfied with the process. The continuity of treatment rate (Table 5) was monitored to determine whether SDM with pharmacist participation was clinically effective; although not directly comparable or statistically meaningful, it was better than that observed in a previous study [21].
Understanding and catering to patient preferences are associated with adherence and a good treatment response [22, 23]. Pharmacists need to evaluate the patients’ drug therapies in clinical practice in terms of efficacy, safety, cost of health services (economy), and utilization of health services (necessity) [24, 25]. In SDM in our usual clinical practice, necessity was further divided into two categories from the pharmacist’s perspective to obtain more specific views of the patient’s values: daily life and others. Thus, we classified the values related to patient decision-making into five categories. The five categories were effectiveness, safety, economics, daily life, and others. In this study, the relationship between these five values and the patient background was diagrammed in MCA and K-means cluster analysis to clarify their relationship. Effectiveness was at the center of patient values in rheumatic diseases, with other values as sub-values of effectiveness. In particular, safety was placed opposite efficacy. Effectiveness was considered a positive factor for patients; safety, economics, daily life, and others were considered negative factors; and positive and negative values appeared to conflict with each other. Depending on the effect of the limiting factor (such as these negative factors) on the patient’s values, the values that the patient considers important may shift from effectiveness to other values.
In terms of sex, women were on the border of the effectiveness and safety clusters on the map, and significantly more women chose safety in multiple-selected values. Thus, women were more likely to select safety. Women are more concerned about safety when switching from biologics to biosimilar drugs [26], which is also consistent with our results.
Patients who had experienced allergies or side effects in the past due to drugs for rheumatic diseases and/or other diseases tended to emphasize safety and showed deep concern about adverse effects after changing drugs. The results of MCA, in which the history of allergy or side effects caused by biologics or Janus kinase inhibitors (the main therapeutic agents for rheumatic diseases) was closer to the value of safety than to that of history caused by overall drugs the patient was taking, indicated that the priority of values shifts from effectiveness to safety after experiencing allergy or side effects caused by therapeutic agents for rheumatic diseases. However, the results may differ for other diseases. In rheumatic diseases, efficacy must be ensured first, followed by side effect management [27, 28]. However, the value priorities of patients with malignant tumors and other diseases may differ from those of patients with autoimmune diseases due to different drug treatment intensities and frequency of allergy or side effects.
Age is one of the most important aspects of experience, and it is inferred that age influences patient values. In the results, the value with the highest ratio of patients <65 years in both multiple and single selections was economics, indicating a shift in the importance of the value from effectiveness to economics for younger patients. Younger patients generally chose intensive pharmacological treatment more frequently [29]. Thus, we hypothesized that patients <65 years would select effectiveness; however, the result of the map indicated that the economic limitations of early age outweigh effectiveness. Younger patients have higher blood levels of tumor necrosis factor-α, while older patients have higher levels of interleukin-6, indicating a difference in signaling pathways in the pathogenesis of RA between juvenile RA and senile RA [30]. *In* general, as age increases, physical deterioration due to aging and osteoarthrosis causes movement limitations, especially in RA, which are often accompanied by joint destruction and degeneration due to inflammation [31]. Therefore, it was inferred that the values that patients consider important differ with age, and it was observed on the map that the values shifted with age from economics to the border between effectiveness and daily life.
It is important to discuss the economic issues of the patient regardless of the patient’s awareness and share the importance of these issues with the healthcare provider and patient for joint decision-making [32]. Previous studies have indicated that biologics are more effective than conventional synthetic disease-modifying antirheumatic drugs such as methotrexate and salazosulfapyridine [27, 28]. The usage history of biologics or Janus kinase inhibitors lies in the effectiveness cluster on the map, suggesting that patients with these histories experienced high treatment efficacy with these drugs and valued their effectiveness. However, these were not used in the economic cluster. These patients had no experience with treatment with highly effective but expensive drugs and were more concerned about economics than effectiveness, which suggested that their values shifted from effectiveness to economics. This may indicate that patients who should be treated with biologics or Janus kinase inhibitors do not have access to appropriate drugs due to a lack of experience with therapeutic efficacy.
The time spent on drug treatment and the actions related to the drugs themselves are included in daily life and others, which affect the physical and mental burden, leading to loss of patient productivity. For patients to continue drug treatment, this burden must be alleviated. Some patients prefer being managed by a healthcare provider and opt for intravenous infusion [33]. Not preferring self-injection, less frequent administration, and preferring to be administered by a healthcare provider were factors in this outcome. However, in recent years, patient orientation has changed with the introduction of simple self-injection preparations, such as auto-injectors and oral small molecular targeted drugs [34]. Recent studies have reported a preference for oral drugs over injections [33, 34], and a trend towards patient preference for oral drugs is becoming apparent. In our study, the route of administration chosen was different for each patient, but most patients took the drug orally, in addition to choosing routes such as intravenous infusion and subcutaneous injection (administered by themselves and nurses). In other words, this may mean that patients do not prioritize the route of administration in their effectiveness-centered values.
Thus, SDM has the potential to support the provision of tailor-made medical care that matches patient values. Biologics and Janus kinase inhibitors had similar efficacy on average and the same level of recommendation [27, 28]. In the absence of clear drug superiority, there is room for interventions in decision-making based on patient values. In doing so, the healthcare provider needs to play a role in supporting decision-making in a non-paternalistic manner [35, 36].
There are some limitations to the study: this was a single-center study, the study population was small, and lifestyle-related diseases or other comorbidities and the type and route of administration of rheumatic disease medications were not included and considered as MCA items. Future studies with a larger sample size should examine the relationship between the values associated with each drug and the route of administration. The hospital where this study was conducted was a rural hospital with a high patient age range due to its regional characteristics, and the median age of the patients with rheumatic disease included in the study was as high as 66 [52–71] years. In addition, the duration of disease was 8 [3–16] years, and $43\%$ of the patients had had the disease for more than 10 years. Patients with a wide range of ages and disease durations who needed SDM and participated in the SDM in the outpatient clinic were enrolled, and there was no selection bias. Since changes in life stage can affect the continuation of medications, the fact that a wide range of ages and durations of disease were adequately represented in the study is a strength of this study. Moreover, this was a pilot study involving pharmacists in the SDM of drug therapy for rheumatic diseases, which allowed us to accurately evaluate patients’ values and analyze their interrelationships at a single center.
Disease activity is a factor that influences treatment choice in the field of rheumatic diseases, as indicated in the treatment recommendations [27, 28]. In the practice that we conduct according to treatment recommendations, we consider clinical remission to be the goal of medical care and tolerate low disease activity in some cases. Therefore, we classified patients into remission or low disease activity (Inactive) and moderate or high disease activity (Active) groups based on patient characteristics. Inactive patients used biologics or Janus kinase inhibitors before SDM more frequently than did Active patients, and their disease activity was controlled. They also rated therapeutic safety higher. Active patients were included in the same cluster as effectiveness; hence, we can say that disease activity is a factor that influences effectiveness and safety. However, there were no differences in other patient characteristics among disease activity, and values such as economical, daily life, and other seemed to be related to factors other than disease activity. Further research is needed to determine how disease activity influences other factors and values, as well as treatment choice.
## Conclusion
Even among patients with the same rheumatic disease, the subject experiences of patients with the treatment and/or the stage of life in which they were treated shaped the values they prioritized. Moreover, the relationships between each value affected the decision-making of patients regarding drug therapy. Patients make decisions based on multiple values rather than just one. There were values most influential to the patient that were important in decision-making; non-etheless, other associated values including patient background were also key factors. Since each patient has different values, the information that the pharmacist should provide as a healthcare provider may differ from the information wanted by the patient. To improve patient adherence and avoid the nocebo effect [37], it is important to positively link patient values and information about the treatment plan in SDM while establishing rapport [38] with the patient, rather than provide information based on the values of the healthcare provider.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
All authors contributed to the study conception and design. Material preparation and data collection and analysis were performed by IH and SH. The first draft of the manuscript was written by IH and SH, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript and agreed to accept full responsibility for the entire work.
## Conflict of interest
IH received lecture fees from Asahi Kasei Co., Astellas Pharma Inc., AbbVie GK., Eli Lilly & Co., Eisai Co., Pfizer Japan Inc., Mitsubishi Tanabe Pharma Provision Co., Janssen Pharmaceutical K.K., and UCB Japan Co., Ltd. RR received lecture fees from Asahi Kasei Co., Astellas Pharma Inc., AbbVie GK., Eli Lilly & Co., Eisai Co., Mitsubishi Tanabe Pharma Provision Co., Janssen Pharmaceutical K.K., UCB Japan Co., Ltd., and AstraZeneca Co., Ltd. RF received lecture fees from Daiichi Sankyo Propharma Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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|
---
title: 'Distinct roles of ADIPOR1 and ADIPOR2: A pan-cancer analysis'
authors:
- Zhuoyuan Chen
- Huiqin Yang
- Yunfeng Ren
- Ze Yang
- Jiazheng Huang
- Cheng Li
- Ying Xiong
- Bin Yu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9990624
doi: 10.3389/fendo.2023.1119534
license: CC BY 4.0
---
# Distinct roles of ADIPOR1 and ADIPOR2: A pan-cancer analysis
## Abstract
### Introduction
AdipoR1 and AdipoR2 proteins, encoded by ADIPOR1 and ADIPOR2 genes respectively, are the receptors of adiponectin secrected by adipose tissue. Increasing studies have identified the vital role of adipose tissue in various diseases, including cancers. Hence, there is an urgent need to explore the roles of AdipoR1 and AdipoR2 in cancers.
### Methods
We conducted a comprehensive pan-cancer analysis for the roles of AdipoR1 and AdipoR2 via several public databases, including expression differences, prognostic value, and the correlations with tumor microenvironment, epigenetic modification, and drug sensitivity.
### Results
Both ADIPOR1 and ADIPOR2 genes are dysregulated in most cancers, but their genomic alteration frequencies are low. In addition, they are also correlated with the prognosis of some cancers. Although they are not strongly correlated with tumor mutation burden (TMB) or microsatellite instability (MSI), ADIPOR$\frac{1}{2}$ genes display a significant association with cancer stemness, tumor immune microenvironment, immune checkpoint genes (especially CD274 and NRP1), and drug sensitivity.
### Discussion
ADIPOR1 and ADIPOR2 play critical roles in diverse cancers, and it is a potential strategy to treat tumors through targeting ADIPOR1 and ADIPOR2.
## Introduction
Obesity is a significant contributor to the risk of cardiovascular disease, diabetes, and cancers. With the obesity epidemic, adipose tissue attracts more and more attention. Adipose tissue is an organ with a variety of roles in biological activity, including energy storage, thermogenesis, as well as endocrine. As an endocrine organ, adipose tissue is capable of secreting several hormones, such as leptin, fibroblast growth factor-21, interleukin-6, and adiponectin [1]. Adiponectin is a peptide that predominately exists in the endocrine factors from adipocytes [2]. After posttranslational modification, adiponectin is released into circulation and then binds to AdipoR1 and AdipoR2 (encoded by ADIPOR1 and ADIPOR2, respectively), which consequently initiates a range of downstream signal pathways [3].
A number of diseases have been linked to ADIPOR1 and ADIPOR2. Insulin resistance associated with obesity is accompanied by the downregulation of ADIPOR1 and ADIPOR2 [4]. Adiponectin signaling is able to inhibit liver fibrosis and AdipoR2 is the main adiponectin receptor responsible for anti-fibrosis effect [5]. Besides, ADIPOR1 is essential for vision, and its deletion causes photoreceptor dysfunction in mice model [6]. AdipoR1 and AdipoR2 also play distinctive functions. They are required for the maintenance of membrane fluidity in most human cell types, which is independent of adiponectin [7]. The dysregulation of ADIPOR$\frac{1}{2}$ is observed in various cancers. A study reported that both ADIPOR1 and ADIPOR2 are highly increased in gastric cancer samples from patients in Iran [8]. Adrenal cancers are also associated with ADIPOR1 and ADIPOR2. Cortical cancer and pheochromocytomas had considerably higher levels of ADIPOR1 and ADIPOR2 as well [9]. In addition, increasing studies have showed that the functions of immune cells are significantly influenced by AdipoR1 and AdipoR2. It has been determined that a subpopulation of Treg cells that secret more IL-10 express AdipoR1 [10]. AdipoR1 is also able to induce the differentiation of naïve T cell into Th17 cells, and its deletion downregulates the expression of a series of T cell related genes [11]. AdipoR1 and AdiporR2 are also found to be enriched in the dendritic cells (DCs) from patients with metastatic or locally advanced breast cancer. The anti-tumor impact of DCs can be diminished by AdipoR1 activating the AMPK and MAPKp38 pathways and AdipoR2 initiating the COX-2 and PPAR pathways [12].
In summary, ADIPOR1 and ADIPOR2 display the capacity to influence anti-cancer immunity and play vital roles in numerous tumors. However, there is no study to investigate their roles in pan-cancer. Hence, using a number of public databases, we carried out this study to explore the roles of ADIPOR1 and ADIPOR2 across cancers.
## The expression of ADIPOR1 and ADIPOR2
Firstly, the RNA expression levels of ADIPOR1 and ADIPOR2 in normal tissues were explored through The Human Protein Atlas database (THPA, https://www.proteinatlas.org/). Secondly, the RNA expression levels of ADIPOR1 and ADIPOR2 in tumor tissues were analyzed using TIMER2.0 database (http://timer.cistrome.org/) and GEPIA2 database (http://gepia2.cancer-pku.cn/#index). TIMER2.0 provides a web tool to perform biological analysis based on the data from TCGA database. GEPIA2 is a visualization website to conduct biological analysis based on the data from TCGA and GTEx database. Tumor acronyms were shown at the “Abbreviation” section. Thirdly, the protein levels of ADIPOR1 and ADIPOR2 were analyzed using UALCAN database (http://ualcan.path.uab.edu/).
The RNA expression levels of ADIPOR1 and ADIPOR2 in normal tissues were exhibited in Figure 1. Generally, ADIPOR1 had a higher expression level in normal tissues then ADIPOR2. Bone marrow was the tissue with highest expression of ADIPOR1, followed by tongue and skeletal muscle (Figure 1A). White matter was the tissue with highest expression of ADIPOR2, followed by liver and medulla oblongata (Figure 1B).
**Figure 1:** *The expression levels of ADIPOR1 (A) and ADIPOR2 (B) in various normal tissues.*
ADIPOR1 and ADIPOR2 exhibited a wide positive correlation across cancers (Figure 2A). The results from TIMER2.0 database revealed that ADIPOR1 was upregulated in breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical (CESO), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC), but was downregulated in kidney Chromophobe (KICH), and kidney renal papillary cell carcinoma (KIRP); ADIPOR2 was upregulated in HNSC, KICH, and LUSC, but was downregulated in COAD, kidney renal clear cell carcinoma (KIRC), LUAD, PCPG, PRAD, thyroid carcinoma (THCA), and UCEC (Figure 2B). Additionally, ADIPOR2 was upregulated in HNSC-human papillomavirus (HPV) (+) samples compared with HNSC-HPV [-] samples. The results from GEPIA2 database showed that ADIPOR1 was upregulated in BRCA, CESC, CHOL, COAD, glioblastoma multiforme (GBM), brain lower grade glioma (LGG), LIHC, ovarian serous cystadenocarcinoma (OV), PAAD, PCPG, PRAD, READ, and STAD, but was downregulated in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), and thymoma (THYM); ADIPOR2 was upregulated in DLBC, KICH, LGG, PAAD, skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), and THYM, but was downregulated in LUAD, OV, PCPG, THCA, UCEC, and uterine carcinosarcoma (UCS) (Figure 2C).
**Figure 2:** *The expression of ADIPOR1 and ADIPOR2 across cancers. (A) The correlation between ADIPOR1 and ADIPOR2 across cancers. (B) The difference analysis ofADIPOR1 and ADIPOR2 across cancers via TIMER2.0 database. Red represented tumor samples, and black represented normal samples. *P < 0.05, **P< 0.001, ***P < 0.0001. (C) The difference analysis of ADIPOR1 and ADIPOR2 across cancers via GEPIA2 database. *P < 0.05.*
Data on ADIPOR$\frac{1}{2}$ proteins in cancer tissues were supplied from the UALCAN database. ADIPOR1 protein was dysregulated in breast cancer, clear cell renal cell carcinoma, glioblastoma multiforme, head and neck squamous carcinoma, hepatocellular carcinoma, and lung adenocarcinoma (Figure 3A). ADIPOR2 protein was dysregulated in breast cancer and hepatocellular carcinoma (Figure 3B).
**Figure 3:** *The protein abundance of ADIPOR1 and ADIPOR2 in cancers. (A) The ADIPOR1 protein abundance in UALCAN database. (B) The ADIPOR2 protein abundance in UALCAN database.*
## The prognostic value of ADIPOR1 and ADIPOR2
The prognostic value of ADIPOR1 and ADIPOR2 were estimated by UCSCXenaShiny and GEPIA2. UCSCXenaShiny is a R shiny application that provides an interactive tool to analysis data from TCGA, GETx, CCLE, and PCAWG, using which we explored the prognostic roles of ADIPOR1 and ADIPOR2 via univariate Cox analysis. In the GEPIA2 web, “Survival Map” module was use to draw the prognosis-associated heatmap, and “Survival Analysis” was used to draw the Kaplan-Meier plots (KM plots).
The results of univariate cox analysis revealed that ADIPOR1 worked as a risky factor in adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), CESC, KICH, KIRP, and LGG, but worked as a favorable factor in KIRC and sarcoma (SARC); ADIPOR2 served as a risky factor in BLCA, LUAD, and mesothelioma (MESO), but served as favorable factor in KIRC and READ (Figure 4A). The survival analysis from GEPIA2 provided similar results. Figure 4B exhibited the overall survival (OS) analysis results of ADIPOR$\frac{1}{2}$ across cancers. The higher expression of ADIPOR1 was correlated with ACC and LGG, but was correlated with better OS in KIRC and SARC. The higher expression of ADIPOR2 was correlated with worse OS in LUAD, MESO, and PAAD, but was correlated with better OS in KIRC. Figure 4C showed the results of disease free survival (DFS) analysis. The higher expression of ADIPOR1 was correlated worse DFS in ACC, BLCA, and LGG. The higher expression of ADIPOR2 was correlated with worse DFS in MESO and PAAD, but was correlated with better DFS in KIRC and THCA.
**Figure 4:** *The roles of ADIPOR1 and ADIPOR2 in cancers. (A) The univariate cox analysis for ADIPOR1 and ADIPOR2. (B) The overall survival analysis for ADIPOR1 and ADIPOR2. (C) The disease free survival analysis for ADIPOR1 and ADIPOR2.*
## The genetic alteration and epigenetic modulation
We explored the genomic alteration of ADIPOR$\frac{1}{2}$ in cBioPortal database (https://www.cbioportal.org/). The TCGA pan-cancer atlas studies were chosen as data source. The DNA methylation level of ADIPOR$\frac{1}{2}$ were analyzed via UCSCXenaShiny. Finally, the correlation of ADIPOR$\frac{1}{2}$ with DNA methyltransferases and N6-methyladenosine (m6A) enzymes were analyzed through TIMER2.0 database.
## Immune infiltration analysis
The expression of ADIPOR$\frac{1}{2}$ in normal immune cells were explored through THPA database. The immune infiltration cells across cancers were estimated through TIMER2.0 database. EPIC was chosen as the calculating method. Spearman test was chosen to estimate the correlation between immune cells and ADIPOR$\frac{1}{2.}$ The roles of ADIPOR$\frac{1}{2}$ in immune subtypes were analyzed in TISDIB database (http://cis.hku.hk/TISIDB/).
The results from THPA database showed that neutrophil was the immune cell with the highest expression of ADIPOR1 (Figure 6A), and Non-Vd2 gdTCR was the immune cell with the highest expression of ADIPOR2 (Figure 6B). According to the EPIC findings, ADIPOR1 and ADIPOR2 were positively correlated with CD8+ T cell and negatively correlated with NK cells widely (Figures 6C, D). ADIPOR1 was correlated with immune subtypes in 17 cancers (Figure 6E). *In* general, subtype C4 had higher ADIPOR1 expression levels. ADIPOR2 was correlated with immune subtypes in 12 cancers (Figure 6F). ADIPOR2 was found to be mostly strongly expressed in subtype C4 and C5.
**Figure 6:** *The correlation of ADIPOR1/2 with tumor immune microenvironment. (A, B) The expression of ADIPOR1/2 in immune cells. (C, D) The correlation between ADIPOR1/2 and tumor microenvironment. (E, F) The distribution of ADIPOR1/2 in immune subtypes of cancers.*
## Stemness, TMB, MSI, and immunotherapy
The correlation between ADIPOR$\frac{1}{2}$ and stemness, tumor mutation burden (TMB), and microsatellite instability (MSI) were estimated via UCSCXenaShiny. The roles of ADIPOR$\frac{1}{2}$ in immunotherapy were analyzed through TIGER database (http://tiger.canceromics.org/#/), which provides a web-accessible tool to analyze the gene expression data correlated with immunotherapy. Finally, the correlation of ADIPOR$\frac{1}{2}$ with immune checkpoint genes across cancers was estimated through TIMER2.0 database.
ADIPOR1 was correlated with stemness in ACC, BRCA, ESCA, GBM, KICH, LAML, LGG, LIHC, LUSC, PCPG, PRAD, SKCM, STAD, TGCT, THCA, and THYM, TMB in OV and STAD, and MSI in HNSC, KIRC, LUSC, PCPG, READ, and UCEC. ADIPOR2 was correlated with stemness in BLCA, ESCA, HNSC, KICH, KIRC, KIRP, LAML, LIHC, LUSC, SKCM, STAD, and THYM, TMB in DLBC, ESCA, LUAD, and THCA, and MSI in ESCA, KIRP, LUAD, and SKCM (Figure 7A). The results from TIGER database showed that ADIPOR2 was upregulated after DCs treated in GSE10618 cohort (Figure 7B), and the patients with low expression of ADIPOR2 had better clinical outcomes in GSE91061 anti-PD-1 cohort (Figure 7C). However, ADIPOR1 did not exhibited correlation with response to immunotherapy in clinical cohorts. Both ADIPOR1 and ADIPOR2 exhibited correlations with diverse immune checkpoint genes in various cancers. ADIPOR1 was predominantly positively correlated with TNSF4, TNFSF18, TNFSF15, TNFSF14, NRP1, CD44, CD276, and CD274, but was predominantly negatively correlated with TNFRSF4, TNFRSF25, TNFRSF18, TNFRSF14, and TMIGD2 (Figure 7D). ADIPOR2 was mainly positively correlated with TNFSF15, NRP1, ICOSLG, HHLA2, CD44, CD274, and CD200, but was mainly negatively correlated with TNFRSF4, TNFRSF25, TNFRSF14, TMIGD2, CD70, and CD48 (Figure 7E).
**Figure 7:** *The correlation between ADIPOR1/2 and stemness, TMB, MSI, immunotherapy, and immune checkpoint genes. (A) The correlation between ADIPOR1/2 and stemness, TMB, and MSI across cancers. (B) The expression level of ADIPOR2 between Pre-Therapy and Post-Therapy. (C) The Kaplan-Meier plot of ADIPOR2 in immunotherapy cohort. (D, E) The correlation between ADIPOR1/2 and immune checkpoint genes. *P < 0.05, **P < 0.01, ***P < 0.001.*
## Function analysis
Firstly, we collected ADIPOR$\frac{1}{2}$ related genes from several public databases. We got 20 genes from geneMANIA database (https://genemania.org/), 20 genes from STRING database (https://string-db.org/), and 100 genes from GEPIA2 database. Totally, 138 ADIPOR1-related genes and 134 ADIPOR2-related genes were collected. Then, based on these genes, we explored the function of ADIPOR$\frac{1}{2}$ through Metascape database (https://metascape.org/gp/index.html#/main/step1). Thirdly, we analyzed the role of ADIPOR$\frac{1}{2}$ in cancers via CancerSEA database (http://biocc.hrbmu.edu.cn/CancerSEA/). CancerSEA provides a web tool for the exploration of genes across cancers at single cell level [13]. Finally, based on the Pan-cancer data from UCSCXena database, the correlations between hallmark pathways and ADIPOR$\frac{1}{2}$ were calculated by spearman test.
The functional enrichment analysis showed that both ADIPOR1 and ADIPOR2 were associated with AMPK signaling, adiponectin-activated signaling pathway, vesicle transport, and lipid and glucose metabolism (Figures 8A, B). ADIPOR1 also exhibited a correlation with protein phosphorylation and dephosphorylation process, whereas ADIPOR2 exhibited a stronger correlation with protein ubiquitination and autophagy. The results from CancerSEA database revealed that ADIPOR1 was associated with quiescence, differentiation, DNA damage, and DNA repair (Figure 8C), and ADIPOR2 was associated with DNA damage, DNA repair, and angiogenesis (Figure 8D). The correlation analysis of hallmark pathways showed that ADIPOR1 was mainly correlated with HEME_METABOLISM, MTORC1_SIGNALING, PROTEIN_SECRETION, and UNFOLDED_PROTEIN_RESPONSE, and ADIPOR2 was mainly correlated with ANDROGEN_RESPONSE, MITOTIC_SPINDLE, E2F_TARGET, G2M_CHECKPOINT, MITOTIC_SPINDLE, MYOGENESIS, and PROTEIN_SECRETION (Figure 9).
**Figure 8:** *Function analysis of ADIPOR1 and ADIPOR2. (A, B) The function enrichment analysis of ADIPOR1/2 using MetaScape database. (C, D) The function analysis of ADIPOR1/2 using CancerSEA database.* **Figure 9:** *The correlations of HallMark pathways with ADIPOR1 (A) and ADIPOR2 (B). *P < 0.05, **P < 0.01, ***P < 0.001.*
## Drug sensitivity
CellMiner is a query tool for the genetic and drug sensitivity data of NCI-60 cancer cell lines, based on which we made an investigation into the correlations between ADIPOR$\frac{1}{2}$ and drug sensitivity.
Totally, ADIPOR1 was associated with 36 drugs, and ADIPOR2 was associated with 43 drugs. The top 10 drugs with highest significance were presented in Figure 10. LGK-974 was the drug mostly correlated with ADIPOR1, followed by Lexibulin and Cpd-401 (Figure 10A). SCH-772984 was the drug mostly correlated with ADIPOR2, followed by Altiratinib and OTS-964 (Figure 10B).
**Figure 10:** *Drug sensitivity analysis. (A) The correlation between ADIPOR1 and drug sensitivity. (B) The correlation between ADIPOR2 and drug sensitivity.*
## Statistical analysis
This study was conducted by several public database. Spearman test was used to estimate the correlations of ADIPOR$\frac{1}{2}$ with molecular features.
## Genetic alteration and epigenetic modulation
*Low* genetic alteration frequency was seen for both ADIPOR1 and ADIPOR2 across cancers (Figures 5A, B). Amplification is the most common alteration of ADIPOR$\frac{1}{2.}$ The tumor with the highest frequency of ADIPOR1 alteration is breast cancer, while the tumor with the highest frequency of ADIPOR2 alteration is ovarian epithelial tumor. Figures 5C, D exhibited the DNA methylation levels of ADIPOR$\frac{1}{2}$ across cancers, respectively. The DNA methylation level of ADIPOR1 was downregulated in BLCA, LIHC, PAAD, and UCEC, but was upregulated in BRCA, HNSC, LUAD, and LUSC. The DNA methylation level of ADIPOR2 was downregulated in BLCA, BRCA, KIRC, LIHC, LUSC, and PAAD, but was upregulated in UCEC. ADIPOR$\frac{1}{2}$ also exhibited a wide correlation with DNA methyltransferases (Figures 5E, F) and m6A enzymes (Figures 5G, H).
**Figure 5:** *The genetic alteration and epigenetic modulation of ADIPOR1 and ADIPOR2. (A, B) The genetic alteration of ADIPOR1 and ADIPOR2 across cancers. (C, D) The DNA methylation of ADIPOR1 and ADIPOR2. (E, F) The correlation between ADIPOR1/2 and DNA methyltransferases. Black represented the relationship without significance. (G, H) The correlation between ADIPOR1/2 and m6A enzymes. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 and ns presented P > 0.05.*
## Discussion
AdipoR1 and AdipoR2, encoded by ADIPOR1 and ADIPOR2 respectively, are the receptors of adiponectin. The activation of AdipoR1 and AdipoR2 by adiponectin result in the initiation of a series of signaling pathways, including AMPK and MAPKp38 pathways. Besides, they also function as a form of ceramidase activity to reduce the intracellular level of ceramide, which are linked to inflammation and cell death [3]. Considering the complex biological functions of AdipoR1 and AdiopoR2, there is an urgent need to thoroughly analyze their contribution in cancer and anti-cancer immunity. Here, we conducted a pan-cancer analysis to investigate the roles of AdipoR1 and AdiopoR2 across cancers.
Although they both serve as the receptors of adiponectin, AdipoR1 and AdipoR2 possess different tissue specificities, indicating that they focus on different functional objectives. According to the THPA database, AdipoR1 was abundant in bone marrow, whereas AdipoR2 was abundant in white matter. A study from Korean found that ADIPOR1 rs16850799 and rs34010966 polymorphisms are correlated with bone mineral density in postmenopausal women [14]. In the aged 5XFAD mouse model of Alzheimer’s disease, AdipoR2 was observed highly expressed in activated astrocytes [15]. Our investigation found that the expression of ADIPOR$\frac{1}{2}$ was dysregulated in a large number of tumors, which is consistent with the findings of earlier studies showing ADIPOR1 and ADIPOR2 were implicated in various malignancies. Although they possessed significant correlations across cancers, the expression patterns of ADIPOR1 and ADIPOR2 were varied. ADIPOR1 was predominantly upregulated in cancers, whereas ADIPOR2 was downregulated in many cancers. In addition, the protein abundance of ADIPOR$\frac{1}{2}$ was observed dysregulated in many cancers as well. However, among cancers, the frequency of ADIPOR$\frac{1}{2}$ genetic alterations was low.
Some studies have focused on how ADIPOR1 and ADIPOR2 affect cancer prognosis. According to a study based on 369 patients, the overexpression of ADIPOR1 is related to lower overall survival outcomes in colorectal cancer [16]. According to a study involving 866 patients, ADIPOR2 was related to lethal prostate cancer [17]. In this study, we discovered that the expression levels of ADIPOR$\frac{1}{2}$ were linked to some clinicopathologic features, including OS and DFS. According to the results of univariate cox analysis and Kaplan-Meier plot analysis, ADIPOR1 was identified as a risky factor in ACC and LGG, and its overexpression was associated with their OS and DFS; ADIPOR2 was identified as a favorable factor in KIRC and a risky factor in MESO. Jehonathan H Pinthus et al. also reported that KIRC with metastasis expressed lower ADIPOR.
The aberrant epigenetic modulation is one of common characteristics in cancer. The current study indicated that the promoter DNA methylation levels of ADIPOR$\frac{1}{2}$ were rarely dysregulated in most cancers. Besides, they only exhibited slight correlations with 4 DNA methyltransferases. However, there were substantial correlations between ADIPOR$\frac{1}{2}$ and m6A enzymes. Especially in DLBC, they both exhibited positive associations with almost all the m6A readers, writers, and erasers.
Recently, ADIPOR1 and ADIPOR2 were discovered to be strongly linked to metabolic and immunological homeostasis in colorectal cancer [18]. Therefore, we performed a comprehensive analysis to determine the correlation between ADIPOR$\frac{1}{2}$ and the tumor immune microenvironment. In General, immune cells express more ADIPOR1 than ADIPOR2. Compared to other immune cells, neutrophils were shown to express ADIPOR1 at a high level. It was reported that porcine ADIPOR1 transgenic mice exhibited higher expression levels of neutrophil chemokines, which indicated that ADIPOR1 owned a potential correlation with the recruitment of neutrophils [19]. A study explored the distribution of ADIPOR$\frac{1}{2}$ on human peripheral blood mononuclear cells using flow cytometry. They found that, approximately, ADIPOR1 was present on $1\%$ of T cells, $21\%$ of NK cells, $47\%$ of B cells, and $93\%$ monocytes, and ADIPOR2 also exhibited similar distribution [20]. ADIPOR$\frac{1}{2}$ also exhibited a correlation with the infiltration of immune cells. Our study revealed that both ADIPOR1 and ADIPOR2 were positively associated with CD4+ T cell and negatively associated with NK cell in most cancers. Qian Zhang et al. have confirmed that AdipoR1-deficent CD4+ T cells expressed lower Hypoxia-Inducible Factor-1α, and consequently was is suppressed to differentiate into Th17 cells because of the glycolysis inhibition [11]. Additionally, ADIPOR2 exhibited a positive correlation with endothelial cells in many cancers. Angiogenesis is required for the progression of tumors, and endothelial cells are linked to the tumor metastasis and the formation of cancer-associated fibroblasts [21]. ADIPOR1 and ADIPOR2 were also found differentially expressed in immune subtypes. In many cancers, subtype C4 (lymphocyte depleted) expressed higher ADIPOR1. C4 showed Th1 suppressed and a high M2 response [22]. In KIRC and LGG, subtype C5 (immunologically quiet) expressed significantly high ADIPOR2. C5 displayed the lowest lymphocyte and highest macrophage in comparison to other immune subtypes [22].
ADIPOR1 and ADIPOR2 were correlated with stemness in many cancers, even though they only had weak correlations with TMB and MSI. It has been established that ADIPOR1 protects neural stem cells. J Song et al. found that AdipoR1 shielded neural cells against hyperglycemia both in vivo and in vitro [23]. Nevertheless, more research is needed to fully understand the connections between ADIPOR$\frac{1}{2}$ and cancer stem cells. Compared to ADIPOR1, ADIPOR2 exhibited a stronger correlation with immunotherapy. In GSE106128, a DCs treated cohort, the expression of ADIPOR2 was upregulated after therapy. In GSE91061, an anti-PD-1 cohort, the patients with lower ADIPOR2 expression had better results. In addition, both the ADIPOR1 and ADIPOR2 exhibited extensive correlations with immune checkpoint genes in the majority of cancers, indicating that they may be used as predictors or novel targets of immunotherapy. CD274, encoding PD-L1, was observed significantly correlated with ADIPOR1 and ADIPOR2 in almost all the cancers. As a result, targeting ADIPOR$\frac{1}{2}$ is a viable tactic to increase the efficacy of anti-PD-L1. Besides, NRP1 also exhibited a strong correlation with ADIPOR$\frac{1}{2.}$ As an immune memory checkpoint, NRP1 is capable of limiting long-term antitumor immunity [24]. NRP1 not only promotes Treg activation, but also hinders CD8+ T cell responsiveness to immune checkpoint inhibitors [25].
Then, we investigated the function of ADIPOR1 and ADIPOR2 using bioinformatic methods. They possess certain distinctive features as well as some biological roles in common. ADIPOR1 exhibited potential correlations with phosphate metabolic process and regulation of dephosphorylation, whereas ADIPOR2 displayed potential correlations with protein ubiquitination and autophagy. CancerSEA database makes it possible to analyze the functions of gene at the single cell level. ADIPOR1 was found correlated with Quiescence status. Quiescent cancer cells are resistant to chemotherapy [13]. Adiponectin-AdipoR1 axis enhances sensitivity to tyrosine kinase inhibitor sunitinib in metastatic renal cell carcinoma via inhibiting PI3K/AKT/NF-κB signaling [26]. ADIPOR2 was correlated with angiogenesis, DNA damage, and DNA repair. Jennifer R Ridar et al. found that ADIPOR2 was positively associated with two measures of angiogenesis in lethal prostate cancer via immunohistochemistry [17]. We also conducted a correlation analysis between ADIPOR$\frac{1}{2}$ and drug sensitivity. ADIPPOR1 exhibited a significant correlation with LGK-974, a small-molecule Porcupine (PORCN) inhibitor. PORCN is required for the Wnt ligand secretion, and the inhibition of PORCN by LGK-974 is a promising strategy to target Wnt-driven cancers [27]. ADIPOR2 was strongly correlated with SCH-772984, an ERK inhibitor. Laura Broutier et al. identified the SCH-772984 as a potential drug for primary liver cancer through organoid culture system [28].
Of course, we have to acknowledge that there are some limitations in this study. Although we conducted a comprehensive analysis for the diverse roles of ADIPOR1 and ADIPOR2 across cancers, further experimental evidences are required to identify our results. Only a few tumor’s ADIPOR$\frac{1}{2}$ protein data are available in the public databases. The protein levels of ADIPOR$\frac{1}{2}$ in most cancers need to be validated by experiments. In certain malignancies, ADIPOR1 and ADIPOR2 performed advantageous roles; in other tumors, they played detrimental roles. Therefore, further investigation is needed into the roles played by ADIPOR$\frac{1}{2}$ in the particular malignancy.
## Conclusion
Although obesity has been associated with various tumors, it is still unclear how adipose tissues interact with malignant tissues. As receptors of endocrine factors secreted by adipose, AdipoR$\frac{1}{2}$ proteins (encoded by ADIPOR$\frac{1}{2}$ genes) are potential bridges between adipose tissues and tumors. To investigate the functions of ADIPOR1 and ADIPOR2 genes in tumors, we carried out a thorough pan-cancer analysis. Our research revealed that ADIPOR1 and ADIPOR2 are dysregulated in a wide range of tumors, and that they play critical roles in anti-tumor immunity as well as drug sensitivity, which indicated that adipose tissues might affect tumor tissues via interacting with AdipoR$\frac{1}{2}$ receptors. In all, AdipoR$\frac{1}{2}$ show capacities of prognostication in many tumors, and might serve as the potential biomarkers and drug targets in malignancies.
## 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
BY, ZC, and HY designed this study. BY, ZC, HY, YR, ZY, JH, CL, and YX conducted this study, including data collection, data analysis, and manuscript writing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Understanding the role of physical activity on the pathway from intra-articular
knee injury to post-traumatic osteoarthritis disease in young people: a scoping
review protocol'
authors:
- Karl Morgan
- James Cowburn
- Matthew Farrow
- Josh Carter
- Dario Cazzola
- Jean-Philippe Walhin
- Carly McKay
journal: BMJ Open
year: 2023
pmcid: PMC9990625
doi: 10.1136/bmjopen-2022-067147
license: CC BY 4.0
---
# Understanding the role of physical activity on the pathway from intra-articular knee injury to post-traumatic osteoarthritis disease in young people: a scoping review protocol
## Abstract
### Introduction
The prevalence of intra-articular knee injuries and reparative surgeries is increasing in many countries. Alarmingly, there is a risk of developing post-traumatic osteoarthritis (PTOA) after sustaining a serious intra-articular knee injury. Although physical inactivity is suggested as a risk factor contributing to the high prevalence of the condition, there is a paucity of research characterising the association between physical activity and joint health. Consequently, the primary aim of this review will be to identify and present available empirical evidence regarding the association between physical activity and joint degeneration after intra-articular knee injury and summarise the evidence using an adapted Grading of Recommendations Assessment, Development and Evaluations. The secondary aim will be to identify potential mechanistic pathways through which physical activity could influence PTOA pathogenesis. The tertiary aim will be to highlight gaps in current understanding of the association between physical activity and joint degeneration following joint injury.
### Methods
A scoping review will be conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and best-practice recommendations. The review will be guided by the following research question: what is the role of physical activity in the trajectory from intra-articular knee injury to PTOA in young men and women? We will identify primary research studies and grey literature by searching the electronic databases Scopus, Embase: Elsevier, PubMed, Web of Science: all databases, and Google Scholar. Reviewing pairs will screen abstracts, full texts and will extract data. Data will be presented descriptively using charts, graphs, plots and tables.
### Ethics and dissemination
This research does not require ethical approval due to the data being published and publicly available. This review will be submitted for publication in a peer-reviewed sports medicine journal irrespective of discoveries and disseminated through scientific conference presentations and social media.
### Trial registration number
https://osf.io/84pnh/.
## Introduction
Knee injuries, resulting from both contact and non-contact mechanisms,1–9 are a common orthopaedic complaint in young, sporting populations worldwide10 and often involve damage to multiple tissues inside the joint.11–14 Reconstruction surgery is commonly used to treat the injured tissue,9 11 13 15 16 with hundreds of thousands of knee injuries and reparative reconstruction procedures performed worldwide every year.13 17–19 *Current data* suggests rates of intra-articular injury and tissue repair are rising in several countries,17–19 with knee injuries predominantly affecting people under 40 years of age,13 17–19 which is alarming considering injury greatly increases the risk of developing knee osteoarthritis (OA).20 Knee OA is a highly prevalent disorder21 which is becoming more common,21–23 and is composed of both a disease and an illness component.24 OA disease is associated with illness,25 and the threshold for disease inducing illness is low.24 However, the presence of illness does not always indicate structural deterioration and disease does not always induce symptomology,26 27 with history of knee injury alone enough to induce illness.27 Knee OA affecting people with a history of intra-articular injury is defined as post-traumatic osteoarthritis (PTOA).28–31 *There is* 10-fold increased odds of experiencing PTOA disease between 3-10 years following a knee injury.27 Further, the prevalence of the disease 11–17 years following knee trauma (48–$52\%$)29 32 is concerning when considering average age at diagnosis for idiopathic knee OA is 55 years,33 which indicates a typical case of PTOA would increase disease burden duration. After disease diagnosis, the joint may deteriorate to end-stage OA, resulting in increased odds for total knee replacement (TKR), which occurs at a noticeably younger age in populations with an injury history than those without.34 Knee OA is the primary cause for TKR35 and the number of TKRs in people aged under 55 is increasing.36 As TKRs typically last 25 years,37 these younger patients experiencing PTOA may sustain subsequent prosthesis failure within their lifetime.38 In terms of PTOA illness, after intra-articular knee injury or anterior cruciate ligament reconstruction there is a high prevalence of knee symptomology (eg, pain, stiffness, swelling, crepitus) and loss of function.39–43 Knee pain is described as the cardinal symptom of OA,44 and often drives people to seek medical care.45–47 When applying the pain-avoidance model,48 developed from idiopathic knee OA populations, to PTOA, experiencing disease and illness due to PTOA may exacerbate loss of function across the lifespan, thereby accelerating the decline below the threshold of independence (figure 1).46 49 Consequently, understanding joint deterioration and characterising risk factors for PTOA in knee injury populations, with a view to prevent or slow condition progression, is paramount.
**Figure 1:** *Hypothetical trajectory of injury to lifelong disease burden. PTOA, post-traumatic osteoarthritis; TKR, total knee replacement.*
Physical inactivity is postulated as one of several risk factors that may compound joint degeneration following intra-articular knee injury.50 51 Subjective and objective measurements of physical activity (PA) levels indicate reduced participation following knee injury52–60 and many individuals do not return to the same level of sports participation or return to sport at all after anterior cruciate ligament reconstruction.61–63 Therapeutic exercise is a core treatment for knee OA, and is considered beneficial to joint health,64 while physical inactivity has been touted as a key contributor to joint deterioration in idiopathic OA.22 50 However, a recent narrative review suggested there is little minimal robust scientific evidence linking PA level and joint health following injury.51 Furthermore, although PA often involves knee joint loading (eg, walking or running), there is limited research investigating the association between cumulative joint loading (dictated by PA) and joint health after knee joint trauma. Lastly, there are several physiological maladaptations that can occur after a traumatic knee injury, such as; increased adiposity,41 54 59 65–67 knee extensor and flexor weakness,68–70 muscle atrophy,71 72 intra-muscular fat accumulation,73 74 and bone mineral density reduction,75 which could influence joint health. However, these associations with PTOA are yet to be fully understood. Considering PA is causally associated with systemic inflammation,76–78 adiposity,79 muscle size80 81 and quality,81–86 and bone mineral density87 88 in uninjured populations, exploring the associations between these factors in injured populations may help to explain the complex relationship between PA and joint health following intra-articular knee injury.
This protocol documents the procedure for conducting the review. The primary aim of the scoping review will be to identify and present available empirical evidence regarding the association between PA and joint degeneration after intra-articular knee injury and summarise the evidence using an adapted Grading of Recommendations, Assessment, Development and Evaluations (GRADE).89–91 The secondary aim will be to establish potential mechanistic pathways through which PA could influence PTOA pathogenesis. The tertiary aim will be to highlight gaps in current understanding of the association between PA and joint health following injury.
## Methods
This scoping review is an exploratory project which will systematically map the literature relevant to the research question, identify key concepts, theories and sources of evidence and gaps in the research.92 93 This protocol has been prepared to ensure the transparency of the review process and will be used as a guide when conducting the review and reporting the outcomes to minimise the risk of reporting bias.94 95 Further, this protocol has been developed in accordance with the adapted Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P)96 97 for scoping reviews95 (online supplemental material 1). This review is registered on Open Science Framework (https://osf.io/84pnh/) and the final output will be underpinned both by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews98 and best practice recommendations.99–101 Deviations from this protocol when conducting the review will be documented and published in supplementary material of the final output. This scoping review is currently in progress, with the first search completed in August 2022 and has a projected end date of May 2023.
## Patient and public involvement
During protocol design and planning, there was no patient or public involvement.
## Framework
This review will follow the Arksey and O’Malley framework and adhere to the Joanna Briggs Institute quality assessment recommendations (figure 2).99 102
**Figure 2:** *Outline of the study methodology. JBI, Joanna Briggs Institute.*
## Stage 1: identifying the research question
The review will be guided by the following research question: *What is* the role of PA on the trajectory from intra-articular knee injury to PTOA in young men and women? As PTOA is a joint condition with a ‘disease’ component and an ‘illness’ component,24 we will only describe the association between PA, and the factors PA underpins, with PTOA disease in this review. Disease will be defined as: ‘abnormalities of the structure and function of body organs and systems that can be specifically identified and described by reference to certain biological, chemical or other evidence’.103 Additionally, we will only include studies working with young participants (average age of study population at study start or follow-up measurement is 18–40 years old) in this review. Within the research question, the following themes have been identified a priori: PA, systemic inflammation, knee joint load, adiposity, strength (lower body), intra-muscular adipose tissue (lower body), muscle size (lower body), and bone mineral content. For this review, we will use consensus definitions for intra-articular knee injury, PA, the themes underpinned by PA, and PTOA (table 1). If there is no agreed or clear definition, then best practices used by previous literature or consensus among the research team will be adopted.
**Table 1**
| Term | Definition |
| --- | --- |
| Post-traumatic osteoarthritis | Joint disorder characterised by extracellular matrix degradation initiated by micro-injury and macro-injury resulting in the manifestation of molecular derangement (eg, abnormal joint tissue metabolism) followed by anatomical, and/or physiological derangements (eg, cartilage degradation), that can culminate in illness.24 In this review, we will focus on the disease component of the disorder. |
| Intra-articular knee injury | Previous research has identified cruciate ligament and meniscus injuries have the highest risk of developing into post-traumatic osteoarthritis.20 Considering these are also the most common types of intra-articular knee injury, our review will focus on both ligament and meniscus injuries only. |
| Physical activity | Any bodily movement produced by skeletal muscles that results in energy expenditure.146 Further, we will include the term ‘exercise’ within this definition of physical activity, as although they describe different concepts, they are often used as synonyms.146 Exercise is defined as a subset of physical activity that is planned, structured, and repetitive and has as a final or an immediate objective of improvement or maintenance of physical fitness.146 |
| Systemic inflammation | A consensus definition on the type, number and value of inflammatory biomarkers for systemic inflammation diagnosis has not been devised. In this review, we will use concentrations of key circulating proteins and cytokines identified in previous research as being involved with inflammation processes and osteoarthritis.147–152 |
| Knee joint load | The research team defines knee joint load as quantification of the forces and/or moments applied to the knee joint specifically. |
| Strength (lower body) | Strength is the ability to produce a maximal voluntary muscular contraction against an external resistance.153 We consider the following movements as relevant to this review:Hip extension/flexion/abduction/adduction.Knee extension/flexion/abduction/adduction.Ankle dorsi/plantar flexion, supination/pronation. |
| Adipose tissue | Specialised loose connective tissue that is extensively laden with adipocytes.154 Adipose tissue is arranged in discrete depots,155 and we consider the following depots at any body location relevent to this review:Subcutaneous.Intraperitoneal/viseral.Intermuscular (considered to be similar to intraperitoneal/visceral).156 |
| Intramuscular adipose tissue (lower body) | Intramuscular adipose tissue is the visible fat found within a muscle.156 We consider the intramuscular adipose content in any of the following muscle groups/muscles relevant to this review:Gluteal.Hamstring.Bicep femoris.Semitendinosus.Semimembranosus.Quadriceps.Vastus lateralis.Rectus femoris.Vastus medialis.Vastus intermedius.Tibialis anterior.Gastrocnemius.Soleus. |
| Muscle size (lower body) | Muscle size if the dimension of skeletal muscle, which can be devised into a multiscale157 consisting of;Organ.Tissue.Cellular.Subcellular.Molecular.We consider the muscle size of muscle groups/muscles relevant to this review:Gluteal.Hamstring.Bicep femoris.Semitendinosus.Semimembranosus.Quadriceps.Vastus lateralis.Rectus femoris.Vastus medialis.Vastus intermedius.Tibialis anterior.Gastrocnemius.Soleus. |
| Bone mineral content | The research team defines bone mineral content as the amount of minerals (mostly calcium and phosphorus) contained in bone.Total bone mineral content of the body is considered relevant to this review. Additionally, the mineral content of the following bones is also considered relevant to this review:Femur.Tibia.Fibular. |
## Stage 2: identifying relevant studies
The search terms include population, independent variable, and outcome (online supplemental material 2). Using the Peer Review of Electronic Search Strategy (PRESS)104 guidelines, a professional health science librarian was consulted to develop and tailor search strategies for each database. Electronic databases Scopus, Embase: Elsevier, PubMed, Web of Science: all databases, and Google Scholar will be searched. Literature published between 1970 and 2022 will be included, covering the period during which clinical knee injury diagnosis was formalised and surgical reparative techniques were popularised.105 106 Primary research studies and grey literature will be included.93 107 108
## Stage 3: study selection
Inclusion and exclusion criteria are presented in table 2.
**Table 2**
| Inclusion | Exclusion |
| --- | --- |
| Includes participants with a history of experiencing intra-articular injury or reparative surgery for the injury. | Reporting of any other medical condition (disease, illness, or disorder):Musculoskeletal.Immunological.Cardiological.Respiratory.Neurological.Metabolic. |
| Measures any association between PA or factors determined by PA (systemic markers of inflammation, joint loading, adiposity, muscle strength, lower body muscle size, intermuscular fat, bone density), and either post-traumatic osteoarthritis diagnosis or indicators of joint degeneration. | Evidence of intra-articular injury incidence to any of the following joints prior to or following intra-articular knee injury:Ankle.Knee.Hip.Shoulder.Elbow. |
| 18–40 years of age | Revision surgery if undergone initial reparative surgery. |
| Primary research studies, grey literature | Animal models, in vitro studies, purely in silico studies with no human component (ie, simulated data with no data capture element working with humans), meta-analyses, narrative reviews, systematic reviews, scoping reviews, protocols, commentaries, position or consensus statements, and purely cadaveric studies. |
| Human research | |
| Published in English | |
| Literature published between 1970 and 2022 | |
Outcome measures for PTOA disease must include medical imaging or molecular indicators of joint health (eg, metabolic, proteomic, genomic, transcriptomic).24 *Imaging diagnosis* of disease may include radiography or magnetic resonance imaging (MRI).109–113 Imaging indicators for disease processes include MRI114–117 and ultrasound techniques.118–120 For molecular indicators of disease processes, OsteoArthritis Research Society International (OARSI) recommended biomarkers121 will be included alongside other biomarkers of cartilage metabolism which have been deemed of interest in previous reviews due to their association with joint health (online supplemental material 3).122–125 This list of outcome measures is not exhaustive and will be added to if other relevant outcomes are identified during Stage 2: identifying relevant studies.
To establish the face and content validity of the inclusion and exclusion criteria, and the selected outcome measures, feedback was sought from a surgeon, an osteopath, a physiotherapist, a physiologist and a biomechanist independent from the research team, with experience ranging from clinical practice to sport research. These inclusion and exclusion criteria will be applied to title/abstract screening, which will be conducted using Covidence (Covidence Systematic Review Software, Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org). Prior to screening start, JCo, JCa and MF will be familiarised and trained126 by the lead author (KM) to conduct title/abstract screening using a screening tool hosted on Research Electronic Data Capture platform (REDCap)127 (online supplemental material 4). Following the training session, inter-rater reliability of the inclusion and exclusion criteria for title/abstract screening will be assessed based on a random sample of 120 titles/abstracts. Percentage agreement and Cohen’s kappa (к)128 will be determined between all reviewers (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$). The inclusion and exclusion criteria will be deemed reliable if the agreement is ‘strong’ (percentage agreement >$63\%$ and к >0.60),129 and title/abstract screening will only begin once the agreement level is strong. Following training and provided ‘strong’ agreement between reviewers is achieved, title/abstract screening will begin. During this process reviewing pairs (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$) will independently screen all titles and abstracts to reduce the quantity of errors.130 If disagreements cannot be resolved within reviewing pairs, a consensus decision across the wider research team will be made following a discussion. Duplicate results returned during title/abstract screening will be removed using Covidence.
Following title and abstract screening, full-text screening will be conducted by reviewing pairs (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$).126 Prior to full-text screening, JCo, JCa and MF will be familiarised and trained126 by the lead author (KM) to conduct full text screening using a screening tool hosted on REDCap127 (online supplemental material 5). Following the training session, inter-rater reliability of the inclusion and exclusion criteria for the full-text screening will be assessed based on a random sample of 20 full texts. Once ‘strong’ agreement between reviewers is achieved, full text screening will begin. All full texts will be independently screened (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$) to reduce quantity of errors.130 If disagreements cannot be resolved within reviewing pairs, a consensus decision across the research team will be made following a discussion. In line with common practice131 132 and best-practice recommendations,133 we will contact corresponding and coauthors via email if details of a study are unclear during title/abstract or full-text screening (online supplemental material 6). A maximum of two attempted contacts will be made. The number of corresponding authors contacted, coauthors contacted, replies, and whether adequate information was provided, will be reported. Following full text screening, the remaining relevant studies to the review will undergo forward/backward screening.
## Quality and critical appraisal
In accordance with best-practice recommendations,99 we will assess methodological quality before conducting data charting of included studies. As this review will include various study designs, we will employ Joanna Briggs Institute (JBI) appraisal tools respective to each study design (online supplemental material 7).134–137 Individual items within each JBI appraisal tool will be assigned either a ‘yes’ [1], ‘no’ [0], or ‘unclear’ (seek clarification/further information) response to questions relating to the research quality. If study details are unclear, corresponding authors will be contacted following the same process as described in Stage 3: study selection (online supplemental material 5). A quality percentage score will be calculated, with smaller scores indicating lower quality and larger scores indicating higher quality. Identified studies will be included regardless of the quality appraisal score. The appraisals will be conducted by reviewing pairs (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$) using REDCap.127
## Stage 4: charting the data
Data charting will be conducted in REDCap127 by reviewing pairs (KM—$100\%$ of records, JCo—$33\%$, JCa—$33\%$, MF—$33\%$) to reduce the risk of errors in data extraction.130 Data charting will include author(s), country of study, study year, study design, participant age and sex/gender distributions, injury type, surgery type, type of injury management, time since injury/surgery, sporting history before injury (type and level), aim of study, independent variables, supervision level (if intervention), quantity (if intervention), adherence rate (if intervention), drop out (if intervention), outcome variables, who assessed the measures, statistical methodology, and the strength of the treatment response or relationship between variables. This form (online supplemental material 8) will be pilot tested by two reviewers and necessary amendments will be made if required (eg, other relevant measurement tools or outcome variables). If study details are unclear, corresponding authors will be contacted following the same process as described in Stage 3: study selection.
## Stage 5: summarising the data
Data will be summarised in three steps.99 The first step will present the results as a descriptive numerical summary which will include: number of studies on each theme of the research question; frequency of study design; definitions and variables used for joint disease; overview of methodological quality of studies. The second step will present the results as individual sections for each theme of the research question. Each theme will be structured as follows: The third step will be to discuss and consider the meaning of the findings as they relate to the overall review purpose. We will also discuss implications for future PTOA research.
## Ethics and dissemination
Ethical approval is not required as this review maps and synthesises data generated from published literature. This review will be submitted for publication in a peer-reviewed sports medicine journal, irrespective of findings which indicate a positive or negative association between PA and joint health. Knowledge synthesised by this review will be presented at scientific conferences and disseminated via social media platforms.
## Conclusion
This study seeks to address a salient topic for people who experience a traumatic intra-articular knee injury and clinicians working with this population. Using a scoping review methodology to examine the known influence of PA on PTOA pathogenesis is novel and appropriate, considering the complexity of the topic and the multi-disciplinary evidence base. This research may inform future PTOA research by helping to guide research priorities and identifying outcome measures for clinical interventions.
## Patient consent for publication
Not applicable.
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---
title: The mechanism of Leonuri Herba in improving polycystic ovary syndrome was analyzed
based on network pharmacology and molecular docking
authors:
- Mali Wu
- Hua Liu
- Jie Zhang
- Fangfang Dai
- Yiping Gong
- Yanxiang Cheng
journal: Journal of Pharmacy & Pharmaceutical Sciences
year: 2023
pmcid: PMC9990637
doi: 10.3389/jpps.2023.11234
license: CC BY 4.0
---
# The mechanism of Leonuri Herba in improving polycystic ovary syndrome was analyzed based on network pharmacology and molecular docking
## Abstract
Background: Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder affecting women. Chinese herbs have been considered as an alternative treatment for PCOS, and Yi-mu-cao (Leonuri Herba) is one of the most commonly used herbs to treat PCOS, which can relieve symptoms of PCOS patients. But the mechanism of its treatment remains unclear.
Method: The main active ingredients and potential targets of Leonuri Herba were obtained by TCMSP and Swiss Target Forecast, and the related targets of PCOS were obtained by searching DrugBank, GeneCard and DisGeNet databases. The Protein-Protein Interaction (PPI) network was constructed using STRING database. GO and KEGG were used to detect the enrichment pathways of key targets. Cytoscape software was used to construct the component-target-pathway network, analyze the PPI network core, and verify the reliability of target binding by molecular docking technology.
Result: 8 components and 116 targets of Leonuri Herba on PCOS were screened. Common targets mainly involve the Lipid and atherosclerosis, Endocrine resistance, AGE-RAGE signaling in diabetic complications and other signaling pathways. It is suggested that it can form multi-target and multi-pathway regulatory network through quercetin, kaempferol and other active substances to regulate endocrine disorders and reduce inflammatory response, so as to systematically improve PCOS. Molecular docking experiments showed that the active constituents of Leonurus had good binding activity with potential targets of PCOS.
Conclusion: In summary, this study elucidates the potential effect of Leonuri Herba on PCOS, which is helpful to provide reference for clinical practice. This is also conducive to the secondary development of motherwort and its monomer components, and precision medicine for PCOS.
## Introduction
Polycystic ovary syndrome (PCOS) can affect 5–$18\%$ of women [1]. It is characterized by androgen excess, infertility, irregular menstrual cycle, and abnormal ovarian androgen production caused by PCOM [2]. PCOS increases the risk of infertility, endometrial dysfunction, cardiovascular disease, diabetes, metabolic syndrome, and other diseases [3]. It seriously affected the quality of life of patients. At present, PCOS treatment mainly relies on antiandrogen drugs, insulin sensitizers, ovulation promoting drugs, oral contraceptives and so on (4–6). However, the treatment of PCOS is still a difficult problem in obstetrics and gynecology. As PCOS is a multi-system disease with complex pathological mechanism, heterogeneous symptoms and numerous complications, sometimes western medicines cannot achieve good therapeutic effects, and more drug targets need to be explored.
Yi-mu-cao (Leonuri Herba) is naturally found in plants and has traditionally been used in China for thousands of years for uterine contractions, postpartum congestion, breast tenderness, and other gynecological disorders [7, 8]. It has been reported as a prescription single herb with antioxidant activity that can treat dysmenorrhea by relieving uterine spasms, reducing inflammation, reducing concentrations of prostaglandin F2α and prostaglandin synthase-2 in uterine smooth muscle, increasing serum progesterone levels, and effectively relieving symptoms of PCOS (9–11). However, because Leonuri *Herba is* a kind of herbal medicine with diverse ingredients and targets, the therapeutic mechanism is not clear at present, and it is necessary to further explore its therapeutic mechanism.
In recent years, Chinese herbal medicine has become a new research hotspot because of its multi-component and multi-target characteristics. But the mechanism of treatment is complex and unclear.
In this study, we applied a network pharmacology approach to achieve a multilevel study to determine the interaction between motherwort and PCOS. Network pharmacology is a new strategy to study the interaction between drugs and diseases [12]. This research method can bring a lot of benefits to traditional Chinese medicine (TCM), because the underlying mechanism of a large proportion of TCM has not been fully understood [13]. After network pharmacological analysis, we further confirmed the potential pharmacological effects of Leonuri Herba components on PCOS by molecular docking of the analyzed core genes with the main effective drugs. The entire study can be seen in Figure 1.
**FIGURE 1:** *The workflow of the current network pharmacology study.*
## Screening and target analysis of active constituents from Leonuri Herba
The active components were found by TCMSP (http://tcmspw.com/tcmsp.php), which were screened according to the two ADME attribute values of oral availability (oral bio-availability, OB) ≥$30\%$ and drug-like drugs (drug-likeness, DL) ≥ 0.18 [14]. A total of 8 active components were analyzed in Table 1. In addition, using the PubChem website (https://pubchem.ncbi.nlm.nih.gov/), get PubChem ID, smiles number, and 2D chemical structures. Then, SwissTargetPrediction (http://swisstargetprediction.ch/) was used to predict protein targets, the feasibility of value as the >0.5, get the target of 189. All target genes are converted into gene symbols using the UniProt knowledge base (http://www.UniProt.org).
**TABLE 1**
| Mol ID | Molecule name | ОВ (%) | DL |
| --- | --- | --- | --- |
| MOL001418 | galeopsin | 61.01548 | 0.3753 |
| MOL001420 | ZINC04073977 | 37.99619 | 0.75755 |
| MOL001421 | preleoheterin | 85.97259 | 0.33044 |
| MOL001422 | Iso-preleoheterin | 66.28878 | 0.33032 |
| MOL000098 | quercetin | 46.43335 | 0.27525 |
| MOL001439 | arachidonic acid | 45.57325 | 0.20409 |
| MOL000354 | isorhamnetin | 49.60438 | 0.306 |
| MOL000422 | kaempferol | 41.88225 | 0.24066 |
## Collection of disease targets for PCOS
With “PCOS” and “Polycystic ovary syndrome” as key words, Mine Gene in GeneCard database (https://www. genecards.org) and Map database of OMIM (http://www.omim.org), DisGeNet database (http://bidd.nus.edu.sg/group/cjttd) the potential targets of PCOS, Enter DrugBank database (https://www.drugbank.ca) to search for first-line clinical western drug targets for the treatment of PCOS. In the GeneCards database, a higher score indicates that the target is closely related to the disease. According to experience, if there are too many targets, the target whose *Score is* greater than the median is set as the potential target of PCOS twice. After merging the four disease database targets together, there are 2,440 disease target intersection genes in the four databases in Figure 2A.
**FIGURE 2:** *(A) Venn diagram of disease targets in four databases. (B) Venn diagram of active ingredients and disease targets. PCOS, polycystic ovarian syndrome.*
## Construction and analysis of disease-medicine network
To explore active ingredients of Leonuri Herba targets with PCOS disease targets, the online mapping tool platform (http://www.bioinformatics.com.cn/) was used to draw the Venn diagram, get 116 intersection genes in Figure 2B. Cytoscape3.7.2 software was used to map the TCM-active ingredients-disease target network.
## Constructing PPI network of intersection targets between PCOS and Leonuri Herba
The STRING database (https://string-db.org) was used to construct the PPI network diagram of 116 related targets: Set the biological species as “Homo sapiens,” and the minimum interaction threshold Highest confidence >0.7 as the screening condition. Each node represents a protein and its structure, and each edge represents the association between different proteins.
## Screening key targets
The PPI results are exported as TSV files and then imported into Cytoscape3.7.2 for further network analysis using the CytoNCA plug-in [15]. *Each* gene receives a score based on the six dimensions of the “Betweenness/Closeness/Degree/Eigenvector/LAC/Network.” 14 key targets were screened out under the condition that the median score was more than 6 parameters.
## GO and KEGG pathway enrichment analyses
The results of pathway enrichment analysis from Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) were applied to the STRING online database (https://string-db.org/) to annotate and classify common targets [16]. After setting an adjusted P value cutoff of 0.05, we collected and analyzed the data by Rstudio 3.6.3 (Bioconductor, clusterProfiler).
## Molecular docking between Leonuri Herba and key targets
According to the enrichment results of compounds KEGG and GO and the comprehensive analysis of the current research status, we selected the two most critical molecules of this drug: quercetin (MOL000098) and kaempferol (MOL000422). TCMSP database (https://tcmspw.com/tcmsp.php) to download the molecular structures and transform them into mol2 formats. The structure of the receptor can be downloaded from the PDB Protein Database (http://www.rcsb.org). The docking simulation was performed with selected key proteins such as AKT1, IL6, EGFR, and MMP9 by AutoDock Vina 1.5.6. The binding affinity between molecules and proteins is predicted based on the docking minimum free energy. The lower the free energy, the higher the affinity. The results are saved in the pdbqt file. Finally, the results were analyzed and demonstrated by PyMOL.
## Identification of the ingredients of Leonuri Herba and predicted target genes of PCOS
There was a total of 8 active compounds of Leonuri Herba, as shown in Table 1. 189 potential targets could be obtained after prediction and deweighting of the active compounds with potential targets through screening in SwissTargetPrediction database. The PCOS related target genes were downloaded from four disease databases, and the genes obtained from GeneCards, DisGeNET, OMIM and DrugBank were screened and de-weighted to obtain 2,440 genes. They were then combined with 189 target genes from Leonuri Herba for analysis. Finally, 116 common target genes were extracted. Venn diagrams were plotted accordingly in Figure 2.
## Construction and analysis of target PPI network
*Target* genes were uploaded to STRING online database to form PPI network. 116 nodes (genes) and 726 edges (interactions) were identified, representing the major genes corresponding to the active ingredient of Leonuri Herba in Figure 3. The more interacting target genes are located in the central region of the network. RAC-alpha serine/threonine-protein kinase (AKT1), interleukin-6 (IL6), epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA), matrix metalloproteinase-9 (MMP9), transcription factor Jun (JUN), myc proto-oncogene protein (MYC), interleukin-1 beta (IL1B), and hypoxia-inducible factor 1-alpha (HIF1A) are most important genes in Leonuri Herba ‘s pharmacological effects on PCOS according to their degree.
**FIGURE 3:** *(A) Protein–protein interaction network of the common active ingredients and disease targets. 122 nodes (target genes) and 726 edges (associations between proteins) are presented. (B) Top 30 PPI network core gene visualization.*
## The PPI network core of Leonuri Herba and PCOS cross targets
The PPI results are exported as TSV files and then imported into Cytoscape3.7.2 for further network analysis using the CytoNCA plug-in. Through 6 parameters in CytoNCA “Betweenness/Closeness/Degree/Eigenvector/LAC/Network” to filter 116 genes. First filter condition, “Betweenness: 49.38088469/Closeness: 0.436213992/Degree: 24/Eigenvector: 0.063172609/LAC: 11.38461538/Network: 12.8”. Getting 35 nodes, 598 edges; Second filter condition, “Betweenness: 11.15689699/Closeness: 0.653846154/Degree: 32/Eigenvector: 0.156082243/LAC: 20.66666667/Network: 23.41193154”. Get 14 nodes (core genes), 152 edges, specific genes such as JUN, AKT1, HSP90AA1, CASP3, FOS, MYC, EGFR, HIF1A, TP53, TNF, IL6, IL1B, MMP9, MAPK14 in Figure 4. Betweenness/Closeness/Degree/Eigenvector/LAC/Network of the 14 core genes in Supplementary Figure S1.
**FIGURE 4:** *Topological analysis of the PPI network. CytoNCA score greater than median. The yellow squares represent the core genes for screening. The blue squares represent unscreened genes. (A) The PPI network of PCOS-Leonuri Herba target genes. (B) The PPI network of significant proteins extracted from (A). (C) The PPI network of crucial Leonuri Herba targets for PCOS treatment extracted from (B).*
## Biological functional analysis
Subsequently, GO enrichment analysis was performed. The top 8 enrichment results for BP, MFs and CCs are listed in Figure 5. The results suggest that biological processes include cellular responses to lipopolysaccharide, reproductive phylogeny, oxidative stress, and reactive oxygen species. In the drug-disease interaction, the molecular function is manifested by high level of nuclear steroid receptor activity, protein serine/threonine/tyrosine kinase activity, DNA binding and transcription factor binding, and the interaction was mainly enriched in membrane raft and membrane microdomain. The related pathway of Leonuri Herba was obtained through KEGG enrichment analysis. 166 signaling pathways were discovered, and the top 20 were shown in Figure 6. Lipid and atherosclerosis (has05417), AGE-RAGE signaling pathway (hsa04933) and fluid shear stress and atherosclerhass (hsa05418) are most prominent in the bar graph of Figure 6A.
**FIGURE 5:** *GO Pathway Enrichment Analysis. (A) The horizontal axis of BP, CC, and MF bar represents the number of genes enriched in each, while the color visualizes the significance based on the corrected P value. (B) The bubble diagram demonstrates the gene proportion enriched in each subset. BP, biological process; MF, molecular function; CC, cell component.* **FIGURE 6:** *KEGG pathway enrichment analysis. (A) The red color in the upper part represents greater significance, while the blue represents less significance according to corrected P value. (B) The bubble diagram demonstrates the gene proportion enriched in each entry.*
## Construction of compound-target-disease pathway
Plot the composition-target-pathway network between PCOS and Leonuri Herba (Figure 7). The analysis of Figure 7 shows that Leonuri Herba ort may have a therapeutic effect on PCOS through multiple active components, targets, and pathways. Among them, quercetin, isorhamnetin, and kaempferol are important components, and the main targets are AKT1, EGFR, IL6, MMP9 and so on.
**FIGURE 7:** *Chinese medicine-active ingredient-disease target network. PCOS, polycystic ovarian syndrome.*
## Molecular docking of the compounds and the core targets
Molecular docking verification was carried out by AutoDock Vina. The results showed that the minimum free energy of quercetin (MOL000098), kaempferol (MOL000422) and key targets AKT1, IL6, EGFR and MMP9 were shown in Table 2. Especially among all the possible binding structures, kaempferol has the best affinity with MMP9 (−9.6 kcal/mol). Other detailed results are shown in Figures 8, 9.
## Discussion
PCOS is one of the most common endocrine disorders among women of reproductive age and is heterogeneous in that women may develop reproductive, endocrine, and/or metabolic symptoms that vary throughout their lives [17, 18]. Due to its wide range of causes, it can lead to a range of disease symptoms. Such as low fertility, sparse menstruation, hirsutism, and insulin resistance pose a serious threat to women’s reproductive health and daily life [19]. However, the current western medicine treatment may be unable to solve parts of the symptoms of PCOS. The Chinese medicine and TCM may make up for the shortcomings of Western medicine.
In recent years, more and more Chinese medicines and their preparations have come into the world’s attention. Of course, these TCMs can also be effective in treating PCOS, and many studies on the treatment of PCOS with TCMs have been reported [20]. For example, TCM can be used to treat PCOS with oligomenorrhea and amenorrhea, relieve ovulation dysfunction, obesity, insulin resistance, and improve ovulation and pregnancy rates (21–24).
Leonuri Herba in this study is one of the traditional Chinese herbs, which has been widely used to treat gynecological and obstetric diseases for thousands of years. Its main ingredients are reported to include leonurine, 4′,5-dihydroxy-7-methoxyflavone, rutin, hyperoside, apigenin, quercetin, kaempferol and salicylic acid [25]. So far, its main components have been found to have antioxidant stress, ROS reduction levels, anti-inflammatory, treatment of infertility or menstrual disorders, treatment of cardiovascular and cerebrovascular diseases [9, 26, 27]. However, due to the complex chemical composition and unclear pharmacological mechanism of TCM, it faces great obstacles in pharmacological research, quality control and supervision [28]. At present, TCM database and target prediction technology have brought new ideas and strategies for the research on the basis and mechanism of TCM pharmacodynamic substances and helped to identify the advantages of TCM such as good efficacy, high safety, multi-component, and multi-target [29]. Network pharmacology allows researchers to study the interaction between the chemical composition of Leonuri Herba and PCOS-related genes.
In this study, we used the newly developed bioinformatics technology to explore the possible interaction between Leonuri Herba and PCOS in the network. We found that quercetin and kaempferol are the main active components of this drug, which can play an important role in anti-inflammation and antioxidation, which has been effectively verified in molecular docking research. In addition, quercetin, the main ingredient, has been shown to be effective in treating PCOS. It can significantly reduce the expression of testosterone (T), estradiol (E2), luteinizing Hormone (LH), Bax, IL-1β, IL-6 and TNF-α, increase the expression of FSH and Bcl-2, and inhibit the expression of androgen receptor (AR), thus restoring the maturation and ovulation of oocytes [30, 31]. Moreover, it can also be anti-inflammatory to improve insulin resistance and relieve PCOS endocrine disruption [32, 33]. It has been reported that kaempferol-7-O-methylether, another major component, may increase the activity of PPAR-γ and inhibit the TGF-β pathway, thus improving the metabolic disorder and ovarian fibrosis in PCOS rats [34].
In addition, we screened out 14 core genes (JUN, AKT1, HSP90AA1, CASP3, FOS, MYC, EGFR, HIF1A, TP53, TNF, IL6, IL1B, MMP9, MAPK1). Among them, High levels of AKT1 is associated with Granulosa cells (GC) dysfunction [35]. MMP9 has been confirmed to be associated with atherosclerotic thrombosis, endothelial dysfunction, and non-alcoholic fatty liver disease in PCOS patients (36–38). IL1B, IL6 and TNF are associated with PCOS inflammation, endoplasmic reticulum stress and recurrent abortion, and can be regulated by the active components of Leonuri Herba (39–44). Leonuri Herba regulates the expression of these genes through AGE-RAGE, PI3K-Akt and MAPK signaling pathways.
To the best of our knowledge, this is the first time to reveal the active ingredients of Leonuri Herba and their pharmacological effects on PCOS. This helps researchers and pharmacologists understand the mechanisms of motherwort. However, further in vitro experiments are needed to verify the predicted process.
## Conclusion
Through the analysis of network pharmacology, the step-by-step mining of data and the analysis of multi-target and multi-way methods, we can more clearly understand the important role of Leonuri Herba in PCOS. Our current research is only from the simple prediction of drug components on the target nuclear pathway of network pharmacology. However, further experiments are needed to confirm the specific therapeutic mechanism of Leonuri Herba in the treatment of PCOS. Look forward to the accurate treatment of PCOS with active ingredients of TCM 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 in the article/Supplementary Material.
## Author contributions
MW and HL designed the study. MW and JZ analyzed the data. MW, JZ, and FD wrote the article. HL gave key advice and embellished the article during the process of revising the manuscript. YG and YC are in charge of the project and guides the writing of the thesis. All authors read and approved the final manuscript.
## Funding
This work was supported by the Key Research and Development Program of Hubei Province (2020BCB023); National Natural Science Foundation of China (82071655 and 81860276); China Medical Association Clinical Medical Research Special Fund Project [17020310700]; the Fundamental Research Funds for the Central Universities (2042020kf1013); Educational and Teaching Reform Research Project [413200095]; Graduate credit course projects [413000206].
## 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.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontierspartnerships.org/articles/10.3389/jpps.2023.11234/full#supplementary-material
## Abbreviation
AR, androgen receptor; AKT1, RAC-alpha serine/threonine-protein kinase; E2, estradiol; EGFR, epidermal growth factor receptor; FSH, follicle stimulating hormone; GO, gene ontology; HIF1A, hypoxia-inducible factor 1-alpha; IL-6, interleukin-6; IL1B, interleukin-1 beta; JUN, transcription factor Jun; KEGG, kyoto encyclopedia of genes and genomes; LH, luteinizing hormone; MAPK, mitogen-activated protein kinase; MMP9, matrix metalloproteinase-9; MYC, Myc proto-oncogene protein; PCOS, polycystic ovary syndrome; PPI, protein-protein interaction; T, testosterone; TCM, Traditional Chinese medicine; TCMSP, Traditional Chinese Medicine System Pharmacology Database and Analysis Platform; VEGFA, vascular endothelial growth factor A.
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|
---
title: 'Education mediating the associations between early life factors and frailty:
a cross-sectional study of the UK Biobank'
authors:
- Asri Maharani
- Altug Didikoglu
- Terence W O'Neill
- Neil Pendleton
- Maria Mercè Canal
- Antony Payton
journal: BMJ Open
year: 2023
pmcid: PMC9990643
doi: 10.1136/bmjopen-2021-057511
license: CC BY 4.0
---
# Education mediating the associations between early life factors and frailty: a cross-sectional study of the UK Biobank
## Abstract
### Objectives
Exposures in utero and during infancy may impact the development of diseases later in life. They may be linked with development of frailty, although the mechanism is unclear. This study aims to determine the associations between early life risk factors and development of frailty among middle-aged and older adults as well as potential pathways via education, for any observed association.
### Design
A cross-sectional study.
### Settings
This study used data from UK Biobank, a large population-based cohort.
### Participants
502 489 individuals aged 37–73 years were included in the analysis.
### Primary and secondary outcome measures
Early life factors in this study included being breast fed as a baby, maternal smoking, birth weight, the presence of perinatal diseases, birth month and birth place (in or outside the UK). We developed a frailty index comprising 49 deficits. We used generalised structural equation modelling to examine the associations between early life factors and development of frailty and whether any observed association was mediated via educational attainment.
### Results
A history of breast feeding and normal birth weight were associated with a lower frailty index while maternal smoking, the occurrence of perinatal diseases and birth month with a longer day length were associated with a higher frailty index. Educational level mediated the relationship between these early life factors and frailty index.
### Conclusions
This study highlights that biological and social risk occurring at different stages of life are related to the variations in frailty index in later life and suggests opportunities for prevention across the life course.
## Introduction
As the world’s population ages, a major goal is the attainment of increased life expectancy accompanied by fewer years spent in poor health and with disability and dependency. The worldwide population of older people (65 years and above) is predicted to double from 0.7 billion ($9\%$) in 2019 to 1.5 billion ($16\%$) in 2050.1 In addition, there is evidence that the number of disability-adjusted life years among those aged 60 years and older is increasing (from 434 million in 1990 to 574 million in 2010),2 which will increase demand for health and care services. As physical disability is an adverse outcome of frailty,3 more research in geriatrics and gerontology has focused on defining and recognising frailty among older people with the aim of determining preventive and interventional measures.4 Frailty can be defined as a state of increased vulnerability resulting from an age-related decline in physiological and cognitive reserves and function following stressor events.5 The frailty index approach, developed by Rockwood and Mitnitski,6 measures frailty level as the number of deficits presents over the number of deficits considered, including symptoms, diagnoses, disabilities and functional impairments. Frailty has become more common with the ageing of the population. A systematic review including 240 studies from 62 countries showed that $24\%$ of people aged 50 years and older are frail as calculated using the frailty index approach.7 Frailty has been found to be associated with adverse health outcomes including loss of mobility, disability, falls, hospitalisation, need for long-term care and death.8–10 Understanding the factors that are associated with frailty is thus important for developing interventions to prevent frailty and for providing directions for future public health policies.
A growing body of literature acknowledges that the first two decades of human life are critical in determining adult life trajectories. Among the early life factors, body size at birth,11 12 cigarette smoke exposure in utero,13 infants exclusively breast fed,14 birth month15 and the presence of perinatal diseases16 have been found to be associated with adult chronic diseases. However, the study linking those factors and frailty is limited. In addition, the evidence on the link between early life factors and occurrence of frailty have been mixed.17 18 The present study thus aims to determine the associations between early life factors, including a history of being breast fed, maternal smoking, birth weight, the presence of perinatal diseases, birth in or outside the UK and birth month and frailty in UK adults.
Furthermore, this study contributes to the literature investigating the determinants of health in later life by exploring the pathways of early life factors that have a lasting impact on health in middle and old age. The pathway hypothesis posits that early life conditions are important because they are directly associated with late life and because they shape later life experiences,19 20 including restricted educational attainment and life chances. The most frequently hypothesised pathway between circumstances in early stages of life and adult health is adult socioeconomic status. Pakpahan et al showed that socioeconomic factors in adulthood, including education, mediate the link between childhood health and socioeconomic conditions and self-rated health among older Europeans.19 Because interventions that target common pathways have the potential to reduce frailty, the identification of the pathways of early life factors leading to frailty later in life has substantial public health relevance for the translation of life course epidemiology into practice. The present study considers whether any observed association between early life factors and frailty could be attributed to differences in education attainment (figure 1).
**Figure 1:** *The pathways of early life factors and impact on frailty among adults.*
## Source and sample
Data were drawn from the UK Biobank, a prospective cohort study of the genetic, environmental and lifestyle causes of diseases among adults in the UK.21 The study involved the collection of extensive questionnaire data and biological samples from, and the performance of, physical examinations of >500 000 respondents enrolled at 22 assessment sites in England, Scotland and Wales between 2006 and 2010. Subjects who took part provided written informed consent for data collection, analysis and linkage; they also completed a touchscreen questionnaire, a nurse-led interview and had their physical measurements taken. The UK Biobank invited adults who were registered with a general practitioner and who lived within reasonable travelling distance of the assessment centre. The current study includes 502 489 individuals aged 37–73 years who had study-specific available data and were not withdrawn from the study.
## Early life factors
Information by questionnaire was obtained on: maternal smoking in the prenatal and postnatal period, history of being breast fed as a baby, birth month, birth weight, the presence of perinatal diseases and place of birth. We defined maternal smoking based on the question “Did your mother smoke regularly around the time when you were born?” ( Data-Field 1787). Respondents were categorised as having been breast fed as babies if they answered ‘yes’ to the question: “Were you breast fed when you were a baby?” ( Data-Field 1677). We retrieved information on birth month from the birth date (Data-Field 52) and treated it as the cosine of the values, representing the rhythmic seasonal length of day and night. We considered this might represent daylight time better than treating it as a categorical variable. This is an approach which we have used in a previous study.22 Birth months of participants born in the UK and other countries in the southern hemisphere were converted to their antiphase. Information on birth weight was gathered by means of self-reported birth weight in kilograms (Data-Field 20022). We categorised the birth weight into low birth weight (<2500 g), normal birth weight (2500–4000 g) and high birth weight (>4000 g). The presence of perinatal diseases (‘ICD-10 chapter XVI: certain conditions originating in the perinatal period’) was coded as one based on self-reported medical history (category 2416). We categorised the place birth of the respondents as born in the UK or outside the UK (Data-Field 1647). Answers of ‘Do not know’ or ‘Prefer not to answer’ were accepted as missing for all questions.
## Education
The education variable represents the highest educational level completed by the respondents. Qualifications were categorised as high school or less (reference) and college or university degree (Data-Field 6138).
## Frailty index
Following William et al,23 we derived the frailty index using 49 functional, psychological and social deficits within the range of data variables in the UK Biobank (see online supplemental table 1). We coded the binary variables as 0 or 1, and for ordinal and continuous variables, coding was based on distribution. The total number of deficits was summed and divided by total possible deficits to create a frailty index between 0 and 1, where higher scores indicated greater frailty.
## Covariates
We included demographic and health behaviour as covariates. Demographic information included age (in years; Data-Field 21003), gender (with male as the reference; Data-Field 31) and ethnicity (other than Caucasian as the reference or Caucasian; Data-Field 21000). Health behaviours included physical activity, alcohol intake and smoking status. Physical activity was measured as the number of days per week respondents engaged in at least 10 min of moderate or vigorous physical activity (Data-Field 884, Data-Field 904). Respondents were classified as non-current smokers (reference) or current smokers (Data-Field 20116). Alcohol intake status was classified as non-current (reference) or current alcohol drinking (Data-Field 20117).
## Statistical analyses
Descriptive statistics were used to summarise subject characteristics including means and SD for continuous variables and frequencies and percentages for categorical variables. We looked at the associations between frailty index and both early life factors and other covariates using unpaired t-tests (dichotomous variables), analysis of variance (categorical variables) and Pearson’s correlation (continuous variables).
We first performed a multivariate regression model including early life factors, education and covariates (age, gender, ethnicity, smoking, alcohol drinking, physical activity). We further handled missing data using multivariate imputation by chained equations24 (using Stata’s mi program).25 Twenty imputations were used.
The structural equation model (SEM) has been widely used to investigate complex relationships between variables in epidemiological studies.26 SEM can be used to resolve the endogeneity problem between variables and to explore direct, indirect and total effects between exogenous and endogenous variables. It can jointly test a variety of hypotheses that involve different types of complicated cause-effect relationships. However, all responses are assumed to be continuous, even when a variable is binary or categorical. In our analysis we include binary (education). To address this, we used a generalised structural equation model (GSEM) to identify the link between early life factors and frailty index and the mediating effect of education on that relationship. A GSEM combines generalised linear model (GLM) estimation and SEM modelling estimation; it can accommodate binary, ordinal, counted and categorical data.27 *Using maximum* likelihood estimators, GLM estimators are based on a density function, allowing the direct use of all types of data.28 The analyses were performed using MPlus V.8. We examined education as mediators of the relationship in the GSEM model, which were controlled for age, gender, ethnicity and health behaviours (model fit information: χ2=5049.35, $$p \leq 0.00$$; RMSEA (root mean square error of approximation)=0.06, CFI (comparative fit index)=0.82; WRMR (weighted root mean square residual)=13.01).
## Patient and public involvement
Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
## Subjects
The study sample consisted of 502 489 respondents with an average age of 56.53 years (SD=8.10 years) (table 1). Just under half ($45\%$) of the respondents were male, and most were Caucasian ($94.59\%$). Around one-third of the respondents had graduated from college or university. The proportion of respondents whose mothers smoked regularly around the time of their birth was $29\%$. More than $72\%$ of respondents were breast fed as babies, and $0.18\%$ had perinatal diseases. Ten per cent of respondents had low birth weight, while $13\%$ of them had high birth weight; $91\%$ of the respondents were born in the UK. Just over two-thirds of subjects reported engaging in at least 10 min of moderate or vigorous physical activity at least 3 days per week; $92\%$ consumed alcohol and $11\%$ were current smokers.
**Table 1**
| Variable | Percentage or mean (SD)* | Mean (SD) of frailty index† | Bivariate association with frailty index‡ |
| --- | --- | --- | --- |
| Frailty index, mean (SD) | 0.14 (0.08) | | |
| Early life factors | | | |
| Maternal smoking around birth, % | | | P<0.0001 |
| No | 70.75% | 0.133 (0.073) | |
| Yes | 29.25% | 0.146 (0.078) | |
| Breast fed as a baby, % | | | P<0.0001 |
| No | 27.65% | 0.137 (0.076) | |
| Yes | 72.35% | 0.134 (0.074) | |
| Birth weight, % | | | P<0.0001 |
| Low birth weight | 10.26% | 0.149 (0.080) | |
| Normal birth weight | 76.34% | 0.131 (0.073) | |
| High birth weight | 13.40% | 0.136 (0.076) | |
| Birth month, % | | | P=0.0002 |
| January | 8.44% | 0.138 (0.076) | |
| February | 7.96% | 0.137 (0.075) | |
| March | 8.98% | 0.138 (0.075) | |
| April | 8.59% | 0.139 (0.076) | |
| May | 8.98% | 0.138 (0.076) | |
| June | 8.45% | 0.139 (0.076) | |
| July | 8.48% | 0.139 (0.076) | |
| August | 8.24% | 0.138 (0.076) | |
| September | 8.14% | 0.138 (0.075) | |
| October | 8.06% | 0.137 (0.076) | |
| November | 7.63% | 0.137 (0.075) | |
| December | 8.03% | 0.138 (0.076) | |
| Perinatal diseases, % | | | P<0.0001 |
| No | 99.82% | 0.138 (0.075) | |
| Yes | 0.18% | 0.149 (0.084) | |
| Born in the UK, % | | | P=0.0381 |
| No | 8.96% | 0.137 (0.076) | |
| Yes | 91.04% | 0.138 (0.075) | |
| Sociodemographics | | | |
| Age (years), mean (SD) | 56.53 (8.10) | | r=0.16, p<0.0001 |
| Gender, % | | | P<0.0001 |
| Female | 54.40% | 0.141 (0.075) | |
| Male | 45.60% | 0.134 (0.076) | |
| Ethnicity, % | | | P<0.0001 |
| Other | 5.41% | 0.141 (0.078) | |
| Caucasian | 94.59% | 0.138 (0.075) | |
| Education, % | | | P<0.0001 |
| Less than college | 67.27% | 0.145 (0.077) | |
| College or university degree | 32.73% | 0.122 (0.069) | |
| Health behaviours | | | |
| Moderate or vigorous physical activity, % | | | P<0.0001 |
| | 10.75% | 0.160 (0.085) | |
| 1 day | 7.11% | 0.134 (0.072) | |
| 2 days | 13.40% | 0.133 (0.072) | |
| 3 days or more | 68.75% | 0.135 (0.073) | |
| Current alcohol consumption, % | | | P<0.0001 |
| No | 8.08% | 0.166 (0.088) | |
| Yes | 91.92% | 0.135 (0.074) | |
| Current smoking, % | | | P<0.0001 |
| No | 89.39% | 0.135 (0.074) | |
| Yes | 10.61% | 0.159 (0.084) | |
## Early life factors, covariates and frailty index
In bivariate analyses, compared with those whose mothers did not smoke around birth, maternal prenatal and postnatal smoking was associated with a significantly higher frailty index (0.146 vs 0.133) as was the presence of perinatal diseases (0.149 vs 0.138) and being born in the UK (0.138 vs 0.137). A history of breast feeding was associated with a lower frailty index (0.134 vs 0.137). Low (0.149 vs 0.131) and high (0.136 vs 0.131) birth weight were associated with higher frailty scores compared with normal birth weight. Shorter daylight hours at birth (r=−0.01) were associated with lower frailty indices. As expected, the frailty index was higher among women than among men and in those with lower educational attainment. The frailty index was also higher in smokers, non-drinkers and those who engaged in less physical activity.
In regression analyses, the effects of early life factors and covariates on the frailty index appeared similar in terms of both magnitude and direction when using both non-imputed and imputed data (see online supplemental table 2). In these multivariate regression analyses, adjusting for age, gender and health behaviours, birth month with longer hours of daylight, having a low and high birth weight, maternal smoking, not being breast fed as baby, perinatal diseases and born in the UK had positive and significant associations with frailty index.
## Mediation analysis
In the GSEM model, education mediated the association between early life factors and frailty index among middle-aged and older adults, supporting the pathway hypothesis. Table 2 presents the total, direct and indirect effects for each of the early life factors on the frailty index. Maternal smoking (direct effect: coefficient=0.068, $z = 33.40$; indirect effect: coefficient=0.011, $z = 25.54$) and low birth weight (direct effect: coefficient=0.041, $z = 20.93$; indirect effect: coefficient=0.003, $z = 9.18$) and high birth weight (direct effect: coefficient=0.013, $z = 6.34$; indirect effect: coefficient=0.001, $z = 4.09$) directly and indirectly affected the frailty index compared with normal birth weight. The direct and indirect effects of being breast fed as a baby on having a lower frailty index were −0.022 (z=−10.36) and −0.009 (z=−22.91). Perinatal diseases had significant direct effect on higher frailty index (coefficient=0.007, $z = 3.83$), but it had no indirect effect on the frailty index (coefficient=0.000, $z = 0.27$). Being born in the UK, differently, had a significant indirect effect on higher frailty index (coefficient=0.016, $z = 31.24$), but it had no direct effect on the frailty index (coefficient=0.002, $z = 0.74$). Birth months with a short daylength were directly (coefficient=−0.006, z=−2.91) and indirectly (coefficient=−0.001, p=−2.35) associated with lower frailty scores.
**Table 2**
| Unnamed: 0 | Total effects | Direct effects | Indirect effects |
| --- | --- | --- | --- |
| Breast fed as a baby | –0.031 (0.002)* | –0.022 (0.002)* | −0.009 (0.000)* |
| Maternal smoking around birth | 0.079 (0.002)* | 0.068 (0.002)* | 0.011 (0.000)* |
| Low birth weight | 0.045 (0.002)* | 0.041 (0.002)* | 0.003 (0.000)* |
| High birth weight | 0.015 (0.002)* | 0.013 (0.002)* | 0.001 (0.000)* |
| Birth month (cos) | –0.007 (0.002)† | –0.006 (0.002)† | –0.001 (0.000)† |
| Perinatal diseases | 0.007 (0.002)* | 0.007 (0.002)* | 0.000 (0.000) |
| Born in the UK | 0.018 (0.002)* | 0.002 (0.002) | 0.016 (0.001)* |
Education mediated the links between early life factors and frailty index (figure 2). Participants born in the UK had a lower probability of completing higher education (coefficient=−0.130, z=−44.65). Having been breast fed as a baby (coefficient=0.076, $z = 26.87$) was associated with higher educational attainment, while maternal smoking was associated with lower educational attainment (coefficient=−0.087, z=−31.41). Both low (coefficient=−0.027, z=−9.39) and high birth weight (coefficient=−0.011, z=−4.11) was related to lower education attainment compared with normal birth weight. Birth months with short daylight was related to higher education with the lowest effect size (coefficient=0.006, $z = 2.35$). Higher education was also associated with a lower frailty index (coefficient=−0.123, z=−44.30). Among covariates with greater effect sizes, older age (coefficient=0.178, $z = 83.25$), lower activity levels (coefficient=−0.088, z=−45.61) and smoking (coefficient=0.106, $z = 56.36$) were associated with a higher frailty index. Drinking alcohol is related to lower frailty index (coefficient=−0.093; z=−51.63).
**Figure 2:** *Generalised structural equation models to identify the association between early life factors and frailty index, and education as mediators of the relationship between early life factors and frailty index. *p<0.05; †p<0.001.*
## Discussion
Using data from UK Biobank, we found that a history of breast feeding was associated with a lower frailty index, while maternal smoking, having low or high birth weight, perinatal diseases and birth month with longer day length were associated with a higher frailty index. This study provides the first evidence that educational attainment level mediates the association between early life factors and frailty index.
Early life factors have previously been linked with higher frailty and chronic disease risk later in life.29 30 Our findings highlight the importance of early life factors in determining frailty in middle age and older individuals. Maternal smoking was directly associated with higher frailty compared with those who were not exposed to maternal smoking. Evidence has suggested that cigarette smoke exposure in utero is linked to the development of chronic diseases later in life, including type 2 diabetes, obesity, certain cancers and respiratory disorders.13 We also showed that this association was mediated by educational attainment. This is in line with a previous study which reported lower academic achievements of adolescents whose mothers smoked during pregnancy.31 Maternal smoking during pregnancy was also found to be correlated with the children’s cognitive function.32 *There is* some evidence of a link between early life factors and occurrence of frailty. In a recent study in Finland, greater weight, length and body mass index at birth were associated with a lower risk of frailty later in life.17 In our study, having low or high birth weight were associated with higher frailty index compared with having a normal birth weight, both directly and indirectly through education. Bleker et al found that prenatal undernutrition was not associated with frailty but was associated with poorer health in old age, including slower gait speed and lower physical functioning which are components of the frailty phenotype, and the findings remained significant after inclusion of an extensive set of control variables including adult socioeconomic status.18 Low birth weight is associated with increased risk of age-related diseases in prior review, and insulin-like growth factor 1 is the key driver of this process.33 High birth weight may be the results of maternal obesity34 and a study in Finland found that being born large for gestational age at term was associated with thicker carotid intima medial as the marker of subclinical atherosclerosis.35 We also found that individuals who reported that they were breast fed have a lower frailty score. Infants exclusively breast fed have been found to have a lower risk of obesity, type 2 diabetes and high blood pressure in adulthood.14 Birth month is associated with lower frailty index scores with a limited effect size in our study. In a large study in the USA with 1 749 400 individuals showed that spring summer-born individuals have a relatively higher cardiovascular disease risk than autumn-winter born individuals and these seasons coincide with lower life expectancy.36 This study showed that cardiovascular diseases and several chronic diseases were associated with season of birth, having a different seasonal pattern. The underlying mechanisms may differ for each of these associations, such as sensitisation to allergens or vitamin D deficiency.36 Another possible mechanism is that differential light exposure during perinatal period influences development of the biological clock, in turn influencing later-life circadian rhythms and the sleep system, which are essential for health.37 In European countries, it was shown that spring/summer born participants compared with autumn had higher frailty scores but this effect seemed independent of education.38 However, we found an indirect effect of season of birth through education. The indirect relationship of season of birth and frailty may be due to social factors such as the UK September date cut-off for starting education, which is in line with our findings showing an association between winter-born individuals and higher educational attainment.39 40 Our results further suggest that having a perinatal disease was associated directly with higher frailty index scores. This finding is in keeping with that newborns’ perinatal complications are related to accelerated ageing at midlife.16 Being born in the UK affected the frailty index indirectly through education, but not directly. Respondents who were not born in the UK were likely to have higher education attainment, which may enable better maintenance of health during older ages. However, we should note that our sample in this analysis may not be representative of the general population, and that participants were categorised as being born outside the UK without taking into account the country of origin and their socioeconomic background. In our analysis, we observed that education levels mediate the link between the other early life factors and the frailty index. Early life factors have a significant relationship with educational attainment, and higher education attainment is linked to a lower frailty index. This result is broadly in keeping with a prior study in Sweden which found that the associations between childhood conditions and various old age health indicators (musculoskeletal disorders, cardiovascular disease, self-rated health and impaired mobility) are mediated by education.41 Prior research on the biological and psychological pathways linked childhood health and socioeconomic conditions to self-reported health status among older adults in 15 European countries.19 Prior studies have shown that the life-course trajectories of socioeconomic attainment could be altered by physical and social conditions,42 and both childhood and adult conditions may impact health decades later.43 Our findings have potential implications for policies aiming at preventing frailty among older adults. Subsequent circumstances mediate the impact of early life factors on frailty later in life, and our study suggests that interventions such as improving education in midlife may mitigate early life disadvantages.
Our findings are based on a large and well-characterised cohort. There are, however, a number of limitations to be consider in interpreting the results. First, information concerning early life factors in this study was based on self-report and is therefore subject to recall error. The likely effect of such error would be to underestimate the relationship between these factors and the frailty index. Second, we have limited access to the health conditions of the parents. A broad range of conditions which are comprised in the frailty index bear a hereditary risk, thus taking into account the health conditions of the parents is important in assessing the independent associations with frailty. Future studies may include the health conditions of the parents as the covariates. Third, the information on breastfeeding duration is unavailable. Breast feeding for weeks rather than months may confer different outcomes. A dose-response relationship thus cannot be assessed. Finally, these data were based on a sample of predominantly Caucasian men and women and should be extrapolated beyond this group with caution.44 In conclusion, this study indicates an association between early life factors and frailty later in life. Early life conditions are important as the start of a mediated, incremental process during the life course. A comprehensive understanding of the determinants of frailty among middle-aged and older adults requires attention to exposures throughout the entire life course, with a special focus on the in utero and infancy stages and the chains of associated socioeconomic conditions that connect over the life course. Applying a life-course perspective to health in adulthood and old age should have implications for public health interventions, social policy and further research. Early life is not the only period for any potential successful intervention; as our findings show, early life disadvantages may be offset by education. Interventions throughout the life course, and especially during early life, could substantially reduce the health burden later in life.
## Data availability statement
Data may be obtained from a third party and are not publicly available. Data are available in a public, open access repository. Other researchers can apply for UK *Biobank data* to answer specific research questions.
## Patient consent for publication
Not applicable.
## Ethics approval
This study was conducted as part of UK Biobank Project Number 41877 and is covered by the generic ethics approval for UK Biobank studies from the NHS National Research Ethics Service (16/NW/0274). Participants gave informed consent to participate in the study before taking part.
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|
---
title: 'Association between obstructive sleep apnoea and cancer: a cross-sectional,
population-based study of the DISCOVERY cohort'
authors:
- Andreas Palm
- J Theorell-Haglöw
- Johan Isakson
- Mirjam Ljunggren
- Josefin Sundh
- Magnus Per Ekström
- Ludger Grote
journal: BMJ Open
year: 2023
pmcid: PMC9990651
doi: 10.1136/bmjopen-2022-064501
license: CC BY 4.0
---
# Association between obstructive sleep apnoea and cancer: a cross-sectional, population-based study of the DISCOVERY cohort
## Abstract
### Objectives
Nocturnal hypoxia in obstructive sleep apnoea (OSA) is a potential risk factor for cancer. We aimed to investigate the association between OSA measures and cancer prevalence in a large national patient cohort.
### Design
Cross-sectional study.
### Settings
44 sleep centres in Sweden.
### Participants
62 811 patients from the Swedish registry for positive airway pressure (PAP) treatment in OSA, linked to the national cancer registry and national socioeconomic data (the course of DIsease in patients reported to Swedish CPAP, Oxygen and VEntilator RegistrY cohort).
### Outcome measures
After propensity score matching for relevant confounders (anthropometric data, comorbidities, socioeconomic status, smoking prevalence), sleep apnoea severity, measured as Apnoea-Hypopnoea Index (AHI) or Oxygen Desaturation Index (ODI), were compared between those with and without cancer diagnosis up to 5 years prior to PAP initiation. Subgroup analysis for cancer subtype was performed.
### Results
OSA patients with cancer ($$n = 2093$$) ($29.8\%$ females, age 65.3 (SD 10.1) years, body mass index 30 (IQR 27–34) kg/m2) had higher median AHI (n/hour) (32 (IQR 20–50) vs 30 (IQR 19–45), n/hour, $$p \leq 0.002$$) and median ODI (n/hour) (28 (IQR 17–46) vs 26 (IQR 16–41), $p \leq 0.001$) when compared with matched OSA patients without cancer. In subgroup analysis, ODI was significantly higher in OSA patients with lung cancer ($$n = 57$$; 38 (21–61) vs 27 [16-43], $$p \leq 0.012$$)), prostate cancer ($$n = 617$$; 28 (17–46) vs 24, (16–39)$$p \leq 0.005$$) and malignant melanoma ($$n = 170$$; 32 (17–46) vs 25 (14–41), $$p \leq 0.015$$).
### Conclusions
OSA mediated intermittent hypoxia was independently associated with cancer prevalence in this large, national cohort. Future longitudinal studies are warranted to study the potential protective influence of OSA treatment on cancer incidence.
## Introduction
Patients with obstructive sleep apnoea (OSA) have recurring airway collapses during sleep with corresponding episodes of hypoxia. Animal models suggest an association between intermittent hypoxia and increased tumour growth, increased angiogenesis, changes in immune function and increases in inflammation and oxidative stress.1 Several studies investigated the potential causal relationship between OSA and cancer2–10 and some but not all report an increased prevalence of cancer in the OSA population studied. A recent meta-analysis provided evidence for a $40\%$–$50\%$ increased risk ratio for cancer incidence in OSA patients.11 As OSA is associated with several other risk factors for cancer diseases, such as obesity, cardiometabolic disease and lifestyle factors, the independent influence of OSA-related hypoxia for an increased cancer risk remained unsolved. The validity of existing data may be in part hampered by the relatively low number of reported cancer cases, a potential reporting bias due to patient-reported cancer diagnosis without the validation of independent sources, as well as by a selection bias due to single-centre studies.
In Sweden, $85\%$ of patients with OSA receiving continuous positive airway pressure (CPAP) are reported to the Swedevox registry12 13 and almost all patients diagnosed with cancer are reported to the mandatory governmental Cancer Registry.14 The Swedish system with personal identity numbers15 allows for cross-linkage of various registries, creating large databases with reliable data quality. Thereby, our current study aimed to overcome some of the limitations mentioned above by exploring the association between measures of OSA and cancer prevalence in a large, multicentric national cohort of OSA patients referred for PAP treatment using validated data from the national cancer registry for verified cancer diagnosis and subclassification.
## Study design and population
This was a national, population-based, cross-sectional study from the ‘course of DIsease in patients reported to Swedish CPAP, Oxygen and VEntilator RegistrY’ cohort. The study has been detailed elsewhere.13 *In this* study, data on patients aged ≥16 years with OSA on CPAP therapy, correctly and prospectively reported to the Swedevox registry12 between 1 July 2010 and 31 December 2017 were cross-linked with data from the mandatory National cancer registry16 for information about cancer diagnosis, the Prescribed Drug Registry with information about all prescriptions of medications17 and the National Patient Registry (NPR) covering data and diagnosis from all inpatient and outpatient visits at governmental hospitals.18 *Socioeconomic data* (marital status, education level) were covered from Statistics Sweden.19 An overview of the study flow is presented in figure 1.
**Figure 1:** *Study flow chart. CPAP, continuous positive airway pressure; OSA, obstructive sleep apnoea*
## End-point variables and covariables
The primary variable of interest was captured by the diagnose codes ‘all-cause cancer’ (International classification of Disease version 10 (ICD-10) code C00-99) up to 5 years prior initiation of CPAP therapy. Additional information was captured from organ specific cancer: ear, neck and throat cancer (ENT) (C1–14), colon (C18), lung (C34), malignant melanomas (C43), breast (C50), prostate (C61), urinary (C66–67) and malignant brain tumours (C71). Information on ‘skin cancer’ (C44) other than malign melanoma was excluded.
Information on sex, body mass index (BMI), Apnoea–Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Epworth Sleepiness Scale score (ESS) and date of initiation of treatment were derived from the Swedevox registry.12 13 Comorbid heart failure (ICD-10 codes I11, I42 and I50) and ischaemic heart disease (ICD-10 code I20–25) was derived from NPR.18 Comorbid obstructive lung disease (OLD) was classified according to accepted practice by ≥2 collections of antiobstructive drugs (Anatomic Therapeutic Chemical classification system (ATC) code R03) ≤12 months prior to initiation of CPAP and comorbid diabetes was classified by ≥1 collection of antidiabetic drugs (A10) ≤6 months prior initiation of CPAP therapy was registered in the national Prescribed Drug Registry.17 Information on educational level was obtained from Statistic Sweden and was categorised as low (≤9 years), medium (10–12 years) or high (≥13 years), corresponding to compulsory school, secondary school and postsecondary school (college and university), respectively. The Swedevox registry does not contain information about individual smoking data. A marker for smoking exposure was generated from information on annual smoking prevalence between 2006 and 2018, in each county according to Statistics Sweden’s Living Conditions Surveys (Undersökningen av levnadsförhållanden/Survey on Income and Living Conditions, ULF/SILC).20 Counties were trichotomised according to mean smoking rate ($8.3\%$–$12.0\%$, $12.1\%$–$13.5\%$ and $13.6\%$–$14.8\%$).
## Statistical analyses
OSA severity was graded by the AHI and ODI both as continuous and categorical variables (AH! <5, 5 to <15 (mild OSA), 15 to <30 (moderate OSA) and ≥30 (severe OSA)). Clinical cut-off values for ODI severity are not defined, and we, therefore, classified ODI into quartiles when used as a categorical variable. Association between sleep recording variables and all-cause cancer diagnosis ≤5 years prior to the initiation of CPAP therapy was analysed. In detail, the relationship between prevalent all-cause cancer and organ-specific cancer and AHI and ODI were analysed using propensity score matched models.21 For propensity score matching, independent covariables were used as anthropometrics (age, sex, BMI), presence of comorbidities at baseline (ischaemic heart disease, heart failure, diabetes, OLD), socioeconomic data (civil status and educational level), start year of CPAP treatment and smoking prevalence (by county, trichotomised). These factors were chosen based on direct acyclic graphs using the browser-based environment DAGitty (www. dagitty.net).22 In addition, we performed a parallel analysis applying logistic regression analysis in the entire baseline population to predict ‘all-cause cancer diagnosis’ independent of all mentioned covariates used for the propensity matching procedure.
Normally distributed continuous data were expressed as mean±SD, and skewed distributed data were expressed as median with IQR. Categorical data were presented as frequencies and percentages. Differences between groups were analysed using χ2 test for categorical variables and Student’s t-test for continuous variables. A $p \leq 0.05$ was considered statistically significant. Statistical analyses were conducted using the software packages Stata, V.17.0 (StataCorp).
## Patient and public involvement
None.
## Patient characteristics
Clinical data, in particular age and frequency of comorbidities varied significantly between unmatched cases ($$n = 2933$$) and controls ($$n = 59$$ 878). The final propensity score matched study population consisted of 2096 patients with cancer ($29.8\%$ females, mean age 65.3 (SD 10.1) years, median BMI 30 (IQR 27–34) kg/m2) and 2096 matched OSA patients without cancer (table 1). Totally in the cohort, 91 patients had lung cancer, 46 ENT, 798 prostate cancer, 239 breast cancer, 191 colon cancer, 134 urinary cancer, 254 malignant melanoma and 16 malignant brain tumours ≤5 years prior to CPAP initiation.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Unmatched | Unnamed: 3 | Matched |
| --- | --- | --- | --- | --- |
| | No cancer | All-cause cancer | No cancer | All-cause cancer |
| | N=59 878 | N=2933 | N=2093 | N=2093 |
| Sex, females (%) | 17 647 (29.5%) | 903 (30.8%) | 661 (31.6%) | 623 (29.8%) |
| Age, years | 56.8 (12.5) | 65.5 (10.2) | 65.3 (10.2) | 65.3 (10.1) |
| BMI, kg/m2 | 31 (28–35) | 30 (27–34) | 30 (27–34) | 30 (27–34) |
| ESS, units | 10.3 (5.0) | 9.9 (4.9) | 9.6 (4.9) | 10.0 (4.9) |
| Ischaemic heart disease | 5671 (9.5%) | 425 (14.5%) | 311 (14.9%) | 309 (14.8%) |
| Heart failure | 3263 (5.4%) | 277 (9.4%) | 180 (8.6%) | 186 (8.9%) |
| Diabetes mellitus | 8548 (14.3%) | 526 (17.9%) | 360 (17.2%) | 366 (17.5%) |
| OLD | 7610 (12.7%) | 462 (15.8%) | 325 (15.5%) | 327 (15.6%) |
| Civil status | | | | |
| Married | 31 926 (53.5%) | 1771 (61.0%) | 1300 (62.1%) | 1280 (61.2%) |
| Unmarried | 14 675 (24.6%) | 392 (13.5%) | 289 (13.8%) | 275 (13.1%) |
| Divorced | 10 561 (17.7%) | 520 (17.9%) | 340 (16.2%) | 378 (18.1%) |
| Widower/widow | 2529 (4.2%) | 219 (7.5%) | 164 (7.8%) | 160 (7.6%) |
| Level of education | | | | |
| Low (≤9 years) | 10 547 (21.7%) | 605 (26.1%) | 540 (25.8%) | 546 (26.1%) |
| Medium (10–12 years) | 25 065 (51.5%) | 1061 (45.8%) | 977 (46.7%) | 959 (45.8%) |
| High (≥13 years) | 13 060 (26.8%) | 653 (28.2%) | 576 (27.5%) | 588 (28.1%) |
After matching procedure, clinical characteristics did not differ between cases and controls except for the ESS score which was slightly elevated in cases compared with controls (ESS score 10.0 (SD 4.9) vs 9.6 (SD 4.9), $$p \leq 0.02$$, respectively).
## Sleep apnoea severity in OSA patients with and without cancer
Cases with cancer disease had higher AHI (median 32 (IQR 20–50) vs 30 (19–45), n/hour, $$p \leq 0.002$$) and ODI (28 (17–46) vs 26,16–41 $p \leq 0.001$) when compared with matched controls without cancer (table 2 and figure 2). Severe nocturnal intermittent hypoxia (ODI quartile IV (>45 n/hour)) was significantly more prevalent in cases ($25.7\%$) compared with controls ($20.8\%$), $$p \leq 0.001$$ for ODI categories. Corresponding numbers for the highest AHI class (≥30 n/hour) were $54.9\%$ in cases and $52.4\%$ in controls but the differences in AHI categories did not reach statistical significance.
**Figure 2:** *Distribution of OSA patients with (red bars) and without (blue bars) cancer diagnosis in the four ODI quartiles (x-axis). Propensity score matched analysis in 2093 cases and 2093 controls, ODI distribution differs significantly between cases and controls, $p \leq 0.001.$ ODI, Oxygen Desaturation Index; OSA, obstructive sleep apnoea.* TABLE_PLACEHOLDER:Table 2 *Subgroup analysis* confirmed that ODI was significantly higher in OSA patients with lung cancer ($$n = 57$$, median 38 (IQR 21–61) vs 27 (16–43), $$p \leq 0.012$$)), prostate cancer ($$n = 617$$, 28 (17–46) vs 24,16–39 $$p \leq 0.005$$) and malignant melanoma ($$n = 170$$, 32 (17–46) vs 25,14–41 $$p \leq 0.015$$) when compared with corresponding OSA patients without cancer (table 3). The remaining cancer subtypes did not differ in OSA severity measures.
**Table 3**
| Unnamed: 0 | No lung cancer | Lung cancer | P value | No ENT cancer | ENT cancer | P value.1 | No prostate cancer | Prostate cancer | P value.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | N=57 | N=57 | | N=31 | N=31 | | N=617 | N=617 | |
| AHI, events/hour | 28 (18–44) | 38 (25–56) | 0.019 | 29 (18–46) | 30 (17–41) | 0.30 | 28 (19–44) | 33 (20–50) | 0.006 |
| AHI category, events/hour | | | 0.10 | | | 0.036 | | | 0.035 |
| AHI<5 | 0 | 0 | | 0 | 0 | | 4 (0.6%) | 2 (0.3%) | |
| AHI 5–14.9 | 8 (14.0%) | 6 (10.5%) | | 1 (3.2%) | 7 (22.6%) | | 70 (11.3%) | 66 (10.7%) | |
| AHI 15–29.9 | 21 (36.8%) | 12 (21.1%) | | 15 (48.4%) | 8 (25.8%) | | 251 (40.7%) | 208 (33.7%) | |
| AHI>30 | 28 (49.1%) | 39 (68.4%) | | 15 (48.4%) | 16 (51.6%) | | 292 (47.3%) | 341 (55.3%) | |
| ODI, events/hour | 27 (16–43) | 38 (21–61) | 0.012 | 24 (15–46) | 23 (13–39) | 0.22 | 24 (16–39) | 28 (17–46) | 0.005 |
| ODI, quartiles, events/hour | | | 0.034 | | | 0.21 | | | 0.009 |
| ODI 0–15.9 | 13 (22.8%) | 6 (10.5%) | | 8 (25.8%) | 11 (35.5%) | | 147 (23.8%) | 129 (20.9%) | |
| ODI 16–27.9 | 17 (29.8%) | 17 (29.8%) | | 10 (32.3%) | 6 (19.4%) | | 210 (34.0%) | 170 (27.6%) | |
| ODI 28–44.9 | 15 (26.3%) | 9 (15.8%) | | 5 (16.1%) | 10 (32.3%) | | 139 (22.5%) | 161 (26.1%) | |
| ODI >45 | 12 (21.1%) | 25 (43.9%) | | 8 (25.8%) | 4 (12.9%) | | 121 (19.6%) | 157 (25.4%) | |
| | No Breast cancer | Breast cancer | P value | No colon cancer | Colon cancer | P value | No urinary cancer | Urinary cancer | P value |
| | N=162 | N=162 | | N=123 | N=123 | | N=93 | N=93 | |
| AHI, events/hour | 28 (17–46) | 28 (18–46) | 0.49 | 33 (21–53) | 30 (21–48) | 0.41 | 31 (19–47) | 35 (24–52) | 0.15 |
| AHI category, events/hour | | | 0.59 | | | 0.31 | | | 0.40 |
| AHI <5 | 4 (2.5%) | 2 (1.2%) | | 4 (3.3%) | 2 (1.6%) | | 1 (1.1%) | 1 (1.1%) | |
| AHI 5–14.9 | 29 (17.9%) | 24 (14.8%) | | 10 (8.1%) | 13 (10.6%) | | 13 (14.0%) | 6 (6.5%) | |
| AHI 15–29.9 | 51 (31.5%) | 60 (37.0%) | | 35 (28.5%) | 46 (37.4%) | | 26 (28.0%) | 30 (32.3%) | |
| AHI >30 | 78 (48.1%) | 76 (46.9%) | | 74 (60.2%) | 62 (50.4%) | | 53 (57.0%) | 56 (60.2%) | |
| ODI, events/hour | 24 (14–39) | 27 (17–49) | 0.26 | 28 (18–50) | 26 (17–43) | 0.47 | 28 (16–47) | 32 (21–50) | 0.11 |
| ODI, quartiles, events/hour | | | 0.30 | | | 0.71 | | | 0.67 |
| ODI 0–15.9 | 49 (30.2%) | 38 (23.5%) | | 24 (19.5%) | 26 (21.1%) | | 20 (21.5%) | 14 (15.1%) | |
| ODI 16–27.9 | 39 (24.1%) | 49 (30.2%) | | 34 (27.6%) | 36 (29.3%) | | 26 (28.0%) | 25 (26.9%) | |
| ODI 28–44.9 | 40 (24.7%) | 34 (21.0%) | | 28 (22.8%) | 32 (26.0%) | | 22 (23.7%) | 25 (26.9%) | |
| ODI >45 | 34 (21.0%) | 41 (25.3%) | | 37 (30.1%) | 29 (23.6%) | | 25 (26.9%) | 29 (31.2%) | |
| | No malignant melanoma | Malignat melanoma | P value | No malignat brain tumour | Malignant brain tumour | P value | | | |
| | N=170 | N=170 | | N=8 | N=8 | | | | |
| AHI, events/hour | 28 (17–45) | 35 (23–52) | 0.003 | 31 (12–40) | 40 (16–54) | 0.25 | | | |
| AHI category, events/hour | | | 0.072 | | | 0.86 | | | |
| AHI <5 | 2 (1.2%) | 1 (0.6%) | | 0 | 0 | | | | |
| AHI 5–14.9 | 25 (14.7%) | 15 (8.8%) | | 2 (25.0%) | 2 (25.0%) | | | | |
| AHI 15–29.9 | 65 (38.2%) | 53 (31.2%) | | 1 (12.5%) | 1 (12.5%) | | | | |
| AHI >30 | 78 (45.9%) | 101 (59.4%) | | 5 (62.5%) | 5 (62.5%) | | | | |
| ODI, events/hour | 25 (14–41) | 32 (17–46) | 0.015 | 29 (13–34) | 36 (15–56) | 0.19 | | | |
| ODI, quartiles, events/hour | | | 0.13 | | | 0.28 | | | |
| ODI 0–15.9 | 48 (28.2%) | 32 (18.8%) | | 3 (37.5%) | 2 (25.0%) | | | | |
| ODI 16–27.9 | 47 (27.6%) | 45 (26.5%) | | 1 (12.5%) | 1 (12.5%) | | | | |
| ODI 28–44.9 | 44 (25.9%) | 50 (29.4%) | | 4 (50.0%) | 2 (25.0%) | | | | |
| ODI >45 | 31 (18.2%) | 43 (25.3%) | | 0 (0.0%) | 3 (37.5%) | | | | |
In a parallel analysis including the entire cohort ($$n = 62$$ 811), multivariable logistic regression analysis with cancer as the independent variable, and adjustment for the same variables as in the propensity score matching, we identified significant ORs for ODI and AHI (1.05 (1.03–1.07) and 1.04 (1.02–1.07) per 10 units, $p \leq 0.001$, respectively) confirming the data obtained by propensity score matching.
## Main findings
Our study identified several important findings. First, OSA is associated with cancer prevalence independent of a substantial number of important confounders. The added risk is small and predominantly seen in patients with severe intermittent hypoxia during sleep measured by ODI rather than in those with frequent respiratory events during sleep measured by AHI. Second, the association between intermittent hypoxia and cancer was identified in lung cancer, malignant melanoma and prostate cancer. Our study underlines the importance of further studies to investigate if preventing and treating sleep apnoea may have beneficial effects on cancer incidence and any outcomes of cancer treatment.
## Cancer prevalence in other cohort studies
Results from previous studies on OSA and cancer are conflicting. One early Spanish multicentre study (4910 patients) identified an increased cancer incidence in OSA patients with an overall OR of 1.45 ($95\%$ CI 1.1 to 1.9).2 *In a* Canadian cohort study ($$n = 10$$ 149) from a single sleep clinic at an academic hospital, 520 patients had a cancer diagnosis at baseline and 627 patients of the cancer-free patients at baseline were reported with incident cancer at follow-up (7.8 years).10 No association between the severity of OSA and cancer was found for prevalent or incident cancer. In a twice as large European cross-sectional study ($$n = 19$$ 556 of whom 388 self-reported cancer diagnosis at the time of sleep study), an association between AHI and ODI was found in women but not in men.8 *In a* recent French cohort study ($$n = 8748$$, 5.8-year follow-up, 718 incident cancer cases), an association between nocturnal hypoxia, defined as the per cent of the night spent with saturation <$90\%$ and incident cancer was found while there were no associations between AHI or ODI and incident cancer after adjustment for confounders.9 A recent meta-analysis of 12 studies, including 184 915 patients with OSA ($$n = 75$$ 367 and 3805 cancer cases) and patients without OSA ($$n = 109$$ 548 and 2110 cancer cases) identified a dose-dependent increased risk of incident all-cause cancer with an OR of 1.14 ($95\%$ CI 1.04 to 1.25, $$p \leq 0.006$$) for mild OSA, 1.36 ($95\%$ CI 1.32 to 1.92;$p \leq 0.001$) for moderate OSA and 1.59 ($95\%$ CI 1.45 to 1.74; $p \leq 0.001$) for severe OSA.11 Unfortunately, this meta-analysis did not evaluate the association between the amount of OSA-related hypoxic measures and cancer incidence. In this context, our cross-sectional data in one of the largest OSA patient populations so far is in line with previous findings. OSA appears to be associated with cancer, but effect sizes are generally small, and large cohorts are needed to detect any independent effect of OSA on cancer incidence. It cannot be excluded that other confounders, not yet considered in any of the published analyses, may further explain part of the effect sizes accounted for OSA. Notably, several studies show that OSA measures reflecting hypoxic burden appear to better predict the risk for cancer when compared with AHI as traditional OSA severity marker.8 9 Due to the exact classification of cancer type through the Swedish national registry, we could perform subtype analysis on the influence of OSA-related cancers. We identified more severe OSA in lung cancer, malignant melanoma and in prostate cancer which is in line with previous study results.23–26 In contrast, we did not identify an increased prevalence of OSA in patients with breast cancer.27 28
## Potential mechanisms between the association of OSA and cancer
Previous studies have explored potential mechanistic pathways for the association between OSA and cancer. Experimental studies mimicking OSA by using an intermittent hypoxia model show that intermittent hypoxia promotes incident tumour cell transition, tumour growth and metastasis. Several in vitro cell models identified intermittent hypoxia to promote tumour cell growth in different cell lines.29 In vivo mouse models showed that melanoma metastasis was increased in animals exposed to intermittent hypoxia mimicking severe OSA.30 Although these data suggest a possible OSA-related mechanistic pathway for cancer growth and aggressiveness, further studies are needed to explore if and how OSA promote the initial occurrence of cancer. In this context, obesity is an important confounder of the association between OSA and cancer. Obesity in the general population has nearly doubled in the last 20 years and by 2017, $17\%$ of the Swedish adult population are obese, defined as BMI exceeding 30 kg/m2.31 Presence of OSA is closely linked to obesity,32 and in Sweden, the mean BMI when initiating CPAP therapy is 32 kg/m2.12 Several cancer forms, that is, breast, colon, kidney, pancreas and oesophageal cancer, are also associated with higher BMI.33 As both age-standardised cancer incidence34 and the prevalence of obesity are increasing31 in parallel, we carefully controlled for both factors in our analysis.
Inflammation may be considered as a third potential pathway for the link between OSA and cancer. OSA increases systemic inflammation with increased levels of high-sensitivity C-reactive protein (hsCRP), Interleukin-6 (IL-6), and *Tumour necrosis* factor α (TNF-α).35 36 Systemic inflammation has been linked to the risk of cancer/cancer development but also tumour growth.37 In addition, a low degree of inflammation has been identified in adipocytes which may contribute to the overall increased inflammation in obesity. Further studies linking markers of inflammation to cancer incidence in OSA populations are of particular interest.
Our study has several strengths. First, the study has a high validity as we included a large number of patients with a verified cancer diagnosis and concomitant OSA. The National cancer registry has the highest quality due to regularly performed validation studies by the National Social Welfare Board. The sleep apnoea-related data within the Swedevox registry has recently been validated against the medical record data entries and showed more than $95\%$ correctness for age, BMI and AHI values.38 Second, our study cohort is representative for studying the association between OSA and cancer prevalence. The Swedevox registry reflects almost $90\%$ of new CPAP treatment starts in Sweden initiated by 44 sleep centres across the country.12 The national cancer registry has nearly $100\%$ coverage.16 *Socioeconomic data* are based on information from the National Tax Agency. No patient was lost due to missing data in the national registries. Third, we used a strong analysis methodology with sufficient power due to a large number of cases. The propensity score matching of cases and controls adjusting for many important confounders can be viewed as close to a randomised study setting. A parallel multivariable Cox-regression analysis confirmed the results of the propensity score matched analysis.
Nonetheless study limitations need also to be addressed. First, sleep apnoea within the Swedevox registry is almost exclusively classified by polygraphy and not by polysomnography. However, device methodology is very homogeneous in Sweden and national guidelines for the analysis of polygraphic recordings are published.39 Polygraphic recording may underestimate the actual AHI classification but to a lesser extent, the ODI count. This may, in part explain why ODI is a stronger predictor of cancer prevalence than AHI. Second, we do not have other measures of nocturnal hypoxia other than ODI, for example, the time spent in saturation below $90\%$ or hypoxic burden analysis of the saturation curve, recently described as a significant predictor of cancer in OSA patients.2 8 9 40 Third, important lifestyle risk factors for cancer, including smoking, physical activity or food preferences, have not been captured on an individual level in our study. However, we have a strong classification of socioeconomic factors. We also included data on smoking prevalence in the different regions of Sweden to capture the potential influence of smoking on cancer prevalence. Research into the impact of socioeconomic factors on cancer incidence typically find a larger prevalence of solid tumours in groups with less fortunate social standing with breast cancer being the sole outlier. The single most important variable in cancer incidence is tobacco smoking where large differences are seen between socioeconomic groups.41 In case of lacking information on individual patient data for such risk factors, the use of socioeconomic stratification represents the best available method to adjust for this. Fourth, our study included only PAP-treated OSA patients reflecting the upper range of OSA severity and not the entire OSA spectrum. This patient selection may have limited our possibility to fully assess the dose response relationship between OSA severity and cancer prevalence. However, the OSA patient group with severe intermittent hypoxia demonstrated the strongest risk increase for cancer prevalence. Finally, due to the cross-sectional study design, our study cannot speculate about the causality in the association between OSA and cancer.
## Clinical implications and research agenda
Our study provides further cross-sectional evidence to the important clinical question of the association between OSA and cancer in OSA patients. Even if we did not analyse longitudinal data on the incidence of cancer, we see a significant yet limited effect size for OSA on the risk for prevalent cancer. More importantly, future analysis needs to focus on the effect of OSA treatment with PAP on cancer incidence and survival.
## Conclusion
OSA mediated intermittent hypoxia is an independent risk factor for cancer in this large, national OSA patient cohort. Future longitudinal studies are warranted to study the potential influence of OSA treatment on cancer incidence in patients with OSA.
## Data availability statement
Data are available on reasonable request. The steering committee of the Swedevox quality registry will consider reasonable requests for the sharing of deidentified patient-level data. Requests should be made to the corresponding author.
## Patient consent for publication
Not applicable.
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|
---
title: 'Treatment burden in survivors of prostate and colorectal cancers: a qualitative
interview study'
authors:
- Rosalind Adam
- Lisa Duncan
- Sara J Maclennan
- Louise Locock
journal: BMJ Open
year: 2023
pmcid: PMC9990667
doi: 10.1136/bmjopen-2022-068997
license: CC BY 4.0
---
# Treatment burden in survivors of prostate and colorectal cancers: a qualitative interview study
## Abstract
### Objectives
Treatment burden is the workload of healthcare and the impact this has on the individual. Treatment burden is associated with poorer patient outcomes in several chronic diseases. Illness burden has been extensively studied in cancer, but little is known about treatment burden, particularly in those who have completed primary treatment for cancer. The aim of this study was to investigate treatment burden in survivors of prostate and colorectal cancers and their caregivers.
### Design
Semistructured interview study. Interviews were analysed using Framework and thematic analysis.
### Setting
Participants were recruited via general practices in Northeast Scotland.
### Participants
Eligible participants were individuals who had been diagnosed with colorectal or prostate cancer without distant metastases within the previous 5 years and their caregivers. Thirty-five patients and six caregivers participated: 22 patients had prostate and 13 had colorectal cancers (six male, seven female).
### Results
The term ‘burden’ did not resonate with most survivors, who expressed gratitude that time invested in cancer care could translate into improved survival. Cancer management was time consuming, but workload reduced over time. Cancer was usually considered as a discrete episode. Individual, disease and health system factors protected against or increased treatment burden. Some factors, such as health service configuration, were potentially modifiable. Multimorbidity contributed most to treatment burden and influenced treatment decisions and engagement with follow-up. The presence of a caregiver protected against treatment burden, but caregivers also experienced burden.
### Conclusions
Intensive cancer treatment and follow-up regimens do not necessarily lead to perceived burden. A cancer diagnosis serves as a strong motivator to engage in health management, but a careful balance exists between positive perceptions and burden. Treatment burden could lead to poorer cancer outcomes by influencing engagement with and decisions about care. Clinicians should ask about treatment burden and its impact, particularly in those with multimorbidity.
### Trial registration number
NCT04163068.
## Introduction
Modern cancer survivorship care places physical, financial, psychosocial and practical demands on individuals and their families.1–4 *There is* a move away from hospital and clinician-led cancer care towards supported self-monitoring and self-management by patients and their families in the community.5 6 Treatment burden is the workload of healthcare and the impact that this work has on the individual.7 Treatment burden is of increasing importance8 due to ageing populations, a rising prevalence of multimorbidity9 10 and increased pressure on healthcare systems. Healthcare workload can encompass a wide range of tasks, including ‘sense-making’ work,11 monitoring/managing symptoms, managing medicines, navigating the healthcare system and changing health-related behaviours.8 11 Treatment burden and illness/disease burden are closely linked but conceptually different. Illness burden describes the impact of an illness on an individual, such as morbidity and mortality.12 The actions taken to manage health and their consequences can lead to treatment burden.13–15 Sav et al noted six key domains of treatment burden, encompassing ‘financial, medication, administrative, time/travel, lifestyle, and healthcare’ dimensions, and ‘antecedents’ which can influence the severity of treatment burden, such as age, gender, treatment characteristics and disease type.16 17 Having good social support or a caregiver can lower treatment burden for patients,17 but caregivers can also become burdened.18 19 The impact of treatment burden on informal caregivers is under-researched.18
Treatment burden is likely to be important in cancer survivors (The term ‘survivor’ is used here to describe individuals living with and beyond cancer. It is a term that is widely used in research, guidelines and charities but which can divide opinion in some individuals with cancer) and their caregivers. Questionnaire studies have detected high levels of treatment burden after cancer in patients, particularly in those with lower health literacy,20 21 multiple comorbidities20–23 and lower social support.20 22 One qualitative interview study investigated treatment burden in individuals who were undergoing or had recently completed treatment for lung cancer.24 Only one caregiver participated in the study. Patients had restructured their lives to accommodate treatment-associated workload. This was accepted by patients as a necessity due to the severe and life-threatening nature of lung cancer.24 *It is* unclear how treatment burden might be perceived by survivors of different types of cancer in whom the prognosis is more favourable than lung cancer.
Prostate and colorectal cancers are the third and fourth most common invasive cancers worldwide.25 They have excellent prognoses when detected and treated early.3 26 Prostate and colorectal cancers were chosen to explore treatment burden in this study because they encompass a wide range of treatment modalities (such as surgery, radiotherapy, and chemotherapy), lasting sequelae (eg, fatigue, persistent pain, incontinence, sexual problems and stoma management) and follow-up activities. Individuals play a key role in improving their own prognosis by self-monitoring for symptoms and attending for scans and blood tests to detect recurrence, and by adhering to diet and exercise recommendations,4 27 and may therefore be at risk of treatment burden. Informal caregivers are key supporters of these activities.28 The aim of this study was to investigate perceptions of treatment burden in individuals who had completed treatment for colorectal or prostate cancer and their caregivers and the impact of treatment burden on these individuals. It is held that individuals who become overburdened by the workload of healthcare disengage from self-management activities, leading to poorer outcomes.21–23 Treatment burden could be an important mediator of poorer outcomes in cancer survivors and patients/caregivers are best placed to give insights into mechanisms of treatment burden and aspects that are potentially modifiable.
## Setting and design
A qualitative semistructured interview study was conducted in National Health Service (NHS) Grampian in Northeast Scotland. The NHS is a publicly funded healthcare system which is free at the point of delivery. In Grampian, cancer care is centred around a university teaching hospital in Aberdeen with academic links and care pathways that are integrated with local cancer charities29. Grampian had a 2011 census population of 569 061, and around one-third of the population live rurally.30
## Participants and recruitment
Eligible participants were adults with a history of localised or locally advanced prostate or colorectal cancer, diagnosed within the past 5 years. A 5-year cut-off was chosen so that individuals were reflecting on recent experiences of cancer treatment and follow-up, and because many individuals with low-risk disease are discharged from follow-up after 5 years. Participants were included if they had received any cancer treatment/management, including, and not limited to, active surveillance, surgery, radiotherapy or chemotherapy. Individuals who were undergoing or on waiting lists for chemotherapy, radiotherapy or surgery were excluded because their experiences of treatment burden were likely to be different from individuals who were in the follow-up stages after active cancer treatment. Those with distant metastases were excluded because treatment and follow-up for these individuals would have different aims and formats of delivery.
NHS Research Scotland Primary Care (NRS Primary Care) assisted with recruitment. NRS Primary Care recruited general practices and searched electronic medical records using Read codes for prostate and colorectal cancers. General practices sent invitation packs to eligible patients, and those interested in participating responded directly to the research team. Eligible patients were invited to nominate a caregiver to participate in the interview. Separate invitation letters and information sheets were included for caregivers in packs sent to patients.
## Data collection and management
An interview topic guide (online supplemental file 1) was designed to ensure comprehensive coverage of the topic. A conceptual model (online supplemental figure 1) was produced, drawing on existing literature on treatment burden in stroke,11 normalisation process theory (NPT)31 and the health action process approach (HAPA).32 NPT has been used to understand how individuals embed new practices within their daily life and has been a useful model through which to explore treatment burden after stroke.11 The HAPA describes both how individuals become motivated to engage with healthcare or self-management work, and how individuals then translate this motivation into engagement with and maintenance of self-management practices over time. The HAPA integrates and extends previous behavioural theories by including a range of important constructs such as self-efficacy and intention, which can predict and explain human behaviours.33 The conceptual model was used to inform the topic guide, which included questions about the initial diagnosis and treatment period, current self-management strategies, social support and interactions with others. Participants were asked to reflect on the work of cancer management and any effects that managing their health had on them. The guide was used flexibly, allowing the interview to be directed by participant responses. Participants and caregivers were asked about the same topics and interviewers adapted the questions during the interview to ensure that caregiver perceptions were fully captured.
The study commenced in January 2020 and was put on hold in March 2020 during the COVID-19 pandemic, restarting in May 2021. Interviews conducted prior to March 2020 were conducted face to face or by telephone, according to participant preference. All interviews undertaken after May 2021 were conducted by telephone. Interviews were conducted by two authors (RA and LD), were audio recorded and transcribed verbatim by a university-approved professional transcription company. Transcripts were checked for accuracy, anonymised and then imported into NVivo software (QSR International. V.12, 2018) for analysis.
## Data analysis
Framework analysis34 and thematic analysis35 were used to organise and analyse the data. A framework matrix was created with headings. Notes and quotations from each interview were listed under headings to summarise study data so that all authors could have an overview of key data from the whole sample.
Thematic analysis was conducted on individual interview transcripts and involved: familiarisation; generating initial codes; generating initial themes; reviewing themes; defining and naming themes; and reporting the thematic analysis.35 Familiarisation with data was achieved through checking, reading and re-reading of transcripts. Initial themes were generated by examining relationships between codes and common features in the data. Themes were revised and developed through discussions among authors. Data collection and analysis were conducted in tandem, and data collection continued until no new concepts or themes were found.
The authors’ disciplinary backgrounds include general practice, health psychology and health services research.
## Patient and public involvement
Patients were not involved in the design of this qualitative study, but a key aim of this work was to understand patient and caregiver perceptions and opinions. Participants were sent a summary of the study results and invited to comment on them. Three participants replied and provided general updates on their progress. None suggested any changes or additions to the results.
## Results
One hundred and sixty-three study invitations were sent by six general practices. Thirty-five patients and six caregivers (joint interviews) participated in 13 in-person face-to-face interviews and 22 telephone interviews ($21\%$ response rate). Participant-level demographics are presented in table 1. Participants were aged between 37 and 91 years, mean age 69 years. Twenty-two participants had prostate cancer and 13 had colorectal cancer (seven female, six male). Most participants were from areas of low socioeconomic deprivation, with $\frac{23}{35}$ patients in deciles 6–10 of the Scottish Index of Multiple Deprivation36 (where 1 is most deprived and 10 is least deprived). Most participants were from a large urban area ($45.7\%$) or an accessible rural area ($28.6\%$).
**Table 1**
| Patient ID | Age range(years) | Sex | Cancer site | SIMD decile* | Urban-rural classification† | Comorbidities‡ (n) | Carer participated |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 70–79 | M | Prostate | 10 | 1 | 0 | No |
| 2 | 80–89 | F | Colorectal | 8 | 1 | 1 | No |
| 3 | 90–99 | F | Colorectal | 7 | 1 | 2 | No |
| 4 | 60–69 | M | Prostate | 10 | 1 | 1 | No |
| 5 | 70–79 | M | Colorectal | 3 | 1 | 2 | No |
| 6 | 70–79 | M | Prostate | 5 | 5 | 2 | Yes |
| 7 | 60–69 | M | Prostate | 5 | 1 | 1 | No |
| 8 | 60–69 | M | Prostate | 7 | 5 | 1 | Yes |
| 9 | 70–79 | M | Prostate | 7 | 5 | 1 | Yes |
| 10 | 60–69 | M | Colorectal | 8 | 3 | 1 | No |
| 11 | 60–69 | M | Prostate | 7 | 5 | 0 | No |
| 12 | 60–69 | M | Colorectal | Missing | Missing | 1 | No |
| 13 | 60–69 | F | Colorectal | 9 | 5 | 0 | No |
| 14 | 70–79 | M | Prostate | 10 | 3 | 0 | No |
| 15 | 60–69 | F | Colorectal | 9 | 5 | 1 | Yes |
| 16 | 60–69 | M | Prostate | 3 | 1 | 4 | No |
| 17 | 60–69 | M | Colorectal | 7 | 1 | 1 | No |
| 18 | 60–69 | M | Prostate | 10 | 1 | 1 | No |
| 19 | 80–89 | M | Colorectal | 8 | 1 | 2 | Yes |
| 20 | 70–79 | M | Prostate | 2 | 1 | 1 | No |
| 21 | 70–79 | M | Prostate | 6 | 1 | 1 | No |
| 22 | 60–69 | M | Prostate | 9 | 6 | 4 | No |
| 23 | 50–59 | M | Prostate | 2 | 1 | 1 | No |
| 24 | 70–79 | M | Prostate | 9 | 5 | 1 | No |
| 25 | 70–79 | M | Prostate | 5 | 1 | 1 | No |
| 26 | 70–79 | M | Prostate | 10 | 1 | 5 | No |
| 27 | 70–79 | M | Prostate | 6 | 1 | 1 | No |
| 28 | 70–79 | M | Prostate | 8 | 6 | 3 | No |
| 29 | 50–59 | M | Prostate | 7 | 5 | 0 | No |
| 30 | 50–59 | F | Colorectal | 7 | 1 | 0 | No |
| 31 | 30–39 | M | Colorectal | 4 | 5 | 0 | No |
| 32 | 70–79 | F | Colorectal | 3 | 5 | 0 | No |
| 33 | 70–79 | F | Colorectal | 4 | 6 | 3 | No |
| 34 | 60–69 | M | Prostate | 5 | 6 | 0 | Yes |
| 35 | 60–69 | M | Prostate | 5 | 2 | 0 | No |
Comparing the demographics of those who participated to those who were invited but did not respond, a higher proportion of non-respondents resided in areas of higher socioeconomic deprivation ($42.2\%$ of non-respondents compared with $20\%$ of participants). These differences were not statistically significant (see online supplemental table 1).
Interviews lasted between 21 and 94 min duration, mean 44 min.
## Thematic analysis
Themes and their interactions are summarised in figure 1.
**Figure 1:** *Themes and their interactions.*
## Theme 1: getting on with the work of cancer survivorship
Participants described having little choice but to engage in the work of managing cancer and its sequelae because cancer was a threat to life. Several participants mentioned ‘getting on with it’.
The work of health management after completion of treatment involved planning, organisation, problem solving and psychological work. Individuals developed new routines, for example, pacing daily activities to manage fatigue, and preplanning before travelling (eg, packing necessary stoma equipment or medications).
Participants took active approaches to psychological adjustment after cancer. Many made comparisons with others whom they felt were ‘worse off’ (eg, more advanced cancer, other disabling chronic diseases). Some spoke of being ‘determined’ or ‘positive’. For many, cancer was always present in the background, and work was required to return to ‘near’ normality after treatment.
Health behaviour change was a source of work for some participants, but mainly in the period immediately after cancer diagnosis. Examples included efforts to lose weight prior to prostate cancer surgery to improve urinary continence or increasing exercise levels to prehabilitate before colorectal cancer surgery.
Few participants described efforts to make dietary, exercise or other healthy behaviour changes after completion of treatment. Some considered that they already lived healthy lifestyles, while others considered cancer to have been cured and no longer requiring attention. None of the participants spoke of specific efforts to reduce their risk of cancer recurrence. Participants with both prostate and colorectal cancers suggested that there was little emphasis placed on adopting healthy behaviours by healthcare professionals after their initial treatment had finished.
## Theme 2: investing time
Cancer management was time consuming during the initial treatment stages, but treatment-related work reduced over time, even when there were lasting effects or complications of treatment.
Follow-up healthcare appointments and investigations to detect recurrence were viewed positively and were reassuring. Many spoke of their gratitude that cancer was detected at a potentially curable stage and that time invested in treatment and follow-up could translate into a greater amount of time to spend in the future on meaningful activities. Despite cancer management consuming considerable amounts of time, this was not perceived to be burdensome.
## Theme 3: factors modifying the experience of treatment burden
Participants identified individual-related, disease-related and health system-related factors that protected against or increased the likelihood of them experiencing treatment burden. At the level of the individual, the presence of a caregiver or a good social support network lessened the treatment burden associated with cancer. Spouses and other family members shared in the practical and psychological work of cancer management, from driving to and being present at appointments and undertaking domestic tasks, to giving input into treatment decisions and providing emotional support. During interviews, caregiver participants used the terms ‘us’ and ‘we’ when they spoke of diagnostic, treatment and aftercare events, indicating that the experiences had happened to both parties. Patient/caregiver dyads described a team approach to cancer management.
At the individual level, participants found that skills learnt during their working lives helped them manage cancer-related work. Taking action was a way to exert control over the illness.
The flexibility to manage personal time (eg, being retired) and financial security protected participants from treatment burden. Conversely, caring responsibilities, coexisting mental health problems or challenging social circumstances increased the risk of treatment burden.
At the disease level, those with ongoing symptoms or managing complications related to their cancer or comorbidities spent more time managing their health. Several participants reflected on their experiences of supporting friends or loved ones with advanced cancer and believed that treatment workload would be much higher if their disease had been detected at a later stage.
At the health system level, administrative problems were burdensome. Examples included lost appointments, or scans that were not arranged at the expected interval. However, participants with both prostate and colorectal cancers thought that cancer aftercare had been configured to minimise burden for them. Important factors that minimised burden included telephone access to a named specialist nurse or group of nurses; kind, caring and professional staff, close links with local cancer support organisations, timely provision of information and a predetermined, algorithmic treatment and follow-up plan. Participants described being on a ‘pathway’ (patient 24), or ‘in the hands of the machine’ (patient 24), with ‘very [few] rabbit holes that you can jump down’ (patient 22).
## Theme 4: comparisons and interactions between cancer and other comorbidities
Coexisting comorbidities could be more burdensome than cancer. Most participants considered that cancer had been a discrete event which had been cured, whereas other comorbidities such as Parkinson’s disease and diabetes required ongoing monitoring, medication adherence and behaviour change. One participant described making significant dietary changes to manage his hypertension but no behavioural changes after a hemicolectomy for screening detected colorectal cancer. Several participants mentioned the ‘invisible’ nature of cancer, which made it easier to forget.
There was also a sense that cancer care was better resourced and better organised compared with health services for dermatological, respiratory and cardiovascular problems. Two participants suggested that cancer had a high profile in the media and one suggested that cancer was favoured by politicians for financial investment compared with other chronic conditions.
The lasting effects of cancer and its treatments could interact with other comorbidities to increase treatment workload. For example, a participant with Parkinson’s disease and tremor found the management of urinary incontinence more challenging.
## Theme 5: caring for the caregiver
There was not always a clear distinction between patient and caregiver roles. One caregiver spoke of their own experiences of having cancer and two patient participants were the main caregivers for their wives (who did not participate in this study). These patients experienced the multiplicative burden of managing their own health after cancer while continuing to provide care for their loved one. Both spoke of their role as a caregiver being their main priority.
The team approach to cancer management (described in theme 3) affected the caregiver. Some caregivers had taken early retirement or rearranged their working lives to accommodate caring. One caregiver (daughter of patient 19) described driving 40 miles every day to support her parents. Caregivers shared the emotional burden of cancer.
Many men with prostate cancer spoke about the impact that loss of libido and erectile dysfunction had on their relationships. In interviews involving caregiver dyads, sexual dysfunction was acknowledged as ‘a source of sadness’ (patient 8), and something which had a continuing effect on both the patient and the caregiver.
Caregivers spoke of additional difficulties being on the ‘side lines’ (wife of patient 8) during cancer care, and that, despite playing a key role in treatment decisions and enabling successful recovery, they had limited access to advice or individualised support.
## Theme 6: the impact of treatment burden
Most of the participants in this study did not perceive themselves to be burdened by cancer aftercare. However, there were three examples of treatment burden influencing cancer treatment and follow-up decisions.
An individual with type 1 diabetes explained that the workload of diabetes had influenced his decision to opt for a radical prostatectomy over more conservative options.
Another participant with previous primary testicular cancer described ‘never being away from the hospital’ when he required hospital treatments for three separate medical conditions. He became aware that his testicular cancer follow-up had not taken place when it should have: *For this* participant, perceptions about treatment burden changed over time. During active surveillance for prostate cancer, his other comorbidities were better controlled, and he was older and retired from work. He reflected that, despite spending considerable time travelling to appointments, he did not perceive prostate cancer surveillance to be burdensome.
Two participants (patients 16 and 28) were undergoing active surveillance for prostate cancer and reflected on the invisible nature of the disease. For participant 28, this made it easier to forget about than other comorbidities (see theme 4) but participant 16 experienced psychological burden arising from an invisible disease that could only be monitored through medical tests.
The same individual found prostate biopsies to be ‘intrusive’ and ‘traumatic’ and started to disengage with monitoring.
## Main findings
The term ‘burden’ did not resonate strongly with most cancer survivors in this study, despite descriptions of time-intensive treatment and follow-up regimens and the ongoing management of problematic symptoms such as fatigue and incontinence. The notion of burden was incongruent with the gratitude that cancer survivors expressed for curative treatment. Time invested in cancer management would ultimately lead to survival and more time to spend on meaningful activities.
Cancer was framed as a discrete episode—something invisible that had been cut out or contained and that should be consigned to the past. In this sample, cancer was not perceived to require the lifelong adherence to self-monitoring and lifestyle changes that were emphasised in conditions such as diabetes or hypertension. Cancer was not considered to be a chronic disease and none of the participants mentioned work to prevent recurrence.
There were potentially modifiable factors that could make treatment burden more or less likely, including social support, financial stability and health system configuration. In this sample, the most important factors were interacting comorbidities, which increased burden, and the presence of a caregiver, which reduced treatment burden. Caregivers shared in all aspects of the work of cancer survivorship care, and in the emotional and practical consequences of this work such as experiencing worry and disruption to working lives.
Despite few patients perceiving cancer management to be burdensome, there were three examples in which treatment burden had influenced cancer treatment decisions or disengagement with follow-up. Interestingly, individuals with similar treatment regimens had very different perceptions of burden, and perceptions of treatment burden could change over time. Disengagement with cancer monitoring is a cause for concern and is a feasible mechanism through which treatment burden could negatively affect cancer outcomes.
## Comparison with existing literature
Treatment burden has mainly been researched in multimorbidity37 and in cardiovascular disease,38 diabetes39 and stroke.11 Patients are known to be burdened by ‘fragmented’ medical care, poor communication/lack of empathy from health professionals and inadequate information provision.40 These problems were less evident in our sample, and it was suggested by participants that cancer services are prioritised over other chronic diseases because of their prominence in the media and public eye.
A recent scoping review found that financial burden, time/travel burden and medication burden were the most prominent dimensions of treatment burden in older individuals with cancer.16 Several studies have focused on individual factors which can contribute to treatment burden and diminished quality of life, such as financial toxicity, time spent on cancer treatments and the burden of adhering to medications.15 41 42 In our study, treatment burden was considered as a multidimensional concept. Financial burden and time/travel burden were not significant problems in our participants, despite almost half the sample living in rural areas. This emphasises that burden is a subjective perception that is influenced by multiple interacting factors.
We identified patient, disease and health system-level factors that can increase or decrease treatment burden. Many of these fit well with established theory, which suggests a ‘cumulative complexity’ of healthcare work.43 Burden occurs when patient workload and demand exceeds patient capacity to undertake this work.7 43 A prominent difference in our sample of individuals with good prognosis cancers compared with other chronic diseases like stroke or heart failure was that the prospect of cure and extending life enhanced patients’ psychological and motivational capacity to engage with health-related work. This diminished the perception of burden.
An interesting question arising from this work is whether all cancers should be considered as chronic or long-term conditions. Long-term conditions are ‘conditions for which there is currently no cure, and which are managed with drugs and other treatment’.44 Individuals in this study had been treated with curative intent. However, all participants were at risk of recurrence, many had lasting effects of treatment and all were at slightly higher risk of second primary cancers.45 46 A challenge for survivorship care is how to introduce the nuance of long-term health management against the binary messages of ‘curable’ or ‘incurable’ that are presented during active treatment.
In this sample, opportunities to promote exercise, healthy diet and weight management were being missed but patients had capacity to take on this work.
## Strengths and limitations
This study is one of the first to specifically investigate burden of treatment and its impact in survivors of good prognosis cancers and their caregivers. It adds granular, mechanistic details to previous quantitative observations that comorbidities and social support can influence patient ratings of treatment burden.20 22 The selected cancers encompassed a wide variety of treatment modalities, different follow-up regimens and a spectrum of illness burdens. Over 25 hours of rich audio-recorded data were generated from in-depth interviews. The involvement of four authors from different backgrounds enriched the analysis with different theoretical and methodological perspectives. Results were fed back to participants to ensure they reflected participant experiences, and that important findings were not omitted.
There are important limitations to note. As prostate cancer is a male cancer, men are over-represented in this sample. Gender is an important antecedent of treatment burden,17 but gender issues were not specifically probed during interviews. Men gave detailed and open accounts of gender-specific problems relating to hormonal treatment and prostate surgery, such as erectile dysfunction, loss of libido and urinary symptoms. However, it is possible that broader issues relating to gender and treatment burden have been missed. Some research has suggested that women may experience more treatment burden than men,22 47–49 but the contribution of gender and identity to treatment burden is under-researched.50 The addition of a female cancer in this study might have highlighted important gender-related differences in treatment burden experiences.
Only six caregivers were recruited, five of whom were female. Despite this, caregivers contributed meaningful insights into their role and experiences of treatment burden, and it became clear that patient and caregiver roles were not mutually exclusive. In future research, it would be important to target caregivers through specific channels of recruitment. Interviewing caregivers on their own could also highlight aspects of their experience that they might be less likely to discuss with their loved one.
All participants were from a single geographical area, attending an academic teaching hospital and most were from areas of low socioeconomic deprivation with relatively low levels of multimorbidity. Multimorbidity and socioeconomic deprivation are significant risk factors for treatment burden. This study may have underestimated the impact of treatment burden on cancer survivors. There is a paradox that those who are significantly burdened by their treatment may have less capacity to participate in research, and it is important to consider mechanisms of incentivising and including these individuals in future research.
## Implications for cancer survivors, practice and future research
Cancer survivors can be reassured that treatment burden tends to decrease over time after active treatment, and that most of the individuals in this study were not burdened by health-related workload. Nevertheless, there were indications that treatment burden could drive inequities in cancer outcomes, particularly in individuals with limited social support and concurrent comorbidities. Clinicians should carefully consider what they are asking patients and their caregivers to do, and whether they have the capacity to undertake this work.
Future research might focus on innovative ways to provide accessible, structured, holistic care to multimorbid cancer survivors. There may be an increasing role for ‘specialist generalists’.51 Future research should examine the relationship between treatment burden and specific, measurable outcomes after cancer such as survival, recurrence and quality of life. Interventions might target those most at risk of treatment burden to improve their outcomes after cancer.
## Conclusions
There is a continuum between positive perceptions of health-related work and burden in cancer survivors. A cancer diagnosis serves as a strong motivator to engage in health management, and perceptions about burden can be shifted by individual, disease and health system factors. Treatment burden can affect engagement with and decisions about care and is an important consideration in cancer survivorship care.
## Data availability statement
No data are available. Participants of this study did not agree for their interview transcripts to be shared publicly, so supporting data are not available.
## Patient consent for publication
Not applicable.
## Ethics approval
This study involves human participants and was approved by the North of Scotland Research Ethics Committee (reference: 19/NS/0158).
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|
---
title: 'Women’s experiences of over-the-counter and prescription medication during
pregnancy in the UK: findings from survey free-text responses and narrative interviews'
authors:
- Julia Sanders
- Rebecca Blaylock
- Caitlin Dean
- Irene Petersen
- Heather Trickey
- Clare Murphy
journal: BMJ Open
year: 2023
pmcid: PMC9990671
doi: 10.1136/bmjopen-2022-067987
license: CC BY 4.0
---
# Women’s experiences of over-the-counter and prescription medication during pregnancy in the UK: findings from survey free-text responses and narrative interviews
## Abstract
### Objectives
To explore women’s experiences of over-the-counter and prescription medication advice and use during pregnancy.
### Design
A study design consisting of an online survey and nested in-depth interviews with a subsample of participants. We analysed data from survey free-text responses and in-depth interviews using thematic analysis. Quantitative survey data is published elsewhere.
### Setting
The UK.
### Participants
Women were eligible if living in the UK, aged 16–45 years, were pregnant or had been pregnant in the last 5 years regardless of pregnancy outcome. A total of 7090 women completed the survey, and 34 women who collectively had experienced 68 pregnancies were subsequently interviewed.
### Results
Medication prescribing and use during pregnancy was common. The prescribing, dispensing and taking of some advised medications were restricted through women’s or prescribers’ fear of fetal harm. Lack of adherence to national prescribing guidance, conflicting professional opinion and poor communication resulted in maternal anxiety, avoidable morbidity and women negotiating complex and distressing pathways to obtain recommended medications. In contrast, some women felt overmedicated and that pharmacological treatments were used without exploring other options first.
### Conclusion
Increased translation of national guidance into practice and greater personalisation of antenatal care are needed to improve the safety, efficacy and personalisation of prescribing in pregnancy.
## Background
Safe and effective prescribing is an essential component of antenatal care. Prescribing in pregnancy requires additional knowledge and caution due to the potential for teratogenesis, altered pharmacokinetics, maternal concerns1 and potential for short and longer term harm to the fetus.2 3 Antenatal information4 provided to pregnant women states that most medications reach the fetus and all medication use should be discussed with health professionals. Many women discontinue or avoid medications in pregnancy,5 however, lack of treatment can potentially have severe consequences.1 Despite selective avoidance, medication use in pregnancy is common, with around $87\%$ of pregnant women in the UK reporting medication use for short-term or chronic conditions.6 Excluding vitamin and iron supplements, frequently prescribed and over-the-counter (OTC) medications taken during pregnancy include antacids, analgesics, anti-emetics and antibiotics.6 There are few medications that should ideally not be used by pregnant women due to their teratogenicity, for example, thalidomide, sodium valproate and isotretinoin. Most other medications are safe and widely used during pregnancy, and there are several national prescribing guidelines7 8 for treatment during pregnancy. For example, the National Institute for Health and Care Excellence (NICE) guidance on antenatal and postnatal mental health recommends that health professionals should discuss the potential benefits of psychological interventions and psychotropic medication, the possible consequences of no treatment and possible harms associated with treatment, and what might happen if treatment is changed or stopped, particularly if psychotropic medication is stopped abruptly.8 For mild to moderate mental health problems, psychological interventions should form first-line management,8 but access is limited by long waiting lists and high referral thresholds, while a lack of continuity in antenatal care leads to difficulty developing trusting therapeutic relationships.9 *Suicide is* the leading cause of direct maternal death in the first postnatal year,10 and reports have highlighted that treatments for depression may often be discontinued in pregnancy.5 11 The Royal College of Obstetricians and Gynaecologists have set out clear guidance on the diagnosis and subsequent management of nausea and vomiting in pregnancy across community, ambulatory day-care and inpatient settings, including a medication escalation ladder for use when first-line recommended treatments are ineffective.7 *There is* growing concern that public health messages aimed at pregnant women, including those relating to medication use, do not always fully reflect or explain the evidence base underpinning them and the nuances and complexity of information is lost.12 The inability to receive effective medications is not without consequence for women and babies. A survey in 2015 identified that women with severe hyperemesis gravidarum frequently had difficulty obtaining swift treatment and support for debilitating pregnancy sickness resulting in some terminating an otherwise wanted pregnancy.13 Likewise, although mental health problems are common during pregnancy,14 previous reports have highlighted barriers to access appropriate treatment.15
The ‘WRISK project: understanding and improving the way risk in pregnancy is communicated to women’ was established to explore women’s experiences of maternity-related public health and risk messaging, including those relating to medication use in pregnancy. The study aimed to hear women’s voices through public involvement, quantitative and qualitative methodologies with a specific objective of exploring the views of women previously identified as feeling stigmatised or poorly served by current practice.16 This paper focuses specifically on women’s experiences of medication use in pregnancy as reported in open free-text comments in the survey and in-depth interviews.
## Study design
The study design consisted of an online survey and nested in-depth interviews with a subsample of survey participants. The survey was open to UK residents aged 16–45 years, who were pregnant or who had been pregnant in the last 5 years, regardless of pregnancy outcome. The survey was publicised through social media platforms including those of the study’s charity partners. We invited survey respondents to express interest in further research involvement and recruited participants for the interviews from this population. As the survey was exploratory, an a priori sample size calculation was not required.
For subsequent in-depth interviews, we used a sampling frame to ensure that we heard the voices of women at greater risk of the most severe pregnancy outcomes, maternal death or stillbirth.11 17 The sampling frame aimed to ensure that 30–35 women were interviewed and included a minimum of $20\%$ who were eligible for means-tested state benefits, a minimum of $20\%$ from Black, Asian and Minority Ethnic backgrounds, and at least 6 women were interviewed with experience of the following pregnancy experiences: pregnancy <20 years of age, body mass index >30 kg/m2, antenatal mental health problems or experience of hyperemesis gravidarum. Interested respondents who met the sampling criteria were selected using a random number table, until required numbers were obtained.
## Survey
A participant information sheet and consent form were integrated into the survey. The survey included questions on participants’ experience of the advice, information and support they received from different sources during pregnancy, with the focus on the respondent’s most recent pregnancy. Questions with open free-text responses were included alongside questions with Likert scales and multiple-choice responses. There were 22 questions in total, two of which asked for open free-text responses and are included in this analysis. The two questions asked were as follows: We describe the survey and demographic characteristics of respondents in full in a previous publication.16 For the purposes of this publication, we present free text responses from the survey pertaining to medication use in pregnancy only.
## One-to-one interviews
The participant information sheets, consent forms and topic guides for the interviews were developed in collaboration with the combined researcher and charity project advisory group. Narrative topic guides allowed participants to tell their pregnancy stories and were tailored to various pregnancy outcomes (see online supplemental file 1). Participants were asked to primarily discuss their most recent pregnancy and were asked about antenatal prescribing and use of over-the-counter medications, among other topics.
Participants were offered the option of in-person or telephone interviews. Interviewers (RB and HT) made detailed field notes following each interview. Participants were offered, and all received a high street vouchers worth £20. Audio files were transcribed verbatim using a commercial transcription service. Electronic transcripts were stored separately to any identifiable data on a secure IT system, and audio files deleted once they had been checked for accuracy.
## Research team
The trained, all female research team had extensive experience of working in pregnancy-related research and practice including social sciences (RB and HT), medical law (JS), public health (RB, JS and HT) and clinical midwifery (JS). Interviews were conducted and analysed by RB and HT.
## Terminology
The WRISK project was inclusive of all people who had experienced pregnancy in the previous 5 years. The project team always referred to individuals according to their self-determined gender. In this paper, we use the words ‘woman/women’ as the vast majority of participants self-identified as women. We use the term ‘BAME’, but acknowledge that this is problematic and present data on participant sociodemographics on a more granular level.18
## Data analysis
The interviews and free text comments in the survey were analysed thematically following Braun and Clarke’s method.19 Transcripts were coded and analysed using Dedoose20 by RB and HT. All transcripts and accompanying data such as researcher’s field notes were read in detail several times by both interviewers, and high-level codes pertaining to the original research questions were identified. Further codes were identified in the data during an inductive process, resulting in a coding framework informed by the data itself. Narrative summaries of data pertaining to each code were produced and cross-checked by RB and HT, and subsequently organised into high-level themes. We used Excel and STATA SE 15 to generate descriptive statistics on the survey and interview populations.21 22
## Patient and public involvement
The project oversight group included representatives from five diverse maternity user groups, healthcare professionals and researchers in addition to the study team. The group met regularly throughout the project from conception to dissemination and collectively informed all aspects of study design and delivery.
## Results
The survey was completed by 7090 women, of whom 3175 ($44.1\%$) expressed willingness for further involvement in research, and 34 were subsequently interviewed. The two questions which asked for open free-text responses and included in this analysis were completed by 2197 and 737 participants, respectively. Sociodemographic characteristics of all survey respondents and quantitative survey findings are published elsewhere.16 Interviews were carried out by RB and HT between July and November 2019, lasting approximately 45–60 min. Two pilot interviews were conducted in-person in April 2019 and were deemed to be of sufficient quality to be included in the analysis. All subsequent interviews were conducted by telephone and recorded using a dictaphone. The 34 women interviewed included those with experience of: a BMI>30 ($$n = 7$$); antenatal medication use for mental health conditions ($$n = 6$$); medication for hyperemesis gravidarum ($$n = 9$$); age <20 years during pregnancy ($$n = 7$$); or having a termination due to a perceived or actual risk either to themselves or their baby ($$n = 7$$). Some participants fitted more than one category, while five had none of these experiences.
Sociodemographic characteristics of those interviewed are described in table 1. Quotes that are followed by a unique identifier, for example, (WRI…) are from interview participants, and quotes that are from survey respondents are clearly identified.
**Table 1**
| Sociodemographics | N (34) | % |
| --- | --- | --- |
| Age (years) | | |
| 19–20 | 2 | 5.9 |
| 21–25 | 9 | 26.5 |
| 26–30 | 7 | 20.6 |
| 31–35 | 7 | 20.6 |
| 36–40 | 7 | 20.6 |
| 41–45 | 0 | 0 |
| 45+ | 1 | 2.9 |
| Missing | 1 | 2.9 |
| Highest level of education | | |
| Secondary school | 2 | 5.9 |
| Apprenticeship/HND/NVQ | 3 | 8.8 |
| A-levels | 6 | 17.6 |
| Undergraduate degree | 13 | 38.2 |
| Postgraduate degree | 10 | 29.4 |
| Relationship status | | |
| Married | 23 | 67.6 |
| Have a partner and live with them | 9 | 26.5 |
| Have a partner and live separately | 1 | 2.9 |
| Polyamorous | 1 | 2.9 |
| Ethnicity | | |
| White: English/Welsh/Scottish/Northern Irish/British | 24 | 70.6 |
| Black/African/Caribbean/ Black British: African | 3 | 8.8 |
| Mixed/multiple ethnic groups: White and Black African | 1 | 2.9 |
| Asian/Asian British: Chinese | 1 | 2.9 |
| Asian/Asian British: Indian | 1 | 2.9 |
| Black/African/Caribbean/ Black British: Caribbean | 1 | 2.9 |
| Mixed/multiple ethnic groups: White and Black Caribbean | 2 | 5.9 |
| Mixed/multiple ethnic groups: White British and Middle Eastern | 1 | 2.9 |
| Gender | | |
| Female | 33 | 97.1 |
| Non-binary | 1 | 2.9 |
| Receive state benefits? | | |
| Yes | 10 | 29.4 |
| No | 23 | 67.6 |
| Missing | 1 | 2.9 |
| Pregnancy history | Mean | Range |
| Gravidity | 2 | 1–5 |
| Live births | 1.2 | 1–4 |
| Terminations/abortion | 0.3 | 0–2 |
| Miscarriage/stillbirth | 0.3 | 0–2 |
Four themes were identified in relation to medication use in pregnancy: ‘fear of medications and self-regulation’, ‘feeling overmedicated’, ‘conflicting opinions’ and ‘running the gauntlet’. Some themes related to women’s experiences of medication use in general, while others were condition specific.
## Fear of medications and self-regulation
Fear or anxiety for the potential for fetal harm caused by OTC and prescribed medications was expressed by women. They also reported prescribers’ reluctance to prescribe for this reason. Participants expressed support for the precautionary approach that medications should be kept to a minimum during pregnancy. The fear of causing fetal harm through taking medication resulted in women reducing medication even when this was required to control serious medical conditions.
Two women described the consequences of stopping prescribed medication for pre-existing medical conditions. One woman stopped taking her asthma medication, which resulted in hospitalisation: Similarly, another woman experienced a serious migraine and temporary blindness, following discontinuation of medication for cranial hypertension: Self-regulation of medication particularly related to the use of analgesics. Women described conflict between their understanding of some medications being safe to take in pregnancy, with their desire to try ‘not to take them’ [WRI11] due to the possibility of potential risks to the fetus.
Another woman described managing with paracetamol for pelvic girdle pain even after she had been prescribed codeine-based medication: While women generally tried to avoid prescribed medication, recommended vaccines, including for influenza, appeared to be accepted. Supplements were viewed as positive and often taken without any apparent fear of causing an adverse pregnancy outcome.
There were many examples of health professionals reinforcing the message that medication should be avoided in pregnancy.
Health professionals expressing their own concerns around prescribing had a particularly negative impact on women. One interviewee [WRI17] explained her concern when a hospital consultant prescribing “put his hands together in prayer and said, ‘God, forgive me for giving you this. I hope your baby’s okay’” when prescribing ondansetron for severe hyperemesis gravidarum (HG).
Other women had similar experiences when accessing medication for their mental health. WRI33 said:
## Feeling overmedicated
Some participants reported feeling overmedicated and that pharmacological treatments were used without exploring other options first. This was the case for women with both mental and physical health concerns: Current guidance relating to gestational diabetes recommends that metformin is introduced if good blood glucose control is not achieved with 1–2 weeks of dietary change and exercise.23 One survey respondent would have preferred to extend this duration before commencing medication.
## Conflicting opinions
Women who required prescribed medication in pregnancy described frustration and distress resulting from conflicting opinions of health professionals. This conflict was particularly felt by women with hyperemesis gravidarum or mental health problems. One woman who required ondansetron for severe hyperemesis gravidarum was prescribed it, later to have this decision questioned by another general practitioner (GP) in the same practice: Another participant described the distress that resulted when a pharmacist would not fulfil a prescription for anti-depressants prescribed by her GP.
Two interview participants said that conflicting and contradictory advice from healthcare professionals was ‘staggering’.
The conflict was seen across all professional groups. One respondent described conflicting information from her midwife and GP.
## Running the gauntlet
This theme particularly related to prescribing for hyperemesis gravidarum. Some women experienced prolonged periods prior to getting effective treatment. Four women who were suffering with hyperemesis gravidarum described their experience of the current guidance.
Even when women were prescribed treatment for hyperemesis, they could then be discouraged from taking it by the prescriber: Another woman who was prescribed medication received comments from a pharmacist who suggested they may be unnecessary:
## Discussion
This study adds important understanding to the seldom explored topic of women’s experiences of antenatal medication use. Many women wished to reduce exposure to OTC and prescribed medication use during pregnancy, but dietary supplements and vaccines were generally accepted by pregnant women. This contrasted to treatments for mental health problems and hyperemesis gravidarum, conditions where pregnant women described having to negotiate conflicting opinions of health professionals to obtain recommended or effective treatments. Too often prescribing was more restrictive than recommended in national guidance resulting in avoidable, or prolonged, maternal morbidity, distress and anxiety. Similar to the lack of preconceptual and antenatal care for women with epilepsy highlighted in reviews of maternal deaths,11 where information on medications used for chronic conditions was not shared, some women discontinued treatments without medical consultation resulting in hospitalisation or exacerbation of symptoms.
In the UK, prescribing for pregnant women is undertaken by different health professionals, which complicates communication. Women with existing medical conditions, including epilepsy, require preconceptual advice on medication use.24 GPs are frequently involved in prescribing decisions, and commonly prescribed medications such as iron therapy and low-dose aspirin may be prescribed and administered by midwives. Women with additional obstetric needs will receive care led by obstetricians, who may share prescribing with GPs or specialist physicians. This arena is further complicated by public health bodies who are responsible for producing public health risk messages for pregnancy being independent from both primary and secondary care where prescribing occurs. This multidisciplinary approach to antenatal prescribing was found often to be fragmented, with women hearing conflicting opinions even from different members of the same professional group. The role of pharmacists as medication ‘gatekeepers’ and their refusal to dispense prescribed medications highlights the importance of their inclusion in system improvements.
Even when effective treatments were endorsed in national guidance, challenges in implementation were found. Despite RCOG guidance on the management of hyperemesis gravidarum,7 women reported being denied access to effective treatments. Ensuring health professionals have easy access to up-to-date guidance on specialist aspects of care and sufficient time and support to incorporate guidance into their practice is imperative.
While prescribers need to balance maternal benefit with potential fetal harm when prescribing in pregnancy, women’s individual circumstances were not always considered, and they were not fully engaged in decision making. Possibly reflecting the tendency of health professionals to overestimate the teratogenic potential of drugs,25 we found many examples where health professionals used fear of fetal harm to justify a refusal to prescribe or dispense otherwise recommended medications. This had a significant impact not only on women’s health, but on their emotional well-being with one reporting they were made to feel like the ‘world’s worst mother’.
Some women felt antidepressants and metformin for gestational diabetes were offered in preference to non-pharmacological options. This may reflect that the availability of talking therapies does not meet demand, or in relation to gestational diabetes, a lack of informed personalised conversations on the effectiveness of methods to obtain glycaemic control.
High-quality, easily accessible information on the safety of medicines is available26 but appeared to be underutilised in informing individualised discussions. Better reporting systems on outcomes related to medication use in pregnancy are needed, including making the results of clinical trials more generalisable through, where appropriate, the inclusion of pregnant women.27 Minimising prescribing in pregnancy became deeply entrenched in the ethos of antenatal care following the thalidomide tragedy.28 The belief that all medication use during pregnancy carries risk is commonly held among women, with paracetamol, antibiotics and antidepressants29 considered to be on a continuum of increasing risk. The potential for this position to cause harm is increasingly being realised. Some women with epilepsy and others with serious mental health conditions have died because of an over stringent position on medication avoidance.11 More recently, the numbers of pregnant women who died during the COVID-19 pandemic could have been reduced through earlier acceptance of the safety of vaccination in pregnancy, better public health communication and greater use of effective treatments among those seriously ill with COVID-19.30 While the need to reduce maternal mortalities through improved prescribing is already appreciated, our study suggests that physical and mental morbidity caused through the lack of access to effective treatments for mental health conditions and hyperemesis gravidarum is likely to be very common and requires improvement.
Strengths of this study include adding to knowledge on a seldom explored topic, the geographical spread and high number of survey participants from across the UK, and use of a sampling frame for the selection of interview participants. A weakness was that the sampling frame design may have shaped our findings and focused attention on certain experiences of medication use at the expense of others of equal concern to women. Our survey was self-selecting and may reflect the views of those more motivated to participate in research. The survey was only available in English and via the internet, excluding some groups from participation including non-English reading women and those with limited or no access to the internet. This study would have been strengthened by the inclusion of clinicians to better understand their attitudes and understandings of prescribing and dispensing during pregnancy.
## Conclusion
Medication prescribing and use during pregnancy is common. However, fear of fetal harm restricts the prescribing and taking of some advised medications. Analgesics were commonly avoided or taken at lower than the therapeutic or prescribed dose and some women reduced or discontinued medications for chronic conditions without medical oversight. Where existing clinical guidance was not followed, or there was conflict in professional opinion, reluctance to prescribe or dispense resulted in women needing to negotiate complex and distressing pathways to obtain required medications. The study identified aspects of antenatal prescribing where improvement in knowledge, communication or practice is required to ensure maximisation of the safety, efficacy and personalisation of prescribing in pregnancy.
## Data availability statement
Data are available upon reasonable request. Data are available on request.
## Patient consent for publication
Not applicable.
## Ethics approval
This study involves human participants and ethical approval was granted by the Research and Ethics committee of the School of Social Sciences at Cardiff University SREC/3201. Participants gave informed consent to participate in the study before taking part.
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|
---
title: Brucea javanica oil alleviates intestinal mucosal injury induced by chemotherapeutic
agent 5-fluorouracil in mice
authors:
- Xinghan Zheng
- Liting Mai
- Ying Xu
- Minghui Wu
- Li Chen
- Baoyi Chen
- Ziren Su
- Jiannan Chen
- Hongying Chen
- Zhengquan Lai
- Youliang Xie
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9990700
doi: 10.3389/fphar.2023.1136076
license: CC BY 4.0
---
# Brucea javanica oil alleviates intestinal mucosal injury induced by chemotherapeutic agent 5-fluorouracil in mice
## Abstract
Background: *Brucea javanica* (L.) Merr, has a long history to be an anti-dysentery medicine for thousand of years, which is commonly called “Ya-Dan-Zi” in Chinese. The common liquid preparation of its seed, B. javanica oil (BJO) exerts anti-inflammatory action in gastrointestinal diseases and is popularly used as an antitumor adjuvant in Asia. However, there is no report that BJO has the potential to treat 5-Fluorouracil (5-FU)-induced chemotherapeutic intestinal mucosal injury (CIM).
Aim of the study: To test the hypothesis that BJO has potential intestinal protection on intestinal mucosal injury caused by 5-FU in mice and to explore the mechanisms.
Materials and methods: Kunming mice (half male and female), were randomly divided into six groups: normal group, 5-FU group (5-FU, 60 mg/kg), LO group (loperamide, 4.0 mg/kg), BJO group (0.125, 0.25, 0.50 g/kg). CIM was induced by intraperitoneal injection of 5-FU at a dose of 60 mg/kg/day for 5 days (from day 1 to day 5). BJO and LO were given orally 30 min prior to 5-FU administration for 7 days (from day 1 to day 7). The ameliorative effects of BJO were assessed by body weight, diarrhea assessment, and H&E staining of the intestine. Furthermore, the changes in oxidative stress level, inflammatory level, intestinal epithelial cell apoptosis, and proliferation, as well as the amount of intestinal tight junction proteins were evaluated. Finally, the involvements of the Nrf2/HO-1 pathway were tested by western blot.
Results: BJO effectively alleviated 5-FU-induced CIM, as represented by the improvement of body weight, diarrhea syndrome, and histopathological changes in the ileum. BJO not only attenuated oxidative stress by upregulating SOD and downregulating MDA in the serum, but also reduced the intestinal level of COX-2 and inflammatory cytokines, and repressed CXCL$\frac{1}{2}$ and NLRP3 inflammasome activation. Moreover, BJO ameliorated 5-FU-induced epithelial apoptosis as evidenced by the downregulation of Bax and caspase-3 and the upregulation of Bcl-2, but enhanced mucosal epithelial cell proliferation as implied by the increase of crypt-localized proliferating cell nuclear antigen (PCNA) level. Furthermore, BJO contributed to the mucosal barrier by raising the level of tight junction proteins (ZO-1, occludin, and claudin-1). Mechanistically, these anti-intestinal mucositis pharmacological effects of BJO were relevant for the activation of Nrf2/HO-1 in the intestinal tissues.
Conclusion: The present study provides new insights into the protective effects of BJO against CIM and suggests that BJO deserves to be applied as a potential therapeutic agent for the prevention of CIM.
## 1 Introduction
Up to right now, chemotherapy is still one of the first-line therapies for cancers. However, epidemiological studies reveal that practically $40\%$ of cancer patients treated with a standard dose of chemotherapy develop clinical intestinal mucositis, which is referred to chemotherapeutic intestinal mucosal injury (CIM) and is characterized by severe diarrhea and inflammation (Thomsen and Vitetta, 2018; Cai et al., 2021). Chemotherapeutic agent 5-Fluorouracil (5-FU) plays an essential role in management of various cancers, and can cause CIM which threatens the effectiveness of therapy due to dose reduction and quality-of-life impairment among patients (Sonis et al., 2004). Since 5-FU-induced CIM exhibits stable, high incidences and similar pathological manifestations in mice, it is commonly used in animal models for CIM. The pathogenesis mechanisms of 5-FU-induced CIM include oxidative stress, inflammatory reaction, activation of apoptosis, interruption of proliferation in the intestinal epithelium, as well as destruction of intestinal mucosal barrier (Ribeiro et al., 2016). Currently, pharmacological interventions for CIM are still unavailable, except for the combined application of loperamide octreotide (LO), sulfasalazine, and probiotics for symptomatic relief and infection control (Keefe, 2007; Yeung et al., 2015; Bowen et al., 2019). Hence, it is in urgent need to develop safe and feasible therapeutic medications for the treatment of CIM. Recent research indicate that traditional Chinese medicine, such as patchouli oil (Gan et al., 2020), andrographolide (Xiang et al., 2020), berberine (Chen et al., 2020) and so on, show potent efficacy and safety in 5-FU-induced CIM in animal models, thus shedding new lights on the application of traditional Chinese medicine in the management of CIM.
Brucea javanica (L.) Merr, widely distributed in South China, has thousands years of history to be used for treating diarrhea and gastrointestinal diseases. ( Yoon et al., 2020). The pharmacologically active composition of B. javanica is referred to as *Brucea javanica* oil (BJO). It is generally considered to be the fatty oil extraction from the dried ripe fruit of *Brucea javanica* by petroleum ether or hexane. BJO contains multiple effective ingredients, including quassinoids and fatty acids such as oleic acid and linoleic acid (Chen et al., 2013; Yan et al., 2015). In our former studies, BJO and its active components demonstrated antioxidant and anti-inflammatory properties and maintained intestinal barrier integrity, contributing to amelioration of ulcerative colitis and inflammatory bowel diseases in mice (Huang et al., 2017; Huang et al., 2019). Additionally, BJO treatment is safe and has fewer side effects and thus widely used as an effective assistant treatment of various malignant tumor patients during radiotherapy and chemotherapy (Xu et al., 2016; Thomsen and Vitetta, 2018).
Taken into considerations that CIM shares similar symptoms and pathological mechanisms with inflammatory bowel diseases and dysentery (Hamouda et al., 2017; Rtibi et al., 2018), the present work was focused on the potential effects of BJO in the treatment of CIM in a 5-FU-induced mice model. Additionally, the protective effects of BJO against oxidative stress, inflammation, apoptosis of intestinal epithelium and intestinal barrier disruption were investigated to explore the pharmacological mechanisms of BJO.
## 2.1 Reagents
BJO was provided by Ming Xing Pharmaceutical Co. Ltd. (Guangzhou, Guangdong, China) with the number (Lot: 20180902), stored in 4°C refrigerator of room A204, laboratory of the School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China. To obtain the emulsion, BJO was prepared as previously reported (Wang et al., 2020). We mixed the appropriate amount of soybean lecithin and water to obtain the blank emulsion. After mixing the proper amount of BJO with the blank emulsion for 9 min, we obtained the milky white oil emulsion like cream with high-pressure homogenization. All processes were followed the standard of the ministry of Health of the people’s Republic of China (WS3-B-3646-98).
5-FU was purchased from Sigma Corporation (United States). LO was produced by Yang Senlin Pharmaceutical (Xi’an, China). Malondialdehyde (MDA) and superoxide dismutase (SOD) assay kits were bought from Jiancheng Biotechnology Company (Nanjing, Jiangsu, China). The ELISA (enzyme-linked-immunosorbent serologic assay) kits (tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-4 (IL-4), interleukin-1β (IL-1β), inducible nitric oxide synthase (iNOS), and diamine oxidase (DAO)) were obtained from Shanghai MLBIO Biotechnology (Shanghai, China). Primary antibodies against proliferating cell nuclear antigen (PCNA), cyclooxygenase-2 (COX-2), zonula occludens-1 (ZO-1), occludin, claudin-1, and β-actin were purchased from Affinity Biosciences (OH, United States). TRIzol® reagent was provided by Thermo Fisher (Waltham, MA, United States). Mice primers for occludin, claudin-1, CXC chemokine ligand 1 (CXCL1) and CXC chemokine ligand 2 (CXCL2) were purchased from (Sangon Biotech, Shanghai, China). Other required materials were bought from Vazyme Biotech (Nanjing, China).
## 2.2 Animals
The experimental animals were Kunming mice (weighting 22–25 g, half male and female) provided by the Laboratory Animal Centre, Guangzhou University of Chinese Medicine, Guangzhou, China (approval number: 44007200079662). Mice were cared under the standard laboratory conditions, which is temperature 22°C ± 2°C, humidity $50\%$ ± $10\%$, and 12 h dark-light cycle. And they were allowed to freely consume sterilized water and standard chow. All experimental procedures were approved by the Animal Ethics Committee of Guangzhou University of Chinese Medicine.
## 2.3 Induction of CIM by 5-FU and treatments
Kunming mice were randomly assigned into six groups ($$n = 10$$): normal group; 5-FU group (5-FU, 60 mg/kg/day for 5 days, intraperitoneal injections); LO group (loperamide; 4 mg/kg/day for 7 days, oral administration); BJO group (0.125 (BJOL), 0.250 (BJOM), 0.500 (BJOH) g/kg/day for 7 days, oral administration). According to previous research (Zheng et al., 2019), 5-FU intraperitoneal injection (from day 1 to day 5); 30 min later, oral administration of BJO and LO was carried out (from day 1to day 7). The normal group mice were injected with physiological saline and orally received blank emulsion. The 5-FU group mice were injected with 5-FU and orally given blank emulsion. Body weight, stool consistency, food intake, and general appearance of mice were recorded daily. Diarrhea was measured using the mean scores. As shown in Table 1, the severity was quantified according to a previously described procedure (Huang et al., 2009). After the 7-day treatment, the blood was obtained after enucleation of eyeball, and mice were sacrificed by cervical dislocation. After 2 h, blood was centrifuged for 15 min at 3500 rpm and 4°C in refrigerate centrifuge, and serum was collected.
**TABLE 1**
| Score | Feature |
| --- | --- |
| 0-normal | normal stool or the absence of stool |
| 1-slight | slightly wet and soft stool |
| 2-moderate | wet and unformed stool with moderate perianal staining of the coat |
| 3-severe | watery stool with severe perianal staining of the coat |
## 2.4 Histopathological evaluation of intestine
After the mice were sacrificed, the ileum tissue was removed immediately and put into the fixation solution ($4\%$ paraformaldehyde) for 24 h, then the tissue was immersed in wax, and sections (5 μm thick) were dewaxed and stained as recommended by the standard operating protocols. The macroscopic injuries of H&E staining segments of the ileum were assessed after microscopical observation.
## 2.5 Immunohistochemical analysis
The ileum tissue was dewaxed and hydrated by paraffin-embedded slices, then placed in citrate antigen repair buffer (pH 6.0) for antigen repair. After that, the tissue endogenous peroxidase was blocked and $3\%$ BSA was closed. According to experimental conditions, the primary and secondary antibodies were incubated successively. Finally, the section was colored by DAB (diaminobenzidine method) and the hematoxylin stained cell nucleus for 3 min. Immunohistochemical staining was used to determine either PCNA or COX-2, and the results were analyzed by using the graphic analysis system. The immunohistochemical images were captured by a microscope.
## 2.6 Cytokines analysis by commercial assay kits and ELISA
The SOD and MDA assay was performed in accordance with the instructions of commercial assay kits. At a low temperature, the samples were homogenized with appropriate amount of pre-cold PBS and centrifuged (4000 rpm/min, 20 min), and then collect the supernatant. The content of proinflammatory factors (TNF-α, IL-6, IL-4, IL-1β, iNOS) and DAO were measured by ELISA kits in the light of the manufacturer’s instruction.
## 2.7 RT-polymerase chain reaction
Total RNA was collected from mice intestine tissues using TRIzol® reagent. 1 μg RNA was reverse transcribed to produce cDNA using the HiScript™ cDNA Synthesis Kit. Then amplification program was done in line with the protocols. Primer sequences for claudin-1, occludin, CXCL1, CXCL2, NLRP3, and GAPDH are stated in Table 2.
**TABLE 2**
| Gene | Unnamed: 1 | Primer sequences (5′-3′) |
| --- | --- | --- |
| CXCL1 | FORWARD | ATGGCTGGGATTCACCTCAAGAAC |
| CXCL1 | REVERSE | AGTGTGGCTATGACTTCGGTTTGG |
| claudin-1 | FORWARD | GCTGGGTTTCATCCTGGCTTCTC |
| claudin-1 | REVERSE | CCTGAGCGGTCACGATGTTGTC |
| Occludin | FORWARD | TGGCTATGGAGGCGGCTATGG |
| Occludin | REVERSE | AAGGAAGCGATGAAGCAGAAGGC |
| CXCL2 | FORWARD | CACTGGTCCTGCTGCTGCTG |
| CXCL2 | REVERSE | GCGTCACACTCAAGCTCTGGATG |
## 2.8 Western blot
The total protein was isolated and extracted from intestine tissues using cold RIPA lysis buffer supplemented with protease inhibitor, and then determined and denatured. Equal amounts of the protein samples were subjected to SDS-PAGE and transferred onto PVDF membranes. The non-specific binding sites were blocked with $5\%$ skimmed milk at 25°C, and then the membranes cleaned in TBST for 3 times. After that, the blocked membranes were incubated at 4°C overnight with the indicated primary antibodies, and then treated with HRP-conjugated secondary antibody for 2 h at room temperature. Protein bands were visualized using ECL luminescence solution in the detection system and quantified with ImageJ software.
## 2.9 Data analysis
All values were represented as means ± standard error (SEM) except for data that did not obey normal distribution, which were expressed as M (P 25∼P 75). SPSS 23.0 software (SPSS Inc., Chicago, IL, United States) was used to accomplish the statistical analyses. Data processing was performed by one-way analysis of variance (ANOVA), followed by Tukey’s post hoc test to perform multiple comparison procedures. Significant differences between groups were reflected as $p \leq 0.05.$
## 3.1 BJO improved 5-FU-induced intestinal mucositis
The clinical symptoms of 5-FU-induced CIM generally included weight loss, abdominal pain, and severe diarrhea (Atiq et al., 2019; Xiang et al., 2020). In this experiment, the potential effect of BJO treatment on 5-FU-induced CIM was investigated through assessment of body weight, diarrhea status, and histological changes in the ileum. As illustrated in Figure 1A, the mice’s weight showed a gradual decrease from the fourth day of 5-FU injection. Oral medication of 0.500 g/kg BJO or the positive control drug LO significantly ameliorated weight loss induced by 5-FU ($p \leq 0.01$) (Figure 1A). Similarly, extensive diarrhea resulted from the 5-FU injection was alleviated by administration of BJO in a concentration-dependent manner ($p \leq 0.01$) (Figure 1B). The H&E staining demonstrated mucosal erosion, disruption of crypt-villus structures, and subacute inflammation in the ileum of mice in the 5-FU-treated groups (Figure 1C). However, BJO treatment significantly reversed the damage of mucosal epithelium and subacute inflammation, as implied by the recovery of mucosa thickness, villus height, crypt depth, and prevention of inflammatory cells infiltration (Figure 1C). Altogether, these results suggested that BJO treatment ameliorates intestinal mucositis caused by 5-FU.
**FIGURE 1:** *BJO improved 5-FU-induced intestinal mucositis. (A) Percentage of initial body weight (%, n = 10) (B) Diarrhea score (n = 10). (C) The ileum histopathology of CIM mice (200×, n = 6). Red arrow: shortened intestinal villus; green arrow: loss of crypt architecture. **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
## 3.2 BJO repressed oxidative stress induced by 5-FU
We evaluated whether BJO decreased 5-FU-induced oxidative stress in the serum. SOD exerts an important influence in maintaining the oxidation and antioxidant balance of the body (Cao et al., 2018). The lipid peroxidation results in the accumulation of MDA, and then ultimately leads to disturbance of bio-membranes’ fluidity and increase of permeability of bio-membranes (Cui et al., 2018). As shown in Figure 3, there was a significant depletion of serum SOD activity accompanied by an elevation of serum MDA levels in the 5-FU-treated mice compared to that in the normal control ($p \leq 0.01$). Following treatment with BJO (0.250 g/kg, 0.500 g/kg), SOD activity significantly rebounded ($p \leq 0.05$, $p \leq 0.01$), and MDA content was reduced ($p \leq 0.05$, $p \leq 0.01$) (Figure 2). Thus, our observations show that BJO has antioxidant capacity and contributes to elimination of oxidative stress in 5-FU-induced CIM.
**FIGURE 2:** *The effect of BJO in markers of oxidative stress in serum (n = 8). (A) SOD activity, (B) MDA concentration. *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
## 3.3 BJO suppressed 5-FU-triggered intestinal inflammation
To further investigate the impact of BJO against 5-FU-induced inflammation in intestine tissue, the expression levels of intestinal inflammatory cytokines were tested. As shown in Figure 3A, compared with normal group, the pro-inflammatory factors IL-1β, TNF-α, and IL-6 of 5-FU group were significantly raised (all $p \leq 0.01$), however, administration of BJO led to an evident dose-dependent reduction in 5-FU-induced elevation of pro-inflammatory factors IL-1β ($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.01$, respectively), TNF-α ($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.01$, respectively), and IL-6 ($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.01$, respectively). For the release of anti-inflammatory IL-4, BJO shown an obvious dose-dependent increase ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$, respectively).
**FIGURE 3:** *The effect of BJO on inflammatory cytokines and intestinal permeability in intestine tissue of CIM mice. (A)The levers of IL-1β, IL-4, TNF-α and IL-6 in intestinal tissue. (B) The levers of iNOS and DAO in intestinal homogenates. *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
Furthermore, the intestinal levels of iNOS and DAO were investigated. The inflammatory marker iNOS was significantly upregulated after 5-FU treatment ($p \leq 0.01$), but it was reversed by BJO ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.05$, respectively) (Figure 3B). DAO is especially active in the intestinal mucosa and regulates the rapidly proliferating intestinal mucosa (Fukuda et al., 2019). DAO can penetrate across the intestinal barrier and shuttle to the circulation when the mucosal barrier is disrupted, so a decrease of intestinal DAO is obvious during intestinal mucositis. The activity of DAO was repressed by 5-FU ($p \leq 0.01$), but activity was recovered following BJO administration ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$, respectively) (Figure 3B).
Moreover, compared with the 5-FU group, the IHC staining and Western blot data of BJO shown a downward trend ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$; $p \leq 0.01$, $p \leq 0.01$, $p \leq 0.01$, respectively), indicating that the 5-FU-induced increase in COX-2 was attenuated by BJO (Figure 4).
**FIGURE 4:** *The effects of BJO on the expression of COX-2. (A, B) The expression of COX-2 (A, 200×) was detected by immunohistochemical staining in ileum sections (n = 3). (C, D) The expression of COX-2 was detected by Western blot (n = 3). *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
CXCL1 and CXCL2 are the potent neutrophils chemoattractants that mediate intestinal inflammatory responses and are related to the pathogenesis of CIM (Guo et al., 2020; Hu et al., 2021). We detected the mRNA amounts of CXCL1 and CXCL2 in intestinal tissue. Both of their productions were enhanced by 5-FU ($p \leq 0.01$), while BJO significantly reduced the productions of them ($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.01$; $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$, respectively) (Figures 5B, C). Moreover, Chemokines CXCL1 and CXCL2 are responsible for the inflammasome activation, and release IL-1β in macrophages (Boro and Balaji, 2017). We observed that compared with the normal group, mRNA expression of NLRP3 was increased more than three-fold in the intestine of the 5-FU group. In all dose, BJO suppressed NLRP3 expression in intestinal tissue ($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.05$, respectively) (Figure 5A).
**FIGURE 5:** *The effects of BJO on the transcription levels of NLRP3 and chemokines in intestinal tissues of CIM mice (n = 6). (A) NLTP3 mRNA expression; (B) CXCL1 mRNA expression; (C) CXCL2 mRNA expression. *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
Therefore, these findings support the conclusion that BJO exerts anti-inflammatory properties against 5-FU-induced CIM.
## 3.4 BJO inhibited 5-FU-triggered epithelial apoptosis and proliferation inhibition
Activation of apoptosis and proliferation inhibition of crypt cells in the intestinal epithelium is one of the key links to CIM (De Angelis et al., 2006). To explore whether BJO could exert an anti-apoptotic effect in 5-FU-induced CIM, the apoptosis-related molecules’ expressions were quantified. As shown in Figure 6, the 5-FU group exhibited significant augmentation on the protein level of Bax and cleaved-caspase-3 (all $p \leq 0.01$), together with a diminution of Bcl-2 expression ($p \leq 0.01$), compared with the normal group, indicating that 5-FU facilitated cell apoptosis. Treatment of BJO (0.500 g/kg) remarkably inhibited 5-FU- stimulated cell apoptosis in CIM mice (all $p \leq 0.05$).
**FIGURE 6:** *The effects of BJO on the protein expression of Bax、Bcl-2 and caspase-3 (cleaved) in intestinal tissues in CIM mice. (A) The protein bands of Bax、Bcl-2 and caspase-3 (cleaved); (B) The statistical analysis of Bax、Bcl-2 and caspase-3 (cleaved) protein expressions (n = 3). *p < 0.05, **p < 0.01 compared with 5-Fu group and ##
p < 0.01 compared with normal group.*
The cell-proliferation capability of enterocyte was evaluated by determining the PCNA expression using immunohistochemistry assay and Western blotting. Treatment with 5-FU at 60 mg/kg dose hampered PCNA expression compared to normal control group (all $p \leq 0.01$). By contrast, BJO concentration-dependent increased the expression of PCNA ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$, Figures 7C, D), especially the crypt-localized PCNA (all $p \leq 0.01$, Figures 7A, B). These data suggest that BJO is able to enhance the survival of crypt cells following chemotherapy and assist in the recovery of the impaired mucosal membrane.
**FIGURE 7:** *The effects of BJO on the expression of PCNA in ileum sections. (A, B) The expression of PCNA was detected by immunohistochemical staining (200×, n = 3). (C, D) The expression of PCNA was detected by western bolt (n = 3). *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
## 3.5 BJO alleviates 5-FU induced disruption of tight junction proteins
Destruction of the mucosal barrier induced by chemotherapy might cause mucosal barrier dysfunction (Sanchez de Medina et al., 2014). We examined the key indicator of the tight junction markers, including ZO-1, occludin, and claudin-1, which are closely associated with the epithelial cell permeability and mucosal barrier function. As shown in Figures 8A, C, the tight junction protein expressions of ZO-1, occludin, and claudin-1 were markedly decreased in mice stimulated with 5-FU (all $p \leq 0.01$), but they were obviously restored by BJO with 0.500 g/kg (all $p \leq 0.01$). By contrast, the routine anti-diarrheal drug loperamide did not show an ameliorative effect on their expressions (all $p \leq 0.05$, Figures 8A, B). As shown in Figure 8C, the mRNA expressions of occludin and claudin-1 were markedly decreased in mice stimulated with 5-FU (all $p \leq 0.01$), while they were significantly increased in BJO (0.500 g/kg, all $p \leq 0.05$) Thus, these data suggested that BJO could sustain the intestinal mucosa barrier function and recover from damage caused by 5-FU.
**FIGURE 8:** *The effects of BJO on the expression of ZO-1, occludin, and claudin-1 in intestinal tissues of CIM mice. (A) The protein bands of ZO-1, occludin, and claudin-1;(B) The statistical analysis of ZO-1, occludin, and claudin-1 protein expressions (n = 3); (C)The mRNA expressions of claudin-1 and occludin (n = 6). *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
## 3.6 BJO alleviated CIM by activating Nrf2/HO-1 signaling pathway
We further explored the mechanisms underlying the antioxidant and anti-inflammatory effects of BJO by investigating Nrf2/HO-1 signaling pathway. As shown in Figure 9, we observed a significant elevation in the cytoplasmic content of Nrf2, accompanied by a reduction of nuclear content of Nrf2 and the downstream target protein HO-1 ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.05$, respectively), in the 5-FU group, suggesting that Nrf2 is inactivated during CIM. However, BJO significantly facilitated the nuclear potion of Nrf2 ($p \leq 0.05$, $p \leq 0.01$, $p \leq 0.01$, respectively) and the followed transcription of HO-1($p \leq 0.05$, $p \leq 0.05$, $p \leq 0.05$, respectively), thus indicating that BJO is able to activate the anti-oxidant Nrf2/HO-1 signaling pathway (Figure 9).
**FIGURE 9:** *The effects of BJO on the signaling pathways of Nrf2/HO-1 involved in CIM mice. (A) The related protein bands of Nrf2/HO-1 signaling pathways; (B) The statistical analysis of Nrf2/HO-1 signaling pathways (n = 3). *p < 0.05, **p < 0.01 compared with 5-FU group; ##
p < 0.01 compared with normal group.*
Consistent with the above observations, our results indicated that the antioxidant and anti-inflammatory effects of BJO are associated with activating Nrf2/HO-1 signaling pathway.
## 4 Discussion
BJO emulsion injection has been extensively applied as adjunctive therapies in combination with radiochemotherapy for colorectal carcinoma, gastric cancer, liver cancer, and other diseases (Ye et al., 2016; Chen and Wang, 2018; Wang et al., 2020). BJO exerts synergetic antitumor effects by increasing the sensitivity of tumor cells to chemotherapeutic agents, reversing drug resistance, maintaining the quality of life, and decreasing the frequency of adverse reactions during treatment (Ye et al., 2016; Chen and Wang, 2018; Wang et al., 2020). Our study extends cognizance to the therapeutic effects of BJO in chemotherapy-induced intestinal mucositis, considering that BJO treatment prevented weight loss, ameliorated diarrhea syndrome, and recovered mucosal epithelium impairment in a 5-FU-stimulated CIM mice model. These mucosal protective effects of BJO were attributable to its antioxidant and anti-inflammatory properties, anti-apoptosis and proliferation activation in mucosal epithelial cells, and improvement of mucosa barrier function. Indeed, these findings are consistent with our former observations that BJO has the therapeutic potential for treating intestinal diseases (Huang et al., 2017; Yoon et al., 2020), and that BJO can help reduce the adverse effects of radiochemotherapy, such as abdominal pain, diarrhea, and myelotoxicity in clinics (Xu et al., 2016). Thus, the present findings may broaden our understanding of the clinical application of BJO in treatment of chemotherapy-induced intestinal mucositis.
Mucositis contains complex biological mechanisms that occur in five stages: initiation, primary damage reaction, signal multiplication, ulceration, and recovery (Sonis, 2004). The increasing ROS and the antioxidant defense mechanisms‘ damage are considered to be causative mediators in the initial stage of CIM (Keefe, 2007). Indeed, our results demonstrated that the serum activity of SOD was reduced, but that of MDA was elevated in 5-FU-treated mice. BJO treatment could reverse the changes of SOD and MDA content, thus confirming that BJO could prevent oxidative stress and protect against CIM at the initial stage.
Excessive reactive oxygen species (ROS) generation in mucosal cells further induces negative events, including inflammatory and immune responses, which have detrimental effects on intestinal epithelial cells, subsequently initiating inflammatory signaling cascades and leading to intestinal disorder (Ribeiro et al., 2016). Our results suggested that BJO had an anti-inflammatory effects in the following ways: [1] BJO repressed the secretion of proinflammatory factors in the mucosa, including TNF-α, IL-1β and IL-6; [2] BJO decreased the expression of COX-2, which is the rate-limiting enzyme for the process of prostanoids’ synthesis and is irregularly upregulated during pathogenesis of intestinal inflammation (Lu and Zhu, 2014); [3] BJO suppressed the release of the chemical attractors CXCL1/CXCL2, which mediate the neutrophil recruitment, an essential preliminary action of tissue inflammation or repair (De Filippo et al., 2013); and [4] BJO restrained the activation of NLRP3 inflammasome, which drives host and immune responses by releasing cytokines and alarmins into circulation and has been revealed to be the vital contributor in the pathogenesis of various diseases involving inflammation (He et al., 2019; Li et al., 2020; Xu et al., 2022). In fact, these inflammatory cascades are not separated because proinflammatory cytokines might amplify the primary inflammatory signal, resulting in the transcription of COX-2 (Sonis, 2004), and the release of the chemical attractor CXCL1/CXCL2 may participate in NLRP3 activation (Serdar et al., 2020). Thus, it is most likely that BJO exerts synergetic effect on these inflammatory cascades.
Chemotherapy-induced intestinal mucositis cause a significant elevation in intestinal epithelial cells apoptosis, subsequently resulting in mucosal damage (Keefe et al., 2000). According to the present observations, BJO downregulated cleaved-caspase-3 and the pro-apoptotic Bax while augmented the anti-apoptotic Bcl-2, thus suggesting that BJO could alleviate the apoptotic process in CIM and might prevent atrophy of the villus and injury to the enterocytes in the intestine (Boeing et al., 2021).
Additionally, BJO upregulated crypt-localized PCNA, a major endogenous marker of cell proliferation capability (Phoophitphong et al., 2012), indicated that BJO could enhance proliferation of intestinal epithelial cells. Taken together, these findings demonstrated that BJO maintains the balance between apoptosis and proliferation of intestinal epithelial cells, finally recovering chemotherapy-induced mucosal damage.
Furthermore, disruption of mucosal barrier function is accompanied by oxidative stress, excessive inflammatory, and extensive apoptosis of epithelial cells (Turner, 2009). The tight junctions are multi-protein complexes that can constitute paracellular barriers to maintain the homeostasis of mucosal. In this experiment, the pharmacological action of BJO on mucosal barrier function was evaluated by detecting the levels of ZO-1, occludin, and claudin-1. Among these proteins, the claudin family reflects tight junction permeability (Tsukita et al., 2019). Peripheral membrane proteins like ZO-1, presents to be contributory to tight junction assembly and maintenance (Buckley and Turner, 2018), while occludin interacts directly with Caudins and actin in (Müller et al., 2005).
Our results showed that BJO rebounded 5-FU-induced decrease of these tight junction proteins, confirming that BJO could improve mucosal barrier function. When high permeability occurrs in the intestine during intestinal mucositis, DAO penetrates across the intestinal barrier and enters the bloodstream (Li et al., 2018; Jiang et al., 2020). Hence, the decrease of intestinal DAO level reflects the impairment of mucosa integrity (Miyoshi et al., 2015). Our observations that intestinal DAO level was elevated by BJO, provide further evidence that BJO could restore tight junction damage in CIM.
To investigate the biological mechanisms of the protective potential of BJO in CIM, we studied Nrf2/HO-1 pathway that are critical in the regulation of oxidative stress (Zorov et al., 2014). The enhancement of Nrf2 nuclear transportation and the increased expression of Nrf2 target gene HO-1 by BJO in CIM mice implied that the intestinal protective effects of BJO are probably attributable to activation of Nrf2. Indeed, the Nrf2/HO-1 signaling pathway is involved in orchestrated regulation of oxidative and inflammatory stress (Vasileva et al., 2020), as well as in the function of epithelial tight junction (Lau et al., 2015; Liu et al., 2017). Thus, the activation of Nrf2/HO-1 might help to provide one possible explanation for the antioxidant and improving anti-inflammatory status of BJO in the small intestine, and the restoration of mucosal barrier function by BJO. Collectively, the protective effects of BJO in CIM are most likely attributable to activation of Nrf2/HO-1.
As stated above, the present study demonstrated that BJO could ameliorate inflammation and diarrhea in 5-FU-induced CIM. Therefore, we believe that appropriate dose of BJO could be used as a potential novel therapeutic strategy for the prevention against intestinal mucositis event. Moreover, a previous study showed that the traditional Chinese medicine relieves mucositis corresponds with an amended intestinal flora. These components either directly improve the gut microbiome diversity and composition or have been metabolized by gut bacteria before being absorbed into the body. Therefore, we speculate that the intestinal flora might partially contribute the ability of BJO to ameliorate the intestinal mucositis induced by 5-FU. This is a hypothesis and needs further investigation for confirmation.
## 5 Conclusion
The present study identified that BJO has the potential to be used as a therapeutic agent for improved management of CIM. BJO significantly improved CIM syndrome in 5-FU-treated mice. The protective effects of BJO against CIM were attributed to the following respects: [1] relieving oxidative stress; [2] inhibiting intestinal inflammation; [3] preventing intestinal epithelial cell apoptosis but enhancing cell-proliferation capability of enterocyte; and [4] improving mucosal barrier function by upregulation of tight junction proteins. The possible mechanisms underlying the protective effects of BJO may be via activation of Nrf2/HO-1.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by Animal Ethics Committee of Guangzhou University of Chinese Medicine.
## Author contributions
The contribution of the authors in this work is as follows, XZ and LM designed and conducted the experiments. XZ wrote the original draft. YX and MW carried out the data processing. LC and BC assisted in the investigation and conducting experiments. ZS and JC contributed to technical support, data curation, and project administration. HC provided the test medicine and technical support. ZL and YX supervised the work, reviewed the paper, and secured funding contribution.
## Conflict of interest
Author HC is employed by Guangzhou Baiyunshan Mingxing Pharmaceutical Co. Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Abbreviations
5-FU, 5-Fluorouracil; BJO, *Brucea javanica* oil; CIM, chemotherapeutic intestinal mucosal injury; Nrf2, Nuclear factor E2-related factor 2; HO-1, Heme Oxygenase-1; SOD, superoxide dismutase; MDA, malondialdehyde; COX-2, cyclooxygenase-2; CXCL1, CXC chemokine ligand 1; CXCL2, CXC chemokine ligand 2; Bax, BCL2-associated X protein; Bcl-2, B-cell lymphoma-2; Caspase-3, Cysteinyl aspartate specific proteinase-3; PCNA, proliferating cell nuclear antigen; ZO-1, Zonula occludens-1; ELISA, enzyme-linked-immunosorbent serologic assay; IL-1β, Interleukin-1β; IL-4, Interleukin-4; IL-6, Interleukin-6; TNF-α, tumor necrosis factor-α; iNOS, Inducible nitric oxide synthase; DAO, Diamine oxidase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LO, loperamide; DAB, diaminobenzidine method; ROS, reactive oxygen species; ANOVA, one-way analysis of variance; PVDF, polyvinylidene fluoride.
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|
---
title: Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with
familial hypercholesterolemia
authors:
- Sergiy Sukhanov
- Yusuke Higashi
- Tadashi Yoshida
- Svitlana Danchuk
- Mitzi Alfortish
- Traci Goodchild
- Amy Scarborough
- Thomas Sharp
- James S. Jenkins
- Daniel Garcia
- Jan Ivey
- Darla L. Tharp
- Jeffrey Schumacher
- Zach Rozenbaum
- Jay K. Kolls
- Douglas Bowles
- David Lefer
- Patrice Delafontaine
journal: JCI Insight
year: 2023
pmcid: PMC9990768
doi: 10.1172/jci.insight.165713
license: CC BY 4.0
---
# Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with familial hypercholesterolemia
## Abstract
Although murine models of coronary atherosclerotic disease have been used extensively to determine mechanisms, limited new therapeutic options have emerged. Pigs with familial hypercholesterolemia (FH pigs) develop complex coronary atheromas that are almost identical to human lesions. We reported previously that insulin-like growth factor 1 (IGF-1) reduced aortic atherosclerosis and promoted features of stable plaque in a murine model. We administered human recombinant IGF-1 or saline (control) in atherosclerotic FH pigs for 6 months. IGF-1 decreased relative coronary atheroma in vivo (intravascular ultrasound) and reduced lesion cross-sectional area (postmortem histology). IGF-1 increased plaque’s fibrous cap thickness, and reduced necrotic core, macrophage content, and cell apoptosis, consistent with promotion of a stable plaque phenotype. IGF-1 reduced circulating triglycerides, markers of systemic oxidative stress, and CXCL12 chemokine levels. We used spatial transcriptomics (ST) to identify global transcriptome changes in advanced plaque compartments and to obtain mechanistic insights into IGF-1 effects. ST analysis showed that IGF-1 suppressed FOS/FOSB factors and gene expression of MMP9 and CXCL14 in plaque macrophages, suggesting possible involvement of these molecules in IGF-1’s effect on atherosclerosis. Thus, IGF-1 reduced coronary plaque burden and promoted features of stable plaque in a pig model, providing support for consideration of clinical trials.
## Introduction
Despite lipid-lowering and emerging antiinflammatory agents, atherosclerosis remains the leading cause of death in both men and women in the United States and worldwide [1]. Approximately every 40 seconds, someone in the United States will have a myocardial infarction, according to the 2022 heart disease statistics update [2]. The estimated total cost of heart disease in the United States alone is more than $329 billion [1]. Thus, interventions that reduce coronary atherosclerotic disease (CAD) would have substantial health and economic benefits.
Coronary atheroma burden is the main determinant of CAD patient outcomes [3]; thus, animal models that closely resemble human coronary atherosclerotic disease are of particular interest. Although murine models have provided mechanistic insights (4–7), they have not often translated to new clinical therapies. Unlike murine models, pigs have plasma lipid profiles very close to humans [8] and develop coronary disease that is similar to humans [9]. Pigs are the FDA-preferred species for testing human cardiovascular devices and the primary choice for preclinical toxicological testing of antiatherosclerotic drugs, including statins [10]. Mild atherosclerotic lesions first appear in coronary arteries, and both plaque distribution and composition follow a pattern comparable to that of humans [11], with early lesions transitioning to complex plaques. Familial hypercholesterolemia is a human genetic disorder with high circulating cholesterol and low-density lipoprotein (LDL) levels, resulting in excessive atherosclerosis [12]. Pigs with familial hypercholesterolemia (FH pigs) have been described by Rapacz and others [13]. FH pigs harbor a point mutation in both LDL receptor alleles that reduces receptor binding, as well as allele variations in apolipoprotein B that may further contribute to the phenotype. FH swine are an excellent model for translational atherosclerosis-related research [14]. Even when consuming a normal diet, FH pigs develop hypercholesterolemia and atherosclerotic lesions ranging from fatty streaks to advanced plaques, with accompanying calcification, neovascularization, hemorrhage, and rupture [11].
Insulin-like growth factor 1 (IGF-1) is an endocrine and autocrine/paracrine growth factor that has pleiotropic effects on development, metabolism, cell differentiation, and survival. There is ongoing debate on the role of IGF-1 in cardiovascular disease. Traditionally, the role of growth factors in atherosclerosis has been thought to be permissive by stimulating vascular smooth muscle cell (SMC) migration and proliferation, thereby promoting neointima formation [15]. Some cross-sectional and prospective studies suggest a positive association between IGF-1 and atherogenesis [16], but others have found that low IGF-1 is a predictor of ischemic heart disease and mortality, consistent with the potential antiatherosclerotic and plaque stabilization effects of IGF-1 [17]. Methodological constraints could explain these contradictions because measurement of total IGF-1 levels represents only a crude estimate of the biologically active IGF-1. IGF-1 levels negatively correlate with body weight (BW) and age, and this additionally confounds examination of the role of IGF-1 in cardiovascular disease.
We have shown that IGF-1 is an antiatherogenic factor in mice [18]. Systemic IGF-1 infusion reduced aortic root plaque area by approximately $30\%$ in apolipoprotein E–knockout (Apoe–/–) mice [19]. This effect is similar to that of statins in the same mouse model (e.g., high-dose rosuvastatin, ref. 20), and statins greatly reduce acute coronary events in patients with atherosclerosis [21]. Consistent with our results, another group reported that long arginine-3 IGF-1 increased plaque SMCs and cap thickness and reduced the rate of intraplaque hemorrhage, indicating that IGF-1 promotes plaque stabilization [17]. These findings are in line with most clinical studies [22, 23] but not all [24], which have suggested that lower circulating IGF-1 levels and higher levels of IGF-1 binding protein 3 are associated with increased risk of atherosclerotic disease. Validation of our murine studies in a large animal model is critical to consider use of IGF-1 to treat atherosclerosis in humans. Antiatherosclerotic effects of novel drugs are routinely tested in pigs (including statins, ref. 25); however, to our knowledge there are no reports directly testing IGF-1 effects on atherogenesis in a large animal model. Furthermore, since epidemiological studies have reported a positive association between circulating IGF-1 levels and various primary cancers, such as breast, colorectal, and prostate cancer [26], it is critical to examine potential carcinogenic effects of long-term IGF-1 administration in a large animal model.
Here, we document IGF-1 effects on coronary atherosclerosis in FH pigs, using recombinant human IGF-1 at a dose that is FDA approved for long-term treatment of growth failure in children with severe primary IGF-1 deficiency. Furthermore, we use spatial transcriptomics (ST), an innovative groundbreaking technology that quantifies changes in the whole transcriptome within a morphological context [27], to detect spatially and differentially expressed genes targeted by IGF-1. Our results provide mechanistic insights into IGF-1–induced effects on atherosclerosis and to our knowledge represent the first report on the use of ST to analyze atherosclerotic tissue from animals or humans.
## Phenotype of FH pigs.
We used 14-month-old FH noncastrated males ($$n = 5$$/group) and gilt (never been used for breeding) females ($$n = 9$$/group) and administered recombinant human IGF-1 (rhIGF-1), 50 μg/kg/d, twice per day, or saline for 6 months (Figure 1A). Of note, the IGF-1 dose is within the range of the FDA-approved dosage for long-term treatment of growth failure in children with primary IGF-1 deficiency [28], and the amino acid sequence structure of porcine IGF-1 is identical to human IGF-1 [29]. Safety, pharmacokinetics, and efficacy of IGF-1 are reported for patients [28, 30, 31]. To verify that IGF-1 administration into FH pigs stimulates specific downstream signaling in porcine vasculature and blood cells, we injected recombinant human rhIGF-1 (or saline, control) into pigs and isolated carotid arteries and peripheral blood mononuclear cells (PBMCs) after 4 hours. Akt phosphorylation was increased by almost 4-fold ($P \leq 0.001$) in both vascular tissue and PBMC sin IGF-1–injected pigs versus control (Figure 1B), indicating that IGF-1 promotes specific downstream signaling. IGF-1 levels at T0 were higher in males versus females (Figure 1, C and D, $P \leq 0.005$). IGF-1–injected pigs had significantly higher IGF-1 levels compared with control pigs at all tested time points. The average increase in IGF-1 group versus saline in males was $88.0\%$ ± $19.4\%$ and $83.3\%$ ± $9.7\%$ in females.
Male and female animals had a similar initial BW on average, and both IGF-1– and saline-injected pigs gained BW steadily throughout the study, with no difference found between saline and IGF-1 groups (Supplemental Figure 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.165713DS1). FH males had a larger BW increase compared with FH females (M, $69.2\%$ ± $2.9\%$ increase at T6 versus T0; F, $45.0\%$ ± $5.2\%$ increase, $P \leq 0.01$).
Blood pressure and heart rate were measured in sedated pigs at each IVUS procedure. We found no difference in systolic/diastolic blood pressure and heart rate between sexes and between saline and IGF-1 groups (Supplemental Table 1). There was no time-dependent change in blood pressure or in heart rate.
Blood tests were performed monthly. We found no statistically significant difference in complete metabolic profiles and complete blood count (CBC) with differential between sexes and saline versus IGF-1 group (Supplemental Tables 2 and 3). However, we found that females had a striking 2.3-fold higher cholesterol levels at T0 compared with males ($P \leq 0.001$, Figure 1, E and F). HFD feeding caused a significant and sustained elevation of total cholesterol levels in both sexes. IGF-1 did not change total cholesterol in both males and females. FH females had significantly higher triglyceride levels compared with males at T0 ($P \leq 0.005$, Figure 1, G and H), and HFD feeding did not change triglycerides. IGF-1 reduced triglycerides in FH pigs (3-way ANOVA, $P \leq 0.005$, Figure 1, G and H).
## Necropsy/histopathology findings.
IGF-1 levels have been reported to be associated with an increased risk of cancer [26]; thus, we performed autopsies and collected all major organs for histopathological analysis. No gross abnormalities were found either in saline-injected or IGF-1–injected FH pigs, and no tissue was considered carcinogenic by a certified pathologist. All female pigs had multiple coalescing yellow to tan plaques on the intimal surface of the aorta and large visible lipid deposits in the right coronary artery (RCA), left anterior descending artery (LAD), and circumflex artery. Two female pigs (saline, 1; IGF-1, 1) had evidence of a myocardial infarction. Abdominal fat deposits, hepatic lipidosis/fatty liver, and fatty lymph nodes were found in several saline- and IGF-1–injected females. Two female pigs in the saline group had pleural adhesions and moderate splenic enlargement, suggesting inflammation. Sections of the lung, liver, kidney, spleen, lymph node, stomach, small and large intestines, urinary bladder, ovary, uterus, pancreas, salivary gland, skeletal muscle, thyroid, and aorta from each animal were examined. No significant microscopic lesion including inflammatory or neoplastic process was noted in all animals (not shown) except atherosclerosis in the aorta and in coronary arteries.
## IGF-1 decreases coronary atherosclerosis.
IVUS was performed in the RCA and LAD at T0, T3, and T6 time points (Figure 1A). We found no significant difference in vessel volume, lumen volume, and plaque + media volume between RCA and LAD and between sexes at T0 (Table 1). There was a time-dependent increase of the vessel volume, presumably due to normal animal growth and to vascular remodeling concomitant with intimal thickening. In fact, the lumen volume time dependently decreased in both saline- and IGF-1–injected pigs ($P \leq 0.001$ for both RCA and LAD), consistent with intimal thickening. IGF-1–injected pigs had a larger time-dependent increase in RCA and LAD artery volume compared with controls ($P \leq 0.05$), suggesting vascular hypertrophy. Coronary arteries in IGF-1–injected FH females had larger lumen volume at T3 and T6 ($P \leq 0.005$) compared with control.
Pigs had approximately $16\%$ of relative atheroma volume at T0. IGF-1– and saline-injected pigs had a time-dependent increase in absolute plaque + media volume and in relative atheroma volume ($P \leq 0.001$ for RCA and LAD). FH females had a significantly larger time-dependent increase in RCA and LAD relative atheroma at T3 (females, $36.6\%$ ± $2.6\%$, vs. males, $23.3\%$ ± $0.2\%$) and at T6 (females, $47\%$ ± $4\%$ vs. males, $35.9\%$ ± $1.9\%$), indicating the presence of a strong sex effect. IGF-1 did not significantly change the absolute plaque + media volume in RCA and LAD. We found no interaction between IGF-1 effect on relative atheroma and sex (3-way ANOVA). As sex does not influence IGF-1’s potential effect on relative atheroma volume, we combined measurements of both sexes and performed a 2-way analysis (treatment vs. time) using Bonferroni’s correction for repeated measurements. IGF-1 time dependently decreased relative atheroma volume in RCA ($P \leq 0.05$ vs. saline) and in LAD ($$P \leq 0.054$$, Figure 2). The IGF-1–induced increase in vessel lumen and reduction in relative atheroma indicate that IGF-1 reduces coronary atherosclerosis.
Trichrome-stained RCA and LAD cross sections were used for histological analysis. FH females developed larger and more complex plaques in coronaries compared with males, in agreement with IVUS results (Figure 3). Plaques in males contained diffuse homogeneous collagen material and lipid droplets and had neither necrotic cores nor fibrous caps. We classified plaques in FH males as type III pre-atheroma in accordance with histological classification of human atherosclerotic lesions [32]. Coronary plaques in FH females were significantly larger (RCA: 2-fold increase in CSA versus males, LAD: 1.5-fold increase, $P \leq 0.05$, data for saline group); they contained dense fibrous caps, large acellular/necrotic cores, and multiple cholesterol clefts. We observed the presence of strong calcification (Alizarin Red staining, data not shown) and neovascularization (IHC with CD31, endothelial cell marker, data not shown) in coronary plaques in FH females but not in males. Plaques in FH females were classified as advanced type V fibroatheroma [32].
FH males had a thicker tunica media compared with females in both IGF-1– and saline-injected groups ($P \leq 0.05$ in each case, Figure 3B). IGF-1 significantly increased medial CSA (males, RCA: $13\%$ increase vs. control; LAD: $12\%$ increase; females, RCA: $34\%$ increase; LAD: $26\%$ increase), consistent with vascular hypertrophy (Figure 3B). IGF-1 did not change total vessel CSA (outlined by external elastic membrane boundary, data not shown). IGF-1 reduced relative atherosclerotic plaque area in males (RCA, $15.2\%$ ± $4.5\%$ decrease; LAD, $16.6\%$ ± $7.1\%$ decrease compared with control) and in females (RCA, $21.5\%$ ± $2.7\%$ decrease; LAD, $17.4\%$ ± $2.1\%$ decrease compared with control) (Figure 3C), consistent with IVUS data. Plaques in IGF-1–injected females were more cellular and contained reduced necrotic cores compared with controls (RCA: $50.1\%$ ± $1.6\%$ decrease; LAD: $47.8\%$ ± $1.4\%$ reduction, Figure 3D). IGF-1 significantly increased the thickness of fibrous caps in coronary plaques in female pigs (Figure 3E). Our results indicate that IGF-1 induces vascular hypertrophy, reduces coronary atherosclerotic burden, and promotes features of plaque stability.
## IGF-1 reduces macrophage-like cells and upregulates endothelial-like cells in coronary plaque.
We reported previously that IGF-1 increases plaque SMCs [33], downregulates macrophages (MFs), and elevates levels of circulating endothelial cell (EC) progenitors [19] in HFD-fed Apoe–/– mice. SMCs and MFs share cell markers in the atherosclerotic plaques [34], and plaque ECs undergo a change in phenotype toward a mesenchymal cell type [35]. Such phenotype switching complicates marker-based cell identification. To validate the IHC protocol, serial RCA sections were stained with a set of cell marker antibodies and immunopositivity pattern was compared. We found that each of 4 SMC marker antibodies stained virtually identical cell populations in the plaque, and a similar conclusion was made for 3 tested MF and 3 EC marker antibodies (Supplemental Figure 2). These data show that IHC with antibody for a single cell marker identified plaque cells expressing multiple markers, increasing confidence in identifying specific plaque cells. We also verified that cells immunopositive for macrophage scavenger receptor A (MSR), an MF marker, were immunonegative for α–smooth muscle actin (α-SMA), an SMC marker, and vice versa (Supplemental Figure 2A), showing that these antibodies have no cross-reactivity.
We used α-SMA, MSR, and CD31 antibodies to quantify SMC-like, MF-like, and EC-like cells, respectively, by IHC. SMC-like cells were abundant in the vascular media, and a mixture of SMC- and MF-like cells was found in the plaque fibrous cap (Figure 4 and Supplemental Figure 2A). In addition, MF-like cells were present in the area surrounding the plaque necrotic core and colocalized with cholesterol clefts in lipid cores. IGF-1–injected pigs had a slight increase in plaque SMC-like cells (P = NS) (Supplemental Figure 3) and a dramatic 2-fold reduction in plaque MF-like cells in females ($P \leq 0.05$ for RCA and LAD) (Figure 4). IGF-1 increased EC marker–positive area and this effect reached significance for RCA and LAD in females and LAD in males (Figure 4). Notably, EC marker–positive area was markedly larger in males compared with females (Figure 4A). We noted discontinuous CD31+ staining in endothelium layers of both IGF-1– and saline-injected pigs, suggesting either CD31 downregulation, or focal loss of EC, suggesting reduced endothelial integrity. To further obtain a surrogate index of endothelium layer integrity, we normalized CD31+ area per lumen perimeter. IGF-1 significantly increased CD31+ area/lumen perimeter ratio in the RCA and LAD in the female group (pixels2/pixels, RCA: IGF-1, 5.04 ± 0.84 vs. saline, 2.76 ± 0.64, $P \leq 0.05$; LAD: IGF-1, 10.31 ± 1.53 vs. saline, 5.52 ± 0.51, $P \leq 0.01$), suggesting that IGF-1 reduced the number of CD31+ endothelium layer breaks.
Systemic IGF-1 administration increased expression of pro–α 1(I) collagen in aortic lysates [36], and SMC-specific IGF-1 overexpression increased collagen fibrillogenesis in the atherosclerotic plaque in Apoe–/– mice [37]. We found that IGF-1–injected pigs had a trend toward increased collagen levels in the vascular media and in coronary plaques (~$10\%$ increase, P = NS) (Supplemental Figure 3, B and C). Thus, IGF-1 changed the cellular composition of porcine coronary plaques. Atherosclerotic lesions in IGF-1–injected pigs had decreased levels of MF-like cells and increased endothelial-like cells compared with controls.
## IGF-1 decreases cell apoptosis, reduces systemic oxidative stress, and suppresses inflammation.
IGF-1 is a mitogen and prosurvival molecule [38] and exerted antiapoptotic, antioxidant, and antiinflammatory effects in Apoe–/– mice [19, 33]. Apoptotic cells in porcine coronary plaques were localized on the luminal border, in the fibrous cap (Figure 5A), and around necrotic cores. IGF-1–injected pigs had an almost 3-fold decrease in cell apoptosis in the male group ($P \leq 0.05$ for LAD) and an approximately 2-fold reduction in apoptosis rate in females (Figure 5D). Proliferating cell nuclear antigen (PCNA) is a cell proliferation marker. Atherosclerotic plaques in the female group had increased PCNA immunopositivity compared with males. IGF-1 upregulated PCNA levels in coronary plaques in females ($P \leq 0.05$ in RCA) and did not change the PCNA signal in the male group (Figure 5, B and E).
Oxidative stress is a major characteristic of hypercholesterolemia-induced atherosclerosis [39]. Oxidative DNA damage promotes cell apoptosis and contributes to formation of unstable plaques. Histone H2A.X phosphorylation is a highly specific molecular marker to quantify DNA damage [40]. We found that $15\%$–$35\%$ of plaque cells in coronaries contained detectable levels of phosphorylated S139-histone H2A.X (pH2A.X) (Figure 5, C and F), and higher pH2A.X levels correlated with larger atherosclerotic burden seen in females. IGF-1 significantly decreased the number of pH2A.X+ cells in plaques in the female group. Circulating levels of N-tyrosine and total plasma antioxidant capacity (TAC) were measured as indices of systemic oxidative stress (Figure 6). IGF-1 decreased plasma N-tyrosine levels in females at both T3 and T6 time points ($56\%$ and $47\%$ decrease, respectively, vs. saline, $P \leq 0.05$) (Figure 6A). FH female pigs had a 2-fold reduction in TAC compared with males at T6. IGF-1 upregulated TAC in males and females (Figure 6B), though the increase in males did not reach statistical significance. Taken together, the N-tyrosine and TAC data indicate that IGF-1 suppressed systemic oxidative stress.
CRP is an acute marker of inflammatory responses, and circulating levels of CRP correlate with progression of CAD [41]. Human CRP transgene expression causes accelerated aortic atherosclerosis in Apoe–/– mice [42]. FH females had higher circulating CRP levels compared with males. IGF-1 significantly decreased CRP levels of both sexes at T3 and T6 (Figure 6C), suggesting a reduction of inflammatory responses. Macrophage-specific IGF-1 reduces chemokine CXCL12 levels, and this effect is associated with decreased atherosclerotic burden in Apoe–/– mice [43]. IGF-1 significantly reduced circulating CXCL12 in FH males at T3 and T6 time points and in the female group at T6 (Figure 6D).
The frequency of monocyte subsets has been linked to severity of atherosclerosis in patients with stable CAD [44]. We measured 2 subsets of circulating monocytes, defined by surface expression levels of CD163 and CD14. Monocyte subpopulations were assessed by flow cytometry. CD172a+ myeloid cells were size-gated to monocytes, which were further assessed for CD163 and CD14 expression levels, classifying cells as CD163hiCD14lo monocytes and CD163loCD14hi monocytes. The population size of CD163hiCD14lo monocytes was larger in male animals than in female animals ($P \leq 0.001$) (Figure 6). No significant effects were induced by IGF-1 administration.
## IGF-1 changes the global transcriptomic profile of coronary plaques.
To obtain further insights into the effects of IGF-1 on coronary atherosclerosis, we performed an exploratory analysis of advanced plaques from FH females, using the newly developed technology spatial transcriptomics. ST provides an unbiased picture of entire transcriptome changes within a spatial context [27]. Prior to running ST analyses, we confirmed the quality of RNA preparations. Total RNA was extracted from plaque-containing RCA cryosections ($$n = 4$$), and RNA integrity number (RIN) [45] was quantified using an Agilent Bioanalyzer. RIN was greater than 7, which is considered suitable for ST analysis according to the manufacturer’s (Visium, 10x Genomics) recommendations. RCA cryosections from IGF-1– and saline-injected FH females ($$n = 2$$/group) were processed, and ST quality controls (Supplemental Table 4) were consistent with a successful experiment. Correlation of ST gene expression with protein expression (measured by IHC) was verified for selected gene/protein combinations, including IGF-1 binding protein 7 (IGFBP7) and α-SMA (data not shown). Furthermore, changes in gene expression of MMP9, IGFBP7, and FOS measured by ST were validated by real-time PCR using aliquots of mRNA isolated from tissue sections (data not shown).
We performed unsupervised clustering of all ST spots in IGF-1 and saline specimens by using a manufacturer-suggested algorithm with R toolkit Seurat [46]. ST spots were grouped into 9 clusters (numbered 0–8) based on their transcriptome (Figure 7B). We identified the top 10 genes overexpressed in each cluster (versus all other clusters) to obtain the heatmap (Figure 7C). In parallel, plaque FC, necrotic/lipid core, tunica media, and tunica adventitia were outlined using H&E images, and transcriptome clusters and histological annotations were compared side by side. FC contained mainly ($94.4\%$) transcriptome cluster 1 and 2, and tunica adventitia cluster 0 and 3 (Figure 7B and Supplemental Figure 4A).
The mixed cell deconvolution algorithms use single-cell RNA-sequencing (scRNA-Seq) data as a reference to characterize cellular heterogeneity in a spatial context [47]. However, to our knowledge, there are no scRNA-*Seq data* available for pig atherosclerotic specimens. Therefore, we used scRNA-*Seq data* obtained for human atherosclerotic RCA (Gene Expression Omnibus GSE131778) [48] as a reference to assess the cellular composition in our porcine ST data set. Using a deconvolution algorithm [48], we calculated the cell type ratio for each ST spot to identify spots enriched in SMCs (SMC-high), MFs (MF-high), or fibroblast-like cells, termed fibromyocytes (FM-high) (Figure 7D). Of note, we observed good agreement between localization of ST-defined SMC- and MF-high spots and IHC-detected immunopositivity pattern obtained for cell markers. The cell type ratio shows that transcriptomic cluster 4 contained around $80\%$ MF-high spots, cluster 5 had more than $70\%$ SMC-high spots, and virtually all FM-high spots (>$95\%$) were assigned to cluster 2 (Supplemental Figure 4B).
Table 2 shows the top up- and downregulated genes in IGF-1 versus saline specimens identified in all ST spots, and Table 3 contains a list of DEGs in SMC-, MF-, and FM-high spots. IGF-1 dramatically (>10-fold reduction vs. saline) downregulated FOS and FOSB proto-oncogenes in all ST spots and in SMC-high spots. IGF-1 downregulated expression of the cytoskeletal molecule desmin in all spots and upregulated ribosomal protein S27 (Table 2), a component of the translational machinery [49]. Activation of translation is in line with known growth-stimulating IGF-1 action [38]. MMP9 gene expression level was reduced by IGF-1 in all ST spots and in MF-high spots. This is consistent with our recent report that MF-specific IGF-1 overexpression downregulates MMP9 in peritoneal MFs and decreases aortic atherosclerosis in Apoe–/– mice [43]. Cytokine CXCL14 was the top IGF-1–downregulated gene in MF-high spots (Table 3). CXCL14 was upregulated in MF-derived foam cells, and CXCL14 peptide-induced immunotherapy suppresses atherosclerosis in Apoe–/– mice [50], suggesting a proatherogenic role of CXCL14.
We observed a clear boundary between transcriptome cluster 1 and 2 within FC histological area (Figure 7, A and B), showing the capability of ST analysis to identify plaque areas with different gene expression patterns that are not histologically distinguishable. To take advantage of this likely unique ST feature and considering the importance of the FC for overall stability of atheroma [51], we focused our subsequent analysis on cluster 1 and 2. First, we found discrete differences in the cellular composition of cluster 1 and 2. More T cells, B cells, and MFs were in cluster 1, whereas fibroblast levels were lower (Supplemental Figure 4B). The top upregulated genes in cluster 1 (versus other clusters) included components of the complement system (C3, C1qA, C1qC, and C1qB) and cathepsin D (CTSD; Figure 7C). The top genes in cluster 2 included CCN2 and CCN3 (CCN molecules are involved in wound healing and fibrosis, ref. 52) and fibronectin 1, which is known to play a vital role in tissue repair [53]. A comparison of DEGs between cluster 1 and 2 showed that upregulated CTSD was the top-ranked DEG in cluster 1 versus 2 (~2.1-fold higher expression in cluster 1 vs. 2; adjusted P value = 1.71 × 10–220). CTSD is a proapoptotic molecule and collagen-degrading protease that is highly expressed in MFs [54]. Higher cathepsin activity is associated with unstable plaque [54]. Ingenuity Pathway Analysis (IPA; QIAGEN) predicted that necrosis and apoptosis pathways are upregulated in cluster 1 versus 2 (data not shown), consistent with increased expression of CTSD. Importantly, cluster 1 is the thinnest part of the FC. Taken together, these data suggest that cluster 1 is a tissue-degrading and less fibrotic compartment of the FC compared with cluster 2. We speculate that cluster 1 represents a plaque site with potentially increased vulnerability and propensity to erode or rupture.
We also compared cell type ratio and DEGs in cluster 1 and 2 in IGF-1 versus saline specimens. IGF-1 slightly decreased MFs in both cluster 1 and 2, and IGF-1 upregulated FMs in cluster 2 (Figure 7E). SMC transition into FMs prevents FC thinning [48], suggesting that higher FM levels in the FC are beneficial during the disease process. IGF-1 reduced CTSD expression in both clusters, whereas vimentin expression (VIM) was decreased only in cluster 1. The top-ranked DEGs in comparing IGF-1 versus saline groups in cluster 1 and 2 included downregulated chemokine CXCL14 and MMP9 (Figure 7F). Thus, use of ST combined with deconvolution algorithms provided, to our knowledge for the first time, a profile of the spatial transcriptome of advanced atherosclerotic plaque and changes induced by IGF-1.
## Discussion
To our knowledge, the current study is the first to test the effects of IGF-1 given long term in a large animal model of atherosclerosis. We found that IGF-1 induced vascular hypertrophy, reduced coronary atheroma volume, and decreased plaque CSA over 6 months and that there was no evidence of IGF-1–induced tumorigenesis over this period. There was evidence of significant sex-specific differences in atherosclerosis development. Females had higher cholesterol and triglyceride levels and an advanced plaque phenotype. IGF-1 increased FC thickness, and reduced necrotic core size, macrophage content, and cell apoptosis, changes consistent with promotion of a more stable plaque phenotype. IGF-1 also reduced circulating triglycerides and markers of systemic oxidative stress. ST analysis of plaques from female FH pigs, combined with a mixed cell deconvolution algorithm, allowed us to spatially profile gene expression in advanced coronary plaques and to identify DEGs in IGF-1–treated animals. We detected 9 specific gene clusters and found that the plaque FC was composed almost exclusively of cluster 1 and 2, showing the potentially unique capability of ST to identify plaque subcompartments that are not histologically distinguishable. IGF-1 induced marked decreases in FOS/FOSB transcription factor and in MMP9 and CXCL14 gene expression in plaque macrophages, suggesting involvement of these molecules in IGF-1’s antiatherogenic effects.
Although multiple studies have shown that IGF-1 exerts antiatherosclerotic effects in murine models [17, 18], the role of IGF-1 in the development of human atherosclerotic disease is unclear [22, 23]. Both IGF-1 administration and gain-of-function and loss-of-function approaches in mice have provided mechanistic insights, but consideration of IGF-1 for treatment of cardiovascular disease in humans mandates demonstration of IGF-1’s efficacy and safety in a large animal model that is physiologically closer to humans.
We administered IGF-1 at a dose approved for use in children with primary IGF-1 deficiency [28]. FH males were noncastrated to fully evaluate potential sex-dependent effects. Coronary plaque location, size, and frequency (as assessed by coronary angiography, data not shown) were similar to ones reported for humans [55]. Coronary atheroma cellular composition, presence of large necrotic/lipid cores, neovascularization, and calcification in FH females closely resembled the phenotype of advanced fibroatheromas reported for patients with CAD [56]. These data demonstrate that FH pigs are a valuable model to study CAD and test antiatherosclerotic drugs.
The number of preclinical studies comparing plaque development between the sexes is extremely limited relative to the vast literature exploring atherosclerosis mechanisms [57]. FH females had reduced circulating IGF-1 levels and higher plasma cholesterol and triglyceride levels compared with males. HFD feeding caused a substantial and sustained elevation of total cholesterol levels in both sexes. The causative role of high cholesterol in promoting atherogenesis has been well established using multiple animal models, including miniature pigs [58, 59] and genetically modified mice and rabbits [60, 61]. High cholesterol and triglycerides are classical risk factors for human CAD [62]. We hypothesize that high lipid levels in FH females were a major cause of the increased atherosclerosis in this group, though it is possible that lower basal IGF-1 levels in female pigs also increased susceptibility to atherosclerosis development. Of note, although IGF-1 had no effect on total cholesterol (Figure 1, E and F), it reduced triglycerides (Figure 1, G and H). Our study is, to our knowledge, the first to report higher cholesterol levels and atherosclerotic burden in FH females compared with males. Further studies will be required to establish mechanisms mediating these sex-specific differences.
We demonstrated that IGF-1 exerted atheroprotective effects on pre-atheroma (in males) and advanced fibroatheromas (in females). Accumulating evidence suggests that endothelial dysfunction is an early marker for atherosclerosis, and damage of coronary endothelium has been shown to constitute an independent predictor of cardiovascular events [63]. The barrier function of the endothelium is impaired in atherosclerosis, leading to uncontrolled leukocyte extravasation and vascular leakage [64]. Intriguingly, we found that the IGF-1–induced antiatherosclerotic effect in FH pigs was associated with reduced EC damage. Indeed, we found multiple breaks in the EC layer in porcine plaque, indicating loss of endothelial integrity. IGF-1–injected pigs had a reduced number of EC layer breaks in coronary plaques, suggesting improved EC function. This result is in line with our recent report showing that EC-specific IGF-1 receptor deficiency downregulated endothelial intercellular junction proteins, elevated endothelial permeability, and enhanced atherosclerotic burden in Apoe–/– mice [65]. Increased EC lining in plaques in IGF-1–treated pigs is potentially due to elevated EC proliferation or increased recruitment of circulating EC progenitors to the plaque area. IGF-1 has been shown to promote EC proliferation [66] and increase levels of EC progenitors in Apoe–/– mice [19].
We found that IGF-1 exerted antioxidant and antiapoptotic effects in FH pigs, which may have contributed to attenuation of atherosclerosis progression, leading to smaller necrotic core size and a more stable plaque phenotype. Indeed, IGF-1 reduced plasma N-tyrosine levels, increased TAC, and concomitantly reduced the number of plaque TUNEL+ and pH2A.X+ cells. Of note, these findings are consistent with the ability of IGF-1 to upregulate expression of the antioxidant enzyme glutathione peroxidase in cultured ECs [67] and to downregulate $\frac{12}{15}$-lipoxygenase levels in murine plaques [68], suggesting that these mechanisms may be relevant to the effects of IGF-1 in the FH pig model. Furthermore, elevated SMC apoptosis has been associated with low IGF-1 expression in human advanced plaques [69], and IGF-1 receptor activation inhibits oxidized lipid-induced apoptosis in SMCs through the PI3K/Akt signaling pathway [70], indicating its involvement in IGF-1–induced suppression of apoptosis.
Single-cell transcriptomic analysis of human atherosclerotic plaques has been reported [71], but critical information linking changes in cell-specific transcriptome with plaque morphology is missing. ST captures all polyadenylated transcripts and provides an unbiased picture of entire transcriptome changes within a spatial context [27]. Thus, ST encodes positional information onto transcripts before sequencing, allowing visualization of expression of virtually any gene of interest within the original tissue section [27]. There are ST-generated spatial atlases of disease-free organs and diseased tissues (cancer, Alzheimer’s disease, amyotrophic lateral sclerosis, and rheumatoid arthritis) [27]. However, there are no published reports to our knowledge using ST with atherosclerotic tissue obtained from animal models or humans. We used FH pig coronary plaque specimens to optimize the ST technology, and quality controls obtained at the end of experiment correlated well with the manufacturer’s recommendations. We found good agreement between transcriptomic profiles and protein expression data detected by IHC. ST revealed distinct clusters of gene expression within histologically homogeneous parts of the plaque, e.g., the FC, which contained almost exclusively cluster 1 and 2 with a clear boundary between these clusters. Cluster 1 had a gene expression pattern, cellular content, and a morphological feature (reduced thickness) suggesting elevated sensitivity of this site to plaque rupture. Further studies will be required to determine the clinical relevance of this finding.
ST combined with the cell deconvolution algorithm identified genes differentially expressed in response to IGF-1 in spots enriched in SMCs, MFs, and FMs. ST identified FOS and FOSB genes as molecules with the highest IGF-1–induced reduction in expression among all ST spots and SMC-high spots. A recent study showed that c-FOS (protein encoded by the FOS gene) is involved in oxidized lipid-induced formation of SMC-derived foam cells. SMC-specific c-FOS deficiency prevents formation of foam cells in vitro and suppresses atherosclerosis in HFD-fed Apoe–/– mice [72], demonstrating a fundamental proatherogenic role of FOS. FOS and FOSB encode leucine zipper proteins that dimerize with JUN proteins, thereby forming the transcription factor complex activator protein-1 (AP-1). In vitro and in vivo studies have implicated AP-1 as a critical common inflammatory transcription factor mediating progression of atherogenesis [73] and have suggested that AP-1 inhibition is a promising strategy to treat atherosclerosis [74]. AP-1 was reported to mediate oxidized lipid-induced CXCL14 upregulation in MF-derived foam cells [50]. Here we report that IGF-1–induced FOS/FOSB downregulation correlated with CXCL14 gene expression decrease in MF-high spots. CXCL14 is known to bind and activate another proatherogenic cytokine, CXCL12 [75]. We reported recently that MF-specific IGF-1 overexpression decreases CXCL12 plaque and circulating levels and promotes atherosclerosis in mice [43]. Of note, in the current study, we found that IGF-1 markedly decreased circulating CXCL12. Taken together, these results suggest that downregulation of the AP-1 complex and suppression of the CXCL14/CXCL12 axis are potential mechanisms contributing to IGF-1–induced antiatherogenic effects.
Most acute coronary events are related to rupture or erosion of atherosclerotic plaques that are not hemodynamically significant [76]. Thus, plaque stability is a critical determinant of clinical events. Stable plaques are characterized by increased collagen, reduced apoptosis, thicker FC, smaller necrotic cores, and decreased number of inflammatory cells [56]. We have shown that coronary plaques in IGF-1–injected FH pigs had reduced necrotic cores, increased FC thickness, decreased levels of MF-like cells, and reduced cell apoptosis, and these changes are consistent with promotion of a stable plaque phenotype. We also found that IGF-1 decreased the number of MF-high spots, reduced gene expression of CXCL14 chemokine, and downregulated MMP9 gene expression specifically in cluster 1, a potentially more vulnerable component of the FC. Taken together, these results suggest that IGF-1 has substantial plaque-stabilizing properties.
In summary, we report here that IGF-1 administered over 6 months decreased coronary atherosclerosis and promoted features of stable atheroma in FH pigs, without evidence of a tumorigenic effect. IGF-1 stimulated an array of potentially antiatherogenic mechanisms, including suppression of oxidative stress, systemic inflammatory response, and cell apoptosis. Furthermore, IGF-1 decreased EC damage in coronary plaques. ST technology combined with cell deconvolution analysis identified an area of the FC with potentially more propensity for erosion or rupture. Furthermore, ST revealed that IGF-1 induced major changes in the plaque transcriptome. IGF-1 dramatically suppressed gene expression of FOS/FOSB transcription factors and of CXCL14 chemokine and MMP9 in plaques, and these data suggest involvement of these molecules in mediating IGF-1 effects. Our results provide mechanistic insights into IGF-1–induced effects on atherosclerosis, are a critical step in taking IGF-1 to human studies, and to our knowledge represent the first report on the use of ST to analyze atherosclerotic tissue from animals or humans.
## Methods
Extended methods are available in the Supplemental Methods.
## Animals.
The Rapacz familial hypercholesterolemic swine (Sus scrofa) (FH pigs) were received from the Swine Research and Teaching Center at University of Wisconsin. We administered recombinant human IGF-1 (INCRELEX, IPSEN) 50 μg/kg, s.c. every 12 hours, twice per day) to FH pigs for 6 months. The control group received an equal volume of saline (Figure 1A). We used a total of 28 FH pigs for experiments (males, $$n = 5$$ per group; females, $$n = 9$$ per group). All pigs received an HFD starting the day after T0. We performed serial IVUS at T0 and at T3 and T6 to assess coronary atherosclerotic burden (Figure 1A). FH pigs were sacrificed after T6 IVUS. We followed guidelines of American Veterinary Medical Association for the euthanasia of animals (2020 edition) [77].
## IVUS analysis.
IVUS was performed using the IVUS imaging system (Volcano Corporation) and a 20 MHz 3.5 F Visions PV 0.035 Digital IVUS Catheter (Volcano Corporation). For the current study 20 mm of IVUS pullback segment distal to the ostia of the RCA and the LAD was selected. The area circumscribed by the outer border of the echolucent tunica media and the luminal border was manually traced on each 1 mm IVUS frame within selected fragment. The following indices of vessel morphology were assessed: 1) lumen volume (mm³) = lumen area (the area bounded by the luminal border) × length of fragment; 2) vessel volume (mm³) = EEM area × length of fragment; 3) plaque + media volume (mm³) = vessel volume – lumen volume; 4) relative atheroma volume (%) = plaque plus media volume divided by the vessel volume × $100\%$.
## Atherosclerotic burden and plaque composition analysis.
The entire RCA and LAD were collected, and the proximal 30 mm fragment of RCA and LAD was further cut onto six 5 mm fragments for embedding in paraffin. Histological blocks were sequentially labeled 1–6; serial 6 μm cross sections were cut from each block and stained with Gomori’s trichrome stain (Polysciences Inc). Sections from each block were used for morphological analysis. EEM, internal elastic membrane, and luminal border were manually outlined in CellSens Dimension 1.18 software (Olympus Corp), and corresponding CSAs were measured by 2 independent researchers, 1 of them under a blinded protocol. The cellular content of coronary plaques was assessed by IHC with serial sections obtained from the middle (no. 3) coronary fragment. Cell marker–specific antibodies for α-SMA (MilliporeSigma, CBL171, clone ASM1), MSR (TransGenia Inc, KT022, clone SRA-E5), and CD31 (Abcam, 134168, clone EP3095) were used for IHC to identify SMCs, MFs, and ECs, respectively. Proliferating cells and cells with DNA damage were quantified by IHC with antibody against PCNA (MilliporeSigma, MAB424R, clone PC10) and pH2A.X (Abcam, ab2893), respectively. Cell apoptosis was quantified with In Situ Cell Death Detection Kit, TMR red (MilliporeSigma, 12156792910), as per manufacturer’s instructions.
## Blood biochemistry.
Fasting blood samples were collected from the jugular vein at baseline and monthly. Blood was collected in 0.1 mol/L citrate-containing EDTA. Fresh whole blood was submitted to Antech Diagnostics for CBC with differential and biochemistry measurements (Superchem w/CBC, SA020). Plasma IGF-1 levels were quantified by human IGF-I Quantikine ELISA Kit (R&D Systems, DG100B) and CRP levels by porcine C-reactive protein/CRP DuoSet ELISA (R&D Systems, DY2648). Quantification of plasma N-tyrosine and TAC assay were performed with OxiSelect Nitrotyrosine ELISA Kit (STA-305) and TAC assay kit (STA-360) (both from Cell Biolabs Inc).
## ST.
ST uses spotted arrays of specialized mRNA-capturing probes containing a spatial barcode unique to that spot. When a cryosection is attached to the slide, the capture probes bind mRNA from the adjacent point in the tissue. After mRNA extraction, a cDNA library is generated and sequenced. ST was conducted using the Visium Spatial Gene Expression System (10x Genomics) in accordance with manufacturer’s instructions. RCA cryosections (IGF-1, $$n = 2$$, saline, $$n = 2$$) were H&E-stained, and mRNA was extracted to ST array, followed by cDNA synthesis, library construction, and sequencing with Illumina NextSeq 1000 system (800 million paired-end reads). Pig reference genome was created from *Sus scrofa* genomic sequence (Sscrofa 11.1) and Ensembl annotation, and reads were aligned and counted by Space Ranger (10x Genomics; ver 2.0.0). ST data were deposited to Gene Expression Omnibus (GSE220218). ST quality was verified by the percentage of valid barcodes, Q30 bases in barcodes, valid unique molecular identifiers, and reads mapped to the genome (Supplemental Table 4). All the downstream analyses were performed using R toolkit Seurat [46] and IPA software (QIAGEN). The mixed cell deconvolution was performed in accordance with the algorithm developed by Wirka et al. using scRNA-*Seq data* obtained for human atherosclerotic RCAs (GSE131778) [48] as a reference. *Differential* gene expression analysis was performed using Seurat’s FindMarkers function, which identifies DEGs based on the nonparametric Wilcoxon rank-sum test. P values were adjusted by Bonferroni’s correction. Error bars indicate SEM.
## Statistics.
Statistical comparisons for histology data were performed by unpaired 2-tailed t test. IVUS results and blood biochemistry data were analyzed using a 3-way repeated measures ANOVA taking treatments (saline vs. IGF-1 administration), time (periodical, repeated measurements), and animal’s sex (female vs. male) as variables. Grubb’s z test was used to identify outliers. Data sets were first assessed for residual distribution using D’Agostino-*Pearson omnibus* normality test and for equal variances using Levene’s test for equality of variances. Differences in outcomes were determined by ANOVA and Bonferroni’s multiple comparisons test, Kruskal-Wallis test, or Mann-Whitney U test, accordingly with the normality of residual distribution. For all comparisons, $P \leq 0.05$ was considered statistically significant. Adjusted P value [78] was used for statistical comparison of genomic data sets obtained with ST. Data were analyzed using Microsoft Excel and Prism v.6.0 (GraphPad Software). Data presented in figures are individual data points (circles) and mean ± SEM (bars). Artwork was generated in GraphPad Prism and Adobe Photoshop 15.0.
## Study approval.
All animal experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals (National Academies Press, 2011), the Public Health Service Policy on the Humane Care and Use of Laboratory Animals, and the Animal Welfare Act. Institutional Animal Care and Use Committee approvals were obtained from the University of Missouri-Columbia and Louisiana State University Health Sciences Center New Orleans, New Orleans, Louisiana, USA, before initiation of experimental studies.
## Author contributions
SS designed the study, performed histological assays, performed IVUS analysis, ran ST, and wrote the manuscript; YH designed the study, performed blood biochemistry and monocyte subset assessment, and contributed to writing the manuscript; SD and MA carried out ELISAs; TY and JKK handled ST data, and ran bioinformatics; TG, AS, TS, JSJ, DG, JI, DLT, and DB performed IVUS surgery; JS carried out necropsy; ZR assisted with IVUS analysis; and DB, DL, and PD designed the study and contributed to writing the manuscript.
## 01/05/2023
In-Press Preview
## 02/22/2023
Electronic publication
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|
---
title: A MALDI-TOF MS library for rapid identification of human commensal gut bacteria
from the class Clostridia
authors:
- Paul Tetteh Asare
- Chi-Hsien Lee
- Vera Hürlimann
- Youzheng Teo
- Aline Cuénod
- Nermin Akduman
- Cordula Gekeler
- Afrizal Afrizal
- Myriam Corthesy
- Claire Kohout
- Vincent Thomas
- Tomas de Wouters
- Gilbert Greub
- Thomas Clavel
- Eric G. Pamer
- Adrian Egli
- Lisa Maier
- Pascale Vonaesch
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC9990839
doi: 10.3389/fmicb.2023.1104707
license: CC BY 4.0
---
# A MALDI-TOF MS library for rapid identification of human commensal gut bacteria from the class Clostridia
## Abstract
### Introduction
Microbial isolates from culture can be identified using 16S or whole-genome sequencing which generates substantial costs and requires time and expertise. Protein fingerprinting via Matrix-assisted Laser Desorption Ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid bacterial identification in routine diagnostics but shows a poor performance and resolution on commensal bacteria due to currently limited database entries. The aim of this study was to develop a MALDI-TOF MS plugin database (CLOSTRI-TOF) allowing for rapid identification of non-pathogenic human commensal gastrointestinal bacteria.
### Methods
We constructed a database containing mass spectral profiles (MSP) from 142 bacterial strains representing 47 species and 21 genera within the class Clostridia. Each strain-specific MSP was constructed using >20 raw spectra measured on a microflex Biotyper system (Bruker-Daltonics) from two independent cultures.
### Results
For validation, we used 58 sequence-confirmed strains and the CLOSTRI-TOF database successfully identified 98 and $93\%$ of the strains, respectively, in two independent laboratories. Next, we applied the database to 326 isolates from stool of healthy Swiss volunteers and identified 264 ($82\%$) of all isolates (compared to 170 ($52.1\%$) with the Bruker-Daltonics library alone), thus classifying $60\%$ of the formerly unknown isolates.
### Discussion
We describe a new open-source MSP database for fast and accurate identification of the Clostridia class from the human gut microbiota. CLOSTRI-TOF expands the number of species which can be rapidly identified by MALDI-TOF MS.
## Introduction
Clostridia is an important class of Gram-positive, often strictly anaerobic, rod-shaped, spore-forming bacteria within the phylum Bacillota (formerly Firmicutes). Some Clostridia spp. are pathogenic in humans and animals such as C. botulinum, C. tetani, and C. difficile. However, most members of this class play an important role in the human intestinal tract, where they contribute, among others, to the production of the short-chain fatty acid butyrate (Beyer-Sehlmeyer et al., 2003; Paparo et al., 2017; Cruz-Morales et al., 2019).
Depletion of butyrate-producing Clostridia from the intestinal microbiota has been associated with multiple intra-and extraintestinal diseases such as, i.e., childhood stunting (Vonaesch et al., 2018), inflammatory bowel disease (Eeckhaut et al., 2013; Gevers et al., 2014), colorectal cancer (Wu et al., 2013), and cardiometabolic disease (Karlsson et al., 2012; Wang et al., 2012; Le Chatelier et al., 2013). Further, depletion in butyrate producers was shown to decrease colonization resistance to enteric pathogens (Rivera-Chávez et al., 2016). Overall, there is thus a clear link between disease and the absence of butyrate producers. Owing to their important role in health and disease, efforts have multiplied to isolate and characterize these bacteria and eventually use them as so-called next-generation probiotics (NGPs); bacteria directly isolated from healthy humans, that are re-introduced in diseased individuals as health-promoting strains (O’Toole et al., 2017; Chang et al., 2019; Lin et al., 2019). The identification and characterization of novel NGPs heavily rely on culture collections and thus on tools that can rapidly identify commensal bacteria.
The current gold standard to identify bacterial isolates are full-length 16S rRNA gene or whole-genome sequencing (WGS) (Browne et al., 2016; Lagkouvardos et al., 2019). WGS is time-consuming, costly and requires a specific expertise. For this reason, it is rarely used for species identification in clinical routine diagnostics (Rossen et al., 2018). Therefore, there is an ongoing interest in rapid, cheap, accurate, and easy-to-use methods to identify bacteria isolated from the human intestinal tract.
Over the last decade, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been established as a technique for rapid and reliable bacterial identification and is mainly used in routine diagnostics (Bizzini and Greub, 2010; Li et al., 2019). The technique measures the mass spectral profiles (MSPs) within a range of 2000 to 20,000 Daltons, where also ribosomal proteins of bacteria occur. Usually, MSPs are compared within a commercial database to pre-recorded reference spectra and matching marker masses allow the identification of a species (Croxatto et al., 2012; Schumann and Maier, 2014; Seuylemezian et al., 2018). Quality of the spectra (Cuénod et al., 2021, 2022) and the database used are two critical factors for a reliable identification. The currently available commercial databases [we used the MBT Compass reference library (version 4.1.100)], which are widely used in routine diagnostics, only cover a minority of non-pathogenic Clostridia. Therefore, the current usage of MALDI-TOF MS for proper species identification is limited. This hurdle has been overcome in the past by researchers in other domains by the generation of in-house custom reference databases of bacterial strains relevant to their field of study (Calderaro et al., 2014; Fergusson et al., 2020; Moussa et al., 2021).
Considering the robust classification, rapid acquisition times, and low cost per sample of MALDI-TOF MS, we aimed to close this important gap and extend this technology to the identification of commensal members of the gastrointestinal microbiota of the class Clostridia. *We* generated 142 mass spectral profiles (MSPs) for 21 genera and 47 species, using full-length 16S rRNA gene sequence-confirmed bacterial isolates from diverse geographic regions. The newly created MSP library was evaluated for accuracy by re-identifying 58 blind-coded, full-length 16S rRNA gene sequence validated isolates of Clostridia covering the same genera and species using the new database plugin. Finally, we applied the new MSP library on a set of 326 isolates from stool samples of healthy Swiss donors, showing its potential on identifying new isolates from human fecal samples.
## Bacterial strains in the CLOSTRI-TOF database
The number and composition of the mass spectrum profile (MSP) library used reference library have a significant impact on the accuracy of the microflex Biotyper taxonomy assignment. To extend the coverage to the identification of members of non-pathogenic Clostridia, we used 142 sequenced strains. These strains are the main representatives of the human gut commensals of the class Clostridia (Table 1; Supplementary Table 1) and have been chosen to include as many geographically diverse strains as possible, to cover each species as broadly as possible. The 142 strains belonging to 21 genera and 47 species were confirmed for their species identification using full-length 16S rRNA gene sequencing. Fifty-eight [58] 16S rRNA gene full-length identified strains were used to validate the newly generated MALDI-TOF MS database. Strain information is summarized in Supplementary Table 1 (strains used for library construction), and Supplementary Table 2 (validation strains).
**Table 1**
| Genus | Species | Total no. of strains included (no. of reference strains) |
| --- | --- | --- |
| Agathobacter | rectalis | 5 (0) |
| Anaerobutyricum | hallii | 2 (1) |
| Anaerostipes | hadrus | 5 (1) |
| Anaerotruncus | colihominis | 5 (1) |
| Blautia | caecimuris | 2 (1) |
| | coccoides | 1 (1) |
| | faecis | 5 (1) |
| | glucerasea | 3 (0) |
| | hansenii | 1 (0) |
| | luti | 2 (1) |
| | massiliensis | 4 (0) |
| | obeum | 5 (1) |
| | producta | 6 (2) |
| | pseudococcoides | 1 (0) |
| | schinkii | 5 (1) |
| | wexlerae | 4 (1) |
| Clostridium | leptum | 1 (1) |
| | nexilis | 1 (0) |
| | scindens | 5 (2) |
| | symbiosum | 5 (1) |
| Coprococcus | catus | 1 (1) |
| | comes | 5 (1) |
| | eutactus | 2 (0) |
| Dorea | formicigenerans | 5 (1) |
| | longicatena | 5 (1) |
| Enterocloster | aldenensis | 3 (1) |
| | clostridioformis | 5 (1) |
| Eubacterium | ramulus | 2 (1) |
| Faecalibacterium | prausnitzii | 4 (0) |
| | longum | 1 (0) |
| Faecalicatena | fissicatena | 5 (1) |
| Fusicatenibacter | saccharivorans | 3 (1) |
| Gemmiger | formicilis | 1 (0) |
| Intestinibacter | bartlettii | 1 (1) |
| Lachnospira | eligens | 3 (1) |
| Lacrimispora | celerecrescens | 3 (1) |
| | saccharolytica | 1 (1) |
| Peptostreptococcus | stomatis | 1 (1) |
| Roseburia | faecis | 2 (1) |
| | hominis | 1 (0) |
| | intestinalis | 1 (0) |
| | inulinivorans | 2 (1) |
| Ruminococcus | bromii | 3 (1) |
| | gnavus | 4 (1) |
| | lactaris | 3 (1) |
| | torques | 2 (1) |
| Sellimonas | intestinalis | 5 (1) |
## Bacterial culture
The culture medium used for each strain is given in Supplementary Table 1 and included the following: Brain Heart Infusion with Inulin (BHII Agar): BHI Agar (BD Difco, cat. number 279830, Franklin Lakes, NJ, USA) supplemented with inulin from chicory (Sigma, cat. number I2255, Darmstadt, Germany), 1 g/l. Schaedler Agar (Oxoid, cat. number CM0497, Ireland), and modified Gifu Anaerobic Broth (mGAM) (HiMedia, cat. number M2079). All media were prepared according to manufacturer instructions and supplemented with 15 g agar/l whenever appropriate. Bacteria were cultured under strictly anaerobic conditions using an anaerobic workstation (Anaerobic chamber; Coy Laboratories, Ann Arbor, MI, USA) containing a gas mixture of $10\%$ CO2, $5\%$ H2, and $85\%$ N2. Bacteria were incubated at 37°C for 3–4 days before MALDI-TOF MS analysis. Culture conditions are summarized in Supplementary Table 1 (strains used for library construction) and Supplementary Table 2 (validation strains).
## DNA purification and 16S rRNA gene sequencing
Genomic DNA was extracted from bacterial cultures using a bead-beating device (SpeedMill BeadBeater for 30 s or with the vortex adapter on maximum speed for 10 min) followed by the Wizard genomic DNA purification kit (Promega, cat. number A2920, Dübendorf, Switzerland) according to manufacturer instructions. Total DNA was quantified by absorbance at 260 nm using the NanoDrop® ND-1000 Spectrophotometer (Witec AG, Littau, Switzerland). DNA samples were stored at-20°C until further analysis.
To confirm the identity of the isolates, the full region of the 16S rRNA gene was amplified by PCR using the FIREPol® DNA polymerase (Solis BioDyne, Tartu, Estonia) using primers 27F (5′-AGA GTT TGA TCC TGG CTC AG-3′) and 1492R (5′-GGT TAC CTT GTT ACG ACT T-3′) with the following conditions: 95°C for 3 min; 35 cycles of 95°C for 30 s, 50°C for 30 s, 72°C for 30 s; then 72°C for 7 min (Frank et al., 2008). PCR products were visualized using standard gel electrophoresis ($1\%$ agarose in 1× Tris-acetate-EDTA [TAE] buffer) at 100 V for 30 min (Bio-Rad, Cressier, Switzerland), purified using the Promega Wizard™ SV Gel, and PCR Cleanup System according to the manufacturer’s protocol and sequenced bi-directionally to obtain near full-length 16S rRNA gene sequence using Sanger sequencing at Microsynth (Balgach, Switzerland). To identify the closest homologs, the obtained sequences were aligned using the Basic Local Alignment Search Tool (BLAST) (Altschul et al., 1990). We defined sequence homology ≥$99\%$ as a species match, and sequence similarity ≥$97\%$ as a genus match (Becker et al., 2004). To build the phylogenetic tree, 16S rRNA gene sequences were aligned and trimmed using MUSCLE and a maximum-likelihood phylogenetic tree was constructed with 1,000 bootstrap replicates using MEGA 11 (Tamura et al., 2021).
## MALDI-TOF MS spectra preparation for MSP creation
As recommended by Bruker-Daltonics for new library creations, ethanol-formic acid extraction was used to prepare the sample for MSP creation. Formic acid is routinely used when using MALDI-TOF in bacteriology since acidic pH of the matrix improves the extraction of the ribosomal proteins (Croxatto et al., 2012). Briefly, two to three colonies were suspended in 300 μl of high-pressure liquid chromatography (HPLC)-grade water (Sigma-Aldrich, Buchs, Switzerland) and mixed with 900 μl of $100\%$ ethanol (Sigma-Aldrich, Buchs, Switzerland). After centrifugation at 15,000× g for 2 min, the supernatant was removed, and the pellet was dried. For sample extraction, 50 μl of formic acid ($70\%$ in water) was added to the bacterial pellet, the tube was thoroughly mixed by vortexing and 50 μl acetonitrile (Sigma) were added to the mixture. The mixture was centrifuged at 15,000× g for 2 min. For each strain, 1 μl of the supernatant containing the bacterial extract was spotted on a 96-spot steel plate (Bruker-Daltonics) in 15 replicates and allowed to dry at room temperature. One microliter of bacterial test standard (BTS, Bruker-Daltonics) was pipetted on two MALDI target spots for each plate to allow for calibration during acquisition and processing. Subsequently, the samples were overlaid with 1 μl of α-cyano-4-hydroxycinnamic acid (HCCA) matrix and air dried again prior to measurement. A Bacterial Test Standard (BTS) was used to calibrate the instrument, before each acquisition session.
## MALDI-TOF MS data acquisition
MALDI-TOF MS was performed with a Bruker-Daltonics microflex LT/LH bench-top mass spectrometer. Protein mass spectra of samples were acquired in a mass range of 2000–20,000 Da using the flexControl 3.4 program (Bruker-Daltonics) and a laser frequency at 20 Hz with a linear positive mode. The default operating conditions were as follows: ion source 1, 20 kV; ion source 2, 18.25 kV; pulse ion extraction, 370 ns. Each spectrum was summed up to 240 laser shots (40 laser shots/position x 6 different positions). For each strain, 30 single spectra were generated from two independent cultures with 15 technical replicates. All raw spectra have been deposited on Zenodo with the access number 10.5281/zenodo.7573939.
## MALDI-TOF MS quality control and custom MSP library creation
The quality of raw spectra was evaluated using flexAnalysis 3.4 software (Bruker-Daltonics). To ease the quality check of the raw spectra, smoothing parameters and baseline subtraction parameters were applied. For baseline subtraction we used the multipolygon, minimum, penalized least squares, and penalized B-Spline baseline subtraction method, and for smoothing, we used the Savitzky-Golay smoothing method. For each analysis day, the eight known masses of the bacterial test standard (BTS) spectrum were controlled to be within the tolerance range of +/− 300 ppm to prove mass accuracy. Spectra showing any flatline, outliers, dramatic mass shifts, and anomalies were deleted. Flatline spectra were defined as spectra with a completely flat line and outliners were defined as spectra with a unique peak that was detected in no other spectra of the same strain. Mass shifts were assessed on a random peak between 6,000 Da to 7,000 Da and we only accepted spectra with peak shifts below 500 ppm. After processing the raw spectra, a minimum of 27 high-quality spectra per strain were selected and merged using the MALDI Biotyper Compass Explorer software (Bruker-Daltonics) to create the mass spectral profiles (MSPs). The merged spectra per strain constituting the MSP were verified to have a log score greater than 2.7 and a peak frequency greater than $75\%$. The final library was compiled based on the MSP of 142 strains (Figure 1A). An MSP dendrogram was constructed using a Euclidean distance measure and an average linkage algorithm with the MALDI Biotyper Compass Explorer 4.1. The final library has been deposited on Zenodo with the access number 10.5281/zenodo.7573939.
**Figure 1:** *Main steps of CLOSTRI-TOF creation (A), validation (B), and MALDI-TOF MS microbial identification workflow (C).*
## CLOSTRI-TOF database validation
To validate the CLOSTRI-TOF database, we performed a blind test of 58 full-length 16S rRNA gene sequenced isolates in two collaborating laboratories (Figure 1B; Supplementary Table 2). Validation was performed using the extended direct transfer (eDT) technique. Briefly, a few bacterial colonies were directly smeared onto a MALDI-TOF MS target and covered with a 1 μl of saturated HCCA matrix solution as is generally recommended for routine bacterial identification. In case no spectra were detected, we repeated the analysis using the ethanol-formic acid extraction method (as previously described). The raw spectra generated were compared to the two combined databases (Bruker-Daltonics MALDI Biotyper Compass MSP library v. 4.1.100 and CLOSTRI-TOF) (Figure 1B). The identification results were classified using the score values proposed by Bruker: the highest matching scores of the spectra were represented by ranges indicating high confidence identification (2.000–3.000), low confidence identification (1.700–1.999) and no organism identification possible (0.000–1.699).
## Identification of a set of 326 clinical isolates from healthy Swiss volunteers
To test the library on a set of previously unknown isolates, we used the CLOSTRI-TOF database combined with the original Bruker-Daltonics MALDI Biotyper Compass MSP library v. 4.1.100 to identify a total of 326 anaerobic cultures isolated from stool samples of healthy Swiss individuals recruited in the EnrichBut project (Swiss Ethics Number: BASEC ID 2021-00199) (Figure 1C). In brief, stool samples were collected from four self-declared healthy Swiss adults between 30 and 60 years of age who did not consume antibiotics two weeks prior to sample collection. Each stool donor was provided with a custom-made stool sampling kit that allows for the preservation of the oxygen sensitive bacterial groups under anaerobic conditions. Stool samples were stored anaerobically at room temperature prior to processing. Upon receipt (within 48 h of collection), the stool samples were resuspended (1:10 w/v) in anaerobic phosphate-buffered saline (PBS). The suspensions were then serially diluted in PBS and 100 μL was plated onto Columbia Blood, BHII, Schaedler and mGAM Agar. The plates were incubated at 37°C for 5 days. Between 20 and 30 colonies showing different colony morphologies were picked from each medium and re-streaked three times on fresh agar plates to ensure purity of the isolates. The isolates were cultured in liquid broth and mixed with $20\%$ glycerol and stored at – 80°C.
The isolates were prepared for MALDI-TOF MS identification using the extended direct transfer (eDT) technique and were identified based on three technical replicates. The obtained spectra were compared either against the commercial Bruker-Daltonics library or the combined library from Bruker-Daltonics and CLOSTRI-TOF.
## MSP library creation
For the 142 strains analyzed, we generated 2,130 target positions and a total of 4,260 individual mass spectra. The quality of the spectra was high, with an average of 1–3 of the raw spectra removed per strain prior to MSP generation. Thus, we could generate a robust library for the 47 species.
## Evaluation of MSP library performance
To test the newly established library, we subjected 58 additional strains (validations strains, Supplementary Table S2), not included in the set of strains used for library construction but previously identified based on their 16S rRNA gene sequences to MALDI-TOF MS. Combining the original Bruker database with the CLOSTRI-TOF database plugin allowed reliable identification of at the species level (score value ≥2.0) of $\frac{57}{58}$ strains ($98.3\%$) in laboratory A and $\frac{54}{58}$ ($93.1\%$) in laboratory B (Figures 2). Of note, several strains belonging to the genus Blautia were identified with a high score (≥2.0) but were incorrectly assigned at the species level. Further, *Coprococcus eutactus* was correctly identified in both laboratories, but with a score between 1.7 and 2.0. All the validation strains ($\frac{58}{58}$) ($100\%$) were reliably identified at the genus level (score ≥ 1.7 but <2.0) in both laboratory A and B. Thus, using the combination of the original Bruker database and the CLOSTRI-TOF database, we were able to reidentify most of the validation strains to species level and all strains to genus level.
**Figure 2:** *Blind test of Clostridia strains using the newly created CLOSTRI-TOF MS performed in lab A (A) and lab B (B). The highest matching scores of the spectra were represented by ranges indicating high confidence identification (2.000–3.000), low confidence identification (1.700–1.999) and no organism identification possible (0.000–1.699).*
## 16S rRNA gene-based phylogeny and MALDI-TOF MS profiling
To investigate the taxonomic assignment relationship between the microflex Biotyper and 16S rRNA gene-based sequencing, we compared dendrograms generated by either of these techniques. In most cases, the two methods provided identical results where strains belonging to the same species clustered together (Figures 3, 4). However, different strains of Agathobacter rectalis, Blautia spp., Faecalicatena fissicatena, and Lacrimispora celerecrescens formed two or three distinct MALDI-TOF-based clusters (Figure 3). Likewise, the maximum-likelihood phylogenetic tree based on 16S rRNA gene sequences was unable to cluster together strains of the same species of Agathobacter rectalis, Blautia spp., Faecalicatena fissicatena, and Lacrimispora celerecrescens (Figure 4).
**Figure 3:** *Distribution of different species with similar spectral profiles according to MSP dendrogram cluster analysis.* **Figure 4:** *Maximum-likelihood phylogenetic tree based on complete 16S rRNA gene sequences of the strains used in the construction of CLOSTRI-TOF database.*
## Identification of anaerobic gut bacterial isolates using MALDI-TOF MS enhanced by CLOSTRI-TOF
We next analyzed a bank of 326 strains isolated from the feces of healthy Swiss volunteers (Supplementary Table 5) using the microflex Biotyper. Of the 326 analyzed strains, 170 ($52.1\%$) were identified with the original Bruker database, while 156 ($47.9\%$) could not be identified/had no match in the library at a score > 1.7. Combining the original library with the CLOSTRI-TOF library plugin, we increased the number of strains identified at a score > 2.0 to 264 ($80.9\%$) (Figure 5), which corresponds to an identification of formerly unknown strains of $60\%$.
**Figure 5:** *Identification rates for 326 bacteria isolates tested using the Bruker-Daltonics MALDI Biotyper Compass MSP library v. 4.1.100 combined or not with the CLOSTRI-TOF database plugin.*
## Discussion
To date, 16S rRNA gene sequencing is one of the most widely used techniques to identify unknown microbial isolates from the human gastrointestinal tract. Here, we propose a MALDI-TOF MS library plugin, CLOSTRI-TOF, enhancing the power of MALDI biotyping as a rapid, accurate, and economical alternative.
MALDI biotyping is strongly dependent on spectral databases, thus allowing to identify only taxa that already have spectral information for a given species. This hurdle has been overcome by the generation of in-house custom reference databases for specific groups of microorganisms. As an example, previous research has generated database plugins for different species of the genera Borrelia (Calderaro et al., 2014), Burkholderia (Fergusson et al., 2020) or Vibrio (Moussa et al., 2021), different strains of *Clostridium tyrobutyricum* (Burtscher et al., 2020) as well as for helminths (Sy et al., 2022).
The CLOSTRI-TOF library plugin presented here allowed to identify all the 47 bacterial species included in the library plugin at the genus level and most even down to species level. Further, we could show that the combination of the original Bruker library combined with the CLOSTRI-TOF library plugin significantly increase the number of identified strains from a collection of fecal bacterial isolates of healthy Swiss volunteers. One of the species included in our library (Coprococcus eutactus) did not yield reliable identification using the validation strains. Of note, C. eutactus is represented by only two strains in the CLOSTRI-TOF library. It has been shown previously that the number of included strains in the database is critical to account for intraspecific diversity, thus allowing reliable identification (Erler et al., 2015). Future updates to the CLOSTRI-TOF database should therefore expand the number of strains included for species with a low strain coverage.
While our database plugin allowed identification of Blautia at the genus level, it did not allow reliable assignment at species-level. As the strains of Blautia were also polyphyletic in the 16S rRNA gene phylogenetic tree, it would be interesting to compare the phylogenetic relationship within this genus in more detail. The library plugin presented here is focused on the main members of the class Clostridia colonizing the human gastrointestinal tract. We have chosen this group of microorganisms, as they are frequently involved in the production of the key metabolite butyrate (Louis and Flint, 2009) and reduced in dysbiotic diseases, including undernutrition (Vonaesch et al., 2018), ulcerative colitis (Machiels et al., 2014), and type 2 diabetes (Wang et al., 2012). Expanding the database beyond the class of Clostridia to other frequent members of the human microbiome will be key for rapid identification of all bacterial strains isolated from human fecal samples.
Of note, MALDI biotyping allows for rapid identification of bacteria, yet is not able to give the same amount of information on each isolate as would be gained by whole-genome sequencing. Thus, MALDI identification should be seen as a low cost, rapid pre-identification tool to select the strains that will be characterized in greater detail using more advanced methods such as whole-genome sequencing.
## Conclusion
Our data show that MALDI-TOF MS is a promising tool to rapidly identify isolates of commensal bacteria. The new open-source library plugin developed here allows to discriminate all taxa included to the genus and most taxa even down to species level.
## Data availability statement
The datasets presented in this study can be found on Zenodo under the following link: https://zenodo.org/record/7773644#.ZCny2y8Rrfc.
## Author contributions
PA, LM, and PV: conceptualization. VT, TW, GG, TC, EP, and AE: resources. PA, CL, VH, YT, AC, NA, CG, AA, MC, CK, and PV: methodology. PA, CL, and PV: data curation, formal analysis, visualization, and writing of original draft. PA, CL, GG, TC, EP, AE, LM, and PV: review and editing. All authors contributed to the article and approved the submitted version.
## Funding
This work was funded by the Bill and Melinda Gates Foundation (grant number INV-004352 to PV and LM), the Forschungsfonds of the University of Basel (PV) and an Eccellenza Fellowship from the Swiss National Science Foundation to PV (grant number PCEFP3_194545). The Vonaesch and Greub labs are part of NCCR Microbiomes, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 180575). TC received funding from the German Research Foundation (DFG), project ID 403224013—SFB 1382, project ID 395357507—SFB 1371, and project ID 460129525—NFDI4Microbiota. NA, CG, and LM are supported by DFG (CMFI Cluster of Excellence EXC 2124 and Emmy Noether Program).
## Conflict of interest
TW and VH were employed by PharmaBiome AG.
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/fmicb.2023.1104707/full#supplementary-material
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|
---
title: Association Between Serum Lipids and Survival in Patients With Amyotrophic
Lateral Sclerosis
authors:
- Mark R. Janse van Mantgem
- Wouter van Rheenen
- Anemone V. Hackeng
- Michael A. van Es
- Jan H. Veldink
- Leonard H. van den Berg
- Ruben P.A. van Eijk
journal: Neurology
year: 2023
pmcid: PMC9990853
doi: 10.1212/WNL.0000000000201657
license: CC BY 4.0
---
# Association Between Serum Lipids and Survival in Patients With Amyotrophic Lateral Sclerosis
## Body
Lipids act as structural components of neuronal membranes, signaling molecules and energy substrates required for normal functioning of neurons.1 Although the exact pathophysiologic mechanisms underlying amyotrophic lateral sclerosis (ALS) are unknown,2 it is likely that the origins of the condition lie in a multistep process,3 followed by intraneuronal disease propagation, altered neuronal metabolism, and ultimately neuronal death. Dysregulated energy metabolism is a consequence of this process,4 which also affects biomarkers of the lipid metabolism, such as cholesterol, its carriers (i.e. LDL and HDL cholesterol), and triglycerides (TG). Albeit little is known about changes in the preclinical stage, 2 recent studies comprising a Mendelian randomized study,5 and a prospective cohort study of over 500,000 people,6 related premorbid metabolic changes to the risk of ALS.
The association between biomarkers of lipid metabolism, prognosis, and disease progression after disease onset has proven more difficult to characterize. Although high lipid levels have been shown to increase metabolic stress7-9 and potentially lead to a more aggressive disease course,2 some studies have suggested that abnormal lipid levels may actually be beneficial to the patient's prognosis.10-14 Elucidating the interplay between clinical phenotype and lipid metabolism may reveal potential therapeutic interventions and better address the mixed results from dietary interventions obtained thus far.15,16 *In this* study, therefore, we aim to summarize the current literature and to explore the relationships between lipids, ALS survival, polygenic profile scores (PPS) for lipid levels, and markers of disease progression in a large population-based study, to address the disparate data in the literature.
## Abstract
### Background and Objective
To explore the association between lipids, polygenic profile scores (PPS) for biomarkers of lipid metabolism, markers of disease severity, and survival in patients with amyotrophic lateral sclerosis (ALS).
### Methods
We meta-analyzed the current literature on the prognostic value of lipids in patients with ALS. Subsequently, we evaluated the relationship between lipid levels at diagnosis, clinical disease stage, and survival in all consecutive patients diagnosed in the Netherlands. We determined the hazard ratio (HR) of each lipid for overall survival, defined as death from any cause. A subset of patients was matched to a previous genome-wide association study; data were used to calculate PPS for biomarkers of lipid metabolism and to determine the association between observed lipid levels at diagnosis and survival.
### Results
Meta-analysis of 4 studies indicated that none of the biomarkers of the lipid metabolism were statistically significantly associated with overall survival; there was, however, considerable heterogeneity between study results. Using individual patient data ($$n = 1$$,324), we found that increased high-density lipoprotein (HDL) cholesterol was associated with poorer survival (HR of 1.33 ($95\%$ CI 1.14–1.55, $p \leq 0.001$)). The correlation between BMI and HDL cholesterol (Pearson r −0.26, $95\%$ CI −0.32 to −0.20) was negative and between BMI and triglycerides (TG) positive (Pearson r 0.18, $95\%$ CI 0.12–0.24). Serum concentrations of total cholesterol and LDL cholesterol were lower in more advanced clinical stages (both $p \leq 0.001$). PPS for biomarkers of lipid metabolism explained $1.2\%$–$13.1\%$ of their variance at diagnosis. None of the PPS was significantly associated with survival (all $p \leq 0.50$).
### Discussion
Lipids may contain valuable information about disease severity and prognosis, but their main value may be driven as a consequence of disease progression. Our results underscore that gaining further insight into lipid metabolism and longitudinal data on serum concentrations of the lipid profile could improve the monitoring of patients and potentially further disentangle ALS pathogenesis.
## Methods
A two-step approach was used. First, we conducted a systematic review to summarize and meta-analyze the current literature on the prognostic value of biomarkers of lipid metabolism in patients with ALS. Second, we assessed the prognostic value of lipids in a large population-based cohort study, explored their relationship with disease severity, and assessed the causal association between PPSs and survival after disease onset. Throughout the text, we define “biomarkers of lipid metabolism” as an umbrella term for total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and TG.
## Search and Study Selection
We conducted the systematic search in 4 literature databases: PubMed, EMBASE, DARE, and the Cochrane Library; the study protocol for the systematic review is presented in the supplementary material (eAppendix 1: Systematic Review Protocol,, links.lww.com/WNL/C516). Additional forms or information, such as data collection forms, can be provided on request. The primary purpose of the meta-analyses was to provide an explanatory summary of the current literature. All databases were last searched in June 2022. Search terms included the MeSH terms: “Amyotrophic Lateral Sclerosis”; “Motor Neuron Disease”; “Cholesterol”; “Cholesterol, LDL”; “Cholesterol, HDL”; “Triglyceride”; “Lipid”; “Prognosis”; “Survival”; “Mortality”; “Kaplan-Meier estimate”; and “Proportional Hazard Models.” Studies were selected on the basis of the following inclusion criteria: [1] participants diagnosed with ALS according to the revised El *Escorial criteria* (EEC)17; [2] reporting of at least one of the following measurements: TC, HDL-C, LDL-C, or TG, obtained after symptom onset; [3] reporting of survival time and hazard ratio (HR); and [4] written in English or Dutch. Study eligibility was not based on sample size. All articles were screened independently by 2 reviewers for title and abstract (M.J.v. M. and A.H.). Included and excluded articles were discussed; if no consensus was reached, a third reviewer was consulted (R.P.A.v. E.).
## Data Collection and Meta-analysis
For each included study, we extracted the following variables: author, publication year, country, number of participants, and statistical analysis parameters (that is, covariates, HR, and $95\%$ CI). We used the Quality in Prognosis Studies tool to determine the quality and risk of bias of the included articles.18 Studies that provided a HR for at least one, nondichotomized, biomarker of the lipid metabolism were included in the meta-analysis. Standardized HRs (SE) were back-transformed to mmol/L by dividing by the study standard deviation; if studies reported biomarkers of lipid metabolism in mg/dL, data were converted to mmol/L by dividing the HR (SE) by 0.02586 for TC, LDL-C, and HDL-C or by 0.01129 for TG. Meta-analyses were conducted using a Bayesian hierarchical model using a noninformative uniform prior for the log HR and a weakly informative prior for the heterogeneity parameter (half normal with standard deviation of 0.5). As sensitivity analysis, we varied the prior for the heterogeneity parameter using either a standard deviation of 0.25 or 1.0.19 Funnel plots were used to visually inspect publication bias and study heterogeneity (eFigure 1, links.lww.com/WNL/C516). We estimated the heterogeneity between studies using the I2 statistic and expressed this as percentage. The meta-analyses provide the pooled HR on survival across studies for each biomarker of lipid metabolism in mmol/L.
## Population-Based Cohort
For the second part of this study, we conducted a prospective analysis of the national registry of the Netherlands ALS Center, selecting all consecutive patients diagnosed in the University Medical Center Utrecht (UMCU), Utrecht, the Netherlands, between January 1, 2012, and December 31, 2017, to ensure sufficient follow-up time for survival. All patients were diagnosed with possible, probable laboratory supported, probable, or definite ALS.17 The UMCU is a referral center for all patients with ALS across our country. All clinical characteristics were collected at the time of diagnosis. The King's clinical staging system20 was determined according to the standard operating procedures provided by the European Network to Cure ALS (ENCALS).21 Patients with more than 30 hexanucleotide repeats in the C9orf72 gene were considered to be C9orf72 carriers.22 We defined survival time as time between date of diagnosis and date of death or date last known to be alive. Survival information was updated at quarterly intervals by cross-referencing with the municipal population register. All patients were administratively censored on 9 July 2020. Data were further supplemented with the revised ALS functional rating scale (ALSFRS-R) collected at time of diagnosis.15 *For a* subset of patients, longitudinal data of the ALSFRS-R were available, obtained during either clinical follow-up or previous participation in clinical research.
In total, 1,324 patients with ALS were enrolled in our population-based registry. At the time of administrative censoring (July 2020), 1,185 deaths ($89.5\%$ of enrolled population) had occurred during 2,370 person-years of follow-up. The median survival since diagnosis was 16.5 months ($95\%$ CI 15.7–17.5). Baseline characteristics of the cohort are listed in Table 2; 688 patients ($52\%$) had been enrolled in our latest GWAS study and were included in the PPS analysis. Overall, $20.1\%$ of the patients had elevated TC, $42.0\%$ elevated LDL-C, $4.9\%$ reduced HDL-C, and $19.2\%$ elevated TG levels on the day of diagnosis.
After adjustment for age, site of onset, diagnostic delay, prediagnostic progression rate (∆FRS), vital capacity, presence of FTD, C9orf72 repeated expansion, and El Escorial classification,24 a 1 mmol/L increase of HDL-C was found to be associated with a higher risk of death and shorter survival time after ALS diagnosis, HR of 1.33 ($95\%$ CI 1.14–1.55, $p \leq 0.001$, Table 3). This effect was larger for male patients than for female patients: HR (male patients) 1.48 vs HR (female patients) 1.13, although not statistically significantly different (interaction term $$p \leq 0.094$$). The effect was similar for different ages at diagnosis (HR-interaction 1.00; $95\%$ CI 0.98–1.02, $$p \leq 0.97$$). Introduction of a nonlinear term did not result in a significant model improvement ($$p \leq 0.84$$). Additional adjustment for weight loss (HR of 1.37, $95\%$ CI 1.17–1.61) or body mass index (HR of 1.28, ($95\%$ CI 1.09–1.50) did not alter our results.
Longitudinal ALSFRS-R data, that is, 2 or more measurements, were available for 419 of the 1,324 patients ($31.6\%$). Average progression rate after diagnosis was 0.79 points per month ($95\%$ CI 0.73–0.85). With each mmol/L increase in HDL-C, the monthly ALSFRS-R progression rate increased by 0.10 points per month ($95\%$ CI −0.07 to 0.26, $$p \leq 0.21$$), indicating a similar directional effect as observed on survival, albeit not statistically significant. None of the other biomarkers of the lipid metabolism was significantly associated with the monthly progression rate (all $p \leq 0.15$).
Figure 2 and Figure 3 present the standardized distributions of the biomarkers of lipid metabolism stratified by BMI category and King's clinical stage at diagnosis, respectively. Both HDL-C (Pearson r −0.26, $95\%$ CI −0.32 to −0.20) and TG (Pearson r 0.18, $95\%$ CI 0.12–0.24) were associated with BMI at diagnosis (both $p \leq 0.001$); these relationships were similar for male patients and female patients (both interaction terms $p \leq 0.40$). Similarly, TC and LDL-C depended on King's clinical staging and showed a declining trend for more advanced disease stages (both $p \leq 0.001$); again, these associations were similar for male patients and female patients (both interaction terms $p \leq 0.75$). The results were similar when categorizing the ALSFRS-R into 4 equal categories (results not shown).
**Figure 2:** *Biomarkers of the Lipid Metabolism Stratified by BMI Category at DiagnosisBoxplots summarizing the cross-sectional concentrations of the lipids linked to body mass index (BMI) at diagnosis. Scales are standardized to provide a direct comparison between lipids; interpretation is straightforward, where the scale reflects the number of standard deviations above or below the mean lipid level as presented in Table 2. Abbreviations: HDL = high-density lipoprotein; LDL = low-density lipoprotein. p values are based on the likelihood ratio test.* **Figure 3:** *Biomarkers of the Lipid Metabolism Stratified by King's Clinical Staging at DiagnosisBoxplots summarizing the cross-sectional concentrations of the lipids linked to the 4 King's clinical stages. Scales are standardized to provide a direct comparison between lipids; interpretation is straightforward, where the scale reflects the number of standard deviations above or below the mean lipid level as presented in Table 2. Abbreviations: HDL = high-density lipoprotein; LDL = low-density lipoprotein. p values are based on the likelihood ratio test.*
## Blood Sample Collection
Blood samples were collected from patients in a nonfasting state on the day of diagnosis or within one month after diagnosis.23 We determined TC, LDL cholesterol, HDL cholesterol, and TG with the Beckman Coulter AU5800 clinical chemistry analyzer series. Normal ranges were defined according to the central diagnostic laboratory of the UMCU: TC 3.5–6.5 mmol/L, LDL-C < 3.5 mmol/L, HDL-C > 0.90 mmol/L for male patients, HDL-C > 1.1 mmol/L for female patients, and TG 0.0–2.0 mmol/L.
## Statistical Analysis
We performed our statistical analyses using RStudio (version 1.1.4, RStudio: Integrated Development for R, Boston, USA, rstudio.com/). Mean and SD were determined and summarized for continuous variables; for categorical variables, we determined frequency and proportion. The Cox proportional hazard model was applied to assess the association between the risk of death and biomarkers of lipid metabolism at diagnosis. All models were adjusted for the 8 clinical predictors—combined in a linear predictor—from the ENCALS survival model,24 namely age at onset, diagnostic delay, bulbar onset, definite ALS according to the revised EEC,17 prediagnostic progression rate (ΔFRS),25 percentage (%) of predicted forced vital capacity, presence of frontotemporal dementia (FTD), and carrier of the C9orf72 repeat expansion. For each analysis, the following sensitivity analyses were conducted: [1] adding an interaction term between biomarker level and sex (i.e. is the effect of the biomarker different for male patients vs female patients?) and similarly for age at diagnosis, [2] adding quadratic terms to explore potential nonlinear relationships between the risk of death and the biomarker level, and [3] additional adjustment for body mass index (BMI) and weight loss, factors known to be associated with both the lipid level and survival.26 Data missing for any variable except the outcome were addressed by creating multiple imputed data sets ($$n = 100$$), using predictive mean and bootstrapping, discarding the first 100 iterations (burn-in). In total, $9.2\%$ of all observations were missing and, therefore, imputed. All covariates were included in a stratified imputation model per diagnostic year; survival time was included as cumulative hazard rate (Nelson-Aalen estimator).27 The results across imputations were pooled using Rubin rules.28 We further explored longitudinal trends in disease progression rate by assessing the relationship between lipid levels at diagnosis and decrease in ALSFRS-R since diagnosis using linear mixed-effects models. Models contained a fixed effect for time since diagnosis (in months), lipid level, and the interaction between time and lipid level; the random part contained a random slope for time and intercept per patient. We used a likelihood ratio test to assess the significance of the interaction between lipid level and time (i.e., is the rate of ALSFRS-R progression dependent on lipid level?). In addition, we assessed the cross-sectional association between lipid levels, BMI, and King's Clinical Staging20 at diagnosis using linear regression models. Sensitivity analyses were conducted by introducing interaction terms for sex to assess potential male-female differences. All analyses of the TG level were performed on the natural logarithm scale because of their right-skewed distribution.
## PPS
As an exploratory analysis, we estimated PPS for biomarkers of lipid metabolism.29 The PPS estimates the sum of additive genetic effects across all alleles that affect the biomarkers of lipid metabolism at the patient level. We used the PPS to explore a potential genetic link between lipid metabolism, ALS, and survival time by assessing [1] how much of the variance in biomarker levels at diagnosis can be explained by genetic profile scores and [2] whether the genetic profile score itself is associated with overall survival time. Because PPS does not change over time,30 a statistical association between the genetic profile score and survival may be evidence of abnormal lipid levels caused by genetic variation or hold potential for therapeutic interventions.30 Moreover, their time invariance allowed us to estimate the link between the genetic profile score and overall survival time, defined as time between symptom onset and death.
For all individuals who were enrolled in both our population-based registry and our latest genome-wide association study (GWAS),5 we calculated the PPS. PPS was based on summary statistics from a GWAS on biomarker levels of lipid metabolism in the UK Biobank.31 For each single-nucleotide polymorphism, we calculated a weight for each biomarker using the summary-BavesR module in the Genome-Wide Complex Trait *Bayesian analysis* toolkit (default parameters)29 and a linkage-disequilibrium matrix originating from 50,000 unrelated individuals of inferred European ancestries included in the UK Biobank. Because the genotype data originated from several different cohorts in the ALS GWAS, we scaled the PPS per GWAS cohort to a mean of zero and a standard deviation of 1. Linear regression models were used to calculate how much of the variance in the biomarker level was explained by their PPS (expressed as adjusted R2); $95\%$ confidence intervals were obtained by means of bootstrapping. Simple univariable Cox models for overall survival time (i.e., from onset to death) were used to estimate HRs.
## Standard Protocol Approvals, Registrations, and Patient Consents
The medical ethics committee and institutional review board of the University Medical Center Utrecht (METC NedMec) approved this study (Study Registration Number: METC 19–190). Written consent was obtained from all study participants before this study.
## Data Availability
All protocol, analyses, and anonymized data will be shared on request. We take full responsibility for data, analyses and interpretation, and conduct of the research.
## Systematic Review and Meta-analysis
Of the 624 citations screened, 9 articles were included (eFigure 2, links.lww.com/WNL/C516), 5 of which found a significant association between survival time and serum levels of TC, LDL/HDL ratio, HDL cholesterol, or TG; their characteristics are summarized in Table 1. Studies included different prognosticators in their multivariable model; none adjusted for all known prognosticators in patients with ALS.24 4 studies reported a nondichotomized HR and were included in the meta-analysis, resulting in a total sample size of 1,120 patients (Figure 1). The risk of bias assessment of the individual studies is presented in eFigure 3. None of the biomarkers of the lipid metabolism reached statistical significance (Figure 1), although the $95\%$ credible intervals included clinically relevant effect sizes. There was, however, considerable heterogeneity between study results, reflected as τ, indicating possible differences in methodology. Changing the prior assumptions resulted in similar findings (not shown). In eFigure 1, we provide the funnel plot to explore publication bias; it should be noted that, given the small number of studies, their interpretation is limited.
## Analysis of PPSs
Finally, in Table 4, we summarize how much of the variance in lipid levels observed at diagnosis can be attributed to the respective PPS, expressed as adjusted R2, and how the PPS relate to overall survival since symptom onset. Each PPS was significantly correlated with the respective lipid level (Pearson rTC 0.11, $$p \leq 0.002$$; Pearson rLDL-C 0.23, $p \leq 0.001$; Pearson rHDL-C 0.36, $p \leq 0.001$; Pearson rlog-TG 0.33, $p \leq 0.001$), with the explained variance at diagnosis ranging from $1.2\%$ to $13.1\%$. None of the PPS was, however, significantly associated with overall survival time (all $p \leq 0.50$).
## Discussion
In this study, we have shown the extensive variability in the literature regarding the prognostic value of the lipid profile. The study heterogeneity is mainly driven by differences in study design, statistical models, sample size, and the patient population enrolled. In the second part of our study, only HDL cholesterol had additional prognostic value for predicting survival after diagnosis in patients with ALS in a prospective, population-based registry. Changes in components of the lipid profile were primarily related to disease severity. We found no immediate associations, however, between lipid-based polygenic scores and overall survival, yet another indication that changes in the lipid profile may be primarily a consequence of disease. Our results underscore that obtaining greater insight into lipid metabolism and longitudinal data on serum concentrations of the lipid profile could improve the monitoring of patients and potentially further disentangle ALS pathogenesis.
First, our literature search into the relationship between survival and lipid profile showed that the results of these studies are mixed.10-14,32 The included studies analyzed lipids either continuously or as binary factor (e.g., high vs low). Binary categorization of the lipid levels into normal or abnormal may lead to spurious associations and be too limited to describe the gradual associations with prognosis. When pooling results across studies in a meta-analysis, none of the lipids were statistically significantly associated with survival, but individual study results varied considerably. The variation may be explained by [1] differences in the disease stage and the phenotype of the population enrolled and [2] differences in study methodology (e.g., follow-up time, statistical approach, and sample size).
Second, our analysis of a population-based registry confirmed the nonprognostic value of most lipids; HDL-C was, however, found to be predictive of overall survival since diagnosis. This finding was recently confirmed in both Japanese32 and Swedish10 patients, although insignificantly in the latter. We were not able to show the association between HDL-C and disease progression determined by the ALSFRS-R, as follow-up data were limited. The prognostic value of HDL-C could be the result of a surrogate association with disease progression. Respiratory insufficiency or symptoms of dyspnea have been associated with the lipid profile,33 while dietary changes alter lipid concentrations.34 Weight loss is observed in up to $60\%$ of patients with ALS,26 and changes in BMI have a direct impact on the lipid profile.35,36 This impact was also found in our study population; there was a strong association between HDL-C and BMI, where HDL-C increases as BMI decreases. However, adjusting for BMI or other markers of disease severity minimally affected the association between HDL-C and survival. Albeit speculative, one could also hypothesize that the prognostic association might partially reflect a premanifest or prodromal sign of ALS. For example, production of oxidized derivatives of excess cholesterol might be caused by deficiencies in cholesterol metabolism,7 which in turn may induce neuronal damage leading to muscle function loss.7,37 Deficiencies in cholesterol metabolism may also lead to dysregulated transport of cholesterol and result in toxicity in the brain.38 In an attempt to disentangle this potential causality between lipids and survival, we estimated PPS for biomarkers of lipid metabolism to explore genetic links with lipid metabolism and ALS survival time. As PPS does not change over time,30 any association between PPS and survival may be an indication that premorbid changes in lipids result in a more aggressive disease as expressed in overall survival time.29 Our results highlight the predictive value and utility of PPS in patients with ALS as surrogate for actual lipid levels but also underscore that over $80\%$ of the variance in the actual lipid levels were not captured by the PPS. Taking into account, the absence of a large effect between PPS and survival time and the results from other studies in which PPSs were more predictive for actual lipid levels,39 these observations may support reverse causality, where lipid levels change as a consequence of the disease rather than vice versa.
The clinical relevance of these observations depends on the setting and the intended use of the PPS. Despite the large sample size of our cohort, we were primarily powered to detect HRs of 1.1 or greater. An HR of 1.1 would translate to a $46.4\%$ difference in hazard when comparing a patient with -2SD (∼2.5th percentile) vs a patient with +2SD (∼97.5th percentile). Smaller effect sizes, therefore, could still be deemed relevant, although detecting, for example, an HR of 1.05 or greater with $90\%$ power would require approximately 4,500 survival events. Larger GWASs that link overall survival time to PPS may, therefore, be needed to further investigate potential causal or etiological relationships.30 Moreover, determining whether a change in the lipid level precedes a change in clinical progression requires longitudinal observations with repeated blood samples to provide more definite evidence.40 In such studies, it would be key to carefully collect other parameters that influence lipids, which were not collected in our study, such as smoking,41 diet or the use of cholesterol-lowering drugs (CLD),42 and preferably assess serum concentration in a fasting state to minimize variability.43,44 Finally, $42.0\%$ of our patient population had elevated serum concentrations of LDL-C; the mean serum HDL-C was comparable with that of the general Dutch population.43 Studies that enrolled patients with ALS have reported similar serum concentrations.10,45 HDL-C values were more or less the same as those found in the general population; however, an elevated LDL-C can be found in approximately $50\%$–$60\%$ of people of similar age in the Netherlands.46,47 Patients with ALS, therefore, may have lower levels of LDL-C compared with the general population,46 supporting our finding of decreasing levels in more advanced disease stages. Enrollment of a more geographically and culturally diverse population may improve generalizability of the exact association between lipids and overall survival in ALS, but dedicated case-control studies are needed to confirm true differences in lipid levels between patients with ALS and the general population. Moreover, although our study indicates a relationship with cross-sectional clinical stages, determining whether a change in the lipid level precedes a change in clinical progression requires longitudinal observations with repeated blood samples to provide more definite evidence.
In conclusion, lipids may contain valuable information about disease severity and prognosis because serum concentrations seem to be dependent on disease severity. Our results underscore that gaining further insight into lipid metabolism and longitudinal data on serum concentrations of the lipid profile could improve the monitoring of patients. Because our results are not in line with previous studies on a causal effect of the lipid profile on ALS disease progression, we believe that this new information may contribute to ongoing efforts to disentangle ALS pathogenesis.
## Study Funding
The Netherlands ALS Foundation.
## Disclosure
M.R. Janse van Mantgem reports no disclosures; W. van Rheenen reports funding provided by the Dutch Research Council (NWO) [VENI scheme grant 09150161810018] and Prinses Beatrix Spierfond (neuromuscular fellowship grant W.F19-03) and has sponsored research agreements with Biogen; A.V. Hackeng reports no disclosures; M.A. van Es reports grants from the Netherlands Organization for Health Research and Development (Veni & Vidi scheme), Joint Program Neurodegeneration (JPND), The Thierry Latran foundation, and the Netherlands ALS foundation (Stichting ALS Nederland). He received travel grants from Shire (formerly Baxalta) and has consulted for Biogen; J.H. Veldink has sponsored research agreements with Biogen; R.P.A. van Eijk reports no disclosures; L.H. van den Berg reports grants from the Netherlands ALS Foundation (Stichting ALS Nederland) and the Netherlands Organization for Health Research and Development (vici schema and funded through the EU Joint Program—Neurodegenerative Disease Research, JPND (SOPHIA, STRENGTH, ALS-CarE projects)) and personal fees from Shire, Biogen, Cytokinetics, and Treeway, outside the submitted work. Go to Neurology.org/N for full disclosures.
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|
---
title: 'Serum anti-Müllerian hormone levels are associated with perinatal outcomes
in women undergoing IVF/ICSI: A multicenter retrospective cohort study'
authors:
- Yi-Chen He
- Kai-Zhen Su
- Jie Cai
- Qing-Xia Meng
- Yan-Ting Wu
- He-Feng Huang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9990865
doi: 10.3389/fendo.2023.1081069
license: CC BY 4.0
---
# Serum anti-Müllerian hormone levels are associated with perinatal outcomes in women undergoing IVF/ICSI: A multicenter retrospective cohort study
## Abstract
### Introduction
Anti-Müllerian hormone (AMH) level has long been considered as a serum biomarker of ovarian reserve clinically, while emerging data suggest that serum AMH level may also predict pregnancy outcomes. However, whether pregestational serum AMH levels are related to perinatal outcomes among women undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles is unknown.
### Objective
To explore the association between different AMH levels and perinatal outcomes in women with live births in IVF/ICSI.
### Methods
This multicenter retrospective cohort study was conducted among three different provinces in China, from January 2014 to October 2019. A total of 13,763 IVF/ICSI cycles with 5657 live-delivery pregnant women and 6797 newborns were recruited. Participants were categorized into three groups according to the <25th (low), 25 to 75th (average), and >75th (high) percentile of serum AMH concentration. Perinatal outcomes were compared among groups. Subgroup analyses were conducted based on the number of live births.
### Results
Among women with singleton deliveries, low and high AMH levels increased the risk of intrahepatic cholestasis of pregnancy (ICP) (aOR1 = 6.02, $95\%$CI: 2.10-17.22; aOR2 = 3.65, $95\%$CI:1.32-10.08) and decreased the risk of macrosomia (aOR1 = 0.65, $95\%$CI:0.48-0.89; aOR2 = 0.72, $95\%$CI:0.57-0.96), while low AMH reduced the risk of large for gestational age (LGA, aOR=0.74, $95\%$CI:0.59-0.93) and premature rupture of membrane (PROM, aOR=0.50, $95\%$CI:0.31-0.79)compared with the average AMH group. In women with multiple deliveries, high AMH levels increased the risks of gestational diabetes mellitus (GDM, aOR=2.40, $95\%$CI:1.48-3.91) and pregnancy-induced hypertension (PIH, aOR=2.26, $95\%$CI:1.20-4.22) compared with the average AMH group, while low AMH levels increased the risk of ICP (aOR=14.83, $95\%$CI:1.92-54.30). However, there was no evidence of differences in preterm birth, congenital anomaly, and other perinatal outcomes among the three groups in both singleton and multiple deliveries.
### Conclusions
Abnormal AMH levels increased the risk of ICP regardless of the number of live births for women undergoing IVF/ICSI, while high AMH levels increased the risks of GDM and PIH in multiple deliveries. However, serum AMH levels were not associated with adverse neonatal outcomes in IVF/ICSI. The underlying mechanism warrants further investigation.
## Introduction
Anti-Müllerian hormone (AMH), mostly secreted by granulosa cells of preantral and early antral follicles, is a dimeric glycoprotein belonging to the family of transforming growth factor beta (TGF-β) [1, 2]. During follicular development, AMH can inhibit the recruitment of initial follicles as well as participate in the regulation of follicular selection [3, 4]. Lines of evidence demonstrated serum AMH is linearly related to the number of developing follicles as well as remaining relatively stable during the menstrual cycle. Thus, AMH is widely used as a serum marker of ovarian reserve in vitro fertilization (IVF) [1, 5, 6]. However, the relationship of AMH to the quality of the oocyte pool and pregnancy outcomes remains unclear [7].
The interest in the impact of serum AMH levels on pregnancy outcomes has emerged in the last few years. Despite several retrospective cohorts pointing to serum AMH as a weak predictor of live birth after assisted reproductive technology (ART) (low AMH level is associated with decreased live birth), only a few studies focus on pregnancy complications and neonatal outcomes [8, 9]. A cohort study based on the serum AMH collected in the first trimester has demonstrated that low maternal level of AMH is a predictor of pregnancy-induced hypertension (PIH) in naturally conceived women, while associations in other complications included gestational diabetes (GDM), preterm birth and small for gestational age (SGA) were not identified [10]. It is interesting to note that recent studies have reported a significant association between AMH and preterm delivery in patients with polycystic ovarian syndrome (PCOS) after IVF [11, 12], suggesting its potential to be a marker of preterm delivery.
Considering the discrepancies and limited sample size, we want to elucidate if AMH is related to pregnancy outcomes, especially in women conceived with ART. ART has been increasingly used for infertile couples thanks to the advances in technology and provision of services, resulting in more than 300 thousand infants born through it each year in China [13]. While ART affords patients the opportunity to have biologically-related children, potential risks including GDM, PIH, preterm birth and low-birth-weight (LBW) exist as results of the laboratory procedures and genetic background (14–18). Given the general use of AMH to assess ovarian reserve before ART, we hope the test will be given new insights as a marker of perinatal outcomes in specific aspects.
To further analyze the effect of AMH on adverse perinatal outcomes among ART pregnancies, we conducted a multi-center retrospective cohort study of women who underwent IVF/intracytoplasmic sperm injection (ICSI) cycles in different AMH groups.
## Study design and participants
This retrospective, multi-center cohort study was conducted on women who underwent IVF/ICSI cycles and achieved live births from January 2014 to October 2019 in three study centers among different provinces in China, including International Peace Maternity and Child Health Hospital (Shanghai), Ningbo Women and Children’s Hospital (Zhejiang Province), Suzhou Municipal Hospital (Jiangsu Province). The study was approved by the research ethics board of each center and written informed consent forms (ICFs) were obtained from all the participants before inclusion.
Subjects were identified from the database in three centers from January 2014 to October 2019 using the following inclusion and exclusion criteria. The inclusion criteria were set as follows: 1) female participants aged between 20 and 45 years, 2) participants with serum AMH measurement within 12 months before undergoing IVF/ICSI cycles. The participants were excluded if they met the following criteria: 1) participants who underwent pre-implantation genetic testing (PGT), 2) participants using donor semen or donor oocyte, 3) mixed transfers with embryos retrieved from different oocyte retrieval cycles, 4) women with severe chronic diseases, 5) women for whom main data were missing or who were lost to follow-up. The participants were categorized into three groups according to the <25th(low), 25th to 75th(average), and >75th(high) percentile of serum AMH concentration (0.01-1.76, 1.76-5.41, 5.41-25.00ng/ml). The subgroup analysis was conducted based on the number of live births.
## AMH measurement
Serum samples were collected from all participants and measured directly after arriving in the laboratory. In two of our study centers, the serum AMH was measured with chemiluminescent immunoassay (CLIA) by Kaeser 1000 chemiluminescence analyzer of Guangzhou Kangrun Biotechnology Co., Ltd. and its corresponding kit according to the manufacturer’s instructions. The intra-assay and inter-assay coefficient of the variation (CV%) was <$8\%$ and <$15\%$. The limit of detection (LoD) was <0.06 ng/ml. And in the other study center, the electrochemiluminescence method with DXI800 chemiluminescence analyzer of Beckman Company and its corresponding kit was adopted for AMH measurement. The total CV% was <$8\%$ in the analytical measure range of 0.02 to 24 ng/ml, and the limit of detection was 0.02 ng/ml.
## IVF/ICSI procedures
The process of IVF or ICSI was conducted according to the standard protocols of our study centers. We performed different types of controlled ovarian hyperstimulation (COH) protocols (gonadotropin-releasing hormone (GnRH)-agonist protocol, GnRH-antagonist protocol, micro-flare protocol or others) according to the state of each patient (age, ovarian reserve and others). After COH, when the leading follicle reached 20mm in diameter or at least two follicles reached 18 mm, ovulation was induced by giving human chorionic gonadotropin (HCG) or gonadotropin-releasing hormone agonists (GnRH-a). Oocyte retrieval was performed 34-38 hours later and oocytes were fertilized by either conventional IVF or intracytoplasmic sperm injection after the assessment of semen quality. Subsequently, viable embryos were transferred in fresh embryo transfer cycles or frozen-thawed embryo transfer (FET) cycles after oocyte retrieval and routine corpus luteum support was performed after transplantation if conceived.
## Outcome measurements
Maternal baseline information was derived from the electronic database of the hospitals, including sociodemographic characteristics and reproductive history. We further abstracted the ART procedures and most of the perinatal outcomes from the database of the hospitals, while the neonatal morbidity and mortality were followed up and recorded by well-trained clinical personnel. The pregnancy outcomes assessed included hypertensive disorders in pregnancy (HDP), GDM, *Intrahepatic cholestasis* of pregnancy (ICP), placental abruption, placenta previa, oligohydramnios, premature rupture of membrane (PROM), postpartum hemorrhage (PPH) and mode of delivery. While neonatal outcomes were assessed including the gender of neonates, birth weight, preterm birth (PTB), weight for gestational age, neonatal infection, admission to the neonatal intensive care unit (NICU), neonatal asphyxia, neonatal jaundice, and congenital anomaly. Preterm birth was defined as delivery at less than 37 weeks, and very preterm was defined as delivery of baby between 28 and 32 gestational weeks of pregnancy. LGA or SGA was defined as a birth weight more than 90th centile or less than 10th centile of our population for a specific gestational age and sex, respectively [19, 20]. Diagnoses were coded according to the International Classification of Diseases version 10(ICD-10).
## Statistical analysis
Continuous variables were presented as mean (standard deviation (SD)) or median (inter-quartile range) as appropriate. Comparisons of the continuous variables among three AMH groups were performed with the use of the Analysis of Variance (ANOVA) test or Kruskal-Wallis test. Categorical variables were represented as frequencies with proportions, while the Pearson Chi-square test or Fisher’s exact test was used to compare the distribution of demographics between categorical variables. Odds ratios (ORs) and $95\%$ confidence intervals (CIs) were calculated using logistic regression to evaluate the association between serum AMH levels and each perinatal outcome following IVF/ICSI. To analyze the pregnancy and neonatal outcomes in singleton pregnancies, multinomial logistic regression was used to adjust ORs for potential confounding factors. While analyzing the neonatal outcomes of multiples, we performed multilevel logistic regression and adjusted for potential confounding factors [21]. Those factors were selected according to baseline analysis and published literature.
The statistical analyses were performed using R software version 3.4.4 (R Foundation for Statistical Computing, Vienna, Austria). All of the statistical analyses were two-sided with a $5\%$ level of significance.
## Results
The flowchart of the study cohort was shown in Figure 1. A total of 13,763 cycles met the eligibility criteria and were included in the cohort (3440 cycles in the low AMH group, 6882 cycles in the average AMH group, and 3441 cycles in the high AMH group). 5657 women with live-born babies (6797 live births with 4519 singletons and 1138 multiples) were further included in the analysis of perinatal outcomes.
**Figure 1:** *Flow chart of the study cohort.*
The baseline characteristics of the participants with live birth deliveries stratified by AMH levels were presented in Table 1. Socio-demographic characteristics including pre-gestational BMI, education attainment, occupation and smoking status were similar among the three groups. However, the distribution of maternal age, paternal age and residence were different among groups ($p \leq 0.001$). Significant differences were found in the race only in singleton delivery ($p \leq 0.001$). Differences in the reproductive history of the participants were found in the parity, gravidity, duration of infertility, primary infertility, and causes of infertility ($p \leq 0.05$), while no statistically significant differences were found between levels of AMH regarding times of abortion and history of ectopic pregnancy. In women with multiple deliveries, the history of ectopic pregnancy and causes of infertility were different among groups, the history of ectopic pregnancy is more frequent in women with low AMH levels. Additionally, gravidity, parity, times of abortion, and duration of infertility were comparable among the three groups. Characteristics of ART procedures (oocyte retrieval and embryo transfer cycles) according to AMH levels were presented in Table S1.
**Table 1**
| Unnamed: 0 | Singleton delivery | Singleton delivery.1 | Singleton delivery.2 | Singleton delivery.3 | Multiple deliveries | Multiple deliveries.1 | Multiple deliveries.2 | Multiple deliveries.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Low AMH (N=937) | Average AMH (N=2340) | High AMH (N=1242) | P value | Low AMH (N=221) | Average AMH (N= 596) | High AMH (N=321) | P value |
| | n (%) | n (%) | n (%) | P value | n (%) | n (%) | n (%) | P value |
| Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics | Socio-demographic characteristics |
| Maternal age (years) | 32.18±4.02 | 30.58±3.74 | 29.64±3.37 | <0.001 | 31.45±3.53 | 29.68±3.36 | 29.89±3.40 | <0.001 |
| Paternal age (years) | 33.78±5.29 | 32.25±4.78 | 31.24±4.38 | <0.001 | 32.50±4.35 | 31.47±4.40 | 31.15±4.10 | <0.001 |
| Pre-gestational BMI (kg/m2) | 22.02±2.95 | 21.98±3.00 | 22.04±3.06 | 0.897 | 22.29±3.27 | 22.06±2.84 | 22.05±2.99 | 0.814 |
| Race | Race | Race | Race | Race | Race | Race | Race | Race |
| Han | 859 (98.5) | 2153 (99.6) | 1159 (99.7) | <0.001 | 206 (99.5) | 562 (99.6) | 304 (99.7) | 0.956 |
| Minority | 13 (1.5) | 9 (0.4) | 3 (0.3) | <0.001 | 1 (0.5) | 2 (0.4) | 1 (0.3) | 0.956 |
| Residence | Residence | Residence | Residence | Residence | Residence | Residence | Residence | Residence |
| Residents | 830 (88.6) | 2042 (87.3) | 999 (80.4) | <0.001 | 194 (87.8) | 523 (87.8) | 249 (77.6) | <0.001 |
| Immigrants/Nonresidents | 107 (11.4) | 298 (12.7) | 243 (19.6) | <0.001 | 27 (12.2) | 73 (12.2) | 72 (22.4) | <0.001 |
| Education attainment | Education attainment | Education attainment | Education attainment | Education attainment | Education attainment | Education attainment | Education attainment | Education attainment |
| Primary school or lower | 21 (2.2) | 37 (1.6) | 15 (1.2) | 0.415 | 4 (1.8) | 9 (1.5) | 5 (1.6) | 0.98 |
| Middle or high school | 365 (39) | 907 (38.8) | 474 (38.3) | 0.415 | 82 (37.4) | 212 (35.8) | 113 (35.2) | 0.98 |
| Collage or above | 550 (58.8) | 1391 (59.6) | 750 (60.5) | 0.415 | 133 (60.7) | 372 (62.7) | 203 (63.2) | 0.98 |
| Occupation | Occupation | Occupation | Occupation | Occupation | Occupation | Occupation | Occupation | Occupation |
| Employed | 629 (71.2) | 1514 (70.8) | 709 (69.1) | 0.838 | 154 (72.3) | 379 (69.5) | 179 (70.8) | 0.055 |
| Self-employed | 115 (13) | 290 (13.6) | 147 (14.3) | 0.838 | 35 (16.4) | 69 (12.7) | 25 (9.9) | 0.055 |
| Unemployed | 139 (15.7) | 333 (15.6) | 170 (16.6) | 0.838 | 24 (11.3) | 97 (17.8) | 49 (19.4) | 0.055 |
| Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status |
| No | 75 (97.4) | 240 (98.8) | 251 (98.8) | 0.625 | 12 (92.3) | 59 (98.3) | 80 (98.8) | 0.288 |
| Yes | 2 (2.6) | 3 (1.2) | 3 (1.2) | 0.625 | 1 (7.7) | 1 (1.7) | 1 (1.2) | 0.288 |
| History of reproduction | History of reproduction | History of reproduction | History of reproduction | History of reproduction | History of reproduction | History of reproduction | History of reproduction | History of reproduction |
| Parity | Parity | Parity | Parity | Parity | Parity | Parity | Parity | Parity |
| 0 | 473 (50.5) | 1214 (51.9) | 713 (57.4) | <0.001 | 128 (57.9) | 383 (64.3) | 211 (65.7) | 0.236 |
| 1 | 223 (23.8) | 609 (26.0) | 302 (24.3) | <0.001 | 57 (25.8) | 120 (20.1) | 70 (21.8) | 0.236 |
| ≥2 | 241 (25.7) | 517 (22.1) | 227 (18.3) | <0.001 | 36 (16.3) | 93 (15.6) | 40 (12.5) | 0.236 |
| Gravidity | Gravidity | Gravidity | Gravidity | Gravidity | Gravidity | Gravidity | Gravidity | Gravidity |
| 0 | 816 (87.1) | 2106 (90) | 1168 (94) | <0.001 | 207 (93.7) | 569 (95.5) | 305 (95.0) | 0.673 |
| 1 | 114 (12.2) | 218 (9.3) | 69 (5.6) | <0.001 | 14 (6.3) | 25 (4.2) | 15 (4.7) | 0.673 |
| 2 | 7 (0.7) | 16 (0.7) | 5 (0.4) | <0.001 | 0 (0) | 2 (0.3) | 1 (0.3) | 0.673 |
| Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions | Number of previous abortions |
| 0 | 606 (64.7) | 1519 (64.9) | 848 (68.3) | 0.239 | 151 (68.3) | 435 (73.0) | 230 (71.7) | 0.353 |
| 1-2 | 300 (32.0) | 744 (31.8) | 363 (29.2) | 0.239 | 66 (29.9) | 144 (24.2) | 86 (26.8) | 0.353 |
| ≥3 | 31 (3.3) | 77 (3.3) | 31 (2.5) | 0.239 | 4 (1.8) | 17 (2.8) | 5 (1.6) | 0.353 |
| Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy | Previous ectopic pregnancy |
| No | 763 (81.4) | 1917 (81.9) | 1041 (83.8) | 0.262 | 187 (84.6) | 513 (86.1) | 292 (91.0) | 0.048 |
| Yes | 174 (18.6) | 423 (18.1) | 201 (16.2) | 0.262 | 34 (15.4) | 83 (13.9) | 29 (9.0) | 0.048 |
| Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) | Duration of infertility (years) |
| 1-2 | 265 (28.5) | 642 (27.7) | 349 (28.4) | 0.015 | 56 (25.9) | 175 (29.5) | 79 (24.8) | 0.588 |
| 3-4 | 287 (30.9) | 851 (36.7) | 447 (36.3) | 0.015 | 83 (38.4) | 223 (37.6) | 125 (39.2) | 0.588 |
| ≥5 | 378 (40.6) | 826 (35.6) | 434 (35.3) | 0.015 | 77 (35.6) | 195 (32.9) | 115 (36.1) | 0.588 |
| Primary infertility | Primary infertility | Primary infertility | Primary infertility | Primary infertility | Primary infertility | Primary infertility | Primary infertility | Primary infertility |
| No | 464 (49.5) | 1126 (48.1) | 529 (42.6) | 0.001 | 93 (42.1) | 213 (35.7) | 110 (34.3) | 0.149 |
| Yes | 473 (50.5) | 1214 (51.9) | 713 (57.4) | 0.001 | 128 (57.9) | 383 (64.3) | 211 (65.7) | 0.149 |
| Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility | Causes of infertility |
| Tubal infertility | 262 (28.1) | 563 (24.2) | 289 (23.4) | <0.001 | 82 (37.3) | 131 (22.2) | 78 (24.4) | <0.001 |
| PCOS | 10 (1.1) | 35 (1.5) | 128 (10.4) | <0.001 | 0 (0) | 10 (1.7) | 34 (10.6) | <0.001 |
| Anovulation (not PCOS) | 14 (1.5) | 25 (1.1) | 23 (1.9) | <0.001 | 6 (2.7) | 7 (1.2) | 12 (3.8) | <0.001 |
| Endometriosis | 53 (5.7) | 130 (5.6) | 49 (4.0) | <0.001 | 6 (2.7) | 26 (4.4) | 14 (4.4) | <0.001 |
| Male-factor infertility | 100 (10.7) | 434 (18.7) | 166 (13.4) | <0.001 | 19 (8.6) | 107 (18.1) | 47 (14.7) | <0.001 |
| Unexplained infertility | 12 (1.3) | 29 (1.2) | 6 (0.5) | <0.001 | 1 (0.5) | 7 (1.2) | 0 (0) | <0.001 |
| Combined | 483 (51.7) | 1111 (47.7) | 575 (46.5) | <0.001 | 106 (48.2) | 303 (51.3) | 135 (42.2) | <0.001 |
The pregnancy outcomes of different groups stratified by serum AMH levels were shown in Figure 2, we presented the crude and adjusted odds ratios assessing the risks in the low AMH group and high AMH group compared with the average AMH group. After adjusting for confounding factors in logistic regression analyses, an increased risk of ICP was found to be associated with low and high levels of AMH in singleton delivery (aOR1 = 6.02, $95\%$CI: 2.10-17.22; aOR2 = 3.65, $95\%$CI: 1.32-10.08). In multiple deliveries, the low AMH group was also found to have an increased risk of ICP compared with the average AMH group (aOR1 = 14.83, $95\%$CI: 1.92-54.30). In addition, low levels of AMH compared to average levels of AMH were associated with a lower risk of PROM in women with singleton delivery (aOR1 = 0.50, $95\%$CI:0.31-0.79). Although not found in singleton delivery, high levels of AMH were associated with a higher risk of gestational diabetes mellitus and gestational hypertension in multiple deliveries (gestational diabetes mellitus: aOR2 = 2.40, $95\%$CI:1.48-3.91; gestational hypertension: aOR2 = 2.26, $95\%$CI: 1.20-4.22), while low levels of AMH were also associated with increased risk of oligohydramnios in women with multiple deliveries compared to average levels of AMH (aOR1 = 37.75, $95\%$CI: 5.17-145.24). There were no significant differences in risks regarding other pregnancy outcomes among the three groups.
**Figure 2:** *Forest plot summary of logistic regression analysis for risks of pregnancy outcomes in ART pregnancies with (A) singleton and (B) multiple deliveries. OR, odd ration; CI, confidence interval; aOR, adjusted odds ratio. aaOR was adjusted maternal age, paternal age, race, residence, gravidity, parity, duration of infertility, primary infertility, causes of infertility, study center, controlled ovarian stimulation protocol, type of insemination, transfer cycle types, embryo types, number of embryos transferred. baOR was adjusted maternal age, paternal age, residence, gravidity, parity, primary infertility, causes of infertility, study center, controlled ovarian stimulation protocol, type of insemination, transfer cycle types, embryo types.*
Figure 3 presented neonatal outcomes among three groups of serum AMH in women with singleton delivery and multiple deliveries. In singleton delivery, an decreased risk of macrosomia was found in the low AMH group compared with the average AMH group (aOR1 = 0.65, $95\%$CI: 0.48-0.89), while the high AMH group showed a similar effect (aOR2 = 0.72, $95\%$CI: 0.57-0.96). Additionally, there was an decreased risk of large for gestation age (LGA) in a group with lower levels of AMH compared with the average AMH group (aOR1 = 0.74, $95\%$CI: 0.59-0.93). There was no evidence of differences in preterm birth, congenital anomaly, and other neonatal complications among the three groups in both singleton delivery and multiple deliveries.
**Figure 3:** *Forest plot summary of logistic regression analysis for risks of neonatal outcomes in ART pregnancies with (A) singleton and (B) multiple deliveries. OR, odds ration; CI, confidence interval; aOR, adjusted odds ration; SGA, small for gestational age; AGA, appropriate for gestational age; NICU, neonatal care unit. aaOR was adjusted for maternal age, paternal age, race, residence, gravidity, parity, duration of infertility, primary infertility, causes of infertility, study center, controlled ovarian stimulation protocol, type of insemination, transfer cycle types, embryo types, number of embryos transferred. baOR was adjusted maternal age, paternal age, residence, gravidity, parity, primary infertility, causes of infertility, study center, controlled ovarian stimulation protocol, type of insemination, transfer cycle types, embryo types.*
Further analyses were conducted in women with single embryo transfer and singleton live birth delivery. A total of 1985 cycles were included (341 fresh transfer cycles and 1644 frozen transfer cycles). The baseline and characteristics of ART procedures in fresh/frozen single embryo transfer cycles according to AMH levels were provided in Tables S2, S3. The effect of serum AMH levels on pregnancy and neonatal outcomes with fresh/frozen single embryo transfers were generally consistent with those of the primary analysis in singleton delivery, except that the risk of GDM increased in the low AMH group with fresh cycles, the risk of cesarean section decreased in the high AMH group with frozen cycles and the difference in the risk of ICP, macrosomia and LGA was no longer significant. Details are provided in Tables S4 through S7 in the Supplementary Materials.
## Discussion
In this multi-center retrospective cohort study of ART patients, we highlight in women with singleton delivery, low AMH levels increased the risk of ICP. There are also some protective factors, for instance, among women of singleton delivery, high AMH levels are associated with a lower risk of macrosomia as well as low levels of AMH are less likely to have PROM and LGA. Moreover, in women with multiple deliveries, we demonstrated that high levels of AMH increased the risk of ICP, GDM and PIH, while low AMH levels are associated with an increased risk of ICP and oligohydramnios. The findings of our study suggest an association between AMH and pregnancy outcomes among women undergoing IVF/ICSI.
The safety of ART procedures has long been a major concern among people who received the treatment, several meta-analyses of cohort studies have demonstrated adverse pregnancy outcomes among ART pregnancies, including GDM and PIH [16, 22]. Although characteristics of infertility, advanced age and underlying polycystic ovary syndrome might result in confounders of the association, some prospective studies provide significant associations between ART and adverse pregnancy outcomes after adjusting for various confounders [23, 24]. Thus, plasma markers as a screen for adverse outcomes are quite in need. Interestingly, AMH, a clinical marker of ovarian reserve, several studies have suggested its potential relation to specific pregnancy complications (such as preterm birth and PIH), while the relationship remains unclear concerning their limited sample size and conflicting results (10–12).
Transfer of multiple embryos in ART procedures used to bring a large number of multiple pregnancies and related risks in the last few years [25]. Despite single-embryo transfer (SET) has been accepted as the best practice in clinical use, the ratio of twin delivery among total deliveries in ART was $27.9\%$ in 2016(Chinese mainland) [13]. Our study demonstrated that high AMH levels increased the risk of PIH and GDM in multiple deliveries after ART (Gestational hypertension: aOR2 = 2.26, $95\%$ CI: 1.20-4.22; Gestational diabetes mellitus: aOR2 = 2.40, $95\%$ CI: 1.48-3.91), low AMH levels increase the risk of oligohydramnios. Nonetheless, we failed to observe a similar association in singleton deliveries. Hypertensive disorder of pregnancy, which affect up to $10\%$ of all pregnancies, is one of the leading causes of pregnancy-related deaths [26, 27]. The relationship between AMH and HDP has been a controversial topic in recent studies. A case-control study conducted by Birdir et al. observed the median multiple of the expected median value of AMH was comparable between the PE (Preeclampsia) group and the controls (1.040, IQR 0.941–1.081 versus 0.995, IQR 0.939–1.065, $$p \leq 0.147$$), indicating AMH might not be a suitable marker for prediction of PE [28]. However, several studies have observed that low levels of AMH are associated with a higher risk of HDP [10, 29]. As for GDM, the association between AMH and GDM was not identified in previous studies [10]. In the present study, we measure maternal AMH levels before pregnancy instead of measurement during pregnancy in other studies, which might result in the discrepancy. In addition, previous studies have not performed similar research in multiple deliveries. Mechanisms underlying the effects on pregnancy complications need more investigation. Detection of AMH receptors in cardiac tissue suggests the linkage of AMH with the circulatory system [30]. Skałba et al. [ 31] documented that plasma AMH level is associated with insulin resistance (IR) both in PCOS (group) and control group, while Tokmak et al. [ 32] proved a similar correlation in non-obese adolescent females with PCOS. Considering IR is closely related to the development of GDM, AMH might play a role in the development of GDM. In summary, this study suggests that we should put more attention to abnormal AMH levels in women with multiple pregnancies. More specifically, abnormal AMH levels should be concerned when we determine the number of embryos transferred, single-embryo transfer is relatively more recommended.
Our study illustrates the association between abnormal AMH levels and ICP for the first time (low AMH levels are associated with increased risk of ICP in singleton and multiple deliveries). ICP is the most common hepatic disorder related to pregnancy, which usually develops within the third trimester of pregnancy and presents with pruritus as well as elevated levels of bile acid and/or alanine aminotransferase [33]. Estrogen-bile acid axis was thought to play a dominant role in the pathogenesis of ICP [34], yet AMH was proved to decrease FSH-induced CYP19a1 expression, leading to reduced estradiol (E2) levels [1, 35], we could assume that the association between AMH and E2 might attribute to the effects of AMH on the risk of ICP. While the molecular mechanisms need more investigations.
Notably, through the analysis of neonatal outcomes, we also observed that circulating levels of AMH influence the risk of macrosomia and LGA in singleton deliveries, indicating some underlying nutritional and metabolic alterations in the offspring. An increasing number of studies have supported the theory of developmental origins of health and disease (DOHaD), which refers to the theory that predisposing factors to chronic diseases are established in early life, specifically by the intrauterine environment [36]. Both human and animal studies have confirmed that the developing fetus is susceptible to in-utero exposures, including air pollution, high-fat diet and hyperglycemia [37, 38]. Additionally, recent studies also demonstrated that high AMH levels in utero might induce metabolic and reproductive alterations in rodent animals, which suggested the potential effects of AMH on perinatal outcomes [39]. Our results also demonstrated that AMH levels are not associated with the risk of preterm birth in women undergoing IVF/ICSI, which is consistent with a previous study that is also based on women undergoing IVF/ICSI cycles [40]. However, recent studies suggested AMH level as a risk factor of preterm birth in PCOS patients [11, 12]. The differences might attribute to the heterogeneity of the population thanks to the higher AMH level in PCOS patients compared with non-PCOS patients [41]. Future studies including long-term follow-up studies are needed to illustrate the long-term effects and potential mechanisms.
The strengths of our study include the novelty as the first research to present the association between maternal AMH levels and pregnancy outcomes after ART, as well as the size of the cohort (largest to our knowledge). Additionally, maternal levels of AMH before pregnancy give us a more advanced vision to assess the risk of complications compared with measurement in the first or second trimester of pregnancy. Moreover, in this retrospective cohort study, the confounding factors were also adjusted for analysis, either previously reported to have effects on AMH levels or varied significantly among groups stratified by AMH. Despite the limited knowledge of the pathophysiology of AMH, we provide distinctive insights on its potential to be a marker of pregnancy outcomes. Similarly, we recognize that there are still limitations in our study. First, missing data regarding clinical and follow-up information was inevitable thanks to the retrospective cohort, which resulted in information bias. Second, the discrepancy of AMH measurement methods in different centers is also a source of bias, although study center was adjusted as a confounding factor in the logistic regression. Third, the relatively low morbidity restricts us to achieve a more accurate confidence interval, thus leading to limitations in our conclusions.
In conclusion, this is the first multi-center retrospective cohort study to indicate the association between maternal AMH levels and adverse perinatal outcomes in IVF/ICSI. Our results proved the potential role of AMH as a predictive marker for adverse pregnancy outcomes. Abnormal AMH levels increased the risk of ICP regardless of the number of live births, while high AMH levels are associated with risks of GDM and PIH only in women with multiple deliveries. In addition, AMH can also be used as a protective factor concerning PROM, macrosomia and LGA. Fortunately, serum AMH levels were not associated with adverse neonatal outcomes in IVF/ICSI. The findings of our study will extend the application of AMH during pregnancy and provide clinicians with some clues for practice. The association between high AMH levels and pregnancy complications among multiple pregnancies also supports the use of SET in these 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 studies involving human participants were reviewed and approved by The Institutional Review Board of the International Peace Maternal and Child Health Hospital. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
H-FH, Y-TW and Y-CH designed the study concept. Y-CH and K-ZS conducted the statistical analysis and drafted the manuscript. Y-CH, Y-TW, JC and Q-XM were responsible for data collection and data curation. H-FH and Y-TW critically revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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.2023.1081069/full#supplementary-material
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|
---
title: CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality
in COVID-19
authors:
- Sophie I.J. van Bakel
- Hester A. Gietema
- Patricia M. Stassen
- Harry R. Gosker
- Debbie Gach
- Joop P. van den Bergh
- Frits H.M. van Osch
- Annemie M. W.J. Schols
- Rosanne J. H.C.G. Beijers
journal: Chest
year: 2023
pmcid: PMC9990885
doi: 10.1016/j.chest.2023.02.048
license: CC BY 4.0
---
# CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19
## Body
FOR EDITORIAL COMMENT, SEE PAGE 269 Take-home PointsStudy Question: Are CT scan-derived muscle and adipose tissue cross-sectional areas (CSAs) associated with 30-day in-hospital mortality in patients with COVID-19, independent of 4C Mortality Score?Results: In multivariate analyses, low pectoralis muscle CSA was associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score. Interpretation: Low CT scan-derived pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.
COVID-19 caused by the SARS-CoV-2 presents with a highly variable disease course varying from asymptomatic disease to severe illness requiring hospitalization, ICU admission, mechanical ventilation, and eventually death.1,2 However, the high prevalence of SARS-CoV-2 infections resulted in very high absolute numbers of severely ill patients requiring hospitalization and high mortality rates of up to $20\%$ to $25\%$ in several European regions, putting a high burden on hospitals and health-care systems worldwide.3,4 *Early diagnosis* of COVID-19 and identification of patients at high risk for severe illness and mortality are essential for adequate clinical decision-making and managing the large numbers of severely ill patients. For this purpose, chest CT scan imaging was found useful from a very early stage in the pandemic onward.5, 6, 7, 8 Based on systematic classification of intrapulmonary abnormalities, these chest CT scans can provide a likelihood of COVID-19 with high diagnostic accuracy.7, 8, 9, 10 Next to the pulmonary abnormalities, CT scans contain relevant information on muscle and adipose tissue mass and distribution.11 Quantification of muscle cross-sectional area (CSA) at the level of the third lumbar vertebra is considered the reference for estimating whole body muscle mass.11,12 However, analyses at higher vertebral levels available on chest CT scan images, for example, at the level of the first lumbar vertebra or the pectoralis muscle, also recently were validated for assessment of clinically relevant muscle mass.12,13 Additionally, the levels of first and third lumbar vertebrae appeared to be comparable for assessment of adipose tissue mass and distribution.12 Therefore, chest CT scans obtained for the diagnosis and assessment of severity of pulmonary involvement also can be used to gain insight into body composition of these patients.
Multiple authors have investigated the possible prognostic value of CT scan-derived body composition parameters on COVID-19 outcomes.14, 15, 16 Methodology and exact anatomic levels at which these parameters were quantified varied among studies. Still, meta-analyses showed that low skeletal muscle mass predicts short-term mortality and high visceral adipose tissue (VAT), but not subcutaneous adipose tissue (SAT), is associated with more severe disease in patients with COVID-19.14,15 Currently, clinical decision-making in the management of patients with COVID-19 is based on vital parameters and blood analyses that are available rapidly and commonly in the ED. With these parameters, a multitude of models predicting adverse outcomes in COVID-19 have been developed. Based on a living systematic review, the 4C Mortality Score was developed and validated by Knight et al17 in derivation and validation cohorts of 35,463 and 22,361 patients, respectively, and has been identified as the most extensively validated and best model to predict in-hospital mortality in COVID-19 after 30 days.17, 18, 19 The 4C Mortality *Score is* a risk stratification score based on the following highly predictive clinical items; sex, age, number of comorbidities, vital signs, blood urea level, and C-reactive protein (CRP) level. Essentially, it combines information on the acute clinical state of the patient (eg, vital signs, blood urea level, and CRP level) and the preexisting condition of the patient (eg, age and number of comorbidities).
Because the CT scan-derived body composition parameters inherently are associated with clinical parameters commonly used to reflect patients’ preexisting conditions, it can be questioned whether CT scan-derived body composition is associated with mortality independent of the 4C Mortality Score. Therefore, this study aimed to evaluate the association of CT scan-derived body composition parameters independent of a validated set of predictive clinical parameters on 30-day in-hospital mortality in patients with COVID-19.
## Abstract
### Background
COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19.
### Research Question
Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score?
### Study Design and Methods
This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records.
### Results
Data from 578 patients were analyzed ($64.6\%$ men; mean age, 67.7 ± 13.5 years; $18.2\%$ 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; $$P \leq .002$$) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; $$P \leq .013$$). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; $95\%$ CI, 0.96-1.00; $$P \leq .038$$).
### Interpretation
CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.
## Graphical Abstract
## Study Design and Population
This was a retrospective, multicenter cohort analysis of patients with COVID-19 from the Maastricht University Medical Centre+ (MUMC+) and the VieCuri Medical Centre in the province of Limburg, The Netherlands. Both cohorts consisted of consecutive adult patients who sought treatment at the ED of the concerning hospital with a primary clinical suspicion of COVID-19 during the first wave of the pandemic and underwent chest CT scan imaging at presentation. All cases of COVID-19 either were confirmed by reverse-transcription polymerase chain reaction testing or had a high clinical likelihood in combination with a COVID-19 Reporting and Data System (CO-RADS) score of ≥ 4 (eg, a high likelihood based on CT scan abnormalities) and no alternative diagnosis.7 The MUMC+ cohort consisted of both hospitalized patients and patients who presented at the ED, but were not admitted, whereas the VieCuri cohort consisted of only hospitalized patients. Mortality within 30 days was checked systematically for all patients, regardless of hospitalization status. Because of the retrospective nature of the study, the medical ethics committee of MUMC+ waived ethical approval for this study (Identifier: METC 2020-2230), and therefore, no informed consent was required. Additionally, on discharge from the hospital, patients were informed about the possible use of their (anonymized) data for research purposes. In case patients objected to this, they were excluded from the database.
## CT Scan Image Analysis
Skeletal muscle and adipose tissue parameters were retrieved from routinely obtained chest CT scans at ED presentation or admission. All CT scans were obtained without the application of IV contrast. Total cross-sectional area (CSA) of the pectoralis major and minor muscles was measured bilaterally at the level of the fourth thoracic vertebra. Additionally, CSA of skeletal muscle, VAT, and SAT was demarcated at the level of the first lumbar vertebra (L1). The muscles analyzed at the L1 level included the psoas, erector, spinae, quadratus lumborum, transversus abdominis, external and internal oblique, and rectus abdominis. At both levels, following previously described methods, a single transverse image at the most cranial slide with both vertebral transverse processes clearly visible was used (Fig 1).12,13 If the selected image was of poor quality, had artefacts, or did not fully depict tissue of interest, the specific slice or missing tissue was considered as a missing value and was not analyzed. CSA of these structures were quantified by one trained assessor, blinded to clinical outcomes, based on pre-established Hounsfield units (HU) thresholds (skeletal muscle, –29 to 150 HU; SAT, –190 to –30 HU; and VAT, –150 to –50 HU).20,21 Boundaries were corrected manually when necessary. All analyses were performed with Slice-O-Matic software version 5.0 (Tomovision).Figure 1A-D, Representative examples of selected CT scan slices at the fourth thoracic vertebra level (A) with demarcated pectoralis major and minor muscle (B) and at first lumbar vertebra (L1) level (C) with demarcated L1 muscle, visceral, and subcutaneous adipose tissue (D).
## Clinical Parameters and Outcome Measures
Patient demographics (age, sex, body height, and weight), clinical observations (number of comorbidities, respiratory rate, peripheral oxygen saturation on room air, and *Glasgow coma* scale score), blood parameters (urea and CRP levels), and information on disease course (ICU or medium care unit admission, mechanical ventilation) were collected retrospectively from the electronic medical records in both institutions. The eight parameters of the 4C Mortality Score were categorized and scored, resulting in a total 4C Mortality Score ranging from 0 to 21.17 Additionally, date of presentation at the ED, date of CT scan imaging, date of discharge, and (if applicable) date of death also were retrieved from the medical records.
## Statistical Analyses
Baseline clinical variables of individuals who died in hospital within 30 days and of survivors were compared using the χ2 test for categorical variables and the nonparametric Mann-Whitney U test for the continuous CT scan-derived variables with skewed distributions. Data are presented as numbers and percentages for categorical variables and medians and interquartile ranges (IQRs) for continuous variables. Missing values of individual components of the 4C Mortality Score were replaced using multiple imputation if values were missing in < $20\%$ of patients. In case more than two components of the 4C Mortality Score were missing, no imputation was performed. Receiver operating characteristic curve analysis was performed to check the area under the receiver operating characteristic curve (AUC) of the 4C Mortality Score. Univariate Cox proportional hazard regression models were applied to assess the association of independent CT scan-derived skeletal muscle mass and adipose tissue CSA with 30-day in-hospital mortality. All parameters with a P value of ≤.1 were considered for inclusion in a multivariate model, adjusted for the 4C Mortality Score, with a forward selection likelihood ratio approach. Multivariate Cox regression analyses then were applied to assess the association of the CT scan-derived parameters adjusted for the 4C mortality score with 30-day in-hospital mortality. Results of the Cox regressions are presented as hazard ratios (HRs) with $95\%$ CIs.
Because the 4C Mortality Score was validated specifically for hospitalized patients, sensitivity analyses were performed investigating the association between CT scan-derived parameters and 30-day in-hospital mortality in hospitalized patients as well as the association with 30-day overall mortality in all patients. Subsequently, potential interactions between CT scan-derived parameters and individual components of the 4C Mortality Score were evaluated using nonparametric Kruskal-Wallis tests. This allowed the construction of categorical variables using age- and sex-specific cutoffs based on the cohort’s IQRs, which were added to an adjusted 4C Mortality Score. Finally, a comparative receiver operating characteristic curve analysis was performed to quantify the added value of CT scan-derived parameters to the 4C Mortality Score. All statistical analyses were performed using SPSS statistical software (SPSS Statistics for Windows version 27.0; IBM). A P value of ≤.05 was considered statistically significant.
## General Characteristics
Data from 587 patients were analyzed, including 374 patients from the MUMC+ cohort and 213 patients from the VieCuri cohort (Fig 2), with an admission rate of $82.5\%$ (484 patients). Missing values for urea level ($2.7\%$) and CRP level ($0.3\%$) were imputed. Most patients were elderly men, with most being overweight or obese (Table 1). Within 30 days, 107 patients ($18.2\%$) died in hospital with a median time to death of 6 days (IQR, 3-11 days). An additional 13 patients ($2.2\%$) died outside of the hospital within 30 days. Deceased patients had significantly more comorbidities and scored significantly worse on all clinical and blood parameters of the 4C Mortality Score compared with survivors. In univariate Cox regression analysis, the 4C Mortality Score significantly predicted 30-day in-hospital mortality (HR, 4.6; $95\%$ CI, 3.4-6.3; $P \leq .001$). The AUC for the 4C Mortality Score was 0.806 ($95\%$ CI, 0.765-0.848; $P \leq .001$).Figure 2Flow chart showing available CT scan images and tissues of interest. If a selected image was of poor quality, had artefacts, or tissue of interest was not depicted fully, the specific slice or missing tissue was considered as a missing value. L1 = first lumbar vertebra; MUMC+ = Maastricht University Medical Centre+; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue. Table 1General CharacteristicsVariableIn-Hospital Survival (> 30 d; $$n = 480$$ [$81.8\%$])In-Hospital Death (≤ 30 d; $$n = 107$$ [$18.2\%$])P ValueAge, y<.001 < 5049 (10.2)0 [0] 50-6097 (20.2)5 (4.7) 60-70137 (28.5)7 (6.5) 70-80131 (27.3)48 (44.9) > 8066 (13.8)47 (43.9)Male sex302 (62.9)80 (74.8).020BMI, kg/m2.036 < 204 (1.0)5 (5.2) 20-25112 (28.4)26 (26.8) 26-30157 (39.8)42 (43.3) > 30121 (30.7)24 (24.7)No. of comorbidities<.001 0139 (29.0)9 (8.4) 1105 (21.9)17 (15.9) ≥ 2236 (49.2)81 (75.7)SpO2 < $92\%$145 (30.2)53 (49.5)<.001Respiratory rate, breaths/min.001 < 20216 (45.0)28 (26.2) 20-30202 (42.1)56 (52.3) > 3062 (12.9)23 (21.5)*Glasgow coma* scale < 1542 (8.8)22 (20.6)<.001Blood urea, mmol/L<.001 < 7306 (63.7)34 (31.8) 7-14132 (27.5)49 (45.8) > 1442 (8.8)24 (22.4)Blood CRP, mg/L.034 < 50170 (36.5)29 (27.1) 50-100137 (28.8)25 (23.4) > 100173 (34.8)53 (49.5)ICU admission74 (15.7)31 (29.0).001MC admission35 (7.1)10 (9.3).470Mechanical ventilation80 (18.8)39 (38.6)<.001Data are presented as No. (%), unless otherwise indicated. Boldface indicates a P value with statistical significance. CRP = C-reactive protein; MC; medium care, min; minute, SpO2 = peripheral oxygen saturation on room air.
## CT Image Analyses
Within the total cohort of 587 patients, pectoralis muscle CSA could be determined on 571 scans ($97.3\%$), whereas at the L1 level, VAT CSA could be analyzed on 307 scans ($52.3\%$), muscle CSA on 283 scans ($48.2\%$), and SAT CSA on 201 scans ($34.2\%$) (Fig 2). Patients who died within 30 days showed a significantly lower pectoralis muscle CSA ($$P \leq .002$$) and tended to have a lower L1 muscle CSA ($$P \leq .087$$) compared with survivors. Additionally, their VAT, but not SAT CSA was significantly higher ($$P \leq .013$$ and $$P \leq .983$$, respectively) (Table 2). Pectoralis muscle CSA was significantly higher in men (median, 38.8 cm2; IQR, 32.0-46.2) compared with that in women (median, 26.5 cm2; IQR, 22.1-31.7 cm2; $P \leq .001$) and decreased with increasing age ($P \leq .001$ for trend).Table 2CT Scan-Derived Body Composition ParametersVariableIn-Hospital Survival (> 30 d)In-Hospital Death (≤ 30 d)P ValuePectoralis muscle, cm2 No.467104 Median (IQR)35.4 (27.2-44.2)32.6 (24.3-38.8).002L1 muscle, cm2 No.23251 Median (IQR)88.1 (72.1-108.6)85.7 (66.4-103.2).087L1 VAT, cm2 No.25156 Median (IQR)112.9 (63.7-174.1)151.1 (93.6-219.7).013L1 SAT, cm2 No.15942 Median (IQR)98.1 (64.4-146.0)104.5 (71.1-130.4).983Data are presented as median (interquartile range), unless otherwise indicated. Boldface indicates a P value with statistical significance. IQR = interquartile range; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue.
Univariate Cox proportional hazards regression analyses demonstrated that pectoralis muscle CSA, L1 muscle CSA, and L1 VAT CSA were associated with 30-day in-hospital mortality (pectoralis muscle CSA: HR, 0.97 [$95\%$ CI, 0.95-0.99]; L1 muscle CSA: HR, 0.99 [$95\%$ CI, 0.97-1.00]; and L1 VAT CSA: HR, 1.00 [$95\%$ CI, 1.00-1.01]) (Table 3, model 1). In the multivariate Cox regression model adjusted for the 4C Mortality Score, pectoralis muscle CSA remained associated significantly with 30-day in-hospital mortality (4C Mortality Score: HR, 1.4 [$95\%$ CI, 1.3-1.4]; pectoralis muscle CSA: HR, 0.98 [$95\%$ CI, 0.96-1.00]) (Table 3, model 2). The sensitivity analyses (e-Tables 1-4) similarly demonstrated that pectoralis muscle CSA was associated significantly with 30-day in-hospital mortality as well as with 30-day overall mortality. Table 3CT Scan-Derived Body Composition Values as Predictors for 30-Day In-Hospital MortalityVariableModel 1Model 2HR ($95\%$ CI)P ValueHR ($95\%$ CI)P ValuePectoralis muscle, cm20.97 (0.95-0.99).0020.98 (0.96-1.00).038L1 muscle, cm20.99 (0.97-1.00).0560.99 (0.98-1.00).080L1 VAT, cm21.00 (1.00-1.01).0031.00 (1.00-1.00).451L1 SAT, cm21.00 (0.99-1.00).643……Univariate Cox proportional hazards regression analysis (model 1) and multivariate analysis adjusted for 4C Mortality Score (model 2). Boldface indicates a P value with statistical significance. HR = hazard ratio; L1 = first lumbar vertebra; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue.
Explorative analyses allowed for an adjusted 4C Mortality Score to be constructed using age- and sex-specific quartiles for pectoralis muscle CSA. After inspecting the HRs (e-Table 5), patients with pectoralis muscle CSA less than the 25th percentile were appointed two additional points. This adjusted 4C Mortality Score (range, 0-24 points) had an AUC of 0.808 ($95\%$ CI, 0.766-0.851), which was not significantly different from the initial 4C Mortality Score (AUC, 0.806; $95\%$ CI, 0.765-0.848; $$P \leq .750$$).
## Discussion
This large retrospective, multicenter cohort analysis demonstrated that CT scan-derived low pectoralis muscle CSA, high VAT CSA, and low muscle CSA at the L1 level were associated with higher in-hospital 30-day mortality in patients with COVID-19. Additionally, in a multivariate analysis, pectoralis muscle CSA was associated significantly with in-hospital 30-day mortality, independent of the 4C Mortality Score.
These muscle-related findings are in line with existing literature, where multiple studies have demonstrated that low CT scan-derived muscle mass, assessed at different anatomic levels, significantly predict mortality and worse clinical outcome in patients with COVID-19.14,22, 23, 24, 25, 26 *Our data* also demonstrated a significant association of high VAT with 30-day in-hospital mortality; however, with an HR of 1.00, the clinical relevance of this statistically significant association should be questioned. Additionally, when adjusted for the 4C Mortality Score, VAT was not associated significantly with mortality. This is in line with recent meta-analyses showing a significant positive association between VAT as well as obesity and COVID-19 severity, but not mortality.15,27 The predictive components of the 4C Mortality Score demonstrated that an older age, male sex, and having multiple comorbidities increased the risk of mortality in patients with COVID-19. The fact that low pectoralis CSA remains significantly associated with in-hospital 30-day mortality when adjusting for the 4C Mortality Score therefore is an important finding in this study. Pectoralis muscle CSA has been shown to be associated with third lumbar vertebra muscle CSA, which is linearly related to whole-body muscle mass assessed via MRI.11,13 Low total muscle mass is indicative for sarcopenia and is well known to be associated with mortality.28, 29, 30 Especially elderly people with comorbidities are more prone to sarcopenia, and as such have a higher risk for mortality.31 Although the exact underlying mechanisms for this association are not fully clear, it can be speculated that a higher muscle mass reflects an increased metabolic reserve, which during periods of acute catabolic disease, such as a COVID-19 infection, can protect whole-body functioning.
Whereas previously the association of CT scan-derived muscle and adipose tissue CSA with disease severity and mortality in COVID-19 was demonstrated in research settings, determining these body composition parameters in an acute clinical setting is still not applied easily in daily practice. Clinical parameters as included in the highly predictive 4C Mortality Score are already part of standard patient assessment in daily practice, and therefore are readily available. However, some of these clinical parameters inherently also are associated with CT scan-derived muscle and adipose tissue parameters.32 Therefore, we investigated if CT scan-derived muscle and adipose tissue CSA are still associated with mortality when adjusted for a validated set of clinical parameters, eg, the 4C Mortality Score. Based on our data, we can conclude that only pectoralis muscle CSA remains associated with 30-day in-hospital mortality when adjusted for the 4C Mortality Score. Additional sensitivity analyses (e-Tables 1-5) demonstrated no difference in this outcome when using only hospitalized patients compared with the current population, which showed an admission rate of $82.5\%$. Whether pectoralis muscle CSA improves the predictability of the already highly predictive 4C Mortality Score and as such needs to be added to the 4C Mortality Score was not the main question of this study. However, we constructed an adjusted 4C Mortality Score including low pectoralis CSA. In comparative AUC analyses, addition of the pectoralis muscle to the 4C Mortality Score demonstrated a small, yet statistically insignificant, improvement to an already very well-performing score. This further underlines our findings that, despite the HR of 0.98, pectoralis muscle is associated significantly with 30-day in-hospital mortality. To investigate this further, a larger dataset as well as a validation dataset of comparable magnitude as the development and validation cohort of the initial 4C Mortality *Score is* warranted.17 To add pectoralis muscle CSA to the 4C Mortality Score, cutoff values to identify patients with high or low muscle CSA are required. In line with other studies, our data demonstrated a significant association between muscle CSA and age and sex.11,32 However, age- and sex-specific cutoffs for pectoralis muscle CSA are still lacking. Therefore, future studies in large cohorts should focus on developing age- and sex-specific cutoff values for muscle CSA.
The current analyses were performed retrospectively on chest CT scans that were obtained with the sole purpose of assessing intrapulmonary abnormalities in an acute clinical setting. This is an important limitation that needs to be addressed, because it might have caused bias resulting from missing data, specifically regarding the extrapulmonary tissue at the L1 level (missing in $52.3\%$ of scans). A focus on ensuring that chest CT scans include the L1 level in the future will allow for more precise (retrospective) comparison of the prognostic value of pectoralis and L1 muscle CSA.
Furthermore, the current method of semi-automated analysis of CT scan-derived muscle and adipose tissue CSA allows for retrospective analysis of CT scans at admission. This provides opportunities for long-term, longitudinal follow-up of patients with COVID-19 using the CT scans that are part of regular care. Relevant changes in muscle mass (and quality) during admission and 3 months after recovery from COVID-19 already have been described in recent studies.33,34 However, the current method requires trained analysts and is very labor intensive. This makes its application in the dynamic, fast-paced daily clinical practice and prognostic studies nearly impossible. Therefore, fully automated segmentation and analysis of CT scan-derived body composition through artificial intelligence algorithms has sparked attention. Both Goehler et al35 and Hosch et al36 demonstrated the potential of its use in COVID-19 by using an artificial intelligence algorithm to investigate the association of different CT scan-derived muscle and adipose tissue parameters on disease severity and mortality. Additionally, in acute trauma settings, the use of a deep learning algorithm to assess CT scan-derived muscle and adipose tissue recently was validated.37,38 Complete automation of this process provides the potential to move these analyses from research settings to daily clinical practice in different areas of both acute and chronic care.39
## Interpretation
Low CT scan-derived pectoralis muscle, high VAT, and low muscle CSA at L1 are statistically significantly associated with higher 30-day in-hospital mortality in patients with COVID-19. Additionally, CT scan-derived pectoralis muscle CSA remains associated with 30-day in-hospital mortality in patients with COVID-19 independent of the clinical 4C Mortality Score.
## Funding/Support
A research fellowship awarded to R. J. H. C. G. B. by the European Society of Clinical Nutrition and Metabolism and a grant from ZonMw [Project no. 100430040211004] awarded to H. A. G. and A. M. W. J. S. funded this work.
## Financial/Nonfinancial Disclosures
The authors have reported to CHEST the following: J. P. v. d. B. received funding from Amgen that was not related to this work. None declared (S. I. J. v. B., H. A. G., P. M. S., H. R. G., D. G., F. H. M. v. O., A. M. W. J. S., R. J. H. C. G. B.).
## Supplementary Data
e-Online Data
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|
---
title: Impact of gut permeability on the breast microbiome using a non-human primate
model
authors:
- Alaa Bawaneh
- Carol A. Shively
- Janet Austin Tooze
- Katherine Loree Cook
journal: Gut Microbiome
year: 2022
pmcid: PMC9990890
doi: 10.1017/gmb.2022.9
license: CC BY 4.0
---
# Impact of gut permeability on the breast microbiome using a non-human primate model
## Abstract
We previously demonstrated in non-human primates (NHP) that Mediterranean diet consumption shifted the proportional abundance of Lactobacillus in the breast and gut. This data highlights a potential link about gut-breast microbiome interconnectivity. To address this question, we compared bacterial populations identified in matched breast and faecal samples from our NHP study. Dietary pattern concurrently shifted two species in both regions; *Streptococcus lutetiensis* and Ruminococcus torques. While we observe similar trends in Lactobacillus abundances in the breast and gut, the species identified in each region vary; Mediterranean diet increased Lactobacillus_ unspecified species in breast but regulated L. animalis and L. reuteri in the gut. We also investigated the impact of gut permeability on the breast microbiome. Regardless of dietary pattern, subjects that displayed increased physiological measures of gut permeability (elevated plasma lipopolysaccharide, decreased villi length, and decreased goblet cells) displayed a significantly different breast microbiome. Gut barrier dysfunction was associated with increased α-diversity and significant different β-diversity in the breast tissue. Taken together our data supports the presence of a breast microbiome influenced by diet that largely varies from the gut microbiome population but is, however, sensitive to gut permeability.
## Introduction
It is estimated that the human body contains more bacterial cells than human cells (Sender et al., 2016). While the majority of the bacteria biomass is contained to the intestinal tract, microbes in lower abundance have been identified in other organ types located distal to the gut, including the breast tissue. The presence of a mammary gland (MG; adipose/stroma/epithelial cells) bacterial population was identified in human breast tissue samples taken from non-lactating, non-pregnant women undergoing reduction mammoplasty, lumpectomy, or mastectomy surgeries (Urbaniak et al., 2014). There were large differences in the type and proportional abundance of bacterial taxa detected between the two geographical populations. Since then, others have also shown normal breast tissue-specific and breast tumour-specific microbiomes to exist (Banerjee et al., 2018; Chiba et al., 2020; Hieken et al., 2016; Nejman et al., 2020; Parida and Sharma, 2020; Plaza-Diaz et al., 2019; Shively et al., 2018; Soto-Pantoja et al., 2021; Thompson et al., 2017; Tzeng et al., 2021; Urbaniak et al., 2014). Another study investigating the microbiome of breast tissue obtained from patients with benign or malignant breast cancers showed that those with malignant tumours displayed a distinct microbiota population (Hieken et al., 2016), suggesting breast tissue dysbiosis as a possible driver of breast cancer. Our group demonstrated breast cancer patients with obesity displayed different proportional abundances of several family-level bacterial taxa in breast tumour tissue, suggesting that obesity may influence the breast microbiome (Chiba et al., 2020).
The concept of a gut-mammary gland signalling axis initially proposed and investigated in the lactation setting, suggests that the gut microbiome may influence the breast microbiome (Fernandez et al., 2013, 2020; Rodriguez, 2014). We recently demonstrated the impact of dietary-induced microbiota changes on breast cancer risk conferred by obesity (Soto-Pantoja et al., 2021). Microbiome transplantation from mice on a high-fat diet to mice on a low-fat diet increased mammary tumour incidence to that of the high-fat diet group in a carcinogen tumorigenesis model. Oral faecal microbiome transplants shifted both the gut and mammary tumour microbiomes. Consumption of a high-fat diet and faecal transplant of lard-derived faecal microbiota increased systemic and MG levels of lipopolysaccharide (LPS), suggesting a potential gut-breast signalling axis. Using breast tumour and normal tumour-adjacent breast samples from a window-of-opportunity clinical trial, we found that dietary interventions, such as omega-3 polyunsaturated fatty acids supplementation, was associated with changes to the tumour and breast microbiome populations (Soto-Pantoja et al., 2021).
Our previous non-human primate (NHP) model showed that dietary pattern (Western vs. Mediterranean diet) can shift the breast microbiome (Shively et al., 2018). Long-term consumption of a Mediterranean diet resulted in a 10-fold increase in breast Lactobacillus abundance, with no apparent change in total bacteria biomass. This study was paired with untargeted metabolomics in subject-matched plasma and breast samples to indicate specific breast-localised regulation of bile acid metabolites and bacteria-modified bioactive compounds, suggesting the presence of a modifiable breast-specific microbiome. We have also recently reported that dietary pattern and adiposity shifts the gut microbiome in NHP, in which lean Mediterranean diet-fed NHP display sixfold increase in gut *Lactobacillus animalis* (Newman et al., 2021), suggesting potential similar regulation of certain breast and gut microbiota populations by diet. To determine the dietary interactions regulating the gut and breast microbiomes, we compared the gut and breast microbiome populations in matched samples from NHP dietary cohort. We further explored the influence of a “leaky gut” on the breast microbiome. We now demonstrate that the breast has its own bacterial niche sensitive to diet that is largely different from the gut microbiome population but is influenced by gut permeability.
## Non-human primate subjects
Adult female *Macaca fascicularis* were obtained (SNBL USA, Ltd. Alice, TX) and housed in groups with daylight exposure on a $\frac{12}{12}$ light/dark cycle. Animals were randomised to a dietary pattern [Western or Mediterranean; See reference (Newman et al., 2021; Shively et al., 2018, 2019) for further detail on the model and experimental diets]. Faecal samples were collected from subjects at 26 months. Breast tissue samples were collected at the end of the study at 31 months ($$n = 11$$–12 subjects per diet). All animal manipulations were performed according to the guidelines of state and federal laws, and the Animal Care and Use Committee of Wake Forest University School of Medicine.
## Metagenomic and 16S sequencing
DNA was isolated from 100 mg of frozen faeces or MG tissue using the Qiagen DNeasy PowerSoil Pro kit protocol. Metagenomic sequencing and 16S sequencing were performed by CosmosID Inc. (Rockville, MD). For further details on metagenomics sequencing please see references (Newman et al., 2021). For 16S sequencing, DNA libraries were prepared using Illumina 16S Metagenomic Sequencing kit (Illumina, Inc., San Diego, CA) according to the manufacturer’s protocol. The V3–V4 region of the bacterial 16S rRNA gene sequences was amplified using the primer pair containing the gene-specific sequences and Illumina adapter overhang nucleotide sequences. The full-length primer sequences are: 16S Amplicon PCR Forward Primer (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG) and 16S Amplicon PCR Reverse Primer (5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC).
Amplicon PCR was performed to amplify template out of input DNA samples. PCR product was cleaned up from the reaction mix with Mag-Bind RxnPure Plus magnetic beads (Omega Bio-Tek, Norcross, GA). The library (~600 bases in size) was checked using an Agilent 2200 TapeStation and quantified using QuantiFluor dsDNA System (Promega). Libraries were normalised, pooled and sequenced (2 × 300 bp paired-end read setting) on the MiSeq (Illumina, San Diego, CA). Zymo community standard (D6305) was used as a positive control and lab-grade DEPC (diethylpyrocarbonate)-treated water was used as a negative control. 16S read depth per sample was >30,000 (range: 33,665–106,582 reads).
## Intestinal permeability measurements
Formalin-fixed paraffin-embedded intestinal tissue (colon and ileum) were cut into 5 μm sections and stained using a haematoxylin and eosin (H&E), Alcian blue (Abcam Cat#, ab150662), or mucicarmine (Abcam Cat# ab150677) staining protocol. Staining was visualised by Mantra Quantitative Pathology Image System, 20× objective was used in H&E staining for muscularis thickness measurements and 10× objective for villi length, then images were quantified using ImageJ program (2 pixels/μm, and 1 pixel/μm, respectively). Goblet cells were manually counted per villus using 20× objective. Four representative images from each tissue were quantified and averaged per subject. Snap-frozen plasma samples collected at necropsy were used to measure circulating LPS concentrations by ELISA (LSBio, Cat# LS-F17912) following the manufacturer protocol. NHP subjects regardless of dietary pattern were sub-grouped into LPS high NHP subjects ($$n = 10$$) that a mean plasma LPS of 125 ± 73 pg/mL and LPS low subjects ($$n = 13$$) that displayed a mean plasma LPS of 21 ± 9 pg/mL, based upon LPS concentrations of 50 pg/mL (approximately circulating serum levels in healthy human subjects). Two subjects with intermediate LPS plasma levels and were excluded from analysis.
## Statistical analysis
16S sequencing data were analysed by the CosmosID 16S pipeline and database. Results were presented as an operational taxonomic unit (OTU) table, visualised as heatmaps, stacked bar charts, alpha diversity plots, and beta diversity network graphs. Data are presented as bar plots (Figures 1 and 2) and box plots (Figures 3–5). Permutational multivariate analysis of variance (PERMANOVA) was used for β-diversity PCoA comparison. Wilcoxon rank sum test was performed for α-diversity comparisons. For data in Figure 1, Two-way ANOVA followed by Holm–Šídák’s multiple comparisons test. Non-parametric Kruskal Wallis test followed by a Dunn’s post hoc analysis was used to compare specific bacterial species abundances in faecal and breast samples (Figure 2). Non-parametric Spearman’s correlation was used for LPS and Ruminococcus flavefacians associations. Plasma LPS and intestinal pathology comparisons (villi length, goblet cell counts, and muscularis thickness) were assessed using two-tailed unpaired t-test with Welch’s correction. A non-parametric Mann–Whitney t-test was performed for breast species proportional abundance by LPS sub-groups (Figure 5). * p-value < 0.05 was set for determining statistical significance. Figure 1Comparing the proportional abundance of the most abundant microbes between the gut and breast compartments. ( A) Phylum classification of faecal and breast bacterial populations shows populations differ by tissue type and diet administration. ( B) Family level classification of microbes in faeces and breast samples. ( C) Genus level of classification of microbes regulated by diet in faecal and breast tissue. Two-way ANOVA followed by Holm–Šídák’s multiple comparisons test. $$n = 11$$–12. * p-value < 0.05. Figure 2Specific bacterial taxa identified in faecal and breast tissue samples regulated by dietary pattern. ( A) Mediterranean diet consumption increased proportional abundance of *Streptococcus lutetiensis* in both the breast and faeces. ( B) Western diet-fed subjects displayed elevated Ruminococcus flavefacians abundance in the breast tissue, which was unchanged in the faeces. ( C) Western diet consumption displayed elevated proportional abundance of Ruminococcus torques in both the breast and faeces. ( D) Mediterranean diet-fed subjects displayed elevated Lactobacillus-unspecified species in their breast tissue, but not their faecal samples. Mediterranean diet consuming NHP displayed elevated proportional abundance of *Lactobacillus animalis* (E) and Lactobacillus reuteri (F) in the gut but not in their breast tissue. $$n = 11$$–12. * p-value < 0.05. Kruskal Wallis test with Dunn’s post hoc analysis. Figure 3NHP subjects can be sub-grouped by intestinal permeability markers. ( A) NHP subjects in which matched faecal and breast microbiome sequencing was performed were analysed for circulating plasma lipopolysaccharide (LPS) by ELISA. Red line demarks LPS concentration of 50 pg/mL (approximately circulating serum levels in healthy human subjects). Teal-filled circles are Mediterranean diet-fed subjects with high LPS ($$n = 4$$) and chartreuse-filled circles are Western diet-fed subjects with high LPS ($$n = 6$$). The two chartreuse unfilled circles are subjects with intermediate LPS plasma levels and were excluded from analysis. ( B) Regardless of dietary pattern, LPS high NHP subjects ($$n = 10$$) displayed a mean plasma LPS of 125 ± 73 pg/mL which was significantly higher than the mean LPS (21 ± 9 pg/mL) observed in the LPS low subjects ($$n = 13$$). **** $p \leq 0.0001.$ Intestinal health measurement including villi length, muscularis thickness, and goblet cell counts were performed on paraffin-embedded ileum and colon tissue from NHP subjects. Representative images H&E, Alcian blue, and mucicarmine stained tissue is shown in (C). LPS high subjects displayed reduced villi length (D), increased muscularis thickness (E), and decreased goblet cell counts (F,G) when compared to LPS low subjects suggesting decreased barrier function and elevated gut permeability in LPS high subjects. $$n = 10$$–13; *p-value < 0.05, unpaired t-test with Welch’s correction.
## Results
At the phylum level, gut Bacteroidetes proportional abundance was modulated by diet but not the breast population. Both the breast and faecal samples displayed elevated proportional abundance of Proteobacteria at the phylum level when subjects were consuming a Mediterranean diet (Figure 1A). At the family level, diet only similarly shifted Ruminococcaceae proportional abundance in both the breast and gut (Figure 1B). At the genus level, dietary pattern shifted Acinetobacter (p-value < 0.02), Lactobacillus (trend; ≤0.1 faecal, p-value < 0.01 breast), and Ruminococcus (trend; ≤0.05 faecal, p-value = 0.07 breast) in both the gut and breast tissue microbiome (Figure 1C).
We also identified several taxa specifically regulated by diet in both the breast and gut regions. Mediterranean diet consumption increased proportional abundance of *Streptococcus lutetiensis* in both the breast and faeces (Figure 2A). Western diet-consuming subjects displayed elevated proportional abundance of Ruminococcus flavefacians in their breast tissue but not in the faecal samples (Figure 2B). Western diet consumption displayed elevated proportional abundance of Ruminococcus torques in both the breast and faeces (Figure 2C). Mediterranean diet-fed subjects displayed elevated Lactobacillus_unspecified species (Figure 2D) in their breast tissue, while displaying increased *Lactobacillus animalis* (Figure 2E) and Lactobacillus reuteri (Figure 2F) in the gut. Diet did not significantly shift L. reuteri in the breast tissue and L. animalis was not present in breast tissue. Species-specific localization of Coprococcus was observed in the gut and breast regulated by diet (Supplementary Figure S1A). Coprococcus comes and *Coprococcus catus* were elevated in the gut of Mediterranean diet-fed NHP but undetectable in breast tissue. Breast tissue of Western diet-fed NHP displayed higher Coprococcus_unspecified genus, which was not detected in gut populations. While Prevotella copri and *Prevotella stercorea* are present in both tissue compartments, these species only differed by diet in the gut compartment (Supplementary Figure S1B). Species-specific localization of Acinetobacter species in the gut differed by diet (Supplementary Figure S1C), where A. baumanii/calcoaceticus is higher in Mediterranean diet-consuming subjects within the gut, but not in the breast.
Microbial dysbiosis often leads to tight junction protein deregulation enabling bacterial translocation and metabolic endotoxemia (Fuke et al., 2019). To determine whether gut barrier dysfunction modulated the breast microbiome, we first measured gut health parameters in our NHP subjects. Plasma LPS concentration was determined in each subject and graphed by individual subject (Figure 3A). Based upon previous human serum LPS measurements associated with metabolic endotoxemia that established an approximate 50 pg/mL LPS as an average control serum concentration (Kallio et al., 2015), we then sub-grouped the NHP subjects regardless of diet into LPS high ($$n = 10$$) or LPS low ($$n = 13$$). LPS high NHP subjects’ mean plasma concentration was 125 ± 73 pg/mL compared with LPS low NHP subjects’ mean plasma concentration of 21 ± 9 pg/mL (Figure 3B). We also stained paraffin-embedded intestinal tissue to measure villi length, muscularis thickness, and goblet cells by H&E, Alcian blue, and mucicarmine. Representative images are shown in Figure 3C. NHP subjects within the LPS high designation displayed decreased villi length (Figure 3D), increased muscularis thickness (Figure 3E), and decreased goblet cells (Figure 3F,G). These data indicate that NHP subjects in the LPS high group demonstrate impaired gut barrier function and increased permeability.
We then re-analysed the breast 16S microbiome sequencing results by circulating LPS concentrations to investigate whether impaired gut barrier function may influence the breast tissue microbiome. Breast microbiota in NHP subjects from the LPS high group displayed significantly different β-diversity principal coordinate analysis (PCoA) Jaccard distance when compared with the breast samples from the LPS low group (Figure 4A). The LPS high group also displayed significantly elevated Chao1 α- diversity (Figure 4B) and Shannon α-diversity (Figure 4C) when compared with the LPS low group. Figure 4Breast 16S sequencing by plasma LPS levels indicates gut permeability significantly modulates the NHP breast tissue microbiome. ( A) β-diversity principal coordinate analysis (PCoA) Jaccard distance demonstrates LPS high versus LPS low NHP subjects display different breast microbiota populations. $$n = 10$$–13, Permutational multivariate analysis of variance (PERMANOVA) p-value = 0.009. Chao1 (B) and Shannon (C) α-diversity is significantly higher in breast samples from LPS high NHP versus LPS low NHP subjects. $$n = 10$$–13; **p-value < 0.01; unpaired two-tailed t-test.
At the species level, breast tissue from NHP subjects in the LPS high group displayed a significantly higher proportional abundance of Ruminococcus torques (Figure 5A) and Ruminococcus flavefaciens (Figure 5B) than breast tissue from NHP subjects in the LPS low group. While R. torques did not significantly correlate with plasma LPS, R. flavefaciens abundance positively associates with plasma LPS concentrations (Spearman’s correlation $r = 0.562$, p-value = 0.007, $$n = 23$$; Figure 5C). Other microbe species identified as co-regulated by diet within the gut and breast were not significantly regulated by gut barrier dysfunction groups (Streptococcus luteciae, Figure 5D; Lactobacillus_u_s, Figure 5F; Lactobacillus reuteri, Figure 5G; Prevotella copri, Figure 5H; Prevotella stercorea, Figure 5I; and Coprococcus_u_s, Figure 5J). Staphylococcus sciuri was higher in breast tissue from LPS high NHP (Figure 5E). Acinetobacter calcoaceticus was significantly higher in the breast tissue from the LPS low NHP subjects (Figure 5K).Figure 5 Ruminococcus species regulated by diet in breast tissue are modulated by a leaky gut. NHP subjects with high plasma LPS display significantly elevated Ruminococcus torques (A) and Ruminococcus flaveciens (B) proportional abundance within their breast tissue when compared with NHP subjects with low plasma LPS levels. $$n = 10$$–13; *$p \leq 0.05$, **$p \leq 0.01$; non-parametric Mann–Whitney t-test. ( C) Breast Ruminococcus flaveciens abundance positively correlates with plasma LPS concentration. $$n = 23$$; Spearman’s correlation, $r = 0.562$, $$p \leq 0.007.$$ Plasma LPS concentration had no significant effect on the proportional abundance of *Streptococcus luteciae* (D), Lactobacillus_u_s (F), Lactobacillus reuteri (G), Prevotella copri (H), *Prevotella stercorea* (I), or Coprococcus_u_s (J). Breast samples from LPS high NHP displayed higher *Staphylococcus sciuri* (E). LPS low subjects displayed significantly elevated breast *Acinetobacter calcoaceticus* (K) than LPS high subject breast tissue $$n = 10$$–13; *p-value < 0.05; non-parametric Mann–Whitney t-test.
## Discussion
The concept of an entero-mammary transmission route as a potential active mechanism to transfer live bacteria from the gastrointestinal tract to the mammary gland through the mesenteric lymph node has been proposed (de Andres et al., 2017; Jimenez et al., 2008; Perez et al., 2007). Pathological conditions that disrupt the gut barrier increase bacterial translocation from the gut to other tissue types, supporting a “leaky gut” model (Cheng et al., 2018; Mokkala et al., 2016; Ortiz et al., 2014). Strictly interrogating our NHP gut microbiome and breast microbiome by dietary pattern consumption suggests that diet independently regulates the breast and gut bacterial populations with few populations expressed in both regions being similarly regulated by diet, diminishing the role of the entero-mammary transmission route for the breast microbiome. However, NHP subjects with increased intestinal permeability did display a significant difference in both alpha and beta diversity, indicating that a “leaky gut” mode of transmission may indeed influence the colonisation or selection of microbes comprising the breast microbiome.
Comparing microbiota populations in NHP subjects randomised to consume a Western or Mediterranean diet, we are able to show that the majority of gut microbiota species are not present in the breast compartment. For the most part, this is unsurprising as the microenvironmental pressures (pH, oxygen content, glucose availability) widely differ between regions. Only two species (Ruminococcus torques and Streptococcus lutetiensis) are similarly regulated by diet in each compartment, with Western diet consumption correlating with increased R. torques and Mediterranean diet consumption associated with increased S. lutentiensis. S. lutentiensis is a lactic acid-producing, Gram-positive, facultative anaerobe that displays similar proportional abundance and regulation by diet in both the gut and breast regions. R. torques is a mucin-degrading, Gram-positive, anaerobe with approximately 20-fold higher proportional abundance in the gut than the breast tissue in Western diet-fed subjects, suggesting dietary patterns influence this microbe similarly in both locations. Since the majority of microbes identified regulated by diet are dependent on body region, this most likely indicates that dietary metabolites in circulation offer selection pressures to modify the bacteria populations already present in the breast tissue. Further research is needed to determine the physiological relevance of the breast microbiome on tissue homeostasis and signalling.
On the other hand, subjects with elevated circulating plasma LPS which is a marker of a gut barrier dysfunction display a different breast microbiome than NHP subjects with low levels of circulating LPS regardless of diet. Metabolic endotoxemia, characterised by elevated circulating level in plasma/serum LPS resulting in chronic low-grade inflammation, is associated with obesity and metabolic syndrome (Boutagy et al., 2016). Studies measuring serum LPS in obese versus non-obese patients report a significant $26\%$ increase in serum LPS in obese patients compared with non-obese patients (Kallio et al., 2015). Increased gut permeability markers in NHP subjects were associated with increased microbial α-diversity and β-diversity PCoA in breast tissue, suggestive of either a gut-breast signalling axis or a potential LPS-mediated selection pressure on present populations. Of the common species present in both the gut and breast compartment only Ruminococcus torques were associated with gut barrier dysfunction. Ruminococcus torques (a bacterial species categorised within the Firmicute phyla) is anaerobic mucin-degrading bacteria associated with dysbiosis and decreased barrier function in the gut (Cani, 2014; Rajilic-Stojanovic and de Vos, 2014). Elevated R. torques is associated with irritable bowel disease, obesity, autism, and circadian rhythm disruption (Deaver et al., 2018; Hynonen et al., 2016; Png et al., 2010; Wang et al., 2013; Yan et al., 2021). Previous research associated Mediterranean diet adherence in overweight and obese individuals with decreased faecal R. torques abundance (Meslier et al., 2020), supporting our associations with Mediterranean dietary pattern and R. torques abundance in NHP. However, the function of breast-specific R. torques is unknown.
Mucins are large glycoproteins comprising the main structural components of mucus and facilitate interactions between microbes and epithelial surfaces. Mucins display high turnover in the gut, with continuous biosynthesis and degradation to maintain healthy gut homeostasis (Paone and Cani, 2020). Breast tumours also display elevated and aberrant mucin-1 (muc-1) on the cell surface and are associated with poor prognosis (Jing et al., 2019). Several gut bacterial species express the enzymes capable of digesting mucins to free monosaccharides and amino acid residues. These mucin-degrading bacteria, such as R. torques, may increase mucin breakdown byproducts, such as free glycan oligosaccharides, fucose, and sialic acid. These metabolites could be detected systemically or may serve as an energy source for other bacterial species, promoting a community microbial shift (Engevik et al., 2021). N-acetylneuraminic acid (NANA; Neu5Ac) is the major form of sialic acid in humans. Elevated plasma sialic acid was observed in breast cancer patients (Zhang et al., 2018). Therefore, the elevated R. torques in breast of NHP with gut barrier dysfunction or Western diet consumption may promote breast cancer risk. Further studies on the causality between breast and gut-specific R. torques abundance and breast tumorigenesis are needed to explore this potential link.
Breast Ruminococcus torques and Ruminococcus flavefaciens were elevated in NHP subjects with elevated plasma LPS. This may be due, in part, to environmental selection pressures on present breast microbes by elevated LPS presence as lipid A of LPS stimulated growth of lactate-producing bacteria (Dai et al., 2020). However, previous studies demonstrate that a fibre-utilising specific strain, R. flavefaciens FD-1 did not significantly respond to LPS in regards to logarithmic growth or short-chain fatty acid production (Dai et al., 2020), potentially refuting this aspect as a contributor to the shift observed in breast Ruminococcus abundance in subjects with elevated plasma LPS.
In conclusion, our report highlights the overall independence of the breast microbiome from the gut populations as shown by the minimal overlap in species present in both compartments potentially due to differences in environmental factors. Gut barrier dysfunction, characterised by metabolic endotoxemia, was associated with differences in the breast microbiome regardless of dietary pattern suggesting gut health may influence the breast microbiome. However, the exact mechanism is unknown. Moreover, we show dietary pattern modifies both gut and breast compartments and therefore represents a novel mechanism to target for potential health outcomes.
## Disclosure statement
The authors declare none.
## Funding
This work was supported by the Department of Defense Breast Cancer Research Program Breakthrough Level 2 Award (K.L.C., W81XWH-20-1-0014) and the National Institutes of Health National Heart, Lung, and Blood Institute R01 grant (C.A.S., 5R01HL087103-09).
## Author contributions
Conceptualization: K.L.C.; Formal analysis: A.B., J.A.T.; Investigation: A.B.; Methodology: C.A.S.; Resources: C.A.S.; Supervision: K.L.C.; Writing – original draft preparation: C.A.S., J.A.T., K.L.C.; Writing – review and editing: K.L.C. All authors have read and agreed to the published version of the manuscript.
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|
---
title: Berberine governs NOTCH3/AKT signaling to enrich lung-resident memory T cells
during tuberculosis
authors:
- Isha Pahuja
- Kriti Negi
- Anjna Kumari
- Meetu Agarwal
- Suparba Mukhopadhyay
- Babu Mathew
- Shivam Chaturvedi
- Jaswinder Singh Maras
- Ashima Bhaskar
- Ved Prakash Dwivedi
journal: PLOS Pathogens
year: 2023
pmcid: PMC9990925
doi: 10.1371/journal.ppat.1011165
license: CC BY 4.0
---
# Berberine governs NOTCH3/AKT signaling to enrich lung-resident memory T cells during tuberculosis
## Abstract
Stimulation of naïve T cells during primary infection or vaccination drives the differentiation and expansion of effector and memory T cells that mediate immediate and long-term protection. Despite self-reliant rescue from infection, BCG vaccination, and treatment, long-term memory is rarely established against *Mycobacterium tuberculosis* (M.tb) resulting in recurrent tuberculosis (TB). Here, we show that berberine (BBR) enhances innate defense mechanisms against M.tb and stimulates the differentiation of Th1/Th17 specific effector memory (TEM), central memory (TCM), and tissue-resident memory (TRM) responses leading to enhanced host protection against drug-sensitive and drug-resistant TB. Through whole proteome analysis of human PBMCs derived from PPD+ healthy individuals, we identify BBR modulated NOTCH3/PTEN/AKT/FOXO1 pathway as the central mechanism of elevated TEM and TRM responses in the human CD4+ T cells. Moreover, BBR-induced glycolysis resulted in enhanced effector functions leading to superior Th1/Th17 responses in human and murine T cells. This regulation of T cell memory by BBR remarkably enhanced the BCG-induced anti-tubercular immunity and lowered the rate of TB recurrence due to relapse and re-infection. These results thus suggest tuning immunological memory as a feasible approach to augment host resistance against TB and unveil BBR as a potential adjunct immunotherapeutic and immunoprophylactic against TB.
## Author summary
In response to primary infections or vaccination, the host immune system elicits robust effector and memory responses to facilitate immediate pathogen clearance and to establish long-term protection. In this study, we have ascertained the prospects of potent immunomodulator berberine (BBR) in instigating host protective immunological memory responses during tuberculosis (TB). BBR-induced glycolytic flux stimulates pro-inflammatory immune responses against *Mycobacterium tuberculosis* (M.tb). BBR further prompted NOTCH-mediated AKT inhibition and activation of FOXO1, STAT3, STAT4, BLIMP-1, and NFκB signaling thereby enriching M.tb-specific resident memory T cell population in the distinct murine TB disease models and human CD4+ T cells. These results project BBR as an adjunct immunotherapeutic and immunoprophylactic against TB.
## Introduction
In spite of being avertable and treatable, tuberculosis (TB) caused by *Mycobacterium tuberculosis* (M.tb) is the major cause of mortality and morbidity among infectious diseases. Globally 10 million people were diseased with TB and an aggregate of 1.3 million people passed away in 2020 itself [1]. Furthermore, almost one-fourth of humankind is infected asymptomatically (latently) with M.tb, with a 5–$15\%$ risk of progressing into clinical manifestations [1].
Existing anti-tubercular treatment (ATT) comprising of assorted anti-mycobacterial drugs can only exterminate active, drug-sensitive strains of M.tb. However, failure to complete the extensive TB restraint approach, directly observed treatment short-course (DOTS) frequently brings about the emergence of multi-drug resistant (MDR) and extensively-drug resistant (XDR) strains. Moreover, DOTS therapy instigates severe toxicity and impairment of host immune responses. For instance, isoniazid (INH) usage leads to the cessation of antigen-responding CD4+ T lymphocytes, which results in a heightened risk of reactivation and reinfection with M.tb [2]. Further inefficacy of the only available vaccine M. bovis bacille Calmette-Guérin (BCG) to prevent adult pulmonary TB makes it a requisite to employ appropriate strategies to augment the host control of M.tb infection [3].
Subsequent to infection, M.tb is phagocytosed by antigen-presenting cells (APCs) that participate in the extermination of internalized pathogens, promote activation of T lymphocytes and stimulate protective pro-inflammatory cytokines such as IFNγ and IL17 [4–6]. The immunological response in TB is extremely complex and the fate of infection is governed predominantly by subsets of T lymphocytes [7]. For instance, stimulation of T helper 2 (Th2) cells and regulatory T cells (Tregs) result in the advancement of disease by hampering protective Th1 responses [8,9,10] while Th1/Th17subsets are associated with host protective immune responses [11]. Nonetheless, these cytokine responses decline post M.tb clearance and hence, subsets of memory T cells play a crucial role in providing long-term protection in TB [11]. T cell receptor (TCR) signaling following antigen stimulation along with cytokine environment regulates and shapes host memory responses [12]. Sustained AKT activation following TCR stimulation drives terminal T cell differentiation [13] probably by targeting FOXO proteins [14,15]. JAK/STAT pathways also influence the differentiation of naïve T cells into memory subsets [16]. Further, STAT4 and Blimp1 transcription factors are known to regulate resident memory responses at the local site of infection. T effector memory (TEM) cells provoke Th1 type cytokines and protect against acute M.tb infections whereas, T central memory (TCM) can give rise to TEM during disease progression, direct cell-mediated immunity for bacterial clearance and sustain long-term memory responses [17,18]. Hence, a strengthened TCM and TEM population is vital for the continuation of long-term protective immune responses [19]. Apart from these, tissue-resident memory T cells (TRM) localized at distinctive sites of infections like lung and spleen are linked with positive medical consequences and host protective responses [20].
Mostly host immune responses elicited in response to M.tb can successfully contain pathogen [4] but complete sterility is not attained. Hence, a novel immunomodulatory approach is necessitated to boost current therapeutics to ease up drug regimen, lessen therapy-induced adversities and intensify anti-mycobacterial effects [21,22,23]. BBR (C20H18NO4+) a bioactive isoquinoline alkaloid is known for diverse therapeutic effects. Administering BBR adjunct to therapeutics can induce hepatoprotective effects by modulating inflammatory responses [24]. BBR can induce protection against INH-associated inflammation, oxidative stress and liver damage in rats [25]. BBR has shown affirmative outcomes in vitro against clinical drug-resistant bacterial strains [26], and in vivo against drug-sensitive M.tb with limited insight into the mechanism of protection[27]. In this study, we have investigated the anti-mycobacterial potential of BBR against pathogenic laboratory strain H37Rv and drug-resistant clinical isolates of M.tb ex vivo and in the murine model of TB. We observed that BBR significantly lowered the bacterial burden in the lungs and the spleen of M.tb infected mice in solitary or in combination with the first-line anti-TB drug INH primarily by boosting the protective host immune responses such as macrophage activation, Th1/Th17 polarization, memory T cell enhancement and pro-inflammatory cytokine responses. NOTCH mediated AKT inhibition and activation of FOXO1, STAT3, STAT4, BLIMP-1 and NFκB signaling following BBR treatment led to a profound induction of adaptive memory in human CD4+ T cells and in mice model. These immunomodulatory properties of BBR were also exploited to increase the vaccine efficacy of BCG. Furthermore, induction of superior memory responses significantly lowered the recurrence of TB due to re-activation and re-infection. Overall, these results propound BBR as an attractive adjunct immunotherapeutic and immunoprophylactic against TB.
## BBR enhances host resistance against drug-susceptible and drug-resistant strains of M.tb
Numerous studies have evaluated the therapeutic potential of BBR against diverse ailments. However, the effectiveness of BBR against M.tb infection is yet to be uncovered. Since BBR has been shown to possess weak anti-bacterial activity [28], we foremost aimed to determine its anti-mycobacterial activity against different drug-sensitive and -resistant strains of M.tb. Consistent with the previous studies, BBR displayed bacterial toxicity at ≤ 50 μg/ml against all the strains tested (S1A–S1C Fig) however 20 μg/ml of BBR treatment which displayed no toxicity in the mouse peritoneal macrophages (S1D and S1E Fig) significantly decreased the intracellular M.tb growth (Fig 1A) indicating the immunomodulatory effects of BBR on host macrophages. With no variable effect on ROS generation (S1F and S1G Fig), BBR treatment significantly induced apoptosis in uninfected (S1H Fig) and M.tb infected macrophages (Fig 1B and 1C) and led to significant activation of transcription factors (NFkB and STAT3) which play a crucial role in combating TB (Fig 1D). Further, BBR treatment significantly enhanced the expression of CD11b and co-stimulatory molecules CD40 and CD86 on the surface of infected macrophages (Fig 1E–1G). Moreover, BBR treated macrophages also displayed increased expression of M1 specific pro-inflammatory cytokines (Fig 1H). Furthermore, immunomodulatory effect of BBR was consistent in T cells isolated from infected mice wherein the percentage of CD69+ activated CD4+ T lymphocytes was significantly enriched upon BBR treatment (Fig 1I). Co-culturing infected macrophages with BBR primed T cells demonstrated significantly reduced intracellular bacterial burden (Fig 1J and 1K) as compared to infected macrophages as well as untreated T cells co-cultured with macrophages. Few piecemeal studies have reported BBR as an efflux pump inhibitor and thereby is known to increase the intracellular concentration of the antibiotics [29]. We investigated whether BBR co-treatment increased the killing potential of INH. With no significant effect in vitro (S1G Fig) BBR co-treatment significantly reduced the bacterial burden in the INH treated macrophages as compared to INH treatment alone in both human (Fig 1L) and mice derived macrophages (Fig 1M). BBR also reduced the intracellular load of MDR and XDR strains of M.tb (Fig 1N) emphasizing that the immunomodulatory potential of BBR is not restricted to drug-sensitive strain of M.tb. To further corroborate the outcomes of ex vivo experiments, C57BL/6 mice were infected with a low dose (∼110 CFU) of M.tb H37Rv and left untreated or treated with BBR (4mg/kg) either alone or in combination with INH (100mg/L) for 45 days followed by CFU enumeration and immune profiling (Fig 1O). As compared to control or INH treated infected lungs, BBR treatment significantly reduced the extent of granulomatous inflammation alone and in combination with INH (Fig 1P and 1Q). Further, BBR treatment significantly reduced the bacterial burden in the lungs and the spleen of infected mice as compared to the control group (Fig 1R and 1S). Interestingly, BBR co-treatment significantly enhanced the anti-tubercular potential of INH (Fig 1R and 1S). These results demonstrate the adjunct potential of BBR along with INH against M.tb H37Rv. Since drug-resistant variants are one of the major contributors of global TB pandemic, we ascertained the efficacy of BBR against MDR and XDR TB (Fig 1T). Interestingly, BBR treatment significantly lowered the bacterial burden of both the drug-resistant strains tested in the lungs and spleen of mice (Fig 1U–1X).
**Fig 1:** *BBR treatment enhances host resistance against drug-sensitive and drug–resistant TB.(A) Mouse peritoneal macrophages were infected with GFP expressing H37Rv (Rv-GFP) at 1:10 MOI followed by treatment with BBR (20 μg/ml). At different time points, cells were analysed by flow cytometry. Graph represents the GFP fluorescence at indicated time points with and without BBR treatment. (B-C) Mouse peritoneal macrophages were infected with M.tb at MOI of 1:10 followed by treatment with BBR (20 μg/ml) for 48 h followed by apoptosis analysis via flow cytometry. (B) Representative dot plots and (C) Percentage of apoptotic cells with and without BBR (20μg/ml) treatment. (D) Immunoblots depicting the phosphorylation of indicated transcription factors (NFkB and STAT3) in uninfected and infected mouse peritoneal macrophages with or without BBR treatment. (E-G) Infected murine peritoneal macrophages were surface stained with antibodies against CD11b (APC/Cy7), CD40 (PE) and CD86 (PerCPCy5.5) followed by flow cytometry. (E) Expression of CD11b on the surface of infected macrophages. Percentage of (F) CD11b+CD40+ and (G) CD11b+CD86+ infected macrophages with and without BBR treatment. (H) Expression of chemokines and cytokines in M.tb infected macrophages at 24h pi with and without BBR (20 μg/ml) treatment. (I) Percentage of CD4+ and CD8+ T cells expressing CD69 in the infected and BBR (10μg/ml) treated splenocytes. (J) Representative overlay plots and (K) percentage of RvGFP infected macrophages co-cultured with M.tb specific and BBR treated splenocytes. (L) PMA- activated THP1 macrophages were infected with H37Rv at 1:10 MOI followed by treatment with INH (1 μg/ml), BBR (20 μg/ml) or both for 48 h pi after which cells were lysed for CFU enumeration. (M) Experiment L was repeated in mouse peritoneal macrophages. (N) Mouse peritoneal macrophages were infected with MDR (Jal 2261) and XDR (MYC431) clinical strains of M.tb followed by treatment with 20 μg/ml of BBR. Cell lysates were plated for CFU enumeration 48 h pi. (O) Schematic representation of the murine model of infection. C57BL/6 mice were infected with low dose of H37Rv (~110 CFU per lung) and after 15 days of disease establishment, mice were treated with either INH (100 mg/L), BBR (4 mg/kg) or both for 45 days followed by CFU enumeration and immune profiling. (P) Histopathology of infected lungs with arrows indicating the granulomatous lesions. (Q) Immunopathology score of the infected lungs. Bacterial burden in the (R) lungs and the (S) spleen of infected animals. (T) Diagrammatic representation of the infection model. Bacterial burden in (U) the lungs and (V) the spleen of mice infected with MDR strain of M.tb. Bacterial load in (W) the lungs and (X) the spleen of mice infected with XDR strain of M.tb. Data is representative of two independent experiments. The data values represent mean ± SD (n = 3–5). *p<0.05, **p<0.005, ***p<0.0005.*
## BBR strengthens the host protective immune responses against TB
To comprehend the immunomodulatory properties of BBR, we profiled the innate and adaptive immune cell populations driving host protection in both the lungs and the spleen of infected mice. Increased percentage of CD11b+ and CD11c+ cells with enhanced expression co-stimulatory molecules CD80 and CD86 was observed in the lungs (S2A–S2H Fig) and the spleen (S2I–S2P Fig) of infected mice treated with BBR indicating significant innate cell stimulation which plays a crucial role in the activation of adaptive immune responses. Although BBR treatment did not change the percentage of CD4+ and CD8+ T cells (Fig 2A–2C), in combination with INH, BBR convalesced INH induced reduction in percentage of CD4+ T cells (Fig 2B) in the lungs of infected mice. BBR treatment significantly induced the activation of T cell subsets as the expression of early activation marker CD69 on these cells was significantly heightened in the lungs (Fig 2D–2F) and the spleen (S3A–S3E Fig) of infected mice.
**Fig 2:** *BBR strengthens M.tb-specific T cell responses during TB treatment.Single cell suspensions generated from the infected lungs were ex vivo stimulated with M.tb complete soluble antigen (CSA) for 16 h followed by surface staining with antibodies against CD3 (Pacific Blue), CD4 (PerCPCy5.5), CD8 (APCCy7) and CD69 (FITC) followed by flow cytometry. (A) FACS scatter dot plots and percentage of (B) CD4+ and (C) CD8+ T cells in the infected lungs. (D) Representative FACS dot plot and the percentage of (E) CD4+CD69+ and (F) CD8+CD69+ T cells in the infected lungs. (G-K) After stimulation with CSA, the lung cells were treated with monensin and brefeldin A for 2 h and surface stained with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5) and α-CD8 (APCCy7) followed by intracellular staining with α-IFNγ (APC) and α-IL17 (PECy7) and flow cytometry. (G) Representative dot plots and percentage of (H) CD4+IFNγ+, (I) CD4+IL17+, (J) CD8+INFγ+ and (K) CD8+IL17+ cells in the infected lungs. (L) Fold expression of cytokines in the lungs of infected, INH and BBR treated splenocytes. (M-R) To determine the frequency of central memory and resident memory T lymphocytes, ex vivo stimulated lung cells were surface stained with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5), α-CD8 (APCCy7), α-CD69 (PE), α-CD103 (PECy7), α-CD62L (APC) and α-CD44 (FITC) followed by flow cytometry. (M) Representative FACS dot plots and (N) the percentage of CD4+TCM (CD62LHICD44HI) cells, T cell subset. (O) Percentage of CD8+TCM (CD62LHICD44HI) cells, in the infected lungs with or without drug treatment. (P) Representative scatter dot-plot images and percentage of (Q) CD4+TRM and (R) CD8+TRM cells in the lungs of infected mice. (S) Schematic representation of adoptive transfer experiment. CFU enumeration after 21 days of adoptive transfer in (T) the lungs and (U) the spleen of Rag-/- mice. Data is representative of two independent experiments. The data values represent mean ± SD (n = 5). *p<0.05, **p<0.005, ***p<0.0005.*
Hence, it can be inferred that BBR treatment extensively strengthens antigen processing and presentation by APCs and consistently promotes activation of T lymphocytes to impart protection against M.tb infection. Furthermore, BBR treatment advanced differentiation of CD4+ and CD8+ into protective Th1 and Th17 subsets in the lungs of infected mice. This was evident by significant increase in IFNγ and IL17 producing T cell subsets in both the lungs (Fig 2G–2K) and the spleen (S3F–S3J Fig) of BBR and INH treated mice. Furthermore, BBR treatment considerably enriched host-protective chemokines and cytokines such as TNFα, IL1β, IL22 etc., and subsided the effect of INH induced anti-inflammatory cytokines such as IL10 and IL4 in co-treated mice (Fig 2L). We further investigated the impact of BBR treatment on the induction of prolonged immune protection, which is mediated by memory subsets of adaptive immunity. The lungs of BBR treated mice revealed high frequency of TCM cells (Fig 2M–2O). Similar trend was observed in the spleen of BBR treated mice (S4A–S4C Fig). Furthermore, the percentage of resident memory T cells (TRM) which evolve from disseminating effector memory T cells (TEM), remain confined to the tissues and play a key role in stimulating adaptive immune response at the tissue specific sites was considerably enhanced in the lungs (Fig 2P–2R) and in the spleen of infected mice (S4D–S4F Fig). Furthermore, to strengthen our findings regarding positive immunomodulatory effects of BBR treatment on CD4+ T cells, we performed adoptive transfer experiment in Rag-/- mice (Fig 2S). Adoptive transfer of CD4+ T cells from BBR treated infected mice into Rag-/- mice significantly reduced bacterial burden in the lungs (Fig 2T) and the spleen (Fig 2U) of Rag-/- mice upon M.tb infection concluding that BBR exerts host-protective effects by enriching M.tb specific CD4+ T cell responses.
## BBR enriches pathways associated with establishment of TRM in human PBMCs
Fig 3A represents a simplistic model of T cell differentiation into different memory subsets highlighting different regulators and surface markers used to identify these cells. To understand the molecular signaling involved in the enhancement of CD4+ adaptive memory after BBR treatment, we cultured PBMCs isolated from healthy PPD+ individuals in the presence of BBR for 48h. Intriguingly, BBR treatment drove significant differentiation of CD4+ TNAIVE cells into TEM, TEMRA and TRM T cell subsets with no significant increase in TCM cells (Fig 3B–3G). To understand the mechanistic details of heightened memory responses, we performed the whole proteome analysis of human PBMCs with or without BBR treatment. In each individual, BBR treatment induced a distinct proteome landscape with a significant number of differentially expressed proteins (Fig 3H). Interestingly, BBR treatment collectively downregulated the expression of 323 proteins (Fig 3I) and induced the expression of 527 proteins (Fig 3J) in the treated PBMCs. Further analysis on these commonly expressed proteins revealed few pathways which were downregulated upon BBR treatment (Fig 3K). Curiously, diverse pathways associated with key cellular processes such as cell cycle, cell adhesion, cell division and glycolysis were upregulated upon BBR treatment (Fig 3L). Importantly, BBR treatment enhanced the upregulation of critical proteins of Notch signaling pathway (Fig 3L and 3M) which is known to stimulate the differentiation and maintenance of TRM cells [30].
**Fig 3:** *BBR treatment enriches human CD4+ memory T cells by regulating NOTCH/PTEN/Akt/FOXO1 pathway.(A) Schematic representation of T cell differentiation into TEM, TCM and TRM memory subsets. (B-G) Human PBMCs isolated from 7 PPD+ healthy individuals were ex vivo stimulated with CSA and treated with BBR (10 μg/ml) for 48 h followed by surface staining with α-CD3 (PE), α-CD4 (APCCy7), α-CD8 (Pacific Blue), α-CD45RO (PerCPCy5.5), α-CCR7 (PECy7) and α-CD69 (Alexa Flour 700) (B) Gating strategy employed to depict the different memory T cell subsets. Percentage of (C) CD4+ T NAIVE cells, (D) CD4+ TCM cells, (E) CD4+ TEM cells, (F) CD4+ TEMRA cells and (G) CD4+ TRM cells. (H) Whole proteome profiling of untreated and BBR treated human PBMCs derived from 4 PPD+ healthy individuals. Heat map representation of the differentially expressed proteins (Log2 fold, n = 3). Common proteins in all the individuals that are (I) downregulated and (J) upregulated upon BBR treatment. Biological processes that are (K) downregulated and (L) upregulated upon BBR treatment. (M) Notch signalling pathway associated proteins which were upregulated in human PBMCs upon BBR treatment. (N) RT-PCR of genes related to Notch signaling pathway. (O) Human CD4+ T cells expressing p-AKT and (P) human CD4+ T cells expressing p-FOXO1. (Q) MFI of human CD4+ T cells expressing Blimp-1. (R) Extracellular L-Lactate quantification in untreated and BBR treated human PBMCs. (S-W) represents multiplex cytokines assay upon BBR treatment in human PBMCs of derived from 4 PPD+ healthy individuals (refer methodology). (S-V) Fold expression changes of 46 cytokines upon BBR treatment in different individuals. (W) Heat map of common differentially expressed cytokines. In H, I, J, M, and W, Red represents upregulation while blue represents downregulation. Data is representative of two independent experiments. The data values represent mean ± SD (n is 4 to 7). *p<0.05, **p<0.005, ***p<0.0005.*
TCR stimulation along with downstream signalling pathways such as PI3K/AKT/mTOR play a critical role in shaping the T cell memory [12]. While activated AKT is known to phosphorylate FOXO1 triggering its nuclear exclusion, previous literature highlights the importance of FOXO1 mediated gene expression in the generation and maintenance of protective memory cells [31,32] Further, Blimp1 transcription factor is known to regulate resident memory responses at the local site of infection [33,34]. Ironically, Notch3 transactivates PTEN which in turn inhibits the AKT signaling [35] leading to FOXO1 activation (S7A Fig). We validated these targets by RT-PCR and observed that BBR treatment induced significant expression of genes related to notch signaling as well as the downstream targets such as Foxo1, Pten and Blimp1 (Fig 3N). Moreover, BBR inhibited AKT and FOXO1 phosphorylation (Figs 3O and 3P and S7B–S7D) and along with AKT inhibitor (AKTi), BBR synergistically reduced TNAIVE population (S7E Fig), increased TEM (S7F Fig) and TEMRA cells (S7G Fig) with no effect on TCM cells (S7H Fig). BBR treatment also enhanced resident memory functions by modulating the BLIMP-1 expression (Fig 3Q).
Cellular metabolism plays a crucial role in modulating T cell effector functions [36] while BBR is known to induce glycolysis in many cell types [37,38], (S7I Fig). Consistent with the previous findings and our proteomics data, BBR significantly induced glycolysis (Fig 3R) in human PBMCs. Furthermore, to dwell deeper into the immunological milieu induced upon BBR treatment, Bio-plex Pro Human cytokine screening Panel (48-Plex) was utilised to screen the regulation of cytokines in human PBMCs. BBR treatment consistently enhanced the expression of pro-inflammatory cytokines in human PBMCs (Fig 3S–3V) derived from PPD+ individuals. Moreover, 20 pro-inflammatory cytokines and chemokines were upregulated in the PBMCs derived from at least 3 out of 4 individuals (Fig 3W). Overall these results indicate that BBR enhances effector functions of CD4+ T cells and upregulates critical signaling associated with TRM establishment and maintenance.
## BBR drives the expansion of memory T cells by modulating key regulators of T cell development in murine T cells
To validate the molecular signaling involved in the enhancement of CD4+ adaptive memory after BBR treatment, splenocytes isolated from M.tb infected mice were ex vivo stimulated with M.tb complete soluble antigen (CSA) and treated with BBR (10μg/ml) for 48h followed by immune profiling. Analysis of different CD4+ T cell subsets via flow cytometry (Fig 4A) revealed that BBR treatment significantly reduced the percentage of TNAIVE subset (Fig 4B) with a concomitant increase in the TEM cells (Fig 4C). While no difference was observed in the TCM population (Fig 4D), BBR significantly induced the TRM subset (Fig 4E). Consistent with the previous results, BBR treatment significantly reduced the activation of AKT (Fig 4F and 4G) and decreased the phosphorylation of FOXO1 (Fig 4H and 4I) in CD4+ T cells. Inhibition of AKT signalling has been shown to promote central memory responses by increasing nuclear accumulation of FOXO1 [39]. It is also well-established that STAT3 and STAT4 play specific function in the formation of T cell memory subsets in response to infections [16,40]. To further ascertain the influence of BBR treatment on AKT-FOXO1 axis, we repeated the ex vivo T cell stimulation experiment in the presence of AKTi and BBR. AKTi alone did not increase the percentage of CD4+ TEM and TRM populations, whereas, considerable enrichment was observed upon BBR treatment alone or in combination with AKTi (Fig 4J and 4K). This infers the influence of alternate signaling pathways contributing in enhancement of memory responses upon BBR treatment. We observed that BBR treatment induced the activation of STAT3, STAT4 and BLIMP-1 in CD4+ T cells (Fig 4L–4Q) which may lead to enhanced TRM response. Interestingly, heightened NFκB activation was observed in BBR treated CD4+ T cells (Fig 4R and 4S) leading to a significant upregulation of host protective proinflammatory responses (Fig 4T). Furthermore, BBR treatment led to enhanced glycolysis in CD4+ T cells (Fig 4U). Interestingly, 2-Deoxy-D-glucose (2DG) (glycolysis inhibitor) treatment abrogated the expression of IFNγ and IL17 in BBR treated CD4+ T cells (Fig 4V and 4W) indicating that BBR potentiates pro-inflammatory response through metabolic reprogramming. Overall, this concludes that BBR treatment expands memory T cell subsets with proinflammatory characteristics.
**Fig 4:** *BBR induces expansion of antigen-specific memory T cells by targeting TCR signaling and glycolysis.Splenocytes isolated from M.tb infected mice were ex vivo stimulated with M.tb CSA and treated with BBR (10 μg/ml) for 48 h. Ex vivo stimulated splenocytes were surface stained with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5), α-CD8 (APCCy7), α-CD69 (PE), α-CD103 (PECy7), α-CD62L (APC) and α-CD44 (FITC). (A) Representative dot plots and the percentage of (B) CD4+ TNAIVE cells, (C) CD4+ TEM cells, (D) CD4+ TCM cells and (E) CD4+ TRM cells after BBR treatment. (F-I) To analyse the activation status of key signaling molecules and transcription factors, the cells were stained with α-CD3 (Pacific Blue) and α-CD4 (PerCPCy5.5) followed by intracellular staining with antibodies against p-AKT and p-FOXO1 (see methods). (F) Representatives FACS scatter plots and (G) the percentage of CD4+ T cells expressing pAKT. (H&I) Representative scatter plots and the percentage of CD4+ T cells expressing p-FOXO1. (J&K)
Ex vivo stimulated splenocytes were treated with BBR (10 μg/ml), AKTi (2.5μM) or both for 48 h followed by surface staining with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5), α-CD69 (PE), α-CD103 (PECy7). (J) Percentage of CD4+ TEM and (K) CD4+ TRM cells. (L-S) Stimulation of transcription factors involved in memory responses were examined for which the cells were stained with α-CD3 (Pacific Blue) and α-CD4 (PerCPCy5.5) followed by intracellular staining with antibodies against p-STAT3, p-STAT4, BlIMP-1 and p-NFκB (see methods). Representatives FACS scatter plots and percentage of CD4+ T cells expressing (L&M) p-STAT3, (N&O) p-STAT4, (P&Q) Blimp-1 and (R&S) p-NFκB. (T) Expression of cytokines in M.tb specific T cells with or without BBR treatment. (V-X)
Ex vivo stimulated splenocytes isolated from M.tb infected mice were treated with BBR (10 μg/ml), 2-Deoxy-D-glucose (2DG; 200 mM), both or left untreated for 24 h. (U) L-Lactate present in the supernatant of treated splenocytes. (V) Percentage of CD4+IFNγ+ T cells and (W) CD4+IL17+ T cells. The data values represent mean ± SD (n = 3–4). *p<0.05, **p<0.005, ***p<0.0005.*
## BBR induced adaptive memory enhances the BCG vaccine efficacy and reduces the rate of TB recurrence
Having established the potential of BBR to induce significant immunological memory against TB, we next investigated whether BBR co-treatment could enhance the BCG vaccine efficacy in vivo. C57BL/6 mice were divided in 3 groups: Cnt (un-vaccinated), BCG (BCG vaccinated) and BCG-BBR (BCG vaccinated and BBR treated), and were challenged with low dose of H37Rv through aerosol infection model (Fig 5A). 30 days after M.tb infection, the mice were euthanized and analysed for bacterial burden and immune profiling. Pre-challenge immune profiling of the animals revealed increased activation of CD4+ and CD8+ T cells in the lungs (S8A–S8E Fig) and the spleen (S8F–S8I Fig) of the BCG vaccinated and BBR treated animals as compared to the BCG vaccinated alone. Consistent with this, BBR treatment during BCG vaccination significantly decreased the bacterial burden in the lungs (Fig 5B) and the spleen (Fig 5C) of co-treated mice as compared to only BCG vaccination. Immune analysis revealed increased percentage of CD4+ and CD8+ T cells in the lungs (S9A–S9C Fig) as well as in the spleen (S9D–S9F Fig) of co-vaccinated mice. BCG primarily induces effector memory responses as a result of which the anti-TB immunity induced by BCG is short lived and wanes in adults [41]. Interestingly, with no effect in the lungs (S9G–S9I Fig), BBR treatment increased the CD4+ and CD8+ TCM cells in the infected spleen (S9J–S9L Fig). Furthermore, the percentage of TRM cells was significantly high in the lungs (Fig 5E and 5F) and the spleen (S9M–S9O Fig) of infected BCG-BBR vaccinated and treated animals as compared to the BCG vaccination alone.
**Fig 5:** *BBR enhances the BCG vaccine efficacy and protects against recurrent TB by inducing T cell resident memory responses in murine model.(A) Schematic representation of vaccine model used in the study. Bacterial load in (B) the lungs and (C) the spleen of infected mice. Ex vivo stimulated lung cells were surface stained with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5), α-CD69 (PE), α-CD103 (PECy7), followed by flow cytometry. (D) Representative FACS plots and percentage of (E) CD4+ TRM cells and (F) CD8+ TRM cells in the lung of infected animals. (G) Schematic diagram representing the re-activation model used in this study. (H) Rate of disease relapse with and without BBR treatment. (I) FACS plots representing CD69 and CD103 expressing CD4+ T cells and (J) percentage of CD4+ TRM cells and (K) CD8+ TRM cells in the spleen of infected mice. (L) Diagrammatic representation of re-infection model used in the study. Bacterial burden in (M) the lungs and (N) the spleen of re-infected mice. Ex vivo stimulated single cell suspensions of the lungs were stained with α-CD3 (Pacific Blue), α-CD4 (PerCPCy5.5), α-CD69 (PE), α-CD103 (PECy7), α-CD62L (APC), and α-IL17 (BV650) followed by flow cytometry. (O) Representative FACS plots and the percentage of (P) CD4+ TRM cells and (Q) CD8+ TRM cells in the lungs of re-infected mice. (R-T) Percentage of resident memory T cells producing IL17. Data is representative of two independent experiments. The data values represent mean ± SD (n = 5). *p<0.05, **p<0.005, ***p<0.0005.*
Memory T cells are vital for long-term immunity against disease relapse due to re-activation or re-infection. To further provide the in vivo evidence of the above results, we performed reactivation study in murine model (Fig 5G). BBR co-therapy significantly reduced the rate of disease re-activation (Fig 5H) further proving that BBR treatment generates long-term M.tb specific protective memory responses with boosted TCM (S10A–S10C Fig) and TRM populations (Fig 5I–K) in the lungs of infected mice. Furthermore, in re-infection murine model of TB (Fig 5L), the bacterial burden was significantly reduced in the lungs (Fig 5M) and the spleen (Fig 5N) of re-infected mice previously treated with INH+RIF+BBR (BBR group) as compared with INH+RIF group (Cnt group). Immune profiling revealed increased percentage of TRM cells in the lungs (Fig 5O–5Q) and the spleen (S10D and S10E Fig) of re-infected animals treated with INH+RIF+BBR. IL17 plays a crucial protective role in the recall protection to M.tb infection [6,11]. Interestingly, IL17 secreting TRM population was enriched in the lungs (Fig 5R–5T) and the spleen (S10F and S10G Fig) of BBR treated mice. Similar trend was observed for the TCM population in both the organs (S10H–S10Q Fig). Collectively, our preclinical mice and human data projects BBR as an excellent immunotherapeutic and immunoprophylactic candidate against susceptible and drug resistant TB.
## Discussion
Immunological memory can be delineated as modification in immune responsiveness after the primary encounter to elicit prompt and robust immune responses. With more profound insights, the conventional characterization of immunological memory is continuously advancing. Since an upsurge in incidences of drug-resistant TB along with recurrence and reactivation of M.tb infection is the root cause of morbidity and mortality worldwide, the utmost significance of immunological memory to combat TB remains unchanged [42]. Therefore, an efficacious immunomodulatory strategy is deemed indispensable to enhance population-wide immune protection to moderate the global TB burden. Strategic host-directed therapies to augment protective immune responses, accomplish bacterial sterility, and subside detrimental pathology are endeavoured [24] to improve clinical outcomes in TB patients [43,44]. Combinatorial administration of immunotherapeutic has resolved inadequacies of numerous stratagems and was found advantageous in eliminating M.tb infections in several clinical trials [45]. One of the most established immunotherapeutic BBR has been known for ages for its effective therapeutic potential to treat diabetes and many other diseases [46,47]. Despite this, the prospects of BBR for better clinical recovery during M.tb infection by augmenting immunological memory responses have not been evaluated.
In line with the previous literature [27], in this study we demonstrated the anti-mycobacterial potential of BBR in murine macrophages, human monocytic cell line THP-1, and murine model against susceptible and drug-resistant strains of M.tb. Mononuclear phagocytic cells are prominent in activating T cells but during the pathogenic hijack, apoptotic inhibition occurs that restricts the presentation of bacterial antigen and further delays T cell mediated adaptive immune response [48]. We found that BBR prompted apoptotic cell death in infected macrophages which constrains M.tb infection. With little direct anti-mycobacterial activity, BBR greatly enhanced the host defence mechanisms by modulating NF-κB and STAT-3 signaling [49]. BBR also increased the percentage of co-stimulatory molecules on mouse peritoneal macrophages and enriched the host protective chemokines, cytokines in response to M.tb infection which was further trailed in line with T cell responses. BBR treatment also enhanced the killing potential of T cells as was evident by the results of ex vivo co-culture experiments. BBR administration in adjunct to INH reduced the bacterial burden in human monocytic cell line THP-1 as well as peritoneal macrophages. Furthermore, BBR significantly boosted the extermination of drug-resistant MDR and XDR strains of M.tb.
The therapeutic effects of BBR have been evaluated for diverse diseases in murine models, with no significant toxicity [50,51]. So, comprehensive immunological analysis was performed in the murine model of TB. Recently a piecemeal study has evaluated the impact of BBR treatment in adjunct to front-line TB drugs INH and RIF against drug-susceptible M.tb strain [27] wherein BBR as an adjunct did not demonstrate any additive or synergistic effect on the bacterial load. In our study, we have defied numerous shortcomings of this study. First of all, our study extensively validates the effectiveness of BBR in eliciting anti-mycobacterial immune responses against drug-susceptible and resistant strains of M.tb exclusive of ATT drugs. We also demonstrate the effectiveness of BBR in reversing the immune dampening effects of INH. Augmented efficacy of BBR in terms of lower bacterial burden, reduced lung inflammation and heightened host protective immune response either alone or in combination with INH can be attributable to a long treatment regimen of 45 days in our study. Moreover, BBR was administered intraperitoneally for superior circulation. Treatment was scientifically strategized to downgrade hepatotoxicity and to enhance host protective responses. BBR administration significantly reduced the bacterial load in adjunct to frontline anti-TB drug INH and resultant diminution in pathological damage in the lungs as reported recently [27]. Our results also demonstrate the effectiveness of BBR treatment in significantly lowering the bacterial load in MDR and XDR infected animals. Hence, it can be stated decisively that BBR elicits anti-mycobacterial immune responses against a range of M.tb strains. Furthermore, with no prior information on BBR induced mechanisms of protection against TB, we have comprehensively evaluated the impact of BBR on T cell signal transduction linking it with the establishment of durable immunological memory against M.tb.
To decipher the immunological feature of BBR induced reduction in bacterial burden, immune profiling was performed. Activation of adaptive immune cell populations was observed in compliance with an increase in the percentage and the activation of innate immune cells upon BBR treatment. In case of inflammatory bowel disease, BBR is known to induce protective immune responses by modulating IFNγ and IL17 CD4+ T cells by activation of AMP kinase [52]. IFNγ plays a vital role during M.tb infection by stimulating Th1 induction from naïve CD4+ T cells [53] and IL17 plays a significant role by stimulating recall responses during recurrent M.tb infections [11,54,55]. In agreement with the previous reports, BBR treatment resulted in the increased percentage of IFNγ and IL17 secreting CD4+ and CD8+ T cells. Furthermore, BBR treatment enhanced expression of pro-inflammatory cytokines such as CCL2, IL12β, IL1β alone and in adjunct to INH. Furthermore, INH induced anti-inflammatory responses were countered along with BBR treatment.
Long-term memory particularly increased TCM pool is vital for heightened immune responses against M.tb infections. Further, the TRM population residing at the site of infection play a critical role in mediating diverse host protective effector functions. TRM upon encountering antigen stimulates IFNγ production and recruits memory T cell and other immune cell populations to the site of infection [56,57]. Our research presents strong evidence of the augmented TRM and TCM responses upon BBR therapy alone and in adjunct to INH. These results were further strengthened by BCG vaccination experiments wherein we observed a striking reduction in the bacterial burden on administering BBR post BCG immunization in M.tb infected mice. In agreement with previous results, we observed a superior percentage of TCM and TRM cell responses upon BBR treatment subsequent to BCG immunization. This again highlights the prominence of this immunomodulatory strategy to provide ever-lasting immunity against M.tb infections. Further, the biological evidence of long-term protective immune prophylactic effects of BBR were provided by re-infection and re-activation mice experiments wherein BBR treated animals displayed increased TCM and TRM cell responses leading to a significantly lower bacterial burden and reduced relapse rate.
To dwell into the immune mechanisms by which the attributes of immunological memory were enhanced upon BBR treatment, we performed ex vivo studies with M.tb specific T cells and observed that BBR treatment instigated significant differentiation of TNAIVE population into TEM and TRM cells. It is well-established that naïve T cells, TCM and TEM cells are capable to differentiate into TRM cells upon stimulation [58]. Previously it has been reported that the TRM cells have a definite cytokine profile (TGFβ, IL15, Type I IFN, IL12) as well as TRM-specific transcription factors such as Runx3, Hobit, Blimp1etc [59] that distinguish them from other immune cell populations and enriching selective and protective tissue specific immunity. Apart from this, certain studies have ascertained the critical role of STAT4 in regulating TRM differentiation and persistence in achieving tissue-specific immunity against infections [40]. Ex vivo BBR treatment elicited the expansion of T cell memory pool by modulating vital interconnected immune molecules such as AKT, FOXO-1, STAT-3, STAT-4, BLIMP-1 as well as NFκB. Instigation of memory establishment was in agreement with the enhancement of pro-inflammatory cytokines such as IFNγ, IL2, IL23, IL22, CCR7 and CXCR4 in BBR treated M.tb specific T cells. Furthermore, BBR induced metabolic flux towards glycolytic pathway thereby enhancing effector functions of CD4+ T cells secreting key host protective cytokines- IFNγ and IL17.
Many times, the results obtained with mice studies poorly mimic conditions of human physiology. To ascertain the memory inducing potential of BBR in humans, we performed ex vivo experiments with PBMCs isolated from PPD+ healthy donors. To our satisfaction, BBR treatment resulted in a significant reduction in CD4+ TNAIVE population with a concomitant increase in CD4+ TEM, TEMRA and TRM subsets. Moreover, BBR treated human PBMCs also displayed reduced phosphorylation of AKT which led to an enhanced FOXO1 activation as BBR/AKTi treatment synergistically decreased CD4+ TNAIVE population and increased TEM and TEMRA subsets. Additionally, BLIMP-1, a TRM specific transcription factor was also upregulated in the BBR treated PBMCs [60]. Similar to the mice ex vivo experiments, BBR treatment heightened glycolytic flux in human PBMCs along with boosted pro-inflammatory cytokine profile. Furthermore, whole proteome analysis revealed upregulation of vital cellular processes in BBR treated human PBMCs including maintenance of glycolysis which is necessitated for T cell effector functions [61] and Notch signaling pathway critically known for TRM establishment. Receptors, NOTCH$\frac{1}{3}$ and activators APHA1/PSEN were found to be upregulated at the RNA as well as the protein levels in the BBR treated hPMBCs. Notch signaling is known to induce PTEN expression which in turn activates FOXO1 by inhibiting AKT [35,62]. Convincingly, BBR treatment significantly induced the expression of PTEN and FOXO1 in human PMBCs. Based on these results; we propose a probable mechanism of action by which BBR enhances the immunological memory against TB (Fig 6).
Finally, this study substantiates the prospects of BBR as a potent immunomodulator that strikingly augments protective immunological memory responses against M.tb infection. Further corroboration in the higher TB model that shares more similarities with humans such as non-human primates will be valuable to appraise BBR as promising host-directed therapy against susceptible and drug-resistant TB.
**Fig 6:** *BBR instigates host-protective immune responses against M.tb by directing key immunological signaling pathways.In response to M.tb infection, BBR establishes long-lived, host protective resident memory T cells (TRM) at the site of infection. BBR enhances the effector functions of T lymphocytes by enhancing CD69 expression, directing metabolic flux towards glycolysis, activation of key host protective signaling pathways, and pro-inflammatory immune responses. BBR enriches pathways essential for the establishment and maintenance of memory T cells. BBR upregulates NOTCH3 which directs PTEN to simultaneously inhibit AKT and activate STAT signaling. AKT inhibition further decreases FOXO1 phosphorylation thereby enhancing its nuclear retention. BBR-mediated enhancement of activated STAT4 and STAT3-mediated BLIMP1 signaling axis further results in heightened expression of TRM-specific genes for long-term protection against M.tb infections.*
## Ethics statement
Animal experiments were executed as per the regulations stated by the Institutional Animal Ethics Committee of the International Centre for Genetic Engineering and Biotechnology (ICGEB, New Delhi, India) along with the Department of Biotechnology (DBT) standards (Government of India) (Approval ID: ICGEB/IAEC/$\frac{08}{2016}$/IMB-1, ICGEB/IAEC/08092021/IMB-19). All the animals utilized in the investigation were ethically sacrificed by asphyxiation with carbon dioxide adhering to institutional and DBT practices.
All the experiments related to human samples were performed as per the regulations stated by the Institutional Ethics Committee of Institute of Liver and Biliary Sciences (ILBS), New Delhi along with the Department of Biotechnology (DBT) standards (Government of India) (Approval ID:IEC/$\frac{2021}{70}$/NA6). Human samples were handled by a skilled technician, samples were coded to maintain records, and consent forms were signed by all the participants before sample acquisition.
## Mice
C57BL/6 mice of 6–8 weeks were maintained in the Animal facility at ICGEB, New Delhi, India. Mice were accessed and obtained for experimental procedures from the facility.
## Bacteria
The mycobacterial strains used in this study (H37Rv, Rv-GFP, MDR, and XDR) were maintained in 7H9 (Middlebrook, Difco) medium supplemented with $10\%$ ADC (albumin, dextrose, and catalase; Difco), $0.05\%$ Tween 80 and $0.2\%$ glycerol. Axenic mid-log phase cultures were cryopreserved in $20\%$ glycerol (Sigma) and were kept at -80°C for future purpose.
## Peritoneal macrophages
Mice were intraperitoneally injected with 2ml of $4\%$ thioglycolate broth (BD) five days prior to the experiment. Cells were isolated from the peritoneal cavity using ice-cold sterile PBS and were cultured in RPMI-1640 medium supplemented with $10\%$ fetal bovine serum (Thermo fisher scientific Inc or Hyclone) followed by overnight incubation at 37°C and $5\%$ CO2. Non-adherent cells were removed by washing with sterile PBS while adherent monolayer of cells was used for further experiments. The homogeneity of isolated macrophages was analysed by staining with CD11b antibody followed by flow cytometry.
## Ex vivo infection, cell death, Apoptosis assay & CellROX assay
Peritoneal macrophages or PMA- differentiated THP1 cells were used for ex vivo experiments. For checking the cytotoxicity of BBR, mouse peritoneal macrophages were treated with different concentrations of BBR for 48 h followed by propidium iodide (PI) staining as described elsewhere [21]. Since 20 μg/ml of BBR was chosen for future experiments, a time dependent PI cytotoxicity assay was performed at this concentration. For infections, bacterial cryostocks were revived and single cell suspensions were formed. Macrophages were infected with M.tb strains at 1:10 MOI. Four hours post-infection, cells were washed twice with 1X PBS so as to remove the extracellular bacteria. Cells were treated with 20 μg/ml of BBR and then incubated at 37°C for different time points followed by CFU enumeration [63], immunoblotting, RNA isolation or flow cytometry. Apoptosis was detected by staining the cells with Annexin V/7AAD (Biolegend) as per manufacturer’s protocol. CellROX (ThermoFisher Scientific) was used to detect the intracellular ROS in the M.tb infected peritoneal macrophages after treatment with 20 μg/ml of BBR for 24 h [64].
## Human PBMC isolation
Blood samples from 10 healthy individuals were collected in BD vacutainer blood collection tubes and were layered onto Histopaque 1077 (Sigma-Aldrich) followed by centrifugation at 400g for 30 minutes at 25°C. The opaque interface containing PBMCs were gently transferred and suspended in complete RPMI-1640 medium. These cells were then pelleted, counted, and seeded in the 12-well plates for further experiments.
## Western blot analysis
RIPA buffer (50 mM Tris, pH 8.0, 150 mM NaCl, $1.0\%$ NP-40, $0.5\%$ Sodium deoxycholate, $0.1\%$ SDS) supplemented with 1X protease and phosphatase inhibitor cocktail (thermos scientific) was used to lyse the cells. Protein concentration in samples was estimated using Bradford assay. Samples were run on $10\%$ polyacrylamide gel followed by transfer on PVDF membrane (Millipore). Membrane was then blocked with $5\%$ BSA dissolved in PBST (PBS and $0.1\%$ Tween-20), followed by overnight probing for diverse proteins with respective antibodies. Chemiluminescent HRP substrate (ECL, Millipore) was layered to develop blots on ImageQuant LAS 500.
## qPCR analysis
Total RNA was isolated from peritoneal macrophages and splenocytes by standard RNA isolation protocol followed by cDNA synthesis using iScript cDNA synthesis kit (Bio-Rad). Real-time PCR was performed using SYBR Green Master Mix (Bio-Rad). Bio-Rad Real-Time thermal cycler (BioRad, USA) was used for Real-time quantitative RT-PCR analysis. The list of Primers used in the study are provided in the S1 Table.
## Mice infection with M.tb and CFU enumeration
Mice were infected with drug-susceptible and resistant strains of M.tb via aerosol route using Madison aerosol chamber (University of Wisconsin, Madison, WI) with its nebulizer pre-adjusted to deposit approximately 110 CFUs to the lungs of mice. Axenic cultures were sonicated to prepare 15 ml of bacterial single-cell suspension for infection. 5–6 mice from each group were euthanized at different time points to determine the bacterial burden. Lungs and spleen were isolated and homogenized in sterile PBS and were plated onto 7H11 Middlebrooks (Difco) plates containing $0.05\%$ Tween-80, $10\%$ oleic acid, albumin, dextrose, and catalase (OADC) (Difco). The homogenates were plated in different dilutions and were incubated at 37°C for 21–28 days. M.tb colonies were counted and CFU was enumerated at various time points.
## Drug administration
For ex vivo infection experiments, macrophages were treated with 20μg/ml of BBR (Sigma). Furthermore, immunological memory was analysed in human PBMCs and mice splenocytes utilising 10 μg/ml of BBR (Sigma). For mice studies, 4 mg/kg of BBR dissolved in 100 μl of PBS with $5\%$ DMSO was injected intraperitoneally for 45 days thrice a week, whereas the control group received only vehicle. 100 mg/L of INH and 60 mg/L RIF were administered in drinking water which was changed every alternative day.
## Adoptive cell transfer therapy
To ascertain the M.tb specific immune response, the harvested lungs from BBR treated group was macerated using sterile frosted slides to prepare single cell suspension. Subsequently the stained CD4+ T cells were sorted and cultured overnight in complete RPMI. Approximately one million cells were injected intravenously to Rag1-/- mice and further given low dose aerosol 5 days post transfer. Lung and spleen were then isolated after 21 days to determine the bacterial load.
## Multiplex cytokines immunoassay
Supernatant of 24h cultured Human PBMCs was collected and dilutions were prepared for sample and standard using Bio-Plex Pro Human Cytokines Assay Instructions manual. Detecting antibody was further added followed by Streptavidin-PE incubation. Data was acquired using Bio-Plex system and then analysed.
## Mass spectroscopy
10μg of protein was isolated from Control and BBR treated Human PBMCs and then desalted by reduction, alkylation and digestion using Trypsin Gold, Mass Spectrometry Grade (Promega Corporation, WA, USA) for 24hrs at 37°C for LC-MS/MS analysis. The peptides formed were extracted on a 25-cm analytical C18 column (C18, 3 μm, 100 Å), by (5–$95\%$) gradient of buffer B (aqueous $80\%$ acetonitrile and $0.1\%$ formic acid) at a flow rate of 300 nL/min for 2.5 hrs. Subsequently, these peptides were exposed to nano-electrospray ionisation and Tandem mass spectrometry (MS/MS) by the application of Q-Exactive (Thermo Fisher Scientific, San Jose, CA, United States) at the collision-induced dissociation mode with the electrospray voltage of 2.3 kV. Data analysis comprising of standard statistical analysis, network and pathway analysis was done by Proteome Discoverer (version 2.0, Thermo Fisher Scientific, Waltham, MA, United States).
## BCG vaccination
Mice were vaccinated subcutaneously with the single dosage of 1 × 106 colony forming units (CFUs) of BCG Pasteur strain in 100 μL of sterile saline. Subsequent to 7 days rest post-vaccination, the mice were treated with 4 mg/kg of BBR, thrice a week for 14 days intraperitoneally. After 14 days of break, the mice were then challenged via aerosol infection of M.tb strain H37Rv. Organs were harvested to elucidate bacterial burden and immune profiling within 30 days post-infection.
## Reinfection and Reactivation experiments
To undermine the susceptibility of M.tb infection, studies were performed in reactivation and reinfection models. Low dose aerosol of H37Rv was given to the mice and treated with INH (100 mg/L) and RIF (40 mg/kg) in drinking water for 12 weeks and rested for 30 days. Thereafter, the group was again challenged with M.tb infection followed by immune response and bacterial load determination. For reactivation studies, the mice were given dexamethasone (5 mg/kg) intraperitoneally, thrice a week for 30 days and so enumerated CFU and host protective immunological profiles.
## L-Lactate quantification
Extracellular L-Lactate levels were measured in the culture supernatant from splenocytes treated with BBR and 2DG. The experiment was performed using L- Lactate assay kit (Cayman Chemicals) as per the manufacturer’s guidelines.
## Flow cytometry
Lungs and spleen from mice of different groups were isolated and macerated using frosted slides in ice-cold RPMI 1640 (Hyclone) supplemented with $10\%$ FBS to prepare single-cell suspension. RBC lysis buffer was used to lyse RBCs and cells were washed with $10\%$ RPMI 1640. After cell counting, 1×106 cells were seeded in 12 well for staining. For surface staining, cells were activated by 10 μg/mL of H37Rv complete soluble antigen (CSA) stimulation. Subsequently, 0.5 μg/mL Brefeldin A and 0.5 μg/mL of Monensin solution (BioLegend) were added during the last 4 hours of culture. Cells were then washed twice with FACS buffer (PBS + $3\%$ FBS) and stained with antibodies directed against surface markers followed by fixation with 100 μl fixation buffer (biolegend) for 30 min. For intracellular staining, the cells were permeabilized using 1X permeabilizing buffer (Biolegend) and then were stained with fluorescently labelled anti-cytokine antibodies. For non-flurochrome tagged antibodies, secondary antibody tagged with Alexa Fluor 488 was used to measure the flurochrome intensity. The intensity of fluorochromes were assessed by flow cytometry (BD LSRFortessa Cell Analyzer—Flow Cytometers, BD Biosciences) followed by data analysis via FlowJo (Tree Star, USA).
## Antibodies
Following antibodies were used for this study: Anti-Mouse: CD3-Pacific Blue, CD4-PerCPCy5.5, CD8-APCCy7, CD69-PE, CD44-FITC, CD62L-APC, CD103-PeCy7, CD69-FITC, IFNγ-APC, IFNγ-BV510, IL17-PECy7, IL17-BV650, CD11b-APCCy7, CD11c-APC, CD80-FITC, CD86-PerCPCy5.5, CD40-PE, CD4-PE, CD4-APC and CD4-FITC, CD3-BV510, CD3-BV650 from Biolegend, USA.
Anti-human: CD3-PE, CD4-APCCy7, CD8-Pacific Blue, CD45RO (PerCPCY5.5), CCR7 (PeCy7), CD69 (Alexa Fluor 700) from BD Biosciences.
Anti-human/anti-mouse: STAT3, p-STAT3, STAT4, p-STAT4, AKT, p-AKT, FOXO1, p-FOXO1, NFκB, p-NFκB, BLIMP1 and β-Tubulin from Cell Signaling Technology.
## Histopathology
Lungs harvested from infected animals were fixed with $10\%$ neutral buffered formalin, and H&E staining was performed on 5-μm-thick paraffin-embedded tissues and were examined under microscope. Granulomas for each animal in every group were screened in 5 different fields. Submitted images are representative of all the visualized section images.
## Statistical analysis
All the experimental data was analysed using GraphPad Prism Software. Significant differences between the groups were determined by 2 tailed unpaired Student’s t-test or 1-way ANOVA. Human data was analysed by 2 tailed paired Student’s t-test. * $p \leq 0.05$, **$p \leq 0.005$, ***$p \leq 0.0005.$
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|
---
title: Joint modeling of longitudinal change in pulse rate and survival time of heart
failure patients treated at Arbaminch General Hospital, Southern Ethiopia
authors:
- Belay Belete Anjullo
- Sebisibe Kusse Kumaso
- Markos Abiso Erango
journal: PLOS ONE
year: 2023
pmcid: PMC9990934
doi: 10.1371/journal.pone.0282637
license: CC BY 4.0
---
# Joint modeling of longitudinal change in pulse rate and survival time of heart failure patients treated at Arbaminch General Hospital, Southern Ethiopia
## Abstract
### Introduction
Heart failure is a chronic progressive disease in which the heart muscle is unable to pump enough blood to meet the body’s need. It is a severe health problem around the world with high re-hospitalization and death rates. The main aim of this study was to identify the factors associated with longitudinal change of pulse rate and survival time to death of congestive heart failure patients treated at Arba Minch General Hospital.
### Methods
A retrospective study design was undertaken on congestive heart failure patients admitted to the Arba Minch General Hospital from January 2017 to December 2020. Data was collected from a total of 199 patients. After evaluating the longitudinal data with a linear mixed model and the survival time to death data with cox proportional model, Bayesian joint model of both sub models was fitted in R software using JMbayes2 package.
### Results
Findings from Bayesian joint model revealed that the estimated value for the association parameter was positive and statistically significant. This indicates that there is significant evidence of an association between the mean longitudinal change of pulse rate and the risk of death. Weight of patients at baseline, gender, chronic kidney disease, left ventricular ejection fraction, New York Heart Association classification, diabetes, tuberculosis, pneumonia and family history were statistically significant factors associated with mean evolution of pulse rate of congestive heart failure patients. Left ventricular ejection fraction, etiology of congestive heart failure, type of congestive heart failure, chronic kidney disease, smoking, family history, alcohol and diabetes were found to be statistically significant factors associated with survival time to death.
### Conclusion
To reduce the risk level, health professionals should give attention to congestive heart failure patients with high pulse rate, co-morbidities of chronic kidney disease, tuberculosis, diabetic, smoking status, family history, and pneumonia in the study area.
## 1. Introduction
Heart failure (HF) is a chronic progressive disease in which the heart can’t keep up with its workload. It results from the failure of the heart to pump enough blood into the circulation due to ventricular a systolic dysfunction defined as left ventricular ejection fraction(LVEF)< $40\%$ to $50\%$ (HF with depressed ejection fraction) [1]. It is the final stage of all cardiac disorders and could be a major cause of morbidity and death [2]. With emergent urbanization, changes in lifestyle habits, and the ageing of the population, the range of causes of heart failure has also extended which results in a significant burden of both communicable and non-communicable etiologies [3].
Heart failure is a severe health problem around the world with high re-hospitalization and death rates [4]. The global re-hospitalization rate in patients with HF is over $50\%$ [5]. As a result, heart failure affects 33 million people worldwide, or 26.4 percent of the adult population. The developed world accounts for 65.73 percent of the adult population, while developing countries account for 34.27 percent. It is estimated that by the year, there will have been a $60\%$ increase over the year 2000 [6]. It is the world’s fastest-growing cardiovascular disease, putting a significant strain on healthcare systems around the world [7]. Congestive heart failure (CHF) has become one of the top causes of death among those who have a lower quality of life and a shorter lifespan. In 2017, 960,000 new instances of congestive heart failure were identified in the United States alone, and this number is expected to rise year after year as the population ages. It is estimated that by 2030, the global prevalence will have increased by 8 million people [8].
Low and middle-income countries had more prevalent heart failure associated death than high-income countries [9]. It was reported that Sub-Saharan *Africa is* the one of the low-income countries in which the magnitude of risk factors associated with heart failure is increasing and heart failure has been established as a significant contributor to the burden of cardiovascular illness [10,11]. It was reported that patients with heart failure in Africa were the youngest and most likely to be in New York Health Association functional class type IV [12]. A study conducted in Sub-Saharan Africa Survey of Heart Failure identified a growing cause of heart failure from $23\%$ to $43\%$ in persons with heart failure [13]. It has become one of the top causes of death among those who have a lower quality of life and a shorter lifespan [14]. According to the WHO [15] report, in 2012 around $9\%$ of all deaths in Ethiopia was due to heart failure disease.
In order to monitor the burden of congestive heart failure disease, it is required to jointly model the biomarker of the disease and survival time of patients. Hence, the main aim of this study was to identify the factors associated with longitudinal change of pulse rate and survival time to death of congestive heart failure patients treated at Arba Minch General Hospital using a Bayesian joint modeling approach.
## 2.1. Cohort-based data
The data used in this study was obtained from a cohort-based retrospective study of patients diagnosed with congestive heart failure at Arba Minch General Hospital. Patients were followed on pulse rate every month from January 2017 to December 2020 and the target population of this study includes all congestive heart failure patients under follow-up who had at least three pulse rate measurements after the first report of congestive heart failure. Patients whose medical cards were incomplete and registered during the data collection time were excluded. Therefore, a total sample of 199 patients who have full records or complete history during the study period was considered in this study. The study was carried out after getting approval on data collection from ethical committee at Arba Minch General Hospital. In addition, all study methods were performed based on relevant guidelines and regulations laid down by the Committee.
## 2.2. Variables in the study
The response variable considered in this study was the longitudinal pulse rate and survival outcome variable, where the pulse rate of the patients was measured every month for each congestive heart failure patient under follow-up, and the survival outcome variable was the time to-death of the patient under follow-up. The explanatory variables considered in this study were patients age (in year), weight(in kilogram), visiting time (monthly follow up), body temperature, blood pressure, and left ventricular ejection fraction in percent, patient gender, place of residence, smoking status of patients (smoker, nonsmoker), diabetes status of patients (present, absent), tuberculosis status of patients (positive, negative), family history of patients (present, absent), chronic kidney status of patients (present, absent), anemia status of patients (anemic, non-anemic), alcohol intake (yes, no), pneumonia status of patients (present, absent), etiology of heart failure (Valvular Heart Disease, Hypertensive heart disease, Ischemic Heart disease, Other), type of congestive heart failure patient (left ventricular, right ventricular, biventricular) and New York Heart Association classification (class I, class II, class III, class IV).
## 2.3. Methods of data analysis
Before modeling, exploratory data analysis was conducted to investigate various structures and patterns exhibited in the data set. This consists of obtaining the summary statistics such as mean and variance for pulse rate. Besides, the individual profile plots, mean structure, and variance structure plots were used to gain some insights into the data on the longitudinal outcome. While, the individual profile plots and the variance structure were used to gain insight into the variability in the data and to determine which random effects to be considered in the linear mixed model. Also, the mean structure was used to gain intuition on the time function that can be used to model the data.
## 2.3.1. Joint longitudinal-survival models
The joint model consists of two linked sub-models, known as the longitudinal sub-model, and the survival sub-model was used as given subsequently.
1. Linear Mixed Model. Linear mixed model (LMM) is the most frequently used random effects model in the context of continuous repeated measurements from longitudinal responses when the measurements are taken on the same or related subjects at different times, in both cases, the responses are likely to be correlated [16–18]. When modeling longitudinal data, our interest is to study the association between dependent variable and a set of explanatory variables [19]. In this study, the dependent variable, pulse rate was taken on the same subject at different times with different baseline characteristic, LMM was used to model longitudinal measurement on pulse rate taken on the same subject at different time points and the model has the form given in Eq [1]: Yi(t)=Xi(t)β+Zi(t)ui+εi(t)=mi(t)+εi(t)mi(t)=Xi(t)β+Zi(t)ui [1] {ui∼N(0,D)εi∼N(0,Σ)u1,…,unandε1,…,εnareindependent Where Yi(t) is the ni×1 pulse rate for the ith patient at time t, *Xi is* ni×p dimensional design matrix of fixed predictors linking β to the set of longitudinal measurements of pulse rate, *Zi is* ni×q dimensional design matrix values of the random factors linking ui to Yi for ith patient, β is a p dimensional vector containing fixed effects, ui is a q dimensional vector containing random effects of ith patient and εi is distributed as N(0, Σi) is a vector of residual components, combining measurement error and serial correlation. Then ui is distributed as N(0,Ω), independently of each other. That is, cov(ui, εi) = 0. Furthermore Σi = δ2Ini is the ni×ni. positive-definite variance-covariance matrix for the errors in subject i,where Ini denotesthe ni×ni identity matrix.
2. The Cox Proportional Hazards Model. The Cox proportional-hazards model is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables [20]. A proportional hazards model proposed by [20] assumes at the hazard function h (t, X, γ) is related to the covariates as a product of a baseline hazard ho (t) and a function of covariates, has a form as shown in Eq [2]: hi(t|Xi)=ho(t)exp(γTXi)=ho(t)exp(γ1X1+γ2X2+⋯+γpXp) [2] Where, ho (t) is the baseline hazard function, X = (X1, X2, X3,…,Xp) is a set of covariates from ith patient and γ = (γ1, γ2, γ3,…,γp) is unknown p regression parameters which measure the effect of the covariates on the risk of death. Hence, the joint model [21] that link the longitudinal response to the time-to-event process through current value parameterization has the form given in Eq [3]: hi(t|mi(t),Xi)=ho(t)exp(γTXi+αmi(t)),t>0 [3] where mi(t) = {mi(s), 0≤s<t} denotes the history of the true unobserved longitudinal process up to time point t given in Eq [1], ho(t) denotes the baseline risk function, and *Xi is* a set of baseline covariates with a corresponding vector of regression coefficients γ and α is association parameter which quantifies the effect of the underlying longitudinal outcomes to the risk for an event. Here in this study all the parameters of joint model are estimated under a Bayesian framework using Markov chain Monte Carlo (MCMC) methods with Gibbs sampling using the JMbayes2 package in R software. The empirical results from a given MCMC analysis are not deemed reliable until the chain has reached its stationary distribution. On account of this, the term convergence of an MCMC algorithm refers to whether the algorithm has reached its target distribution. Hence, monitoring the convergence of the algorithm is essential for producing results from the posterior distribution of interest [22]. Among several ways the most popular and straight forward convergence assessment methods; time series(history) plot and density plot [23] were used to assess whether the sample has reached its stationary distribution or not. Summary statistics (posterior mean and credible interval of posterior mean) was computed for each parameter. Finally, the importance of each of the explanatory variables is assessed by carrying out statistical tests of the significance of the regression coefficients (posterior mean) via $95\%$ Bayesian credible interval of the posterior mean [24].
## 2.4. Ethical approval and consent
The study was carried out after getting permission from the Statistics Department, Arba Minch University. In this regard, the official letter of co-operation referenced with stat/$\frac{534}{2013}$ was written to ethical approval committee at Arba Minch General Hospital. Then, the ethical committee approved the letter and gave permission to collect data from patients’ record and to use in the study. For the purpose of confidentiality, there was no links with individual patients and all data had no personal identifier. Therefore, informed consent to the patient has been waived by Arba Minch General Hospital ethical committee.
## 3.1. Descriptive analysis
As indicated in Table 1, from a total of 199 congestive heart failure patients, $50.3\%$ who received treatment were women, whereas the remaining $49.7\%$ were men. The majority, $58.8\%$ of the patients with congestive heart failure were from in rural areas. 127 ($63.8\%$) of those with congestive heart failure had a family history of heart failure, whereas 72 ($36.2\%$) had no family history of heart failure. Regarding the New York Heart Association (NYHA) class of congestive heart failure patients, among 199 patients, $5\%$ of them were New York Heart Association class I, $11.6\%$ were New York Heart Association class II, $29.1\%$ were New York Heart Association class III and $54.3\%$ were New York Heart Association class IV. Regarding history of disease status, among 199 patients, $56.3\%$ of heart failure patients had diabetes, $35.7\%$ had chronic kidney disease, $37.2\%$ were smokers, $43.7\%$ had pneumonia, $59.3\%$ had tuberculosis, and $70.9\%$ had anemia as comorbidities.
**Table 1**
| Variables | Variables.1 | Categories | Frequency (%) | Survival status | Survival status.1 |
| --- | --- | --- | --- | --- | --- |
| Variables | Variables | Categories | Frequency (%) | Event (%) | Censored (%) |
| Gender | Female | Female | 100 (50.3) | 24 (12.1) | 76(38.2) |
| Gender | Male | Male | 99 (49.7) | 19 (9.5) | 80(40.2) |
| Residence | Rural | Rural | 117(58.8) | 21 (10.5) | 96(48.3) |
| Residence | Urban | Urban | 82(41.2) | 22 (11.1) | 60(30.1) |
| Family History status | Absent | Absent | 72(36.2) | 16(8.1) | 56(28.1) |
| Family History status | Present | Present | 127(63.8) | 27(13.6) | 100(50.2) |
| Diabetes status | Absent | Absent | 87(43.7) | 9(4.5) | 78(39.2) |
| Diabetes status | Present | Present | 112(56.3) | 34(17.1) | 78(39.2) |
| Tuberculosis status | Negative | Negative | 81(40.7) | 7(3.5) | 74(37.2) |
| Tuberculosis status | Positive | Positive | 118(59.3) | 36(18.1) | 82(41.2) |
| Smoking status | Non smoker | Non smoker | 125(62.8) | 24(12.1) | 101(50.7) |
| Smoking status | Smoker | Smoker | 74(37.2) | 19(9.6) | 55(27.6) |
| Pneumonia status | Absent | Absent | 112(56.3) | 16(8.1) | 96(48.2) |
| Pneumonia status | Present | Present | 87(43.7) | 27(13.6) | 60(30.1) |
| Alcohol intake | No | No | 103(51.8) | 21(10.6) | 82(41.2) |
| Alcohol intake | Yes | Yes | 96(48.2) | 22(11.1) | 74(37.1) |
| Anemia status | Anemic | Anemic | 141(70.9) | 39(19.6) | 102(51.3) |
| Anemia status | Non anemic | Non anemic | 58(29.1) | 4(2) | 54(27.1) |
| Chronic Kidney disease | Absent | Absent | 128(64.3) | 21(10.6) | 107(53.8) |
| Chronic Kidney disease | Present | Present | 71(35.7) | 22(11.1) | 49(24.6) |
| New York Heart Association classification | Class Ⅰ | Class Ⅰ | 10(5.0) | 1(0.503) | 9(4.5) |
| New York Heart Association classification | Class Ⅱ | Class Ⅱ | 23(11.6) | 2(1.0) | 21(10.6) |
| New York Heart Association classification | Class Ⅲ | Class Ⅲ | 58(29.1) | 8(4.0) | 50(25.1) |
| New York Heart Association classification | Class Ⅳ | Class Ⅳ | 108 (54.3) | 32(16.1) | 76(38.2) |
| Types of congestive heart failure | Right ventricular | Right ventricular | 55(27.6) | 5(2.5) | 50(25.1) |
| Types of congestive heart failure | Bi ventricular | Bi ventricular | 64(32.2) | 12(6.1) | 52(26.1) |
| Types of congestive heart failure | Left ventricular | Left ventricular | 80(40.2) | 26(13.1) | 54(27.1) |
| Etiology of congestive heart failure | Valvular Heart Disease | Valvular Heart Disease | 51(25.6) | 16(8.0) | 35(17.6) |
| Etiology of congestive heart failure | Hypertensive heart disease | Hypertensive heart disease | 43(21.6) | 7(3.5) | 36(18.1) |
| Etiology of congestive heart failure | Ischemic heart disease | Ischemic heart disease | 55(27.6) | 12(6.0) | 43(21.6) |
| Etiology of congestive heart failure | Other | Other | 50(25.1) | 8(4.0) | 42(21.1) |
Similarly, out of the total 199 congestive heart failure treatment followers, about 24 ($12.1\%$) female respondents died from treatment and the remaining were censored. On the other hand, about 19 ($9.5\%$) of the male respondents died, and the rest of the respondents were censored. Based on the patient’s residence, out of 82 urban and 117 rural patients, 22 ($11.1\%$) and 21($10.5\%$) of the respondents had an occurrence of an event, respectively (see Table 1).
Regarding continuous predictors, the average of age patients at baseline was 48.6 years with a standard deviation of 17.385 years, average weight and left ventricular ejection fraction of patients at baseline were 54.21 kilograms and $43.18\%$ with a standard deviation of 10.93 kilograms and $13.93\%$ respectively.
## 3.2. Exploratory data analysis
Exploratory analysis of longitudinal data attempts to find patterns of systematic variation across groups of subjects as well as characteristics of random variation that distinguish each patient.
Fig 1 depicted individual profile plot of the longitudinal pulse rate of all patients and fifteen randomly selected congestive heart failure patients by follow-up time. Hence, it can be observed that some trajectories were steeper while others were almost identical; suggesting that the slope and intercept of pulse rate readings may vary. After they begin follow-up, there seems to be a variation in pulse rate measurement over time, with the variability of the pulse rate measurement appearing bigger at the beginning and smaller at the latter. It was seen that there is variability in pulse rate measurement between patients, indicating that random effects for each subject should be included to capture this variability and allow pulse rate measurement for patients within the same patient to be correlated (see Fig 1). From mean profile plot of pulse rate, the patients’ average pulse rate was strictly decreasing until the tenth month of follow-up, after which it began to gradually decrease until the 28th month, when it changed to oscillations and began to decrease. The average pulse started to decline after the follow-up, indicating that patients with congestive heart failure were at risk at baseline. In addition, mean profile plot of pulse rate also shows that the horizontal loss smoothing approach indicates that the mean structure of pulse rate is roughly linear over time (see S1 Fig).
Fig 2 shows the variance of the pulse rate measurements of congestive heart failure patients showed an irregular pattern over the follow-up period. It increases at some point and decreases at another point, suggesting a non-constant variation among congestive heart failure patients over the follow-up period. It was observed that high variation was observed among males until the 18th month and after the 27th month, and the variance of both genders increased at some point and decreased at another point, which suggests there, is no constant variation over time which suggest that the mixed effect model with random intercept could be the candidate starting model to fit the data (see Fig 2).
## 3.3. Bayesian joint model of pulse with survival time
First, longitudinal measurement on the pulse rate and survival outcome time to death was separately modeled using linear mixed model and cox proportional hazard model, respectively. Accordingly, linear mixed-effect model with random intercept, random slope, and random intercept and slope were fitted and compared. Hence, the linear mixed-effect model with random intercept and slope had lower values of AIC and BIC and chosen as the parsimonious model to fit the data on the longitudinal change of pulse rate (result not shown here).
In addition, for Cox proportional hazard model, proportional hazard assumption was tested for each categorical covariate. Hence, P-value of individual covariate and GLOBAL test statistic are greater than $5\%$ level of significance, indicating that all covariates satisfied the proportionality assumption of the Cox model (result not shown here). Following the development of separate models, Bayesian joint model that links longitudinally measured pulse rate to the survival time to death was performed using JMbayes2 package in R software version 4.2. The Gibbs sampler algorithm was implemented with 20,000 iterations in three different chains initialized with over dispersed values for all parameters. Then, samples generated from the full posterior distribution are used to make inference about the joint model parameters. Before undertaking any inference from posterior distribution the convergence of generated Markov chains has been verified by density plot (Fig 3) and time series or history plot (Fig 4). Fig 3 shows density plots for only some selected statistically significant regression coefficients in the joint model and simulated samples from posterior distribution for each regression coefficient is smooth, uni-modal shape of posterior marginal distribution indicating that simulated parameter value indicates convergence to the target distribution. The density plots for the rest of the parameters (not shown here) also tell a similar story.
Fig 4 shows history plots for only some selected statistically significant regression coefficients in the joint model and this option produces iteration number on x axis and parameter value on y-axis). The plots looks like a horizontal band, with no long upward or downward trends and the two independently generated chains demonstrated good "chain mixture" indicating that the chains has converged. The Time series (history) plots for the rest of the parameters (not shown here) also tell a similar story. This implies convergence for the regression parameters in the joint model are attained. Table 2 presents the estimates (estimated parameters, hazards ratio (HR) and $95\%$ credible intervals for estimated posterior mean) from Bayesian joint modeling of longitudinal change of pulse rate with survival time of heart failure patients. Therefore, result from longitudinal sub model revealed that status of tuberculosis, New York Heart Association class type, left ventricular ejection fraction, gender, length of follow-up time, having chronic kidney disease, status Pneumonia, family history, status of diabetes and weight of patients at baseline had statistically significant effects on the average longitudinal change of pulse rate at $5\%$ level of significance ($95\%$ credible interval doesn’t included zero, see Table 2). Likewise result from survival sub model revealed that the covariates status of chronic kidney disease, family history, status of diabetes, smoking status of patients, status of tuberculosis, left ventricular ejection fraction, type of congestive heart failure, etiology of congestive heart failure and status of alcohol intake were associated with the time to death of congestive heart failure patients.
**Table 2**
| Longitudinal sub-model: Fixed effects | Longitudinal sub-model: Fixed effects.1 | Longitudinal sub-model: Fixed effects.2 | Longitudinal sub-model: Fixed effects.3 |
| --- | --- | --- | --- |
| Predictors | Categories | Posterior mean estimate of β’s | 95% CI of Posterior mean estimate of β’s |
| | Intercept | 322.2 | (167.13, 500.03)* |
| | length of follow-up time | -30.7 | (-40. 58–20.59)* |
| | Left ventricular ejection fraction | -0.86 | (-1.29, -0.47)* |
| New York Heart Association classification | Class I (Ref) | | |
| New York Heart Association classification | Class II | 0.38 | (-5.76, 6.52) |
| New York Heart Association classification | Class III | 5.8 | (-0.41, 12.14) |
| New York Heart Association classification | Class IV | 12.9 | (3.73, 21.79)* |
| Gender | Female (Ref) | | |
| Gender | Male | 7.6 | (1.58, 13.75)* |
| Tuberculosis status | Negative (Ref) | | |
| Tuberculosis status | Positive | 9.5 | (3.45, 15.59)* |
| Chronic kidney status | Absent(Ref) | | |
| Chronic kidney status | Present | 7.7 | (1.78, 13.78)* |
| Pneumonia status | Absent(Ref) | | |
| Pneumonia status | Present | 17.3 | (10.99, 23.65)* |
| Family history | Absent(Ref) | | |
| Family history | Present | 12.3 | (6.18, 18.37)* |
| Diabetes Status | Absent(Ref) | | |
| Diabetes Status | Present | 9.4 | (3.42, 15.49)* |
| | Baseline Weight | 0.15 | (0.07, 0.24)* |
| | Baseline Age | -0.01 | (-0.06, 0.03) |
| Random effects | Random | Standard deviation | 95% credible interval |
| Random effects | Intercept | 374.6 | (369.8, 379.4) |
| Random effects | Slope of visiting time | 26.9 | (18.1,35.7) |
| Random effects | Corr (Intercept, slope of visiting time) | 0.14 | (0.11, 0.17) |
| Survival sub model | Survival sub model | Survival sub model | Survival sub model |
| Predictors | Categories | Posterior mean estimate of γ’s(HR) | 95% CI of posterior mean estimate of γ’s |
| | Left ventricular ejection fraction | -0.08(0.91) | (-0.12, -0.05)* |
| Etiology of congestive heart failure | Valvular Heart Disease (Ref) | | |
| Etiology of congestive heart failure | Ischemic Heart disease | -2.38(0.09) | (-4.19, -0.39)* |
| Etiology of congestive heart failure | Hypertensive heart disease | 0.99(2.69) | (-0.53, 2.56) |
| Etiology of congestive heart failure | Other | 2.84(17.16) | (1.11, 4.57)* |
| Type of congestive heart failure | left ventricular(Ref) | | |
| Type of congestive heart failure | Right ventricular | -3.12(0.04) | (-4.75, -1.16)* |
| Type of congestive heart failure | Biventricular | 0.80(2.22) | (-0.16, 1.76) |
| Tuberculosis status | Negative(Ref) | | |
| | Positive | 4.11(61.49) | (2.95, 5.26)* |
| Status of Chronic Kidney | Absent(Ref) | | |
| Status of Chronic Kidney | Present | 2.17(8.80) | (0.93, 3.36)* |
| Smoking status | Nonsmoker(Ref) | | |
| Smoking status | Smoker | 2.68(14.65) | (1.63, 3.67)* |
| Family history | Absent(Ref) | | |
| Family history | Present | 2.77(16.02) | (1.59, 3.83)* |
| Status of Alcohol intake | No(Ref) | | |
| Status of Alcohol intake | Yes | 1.40(4.06) | (0.24, 2.59)* |
| Diabetes Status | Absent(Ref) | | |
| Diabetes Status | Present | 2.07(7.94) | (1.11, 2.97)* |
| | Association parameters(α) | 1.7 (5.47) | (1.39, 2.54)* |
When the other predictors are kept constant, the average longitudinal change on pulse rate was 30.7 beats per minute for a unit increase of length of follow up time of congestive heart failure patients. Similarly, if the left ventricular ejection fraction of congestive heart failure patients increased by $1\%$, the average longitudinal change in pulse decreased by 0.86 beats per minute. Regarding the weight of patients at baseline, as the weight of patient increased by one kilogram the pulse rate of the patient increases by 0.15 beats per minute.
Regarding the New York Heart Association classification, the average longitudinal change of pulse rate of patient increased by 12.9 beats per minute for patient with class type IV congestive heart failure compared to patients with New York Heart Association class I congestive heart failure. Congestive heart failure patients with positive tuberculosis status significantly increased the mean longitudinal change of pulse rate by 9.5 beats per minute compared to patient with negative tuberculosis status.
Furthermore, regarding the gender of patients, male congestive heart failure patient had 7.6 beats per minute higher mean longitudinal change in pulse rate than female congestive heart failure patient. The congestive heart failure patients who had a family history, pneumonia, chronic kidney disease, and diabetes mellitus disease had higher average longitudinal change of pulse rate compared to congestive heart failure patients who didn’t have a family history, pneumonia, chronic kidney disease, and diabetes mellitus disease, respectively. The variation of the random intercepts in pulse rate for the random portion of the linear mixed-effect model was 374.6, with a random slope of 2.69. This indicates that there is a greater baseline difference in pulse rate at the start of their treatment (see Table 2).
When the other covariates are held constant, for a unit increase in percentage of left ventricular ejection fraction risk of death in congestive heart failure patients decreased by 0.08 (HR = 0.91). That means a reduction in left ventricular ejection fraction increases the risk of mortality in individuals with congestive heart failure or vice versa. At baseline, the ischemic heart disease patient group had a hazard rate of 0.09 times higher than the Valvular heart disease patient group. Regarding the type of congestive heart failure, patients with congestive heart failure who tested positive for tuberculosis had a significantly greater risk of death (HR = 61.49). This means that patients who tested positive for tuberculosis had 61.49 times higher risk of death than who tested negative for tuberculosis.
Patients with chronic kidney disease (HR = 8.802), being smoker (HR = 14.658), having a family history (HR = 16.022), drinking alcohol (HR = 4.063), and having diabetes (HR = 7.94) were all statistically associated with higher risk of death in congestive heart failure patients compared to patients who had no chronic kidney disease, patients who were non-smoker, had no family history, non-drinking alcohol, and patients who had no diabetes respectively.
In the joint model, it was found that association parameter that link longitudinal biomarker pulse rate and the survival time to death of congestive heart failure patients was statistically significant($95\%$ credible interval doesn’t included zero, see Table 2). Hence, the estimated value of the association parameter alpha was 1.7 (HR = 5.47), indicating that the average longitudinal change in pulse rate had a positive relationship with the time to death of CHF patients across time.
## 3.4. Discussion
Properties and features of residuals, when longitudinal and survival outcomes are separately modeled have been used to check model assumptions. Result revealed that normality assumption for longitudinal process is satisfied (see S2 Fig) and in order to validate the Cox proportional hazards model assumption of the survival sub-model, a graph of the Schoenfeld residuals was displayed to check the overall goodness of fit for survival sub-models. S3 Fig shows that the scaled Schoenfeld residuals are randomly distributed and a loess smoothed curve do not exhibit much departure from the horizontal line suggests that the proportional hazards assumption is not violated. This was also confirmed via testing the interaction of the covariate with the log of survival time (Global P-value = 0.293). The estimated value for the association parameter (α) in the joint model was statistically significant and indicates a strong association between longitudinal measurement of pulse rate and the risk of death. The average longitudinal change in pulse rate demonstrated a negative relationship with the length of follow-up for congestive heart failure patients. This finding is in line with conclusion from the previous study by [5]. It was found that the left ventricle ejection fraction of congestive heart failure patients had a negative significant effect on the average longitudinal change of pulse and also associated with a risk of death in congestive heart failure patients. This finding is consistent with conclusion by [25,26] and contradicts with finding by [5] which reported that left ventricular ejection fraction disease had no statistically significant effect on the risk of death. This study had no evidence of the significance of age on average change in pulse rate which contradicted the previous studies done by [5]. The baseline weight of a patient had statistically significant increasing effect on the average longitudinal change in pulse rate. This finding is consistent with previous study done by [5].
Congestive heart failure patients who have diabetes disease have a positive and significant effect on the average evolution of pulse rate and this is also related to the risk of death of congestive heart failure patients. This finding is similar to previous findings by [26,27]. Patients who had positive test result for tuberculosis had a positive significant effect on the average evolution of pulse rate and was associated with a risk of death. This result was in line with study by [26]. Furthermore, being male had a positive significant effect on the average longitudinal change in pulse rate. This finding is similar with conclusion by [26–28]. However, it contradicts previous study by [27].
## 4. Conclusions
For the joint modeling of the data, the joint model with random intercepts and slope from the longitudinal sub model and Cox proportional hazard model from survival sub model was found to be appropriate model to fit the data. Based on the result from Bayesian joint modeling of longitudinal change of pulse rate with survival time of heart failure patients, length of follow up time, weight of patients at baseline, gender, chronic kidney disease, left ventricular ejection fraction, New York Heart Association class type, diabetes, tuberculosis, pneumonia and family history were statistically significant factors associated with mean evolution of pulse rate of congestive heart failure patients. From survival sub model, Left ventricular ejection fraction, Etiology of congestive heart failure type, type of congestive heart failure, chronic kidney disease, smoking, family history, alcohol and diabetes were found to be statistically significant factors associated with the risk of death of the congestive heart failure patients. In addition, computed association parameters revealed that mean evolution of pulse rate was found to be statistically significant and positively related with the hazard rate of time to death of congestive heart failure patients in the study area. To reduce the risk level, health professionals should give attention to congestive heart failure patients with high pulse rate, co-morbidities of chronic kidney disease, tuberculosis, diabetic, smoking status, family history, and pneumonia in the study area.
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|
---
title: 'Gender and urban–rural residency based differences in the prevalence of type-2
diabetes mellitus and its determinants among adults in Naghadeh: Results of IraPEN
survey'
authors:
- Nafiseh Ghassab-Abdollahi
- Haidar Nadrian
- Kobra Pishbin
- Shayesteh Shirzadi
- Parvin Sarbakhsh
- Fatemeh Saadati
- Mohammad Sanyar Moradi
- Pouria Sefidmooye Azar
- Leila Zhianfar
journal: PLOS ONE
year: 2023
pmcid: PMC9990936
doi: 10.1371/journal.pone.0279872
license: CC BY 4.0
---
# Gender and urban–rural residency based differences in the prevalence of type-2 diabetes mellitus and its determinants among adults in Naghadeh: Results of IraPEN survey
## Abstract
### Background
Type 2 diabetes mellitus (T2DM) is one of the most important risk factors for cardiovascular diseases, with a high economic burden on health care systems. Since gender and residency can affect people’s lifestyle and health behaviors, this study was conducted to investigate the prevalence of T2DM and its determinants by gender and residency.
### Methods
A secondary analysis study was conducted on the survey data of the IraPEN (Iran’s Package of Essential Non-Communicable Disease) pilot program conducted in 2017 in Naghadeh County, Iran. Data of 3,691 participants aged 30–70 years from rural and urban areas of the County were included into data analysis process. Sociodemographic factors, anthropometric measurements, and cardiovascular risk factors related to T2DM were assessed.
### Results
The overall prevalence of T2DM within the population was $13.8\%$, which was significantly higher among women ($15.5\%$) than men ($11.8\%$), and non-significantly higher in urban ($14.5\%$) areas than rural ($12.3\%$) areas. In both genders, age (male: OR 1.01, $95\%$ CI: 1.00–1.03; $$P \leq 0.012$$; female: OR 1.03, $95\%$ CI: 1.02–1.04; $P \leq 0.001$), blood pressure (male: OR 1.77, $95\%$ CI: 1.13–2.79; $$P \leq 0.013$$; female: OR 2.86, $95\%$ CI: 2.12–3.85; $P \leq 0.001$), and blood triglycerides (male: OR 1.46, $95\%$ CI: 1.01–2.11; $$P \leq 0.04$$; female: OR 1.34, $95\%$ CI: 1.02–1.77; $$P \leq 0.035$$) had a significant relationship with the chance of developing T2DM. Among women, a significant relationship was found between abdominal obesity (OR 1.68, $95\%$ CI: 1.17–2.40; $$P \leq 0.004$$) and the chance of developing T2DM. Age (rural: OR 1.03, $95\%$ CI: 1.01–1.04; $P \leq 0.001$; urban: OR 1.02, $95\%$ CI: 1.01–1.04; $P \leq 0.001$), blood pressure (rural: OR 3.14, $95\%$ CI: 2.0–4.93; $P \leq 0.001$; urban: OR 2.23, $95\%$ CI: 1.66–3; $P \leq 0.001$), and abdominal obesity (rural: OR 2.34, $95\%$ CI: 1.41–3.87; $$P \leq 0.001$$; urban: OR 1.46, $95\%$ CI: 1.06–2.01; $$P \leq 0.019$$), in both rural and urban areas, blood cholesterol (OR 1.59, $95\%$ CI: 1.07–2.37; $$P \leq 0.02$$) in rural areas, and blood triglycerides (OR 1.51, $95\%$ CI: 1.16–1.98; $$P \leq 0.002$$) in urban areas were significant predictors of T2DM.
### Conclusion
Given the higher prevalence of T2DM among females, risk reduction strategies at the community level should be more targeted at women. The higher prevalence of T2DM risk factors among the urban population is a wake-up call for policymakers to pay more attention to the consequences of unhealthy and sedentary lifestyles within urban communities. Future actions should be focused on appropriate timely action plans for the prevention and control of T2DM from early years of life.
## Introduction
Type 2 diabetes mellitus (T2DM) is a complex disorder that results from several pathophysiological complications including decreased insulin secretion, increased glucose production in the liver, and increased insulin resistance [1]. T2DM is the most common type of diabetes and approximately accounts for 90 to $95\%$ of all diagnosed diabetes cases [2]. The number of people aged over 18 years with T2DM in 2014 was 422 million, which was equivalent to a prevalence of about %8.5, worldwide. The highest prevalence of T2DM occurs in middle and low-income countries. The prevalence of T2DM is continuously increasing in these countries [3]. In Iran, as a developing country. the prevalence of diabetes in the population older than 40 is more than $24\%$ [4].
The lack of definitive treatment and its deadly effects have made T2DM one of the most challenging diseases. The disease is also one of the most important risk factors for cardiovascular diseases, and the most common cause of microvascular complications, such as amputation, blindness, and chronic renal failure, which can all affect the patients’ quality of life [1]. Such complications impose a heavy economic burden to health care systems and society, as well [5]. In Iran, diabetes was the sixth-leading cause of death among the population in 2014 [6].
Various studies have identified several risk factors associated with T2DM, such as a family history of diabetes, obesity, age over 45 years, race, history of gestational diabetes, high blood pressure, high cholesterol, low HDL, high LDL, and glucose tolerance disorder [7]. Among them, sociodemographic and lifestyle-related factors were identified to be related to high T2DM risk [8]. Obesity, smoking, alcohol consumption, high blood pressure, and dyslipidemia, as consequences of a sedentary lifestyle, are among the important unhealthy conditions and behaviors that may increase the risk of T2DM [9,10].
Gender differences in health are associated to the difference in lifestyle factors and context [11]. Differences in health-protective behaviors between males and females have previously been reported [12]. Men’s health was more influenced by health behaviors while women’s health was more affected by structural determinants [11]. Gender differences in the prevalence of diabetes have also been reported in the literature [13], and some aspects of gender were identified as T2DM risk factors [14].
In addition to gender, urbanization affects people’s lifestyles and socioeconomic conditions. Diversity in the demographic variables, health behaviors, and lifestyle are effective factors in the existence of differences in the prevalence of diabetes among urban and rural areas. Urbanization is associated to better access to health, education, and social services. On the other hand, adverse changes in health behaviors and western lifestyle among people in urban areas have led to an increase in the rates of obesity, which can be associated to the rates of T2DM [15–17].
Complex pathophysiological processes resulting from interactions between genes and environment suggest that the T2DM risk factors can vary within different populations. Conventional risk factors for predicting diabetes vary among countries and geographical regions. This difference in the prevalence of risk factors may result from population structure such as aging, cultural context, and lifestyle factors, such as diet and physical activity [8,18].
So far, some studies have identified gender and residency differences in the prevalence of diabetes [13–15]. Due to the considerable socio-cultural and environmental diversity within and across different countries, identifying important risk factors of non-communicable diseases (NCDs) in each community may be helpful in preventing adverse outcomes, and improving population’s health [7]. World Health Organization (WHO) recommends identifying and collecting data that help to demonstrate the impact of cultural differences on health [19]. Particularly, prioritizing the lifestyle-related risk factors of T2DM by gender and residency may provide health policymakers with different strategies in the prevention and control of the disease. The present study aimed to investigate the prevalence of T2DM and its determinants in the population older than 30 in Naghadeh-Iran, based on gender and urban-rural differences using IraPEN data.
## About IraPEN program in Naghadeh
The WHO Package of Essential Noncommunicable Disease Interventions (WHO PEN) for primary care in low-resource settings is an innovative and action-oriented set of cost-effective interventions that might not only reduce medical costs, but also increase the patient’s quality of life [20]. WHO has defined the control of NCDs and their underlying factors as the main goal to reduce their associated mortality by 2025. IraPEN (Iran’s Package of Essential Non-Communicable Disease) is a prioritized group of effective interventions that is part of the national health transformation plan created by the Iranian Ministry of Health and Medical Education, in 2014. IraPEN is a response of the Iranian health system to the long-term goals of WHO, with the hope to prevent the four main types of NCDs (diabetes, cancer, respiratory and cardiovascular diseases), and to reduce their associated risk factors among 30 to 70 years old population. The project was based on the WHO PEN, which was adjusted for Iranian setting, and pilot-tested in four counties; Naghadeh, Maragheh, Shahreza, and Baft. IraPEN was then integrated into the primary healthcare services at the health centers, nationwide.
The implementation of this project in the health centers of Naghadeh was carried out according to the "Package of essential noncommunicable diseases in Iran’s primary health care system" developed by the Ministry of health, under the supervision of the representative office of WHO in Iran [21]. The initial goal of IraPEN was to estimate the prevalence of four conditions (diabetes, high blood pressure, cancer, and chronic respiratory diseases) along with their risk factors: sedentary lifestyle, unhealthy diet, alcohol consumption, and smoking [22–24].
Naghadeh *County is* located in West Azerbaijan province, the northwest of Iran. Based on the last *Iranian census* in 2016, the population of Naghaded was 127671 people, and features two Turk and Kurd ethnic groups with almost equal population [25]. The population of 30 to 70 years old men and women in urban and rural areas were 30049 and 29075, and 8403 and 8024, respectively.
## Study design and setting
This study was a secondary analysis study conducted on the survey data of the IraPEN pilot project. IraPEN launched in the County in 2017. We accessed the project data in 2018. The participants were recruited using their household health records in the health centers of both urban and rural areas. Rural area is defined as a land with a specific area and territory, where either at least 20 households or 100 people live. An urban area is also defined as the region in which most inhabitants have nonagricultural jobs. It has a population of at least ten thousand people [26]. In urban areas of Naghadeh, the major economic activities are self-employment, governmental employment, and the trade of agricultural/animal husbandry-associated products. In the rural areas, a majority of residents are directly/indirectly engaged in one of the activities of agriculture, animal husbandry, horticulture, and rural industries. All people aged 30–70 years old who received the care related to the IraPEN project in the health centers, and had data in the project were eligible to be included into our secondary analysis.
## Data collection and measurements
The process of data collection was based on the IraPEN guideline, namely the “Package of essential noncommunicable diseases in Iran’s primary health care system” [21], which was the directive of the Ministry of Health. The IraPEN standard forms were used to collect data. IraPEN form had two sections. In the first section, all participants responded to sociodemographic questions such as age, gender, ethnicity, marital status, education levels, occupation, history of smoking and alcohol consumption, history of hypertension, and history of diabetes in the first-degree relatives. The second section of the form included 1) anthropometric measurements such as weight, height, body mass index (BMI), waist circumference (WC), hip circumference, waist to hip ratio (WHR), and 2) cardiovascular risk factors such as fasting blood sugar, blood lipids (cholesterol, triglyceride), and systolic and diastolic blood pressure (BP). All forms were completed by trained health care providers.
A calibrated scale to the nearest 0.1 kg was used to weigh an individual with light clothing without shoes. A stadiometer to the nearest 0.1 cm was also used to measure height without shoes. BMI was calculated by a participant’s weight in kilograms divided by the square of the height in meters. The BMIs less than 25, from 25 to 29.9, and above 30 were considered to be under/normal weight, overweight and obese, respectively. WC was measured by placing a tape measure around the waist horizontally above the hipbones. Hip circumference was also measured by a tape measure to check around the largest/widest part of the buttocks. WHR was also calculated by dividing waist to hip circumference. WHRs above 0.85 in women and above 0.9 in men were considered as abdominal obesity [27].
Participant’s BP was measured with a mercury sphygmomanometer twice with an interval of at least 5 minutes. To measure BP, digital sphygmomanometer is recommended by WHO [28]. However, mercury sphygmomanometer was used in the IraPEN study, considering that digital blood pressure measuring device was not available in all rural and urban health centers of the country. The average blood pressure of two measurements higher than $\frac{140}{90}$ mmHg was considered to be high blood pressure [21]. In IraPEN, the status of smoking and alcohol consumption were assessed using two separate modified items derived from the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). The practical definition of smoking and alcohol consumption in IraPEN guideline was defined as follow: “In the past three months, have you smoked cigarette and/or hookah (even for one time)?” and “In the past three months, have you had alcohol consumption (even for one time)?” [ 21].
Blood sampling was collected from all participants to measure glycemic and lipid parameters. Venous blood samples (5 ml.) were collected. Before blood sampling, 8 hours of fasting was needed. In IraPEN, Lipid pro® was used to assess blood glucose and lipids. The instructions for using this tool were provided in detail in the IraPEN guideline for health workers [21].
The glucose oxidase method (intra- and inter-assay coefficients of variation $2.1\%$ and $2.6\%$, respectively) was used to measure fasting blood sugar (FBS). T2DM was defined as the fasting plasma glucose level higher than 126 mg/dl (≥7.0 mmol/L), confirmed by a physician [29]. In cases where FBS was above 126 mg/dl in the first measurement, the blood test was repeated, immediately. If FBS was again 126 mg/dl and/or higher in the second measurement, the participant was diagnosed as a diabetic patient. The lipid profile test was also taken in a fasting condition.
Oxidase methods were used for measuring fasting cholesterol and triglyceride levels in plasma. Plasma cholesterol levels more than 200 (mg/dL) and triglyceride levels more than 150 (mg/dL) were considered as high blood cholesterol and high triglyceride, respectively [21].
## Statistical analysis
Data were analyzed using Statistical Package for Social Science (SPSS 23 for windows, SPSS Inc.® headquarter, Chicago, USA). The Kolmogorov–Smirnov test was applied to assess the normality of data. Descriptive statistics including frequency and percentage were employed to express the data. A Chi-square test was used to compare variables by gender and residency.
Simple and multiple logistic regression models were used to examine the odds of developing T2DM based on the risk factors by gender and residency. Considering each risk factor as an independent variable, and gender and residency as dependent variables, a series of multiple logistic regression analysis with stepwise forward variable selection method was used. In this model, we moved from a simpler to a more complex model to understand the contribution of variables. The variables with $p \leq .05$ in the simple regression analysis were included in the multiple models. Adjusted odds ratio (OR) of T2DM risk factors with $95\%$ confidence intervals were provided. The p-values less than 0.05 were considered to be statistically significant.
## Ethical considerations
The ethics committee of Tabriz University of Medical Sciences approved the study protocol (IRTBZMED.REC.1396.965). When data collection, a signed informed consent form was obtained from all participants.
## Overall prevalence of T2DM by gender, residency and socio-demographic characteristics
The records of 3,691 participants of the IraPEN pilot project were included into the study. Sociodemographic characteristics of the population and the prevalence of T2DM are presented in Table 1. Thirty-four percent of the population were illiterate. Nearly $90\%$ were married. The highest prevalence of T2DM was among the population over 60 years ($23.2\%$). The prevalence of T2DM was higher among Turks ($16.2\%$) than Kurds ($11.3\%$) ($P \leq 0.001$) (Table 1).
**Table 1**
| Variable | Overalln (%) | Diabetes mellitusn (%) | Diabetes mellitusn (%).1 | P |
| --- | --- | --- | --- | --- |
| Total prevalence | | 13.8% | 13.8% | |
| Gender | | | | 0.001 |
| Male | 1625 (44.0%) | 191 (11.8%) | 191 (11.8%) | 0.001 |
| Female | 2066 (56.0%) | 320 (15.5%) | 320 (15.5%) | 0.001 |
| Residency | | | | 0.073 |
| Rural | 1159 (31.4%) | 143 (12.3%) | 143 (12.3%) | 0.073 |
| Urban | 2532 (68.6%) | 368 (14.5%) | 368 (14.5%) | 0.073 |
| Age | | Yes | No | <0.001 |
| 30–39 | 807 (21.9%) | 19 (2.4%) | 788 (97.6%) | <0.001 |
| 40–49 | 1187 (32.2%) | 117 (9.8%) | 1072 (90.2%) | <0.001 |
| 50–59 | 953 (25.8%) | 203 (21.3%) | 750 (78.7%) | <0.001 |
| 60+ | 742 (20.1) | 172 (23.2%) | 570 (76.8%) | <0.001 |
| Education | | | | <0.001 |
| Illiterate | 1273 (34.5%) | 264 (20.7%) | 1009 (79.3%) | <0.001 |
| Elementary | 1000 (27.1%) | 111 (11.1%) | 889 (88.9%) | <0.001 |
| Middle school | 557 (15.1%) | 62 (11.1%) | 495 (88.9%) | <0.001 |
| High school | 625 (16.9%) | 55 (8.8%) | 570 (91.2%) | <0.001 |
| University | 234 (6.3%) | 19 (8.1%) | 215 (91.9%) | <0.001 |
| Occupation | | | | <0.001 |
| Employee | 170 (4.6%) | 10 (5.9%) | 160 (94.1%) | <0.001 |
| Manual worker | 594 (16.1%) | 66 (11.1%) | 528 (88.9%) | <0.001 |
| Self-employed | 624 (16.9%) | 67 (10.7%) | 557 (89.3%) | <0.001 |
| Housewife | 2009 (54.5%) | 319 (15.9%) | 1690 (84.1%) | <0.001 |
| Unemployed | 208 (5.6%) | 40 (19.2%) | 168 (80.8%) | <0.001 |
| Other | 82 (2.2%) | 9 (11.0%) | 73 (89.0%) | <0.001 |
| Ethnicity | | | | <0.001 |
| Turk | 1834 (59.9%) | 297 (16.2%) | 1537 (83.8%) | <0.001 |
| Kurd | 1226 (40.1) | 138 (11.3%) | 1088 (88.7%) | <0.001 |
| Marital status | | | | <0.001 |
| Married | 3319 (89.9%) | 438 (13.2%) | 2881 (86.8%) | <0.001 |
| Single | 101 (2.7%) | 12 (11.9%) | 89 (88.1%) | <0.001 |
| Widow | 256 (6.9%) | 59 (23.0%) | 197 (77.0%) | <0.001 |
| Divorced | 14 (0.4%) | 2 (14.3%) | 12 (85.7%) | <0.001 |
| Smoking (yes) | 321 (8.7%) | 38 (11.8%) | 283 (88.2%) | 0.276 |
| Alcohol use (yes) | 15 (0.4%) | 1 (6.7%) | 14 (93.3%) | 0.420 |
| History of hypertension (yes) | 596 (16.1%) | 181 (30.4%) | 415 (69.6%) | <0.001 |
| Family history of diabetes (yes) | 689 (18.7%) | 168 (24.4%) | 521 (75.6%) | <0.001 |
The overall prevalence of T2DM within the population was $13.8\%$. Fifty-six percent of the overall population were female. The prevalence of T2DM was higher among women ($15.5\%$) than men ($11.8\%$) ($$P \leq 0.001$$). Approximately $69\%$ of the population lived in urban areas. However, the prevalence of T2DM was higher among urban population ($14.5\%$) than rural population ($12.3\%$), the difference however was not statistically significant ($$P \leq 0.073$$) (Table 1).
## Prevalence of T2DM risk factors by gender and residency
The prevalence of high blood pressure ($P \leq 0.001$), obesity ($P \leq 0.001$), abdominal obesity ($P \leq 0.001$), and high blood cholesterol ($P \leq 0.001$) were significantly higher in women. The prevalence of smoking ($P \leq 0.001$), alcohol consumption ($$P \leq 0.001$$), overweight ($P \leq 0.001$), and high blood triglycerides ($P \leq 0.001$) were significantly higher in men. Also, the prevalence of all T2DM-associated risk factors was higher in urban areas, compared to rural areas; alcohol consumption ($$P \leq 0.039$$), high blood pressure ($P \leq 0.001$), overweight, ($$P \leq 0.02$$) and high blood cholesterol ($P \leq 0.001$) showed a statistically significant difference. The results showed that the most common risk factor in the population is abdominal obesity (Table 2).
**Table 2**
| Risk Factors | Gender | Gender.1 | P | Residency | Residency.1 | P.1 | Total |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Risk Factors | Male | Female | P | Rural | Urban | P | Total |
| Smoking | 18.8% | 0.8% | <0.001 | 7.5% | 9.2% | 0.083 | 8.7% |
| Alcohol consumption | 0.8% | 0.1% | 0.001 | 0.1% | 0.6% | 0.039 | 0.4% |
| High blood pressure | 15.2% | 24.0% | <0.001 | 14.2% | 22.8% | <0.001 | 20.1% |
| Overweight | 47.1% | 36.6% | <0.001 | 38.5% | 42.5% | 0.02 | 41.3% |
| Obesity | 23.6% | 50.2% | <0.001 | 36.7% | 39.3% | 0.128 | 38.5% |
| Abdominal obesity | 64.8% | 70.8% | 0.001 | 67.7% | 68.9% | 0.517 | 51.2% |
| High blood cholesterol | 31.8% | 41.9% | <0.001 | 28.5% | 41.6% | <0.001 | 37.4% |
| High triglyceride | 48.0% | 40.4% | <0.001 | 43.7% | 43.8% | 0.975 | 42.5% |
## Determinants of T2DM by gender and residency
The results of simple logistic regression model showed that age, high blood pressure, obesity, abdominal obesity, high blood cholesterol, and triglycerides in both genders had a significant association with the chance of developing T2DM (Table 3). According to the results of multivariate logistic regression, having high blood pressure showed a strong association with the risk of developing T2DM in men (OR 1.77, $95\%$ CI: 1.13–2.79; $$P \leq 0.013$$) and women (OR 2.86, $95\%$ CI: 2.12–3.85; $P \leq 0.001$). Also, age and having high blood triglycerides showed significant associations with the risk of developing T2DM in both genders. There was a significant relationship between abdominal obesity and the chance of developing T2DM, only among women (OR 1.68, $95\%$ CI: 1.17–2.40; $$P \leq 0.004$$) (Table 3).
**Table 3**
| Risk Factors | Status | Male | Male.1 | Female | Female.1 | Male.2 | Male.3 | Female.2 | Female.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Risk Factors | Status | OR (95% CI) | p-value* | OR (95% CI) | p-value* | OR (95% CI) | p-value** | OR (95% CI) | p-value** |
| Age | Year | 1.03 (1.02–1.04) | <0.001 | 1.06 (1.05–1.07) | <0.001 | 1.01 (1.00–1.03) | 0.012 | 1.03 (1.02–1.04) | <0.001 |
| Residency | Rural | 1 | | 1 | | - | - | - | |
| Residency | Urban | 1.078 (0.77–1.49) | 0.65 | 1.28 (0.98–1.68) | 0.068 | - | - | - | |
| Smoking | No | 1 | | 1 | | - | - | - | |
| Smoking | Yes | 0.967 (0.65–1.42) | 0.867 | 1.26 (0.35–4.45) | 0.718 | - | - | - | |
| Alcohol consumption | No | 1 | | 1 | | - | - | - | |
| Alcohol consumption | Yes | 0 | 0.999 | 5.47 (0.34–87.68) | 0.23 | - | - | - | |
| Blood pressure | Normal | 1 | | 1 | | 1 | | 1 | |
| Blood pressure | High | 2.534 (1.78–3.59) | <0.001 | 4.93 (3.84–6.34) | <0.001 | 1.77 (1.13–2.79) | 0.013 | 2.86 (2.12–3.85) | <0.001 |
| BMI | Underweight and normal | 1 | | 1 | | 1 | | 1 | |
| BMI | Overweight | 1.18 (0.87–1.59) | 0.283 | 0.98 (0.76–1.25) | 0.875 | | | | |
| BMI | Obese | 1.488 (1.06–2.07) | 0.019 | 1.31 (1.03–1.67) | 0.026 | 1.23 (0.82–1.84) | 0.308 | 1.20 (0.90–1.59) | 0.197 |
| Waist-hip ratio | Normal | 1 | | 1 | | 1 | | 1 | |
| Waist-hip ratio | Abdominal obesity | 1.70 (1.1–2.5) | 0.009 | 2.51 (1.79–3.52) | <0.001 | 1.49 (0.99–2.25) | 0.056 | 1.68 (1.17–2.40) | 0.004 |
| Total Cholesterol | Normal | 1 | | 1 | | 1 | | 1 | |
| Total Cholesterol | High | 1.47 (1.07–2.0) | 0.016 | 1.29 (1.02–1.65) | 0.034 | 1.20 (0.82–1.77) | 0.343 | 0.98 (0.74–1.29) | 0.904 |
| Triglycerides | Normal | 1 | | 1 | | 1 | | 1 | |
| Triglycerides | High | 1.49 (1.1–2.03) | 0.01 | 1.67 (1.31–2.13) | <0.001 | 1.46 (1.01–2.11) | 0.04 | 1.34 (1.02–1.77) | 0.035 |
The results of simple regression analysis showed that age, high blood pressure, being overweight, abdominal obesity, and high blood cholesterol significantly increased the risk of developing T2DM, in both rural and urban areas (Table 4). Multiple regression analysis showed that, in rural areas, age (OR 1.03, $95\%$ CI: 1.01–1.04; $P \leq 0.001$), high blood pressure (OR 3.14, $95\%$ CI: 2.0–4.93; $P \leq 0.001$), abdominal obesity (OR 2.34, $95\%$ CI: 1.41–3.87; $P \leq 0.001$), and high blood cholesterol (OR 1.59, $95\%$ CI: 1.07–2.37; $$P \leq 0.02$$) had significant associations with the chance of developing T2DM. In urban areas, age (OR 1.02, $95\%$ CI: 1.01–1.04; $P \leq 0.001$), high blood pressure (OR 2.23, $95\%$ CI: 1.66–3; $P \leq 0.001$), abdominal obesity (OR 1.46, $95\%$ CI: 06–2.01; $$P \leq 0.019$$) and high triglycerides (OR 1.51, $95\%$ CI: 1.16–1.98; $$P \leq 0.002$$) showed significant relationships with the chance of developing T2DM (Table 4).
**Table 4**
| Risk Factors | Status | Rural | Rural.1 | Urban | Urban.1 | Rural.2 | Rural.3 | Urban.2 | Urban.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Risk Factors | Status | OR (95% CI) | p-value* | OR (95% CI) | p-value* | OR (95% CI) | p-value** | OR (95% CI) | p-value** |
| Age | Year | 1.04 (1.03–1.05) | <0.001 | 1.05 (1.04–1.06) | <0.001 | 1.03 (1.01–1.04) | <0.001 | 1.02 (1.01–1.04) | <0.001 |
| Gender | Male | 1 | | 1 | | 1 | | 1 | |
| Gender | Female | 1.21 (0.84–1.72) | 0.292 | 1.44 (1.14–1.81) | 0.002 | 0 (0–0) | 0 | 1.22 (0.90–1.65) | 0.185 |
| Smoking | No | 1 | | 1 | | - | - | - | - |
| Smoking | Yes | 1.27 (0.68–2.36) | 0.443 | 0.68 (0.44–1.04) | 0.081 | - | - | - | - |
| Alcohol consumption | No | 1 | | 1 | | - | - | - | - |
| Alcohol consumption | Yes | 0 (0–0) | 1 | 0.45 (0.05–3.45) | 0.443 | - | - | - | - |
| Blood pressure | Normal | 1 | | 1 | | 1 | - | 1 | - |
| Blood pressure | High | 4.56 (3.08–6.76) | <0.001 | 3.8 (3.0–4.79) | <0.001 | 3.14 (2.0–4.93) | <0.001 | 2.23 (1.66–3) | <0.001 |
| BMI | Underweight and normal | 1 | | 1 | | 1 | | 1 | |
| BMI | Overweight | 1.47 (1.03–2.09) | 0.033 | 1.44 (1.16–1.80) | 0.001 | | | | |
| BMI | Obese | - | - | - | - | 1.32 (0.89–1.96) | 0.167 | 1.12 (0.84–1.48) | 0.422 |
| Waist-hip ratio | Normal | 1 | | 1 | | 1 | | 1 | |
| Waist-hip ratio | Abdominal obesity | 2.95 (1.82–4.79) | <0.001 | 1.91 (1.41–2.59) | <0.001 | 2.34 (1.41–3.87) | 0.001 | 1.46 (1.06–2.01) | 0.019 |
| Total Cholesterol | Normal | 1 | | 1 | | 1 | | 1 | |
| Total Cholesterol | High | 1.72 (1.19–2.49) | 0.003 | 1.26 (1.0–1.57) | 0.042 | 1.59 (1.07–2.37) | 0.02 | 0.86 (0.65–1.13) | 0.293 |
| Triglycerides | Normal | 1 | | 1 | | 1 | | 1 | |
| Triglycerides | High | 1.40 (0.98–1.99) | 0.061 | 1.63 (1.30–2.03) | <0.001 | 0 (0–0) | 0 | 1.51 (1.16–1.98) | 0.002 |
## Overall prevalence of T2DM by gender and residency
The purpose of this cross-sectional study was to estimate the prevalence of T2DM and its determinants by gender and residency on over 3000 participants in Naghadeh, Iran. Our findings showed that the prevalence of T2DM in the population with 30 years of age and older was $13.8\%$, which was significantly higher in women than men, and in urban residents than rural residents. In the study conducted by Afkhami et al, the prevalence of T2DM in people older than 30 years was $14.5\%$, which is consistent with our findings [30]. Based on another study conducted in Yazd, Iran, the prevalence of T2DM in people older than 30 years was $17.2\%$ [31]. In a meta-analysis on Iranian studies published from 1996 to 2004, the overall prevalence of diabetes among people with 40 years of age and older was reported to be $24\%$ [4]. Such discrepancies can be explained by the variety of the studies, in terms of study type, socio-demographic characteristics, target population, and sample size. Differences in cultural components of communities may lead to the formation of different behavioral patterns, which might affect their health by shaping different beliefs and attitudes [32]. Due to the lack of previous prevalence studies in Naghadeh, we could not evaluate the trend of T2DM prevalence over time in the population.
Naghadeh is composed of two main ethnicities, including Turks and Kurds. Our results showed that the prevalence of T2DM significantly differed between Kurds ($11.3\%$) and Turks ($16.2\%$), which can be attributed, in part, to the greater number of Turkish participants ($59.1\%$). As the second largest ethnic group in Iran, the most of Iranian Turk and Kurd populations reside in the northwest of Iran (the geographical location of the present study). In a previous study, Abbasi et al. found significant discrepancies among seven ethnic groups in Iran, in terms of the modifiable risk factors, and the severity of Coronary Artery Disease (CAD). In their study, Turk ethnic group were found with higher prevalence of T2DM and severity of CAD, compared to Kurd ethnic group [33]. These findings were similar to those found in our study. In another study that Rezazadeh et al. conducted in northwestern of Iran (Urmia, a city close to Naghadeh), a significant difference was found in dietary patterns between Turk and Kurd participants [34]. As they reported, traditional high socio-economic status patterns and traditional low socio-economic status dietary patterns were more common among Turk and Kurd respondents, respectively, which may be considered as a reason for the differences found in the present study.
Greater prevalence of T2DM in women has also been reported in previous studies conducted in Iran [4,31,35]. Based on the study of Esteghamati et al., the prevalence of diabetes was higher in women than men [35]. Some of these gender differences may be attributed to sex hormones [13]. Moreover, obesity thresholds in women and insulin resistance in men can explain some of these differences in T2DM prevalence [36]. As in the present study, the prevalence of obesity and abdominal obesity was higher among women compared to men. In our study, there was a significant difference in all T2DM related risk factors by gender (Table 2) that can also explain the difference in the prevalence of T2DM between genders.
Many previous studies have shown differences in the prevalence of T2DM between urban and rural areas [15–17,35]. In agreement with the results of our study, Esteghamati et al. reported that the prevalence of T2DM was higher in urban areas than in rural areas [35]. Urbanization seems to have contributed to an increase in diabetes-related risk factors by increasing some unhealthy behaviors and Western lifestyles [15–17]. In the present study, all diabetes-related risk factors were more prevalent among urban population compared to the rural population. This may explain the relatively higher prevalence in urban areas than in rural areas. Despite the difference in the prevalence of T2DM in urban ($14.5\%$) and rural ($12.3\%$) areas in the present study, this difference was not statistically significant. It may be due to changes in rural lifestyles and their gradual shift to urban lifestyles.
## Determinants of T2DM by gender
Our results showed age, high blood pressure, and high blood triglycerides in associations with the chance of developing T2DMin both men and women. Age and gender are unchangeable risk factors for diabetes [7]. The risk of developing T2DM increases with aging in both genders [4]. In a study performed by Navipur et al., the odds of progression of T2DM increased by aging (OR = 1.28) [37], which is consistent with those found in our study. The results of another study showed that older adults are at high risk for developing T2DM [24]. Due to the effects of increasing insulin resistance and impaired pancreatic function with aging, older people are at high risk for the development of T2DM [38]. It seems that with increase in older adult population, an increase in the prevalence of T2DM is expected. In the present study, the frequency of smoking was $18.8\%$ and $0.8\%$ among men and women, respectively. In a previous study [39], the prevalence of smoking in the northwestern area of Iran (*Naghadeh is* located in this area) was reported to be less than $1\%$ in women, and from 15 to $30\%$ in men, which is comparable to those found in our study. However, due to the self-report nature of the data, report bias is cautioned for the factors like smoking and alcohol consumption.
High blood pressure increased the chance of developing T2DM among both male and female respondents. Although in our study the prevalence of hypertension was significantly higher in women, the disease was a strong predictor of diabetes in both genders. Meysamie et al., also, reported the prevalence of hypertension to be higher in women than men [35]. In more than two-thirds of the patients with T2DM, hypertension was also reported, and its development coincided with hyperglycemia. Insulin resistance is a disorder that is common patients with high blood pressure and T2DM [40]. It may be a possible reason for the coexistence of hypertension and T2DM. Hypertriglyceridemia is previously reported as a common problem among T2DM patients [41]. Blood triglyceride level is also an independent risk factor and predictor for T2DM [42]. Although high level of blood triglyceride was a determinant of T2DM in both genders in our study, the results showed that the prevalence of high blood triglyceride level was significantly higher in men than women ($p \leq 0.001$). A study on adolescents showed that the high levels of triglyceride were more common in boys than girls [43]. Differences in blood triglyceride levels can be attributed to different eating patterns between genders. Unhealthy eating habits are more common among men. Men tend to eat more high-fat, high-protein foods than women [44]. Despite the higher prevalence of hypertriglyceridemia among men [44,45], obesity and abdominal obesity were more common among women in our study, which may be attributed to the higher inactivity of female participants. Since a majority of women in Naghadeh were housewives, we expected that inactivity to be more prevalent among women than men. Thus, obesity and abdominal obesity were higher in women.
## Determinants of T2DM by residency
In the present study, age, high blood pressure, and waist-to-hip ratio (abdominal obesity) in both rural and urban areas, and high levels of blood cholesterol in rural areas, and high triglyceride levels in urban areas, were significant predictors of T2DM. Similar to our findings, high blood pressure is reported to increase the chance of developing T2DM in both rural and urban areas [46]. Over time, T2DM can damage blood vessels, causing their walls to be stiffen, which leads to an increase in blood pressure [47]. In the present study, hypertension was also more prevalent among urban than rural residents, which was similar to those reported by Meysamie et al [17]. Urbanization is noted as a major social determinant of hypertension [48]. Due to the higher prevalence of hypertension risk factors (such as obesity, smoking, high levels of blood cholesterol, and triglycerides) in urban areas, the higher level of blood pressure in urban populations, compared to rural populations, is predictable. Moreover, lifestyle changes [17], and high levels of stress and anxiety due to urbanization [49], can also be attributed to the higher prevalence of hypertension in urban than rural areas.
In the present study, abdominal obesity was a determinant of T2DM in both urban and rural areas. Alam et al. reported that diabetes prevalence is six times higher among individuals with abdominal obesity compared to individuals with normal weight [50]. In the present study, central (intra-abdominal) obesity was observed in the majority of patients with T2DM. A high level of circulating adipokines secreted by abdominal adipose has an essential role in inflammation and insulin resistance [51]. In our study, compared to rural residents, abdominal obesity was higher in urban residents, although the difference was not statistically significant. As previously mentioned, such a difference may be due to the gradual changes that is being made in rural lifestyles.
According to our findings, high blood triglycerides level was a determinant of T2DM in urban areas. In the study of Esteghamati et al., the prevalence of hypertriglyceridemia was higher in urban areas [35], which is consistent with our results. However, the difference in the prevalence of hypertriglyceridemia in rural and urban areas was not statistically significant. The results also showed that the prevalence of abnormal blood cholesterol was significantly higher in urban residents than in rural residents. Similarly, the results of a systematic review showed that $63\%$ of studies reported higher cholesterol levels in urban areas [52]. As mentioned, this could be due to the unhealthy and sedentary lifestyle of urban residents.
Since this study examined the difference in the prevalence of T2DM in urban and rural areas, the results can be generalized to compare the prevalence of T2DM in urban and rural areas in other geographical regions, especially in low and middle-income countries. On the other hand, gender differences in the prevalence of the chronic diseases can also be considered in the generalizability of the results of our study.
## Limitations and strengths of the study
As we conducted this study based on a secondary data collected by the Iranian Ministry of Health, we did not have complete information about the quality of IraPEN program implementation in Naghadeh health centers. Due to the lack of previous studies in the County, it was not possible for us to evaluate the increasing/decreasing trend of T2DM prevalence over time in Naghadeh. Iran as a multi-ethnic country is composed of six major ethnic groups [33]. Since previous studies have shown that the prevalence of T2DM might be different by ethnicity (due to their cultural and lifestyle differences) [53], the results of our study may be mostly generalizable to the populations with Turk and Kurd ethnicities, in Iran, Azerbaijan Republic, Turkey, Iraq and Syria. Despite our efforts, we could not achieve valid data on the distribution of variables in the overall population. Moreover, the male/female proportion of the participants was not so close to those of the overall population of the County, although it may be due to the fact that women have a higher number of primary care visits compared to men [54]. Despite the recommendation of WHO to use digital sphygmomanometer, mercury sphygmomanometer was used in the IraPEN study, as digital blood pressure measuring device was not available in all rural and urban health centers of the country. So, it could be somewhat extent of variation in blood pressure, which might have some potential effect on the results. Concerning with alcohol consumption, the amount of alcohol drinking is reported to be a key influencer for health [55], and moderate alcohol drinking is associated to lower risk of type 2 diabetes [56]. However, in the IraPEN study, the operational definition for alcohol drinking was “having a history of alcohol consumption in the past three months” which was identified to be vague. So, this limitation should be taken into account while studying the results and data interpretation. As another limitation, it leads to impacts of results, validity and data interpretation. Fruit and vegetable consumption is an important factor for diabetes prevalence. However, this data is missing in our results. As a strength, this study was a large community survey conducted on 3691 participants. Another strength was the evaluation of the most important risk factors involved in the prevalence of T2DM by gender and residency.
## Conclusion
The results of the present study showed that the overall prevalence of T2DM in Naghadeh was $13.8\%$, which was higher among women than men, and in urban areas than rural areas. The recognition of the prevalence of T2DM and its determinants among populations by gender and residency is a key step in establishing community-based interventions for modifying cardiometabolic risk factors in communities. Given the higher prevalence of T2DM among women, risk reduction strategies at the community level should be more targeted at women. Moreover, the higher prevalence of T2DM risk factors among the urban population is a wake-up call for policymakers to pay more attention to the consequences of unhealthy and sedentary urban lifestyles in communities. Our findings might be helpful for health policymakers and health practitioners in making effective and feasible public policies and designing health promotion programs to prevent T2DM, and reduce its-associated co-morbidities and complications. It is recommended that future actions to be focused on appropriate timely action plans for prevention and control of T2DM from the early years of life, especially planning for effective health education and promotion programs.
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|
---
title: Admission of kidney patients to a closed staff nephrology department results
in a better short-term survival
authors:
- Lihi Schwartz
- Omer Rosenshtok
- Leah Shalev
- Ella Schneider
- Anna Basok
- Marina Vorobiov
- Elvira Romanjuk
- Boris Rogachev
- Ismail El-Sayed
- Lina Schwartz
- Idan Menashe
- Ohad Regev
- Yosef S. Haviv
journal: PLOS ONE
year: 2023
pmcid: PMC9990939
doi: 10.1371/journal.pone.0279172
license: CC BY 4.0
---
# Admission of kidney patients to a closed staff nephrology department results in a better short-term survival
## Abstract
### Background
The outcome of patients with chronic kidney disease (CKD) and acute kidney injury (AKI) is often dismal and measures to ameliorate their course are scarce. When admitted to the hospital, kidney patients are often hospitalized in general Medicine wards rather than in a specialized Nephrology department. In the current study, we compared the outcome of two cohorts of kidney patients (CKD and AKI) admitted either to general open-staff (with rotating physicians) Medicine wards or to a closed-staff (non-rotating Nephrologists) Nephrology ward.
### Methods
In this population-based retrospective cohort study, we enrolled 352 CKD patients and 382 AKI patients admitted to either Nephrology or General Medicine wards. Short-term (< = 90 days) and long-term (>90 days) outcomes were recorded for survival, renal outcomes, cardiovascular outcomes, and dialysis complications. Multivariate analysis was performed using logistic regression and negative binomial regression adjusting to potential sociodemographic confounders as well as to a propensity score based on the association of all medical background variables to the admitted ward, to mitigate the potential admittance bias to each ward.
### Results
One hundred and seventy-one CKD patients ($48.6\%$) were admitted to the Nephrology ward and 181 ($51.4\%$) were admitted to general Medicine wards. For AKI, 180 ($47.1\%$) and 202 ($52.9\%$) were admitted to Nephrology and general Medicine wards, respectively. Baseline age, comorbidities and the degree of renal dysfunction differed between the groups. Using propensity score analysis, a significantly reduced mortality rate was observed for kidney patients admitted to the Nephrology ward vs. general Medicine in short term mortality (but not long-term mortality) among both CKD patients admitted (OR = 0.28, CI = 0.14–0.58, $$p \leq 0.001$$), and AKI patients (or = 0.25, CI = 0.12–0.48, $p \leq 0.001$). Nephrology ward admission resulted in higher rates of renal replacement therapy (RRT), both during the first hospitalization and thereafter.
### Conclusions
Thus, a simple measure of admission to a specialized Nephrology department may improve kidney patient outcome, thereby potentially affecting future health care planning.
## Introduction
A number of studies have shown that it is remarkably difficult to ameliorate the outcome of kidney patients using medical or technical measures for both acute kidney injury (AKI) and end stage renal disease (ESRD). In contrast, the outcome of CKD can be improved with novel drugs. On the other hand, administrative steps facilitating kidney patient-Nephrologist interaction may improve their outcome, e.g. earlier out-patient referral to a Nephrologist can reduce mortality and hospitalizations [1]. Thus, specialist involvement may be beneficial in the out-patient Nephrology setting However, in the context of in-patients, whether admission to a specialized Nephrology department improves survival is yet to be determined.
Health care utilization among adult CKD patients is high and $47\%$ of the patients are hospitalized at least once per year [2,3]. When kidney patients are hospitalized their outcome is worse than patients with intact renal function [4]. Kidney patients are often hospitalized in general Medicine wards where Nephrologist consultation may be requested. These patients may be regarded as ‘outliers’ of the Nephrology ward with a substantially lower degree of specialist involvement.
Outliers in general may have an increased length of hospitalization [5], as shown by a study from another field (Neurology) that found a significantly shorter median length of stay in a specialist unit compared to the general wards (9 days vs 13 days respectively) [6]. In the field of Nephrology, Fagugli et al [7] investigated the outcome of patients with acute kidney injury (AKI) requiring dialysis who were admitted to either a Nephrology ward or to general medical wards. The study showed reduced in-hospital mortality in the Nephrology ward ($20\%$ versus $52\%$), thereby suggesting that for the most severe AKI patients requiring dialysis, specialty care may result in better outcomes. Other studies demonstrated that early Nephrologist involvement in patients with AKI may reduce the risk of further decrease in kidney function [8]. Moreover, delayed Nephrology consultation was associated with increased dialysis dependence rates in critically ill AKI patients on hospital discharge [9].
In the current in-patient study, we retrospectively examined whether the outcome of hospitalized kidney patients, i.e. AKI (AKIN classification stages 1–3) not requiring dialysis and CKD (stages G3-G5) patients, was improved following admission to a closed-staff Nephrology ward (see below classification). To the best of our knowledge these patient populations have not been examined in this regard previously.
## Study design
This was a population-based retrospective cohort study comparing two cohorts of kidney patients (either AKI or CKD patients) admitted to general Medicine wards with Nephrology consultation vs. care in a closed-staff Nephrology ward. Short-term (< = 90 days) and long-term (>90 days) outcomes were recorded for mortality, renal outcome (RRT (dialysis or kidney transplantation)) and AV shunt surgery, composite dialysis complication score (CDCs), CREDENCE composite outcome [see below]), and cardiovascular outcomes [MACE, see below]. Of note, AV shunt surgery differs from the other outcomes in predicting a better prognosis in dialysis patients [10].
## Setting
Soroka University Medical *Center is* the 4th largest hospital in Israel and the only one in the Negev district providing medical services to ~ 1 million residents. Because all the kidney patients in the Negev district are referred to Soroka University Medical Center, admissions to Soroka hospital were considered to reflect all hospitalization events.
The Medicine wards are based on an open-staff structure, i.e., both the attending senior physician in the medical wards and the consulting Nephrologist are rotating, the former monthly and the latter daily. In contrast, in the closed-staff Nephrology department the staff is unchanged and board-certified in Nephrology. Daily morning meetings of 6–8 Nephrologists are conducted to guide patient care. The Nephrology floor comprises a 12-bed ward dedicated entirely to kidney in-patients, in addition to peritoneal dialysis outpatient unit, hemodialysis outpatient and in-patient unit, and a kidney transplantation service. The medical staff comprises 7 board-certified Nephrologists and one resident. The nurses all passed a 1-yr Nephrology and dialysis nursing course. In-house dietitian and social worker guide the relevant aspects of therapy.
## Study participants and data sources
The two kidney patient cohorts were defined as AKI or CKD. All patients were adults (>18 years) with renal dysfunction admitted either to the Nephrology Ward or to the General Medicine wards (in the latter only patients with Nephrology consultation were included). The dates of admission were from 21 July 2016 through 31 December 2018 (exclusion criteria common to both cohorts were absence of Nephrologist consultation, need for urgent dialysis on admission, admission to ICU or surgery and ESRD (on chronic dialysis or with a kidney transplant) [S1 Table]. The specific AKI study exclusion criterion was serum creatinine rise below $50\%$ compared to baseline. The latter was calculated as the mean of the available serum creatinine levels measured during the last year before admission.
Additional specific CKD study exclusion criteria were eGFR >60 ml/min. The data collection ended on 31.12.2019; thus, all patients have had at least one year of follow-up. For each patient we calculated the relevant AKIN/CKD KDIGO scores based on their creatinine level and rekevant demographic data. Because the serum creatinine alone does not accurately reflect the kidney function, these data were convertedninto the AKIN stage /CKD as the unit of analysis. The AKIN classification of AKI was used; AKI patients were classified into 3 stages [1.5-fold≤Serum creatinine (Scr) ≤2-fold, 2-fold <Scr≤ 3-fold, Scr> 3-fold] [11]. For CKD, The KDIGO classification was used; [G3a (45≤eGFR<60), G3b (30≤eGFR<45), G4 (15≤eGFR<30) and G5nd (eGFR<15, G5 CKD patients not receiving RRT) [12].
## Data collection
The study was based on two computerized datasets: a Nephrology consultation database, which consists of records of hospitalized patients, from all the hospital wards requesting Nephrology consultation. The second is Soroka’s Chameleon electronic medical records database, which comprises records of all patients treated in Soroka hospital. Based on previous power calculations, two-thirds of the patients were randomly selected using an arbitrary digit of their ID number, as reported before [13]. The study was investigator-initiated and was approved by the Soroka University Medical Center institutional review board (IRB). All diagnoses were classified by the international classification of disease (ICD-9).
## Statistical analysis
For each kidney patient cohort (AKI/CKD), the sociodemographic and medical characteristics of Nephrology and General Ward patients were assessed using appropriate univariate statistics. Next, we assessed the association between admission type and clinical outcomes using appropriate univariate statistics. Categorical variables were assessed using Chi-Square test. Continuous variables were assessed using either T-test (for normal distribution) or Mann-Whitney test (in all other cases).
To assess the independent association between admission type and clinical outcomes, we conducted a multivariate analysis using either logistic regression (for dichotomous variables) or negative binomial regression (for counting variables) adjusted for the sociodemographic variables, which showed significant association with ward type (age, ethnicity, and number of children). In addition, to mitigate the potential admittance bias to each ward, a propensity score (PS) was created using a logistic regression assessing the effect of all medical background variables on the hospitalization ward as a dependent variable. The resulting PS was added as an independent variable to all the multivariate regression models [14]. Details concerning the specific statistical tests conducted for each variable can be seen at the footnote of each table. All analyses were conducted using SPSS Statistics V. 25 and R software. A two-sided test significance level of 0.05 was used throughout the entire study.
The association between admission type and outcome was studied for the following parameters: long- and short-term all-cause mortality,4-point MACE (major adverse cardiac event: nonfatal stroke, nonfatal MI, congestive heart failure (CHF), cardiovascular death), need for dialysis during the first hospitalization, need for chronic RRT (measured as RRT after discharge from first hospitalization), recurrent hospitalization and AV shunt surgery. For long-term Nephrology outcome, we used the CREDENCE composite CKD progression score index, comprising either ESRD, doubling of the serum creatinine level, renal or cardiovascular death [15]. To assess a specific dialysis quality index, we also tested a composite dialysis complication score (CDCs), comprising any of the following: CLABSI (catheter-induced bacteremia), pulmonary edema, hyperkalemia requiring urgent hemodialysis, need for any acute dialysis during first hospitalization and all-cause mortality.
## Baseline characteristics
Table 1 depicts the sociodemographic and comorbidities of kidney patients admitted to the Nephrology ward or to general Medicine wards (of whom only patients with Nephrology consultation were included). The impact of the admitting department on the outcome of kidney patients was studied for 2 groups, i.e. AKI and CKD. Only when significant for both AKI and CKD, the difference between the admitting departments deemed to reflect clinically meaningful difference. Thus, at baseline, age, and the prevalence of cardiovascular disease (composite coronary vascular disease, peripheral vascular disease, acute coronary syndrome, cerebrovascular event), and congestive heart failure (CHF), were all significantly higher in kidney patients admitted to Medicine wards (Table 1). On the other hand, patients admitted to the Nephrology department manifested a more advanced stage of either AKI or CKD (Table 1). To address a potential admittance bias, a propensity score analysis was performed in addition to standard multivariate analysis (adjusted OR, Table 3).
**Table 1**
| Variable | Unnamed: 1 | Study Group a | Study Group a.1 | Nephrology Department | General Department | General Department.1 | Pv |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Sociodemographic Background | Sociodemographic Background | Sociodemographic Background | Sociodemographic Background | Sociodemographic Background | Sociodemographic Background | | |
| Age, Years | Mean±SD | AKI | AKI | 63.6 ±19.6 | 69.4 ±13.9 | 69.4 ±13.9 | <0.001 b |
| Age, Years | Mean±SD | CKD | CKD | 66.2±16.2 | 71.31±13.1 | 71.31±13.1 | 0.001 b |
| Ethnicity (Jewish) | No. (%) | AKI | AKI | 144 (82.3) | 170 (85.4) | 170 (85.4) | 0.409 c |
| Ethnicity (Jewish) | No. (%) | CKD | CKD | 131(77.5) | 158(87.8) | 158(87.8) | 0.011 c |
| Sex (male) | No. (%) | AKI | AKI | 96 (53.3) | 119 (58.9) | 119 (58.9) | 0.273c |
| Sex (male) | No. (%) | CKD | CKD | 108(63.2) | 107(59.1) | 107(59.1) | 0.437c |
| Family Status (Married) | No. (%) | AKI | AKI | 124 (61.7) | 98 (55.4) | 98 (55.4) | 0.213c |
| Family Status (Married) | No. (%) | CKD | CKD | 105(62.1) | 118(65.2) | 118(65.2) | 0.551c |
| Number of Children | Median (IQR) | AKI | AKI | 2(1–4) | 3(2–5) | 3(2–5) | 0.007 d |
| Number of Children | Median (IQR) | CKD | CKD | 3(1–5) | 3(2–6) | 3(2–6) | 0.085d |
| Medical Background | Medical Background | Medical Background | Medical Background | Medical Background | Medical Background | | |
| Diabetes Mellitus | No. (%) | AKI | AKI | 83 (46.1) | 109 (54.0) | 109 (54.0) | 0.126c |
| Diabetes Mellitus | No. (%) | CKD | CKD | 95(55.6) | 112(61.9) | 112(61.9) | 0.228c |
| Cardiovascular Disease | No. (%) | AKI | AKI | 71 (39.4) | 105 (52.0) | 105 (52.0) | 0.014 c |
| Cardiovascular Disease | No. (%) | CKD | CKD | 84(49.1) | 116(64.1) | 116(64.1) | 0.005 c |
| Heart Failure | No. (%) | AKI | AKI | 34 (18.9) | 65 (32.2) | 65 (32.2) | 0.003 c |
| Heart Failure | No. (%) | CKD | CKD | 40(23.4) | 75(41.4) | 75(41.4) | <0.001 c |
| Hypertension | No. (%) | AKI | AKI | 132 (73.3) | 155 (76.7) | 155 (76.7) | 0.443c |
| Hypertension | No. (%) | CKD | CKD | 147(86) | 151(83.4) | 151(83.4) | 0.509c |
| Malignancy | No. (%) | AKI | AKI | 20 (11.1) | 41 (20.3) | 41 (20.3) | 0.014 c |
| Malignancy | No. (%) | CKD | CKD | 21(12.3) | 30(16.6) | 30(16.6) | 0.253c |
| Hemoglobin Levels | Mean±SD | AKI | AKI | 10.93 ± 2.40 | 10.83 ± 2.28 | 10.83 ± 2.28 | 0.704 b |
| Hemoglobin Levels | Mean±SD | CKD | CKD | 10.49±2.00 | 10.50±2.18 | 10.50±2.18 | 0.954b |
| Albumin Levels | Mean±SD | AKI | AKI | 3.11 ± 0.70 | 3.22 ± 0.88 | 3.22 ± 0.88 | 0.244 b |
| Albumin Levels | Mean±SD | CKD | CKD | 3.21±0.62 | 3.20±0.87 | 3.20±0.87 | 0.009 b |
| Staging | AKINNo. (%) | AKI | Stage 1 | 43 (24.0) | 78 (38.6) | 78 (38.6) | <0.001 c |
| Staging | AKINNo. (%) | AKI | Stage 2 | 44 (24.6) | 67 (33.2) | 67 (33.2) | <0.001 c |
| Staging | AKINNo. (%) | AKI | Stage 3 | 92 (51.4) | 57 (28.2) | 57 (28.2) | <0.001 c |
| Staging | KDIGO No. (%) | CKD | G3A | 27 (15.9) | 40 (22.2) | 40 (22.2) | 0.006 c |
| Staging | KDIGO No. (%) | CKD | G3B | 46 (27.1) | 62 (34.4) | 62 (34.4) | 0.006 c |
| Staging | KDIGO No. (%) | CKD | G4 | 75 (44.1) | 71 (39.4) | 71 (39.4) | 0.006 c |
| Staging | KDIGO No. (%) | CKD | G5 | 22 (12.9) | 7 (3.9) | 7 (3.9) | 0.006 c |
## Mortality
In univariate analysis, a significantly higher rate of short-term all-cause mortality was found among the two groups of kidney patients admitted to the open-staff Medicine wards compared to the closed-staff Nephrology ward (Table 2).
**Table 2**
| Variable | Unnamed: 1 | Study Group a | Nephrology Department | General Department | Pv |
| --- | --- | --- | --- | --- | --- |
| Short Term all-cause mortality (< = 90 days) | No. (%) | AKI | 24 (13.3) | 85 (42.1) | <0.001 c |
| Short Term all-cause mortality (< = 90 days) | No. (%) | CKD | 19(11.1) | 71(39.2) | <0.001 c |
| Long Term all-cause mortality (>90 days)b | No. (%) | AKI | 33 (18.3) | 47 (23.3) | 0.237 c |
| Long Term all-cause mortality (>90 days)b | No. (%) | CKD | 36(21.1) | 51(28.2) | 0.121 c |
| RRT in first hospitalization | No. (%) | AKI | 53 (29.4) | 29 (14.4) | <0.001 c |
| RRT in first hospitalization | No. (%) | CKD | 43(25.1) | 24(13.3) | 0.005 c |
| RRT After first hospitalization | No. (%) | AKI | 45 (25.0) | 24 (11.9) | <0.001 c |
| RRT After first hospitalization | No. (%) | CKD | 89(52.0) | 36(19.9) | <0.001 c |
| AV Shunt Surgery | No. (%) | AKI | 14 (7.8) | 6 (3.0) | 0.035 c |
| AV Shunt Surgery | No. (%) | CKD | 34(19.9) | 11(6.1) | <0.001 c |
| Number of Recurrent Hospitalizations | Median (IQR) | AKI | 1 (0–4) | 1 (0–3) | 0.079d |
| Number of Recurrent Hospitalizations | Median (IQR) | CKD | 3(1–5) | 1(0–3.5) | 0.001 d |
| Short Term CDCs | Median (IQR) | AKI | 0 (0–1) | 1 (0–1) | 0.043 d |
| Short Term CDCs | Median (IQR) | CKD | 1(0–1) | 1(0–1) | 0.441d |
| Long Term CDCs | Median (IQR) | AKI | 0 (0–1) | 0 (0–1) | 0.917 d |
| Long Term CDCs | Median (IQR) | CKD | 0 (0–1) | 0 (0–1) | 0.162 d |
| Long Term CREDENCE | No. (%) | AKI | 47 (26.1) | 48 (23.8) | 0.596 c |
| Long Term CREDENCE | No. (%) | CKD | 72(42.1) | 58(32.0) | 0.051 c |
| MACE | No. (%) | AKI | 42 (23.3) | 55 (27.2) | 0.596 c |
| MACE | No. (%) | CKD | 51(29.8) | 71(39.2) | 0.064 c |
Next, univariate analysis (Table 3) showed a significantly lower mortality rate for kidney patients admitted to Nephrology floor for both CKD (OR = 0.19, CI = 0.11–0.34, $p \leq 0.001$) and AKI (OR = 0.21, CI = 0.13–0.35, $p \leq 0.001$).
**Table 3**
| Variable | Study Group | Department | Odds Ratio (OR) | 95% CI | Pv | Adjusted Odds Ratio (aOR)c | 95% CI.1 | Pv.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Short Term all-cause mortality (< = 90 days) a | AKI | General | REF | | <0.001 | REF | | <0.001 |
| Short Term all-cause mortality (< = 90 days) a | AKI | Nephrology | 0.21 | 0.13–0.35 | <0.001 | 0.25 | 0.12–0.48 | <0.001 |
| Short Term all-cause mortality (< = 90 days) a | CKD | General | REF | | <0.001 | REF | | 0.001 |
| Short Term all-cause mortality (< = 90 days) a | CKD | Nephrology | 0.19 | 0.11–0.34 | <0.001 | 0.28 | 0.14–0.58 | 0.001 |
| Long Term all-cause mortality (>90 days)a | AKI | General | REF | | 0.238 | REF | | 0.979 |
| Long Term all-cause mortality (>90 days)a | AKI | Nephrology | 0.74 | 0.45–1.22 | 0.238 | 0.99 | 0.52–1.88 | 0.979 |
| Long Term all-cause mortality (>90 days)a | CKD | General | REF | | 0.112 | REF | | 0.310 |
| Long Term all-cause mortality (>90 days)a | CKD | Nephrology | 0.68 | 0.42–1.11 | 0.112 | 0.71 | 0.37–1.37 | 0.310 |
| RRT in first hospitalization a | AKI | General | REF | | <0.001 | REF | | 0.003 |
| RRT in first hospitalization a | AKI | Nephrology | 2.49 | 1.50–4.14 | <0.001 | 2.57 | 1.39–4.75 | 0.003 |
| RRT in first hospitalization a | CKD | General | REF | | 0.005 | REF | | 0.003 |
| RRT in first hospitalization a | CKD | Nephrology | 2.20 | 1.27–3.81 | 0.005 | 2.81 | 1.42–5.56 | 0.003 |
| RRT after first hospitalization a | AKI | General | REF | | 0.001 | REF | | 0.038 |
| RRT after first hospitalization a | AKI | Nephrology | 2.47 | 1.44–4.26 | 0.001 | 2.06 | 1.04–4.06 | 0.038 |
| RRT after first hospitalization a | CKD | General | REF | | <0.001 | REF | | 0.004 |
| RRT after first hospitalization a | CKD | Nephrology | 4.37 | 2.73–7.01 | <0.001 | 2.50 | 1.35–4.65 | 0.004 |
| AV Shunt Surgery a | AKI | General | REF | | 0.042 | REF | | 0.089 |
| AV Shunt Surgery a | AKI | Nephrology | 2.76 | 1.04–7.33 | 0.042 | 2.94 | 0.85–10.19 | 0.089 |
| AV Shunt Surgery a | CKD | General | REF | | <0.001 | REF | | 0.016 |
| AV Shunt Surgery a | CKD | Nephrology | 3.81 | 1.86–7.70 | <0.001 | 3.36 | 1.25–8.99 | 0.016 |
| Number of Recurrent Hospitalizations b | AKI | General | REF | | 0.040 | REF | | 0.001 |
| Number of Recurrent Hospitalizations b | AKI | Nephrology | 1.29 | 1.01–1.64 | 0.040 | 1.66 | 1.22–2.25 | 0.001 |
| Number of Recurrent Hospitalizations b | CKD | General | REF | | 0.004 | REF | | 0.016 |
| Number of Recurrent Hospitalizations b | CKD | Nephrology | 1.42 | 1.12–1.81 | 0.004 | 1.51 | 1.08–2.10 | 0.016 |
| Short Term CDCs b | AKI | General | REF | | | REF | | |
| Short Term CDCs b | AKI | Nephrology | 0.87 | 0.64–1.20 | 0.402 | 1.10 | 0.74–1.64 | 0.646 |
| Short Term CDCs b | CKD | General | REF | | | REF | | |
| Short Term CDCs b | CKD | Nephrology | 0.97 | 0.70–1.33 | 0.831 | 1.15 | 0.76–1.75 | 0.515 |
| Long Term CDCs b | AKI | General | REF | | | REF | | |
| Long Term CDCs b | AKI | Nephrology | 0.97 | 0.67–1.40 | 0.867 | 1.30 | 0.82–2.07 | 0.272 |
| Long Term CDCs b | CKD | General | REF | | | REF | | |
| Long Term CDCs b | CKD | Nephrology | 1.12 | 0.81–1.56 | 0.490 | 1.12 | 0.72–1.74 | 0.617 |
| Long Term CREDENCE a | AKI | General | REF | | | REF | | |
| Long Term CREDENCE a | AKI | Nephrology | 1.13 | 0.71–1.80 | 0.596 | 1.42 | 0.77–2.62 | 0.261 |
| Long Term CREDENCE a | CKD | General | REF | | | REF | | |
| Long Term CREDENCE a | CKD | Nephrology | 1.54 | 0.99–2.38 | 0.051 | 1.37 | 0.76–2.47 | 0.292 |
| MACE a | AKI | General | REF | | | REF | | |
| MACE a | AKI | Nephrology | 0.81 | 0.51–1.29 | 0.383 | 1.66 | 0.88–3.12 | 0.116 |
| MACE a | CKD | General | REF | | | REF | | |
| MACE a | CKD | Nephrology | 0.66 | 0.42–1.03 | 0.065 | 1.16 | 0.64–2.10 | 0.615 |
Next, using multivariate analysis, adjusted to potential confounders as well as to the propensity score analysis, the Nephrology ward related relative reduction rate in short term mortality was $72\%$ and $75\%$, for CKD and AKI patients respectively (CKD: OR = 0.28, CI = 0.14–0.58, $$p \leq 0.001$$; AKI: OR = 0.25, CI = 0.12–0.48, $p \leq 0.001$). However, the long-term all-cause mortality was not affected by the type of admitting department (Table 3). Remarkably, the propensity score analysis reiterated the protective effect of Nephrology ward admission, relevant for both kidney patient populations.
## Intermediate outcomes
To evaluate intermediate outcomes, we next tested for composite dialysis complication score (CDCs), CREDENCE, MACE and RRT.
Although univariate analysis initially suggested that in AKI patients there could be less acute complications (short term CDCs) for patients admitted to Nephrology (Table 2), further multivariate analyses indicated that this score did not differ significantly between the Nephrology and Medicine departments (Table 3). Similarly, univariate analysis initially suggested that CKD patients had borderline higher long term CREDENCE score and lower MACE score for patients admitted to Nephrology (Table 2). However, in multivariate analysis (Table 3), these differences were no longer significant.
Admission to Nephrology was associated with higher rates of renal replacement therapy (RRT) both in and after the first hospitalization, and consequently AV shunt surgery, observed in all kidney cohorts (Table 3).
## Discussion
In this retrospective cohort study, we found that admission to a closed-staff Nephrology ward was associated in two groups of kidney patients with reduced short-term mortality. These findings for kidney patients are consistent with the benefits of specialized care units in other fields [7–9,16,17].
Fagugli et al [7] have demonstrated the value of a closed-staff Nephrology department in a selected group of AKI patients requiring acute dialysis. In the current study, we found that the value of closed-staff Nephrology department care may further extend to hospitalization of two major kidney patient groups, i.e., AKI and CKD stage 3A-5.
The present study reveals that for these two groups of kidney patients, short-term all-cause mortality rate was significantly lower in the Nephrology department. While the mortality difference in the range of 72–$75\%$ in favor of Nephrology floor care is very high, we acknowledge it may be partially exaggerated by admittance bias. In contrast, the mortality risk was not totally skewed in favor of the Nephrology department as more severe baseline renal dysfunction, known to predict mortality [18], was observed in patients admitted to Nephrology in both groups. We thus employed a propensity score analysis, adjusted to comorbidities and demographic parameters. Since a randomized controlled trial of kidney patient admission is unlikely due to ethical considerations, this limited study may still be of importance in regard to the structure of Department of Medicine and its sub-specialties. The possibility that early kidney patient transfer to an empowered Nephrology Department may substantially increase short term survival cannot be ruled out.
The relative reduction in the mortality rate may be possibly explained by several conjectures. Nephrology ward is a specialized department managed $\frac{24}{7}$ by the same group of Nephrologists. In contrast, rotating attending Nephrologist consultation is requested by the general Medicine wards every few days. Furthermore, the patient to staff ratios in the Nephrology floor are lower, allowing more attention to patients. Third, facilities such as dialysis unit and transplantation clinic are part of the Nephrology Department as are specialist dietitian, transplantation nurse and social worker. It thus appears that a major factor affecting kidney patient short term survival is the human factor where a trained, closed-staff Nephrology team may improve AKI and CKD patient survival. This structure may also account for our finding of higher rate of RRT and AV shunt surgery in AKI and CKD patients admitted to Nephrology department.
In contrast to short-term mortality, long term all-cause mortality was not associated with the ward type. The following factors may account for this observation. First, in the AKI cohort, long-term mortality may have been underestimated. In 16 studies AKI was associated with increased long-term mortality (up to $83\%$ 5-yr mortality risk.) [ 16]. Because our maximum follow-up time was 2.4 years, the long-term effect of Nephrology department on AKI and CKD care may only be realized after a longer period. Second, our finding that admission to the Nephrology Department was beneficial only for short-term mortality may in fact reflect the critical need for expertise in the management of kidney patients, whereas long-term mortality is multi-factorial involving general practitioners, pharmacists, nurses, and dietitians who are not always acquainted with the subtleties of CKD care. In this regard, outpatient Nephrology clinic visit were not accounted for after discharge.
The benefit of in-patient Nephrology care was previously reported primarily for AKI patients. Meier et al found that hospital-acquired AKI patients who had been referred early (within 5 days after development of AKI) to a Nephrologist were at lower risk for in-hospital morbidity and mortality compared with non-Nephrologist referral and late (> 5 days) Nephrology referral [17]. A similar difference in short-term mortality in a subgroup of 296 non critically ill AKI patients requiring acute dialysis was reported between Nephrology and Medicine wards [7], where admission to a closed-staff Nephology department resulted in a $20\%$ mortality rate vs. a $52\%$ mortality rate in the medical wards. Our study focused on 2 different cohorts of non-critically ill kidney patients and further extends the potential benefit of closed-staffed Nephrology care to all AKI stages and to CKD. Thus, we raise the hypothesis that a better outcome for hospitalized kidney patients is possible, not necessarily via advanced sophisticated technology, but rather involving hospital organizational steps promoting closed-staff Nephrology departments.
## Limitations
This study has some limitations. First, this is a single-center study and may not reflect other institutions. Nevertheless, since our single center is the only referral medical center in our region, this may be an advantage as missing data are unlikely. Second, the baseline characteristics of the Nephrology ward patients were more favorable as they were younger and had fewer cardiovascular comorbidities. In contrast, advanced CKD, and AKI stages, also known as major risk factors, were more prevalent in the Nephrology group. Although we used a robust propensity analysis to account for the difference in patients’ comorbidities, we cannot rule out an overlooked baseline risk factor that could have skewed our results. Third, we did not differ between hospitalization conferring bad prognosis, e.g., sepsis or MI, and hospitalization conferring a good prognosis, e.g., AV shunt surgery. Fourth, we don’t have the data regarding Nephology clinic visits after hospital discharge. However, this would seem to affect mostly the long-term outcomes rather than the short-term.
## Strengths
First, our more extensive study supports the results of a previous smaller study in a subgroup of AKI requiring acute dialysis [7] where admission to a Nephrology ward was also associated with reduced short-term mortality. Our study extends this finding to all stages of AKI and to CKD. Second, because missing data are unlikely in a single referral center, our study population appears to accurately reflect the kidney patient population in our area.
## Conclusion
Both AKI and CKD patients admitted to the Nephrology Department demonstrate significantly reduced short-term mortality, when compared to general Medicine departments. These findings highlight the human factor in kidney patient outcome, and support the role of highly trained, closed-staff Nephrology departments for specialized kidney patient care.
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|
---
title: Does the economic growth target overweight induce more polluting activities?
Evidence from China
authors:
- ZhengZheng Shi
- Hongwen Chen
- Kunxian Chen
journal: PLOS ONE
year: 2023
pmcid: PMC9990941
doi: 10.1371/journal.pone.0282675
license: CC BY 4.0
---
# Does the economic growth target overweight induce more polluting activities? Evidence from China
## Abstract
In China, official promotion evaluation based on economic performance motivates local governments to develop high economic growth targets, which has played an active role in boosting China’s economic growth in the past decades, whereas its environmental consequences have not been fully exploited. This paper finds that the economic growth target overweight has a stronger positive impact on the output of high-polluting industries than on the output of low-polluting industries, thus inducing more polluting activities. To deal with the issues of reverse causality and omitted variables bias, we take an instrumental variable approach. Examining mechanisms, we show that economic growth target overweight promotes polluting activities through the deregulation of the polluting activities in high-polluting industries. We also find an increase in the impact of the economic growth target overweight after the 2008 global economic crisis. Our study provides new evidence for explaining the dual presence of rapid economic growth and heavy environmental pollution in China.
## 1. Introduction
Environmental damage is one of the biggest threats to human well-being in the short and long run and has been an eye-catching research topic in economics and environmental science [1–3]. One type of environmental damage is the climate warming. It is documented that carbon dioxide (CO2), one of the leading greenhouse gases, has risen to its highest level in the past 800,000 years [4], and will continue to grow [5]. Massive carbon emission is considered the main cause of climate warming, which is associated with many diseases such as mental disorders [6], primary hypertension [7], and diabetes [8]. Ambient pollution is another type of environmental damage, which has adverse effects on humans in many aspects, including health and life expectancy [9, 10], productive [11, 12], and short-run cognition [13]. Therefore, it is urgent to take action to curb the deteriorating environmental issue, but before it we have to have a good knowledge of the causes of it.
One possible cause is the economic growth, as reflected in the empirical correlation between economic growth and environmental pollution, the so-called environmental Kuznets curve (EKC). The idea behind the EKC is that an inverted U relationship exists between ambient levels of pollution and GDP per capita [14]. The rising part of the EKC is what is going on now in China. Since the launch of the reform and opening up in 1978, China’s economic performance has been remarkable. For instance, according to the data from the National Bureau of Statistics of China, its GDP grew more than 200 times from 1978 to 2018 (at 1978 constant prices), a $9.4\%$ average annual growth rate, which is much higher than the world’s average during the same period. However, rapid economic growth is also accompanied by serious environmental damage [15, 16]. According to the data from the Ministry of Ecology and Environment, China’s economic losses due to environmental damage stood at about 2 trillion yuan in 2015. Clearly, environmental pollution has been a significant impediment to China’s long-term economic development.
Chinese economy is intertwined with its political system. One of salient features of the China’s political economy is that local officials’ chances of promotion are bound up with the economic growth in their jurisdictions. *In* general, the higher economic growth rate is, the higher promotion chances the local officials get [17, 18]. This incentive mechanism encourages local officials to pay much attention to GDP growth to compete for promotions, the so called “promotion tournament” [19], which is a crucial contributor to China’s economic success in the past decades [18]. For more promotion chances, lower-level government typically set higher growth targets than upper-level ones (i.e., the “top-down amplification”) [20], generating the economic growth targets overweight (EGTO).
EGTO brings great pressure of boosting the economy to the local officials, which may lead to the neglection of local governments to environmental protection. As shown in Fig 1, the average output, investment, and employment of the pollution companies are all higher than those of the non-pollution companies. This implies that local officials have incentives to take more care of pollution companies so as to achieve the growth targets [21], which, however, may exert negative effect on environment quality. This argument is evidenced by many studies. For example, it is found that local officials often neglect pollution controls when developing the economy, thus resulting in environmental degradation [22]. Some research shows that government officials have incentives to support local firms through tolerating their heavy pollution and even protecting them from being penalized for producing excessive pollution [23]. A research finds that higher economic growth target can lead to lower intensity of the environmental regulation [20]. Finally, there is a collusion between governments and enterprises, which has been a severe problem hindering environmental protection and green development in China [24, 25]. With the discussion above, a natural question is that how does EGTO affect the environmental pollution? This has not been fully exploited by previous research and is the focus of our paper.
**Fig 1:** *Average output, investment, and employment of enterprises of high-polluting and low-polluting industries.Note: The unit of output and investment is 10000 yuan, and the unit of employment is 100 people. We calculate the average output, employment, and investment of high-polluting and low-polluting enterprises based on the enterprise data in the ASIF.*
This paper aims to explore the impact of the EGTO on polluting activities in China. Using data from the Annual Survey of Industrial Firms (ASIF) and the annual work reports of provincial and prefecture-level governments, we find that the economic growth target overweight can induce more pollution activities, which is reflected in the stronger positive impact of EGTO on the output of high-polluting industries than the output of low-polluting industries. Moreover, the effect of EGTO on pollution activities varies across the ownership and scale of enterprises and the release date of the economic growth target. We provide evidence that EGTO increases polluting activities through reducing the environmental regulations of polluting activities of high-polluting industries. We further explore the influence of the global economic crisis in 2008, and document an increase in the effect of EGTO after the crisis.
Existing literature on environmental pollution has deepened our understanding of pollution from the perspective of economic and institutional factors. For example, some scholars have paid attention to economic growth and reform [26–28], trade and foreign investment [29–31], industrial structure and agglomeration [32, 33]. Some scholars also focus on market segmentation [34], fiscal decentralization [35], and political turnover [36]. This paper investigates whether and how municipal governments’ economic growth targets influence pollution activities. The setting of an economic growth target involves both economic and non-economic factors. Exploring the impact of EGTO can provide a new explanation for the relationship between China’s economic growth and environmental pollution activities. Cities pursuing higher economic growth not only attract the transfer of pollution but also encourage the expansion of pollution. The pollution transfer caused by environmental regulation has attracted the attention of many scholars [15, 37, 38]. Our research finds that the EGTO also causes the transfer of pollution. In the process of China’s development, the economically developed regions set higher targets before the first decade of the 20th century, but after that, the underdeveloped regions set higher targets, which may cause the poor people in the underdeveloped areas to suffer from environmental damage. This issue should be paid close attention to.
In addition, existing studies attribute the success of China’s economy to its unique political system, which effectively encourages economic competition among officials and helps the central government to ensure that prefectural governments are consistent with national policy objectives [39, 40]. We supplement this traditional view. Just like the central government’s economic growth accounting for local governments, due to the principal-agent relationship between them, there may be inconsistent goals.
The rest of the paper is structured as follows. Section 2 introduces the background of economic growth target, promotion system, and environmental regulation in China. Section 3 presents the empirical strategy. Section 4 introduces the data and variables used in our analysis. Section 5 shows the empirical results, including baseline regression results, robustness checks, and heterogeneity analysis. Section 6 examines the mechanism through which EGTO affect polluting activities. Section 7 investigates the time variation of the impact of EGTO on polluting activities before and after the 2008 global financial crisis. Finally, Section 8 concludes and makes some policy suggestions.
## 2.1 The relationship between economic growth target and official promotion
In China, the central government assigns economic growth rate targets to provincial governments, and the provincial governments redistribute the targets to subordinate prefecture-level governments. In order to supervise and inspire local officials, the central government adopts the cadre evaluation system to cascade financial and personnel incentives to local governments along the hierarchy [41]. Based on the performance evaluation system, local officials in China give priority to the policy formulated by higher authorities [42]. There are assessment objectives in policy areas such as economic growth and production safety, and officials go all out to achieve these goals. In terms of economic growth, local governments not only passively accept the tasks of their superiors but actively set higher targets to impress their superiors. For example, most provinces are usually expected to have higher growth than the central government. Prefecture-level cities also set higher growth targets than the provincial targets, resulting in the “layer upon layer”. The career prospects of officials are closely related to the degree to which the targets are fulfilled [41, 43, 44]. Local officials who achieve their goals have more promotion opportunities and will receive more financial transfers from higher-level governments, while those who fail to achieve their goals are punished, such as dismissal and demotion [43, 45]. Since 1978, economic growth has become a top priority for governments at all levels. The promotion tournament on economic growth has been a common phenomenon in municipal and provincial governments [44, 45]. Some scholars [46] verify that economic performance has a significantly positive impact on the promotion opportunities of local officials. The promotion incentive urges local officials to set higher economic goals. One research [43] demonstrates that the target of the higher-level government is an important reference for the subordinate government when setting its own target. The chance of promotion depends on relative performance, i.e., local officials need to outperform their peers, which motivates local governments to set higher economic growth targets [19]. However, the economic growth tournament also makes local governments myopic in economic efficiency. For example, public expenditures are of low profits but play an important role in increasing GDP will be favored by governments [42, 43].
## 2.2 Pollution control practice of the Chinese government
In response to the increasingly severe environmental damage, the two-control zone (TCZ) was proposed in 1998 to reduce acid rain and sulfur dioxide pollution. Starting from the 10th Five-Year Plan in 2001, each five-year plan has set targets for pollutant emission reduction. The main leaders of the cities in areas that fail to achieve the target or have the most serious pollution each year will be interviewed by the Ministry of Environmental Protection (MEP). In 2003, President Hu Jintao formally put forward the Scientific Outlook on Development (SOD) to seek the coordinated development of economic, environmental, and social issues. In order to address the serious water pollution problem in China, hundreds of water monitoring stations have been installed along the main rivers in China, and the importance of water quality monitoring has been emphasized. The MEP set clear water quality targets for monitoring stations and systematically published the readings, which significantly curbed the discharge of pollutants [47]. In 2008, environmental factors were added to the promotion evaluation of officials. This year, MEP was upgraded to the Ministry of Ecological Environment (MEE). The improvement of the administrative level means the increase of the regulatory power of environmental protection departments. However, in the absence of transparency in air quality data, local officials can whitewash environmental quality data to cope with the assessment [48], which still cannot effectively encourage local officials to carry out substantive environmental governance [23, 49].
In short, promotion pressure makes officials focus more on economic growth than environmental protection. This study aims to prove the causality between economic growth target overweight of the prefecture-level governments and pollution activities, the possible mechanism, and the change of this causality under the pressure of economic and environmental protection in different periods.
## 3. Model specification
Since higher EGTO requires more efforts in developing the economy and high-polluting industries can bring more economic benefits compared with low-polluting industries, we conjecture that the impact of EGTO on the output of high-polluting industries is larger than the output of low-pollution industries, and set the following interaction model to identify the heterogeneous effect: yict=β0+β1EGTOct*dirtyi+φit+τic+λct+ϵict [1] where yict is the output of industry i of city c in year t. EGTOct denotes the economic growth target overweight of city c in year t, which is measured by the economic growth target gap between city c and subordinated province. dirtyi is an indicator variable that equals one for high-polluting industries, and zero for low-polluting industries. φit are industry-year fixed effect, which captures all time-variant and time-invariant industry characteristics, such as industry-specific technologies and industrial policies.; τic are industry-city fixed effects that absort different cities to have differential levels of industrial agglomeration and industrial policies. λct are city-year fixed effects which controls all time-variant and time-invariant city characteristics, such as geographical location, productivity change, economic development level, public policy, official characteristics and turnover, etc. The coefficient of interest is β1, which captures the heterogeneous effect of EGTO on high-polluting industries and low-polluting industries. In specific, if β1 > 0, the EGTO has a larger impact on the output of high-polluting industries.
## 4. Data and variables
Our estimation sample is a panel of 260 Chinese cities in 23 provinces from 2006 to 2014. We collect economic growth targets data from official government websites and documents. Other data on city-level variables are from the China City Statistical Yearbook. All nominal values are adjusted by the industrial producer price index (PPI), which is from the National Bureau of Statistics of China, to the 2006 price level.
## 4.1 Measuring the intensity of industrial pollution
We use the pollution intensity index (PII) to measure the intensity of industrial polluting activities following a research [50], which is defined as: pIIij=(Wij/Qi)/(∑$i = 139$Wij/∑$i = 139$Qi) [2] where i and j index industries and pollutants, respectively. Due to the data availability, we focus on three types of pollutants. These are: wastewater, smoke, and sulfur dioxide. Wij denotes the emission of pollutant j of industry i. Qi represents the value added of industry i. The PII of wastewater, smoke, and sulfur dioxide for each industry are shown in S1 Appendix. We refer to industries with PII>1 as “high-polluting” industries, and industries with PII<1 as “low-polluting” industries. S2 Appendix displays the list of high-polluting industries for different pollutants. Pollution emission and value-added data of each industry are from China Statistical Yearbook and China Industrial Statistical Yearbook, respectively.
## 4.2 Industry-level data
The output and investment data of industries are from the Annual Survey of Industrial Firms (ASIF, National Bureau of Statistics 2006–2014). The database contains non-state-owned enterprises with an output value of more than 5 million and all state-owned enterprises. These total output values account for more than $95\%$ of the national industrial output value, which is a representative sample. Based on firm-level data, we calculated the annual gross output value of each two-digit industry in each city from 2006 to 2014. As the pollution industries are determined by the double-digit classification method according to the existing literature, they are divided into high-polluting industries and low-polluting industries based on the pollution emission intensity. The dependent variable is the logarithm of the total output value of high-polluting and low-polluting industries, which measures the intensity of polluting activities. Table 1 displays the definition and summary statistics for the main variables used in our analysis.
**Table 1**
| Variable | Definition | Obs | Mean | Min | Max |
| --- | --- | --- | --- | --- | --- |
| EGTO | Target gap between city and province (%) | 2170 | 2.08 | −7.10 | 15.0 |
| EGTO2 | Target gap between city and the central government (%) | 2170 | 2.51 | -5.7 | 18.0 |
| Target decision-making time before province | | 2170 | 0.41 | 0 | 1.0 |
| GDP per capita (Log) | | 2170 | 10.21 | 4.62 | 13.06 |
| Population density | People per square kilometer | 2170 | 493.79 | 22.31 | 2641.17 |
| Envir_r | Average environment-related text proportion (%) | 2170 | 2.01 | 0.58 | 7.44 |
| Output value of polluting industry (100 million) | | 2170 | 485.65 | 4.43 | 3931.33 |
| Output value of air-polluting industry (100 million) | | 2170 | 429.73 | 3.71 | 3518.07 |
| Output value of water-polluting industry (100 million) | | 2170 | 391.05 | 3.69 | 3448.16 |
## 5.1. Main results
Table 2 reports the estimation results of model [1]. Columns 1 and 2 show that the coefficient of the interaction term between EGTO and the high-polluting industry dummy is positive and statistically significant for air-polluting industries both with and without controlling for fixed effects, which demonstrates that EGTO has a stronger positive impact on the output of high air-pollution industries. Thus, EGTO can induce more air-polluting activities. Columns 3 and 4 display the effect of EGTO on water-polluting activities. The coefficient of the interaction is lower but remains positive and significant, which suggests that EGTO can facilitate water-polluting activities. Finally, we focus on industries that feature both high air pollution and water pollution. Columns 5 and 6 report the results. The coefficients of the interactions are still significantly positive. Taken together, the results in Table 2 suggest that EGTO will induce more polluting activities, thus having a negative effect on the environment.
**Table 2**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 | Dependent variables: industrial output.3 | Dependent variables: industrial output.4 | Dependent variables: industrial output.5 |
| --- | --- | --- | --- | --- | --- | --- |
| | Air-polluting industry | Air-polluting industry | Water-polluting industry | Water-polluting industry | Both | Both |
| | (1) | (2) | (3) | (4) | (5) | (6) |
| EGTO *dirty | 0.083*** | 0.065*** | 0.058*** | 0.045*** | 0.074*** | 0.061*** |
| | (0.023) | (0.017) | (0.018) | (0.016) | (0.018) | (0.017) |
| Industry-year FE | Y | Y | Y | Y | Y | Y |
| City-year FE | N | Y | N | Y | N | Y |
| City-industry FE | N | Y | N | Y | N | Y |
| Observations | 4340 | 4340 | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.867 | 0.924 | 0.728 | 0.836 | 0.843 | 0.889 |
## 5.2.1 Alternative measures of high-polluting industry and EGTO
We now check the robustness of our baseline estimates. We first examine the sensitivity of the baseline estimates to the use of alternative measures of high-polluting industries and EGTO. In this paper, we use the pollution intensity index to measure high-polluting and low-polluting industries. In this subsection, we follow the criterion of the First Nationwide Pollution Source Survey (FNPSS), regard the key pollution source industries as the high-polluting industries, and others as low-polluting industries. The new high-polluting industry dummy is labeled as dirty2. Column 1 in Table 3 presents the estimates. The coefficient of the interaction remains positive and significant.
**Table 3**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 | Dependent variables: industrial output.3 |
| --- | --- | --- | --- | --- |
| | Both | Air-polluting industry | Water-polluting industry | Both |
| | (1) | (2) | (3) | (4) |
| EGTO *dirty2 | 0.061*** | | | |
| | (0.017) | | | |
| EGTO2 *dirty | | 0.095*** | 0.089*** | 0.090*** |
| | | (0.032) | (0.031) | (0.030) |
| Industry-year FE | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y |
| City-industrial FE | Y | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.910 | 0.937 | 0.895 | 0.906 |
Next, we check the robustness to using an alternative measure of the EGTO. Since the political competition of officials may occur between cities in different provinces, we use the difference between the growth targets of prefecture-level cities and the central government to measure the overweight of economic target, and denote it as EGTO2. Results are shown in Columns 2–4 in Table 3. The coefficients for the interaction terms remain positive and significant in all specifications.
## 5.2.2 Endogeneity
Although we have controlled for a large set of fixed effects in Eq [1], we still suffer from the omitted variables bias. For example, government officials in cities with different levels of economic development and population density may attach differential importance to pollution. Specifically, people in developed and densely populated cities generally place more weight on environmental quality. Thus, GDP per capita and population density are two potential omitted variables that confound the estimates. To correct the bias, we further control for the interaction between GDP per capita and the high-polluting industry dummy, and the interaction between annual population density and the high-polluting industry dummy. Since the economic growth target is usually set at the beginning of a year, we lag GDP per capita and population density by one year. Results are presented in panel A of Table 4. Columns 1–3 show that we obtain qualitatively identical results when we condition on the two controls—i.e., the coefficients of the interaction terms between EGTO and the high-polluting industry indicator are all positive and statistically significant. In addition, there are fewer polluting activities in cities with high GDP per capita and high population density.
**Table 4**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 |
| --- | --- | --- | --- |
| | Air-polluting industry | Water-polluting industry | Both |
| | (1) | (2) | (3) |
| Panel A. Controlling for GDP per capita and population density | Panel A. Controlling for GDP per capita and population density | Panel A. Controlling for GDP per capita and population density | Panel A. Controlling for GDP per capita and population density |
| EGTO *dirty | 0.065*** | 0.042** | 0.054*** |
| | (0.020) | (0.020) | (0.019) |
| Log GDP per capita (t-1)*dirty | -0.134** | -0.186*** | -0.175** |
| Log GDP per capita (t-1)*dirty | (0.072) | (0.062) | (0.070) |
| Population density (t-1)*dirty | -0.179*** | -0.102** | -0.151*** |
| Population density (t-1)*dirty | (0.058) | (0.049) | (0.056) |
| Industry-year FE | Y | Y | Y |
| City-year FE | Y | Y | Y |
| City-industry FE | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 |
| R-squared | 0.942 | 0.901 | 0.920 |
| Panel B. IV estimates | Panel B. IV estimates | Panel B. IV estimates | Panel B. IV estimates |
| EGTO *dirty | 0.405*** | 0.328*** | 0.357*** |
| | (0.138) | (0.117) | (0.120) |
| Industry-year FE | Y | Y | Y |
| City-year FE | Y | Y | Y |
| KP F-statistic | 103.15 | 61.47 | 83.79 |
| Observations | 4340 | 4340 | 4340 |
| R-squared | 0.834 | 0.791 | 0.816 |
Although having controlled for a set of fixed effects and the two aforementioned covariates, we can’t completely address omitted variables bias through this way because there are unobservable variables affecting both EGTO and polluting activities. Besides omitted variables bias, another type of endogeneity is the reverse causality. That is, the relative output of high-polluting industries may shape the government’s decision of setting economic growth target, thus affecting EGTO. To address both problems, we employ an instrumental-variables (IV) strategy using the number of prefecture-level cities in a province as the instrument for EGTO.
Causal inference of the IV strategy requires that the selected instrument satisfy both the exclusion restriction and relevance condition, i.e., the number of prefecture-level cities in a province influences polluting activities only through EGTO. We now elucidate the validity of our instrument based on the two criteria. First, in China, local officials are promoted by higher-level officials, but the quota for promotion is a scarce resource. *In* general, the more prefecture-level cities a province comprises, the more intense competition between these cities of the province is. The more competitive environment for promotion encourages local officials to set higher economic growth targets and thus leads to larger EGTO. Second, arguably the number of prefecture-level cities in a province is mainly determined by historical and geographic factors (e.g., conditional on area, geographically more flat provinces generally have more subordinate cities due to the ease of construction and transportation) rather than unobserved drivers of polluting activities. Hence it satisfies the exclusion restriction. Since the number of prefecture-level cities in a province does not change over time, and thus the interaction term between EGTO and the high-polluting industry indicator is subsumed into city-industry fixed effects, we do not control city-industry fixed effects when regressing using the instrument.
Panel B in Table 7 reports the IV estimates. In all three columns, the coefficients of the interactions are still positive and statistically significant. The Kleibergen-Paap Wald rk F-statistic (KP) for weak identification is much larger than the Stock-Yogo critical value of 10. Note that IV estimates are about four times as large as their OLS counterparts, which suggests that OLS estimates underestimate the effect of EGTO.
## 5.2.3 Transfer of polluting activity
If local governments set high economic growth target, they will try hard to attract investments so that the number of new enterprises increase while the number of old enterprises remains steady. To detect the heterogenous effects, we use the information from the enterprise’s opening year to calculate the number of new and old enterprises in each industry of a city. Specifically, old enterprises are those whose opening year is 2005 or earlier; otherwise, they are new enterprises.
Columns 1–3 in Table 5 show that EGTO has a stronger positive impact on the number of new enterprises in high-polluting industries. In contrast, we don’t see significant effects on old enterprises from columns 4–6, which indicates that EGTO increases pollution activities through attracting new enterprises to high-polluting industries.
**Table 5**
| Dependent variables: | Log (number of new firms +1) | Log (number of new firms +1).1 | Log (number of new firms +1).2 | Log (number of old firms +1) | Log (number of old firms +1).1 | Log (number of old firms +1).2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Air- polluting industry | Water- polluting industry | Both | Air- polluting industry | Water- polluting industry | Both |
| | (1) | (2) | (3) | (4) | (5) | (6) |
| EGTO *dirty | 0.048*** | 0.037** | 0.042** | 0.027 | 0.010 | 0.022 |
| | (0.016) | (0.019) | (0.020) | (0.019) | (0.021) | (0.018) |
| Industry-year FE | Y | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.924 | 0.871 | 0.915 | 0.857 | 0.823 | 0.846 |
## 5.3 Heterogeneity
This part examines whether the effects of EGTO are heterogeneous across different contexts, the results of which can help guide policy discussions and future studies on EGTO. First, we explore the heterogeneity regarding the ownership of enterprises. In China, state-owned enterprises (SOEs) are more obedient to government policies, hence are preferred by governments for achieving economic growth targets. Therefore, SOEs may be more susceptible to EGTO. To detect the heterogeneity, we first divide the enterprises in our sample into three groups based on ownership (i.e., SOEs, domestic private enterprises, and foreign enterprises), and then estimate the effect of EGTO for each group. Columns 1–3 in Table 6 display the results. In column 1 of panels A, B and C, we show that EGTO has a significantly stronger positive effect on the output of domestic private enterprises in high-polluting industries. In column 2 of each panel, we find that the effect of EGTO on domestic private enterprises in high-polluting industries is positive and statistically significant as well, but smaller in magnitude. However, there is no significantly stronger effect on foreign enterprises in water-polluting and both-polluting industries, while the effect on foreign enterprises in air-polluting industries remains significant (cols. 3), arguably because the production plans of foreign enterprises are constrained by their overseas parent companies who are insusceptible to the EGTO of Chinese governments.
**Table 6**
| Unnamed: 0 | SOEs | Demestic private | Foreign | Large | Small |
| --- | --- | --- | --- | --- | --- |
| | (1) | (2) | (3) | (4) | (5) |
| Panel A. Air-polluting industry | Panel A. Air-polluting industry | Panel A. Air-polluting industry | Panel A. Air-polluting industry | Panel A. Air-polluting industry | Panel A. Air-polluting industry |
| EGTO *dirty | 0.069*** | 0.045** | 0.043** | 0.071*** | 0.042** |
| | (0.016) | (0.020) | (0.023) | (0.020) | (0.017) |
| Industry-year FE | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.927 | 0.914 | 0.874 | 0.913 | 0.887 |
| Panel B. Water-polluting industry | Panel B. Water-polluting industry | Panel B. Water-polluting industry | Panel B. Water-polluting industry | Panel B. Water-polluting industry | Panel B. Water-polluting industry |
| EGTO *dirty | 0.059*** | 0.034* | 0.020 | 0.052*** | 0.034** |
| | (0.020) | (0.018) | (0.022) | (0.017) | (0.015) |
| Industry-year FE | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.899 | 0.912 | 0.884 | 0.900 | 0.881 |
| Panel C. Both | Panel C. Both | Panel C. Both | Panel C. Both | Panel C. Both | Panel C. Both |
| EGTO *dirty | 0.062*** | 0.048** | 0.030 | 0.064*** | 0.043** |
| | (0.020) | (0.019) | (0.027) | (0.021) | (0.018) |
| Industry-year FE | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 | 4340 | 4340 |
| R-squared | 0.939 | 0.883 | 0.884 | 0.923 | 0.891 |
Second, we investigate the heterogeneous effect of EGTO on large and small companies. We define “large” and “small” by company’s annual sales. Specifically, large companies are those whose annual sales are higher than the median of our sample; Small companies are those with below-median annual sales. Columns 4 and 5 present the results. As shown, EGTO has a larger impact on the polluting activities of large companies in comparison with small companies.
Finally, we explore the heterogeneity in the effect of EGTO regarding the different announcement times of economic growth targets. Local officials refer to the targets of higher-level governments when setting economic growth targets. If local officials set economic growth targets before their superior governments, they are more likely to set targets based on local development conditions. If the growth target of prefecture-level cities postdates the provincial target, prefecture-level cities may refer to the superior target, and tend to set higher targets for promotions. Results in the panel A of Table 7 confirm our conjecture, for cities announcing their targets after the provincial target, the impact of EGTO on polluting activities is larger. This result shows that when the growth targets of local governments are announced after the date when their superior governments announce the growth target, which implies a larger pressure on the promotion, EGTO has a larger impact on polluting activities.
**Table 7**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 | Dependent variables: industrial output.3 | Dependent variables: industrial output.4 | Dependent variables: industrial output.5 |
| --- | --- | --- | --- | --- | --- | --- |
| | Air-polluting industry | Air-polluting industry | Water-polluting industry | Water-polluting industry | Both | Both |
| Panel A. Announcement time | Panel A. Announcement time | Panel A. Announcement time | Panel A. Announcement time | Panel A. Announcement time | Panel A. Announcement time | Panel A. Announcement time |
| | After | Before | After | Before | After | Before |
| | (1) | (2) | (3) | (4) | (5) | (6) |
| EGTO *dirty | 0.093*** | 0.063*** | 0.067*** | 0.043** | 0.081*** | 0.057*** |
| | (0.024) | (0.020) | (0.021) | (0.019) | (0.022) | (0.020) |
| Industry-year FE | Y | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y | Y |
| Observations | 1024 | 3316 | 1024 | 3316 | 1024 | 3316 |
| R-squared | 0.867 | 0.924 | 0.928 | 0.836 | 0.877 | 0.949 |
| Panel B. Resource dependence | Panel B. Resource dependence | Panel B. Resource dependence | Panel B. Resource dependence | Panel B. Resource dependence | Panel B. Resource dependence | Panel B. Resource dependence |
| | Air-polluting industry | Air-polluting industry | Water-polluting industry | Water-polluting industry | Both | Both |
| | Resource-based cities | Non resource-based cities | Resource-based cities | Non resource-based cities | Resource-based cities | Non resource-based cities |
| EGTO *dirty | 0.084*** | 0.057** | 0.092*** | 0.033* | 0.088*** | 0.043* |
| | (0.028) | (0.027) | (0.025) | (0.017) | (0.023) | (0.022) |
| Industry-year FE | Y | Y | Y | Y | Y | Y |
| City-year FE | Y | Y | Y | Y | Y | Y |
| City-industry FE | Y | Y | Y | Y | Y | Y |
| Observations | 1808 | 2532 | 1808 | 2532 | 1808 | 2532 |
| R-squared | 0.892 | 0.901 | 0.889 | 0.913 | 0.897 | 0.900 |
China’s resource-based cities feature a relatively simple industrial structure. These industries largely rely on natural resources and thus are high-polluting. Therefore, if the officials of these cities want to achieve the overweight of economic growth target, they have to pay more attention to the high-polluting industries than the officials of other cities. That is, EGTO will have a larger impact on the polluting-activities of resource-based cities than that of non resource-based cities. To detect the heterogeneity, we first divide the sample cities into resource-based cities and non resource-based cities according to the “National Sustainable Development Plan for Resource-Based Cities” released by the Chinese State Council, and then do the baseline regression for each sample. Panel B of Table 7 shows the results. As expected, the coefficients of interaction terms for resource-based cities are statistically larger than these for non resource-based cities for all specifications, which means that EGTO induces more polluting activities for resource-based cities than for non resource-based cities.
## 6. Mechanisms
We probe the mechanisms underlying the effects of EGTO on polluting activities in this section. More specifically, how did EGTO induce more high-polluting activities? Many studies find that in order to boost GDP growth, local governments turn a blind eye to the pollution generated in the process of production activities, even shielding high-polluting firms from punishment [51]. Since environmental regulation can improve environmental quality but comes at the cost of jobs, productivity, or other undesirable economic effects [52, 53], if the economic growth target is high, local governments will pay much attention to GDP regardless of the environmental pollution. Therefore, we infer that EGTO promotes polluting activities by weakening environmental regulation. To formally test the hypothesis, we first, following a study [15], use the frequency of words as to environmental protection in the government work report to measure the intensity of environmental regulation of a city, and employ the following equation: yict=β0+β1EGTOct*dirtyi*envir_rct+EGTOct*dirtyi+φit+τic+λct+ϵit [3] where envir_rct refers to the intensity of the environmental regulation of city c in year t. Results are presented in Table 8. The coefficients of the triple-interaction terms are significantly negative in all specifications, which means that the more environmental regulations, the weaker impact of EGTO on the relative output of high-polluting industries. Hence, environmental deregulation is the mechanism through which EGTO increases polluting activities.
**Table 8**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 |
| --- | --- | --- | --- |
| | Air-polluting industry | Water-polluting industry | Both |
| | (1) | (2) | (3) |
| EGTO *dirty*envir_r | -0.076** | -0.123*** | -0.097** |
| EGTO *dirty*envir_r | (0.038) | (0.042) | (0.039) |
| EGTO *dirty | 0.023 | 0.084** | 0.046* |
| | (0.052) | (0.037) | (0.029) |
| Dirty*envir_r | -1.746 | -0.451 | -0.356 |
| | (0.901) | (0.553) | (0.112) |
| Industry-year FE | Y | Y | Y |
| City-year FE | Y | Y | Y |
| City-industry FE | Y | Y | Y |
| Observations | 4340 | 4340 | 4340 |
| R-squared | 0.916 | 0.902 | 0.893 |
## 7. Further analysis
To cope with the downward pressure on the economy induced by the 2008 global financial crisis, the Chinese government adopted various measures to spur economic growth. For example, in late 2008, China announced a 4 trillion-yuan ($586 billion) stimulus plan to boost domestic demand. Although the external economic environment was very bad, China still set the 2009 GDP growth target at $8\%$ which is the same as that of the last five years. On the whole, developing the economy was of particular importance during this period, which implies that the government’s tolerance and preference for productive high-polluting industries may be enhanced after the crisis. Therefore, a natural guess is that the impact of EGTO on polluting activities will be larger after 2008. To explore the time variation in the effect of EGTO, we employ the following empirical model: yict=β0+β1EGTOct*dirtyi*post2008+EGTOct*dirtyi+φit+τic+λct+ϵit [4] where post2008 is a dummy variable, which equals 1 if t>2008 and 0 otherwise. The coefficient of the triple interaction term, β1, is of primary interest. A positive coefficient, β1 > 0, indicates that the effect of EGTO on polluting activities increases after 2008.
Table 9 shows the expected results. The coefficients of the triple interaction terms are positive and significant in all specifications, which suggests that the larger positive effect of EGTO on the output of high-polluting industries enhanced after 2008, in other words, EGTO induced more polluting activities after the financial crisis.
**Table 9**
| Unnamed: 0 | Dependent variables: industrial output | Dependent variables: industrial output.1 | Dependent variables: industrial output.2 |
| --- | --- | --- | --- |
| | Air-polluting industry | Water-polluting industry | Both |
| | (1) | (2) | (3) |
| EGTO *dirty*post2008 | 0.049*** | 0.021* | 0.039*** |
| EGTO *dirty*post2008 | (0.013) | (0.012) | (0.012) |
| EGTO *dirty | 0.036*** | 0.024** | 0.038** |
| | (0.012) | (0.010) | (0.015) |
| Industry-year FE | Y | Y | Y |
| City-year FE | Y | Y | Y |
| City-industry FE | Y | Y | Y |
| Observations | 3280 | 3280 | 3280 |
| R-squared | 0.939 | 0.865 | 0.898 |
We restrict our sample period to 2006–2012 so as to exclude the impact of the establishment of the air pollution monitoring system in 2013.
## 8. Conclusion
Using data from the Annual Survey of Industrial Firms and the economic growth targets of provinces and prefecture-level cities from 2006 to 2014, this paper finds that the economic growth target overweight (EGTO) has a positive impact on the output of high-polluting industries relative to low-polluting industries, which demonstrates that EGTO can induce more polluting activities. This result is robust to alternative measures of EGTO and addressing endogeneity. We also find evidence that EGTO promotes polluting activities by relaxing environmental regulations of polluting activities in high-polluting industries. Finally, we document an increase in the effect of EGTO after the 2008 global economic crisis.
Although official promotion evaluation based on economic performance has played a vital role in China’s phenomenal economic growth in the past 40 years, our results show that it also brings a negative impact on the environment. For this reason, China should make the growth-target official evaluation system more environmentally friendly, such as adding more performance indicators related to the environmental quality. Indeed, this is exactly what is happening now in China. For instance, in 2013, the Central Committee of the Communist Party approved the “Decision of the CCCPC on Some Major Issues Concerning Comprehensively Deepening the Reform”, in which the weight of other evaluation indicators such as resource consumption, environmental damage, and ecological benefits has been increased. Thus, an intriguing line for future research is to examine whether the updated official evaluation system improves China’s environment, or brings costs to the economy. Finally, it is worth noting that the latest year when the data of the Annual Survey of Industrial *Firms is* publicly available is 2014. Therefore, the findings of this paper may not support current operations of high-polluting industries.
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|
---
title: Loss of the m6A methyltransferase METTL3 in monocyte-derived macrophages ameliorates
Alzheimer’s disease pathology in mice
authors:
- Huilong Yin
- Zhuan Ju
- Minhua Zheng
- Xiang Zhang
- Wenjie Zuo
- Yidi Wang
- Xiaochen Ding
- Xiaofang Zhang
- Yingran Peng
- Jiadi Li
- Angang Yang
- Rui Zhang
journal: PLOS Biology
year: 2023
pmcid: PMC9990945
doi: 10.1371/journal.pbio.3002017
license: CC BY 4.0
---
# Loss of the m6A methyltransferase METTL3 in monocyte-derived macrophages ameliorates Alzheimer’s disease pathology in mice
## Abstract
Alzheimer’s disease (AD) is a heterogeneous disease with complex clinicopathological characteristics. To date, the role of m6A RNA methylation in monocyte-derived macrophages involved in the progression of AD is unknown. In our study, we found that methyltransferase-like 3 (METTL3) deficiency in monocyte-derived macrophages improved cognitive function in an amyloid beta (Aβ)-induced AD mouse model. The mechanistic study showed that that METTL3 ablation attenuated the m6A modification in DNA methyltransferase 3A (Dnmt3a) mRNAs and consequently impaired YTH N6-methyladenosine RNA binding protein 1 (YTHDF1)-mediated translation of DNMT3A. We identified that DNMT3A bound to the promoter region of alpha-tubulin acetyltransferase 1 (Atat1) and maintained its expression. METTL3 depletion resulted in the down-regulation of ATAT1, reduced acetylation of α-tubulin and subsequently enhanced migration of monocyte-derived macrophages and Aβ clearance, which led to the alleviated symptoms of AD. Collectively, our findings demonstrate that m6A methylation could be a promising target for the treatment of AD in the future.
This study reveals that loss of METTL3, and the corresponding changes in m6A modifications, leads to enhanced brain infiltration of monocyte-derived macrophages in a mouse model of Alzheimer’s Disease, promoting Aβ clearance and ameliorating pathology and cognitive decline.
## Introduction
Alzheimer’s disease (AD), as an age-related neurodegenerative disease, is the most common cause of dementia [1]. Among the common neuropathological features in AD are synaptic and neuronal dysfunction, intracellular neurofibrillary tangles, elevated levels of toxic amyloid beta (Aβ), and extracellular Aβ deposition as neuritic plaques [2–4], which induce cognitive decline. Consequently, there has been tremendous interest in elucidating the molecular mechanism underlying the pathological process of AD.
In recent years, chemical modifications in RNA have been recognized as important mechanisms for the regulation of gene expression and protein translation [5,6]. N6-methyladenosine (m6A), the most diverse and reversible posttranscriptional modification of eukaryotic messenger RNAs (mRNAs), is a strong regulator of mRNA splicing, stability, localization, and translation [5,7], which depends on the combined activity of methyltransferases and demethylases. Currently, the known methyltransferase complex is mainly composed of methyltransferase-like protein 3 (METTL3), methyltransferase-like protein 14 (METTL14), and Wilms tumor 1-associating protein (WTAP), while demethylases include obesity-associated protein (FTO) and AlkB homolog 5 (ALKBH5) [8–11]. Recent studies have demonstrated that m6A is involved in the development of the nervous system and neural degenerative diseases [12–14]. The widespread presence of m6A in the neuronal transcriptome also suggests its various functional roles in brain development and function [15–17]. In addition, accumulating evidence has shown essential roles of m6A modification in learning and memory through regulation of the translation of plasticity-related genes in the mouse brain [13,18–21].
Recently, it has been reported that blood-derived myeloid cells can cross the blood–brain barrier and differentiate into fully functional macrophages [22–24]. Blood-derived myeloid cells, recruited by central nervous system (CNS) damage, are considered microglial reinforcements of comparable functions and are accordingly termed “blood-derived macrophages” [25–27]. Activated microglia and blood-derived macrophages, often collectively referred to as CNS macrophages, adopt a variety of functional phenotypes that contribute to the progression of neurodegeneration as well as CNS repair and protection [28,29]. However, the roles of monocyte-derived macrophages in the development of AD remain to be fully elucidated.
In this study, we aimed to elucidate the role of m6A mRNA methylation in AD progression by conditionally inactivating the *Mettl3* gene specifically in myeloid cells using a Mettl3 conditional mouse line in combination with Lyz2-Cre driver lines. We found that METTL3 ablation enhances the infiltration of monocyte-derived macrophages in an Aβ-induced AD mouse model. Further analysis showed that METTL3 depletion impairs the m6A modification in Dnmt3a mRNAs, which in turn attenuates the translation of DNMT3A. DNMT3A binds to the promoter region of Atat1 and maintains its expression. Loss of METTL3 results in reduced α-tubulin acetylation and enhanced monocyte-derived macrophage migration, Aβ clearance and relief of AD symptoms.
## METTL3 deficiency in monocyte-derived macrophages attenuates the symptoms of Aβ-induced AD
Emerging studies have shown that m6A, the most abundant modification in eukaryotic RNA, plays a critical role in various developmental processes. However, the role of m6A RNA methylation in AD is unclear. To explore the involvement of monocyte-derived macrophages regulated by m6A during AD progression, we first showed that METTL3 did not alter the expression of surface cell markers (CD11b, F$\frac{4}{80}$) on macrophages from WT (Mettl3fl/flLyz2-/-) and KO (Mettl3fl/flLyz2Cre/-) mice (S1A Fig). We then examined whether METTL3 ablation in myeloid cells affects cognition in an Aβ-induced AD mouse model. We assessed the cognitive function of WT and KO mice by the Morris water maze (MWM) task and Y maze test (YMT). The results demonstrated that the learning and memory of the KO mice were improved compared with those of the WT mice (Fig 1A–1H). We also assessed the locomotor and exploratory behavior of mice by the open-field test. No significant difference in rotarod performance was observed between the 2 groups, indicating that the KO mice did not have compromised motor function (S1B Fig). Moreover, we generated chimeric mice by subjecting WT mice to bone marrow transplantation from WT or KO mice. Then, the cognitive function of the mice was tested by the MWM task and YMT. The results suggested that METTL3 deficiency ameliorated learning and memory deficits induced by Aβ in mice (Fig 1I). We also transferred monocytes isolated from the bone marrow of WT or KO mice into WT mice. The MWM task and YMT showed that the mice with METTL3-deficient monocyte transfer displayed ameliorated symptoms of Aβ-induced AD (Figs 1J and S1C). Furthermore, we observed that inhibition of the recruitment of monocyte-derived macrophages into the brain ablated the effect of METTL3 deficiency on AD progression (Figs 1K and S1D). These results suggest that METTL3 depletion in monocyte-derived macrophages plays an important role in AD progression.
**Fig 1:** *METTL3 deficiency in monocyte-derived macrophage improve the cognition in a mouse model of AD.(A–E) The WT or KO mice were trained and learned to find a hidden platform over 4 consecutive days. The escape latencies of the WT or KO mice in the MWM task on training days were recorded (A). On Day 5, probe trials were performed with the platform absent, and the travel distance (B), swim speed (C), and number of platform area crossings (D) were assessed. (E) Representative MWM swim plots for the WT and KO mice on Day 5. (F) Experimental design of the YMT. (G) The spatial working memory of the WT or KO mice induced by Aβ in the YMT. Alternations were counted as the percentage of “correct” alternation/total entries. “Correct” alternation means entry into all 3 arms on consecutive choices. (H) Representative YMT plots for the WT and KO mice induced by Aβ. (I) The learning and memory impairment of the Aβ-treated WT mice reconstituted with either WT or KO BM was examined by the MWM task and YMT. (J) The spatial learning and memory of mice with WT or KO monocyte transplantation were examined by the MWM task and YMT. (K) The learning and memory impairment of the Aβ-treated WT or KO mice injected with the CCR2 antagonist PF-4136309 (2 mg/kg) was assessed by the MWM task and YMT. All data in the figure are shown as the mean ± SD except for (A). P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), NS means no significant difference. Underlying data can be found in S1 Data. Aβ, amyloid beta; AD, Alzheimer’s disease; BM, bone marrow; KO, knockout; MWM, Morris water maze; WT, wild type; YMT, Y maze test.*
## METTL3 deficiency in BMDMs attenuates microtubule acetylation through ATAT1
A previous study reported that α-tubulin acetylation is closely related to neurodegenerative disease [30]. To investigate the role of α-tubulin acetylation in monocyte-derived macrophages involved in AD progression, we first examined the acetylation of microtubules in bone marrow-derived macrophages (BMDMs). As shown in Fig 2A, the level of microtubule acetylation was attenuated significantly in KO BMDMs compared with WT BMDMs. Similar results were also obtained in METTL3 knockdown BMDMs (Fig 2B). Furthermore, we detected the expression of tubulin acetyltransferase (Atat1) [31] and histone deacetylases (Hdac6) [32] and sirtuin type 2 (Sirt2) [33]. The results demonstrated that the expression of the major tubulin acetyltransferase Atat1 was noticeably reduced in the KO BMDMs, while the expression of the major tubulin deacetylases Hdac6 and Sirt2 was not affected (Fig 2C). Furthermore, we observed that the expression of ATAT1 was reduced after knockdown of Mettl3 (Fig 2D). Bioinformatics analysis based on the GEPIA database showed that the expression levels of METTL3 were positively correlated with ATAT1 (Fig 2E). These results indicated that METTL3 could regulate the acetylation of microtubules by targeting ATAT1. Previous studies have shown that METTL3 can bind to the gene promoters and affect their expression [34]. For this, we used a reporter system consisting of a plasmid harboring the Atat1 promoter fragments upstream of the firefly luciferase gene. The results showed that neither wild-type METTL3 (WT METTL3) nor the catalytically dead mutant of METTL3 (mut METTL3; residues 395–398: DPPW→APPA [35]) influenced the luciferase activity (Fig 2F). The results indicated that METTL3 could not directly bind to the promoter region of Atat1, indicating that Atat1 expression might be regulated by METTL3-mediated m6A modification. To assess whether METTL3-mediated m6A modification could influence ATAT1 expression, we next explored the m6A modification of Atat1 in BMDMs. MeRIP-qPCR assays showed that the m6A modification of Atat1 mRNA in KO BMDMs was almost unchanged (Fig 2G). RNA decay assays also showed that the half-life of Atat1 mRNA was not affected by METTL3 (Fig 2H). These results imply that METTL3 cannot directly regulate ATAT1 expression through m6A modification.
**Fig 2:** *METTL3 enhances ATAT1-mediated microtubule acetylation.(A) Western blotting showing the level of α-tubulin acetylation in WT and KO BMDMs. (B) Immunoblotting analysis of the indicated proteins in the BMDMs transfected with NC siRNA or Mettl3 siRNA. (C) Expression of Mettl3, Atat1, Hdac6, and Sirt2 in WT and KO BMDMs evaluated by qRT-PCR. The expression of ATAT1 was measured by immunoblotting. (D) qRT-PCR and immunoblotting analysis of Mettl3 and Atat1 expression in BMDMs transfected with NC siRNA or Mettl3 siRNA. (E) Bioinformatics analysis showed the expression levels of METTL3 and ATAT1 in normal tissue through the GEPIA database. (F) Cells cotransfected with a luciferase reporter construct of the ATAT1 promoter (2 kb) and wild-type or mut METTL3. The results are shown as firefly luciferase activity normalized to Renilla luciferase activity. (G) MeRIP-qPCR analysis of Atat1 expression in the WT and KO BMDMs. (H)
Atat1 mRNA decay assay in the WT and KO BMDMs treated with actinomycin D. All data in the figure are shown as the mean ± SD. P < 0.05 (*). NS means no significant difference. Underlying data can be found in S1 Data. BMDM, bone marrow-derived macrophage; KO, knockout; MeRIP-qPCR, methylated RNA immunoprecipitation-qPCR; NC, negative control; qRT-PCR, quantitative reverse transcription PCR; WT, wild type.*
## METTL3 depletion impairs YTHDF1-mediated translation of DNMT3A
Next, we investigated the mechanisms underlying METTL3’s activity in inhibiting the production of ATAT1. We analyzed our previous m6A-seq dataset GSE146140 [36] in which Mettl3 was knocked out specifically in myeloid cells. The top 50 m6A down-regulated genes (fold-change >4 and p value <0.00001) in KO BMDMs were ranked (S2A Fig). By performing Gene Ontology enrichment analysis for m6A down-regulated genes, we found that 9 of the down-regulated m6A direct targets were strongly enriched in the signaling pathway involved in transcriptional gene regulation (Fig 3A). We hypothesized that METTL3 might enhance ATAT1 expression through the enriched genes regulated by m6A modification. Further analysis demonstrated that the mRNA expression of the 9 genes enriched in transcriptional gene regulation was not affected in the KO BMDMs (Fig 3B). Bioinformatics analysis based on the GEPIA database showed that the expression levels of DNMT3A, KAT6B, and ZHX2 were strongly correlated with ATAT1 (R ≥ 0.5; $P \leq 0.001$) (Fig 3C). Therefore, we further analyzed the 3 candidates and assessed whether Atat1 transcription was regulated by them by RNA interference. As shown in Figs 3D and S2B, knockdown of DNMT3A, but not KAT6B or ZHX2, notably attenuated ATAT1 expression in BMDMs, which was accomplished with reduced acetylation of α-tubulin (Fig 3E). These results indicate that METTL3 may regulate ATAT1 expression through DNMT3A.
**Fig 3:** *DNMT3A is a target gene of METTL3 and maintains ATAT1 expression.(A) GO enrichment analysis (biological process) of m6A down-regulated genes in BMDMs. (B) The expression of the indicated genes in KO BMDMs compared with WT BMDMs. (C) The association between the expression of the indicated genes and ATAT1 expression was analyzed in normal tissue through the GEPIA database. (D) The expression of Atat1 was measured by qRT-PCR in WT BMDMs with Dnmt3a knockdown. (E) Immunoblotting analysis of the indicated proteins in the BMDMs transfected with NC siRNA or Dnmt3a siRNA. All data in the figure are shown as the mean ± SD. P < 0.05 (*). NS indicates no significant difference. Underlying data can be found in S1 Data. BMDM, bone marrow-derived macrophage; KO, knockout; NC, negative control; qRT-PCR, quantitative reverse transcription PCR; WT, wild type.*
Next, we explored whether DNMT3A was a m6A-regulated target gene using m6A immunoprecipitation (m6A-IP) followed by quantitative reverse transcription PCR (qRT-PCR) and confirmed that Dnmt3a was a m6A-regulated target gene (Fig 4A). We next found that DNMT3A protein expression was reduced in KO BMDMs, while its mRNA level was not significantly altered (Fig 4B). Similar results were also obtained in METTL3-knockdown BMDMs (Fig 4C). In addition, mRNA decay assays showed that m6A modification did not significantly influence the decay of Dnmt3a mRNAs (Fig 4D). We hypothesized that down-regulation of DNMT3A protein expression in the KO BMDMs may be due to a difference in protein translation efficiency controlled by the m6A reader protein YTHDF1, which mainly promotes translation of m6A methylated transcripts [37]. Next, we extracted the RNA fractions: nontranslating fraction (<40S), translation initiation fraction (including 40S ribosomes, 60S ribosomes, and 80S monosomes) and translation active polysomes (>80S) from WT and KO BMDMs through polysome profiling. The qRT-PCR results showed that Dnmt3a mRNA from the Mettl3-deficient BMDMs in translation-active polysomes (>80S) was appreciably lower than that in the WT BMDMs (Fig 4E).
**Fig 4:** *METTL3 facilitates DNMT3A translation mediated by YTHDF1.(A) MeRIP-qPCR analysis of Dnmt3a expression in WT and KO BMDMs. (B) qRT-PCR and western blotting analysis of DNMT3A levels in the WT and KO BMDMs. (C) Immunoblotting analysis of the indicated proteins in the BMDMs transfected with NC siRNA or Mettl3 siRNA. (D)
Dnmt3a mRNA decay assay in the WT and KO BMDMs after actinomycin D treatment. (E) Analysis of Dnmt3a mRNA in non-ribosome portion (<40S), 40S, 60S, 80S, and polysome. (F) Schematic representation of mutations of the conserved m6A site in Dnmt3a mRNA. (G) The expression of DNMT3A WT, DNMT3A mut1, DNMT3A mut2, and DNMT3A mut1/2 was measured by western blots. The normalized intensity of DNMT3A WT, DNMT3A mut1, DNMT3A mut2, and DNMT3A mut1/2 was quantified. (H) qRT-PCR analysis of the expression of DNMT3A WT, DNMT3A mut1, DNMT3A mut2, and DNMT3A mut1/2. (I) Binding of YTHDF1 with Dnmt3a mRNAs in the WT or KO BMDMs was analyzed by YTHDF1 RIP-qPCR. (J) Immunoblotting analysis of the indicated proteins in the BMDMs transfected with NC siRNA or Ythdf1 siRNA. All data in the figure are shown as the mean ± SD. P < 0.05 (*). NS indicates no significant difference. Underlying data can be found in S1 Data. BMDM, bone marrow-derived macrophage; KO, knockout; MeRIP-qPCR, methylated RNA immunoprecipitation-qPCR; NC, negative control; qRT-PCR, quantitative reverse transcription PCR; RIP-qPCR, RNA immunoprecipitation PCR; WT, wild type.*
To investigate whether m6A methylation could directly regulate the expression of DNMT3A, we analyzed the conserved m6A motifs of Dnmt3a mRNA and then mutated the 2 potential conserved m6A motifs GGAC to GGTC (Dnmt3a mut1 or Dnmt3a mut2) respectively or (Dnmt3a mut$\frac{1}{2}$) simultaneously (Fig 4F). The results showed that DNMT3A protein levels, but not mRNA expression, were reduced in Dnmt3a mut1 and Dnmt3a mut2, especially in Dnmt3a mut$\frac{1}{2}$, which indicated that m6A motifs in both Dnmt3a mut1 and Dnmt3a mut2 were the main sites for expression regulation (Fig 4G and 4H). Furthermore, RNA-IP analysis showed that the binding between YTHDF1 and Dnmt3a mRNA was significantly attenuated in the KO BMDMs compared with the WT BMDMs (Fig 4I). Western blotting also showed that the expression of DNMT3A was inhibited by YTHDF1 knockdown (Fig 4J). Taken together, these data reveal that METTL3 ablation leads to the reduced expression of DNMT3A mediated by YTHDF1.
## DNMT3A transcriptionally regulates the expression of ATAT1
As an epigenetic modifier, DNMT3A usually binds directly to the gene promoter to regulate transcription [38]. Accordingly, DNMT3A was found to directly bind to both the proximal promoters and distal promoters of Atat1 in BMDMs by chromatin-immunoprecipitation (ChIP) assay (Fig 5A). Furthermore, a previous study reported that the binding of DNMT3A to DNA might be used to maintain active chromatin states together with DNA methylation by antagonizing silencing modifications, such as trimethylation of histone H3 at Lys27 (H3K27me3) catalyzed by EZH2 in specific promoter region [39]. Our results showed that the abundance of H3K27me3 at the promoter region of Atat1 was increased in BMDMs after knockout of METTL3 (Fig 5B). Furthermore, we assessed the ability of EZH2 to bind to the promoter region of Atat1 and found that the binding of EZH2 to the promoter region of Atat1 was greatly increased in the Mettl3 knockout BMDMs (Fig 5B). To explore the mechanism underlying the regulation of ATAT1 by DNMT3A, we first analyzed the expression of EZH2 and H3K27me3 in WT and KO BMDMs. The results showed that METTL3 did not affect the abundance of EZH2 and H3K27me3 (S3A Fig). We also detected the expression of EZH2 and H3K27me3 in DNMT3A knockdown cells and demonstrated that DNMT3A did not significantly affect EZH2 and H3K27me3 levels (S3B Fig). These results indicated that METTL3 and DNMT3A did not regulate the expression of EZH2, which excluded that the enhanced binding of EZH2 to the promoter region of Atat1 was caused by increased expression of EZH2. Next, we performed ChIP assays using EZH2 and H3K27me3 antibodies in DNMT3A knockdown cells to explore whether DNMT3A affects the occupation of EZH2 and H3K27me3 in the Atat1 promoter region. The results exhibited that DNMT3A knockdown enhanced the binding capability of H3K27me3 and EZH2 in Atat1 promoter region (Fig 5C). Previous results have demonstrated that knockdown of DNMT3A inhibited the expression of ATAT1 (Fig 3D and 3E). Furthermore, we observed that double knockdown of both DNMT3A and EZH2 inhibited the reduced expression of ATAT1 induced by DNMT3A knockdown (Fig 5D). Taken together, these results reveal that DNMT3A maintains the expression of ATAT1 by antagonizing H3K27me3 catalyzed by EZH2 in the Atat1 promoter region.
**Fig 5:** *DNMT3A transcriptionally regulates the expression of ATAT1.(A) ChIP analysis of Dnmt3a at positions 5 kilobases (kb) (up5k), 4 kb (up4k), 3 kb (up3k), 2 kb (up2k), or 1 kb (up1k) upstream of the transcriptional start site of Atat1 in BMDMs. (B) ChIP analysis of H3K27me3 or EZH2 at the Atat1 promoter in the WT or KO BMDMs. (C) ChIP analysis of H3K27me3 or EZH2 at the Atat1 promoter in the DNMT3A knockdown BMDMs. (D) The expression of DNMT3A, EZH2, and ATAT1 was measured by qRT-PCR and western blot in the BMDMs transfected with NC, Dnmt3a, or Dnmt3a and Ezh2 siRNAs. All data in the figure are shown as the mean ± SD. P < 0.05 (*). Underlying data can be found in S1 Data. BMDM, bone marrow-derived macrophage; ChIP, chromatin immunoprecipitation; KO, knockout; NC, negative control; qRT-PCR, quantitative reverse transcription PCR; WT, wild type.*
## METTL3 depletion enhances the transmigration of brain-infiltrating macrophages involved in Aβ clearance in an Alzheimer’s disease model
The above results showed that METTL3 depletion in monocyte-derived macrophages impaired α-tubulin acetylation and improved the symptoms of AD. Previous studies have reported that blood-derived monocytes adopt many functional phenotypes that contribute to progressive neurodegeneration [28]. α-Tubulin acetylation has been shown to contribute to cell migration, facilitating fibroblast, and neuronal motility [32,40,41]. These findings prompted us to investigate whether METTL3 could influence the migration of monocyte-derived macrophages into cortical brain regions. As shown in Fig 6A and 6B, the results showed that the infiltration of monocyte-derived macrophages stained with the CD45 and IBA1 markers increased in mice with Aβ-induced AD and aged mice with METTL3 depletion. Mouse brain tissue was also taken for analysis by flow cytometry. We observed that the frequency of monocyte-derived macrophage (CD45+CD11b+F$\frac{4}{80}$+) cells from the KO mice was increased significantly compared to that from the WT mice (Fig 6C). Similar results were also obtained in mice with Aβ-induced AD with Mettl3-deficient BM cells or monocyte transplantation (S4A and S4B Fig). In vitro transwell assays also suggested BMDMs with METTL3 depletion showed an enhanced cell migration (Fig 6D). We also found that the reduced expression of DNMT3A or ATAT1 was closely associated with a higher migration rate of BMDMs (Fig 6E and 6F). In addition, overexpression of ATAT1 in DNMT3A knockdown cells inhibited the enhanced migration (Figs 6G, S4C and S4D). Growing evidence suggests that infiltrating innate immune cells may play a key role in the clearance of Aβ in murine models of AD [42]. This finding prompted us to investigate how monocyte-derived Mettl3-deficient macrophages relate to the Aβ burden. We analyzed Aβ accumulation by Aβ staining in the cortical brain. The results revealed that amyloid burden was significantly decreased in the Aβ-induced AD mice and aged mice with METTL3 depletion compared to the WT mice (Fig 7A and 7B). Chimeric mice injected with Mettl3-deficient BM cells or monocytes also showed a decreased burden of Aβ (S5A and S5B Fig). Furthermore, we assessed the phagocytosis function of macrophages in vitro and determined that Aβ clearance was enhanced in the METTL3 deficient BMDMs in vitro (Fig 7C). Similar results were also obtained in the DNMT3A or ATAT1 knockdown BMDMs (S6 Fig). In addition, overexpression of ATAT1 inhibited the enhanced Aβ uptake induced by DNMT3A knockdown (Fig 7D). Collectively, these results imply that the m6A-DNMT3A-ATAT1 axis plays a key role in AD progression by regulating the migration and Aβ clearance of monocyte-derived macrophages (Fig 7E).
**Fig 6:** *METTL3 inhibits the migration and infiltration of monocyte-derived macrophage into brain.(A) Colocalization of CD45 with IBA1 in the brain of Aβ-induced AD mice. Images are representative of 3 independent experiments. Scale bars: 50 μm. (B) Immunofluorescence showing the colocalization of endogenous CD45 with IBA1 in the brains of 24-month-old mice. The percentage of CD45+IBA1+ cells was quantified. Scale bars: 50 μm. (C) Flow cytometry analysis of brains from the Aβ-treated WT or KO mice. (D) Cell migration in transwell assays was assessed with WT and KO BMDMs. Scale bars: 30 μm. (E) Transwell assays were used to determine the migration of BMDMs transfected with NC siRNA or Dnmt3a siRNA. Scale bars: 30 μm. (F) Transwell assays were performed to determine the migration of the BMDMs transfected with NC siRNA or Atat1 siRNA. Scale bars: 30 μm. (G) Cell migration was quantified in the ATAT1-overexpressing THP1 cells transfected with NC siRNA or Dnmt3a siRNA. All data in the figure are shown as the mean ± SD. P < 0.05 (*), P < 0.01 (**). Underlying data can be found in S1 Data. Aβ, amyloid beta; AD, Alzheimer’s disease; BMDM, bone marrow-derived macrophage; KO, knockout; NC, negative control; WT, wild type.* **Fig 7:** *METTL3 deficiency enhances Aβ uptake.(A) Representative fluorescence micrographs of brains from Aβ-injected WT and KO mice, immunolabeled for anti-Aβ. Scale bars: 50 μm. (B) Representative fluorescence micrographs of brains from the KO (24 months old) and age-matched WT mice, immunolabeled with Aβ antibody. Scale bars: 50 μm. (C) Representative fluorescent micrographs and quantitative analysis of Aβ uptake in WT or KO BMDMs stained with Aβ antibody. Scale bars: 10 μm. (D) Representative fluorescence micrographs and quantitative analysis of Aβ uptake in the ATAT1-overexpressing THP1 cells transfected with NC siRNA or Dnmt3a siRNA. Scale bars: 10 μm. (E) Schematic diagram of the role of the m6A writer METTL3 in AD. All data in the figure are shown as the mean ± SD. P < 0.05 (*), P < 0.01 (**). Underlying data can be found in S1 Data. Aβ, amyloid beta; AD, Alzheimer’s disease; BMDM, bone marrow-derived macrophage; KO, knockout; NC, negative control; WT, wild type.*
## Discussion
In this study, we found that METTL3 deficiency increased the infiltration of monocyte-derived macrophages in an Aβ-induced AD mouse model. Further analysis demonstrated that METTL3 ablation in monocyte-derived macrophages attenuated the m6A modification in Dnmt3a mRNAs and subsequently impaired the translation of DNMT3A and ATAT1 expression and acetylation of tubulin, which enhanced the migration of monocyte-derived macrophages, accelerated the clearance of Aβ and alleviated AD symptoms.
A previous study showed that METTL3 is related to hippocampal memory function [21], while another study demonstrated that in brain tissues, FTO has a wide variety of physiological and pathological functions [14]. In addition, METTL14, another m6A RNA methyltransferase, is important for the transcriptional regulation of striatum function and learning epitopes [19]. Other studies have suggested that m6A RNA methylation is related to the development of the cerebellum and neural development [12,43]. A recent study showed that the dysregulation of RNA methylation is related to AD [44]. These results prompted us to explore the role of monocyte-derived macrophages regulated by m6A modification in AD. Our study will play a key role in improving our understanding of AD.
AD is characterized by gradual memory loss owing to progressive brain atrophy accompanied by amyloid plaques and neurofibrillary tangles [45–47]. Although the participation of myeloid cells in AD has been extensively researched, their contributions to disease pathology and repair are still unclear [27,48–50]. Simard and colleagues showed that bone marrow-derived mononuclear phagocytes can infiltrate into AD mouse brain and accumulate in the area of amyloid-β plaques. Transplantation of bone marrow from WT mice into AD mice was found to reduce AD pathology, supposedly relying on the phagocytic actions of bone marrow-derived cells [27]. Another study showed that AD pathology is ameliorated upon treatment of mice with macrophage colony-stimulating factor (M-CSF) after bone marrow transplantation. Fewer amyloid-β monomers were observed in the extracellular protein-enriched fractions of M-CSF-treated transgenic mice compared with vehicle controls [51]. In our study, we found that METTL3 deficiency enhanced myeloid cell infiltration into the brains of aged mice or mice with Aβ-induced AD. Further analysis showed that METTL3 deficiency improves the capability of Aβ phagocytosis. In vivo experiments also demonstrated that brain sections from KO AD mice showed a decreased burden of Aβ.
ATAT1 catalyzes the acetylation of α-tubulin at lysine 40 in various organisms ranging from tetrahymena to humans [52]. A very preliminary observation shows that the lateral ventricles are large in Atat1−/− mice [53]. Our results demonstrated that decreased α-tubulin acetylation was closely associated with reduced expression of METTL3 in BMDMs. α-Tubulin acetylation has been shown to contribute to cell migration facilitating fibroblast and neuronal motility [32,40,41]. We found that METTL3 depletion enhanced the infiltration of BMDMs into the AD mouse brain. In vitro experiments also showed that METTL3 depletion enhanced the migration of BMDMs. Previous studies have shown that m6A modification is a versatile regulator of mRNA stability, splicing, localization, and translation rate [5,7]. Mechanistically, we found that METTL3 depletion impaired YTHDF1-mediated translation of DNMT3A. DNMT3A usually binds directly to the gene promoter to regulate transcription [38]. Furthermore, a previous study demonstrated that the binding of DNMT3A to DNA contributes to maintaining active chromatin states together with DNA methylation by inhibiting silencing modifications, such as EZH2-mediated trimethylation of histone H3 at Lys27 (H3K27me3) in specific promoter regions [39]. Consistently, our results showed that DNMT3A could bind to the promoter region of Atat1 and maintain its expression by antagonizing H3K27me3 modification.
In summary, we discovered that m6A-DNMT3A-ATAT1 can regulate the acetylation of α-tubulin in BMDMs. METTL3 deficiency enhances the infiltration of monocyte-derived macrophages into the AD brain and Aβ clearance, subsequently improving the cognitive decline. Our results suggest that m6A modifications are potential targets for the treatment of AD. However, the upstream regulation of METTL3 in AD remains to be explored in the future. At present, some clinical trials targeting Aβ are investigated for the treatment of early AD [54,55]. Our results may supply a new choice for AD treatment strategies.
## Ethics statement
All animal care and use protocols were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People’s Republic of China. The animal experiments were approved by the Institutional Animal Care and Use Committee of Xinxiang Medical University (Approval Number: XXLL-2018B005) and the Animal Experiment Administration Committee of Fourth Military Medical University (approval number: IACUC-20221210).
## Mice
Mettl3fl/flLyz2-/- (WT) and Mettl3fl/flLyz2cre/- (KO) mice were generated as previously described [36]. All experimental mice were maintained under specific pathogen-free conditions, fed standard laboratory chow, and kept on 12 h light to 12 h dark cycles. The temperature and humidity were set at 22 ± 1°C and $55\%$ ± $5\%$, respectively. Cohoused Cre-negative littermate mice were used as control animals in all experiments.
## Cell lines
HEK293T cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) containing $10\%$ fetal bovine serum (Gibco), 100 U/ml penicillin, and 100 μg/ml streptomycin (Gibco). The cells were cultured in a $5\%$ CO2 humidified incubator at 37°C. The THP1 cells were cultured at 37°C in an atmosphere of $5\%$ CO2 and maintained in RPMI 1640 medium (Gibco) with $10\%$ fetal bovine serum (Gibco) with 100 U/ml penicillin and 100 μg/ml streptomycin (Gibco).
## Plasmid construction
The ATAT1 promoter was amplified and cloned into the PGL-3.0 vector. FLAG-DNMT3A was constructed by cloning full-length mouse DNMT3A cDNA into the p3×FLAG-CMV-10 vector. FLAG-DNMT3A mut1, FLAG-DNMT3A mut2, or FLAG-DNMT3A mut$\frac{1}{2}$ were generated by mutating the 2 potential conserved m6A motifs GGAC to GGTC separately or simultaneously. HA-ATAT1 was generated by cloning full-length ATAT1 cDNA into the pCDH vector.
## Bone marrow-derived macrophages
Bone marrow was isolated from femurs and tibia of mice by flushing. The cells were cultured in DMEM with $10\%$ fetal bovine serum, streptomycin (100 μg/ml), penicillin (100 U/ml), and M-CSF (40 ng/ml). Cells were cultured for 7 days to generate BMDMs. Cultured BMDMs were used for qRT-PCR analysis, western blotting, and other experiments.
## Intracerebroventricular injection
The experiments were performed as previously described [56]. Aβ1–42 (Anaspec, San Jose, California) was reconstituted in sterile saline solution as a stock solution at a concentration of 100 μm, followed by incubation at 37°C for 4 days. Animals were anesthetized with isoflurane, and the site of injection was stereotaxically reached. The injection point was located 1.8 ± 0.1 mm lateral to the sagittal suture, −1.0 ± 0.06 mm posterior to the bregma, and 2.4 mm in depth. Five microliters of solution containing either $0.9\%$ NaCl or recombinant Aβ1–42 was injected over a period of 3 min.
## Assay for Aβ phagocytosis
BMDMs were seeded at 10,000 cells per well in glass slides. Two days later, the cells were incubated for 12 h with 1 μm Aβ. Thereafter, the cells were rinsed several times with PBS and then fixed with $4\%$ formaldehyde for 15 min at room temperature. The cells were then rinsed 3 times with PBS and probed overnight at room temperature with antibody against Aβ in PBS containing $0.1\%$ Triton X-100, followed by Alexa Fluor-labeled secondary antibodies for 0.5 h at room temperature. Fluorescence images were acquired on a laser confocal microscope (Nikon).
## Transfection
For siRNA transfection, cells were seeded 1 day before transfection. Transfection was performed when the cell confluency was approximately $70\%$ to $80\%$ using Lipofectamine RNAiMAX (Thermo Fisher Scientific) following the manufacturer’s instructions. The siRNA duplex oligonucleotides used are listed in S1 Table. Lipofectamine 2000 (Thermo Fisher Scientific) was used for plasmid transfections following the manufacturer’s instructions.
## Quantitative real-time PCR (qRT-PCR)
Total RNA was isolated from cells using TRIzol (Thermo Fisher Scientific). For qRT-PCR analysis of mRNAs, cDNA was generated using a PrimeScript One Step RT-PCR Kit (TaKaRa). The qRT-PCR experiment was conducted using a SYBR Premix E×Taq Kit (TaKaRa). The primers used for qRT-PCR are listed in S2 Table.
## Immunoblotting
Cells were lysed in RIPA buffer ($1\%$ Triton X-100, 20 mM Na2PO4, 150 mM NaCl (pH 7.4)) containing PMSF and Phosphatase Inhibitor Cocktail (Roche). Subsequently, a BCA assay (Thermo Fisher Scientific) was used to determine the protein concentrations. Proteins were separated by SDS-PAGE gels and transferred onto nitrocellulose membranes (Millipore). The membranes were placed in TBST (10 mM Tris-HCl (pH 7.4), 150 mM NaCl, and $0.1\%$ Tween-20) containing $5\%$ nonfat milk for 1 h at room temperature. Primary antibodies were diluted in TBST containing $5\%$ BSA and used at the indicated concentrations: rabbit anti-acetyl-α-tubulin (1:1,000, 5335, Cell Signaling Technology), rabbit anti-EZH2 (1:1,000, 5246, Cell Signaling Technology), rabbit anti-H3K27me3 (1:1,000, 9733, Cell Signaling Technology), rabbit anti-DNMT3A (1:1,000, ab2850, Abcam), rabbit anti-YTHDF1 (1:1,000, 17479-1-AP, 18810, Proteintech), rabbit anti-METTL3 (1:1,000, ab18810, Abcam), and mouse anti-β-actin (1:5,000, Sigma). The membranes were incubated with primary antibodies overnight at 4°C, washed with TBST 4 times and incubated with HRP-conjugated anti-mouse IgG (1:10,000, 7076, Cell Signaling Technology) or anti-rabbit IgG (1:10,000, 7074, Cell Signaling Technology) diluted in TBST at room temperature for 1 h. After 4 final washes with TBST, the membranes were developed by using ECL and visualized using Tanon 5500 or Amersham Imager 680.
## Chromatin immunoprecipitation (ChIP) assay
ChIP analysis of DNMT3A/EZH/H3K27me3 was performed using the SimpleChIP Enzymatic Chromatin IP Kit (Cell Signaling Technology) following the manufacturer’s instructions. Primer sequences for ChIP analysis are listed in S2 Table.
## Luciferase assay
The luciferase assay was performed as previously described [57]. Briefly, HEK293T cells were seeded in 48-well plates at a density of 5,000 cells per well. After 24 h, the cells were transfected with 5 ng of pRL-TK Renilla luciferase reporter, 100 ng required plasmids, and 100 ng of luciferase reporter with the target gene promoter. After 48 h, luciferase activity was determined with the dual luciferase reporter assay system (Promega).
## Immunofluorescence staining
For confocal microscopy, cells were fixed in $4\%$ paraformaldehyde after treatments, permeabilized with $0.3\%$ Triton X-100, blocked with $5\%$ bovine serum albumin, and then incubated with primary antibodies overnight at 4°C. Next, the cells were incubated with fluorescent dye-labeled secondary antibodies: Donkey Anti-Rat IgG Alexa Fluor 594 (1:1,000, A-21209, Thermo Fisher Scientific), Donkey Anti-Rabbit IgG Alexa Fluor 555 (1:1,000, A-31572, Life Technologies), Goat Anti-Rabbit IgG Alexa Fluor 488 (1:1,000, A-11008, Life Technologies), Goat Anti-Mouse IgG Alexa Fluor 488 (1:1,000, A-11001, Thermo Fisher Scientific), and Goat Anti-Mouse IgG Alexa Fluor 555(1:1,000, A-21422, Thermo Fisher Scientific). Nuclei were stained with DAPI. Confocal fluorescence images were captured using a Nikon confocal microscope.
## meRIP-qPCR
Total RNA was isolated from cells using TRIzol (Thermo Fisher Scientific). For meRIP-qPCR, poly (A)+ mRNA was extracted by using the Dynabeads mRNA Direct Purification Kit (61012, Thermo Fisher Scientific). m6A-containing mRNA enrichment was carried out using the Magna MeRIP m6A kit (17–10499, Millipore) from 2 μg of purified poly (A)+ mRNA, and the enriched RNA was purified according to the manufacturer’s protocol. The final product was used for qRT-PCR to determine the enrichment of m6A on gene transcripts. The primers used for testing Dnmt3a and Atat1 mRNA are listed in S2 Table.
## RNA immunoprecipitation-qPCR (RIP-qPCR)
This procedure was performed according to a previously published report [36]. BMDMs were washed twice with PBS and lysed in lysis buffer (150 mM KCl, 10 mM HEPES (pH 7.6), $0.5\%$ NP-40, 2 mM EDTA, 0.5 mM dithiothreitol (DTT), protease inhibitor cocktail, 400 U/ml RNase inhibitor). The cell lysates were centrifuged to obtain the supernatant. A 50-μl aliquot of cell lysate was saved to serve as input, and the remaining lysate was incubated with 20 μl of protein A beads, previously bound to IgG antibody or anti-YTHDF1 antibody (Proteintech) for 4 h at 4°C. The beads were then washed 4 times with washing buffer (50 mM Tris, 200 mM NaCl, 2 mM EDTA, $0.05\%$ NP40, 0.5 mM DTT, RNase inhibitor). RNA was eluted from the beads with 50 μl of elution buffer (5 mM Tris-HCL (pH 7.5), 1 mM EDTA, $0.05\%$ SDS, 20 mg/ml Proteinase K) for 2 h at 50°C, and purified with Qiagen RNeasy columns. RNA was eluted in 100 μl of RNase-free water and were reverse transcribed into cDNA using a PrimeScript qRT-PCR kit (TaKaRa) according to manufacturer’s instructions. The fold enrichment was measured by qRT-PCR. The primers used for testing Dnmt3a mRNA are listed in S2 Table.
## mRNA stability analysis
For determination of mRNA stability, BMDMs were treated with actinomycin D (Sigma) at a final concentration of 5 μg/ml for 0, 3, or 6 h. The cells were then collected, and total RNA was extracted for reverse transcription using a PrimeScript qRT-PCR kit (TaKaRa). The mRNA transcript levels of interest were determined by qRT-PCR.
## Polysome profiling analysis
Polysome profiling was performed as previously published [58]. BMDMs were first treated with cycloheximide (0.1 mg/ml) for 3 min at room temperature to arrest and stabilize polysomes, washed with PBS, and then lysed with 1 ml of cold polysome lysis buffer (0.3 M NaCl; 15 mM MgCl2.6H2O; 15 mM Tris-HCl (pH 7.4)) containing 10 μl of Triton X-100 ($1\%$), 1 μl of 100 mg/ml cycloheximide in DMSO and RNasin. The cell lysates were centrifuged at 13,000 rpm for 15 min at 4°C. The supernatants were fractionated by $10\%$ to $50\%$ sucrose gradient centrifugation (35,000 rpm for 1.5 h) in a Beckman ultracentrifuge. Each fraction of the density gradient was collected, and the absorbance was detected at 260 nm. Ribosomal RNA content measured at 260 nm was plotted to obtain the polysome profile of each sample. RNA from each fraction was isolated and reverse transcribed using a PrimeScript qRT-PCR kit (TaKaRa). The obtained cDNAs were amplified using real-time PCR analysis for polysome abundance on Dnmt3a mRNAs.
## Flow cytometry
For brain tissue analysis, animals were sublethally anesthetized by intraperitoneal injection of pentobarbital. Mice were then perfused transcardially with sterile cold saline solution until the blood was completely washed out. Brains were quickly removed and placed in cold medium and then dissociated using a Neural Tissue Dissociation kit (Miltenyi Biotec) according to the instructions of the manufacturer. Single-cell suspensions were analyzed using the following antibodies: CD45.2-PerCP (Biolegend), CD11b-PE-cy7 (BD Bioscience), F$\frac{4}{80}$-PE (BD Bioscience), Ly6C-FITC (BD Bioscience), and CD115-PerCP (Biolegend) and subjected to flow cytometry analysis.
## Bone marrow chimeras
BM chimeras were prepared as previously described [59]. In brief, mice were exposed to irradiation (9 Gy). The bone marrow cells were aseptically collected from WT or KO mice by flushing femurs and tibia with Dulbecco’s PBS with $2\%$ fetal bovine serum. The samples were filtered through a 40-μm nylon mesh and centrifuged. Irradiated animals were injected via a tail vein with 1 × 107 bone marrow cells, housed in autoclaved cages, and treated with antibiotics 2 weeks after irradiation. After 5 weeks, the chimeric mice were subjected to the AD model.
## Monocytes transplantation
Monocyte transplantation was performed as previously described [60]. In brief, bone marrow cells were collected from WT or KO (CD45.2) donor mice by flushing the femurs and tibia. Cells were passed through a 40-μm pore nylon mesh and rinsed with DPBS containing $2\%$ FBS. CD115+ cells were isolated using a magnetic cell separation system and biotinylated anti-CD115 antibody combined with streptavidin-magnetic beads (Miltenyi Biotec). Then, the isolated monocytes were injected via the tail vein into mice.
## Morris water maze (MWM)
For evaluation of spatial learning and memory after Aβ injections, the MWM test was performed as previously described [61]. Briefly, a circular pool (diameter, 120 cm; height, 30 cm) located in a large room with various distal visual cues was filled with water. A circular platform (diameter, 10 cm) was located at a fixed position approximately 1 cm below the water surface. The swimming activity of each mouse was recorded. In the training phase, the mice were given 4 trials to locate a visible platform for 4 consecutive days starting from different positions. In each of the 4 trials, the mouse was allowed 60 s to find the platform. If the mouse could not find the platform within 60 s, it was guided to the platform and allowed to stay there for 20 s. On Day 5, the platform was removed from the pool in the probe trial. The latency to the first platform area crossing, the number of crossings, and the time spent in the platform quadrant were recorded in 60 s.
## Open-field test
The mouse activity was recorded to study locomotor and exploratory behavior as previously described [62]. Mice were allowed to explore in a gray square box for 5 min. The velocity and time spent in the center area were recorded.
## Y maze test (YMT)
Working memory and exploratory activity were measured using a Y-maze apparatus as previously described [63]. The arms of the apparatus were 30 cm long, 8 cm wide, and 15 cm high. Each mouse was placed at the end of 1 arm. The number of alterations was recorded for 5 min. Working memory was calculated as the number of correct alterations/number of total arm entries. Correct alternation means entry into all 3 arms on consecutive choices (i.e., ABC, BCA, or CAB, but not CAC, BAB, or ABA).
## Statistical analysis
Most experiments were repeated at least twice. Statistical significance was determined by the Mann–Whitney test, and differences were considered statistically significant when $P \leq 0.05$ (*), $P \leq 0.01$ (**), or $P \leq 0.001$ (***).
## References
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|
---
title: Prevalence and determinants of undiagnosed hypertension in the Western region
of Saudi Arabia
authors:
- Walaa A. Mumena
- Sahar A. Hammouda
- Raghad M. Aljohani
- Amal M. Alzahrani
- Mona J. Bamagos
- Wed K. Alharbi
- Bodoor M. Mulla
- Hebah A. Kutbi
journal: PLOS ONE
year: 2023
pmcid: PMC9990946
doi: 10.1371/journal.pone.0280844
license: CC BY 4.0
---
# Prevalence and determinants of undiagnosed hypertension in the Western region of Saudi Arabia
## Abstract
Recent data regarding the prevalence and determinants of undiagnosed hypertension in Saudi Arabia are particularly lacking. This study aimed to investigate the prevalence of undiagnosed hypertension and to identify potential associates of hypertension risk among adults in the Western region of Saudi Arabia. Cross-sectional data for 489 Saudi adults were collected from public places in the cities of Madinah and Jeddah. Demographic, anthropometric (height, weight, waist circumference), and blood pressure (assessed by a digital sphygmomanometer) data were collected from all participants during face-to-face interviews. The American College of Cardiology and American Heart Association guidelines were used to evaluate blood pressure status. Sodium intake was assessed using a semi-validated food frequency questionnaire. The prevalence of undiagnosed, elevated blood pressure, stage I, or stage II hypertension was $9.82\%$, $39.5\%$, and $17.2\%$, respectively. The proportions of individuals with undiagnosed hypertension were higher among men and smokers ($p \leq .001$ for both). Blood pressure status was positively associated with weight, body mass index, and waist circumference among participants ($p \leq .001$ for all). Higher body mass index and waist circumference were associated with increased odds of stage I and stage II hypertension. Sodium intake was not associated with blood pressure status. A strikingly high prevalence of undiagnosed hypertension was observed among the study sample. National intervention programs are necessary to encourage regular screening and follow-up for the early detection and management of hypertension.
## Introduction
Hypertension (HTN) is a significant health issue, affecting more than one-third of the global population [1]. Data obtained from the National Health and Nutrition Examination Survey (2011–2012) indicated that one-third of American adults have high blood pressure [2]. In 2013, based on national data, the prevalence of prehypertension and HTN in Saudi Arabia was $40.6\%$ and $15.2\%$, respectively, with $57.8\%$ of hypertensive Saudis being undiagnosed [3].
High blood pressure is a major cause of cardiovascular disease (CVD) and stroke [4]. Untreated HTN increases the risk of life-threatening health conditions, such as stroke, heart failure, kidney disease or failure, and mortality [5]. In 2019, the World Health Organization (WHO) declared ischemic heart disease and strokes to be the leading cause of death globally and in high-income countries [6]. Similarly, In 2013, the Ministry of Health in Saudi Arabia reported that CVDs cause $42.0\%$ of non-communicable disease-related deaths in the country [7]. To prevent the development of health consequences, health organizations recommend screening for HTN and prehypertension [8].
Available evidence suggests HTN is linked to several risk factors, including excessive sodium intake, poor fruit and vegetable intake, smoking, physical inactivity, and being overweight or obese [9, 10]. Many countries, including Saudi Arabia, are currently undertaking a nutritional transition, where westernized diets are replacing traditional diets, and individuals are becoming less active [11, 12]. This dietary shift has been associated with higher rates of obesity and comorbidities [13, 14].
The Saudi Vision 2030 is a strategic framework that includes several goals that aim to improve the quality of preventive healthcare services and healthcare system in Saudi Arabia. To achieve these goals, identifying target healthcare needs and resources for future interventions is critical, particularly in areas that lack recent and reliable data. This study aimed to investigate the prevalence of undiagnosed HTN and its association with sociodemographic factors, sodium intake, and anthropometric parameters among adults residing in the Western region of Saudi Arabia.
## Materials and methods
This cross-sectional study was conducted between January and March 2020. In order to achieve a $95\%$ confidence level and a standardized confidence interval width of 0.20, calculations demonstrated that the minimum number of participants required was 385 [15]. Four hundred and eighty-nine adults aged between 20–50 years old were recruited from two major cities located in the Western Region of Saudi Arabia: Madinah and Jeddah. Data were collected from several public locations, such as walking paths, malls, parks, and the Jeddah Corniche. Prior to data collection, each participant signed a written consent form after aim and methods used to collect data of the study were described. The exclusion criteria included individuals previously diagnosed with HTN, diabetes, or CVD, who had been prescribed medication to treat high blood pressure, had undergone bariatric surgery, reported following a special diet, or were pregnant. Furthermore, to prevent the overestimation of HTN, individuals engaged in physical activity at the time of data collection were also excluded. This study received ethical approval from the ethical committee of the College of Applied Medical Sciences at Taibah University [SREC/AMS $\frac{2019}{33}$/CND].
Data were collected by trained health professionals (nurses and dietitians) during face-to-face interviews. Chairs and tables were provided to participants so they could be comfortably seated during the data collection. Sociodemographic data, including sex, age, smoking status, household income, and education level, were collected from all participants. Participants were grouped into three age categories: 20–29 years old, 30–39 years old, and ≥ 40 years old.
## Clinical evaluation of blood pressure
Each participant’s blood pressure was assessed using the Cardio Simple digital sphygmomanometer (Cardio Simple, Pic Solution, Italy). All devices were calibrated and validated prior to data collection. Measurements were obtained using standardized procedures according to the instructions provided in the device manual [16]. Prior to measurements being taken, participants were asked to sit still and relax for five minutes. Blood pressure was measured three times at five-minute intervals, with the average reading calculated and documented. If a measurement appeared inappropriate, it was discarded, and a new measurement was obtained. Blood pressure cutoffs were determined based on the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines [9]. Blood pressure was considered normal if the reading was < $\frac{120}{80}$ mm Hg and elevated if systolic blood pressure (SBP) was 120–129 mm Hg and diastolic blood pressure (DBP) was < 80 mm Hg. Participants were determined to have stage I HTN if SBP was between 130–139 mm Hg or if DBP was between 80–89 mm Hg. Stage II HTN was defined as an SBP of at least 140 mm Hg or a DBP of at least 90 mm Hg.
## Assessment of sodium intake
Sodium intake was assessed using a semi-validated food frequency questionnaire (FFQ) that contained 131 items on 11 food groups. The FFQ has previously been used to assess sodium intake among adults in Saudi Arabia [17]. Participants reported their consumption frequency by selecting one of several options: 1, 2–3, 4–5, or ≥ 6 times per day; 1, 2–4, or 5–6 times per week; and less than 1 or 1–3 times per month. Participants were grouped based on their sodium intake, using the AHA recommendation of 2300 mg/day as the cutoff value: < 2300 mg/day or ≥ 2300 mg/day [18].
## Assessment of anthropometric parameters
Anthropometric measurements, including height, weight, and waist circumference (WC), were collected using standardized procedures. All measurements were assessed three times to calculate an average. Weight was measured using an electronic scale (Omron, BF508, Japan), which was placed on a hard surface. Weight was rounded to the nearest 0.1 kg. A measuring tape was placed on a straight wall to measure height, with measurements rounded to the nearest 0.5 cm. The height and weight of each participant were used to calculate their body mass index (BMI). Weight status was determined based on the WHO cutoffs: <18.5 kg/m2 indicated being underweight, 18.5–24.9 kg/m2 indicated a healthy weight; 25–29.9 kg/m2 indicated being overweight, and ≥ 30.0 kg/m2 indicated obesity [19]. The WC of each participant was measured using a fixed measuring tape and was rounded to the nearest 0.5 cm and the WHO cutoffs (male < 102 cm; female < 88 cm) as values above these cutoffs indicate a higher risk of metabolic complications [20]. At the end of the interview, each participant was provided with a card containing their anthropometric measurements and average blood pressure reading for their records.
## Statistical analysis
Descriptive data are presented as the mean (standard deviation [SD]) for continuous variables and as the frequency (percentage) for categorical variables. Associations between two categorical variables were evaluated using Fisher’s exact test. The means of normally distributed continuous variables across the blood pressure status groups (normal blood pressure = 0; elevated blood pressure = 1; stage I HTN = 2; and stage II HTN = 3) were compared using an analysis of variance (ANOVA). Multinomial logistic regression was used to estimate the unadjusted odds ratio (OR) and $95\%$ confidence intervals (CI) for the association between blood pressure status and variables of sex (male = 1; female = 2), BMI (underweight = 1; healthy weight = 2; overweight = 3; obese = 4), and WC (within recommendation = 1; above recommendation = 2). The anthropometric measurements of the participants were correlated ($r = 0.79$, $p \leq .001$). Therefore, separate regression analyses were performed to investigate the associations of BMI (model 1) and WC (model 2) with blood pressure status. Further analyses were performed to estimate the adjusted OR (aOR), controlling for age and smoking status. All tests used were two-tailed, with the significance level set to.05. Statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS 20, SPSS Inc., Chicago, IL).
## Sample characteristics and associations with blood pressure status
Following the exclusion of $0.81\%$ of the sample ($$n = 4$$) due to missing data, data from 489 participants were included in the final analyses. Most participants were < 30 years old, with only $17.4\%$ ($$n = 85$$) being ≥ 40 years old. Females made up $59\%$ of all participants ($$n = 288$$), and $58.1\%$ ($$n = 284$$) of participants reported being married. Only $25.2\%$ of participants were smokers ($$n = 123$$), with most smokers being male ($82.3\%$, $$n = 102$$). Fifty-one per cent of participants ($$n = 247$$) held a university degree or higher, and $53\%$ of participants ($$n = 259$$) earned an income of less than SAR 5,000 (< USD 1,333). The prevalence of elevated blood pressure, undiagnosed stage I HTN, and undiagnosed stage II HTN was $9.82\%$, $39.5\%$, and $17.2\%$, respectively. The sociodemographic characteristics of the participants, stratified by blood pressure status, are provided in Table 1. The prevalence of HTN (stage I and II combined) among males was significantly higher than among females ($62.2\%$, $$n = 125$$ versus $52.2\%$, $$n = 152$$, $p \leq .05$). A higher prevalence of elevated blood pressure and HTN was observed among males and participants who smoked compared with females and non-smokers, respectively, ($p \leq .001$). A large proportion of participants ($90.6\%$, $$n = 443$$) exceeded the AHA recommendation for daily sodium intake. However, sodium intake did not significantly differ among the different blood pressure groups.
**Table 1**
| Unnamed: 0 | Normal blood pressure (n = 164) | Elevated blood pressure (n = 48) | Stage I hypertension (n = 193) | Stage II hypertension (n = 84) | Total (n = 489) | p |
| --- | --- | --- | --- | --- | --- | --- |
| Age, years, n (%) | Age, years, n (%) | Age, years, n (%) | Age, years, n (%) | Age, years, n (%) | Age, years, n (%) | Age, years, n (%) |
| 20–29 | 111 (35.8) | 35 (11.3) | 118 (38.1) | 46 (14.8) | 310 (63.4) | .261 |
| 30–39 | 25 (26.6) | 8 (8.51) | 39 (41.5) | 22 (23.4) | 94 (19.2) | .261 |
| ≥ 40 | 28 (32.9) | 5 (5.88) | 36 (42.4) | 16 (18.8) | 85 (17.4) | .261 |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) |
| Male | 41 (20.4) | 35 (17.4) | 80 (39.8) | 45 (22.4) | 201 (41.1) | < .001 a |
| Female | 123 (42.7) | 13 (4.51) | 113 (39.2) | 39 (13.5) | 288 (58.9) | < .001 a |
| Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) |
| Single | 74 (37.0) | 14 (7.00) | 78 (39.0) | 34 (17.0) | 200 (40.9) | .272 |
| Married | 90 (31.7) | 34 (12.0) | 111 (39.1) | 49 (17.3) | 284 (58.1) | .272 |
| Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) |
| ≤ High-school /Diploma | 83 (34.3) | 25 (10.3) | 96 (39.7) | 38 (15.7) | 242 (49.5) | .844 |
| University/ Postgraduate | 81 (32.8) | 23 (9.30) | 97 (39.3) | 46 (18.6) | 247 (50.5) | .844 |
| Income, SAR b , n (%) | Income, SAR b , n (%) | Income, SAR b , n (%) | Income, SAR b , n (%) | Income, SAR b , n (%) | Income, SAR b , n (%) | Income, SAR b , n (%) |
| < 5000 | 90 (34.7) | 27 (10.4) | 107 (41.3) | 35 (13.5) | 259 (53.0) | .341 |
| 5000–10000 | 34 (32.4) | 12 (11.4) | 36 (34.3) | 23 (21.9) | 105 (21.5) | .341 |
| > 10000 | 40 (32.0) | 9 (7.20) | 50 (40.0) | 26 (20.8) | 125 (25.6) | .341 |
| Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) |
| Smokers | 28 (22.8) | 23 (18.7) | 47 (38.2) | 25 (20.3) | 123 (25.2) | < .001 a |
| Non-smokers | 136 (37.2) | 25 (6.80) | 146 (40.0) | 59 (15.9) | 366 (74.8) | < .001 a |
| Sodium intake c, n (%) | Sodium intake c, n (%) | Sodium intake c, n (%) | Sodium intake c, n (%) | Sodium intake c, n (%) | Sodium intake c, n (%) | Sodium intake c, n (%) |
| < 2300 mg/day | 18 (39.1) | 2 (4.30) | 19 (41.3) | 7 (15.2) | 46 (9.40) | .583 |
| ≥ 2300 mg/day | 146 (33.0) | 46 (10.4) | 174 (39.3) | 77 (17.4) | 443 (90.6) | .583 |
Among the sample, $8.80\%$ ($$n = 43$$) of participants were underweight, $38.2\%$ ($$n = 187$$) were a healthy weight, $26.4\%$ ($$n = 129$$) were overweight, and $26.6\%$ ($$n = 130$$) were obese. Mean BMI and WC measurements were significantly higher among participants with HTN ($p \leq .05$). Weight statuses also differed significantly across the blood pressure status groups ($p \leq .001$). Detailed descriptions of the anthropometric measurements according to blood pressure status are provided in Table 2.
**Table 2**
| Unnamed: 0 | Normal blood pressure (n = 164) | Elevated blood pressure (n = 48) | Stage I hypertension (n = 193) | Stage II hypertension (n = 84) | Total (n = 489) | p |
| --- | --- | --- | --- | --- | --- | --- |
| BMI (kg/m 2 ), mean (SD) | 24.2 (5.06) | 24.6 (5.67) | 26.3 (5.68) | 28.8 (6.42) | 25.9 (5.81) | < .001a |
| WC (cm), mean (SD) | WC (cm), mean (SD) | WC (cm), mean (SD) | WC (cm), mean (SD) | WC (cm), mean (SD) | WC (cm), mean (SD) | WC (cm), mean (SD) |
| Males | 85.4 (12.9) | 85.0 (13.6) | 93.6 (20.4) | 99.1 (15.5) | 91.7 (17.6) | < .001a |
| Females | 75.1 (10.3) | 71.5 (13.2) | 77.3 (11.9) | 82.8 (14.6) | 76.8 (12.0) | .004a |
| Weight status, n (%) | Weight status, n (%) | Weight status, n (%) | Weight status, n (%) | Weight status, n (%) | Weight status, n (%) | Weight status, n (%) |
| Underweight | 20 (46.5) | 5 (11.6) | 12 (27.9) | 6 (14.0) | 43 (8.80) | < .001a |
| Healthy weight | 77 (41.2) | 21 (11.2) | 71 (38.0) | 18 (21.4) | 187 (38.2) | < .001a |
| Overweight | 44 (34.1) | 12 (9.30) | 54 (41.9) | 19 (41.9) | 129 (26.4) | < .001a |
| Obese | 23 (17.7) | 10 (7.70) | 56 (43.1) | 41 (31.5) | 130 (26.6) | < .001a |
## Determinants of undiagnosed hypertension
Multinomial logistic regression analyses were used to assess the association between blood pressure status and sex, BMI, and WC. The unadjusted models indicated that males had higher odds of elevated blood pressure (OR: 8.08, $95\%$ CI: 3.90–16.7, $p \leq .001$), stage I HTN (OR: 2.12, $95\%$ CI: 1.35–3.35, $$p \leq .001$$), and stage II HTN (OR: 3.46, $95\%$ CI: 1.99–6.03, $p \leq .001$). An increased BMI was associated with higher odds of stage I HTN (OR: 1.07, $95\%$ CI: 1.0–1.11, $$p \leq .001$$) and stage II HTN (OR: 1.15, $95\%$ CI: 1.10–1.21, $p \leq .001$). Higher WC also was associated with higher odds of stage I HTN (OR: 1.03, $95\%$ CI: 1.01–1.04, $p \leq .001$) and stage 2 HTN (OR: 1.06, $95\%$ CI: 1.04–1.08, $p \leq .001$).
Multiple regression models, adjusted for participant age and smoking status, were further evaluated. In model 1, males had significantly higher odds of elevated blood pressure (aOR: 6.29, $95\%$ CI: 2.75–14.3), stage I HTN (aOR: 1.82, $95\%$ CI: 1.08–3.08), and stage II HTN (aOR: 2.39, $95\%$ CI: 1.24–4.62) compared to females. A higher BMI was associated with higher odds of stage I HTN (aOR: 1.07, $95\%$ CI: 1.02–1.12) and stage II HTN (aOR: 1.15, $95\%$ CI: 1.09–1.21). In model 2, males had significantly higher odds of elevated blood pressure (aOR: 6.08, $95\%$ CI: 2.54–14.5) than females, while higher WC was associated with higher odds of stage I HTN (aOR: 1.03, $95\%$ CI: 1.01–1.05) and stage II HTN (aOR: 1.06, $95\%$ CI: 1.03–1.08). These findings are further detailed in Table 3.
**Table 3**
| BP status (Outcome) | Variable | Model 1 | Model 1.1 | Model 2 | Model 2.1 |
| --- | --- | --- | --- | --- | --- |
| BP status (Outcome) | Variable | aOR [95% CI] | p | aOR [95% CI] | p |
| Elevated | Male | 6.29 [2.75 to 14.3] | < .001 * | 6.08 [2.54 to 14.5] | < .001 a |
| | BMI or WC | 1.00 [0.93 to 1.07] | .982 | 0.99 [0.97 to 1.02] | .663 |
| Stage I | Male | 1.82 [1.08 to 3.08] | .025 * | 1.46 [0.82 to 2.60] | .205 |
| | BMI or WC | 1.07 [1.02 to 1.12] | .005 * | 1.03 [1.01 to 1.05] | .006 a |
| Stage II | Male | 2.39 [1.24 to 4.62] | .009 * | 1.36 [0.64 to 2.88] | .428 |
| | BMI or WC | 1.15 [1.09 to 1.21] | < .001 * | 1.06 [1.03 to 1.08] | < .001 a |
Associations stratified by age indicated that the younger males (20–29 years) had significantly higher odds of elevated blood pressure (aOR: 8.77, $95\%$ CI: 3.26–23.6) and stage I HTN (aOR: 2.02, $95\%$ CI: 1.03–3.95). A higher BMI in younger participants (20–29 years) was associated with higher odds of stage I (aOR: 1.07, $95\%$ CI: 1.01–1.13) and stage II HTN (aOR: 1.14, $95\%$ CI: 1.07–1.22), whereas a higher BMI among participants aged 30–39 years was only associated with higher odds of stage II HTN (aOR: 1.16, $95\%$ CI: 1.01–1.31). Furthermore, a higher WC in younger participants (20–29 years) was associated with higher odds of elevated blood pressure (aOR: 8.54, $95\%$ CI: 2.94–24.8), stage I HTN (aOR: 1.03, $95\%$ CI: 1.00–1.05), and stage II HTN (aOR: 1.05, $95\%$ CI: 1.02–1.09). Among participants aged between 30–39 years and ≥ 40 years, a higher WC was associated with higher odds of stage II HTN (aOR: 1.07, $95\%$ CI: 1.01–1.14 and aOR: 1.06, $95\%$ CI: 1.00–1.12, respectively). Further details are provided in Table 4.
**Table 4**
| BP status (Outcome) | Variable | 20–29 years | 20–29 years.1 | 20–29 years.2 | 20–29 years.3 | 30–39 years | 30–39 years.1 | 30–39 years.2 | 30–39 years.3 | ≥ 40 years | ≥ 40 years.1 | ≥ 40 years.2 | ≥ 40 years.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| BP status (Outcome) | Variable | Model 1 | Model 1 | Model 2 | Model 2 | Model 1 | Model 1 | Model 2 | Model 2 | Model 1 | Model 1 | Model 2 | Model 2 |
| BP status (Outcome) | Variable | aOR [95% CI] | p | aOR [95% CI] | p | aOR [95% CI] | p | aOR [95% CI] | p | aOR [95% CI] | p | aOR [95% CI] | p |
| Elevated | Male | 8.77 [3.26 to 23.6] | < .001 a | 8.54 [2.94 to 24.8] | < .001 a | 7.33 [0.64 to 84.0] | .109 | 8.77 [0.76 to 102] | .082 | 0.93 [0.08 to 11.5] | .958 | 0.65 [0.04 to 9.99] | .754 |
| | BMI or WC | 0.99 [0.91 to 1.08] | .844 | 0.99 [0.96 to 1.03] | .661 | 0.97 [0.81 to 1.16] | .739 | 0.97 [0.91 to 0.1.03] | .308 | 1.10 [0.88 to 1.38] | .395 | 1.04 [0.95 to 1.13] | .438 |
| Stage I | Male | 2.02 [1.03 to 3.95] | .040 a | 1.75 [0.83 to 3.68] | .141 | 2.06 [0.64 to 6.60] | .227 | 1.72 [0.48 to 6.13] | .404 | 0.95 [0.24 to 3.72] | .937 | 0.55 [0.12 to 2.53] | .442 |
| | BMI or WC | 1.07 [1.01 to 1.13] | .012 a | 1.03 [1.00 to 1.05] | .028 a | 1.04 [0.93 to 1.17] | .453 | 1.00 [0.96 to 1.05] | .871 | 1.09 [0.96 to 1.22] | .173 | 1.05 [1.00 to 1.10] | .063 |
| Stage II | Male | 1.99 [0.83 to 4.77] | .124 | 1.16 [0.43 to 3.19] | .768 | 3.01 [0.72 to 12.6] | .131 | 1.45 [0.27 to 7.80] | .668 | 2.98 [0.68 to 13.1] | .149 | 1.39 [0.26 to 7.32] | .701 |
| | BMI or WC | 1.14 [1.07 to 1.22] | < .001 a | 1.05 [1.02 to 1.09] | < .001 a | 1.16 [1.01 to 1.31] | .027 a | 1.07 [1.01 to 1.14] | .020 a | 1.16 [1.00 to 1.34] | .055 | 1.06 [1.00 to 1.12] | .044 a |
## Discussion
HTN is considered a major risk factor for CVD development and associated mortality [5]. A high prevalence of HTN has previously been documented among the Saudi population [21]. However, recent data on its prevalence is lacking. The results of the current study indicated a high prevalence of elevated blood pressure and HTN among Saudi adults. HTN is a silent, asymptomatic disease, and the high prevalence of undiagnosed HTN observed in the present study indicates an urgent need for immediate action to avoid the manifestation of serious long-term complications [4, 5]. The data also indicated higher odds of elevated blood pressure and HTN in males and individuals with higher BMIs and WCs, independent of age or smoking status.
The prevalence of undiagnosed HTN observed among the study sample was high. Previous epidemiological studies have reported a lower prevalence of undiagnosed HTN among apparently healthy individuals. For example, a study conducted in India examining 3629 participants reported that approximately $26\%$ had undiagnosed HTN [22]. However, another study conducted in Sudan that examined 1099 participants reported a higher prevalence of undiagnosed HTN ($38.2\%$) [23]. These findings highlight the importance of identifying barriers to HTN diagnosis and developing nationwide interventions to both encourage HTN screening and increase awareness of potential HTN complications among the Saudi population.
Several studies have indicated that elevated blood pressure is more prevalent among males than females, irrespective of race and ethnicity [24, 25]. Previous studies have also reported lower levels of HTN awareness among males [25, 26]. Smoking has previously been associated with HTN in a dose-dependent manner, with longer smoking duration associated with a higher risk of HTN [27]. In the present study, elevated blood pressure was observed primarily among young males, the majority of whom were smokers. Therefore, males, in particular, should undergo routine HTN assessment, evaluation, and management, beginning at a young age, with special attention paid to those who are smokers. Intervention campaigns should be conducted to increase awareness regarding the adverse effects of smoking on cardiovascular outcomes.
Current evidence suggests that economic and nutritional transitions are occurring in various developing countries. In Saudi Arabia, fast food consumption has increased over the past few decades [28], leading to an increased intake of energy, fats, and salt [29]. Excessive sodium intake, poor consumption of dietarily important foods, including fruits, vegetables, milk, and dairy products, and more sedentary lifestyles have been reported among the Saudi Arabian population [12, 30]. Although an association between sodium intake and HTN has been reported in previous studies [10, 31], it was not linked to HTN occurrence in the current study. This may have been due to the excessive sodium consumption of sodium among the participants included in the study. Furthermore, the participants in the current study were relatively young, and previous studies have indicated that prolonged periods of high sodium intake can result in HTN, meaning this association may be more apparent among older individuals [32].
The recent dietary and lifestyle changes among the Saudi Arabian population have resulted in a higher incidence of obesity and an increased prevalence of obesity-related comorbidities, including HTN [11]. In line with previous findings, the current study reported positive associations between blood pressure status and higher BMI and WC values [33]. The pathophysiological role of obesity in the development of HTN has been extensively investigated. Various mechanisms involved in the development of oxidative stress and alterations in the renin–angiotensin–aldosterone system and sympathetic nervous system have been identified as playing key roles in elevating blood pressure in overweight and obese individuals [34].
The present study provides updated data regarding the prevalence of undiagnosed HTN and the association between blood pressure status and sociodemographic characteristics, sodium intake, BMI, and WC among Saudi adults. Such data is necessary to fill current gaps in the literature and to guide future research and interventions. However, the current study is limited by its cross-sectional design, meaning cause-and-effect relationships between blood pressure status and sociodemographic variables, sodium intake, and anthropometric measurements could not be determined. Convenient sampling method was used to collect data included in this study, which may influence the generalizability of the study findings. Furthermore, the physical activity levels of participants were not considered in the analyses. However, recent evidence suggests that the majority of Saudi individuals are living sedentary lifestyles [12].
## Conclusions
High prevalence of undiagnosed HTN was observed among the Saudi adults surveyed in this study. Nationwide data on the prevalence of HTN must be established based on recent guidelines. Males and individuals with elevated BMI or WC values were observed to be at particular risk of having undiagnosed HTN. Future longitudinal studies should be conducted to identify the specific causes of undiagnosed HTN among Saudi adults. Furthermore, intervention programs should be implemented to prevent obesity and encourage regular HTN screening and follow-ups to enable early detection and management.
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|
---
title: '“Nutripiatto”: A tool for nutritional education. A survey to assess dietary
habits in preschool children'
authors:
- Greta Lattanzi
- Claudia Di Rosa
- Chiara Spiezia
- Roberto Sacco
- Samanta Cattafi
- Leonardo Romano
- Domenico Benvenuto
- Silvia Fabris
- Laura De Gara
- Yeganeh Manon Khazrai
journal: PLOS ONE
year: 2023
pmcid: PMC9990952
doi: 10.1371/journal.pone.0282748
license: CC BY 4.0
---
# “Nutripiatto”: A tool for nutritional education. A survey to assess dietary habits in preschool children
## Abstract
Childhood obesity is a global public health concern linked to metabolic and psychological comorbidities. There is growing evidence that children’s lifestyle habits are shifting towards obesity, with dire consequences for their future well-being and healthcare costs. In this interventional study, we enrolled 115 children aged between 4–5 years ($53\%$ females and $47\%$ males) and carried out nutrition education interventions to improve their dietary habits. We introduced “Nutripiatto”, a visual plate icon and easy guide, which was used by the children during the study. We investigated the children’s dietary habits using a Food Frequency Questionnaire at the beginning and end of the study, after one month of using “Nutripiatto”. The results showed that the children significantly increased the portion sizes and frequency of vegetable consumption ($P \leq 0.001$) and reduced the consumption of several junk foods such as French fries and crisps ($P \leq 0.001$), reaching the recommended dietary allowances and frequency of consumption. Daily consumption of water also significantly increased, reaching the suggested amount of six glasses per day. Based on these results, “Nutripiatto” can be considered an effective visual guide and helpful tool to achieve small changes and empower families to make healthier food choices. It can also be considered an effective educational tool for nutritionists and healthcare professionals to improve children’s dietary behavior.
## Introduction
Childhood obesity is one of the most significant health challenges of the 21st century [1], as it is rapidly increasing worldwide [2]. According to the World Health Organization (WHO), in 2016, 41 million children under the age of 5, and 340 million children and adolescents aged from 5–19 were found to be overweight or suffering from obesity [2,3]. Data published by the Organisation for Economic Cooperation and Development (OECD) in 2019 revealed that childhood obesity rates are particularly high in Italy [4], ranking fourth worldwide after the United States, New Zealand, and Greece.
The Childhood Obesity Surveillance Initiative (COSI) conducted by "*Okkio alla* Salute" analyzed the prevalence of overweight and obesity among Italian school children aged between 6–7 years in 2019 and found that $20.4\%$ were overweight and $9.4\%$ [5] were obese. The survey also revealed that $8.7\%$ of children skipped breakfast, while $35.6\%$ consumed a small breakfast, and $55.2\%$ compensated by eating more during mid-morning snacks. Additionally, $48.3\%$ of the children consumed sweet and salty snacks at least three times a week, while legumes were consumed less than once a week by $38.4\%$ of the children interviewed. One in five children did not consume any fruits or vegetables daily, while $25.4\%$ consumed sweet drinks. Many children led a sedentary lifestyle and almost half of them spent more than two hours every day watching TV, playing with tablets, mobile phones, and video games.
It is recommended that obesity prevention and healthy lifestyle education start in early childhood as this is the phase that precedes the adiposity rebound that can lead to obesity [6,7]. Studies have also shown that schools are the best setting for nutrition education, as this is where children spend most of their time. The literature suggests that the best results are achieved when interventions include both diet and physical activity, particularly for preschool children [8,9].
The growing prevalence of childhood obesity is associated with metabolic and psychological comorbidities, with a plethora of diseases that used to be related only to adults such as type 2 diabetes, hypertension, dyslipidemia, etc. [ 10] with dire consequences for children’s future well-being and also healthcare costs. Obesity is a multifactorial disease caused by genetic, cultural, and environmental factors, which requires multicomponent interventions that take into account lifestyle changes, behavioral strategies, and active parental involvement [11]. Parents are a powerful role model for their children’s eating behaviors [12,13], which they can influence by improving their own eating patterns. Family mealtimes are also crucial, as they expose children to healthier food choices and smaller portion sizes [13]. Recent studies have evaluated the advantages of family cooking to improve and promote healthy eating habits [14].
The dramatic increase in childhood obesity can also be contained through the concerted efforts of both public and private sectors. The Unit of Food Science and Human Nutrition at Campus Bio-Medico University of Rome, Italy, in collaboration with Nestlé Italia, has thus developed “Nutripiatto”, a visual plate icon along with an intuitive explanatory guide to be used as a dietary education tool for children aged between 4–12 years and their families.
The aim of the present pilot study is to evaluate the effectiveness of “Nutripiatto” as a tangible tool for nutritional education of children aged 4–5 years and their caregivers in reducing portion sizes and improving food choices.
## Materials and methods
The present study took place from September 2019 to March 2020. During the first two weeks, 127 preschool children’s parents were recruited to participate in the study according to the following inclusion and exclusion criteria.
Out of 127, $9\%$ ($$n = 12$$) of the children was not included in the study as they either refused or did not show up to the explanatory meetings organized at T0. Thus, 115 children were enrolled from 5 preschool classes.
Parents were asked to sign the informed consent and to fill in a Food Frequency Questionnaire (FFQ) to evaluate their children’s dietary habits and physical activity. Afterwards parents, grandparents or other caregivers and children were invited together with teachers and school kitchen staff to attend an explanatory meeting to learn how to use “Nutripiatto” and its guide (T0). The explanatory meetings were held by a nutritionist and organized several times for small groups of people (8 persons), accordingly to the time in which parents, grandparents or other caregivers, teachers and school staff were recruited. To allow parents, grandparents or other caregivers to participate, completion of the questionnaire and the explanatory meetings took place at school entrance or exit times.
During the meeting, the importance of the right amount of nutrients and micronutrients in the diet as well as the proper portion sizes was emphasized. Parents, grandparents or other caregivers were recommended to read the explanatory guide and to try cooking the recipes with their children to improve their food choices and healthy eating habits. They also had the opportunity to meet the nutritionist after the explanatory meeting if they need further information on “Nutripiatto” or issues related to childhood nutrition. About $10\%$ of the parents wanted a further meeting with the nutritionist to better understand the project and what was expected of them. The food education interventions for children were composed by two interactive sessions on healthy dietary habits and lifestyle tailored to the children’s age. They were mainly interactive interventions with games and practical activities to encourage active learning. Children played with food-shaped toys to learn about the main nutrients they contained. They built a Mediterranean diet food pyramid with printable word scrambles, using flash cards or food group colouring sheets. Nutritionists explained and showed children how to use “Nutripiatto” correctly. At the end of each session, children were asked to draw foods and put them in their “Nutripiatto” to evaluate how much they had learned on food composition and healthy eating.
They were invited to bring their “Nutripiatto” at home, while parents were asked to allow their children to use it for their main meals for a month.
At the end of the one-month period (T1), parents were asked to fill in a second FFQ to evaluate changes in their children’s eating habits compared to baseline.
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee Campus Bio-Medico University of Rome for studies involving humans (approval code of the study $\frac{131}{21}$).
## “Nutripiatto”
“Nutripiatto” is a tool for nutritional education for children aged 4–12 years designed to promote a correct lifestyle and to reduce the incidence of metabolic diseases among children. “ Nutripiatto” is a real-size plate that shows the proportions of food groups composing principal meals. Vegetables occupy the half of the dish, while cereals and protein a quarter, respectively. In fact, some easy examples of the respective foods are represented in the “vegetable half part” of the dish and in the two quarters.
Healthy eating habits and lifestyle recommendations are also graphically represented on the plate’s edge (Fig 1).
**Fig 1:** *“Nutripiatto”, a visual plate.A tool for nutritional education for children aged 4–12 years.*
The plate is provided with a booklet that contains information on appropriate portion sizes for children according to their age group as well as easy methods to estimate them. Food portion sizes are illustrated in simple, practical and interactive ways. For instance, children’s hands and household tools were used to estimate portion sizes, accordingly to scientific literature [15,16]. This system proved to be fun for children and useful at the same time, as they could measure portion sizes learning through play.
The guide contains recommendations regarding the importance of hydration, fruit consumption, use of extra virgin olive oil as main seasoning and physical activity.
The booklet contains also recipes (see S1–S4 Figs. for few example of the proposed recipes) for the preparation of healthy meals. Each recipe is designed as a single dish (photographed row and cooked) to estimate the correct portion size and the proportion of food groups to be consumed at each meal in relation to children’s age group. Recipes have been developed according to the nutritional references provided by National Recommended Energy and Nutrient Intake Levels (LARN) [17] for the respective age groups taken into consideration in the booklet (4–6 /7-9/ 10–12 years). Breakfast and snacks provided $15\%$ and 5–$10\%$ of the daily energy requirement, respectively, while the principal meals provided about 30–$40\%$ of the total daily energy amount with a protein intake less than $15\%$.
The booklet provided with “Nutripiatto” is based on the National Nutritional Guidelines (NNG). It aims to suggest adequate portion sizes and weekly food consumption frequencies, to improve children’s eating patterns and to prevent metabolic diseases [18–20]. A recommended serving size indicates the amount of food that should be consumed during a meal or a snack, while weekly consumption frequencies imply the number of times that a food or a food group should be consumed in a week.
Table 1 shows the three portion sizes for the different food categories: small (for children aged 4–6 years), medium (for children aged 7–9 years), and large (for children aged 10–12 years) according to NNG [18–20].
**Table 1**
| FOOD CATEGORIES | SMALL PORTION SIZE(age 4–6 yrs) | MEDIUM PORTION SIZE(age 7–9 yrs) | LARGE PORZION SIZE(age 10–12 yrs) |
| --- | --- | --- | --- |
| CARBOHYDRATES SOURCE | | | |
| Pasta/rice/barley/couscous | 50 g | 60 g | 90 g |
| Bread | 60 g | 70 g | 110 g |
| Potatoes | 200 g | 230 g | 360 g |
| Pizza | 150 g | 200 g | 350 g |
| Biscuits/Breakfast cereals | 30 g (3 biscuits)/30 g | 40 g (4 biscuits)/40 g | 40 g (4 biscuits)/40 g |
| PROTEIN SOURCES | | | |
| Meat* | 70 g | 80 g | 100 g |
| Fish** | 110 g | 130 g | 160 g |
| Egg | 50 g (n. 1 egg) | 50 g (n. 1 egg) | 100 g (n. 2 egg) |
| Legumes | 60 g (fresh) and 20 g (dried) | 90 g (fresh) and 30 g (dried) | 120 g (fresh) and 40 g (dried) |
| MILK AND DAIRY PRODUCTS | | | |
| Fresh cheeses/seasoned cheeses | 40 g /20 g | 70 g/30 g | 100 g/50 g |
| Milk | 200 ml | 200 ml | 200 ml |
| Yogurt | 125 g | 125 g | 125 g |
| VEGETABLES AND FRUIT | | | |
| Vegetables | 40 g (raw vegetables) and 120 g (cooked vegetables) | 50 g (raw vegetables) and 150 g (cooked vegetables) | 50 g (raw vegetables) and 200 g (cooked vegetables) |
| Fruit | 100 g | 150 g | 200 g |
| Nuts | 20 g | 30 g | 30 g |
| EXTRA VIRGIN OLIVE OIL | 10 g | 10 g | 10 g |
| SWEETS, CONFECTIONARY, AND SNACKS | 30 g baked desserts (donuts, tarts) or 10 g chocolate or jam or 100 g ice cream | 50 g baked desserts (donuts, tarts) or 25 g chocolate or jam or 100 g ice cream | 100 g baked desserts (donuts, tarts) or 40 g chocolate or jam or 120 g ice cream or 5 g sugar*** |
| WATER | 200 ml | 200 ml | 200 ml |
The daily and weekly frequencies of consumption of some foods according to the Italian Guideline [18] are reported in Table 2.
**Table 2**
| FOOD CATEGORIES | DAILY FREQUENCY OF CONSUMPTION | WEEKLY FREQUENCY OF CONSUMPTION |
| --- | --- | --- |
| CARBOHYDRATES SOURCE | | |
| Pasta/rice/barley/couscous | 2 servings | |
| Bread | 2–3 servings | |
| Potatoes | | 1–2 servings |
| Pizza | | 1 serving (in place of bread, pasta, rice etc.) |
| Biscuits/Breakfast cereals | 1 serving | |
| PROTEIN SOURCE | | |
| Meat | | 3 servings |
| Fish | | 3–4 servings |
| Egg | | 1–2 servings |
| Legumes | | 3 servings |
| MILK AND DAIRY PRODUCTS | | |
| Fresh cheeses/seasoned cheeses | | 2–3 servings |
| Milk | 1 serving | |
| Yogurt | | 5–7 servings |
| VEGETABLES AND FRUIT | | |
| Vegetables | 2 servings | |
| Fruit | 2–3 servings | |
| Nuts | | 3 servings |
| EXTRA VIRGIN OLIVE OIL | 3 servings | |
| SWEETS, CONFECTIONARY AND SNACKS | Sugar: 1 serving for elder children | 2–4 servings |
| WATER | 6 glasses | |
## Food Frequency Questionnaire (FFQ)
Two FFQs [21] were used, one at baseline (T0) and one at the end of the study (T1). Both questionnaires were filled in by parents based on their children’s eating patterns and preferences. The FFQs differed from each other because the T1 questionnaire included also a small section about the efficacy of “Nutripiatto”.
The answers to the questionnaire given at T0 and at T1 have been encoded by assigning them a number.
The answers options were different depending on the questions.
## Section 1
Questions 1,4 and 5: were dichotomous answers “Yes” or “No” that have been encoded in 1 or 2.
Questions 2 and 3: possible answers were always (everyday), often (3–4 times/week) sometimes (1–2 times/week) and never that have been encoded in 1,2,3 and 0 respectively.
## Section 2 and 3
For food portion sizes the possible answers were small, medium and large that have been encoded in 1, 2 or 3, respectively.
For weekly frequencies of consumption, the possible answers were always, often (3–4 times/week) sometimes (1–2 times/week) and never that have been encoded in 1, 2, 3 and 0, respectively.
## Section 4 (only for T1 questionnaire)
Parents were invited to answer “Yes” or “No” to the questions that have been encoded in 1 or 2.
The structure of the two FFQs is presented in Table 3.
**Table 3**
| Section | T0 questionnaire | T1 questionnaire |
| --- | --- | --- |
| Section 1 (dietary habits and lifestyle) | Does your child follow a specific diet? | Does your child follow a specific diet? |
| Section 1 (dietary habits and lifestyle) | How many meals do you eat together with your family during the week? | How many meals do you eat together with your family during the week? |
| Section 1 (dietary habits and lifestyle) | Who cooks in your family? | Who cooks in your family? |
| Section 1 (dietary habits and lifestyle) | Does your child have school lunches? | Does your child have school lunches? |
| Section 1 (dietary habits and lifestyle) | Does your child play any sport? | Does your child play any sport? |
| Section 2 (food group, portion size and frequency of consumption) | Milk and dairy products (milk, yogurt, cheese…) | Milk and dairy products (milk, yogurt, cheese…) |
| Section 2 (food group, portion size and frequency of consumption) | Breakfast foods (biscuits, toasted bread) | Breakfast foods (biscuits, toasted bread) |
| Section 2 (food group, portion size and frequency of consumption) | Cereals (pasta, rice, bread, pizza) and potatoes | Cereals (pasta, rice, bread, pizza) and potatoes |
| Section 2 (food group, portion size and frequency of consumption) | Protein foods (legumes, fish, white and red meat, salami, eggs) | Protein foods (legumes, fish, white and red meat, salami, eggs) |
| Section 2 (food group, portion size and frequency of consumption) | Fruits and vegetables | Fruits and vegetables |
| Section 3 (oils and fat, drinks and snacks frequency of consumption) | Oils and animal fats | Oils and animal fats |
| Section 3 (oils and fat, drinks and snacks frequency of consumption) | Soft drinks | Soft drinks |
| Section 3 (oils and fat, drinks and snacks frequency of consumption) | Snacks (sweets, prepackaged snacks, chewing—gum, chocolate) | Snacks (sweets, prepackaged snacks, chewing—gum, chocolate) |
| Section 4 (Evaluation of “Nutripiatto”’s effectiveness) | It was not present | Did you find “Nutripiatto” effective as a visual guide? |
| Section 4 (Evaluation of “Nutripiatto”’s effectiveness) | It was not present | Did you notice an increase in the consumption of wholegrain cereals, vegetables and water? |
| Section 4 (Evaluation of “Nutripiatto”’s effectiveness) | It was not present | Did you notice a reduction in the porzion size of animal protein sources? |
| Section 4 (Evaluation of “Nutripiatto”’s effectiveness) | It was not present | Did the level of your child’s physical activity increase? |
## Statistical analysis
GraphPad Prism 9 and IBM SPSS were used to perform the analyses.
Data are presented as means ± standard deviation (mean ± DS). Normal distribution of data was checked through the Shapiro-Wilk test.
The non-parametric Wilcoxon signed-rank test was performed to compare the number of children who consumed the right portion size at T1 versus T0. The percentage of children who consumed the medium and large portion sizes and who did not consume some foods at T0 and T1 were compared using the Chi squared test. The level of significance (P-value<0.05) and the effect size (d) (Cohen’s D) were also analysed and their values reported. Daily and weekly frequencies of food consumption were also compared with NNG [18].
## Results
At baseline, 115 children were included in the present study. The majority ($94\%$) were not following a specific diet, except for a small minority, $6\%$ ($$n = 7$$), who were on dietary regimes due to being overweight ($1\%$), familial hypercholesterolemia ($2\%$), lactose intolerance ($2\%$), milk protein allergy ($1\%$), and celiac disease ($1\%$). These children thus participated in the nutritional education meetings and received “Nutripiatto” and the booklet, but their data were not included in the analysis in order to prevent bias. The data of 108 children, $53\%$ females ($$n = 57$$) and $47\%$ males ($$n = 51$$) with a mean age of 4.66±0.48 and 4.57±0.49 respectively, were therefore considered for the analysis. All the children enrolled followed the nutritional program for one month, and at T1, there were no dropouts. Regarding meal timing, $90\%$ of the children’s parents declared that their children consumed breakfast at home, but on working days, they usually had lunch in the school canteen. On the other hand, all children ($100\%$) had dinner at home. Breakfast preparation was equally distributed between parents ($52\%$ mothers and $48\%$ fathers), while mothers prepared lunches and dinners on non-working days ($78\%$ and $80\%$ respectively). Regarding physical activity, at T0, $82\%$ of children reported that they played sports.
To assess the effectiveness of “Nutripiatto” as a visual guide and helpful tool to improve children’s eating habits, changes in terms of portion sizes and food frequency consumption before and after its use were evaluated.
## Effectiveness of “Nutripiatto” on portion sizes
At T1, after one month of using “Nutripiatto”, children showed a greater adherence to portion sizes recommended by NNG [19,20].
Table 4 shows the statistically significant variations in the number of children who consumed the right portion related to the age recommended by NNG before and after using “Nutripiatto”.
**Table 4**
| FOODS | % OF CHILDREN WHO CONSUMED THE RIGHT PORTION SIZE | % OF CHILDREN WHO CONSUMED THE RIGHT PORTION SIZE.1 | P VALUE (p) | EFFECT SIZE (d) |
| --- | --- | --- | --- | --- |
| FOODS | T0 | T1 | P VALUE (p) | EFFECT SIZE (d) |
| White pizza (with oil and salt) | 43.5 | 50.0 | <0.05 | 0.2 |
| Red pizza (with tomato sauce) | 45.3 | 53.0 | <0.05 | 0.2 |
| Pasta and rice | 17.6 | 37.0 | <0.05 | 0.6 |
| Soup with pasta and rice | 20.4 | 34.2 | <0.05 | 0.3 |
| Other cereals | 21.3 | 26.0 | <0.05 | 0.5 |
| Bread | 50.9 | 65.7 | <0.04 | 0.2 |
| Roasted potatoes | 39 | 51 | <0.05 | 0.3 |
| French fries | 40.7 | 50 | <0.001 | 0.5 |
| Crisps | 39.9 | 44.5 | <0.001 | 0.5 |
| Biscuits | 33.3 | 46.3 | <0.01 | 0.5 |
| Fish | 18.5 | 64 | <0.001 | 0.8 |
| White and red meat | 14.8 | 63 | <0.001 | 0.9 |
| Vegetables | 44.4 | 23.1 | <0.001 | -0.6 |
| Eggs | 54.6 | 67.6 | <0.003 | 0.2 |
| Glasses of water | 21 | 51 | <0.001 | 1.56 |
Regarding protein portion sizes, the percentage of children who consumed the right portion of fish increased from $18.5\%$ to $64\%$ ($P \leq 0.001$; $d = 0.8$), while the percentage of children who consumed the right portion of white and red meat increased from $14.8\%$ to $63\%$ ($P \leq 0.001$; $d = 0.9$).
The percentage of children who consumed the right portion size of French fries and crisps shifted from $40.7\%$ to $50\%$ ($P \leq 0.001$; $d = 0.5$) and from $39.9\%$ to $44.5\%$ ($P \leq 0.001$; $d = 0.5$), respectively.
A significant increase in the consumption of daily glasses of water was observed at T1, in fact, the percentage of children who drank 6 or more glasses of water per day shifted from $21\%$ at T0 to $51\%$ at T1 ($P \leq 0.001$; $d = 1.56$) as suggested by the national guidelines [19,20].
There were no statistically significant variations in the consumption of nuts, milk and dairy products, salami and fruit. Regarding legumes, the right portion sizes were already consumed at T0, thus no significant changes were observed.
The recommended vegetable portion size was the only one that significantly decreased ($P \leq 0.001$; d = -0.6). This was due to the fact that $76\%$ of children at T1 increased their consumption of vegetables, from the right portion (small) to the medium or large portion sizes. This was probably due to the emphasis given to the importance of consuming vegetables and fibers during the explanatory meetings for both parents and children since vegetable are usually one of the food groups least consumed by children. It is worth noting that children’s fiber intake is lower than those recommended by NNGs throughout Europe, even though it correlates with a lower risk of developing obesity and type 2 diabetes [22]. On the other hand, it is debatable whether a large consumption of dietary fiber could affect the mineral bioavailability [23,24].
Conversely, Table 5 shows the statistically significant variations in children who consumed the medium and large portion sizes after the use of “Nutripiatto”.
**Table 5**
| FOODS | % OF CHILDREN WHO CONSUMED THE MEDIUM PORTION SIZE | % OF CHILDREN WHO CONSUMED THE MEDIUM PORTION SIZE.1 | P VALUE (p) | EFFECT SIZE (d) | % OF CHILDREN WHO CONSUMED THE LARGE PORTION SIZE | % OF CHILDREN WHO CONSUMED THE LARGE PORTION SIZE.1 | P VALUE (p).1 | EFFECT SIZE (d).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| FOODS | T0 | T1 | P VALUE (p) | EFFECT SIZE (d) | T0 | T1 | P VALUE (p) | EFFECT SIZE (d) |
| Roasted potatoes | | | | | 6 | 0 | 0.01 | 0.5 |
| Crisps | 23 | 7 | 0.001 | 0.6 | | | | |
| Fish | 74 | 32 | 0.001 | 1.6 | | | | |
| White and red meat | 78 | 35 | 0.0001 | 1.5 | | | | |
| Biscuits | | | | | 14 | 4 | 0.008 | 0.5 |
| Vegetables | 45 | 65 | 0.004 | 0.5 | | | | |
The reduction in consumption of the medium and large portions of some foods confirms the increase in the consumption of the right portion size for the considered age group (4–5 years).
Only for vegetables did the percentage of children who consumed the medium portion size increase after the use of “Nutripiatto” due to the importance given to vegetable consumption during the class meetings, as previously stated.
Some children did not consume the foods mentioned in the questionnaire. After using “Nutripiatto”, the percentage of children who completely avoided eating specific foods decreased. Of particular importance, the percentage of children who did not consume vegetables decreased from $7\%$ at T0 to $1\%$ at T1 ($P \leq 0.01$; $d = 0.47$). A statistically significant decrease in the percentage of children who avoided consuming cereals such as pasta and rice was also observed, shifting from $4\%$ at T0 to $0\%$ at T1 ($P \leq 0.001$; $d = 0.4$). Similar results were found for the consumption of biscuits: the percentage of children who did not consume biscuits decreased from $7\%$ at T0 to $1\%$ at T1 ($P \leq 0.01$; $d = 0.4$). Conversely, regarding the consumption of other cereals, white and red pizza, roast potatoes, crisps, French fries and nuts, the percentage of children who did not consume the above-mentioned foods increased, although not in a statistically significant way. The consumption of white and red meat, fish and eggs did not undergo any variations after using of “Nutripiatto”.
## Effectiveness of “Nutripiatto” on frequency of consumption
The results showed a greater adherence to the recommended frequencies of consumption for children aged 4–5 years according to the NNG [19]. After using “Nutripiatto”, the frequency of consumption of various foods such as biscuits, bread, crisps, fish, soft drinks, etc. became more aligned with the guidelines. For example, biscuit consumption decreased from 5 times per week to 4 times per week ($P \leq 0.0006$), and bread consumption decreased from 3 times per day to 2 times per day ($P \leq 0.009$). A significant number of children stopped consuming crisps and soft drinks at T1, compared to consuming them once or "sometimes" per week at T0 ($P \leq 0.003$ and $P \leq 0.001$, respectively). Fish consumption increased from once per week to twice per week ($P \leq 0.003$), and vegetable consumption increased from once a day at T0 to twice a day at T1 ($P \leq 0.001$) as recommended by NGG [19].
## Effectiveness of “Nutripiatto”
Regarding the evaluation of “Nutripiatto” as an effective visual guide, $97\%$ of the children’s parents responded affirmatively. The results also showed that according to the parents, children increased the consumption of wholegrain cereals ($23\%$), consumed more vegetables ($61\%$) and drank more water ($62\%$).
According to $52\%$ of parents, their children reduced their portion sizes of animal protein such as meat and fish.
In addition, $31\%$ of children improved their level of physical activity after the education sessions and the use of “Nutripiatto”.
## Discussion and conclusion
The United States Department of Agriculture (USDA) recommends that nutrition education for pre-schoolers should focus on introducing children to healthy eating habits, appropriate portion sizes, and an increased consumption of whole grains, legumes, vegetables, and fruits. It also suggests that learning should be done in a fun and engaging manner [25]. Our study shows that “Nutripiatto” as an educational tool effectively achieved these objectives in the short term.
As with its predecessors, "My Plate" [26] and "The Healthy Eating Plate" [27], “Nutripiatto” is a visual representation of portion sizes and proportions of different food groups. Our results indicate that when used ap-appropriately, it can be a useful tool for making small changes and empowering families to make healthier food choices [28]. However, there have only been a few intervention studies involving children aimed at evaluating the effectiveness of "My Plate" and "The Healthy Eating Plate" as nutrition education tools. These studies had small sample sizes or high dropout rates. A small study conducted in the U.S. involving children in grades 2–5 showed significant improvements in healthy food choices such as fruits and vegetables compared to the baseline [29]. Another study found no significant differences in food choices between the experimental and control groups, with the exception of increased vegetable and dairy product intake in the intervention group [30]. A study by Metzler et al. [ 2017] designed a nutrition education curriculum consisting of five 30-minute lessons for kindergarten children based on "My Plate" recommendations. Although children increased their knowledge of healthy eating, there was no evidence of behavioural change. The retention rate was low, with only $52.6\%$ of parents responding to the survey sent home after the intervention [31].
In the present study, there were no dropouts, and all parents answered the FFQ at T1. This could be at-tributed to the fact that the schools strongly supported the intervention and encouraged parents’ participation. The presence of a nutritionist who actively interacted with parents and children was an important factor in increasing the effectiveness of the education intervention. Additionally, compared to previous studies, “Nutripiatto” involved an actual plate that children took home and used for at least one month. This helped parents learn how to properly assess portion sizes and maintain their focus on the educational project. The plate’s colourful design appealed to children, who continued to use it consistently.
Portion sizes have been identified as a risk factor for childhood obesity, as parents tend to overfeed their children [32,33]. Parents can be trained to serve appropriate portion sizes using tools such as food pictures or by preparing food and sharing the experience in group meetings with other parents [34]. As larger portion sizes lead to higher energy intake, it has been suggested to increase the amount of vegetables included in the main meal. Used as an iconic model, “Nutripiatto” embodies these concepts, as it considered portion sizes and the right proportion of food groups, with half of the plate filled with vegetables. The booklet recommends that parents prepare foods with their children to enable them to improve their food choices and learn about portion sizes. By the end of one month, children had used the Nutripiatto’s eye-catching graphics to increase their consumption of vegetables and reduce their intake of junk foods [35].
Childhood obesity is often caused by an imbalance between the calories consumed and expended [36]. It is well-known that we live in an obesogenic environment, which encourages the excessive intake of unhealthy foods and a sedentary lifestyle from an early age [37]. Parents often set poor examples of eating habits and behaviour for their children, such as using smartphones or watching TV while sitting at the dinner table.
Moreover, this type of behaviour reduces parents’ sensitivity and their ability to respond to their children’s needs, which can lead to reduced parent-child interactions [38]. In contrast, regular family meals free from electronic devices such as TVs, mobile phones, etc. should be encouraged because they improve relationships and can be used as appropriate situations to discuss healthy foods [39].
According to the OECD document [2019] [4], barely half of the Italian population, including adults and children, eat a healthy diet in accordance with national guidelines. Less than $40\%$ consume five portions of fruit and vegetables per day [40,41]. Italian dietary guidelines [18] recommend that pre-school and school-age children consume healthy foods such as vegetables, fruits, whole grains and legumes, which are rich in fiber and maintain satiety for longer periods [42,43], and also reduces insulin secretion. Simple sugars, saturated and trans fats should be reduced as they have been associated with increased adiposity and exposure to high LDL- cholesterol levels and hyperglycemia in children [44,45]. In this study, preschool children significantly increased their consumption of vegetables, reaching 21 portions per week, and reduced their consumption of bread and potato chips as snacks.
The results of this study show that “Nutripiatto” can be considered an effective educational tool for improving portion sizes and food choices, particularly when it is combined with food education interventions given by an expert. It can be useful for nutritionists and health professionals to improve the eating behaviour of children and, potentially, the whole family. Although the results are encouraging, the sample size should be expanded together with the period of intervention to assess the long-term effectiveness of this new education tool. Furthermore, it would be interesting to customize nutrition education programs to include other age groups covered by “Nutripiatto”, from 6 to 12 years old.
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|
---
title: Theoretical framework for a decision support system for micro-enterprise supermarket
investment risk assessment using novel picture fuzzy hypersoft graph
authors:
- Muhammad Saeed
- Muhammad Imran Harl
- Muhammad Haris Saeed
- Ibrahim Mekawy
journal: PLOS ONE
year: 2023
pmcid: PMC9990954
doi: 10.1371/journal.pone.0273642
license: CC BY 4.0
---
# Theoretical framework for a decision support system for micro-enterprise supermarket investment risk assessment using novel picture fuzzy hypersoft graph
## Abstract
Risk evaluation has always been of great interest for individuals wanting to invest in various businesses, especially in the marketing and product sale centres. A finely detailed evaluation of the risk factor can lead to better returns in terms of investment in a particular business. Considering this idea, this paper aims to evaluate the risk factor of investing in different nature of products in a supermarket for a better proportioning of investment based on the product’s sales. This is achieved using novel Picture fuzzy Hypersoft Graphs. Picture Fuzzy Hypersoft set (PFHSs) is employed in this technique, a hybrid structure of Picture Fuzzy set and Hypersoft Set. These structures work best for evaluating uncertainty using membership, non-membership, neutral, and multi-argument functions, making them ideal for Risk Evaluation studies. Also, the concept of the PFHS graph with the help of the PFHS set is introduced with some operations like the cartesian product, composition, union, direct product, and lexicographic product. This method presented in the paper provides new insight into product sale risk analysis with a pictorial representation of its associated factors.
## 1 Introduction
The world is evolving with humanity learning from processing immense amounts of data available in various forms, no matter how convoluted it may seem, in the hope that it may lead to some insight. Processing the data in multiple ways has allowed for a more analytic and scenario-based understanding of current affairs, allowing for effective decision-making in science and business. Keeping this notion in mind, business individuals in product sales and managing supermarkets require product sales supply and demand analytics so that the stock is always the optimum amount to go off the shelves while in the best condition.
Utilizing this information allows for the design of promotional strategies for product sales benefitting the business. Most customers in supermarkets are not subjected to confer with the prices while buying the product, so there is minimal need for them to confer with their internal reference prices, allowing marketing strategists more room to design marketing strategies for business growth. For this purpose, numerous mathematical and computational tools have been designed to analyze these marketing indicators. Among these computational methods, some methods are discussed, but among these methods, attributes are not divided into sub-attributes. To encounter this problem, this paper presents a novel concept of picture fuzzy hypersoft graphs that allows for graphical analysis of multi-criteria decision-making problems while discussing a particular application regarding investment division and risk analysis of products in a supermarket or micro-enterprise setting. The paper first explains some mathematical literature and techniques used for market risk analysis. The novelty of the picture fuzzy hypersoft graph is defined by applying division of investment and evaluating the risk of supermarket products by tackling it as a multi-criteria decision-making problem.
The concept of fuzzy graph was developed by defining a graphical relationship among fuzzy sets by Rosenfeld in 1975 [1]. With the introduction of this concept, fuzzy set extensions like intuitionistic fuzzy set (IFS) [2] were further improved to picture fuzzy set (PFS) [3]. This extension allowed for addressing opinion based data (i.e., yes, no, abstain, and refusal) by addition of a neutral membership function to the already existing IFS. The operators and basic definitions of the PFS concept have been presented in [4, 5]. PFS has been discussed in numerous application based research in diverse fields like information technology, pharmacy, operational research, and business. The above-discussed fuzzy structures fall short when it comes to addressing data where the attributes are divided into multiple sub-attributes. For this purpose, Soft Set (SS) were extended to Hypersoft Set (HSS) by Smarandache which allowed modifying the one parameter set into a multi-parameter set in the domain set of function [6]. The properties of HSS were developed by Saeed et al. ( i.e., HS subset, union, intersection, etc.) and developed hybrids of this HSS structure with structures from fuzzy and neutrosophic set theory [7, 8]. Literature does revela some fuzzy hyperosft hybrid structures including Pythagorean fuzzy hypersoft set and Intuitionistic Fuzzy Hypersoft Set (IFHSS) which is a generalized version of the Intuitionistic Fuzzy Soft Set (IFSS) [9, 10]. To elaborate the versatility and operation of the hybrid fuzzy hypersoft and neutrosophic hypersoft structures, they were applied for the development of medical decision support systems, pattern recognition to monitor the spread of COVID-19 with real-time population data over the period of 1.5 years [11–18].
## 2 Literature review
The development of a fuzzy graph structure lead to the expansion of hybrid structures of Fuzzy Sets and Graph Theory (i.e., balanced interval-valued fuzzy graphs [19], fuzzy planar graphs [20, 21], m- step fuzzy competition graphs [22], fuzzy threshold graph [23], fuzzy cubic graph [24], and fuzzy k-competition graph [25]). The interval-valued fuzzy threshold graph was developed and its properties were explored by Pramanik et al. [ 26]. The concept of planarity was applied to a simple Bipolar Fuzzy Graph leading to an extension to the already existing structure to a more refined bipolar fuzzy planar graphs [27]. Some other hybrid extension of the Fuzzy Planar graphs include interval-valued fuzzy graph [28] and interval-valued fuzzy planar graph [29]. The concept of Fuzzy Hypergraphs was developed by Voskoglou et al. [ 30] alongside several other related graphical strctures. Another term called Intuitionistic fuzzy competition graph was introduced by Sahoo et al. [ 31] while Balanced intuitionistic fuzzy graphs were introduced by Karunambigai et al. [ 32]. When talking about Intuitionistic Fuzzy Graphs, Sahoo et al. applied these intricate structure to numerous real world applications [33–35].
Numerous studies related to Picture Fuzzy Graphs and their properties like edge domination have been reported in literature alongside their applications and hybrid structures [36–38]. The hybrid structures like the Picture Dombi Fuzzy Graph, interval-valued picture uncertain linguistic generalized Hamacher aggregation operators, and the q-rung picture fuzzy graph have significant applications in decision-making problems, design of decision support systems, and associating fuzzy graphical structures in topological spaces [39–43]. These decision support systems require fuzzy aggregation opertors that combine the observations recorded in the data for manipulation in a fuzzy environment [44]. Some examples of these studies include pattern recognition using novel similarity measures for T-spherical fuzzy sets, a policy design aid by using an averaging operator of T-spherical fuzzy set [45, 46]. The T-spherical fuzzy set was presented as a generalization of the previously existing Fuzzy Set concepts (i.e., Intuitionistic fuzzy set and the Picture fuzzy set) [47].
Fuzzy set theory and concepts from Graph theory have been combined for the development of hybrid structures with better ability to handle and express data to extract better results. Picture fuzzy labeling graphs were reported by Devaraj et al. in 2020 alongside its application in decision making [47, 48]. Another interesting concept of fuzzy cross-entropy for picture hesitant fuzzy sets and introduction of polarity in addressing of attributes using Bipolar soft sets were developed by Mahmood et al. for addressing attributes of different nature in decision making problems [49, 50]. A generalized concept of picture fuzzy soft set alongside its application was put forward by Khan et al. [ 50, 51]. Intuitionistic multiplicative set operational laws and their associated aggregation operators for processing was developed by Garg alongside its application in decision support systems [40]. Balanced Picture Fuzzy Graphs were explored in 2021 by Amanathulla et al. [ 51, 52]. Numerous related hybrid fuzzy graph theoretical concepts relating to different aspects of their applications are presented in the following studies [53–58].
The concept of hybrids of hypersoft structures and graph theory have been discussed by Saeed et al. including intricate concepts like the hypersoft graph, a hypersoft subgraph, a complete hypersoft graph, and a hypersoft tree etc [59]. Due to the fast and evolving nature of businesses nowadays, its essential to calculate every single move with extreme delicacy and process a plethora of information in order to assess the risk with respect to inventory management. Literature reveals some studies that involve the use of mower based on fuzzy logic approach for effective and efficient methods for customer service [60]. Similar economic-mathematical models allow for the forecasting of growth of agricultural sector of Ukraine in order to make suitable assessment for people in business and agarians [61]. These assessments allow for timely making business decision based on facts and calculations reducing the risk in terms of loss. Another approach presented in [62] aims for evaluation of enterprise based systems with multiple criteria for analysis and designing a decision-support system. An approach presented in [63] applied clique covering of a fuzzy graph for parametric optimization for development of optimal business strategies.
## 3 Motivation
Numerous methods have been reported in the literature that has been used to solve MADM problems by using concepts from Fuzzy and Soft Sets. All these methods have some restrictions, such as when elements of the attributes set contain further sub-attributes and elements of each attribute set have no relationship. In order to address these difficulties, the concept of Picture Fuzzy Hypersoft graph (PFHSG) is introduced in this paper which is a hybrid structure of the Picture Fuzzy Hypersoft set with Graph Theory. The presented structure was then employed to develop a decision-support algorithm that uses a combination of concepts form the Hypersoft and Fuzzy Set theory. For a proper explanation, some basic terminologies are defined in Section 1, section 2 focuses on the presentation of properties of the PFHSG, while section 3 highlights the multi-attribute decision support system that was designed to based on the PFHSG for addressing intricate decision-making issues in a graphical manner. In Section 4, the algorithm is numerically applied on a risk assessment problem in order to elaborate its working principle and operations. The final section 4 is presents a brief conclusion of the paper.
## 4 Preliminaries
This section focuses on definitions of IFS, PFS, SS, HS, PFSS, PFG, and HSG.
Atanassov [2] defined IFS a direct extension of the fuzzy set which consist a membership function and a non-membership function. It overcomes defects of fuzzy sets.
Definition 1: Intuisionistic Fuzzy Set [2] An intuitionistic fuzzy set X on universe of discourse X = {x1, x2, …‥, xn} is an object of the form: L˜={⟨μ¨L˜(xi),ν¨L˜(xi)⟩|xi∈X} where μ¨L˜(xi):X→[0,1] is called degree of membership of xi in L˜ ν¨L˜(xi):X→[0,1] is called degree of non membership of xi in L˜ and 0≤μ¨L˜(xi)+ν¨L˜(xi)≤1, ∀xi ∈ X πL˜(xi)=1-μ¨L˜(xi)-ν¨L˜(xi) is called hesitancy degree of xi in L˜, ∀xi ∈ X, 0≤πL˜(xi)≤1.
Definition 2: Picture Fuzzy Set [4] In 2013, Coung [4] introduced PFS in order to solve inconsistent and uncertain information in real life. Picture fuzzy set consists of three functions: positive membership function, neutral membership function, and negative membership function. A good example of PFS is electoral voting. A Picture fuzzy set on universe of discourse X = {x1, x2, …‥, xn} is an object of the form: L˜1={⟨μ¨L˜1(xi),η¨L˜1(xi),ν¨L˜1(xi)⟩|xi∈X} where μ¨L˜1(xi):X→[0,1] is called degree of positive membership of xi in L˜1 where η¨L˜1(xi):X→[0,1] is called degree of neutral membership of xi in L˜1 ν¨L˜1(xi):X→[0,1] is called degree of negative membership of xi in L˜1 and 0≤μ¨L˜1(xi)+η¨L˜1(xi)+ν¨L˜1(xi)≤1, ∀xi ∈ X ρL˜1(xi)=1-μ¨L˜1(xi)-η¨L˜1(xi)-ν¨L˜1(xi) is called degree of refusal membership of xi in L˜1, ∀xi ∈ X The set of all picture fuzzy subsets on universe of discourse L˜1 is denoted by PFSs(X).
Some basic operation of PFS are defined as follows: Operations in Picture Fuzzy Soft Sets [4] Let L˜1={〈μ¨L˜1(x),η¨L˜1(x),ν¨L˜1(x)〉|x∈X} and L˜2={〈μ¨L˜2(x),η¨L˜2(x),ν¨L˜2(x)〉|x∈X} be two PFSs on universe X. Then the operations between the sets L˜1 and L˜2 are defined as follows: Definition 4: Soft Set [64]
A new scientific instrument in which parameter space is finite or infinite, even if universal set is finite, is known as a soft set. It was proposed by Molodtsov [64] for parameterized uncertainty handling purposes. A mapping F:A→P(U) (F, A) is called a soft set over U, where A is set of parameters.
Definition 5: Picture Fuzzy Soft Set [65] In 2015, Yang et al [65] proposed PFSs which is combination of PFS and SS. Let E be a parametric space with a U universal set. Now, the set of all picture fuzzy sets be represented by U. In this scenario, a picture fuzzy soft set (PFSS) is a pair (F, A) where A ⊆ E and F is a mapping as shown: F:A→PF(U).
Definition 6: Hypersoft Set [6] In 2018, Smarandache [6] put forward the concept of Hypersoft Set, which is a generalization of SS by transforming the mapping into a multi-attribute. Suppose b1, b2, ……, bn, for b ≥ 1, be n distinct traits, whose corresponding trait values are respectively the sets Q1,Q2,…‥,Qn, with Qr⋂Qs=ϕ, i ≠ j, and r,s ∈ {1, 2, …., n}. Then the pair (Ψ¨,Q1×Q2×…‥×Qn), where Ψ¨:Q1×Q2×…‥×Qn → P(U) is called a Hypersoft Set over U.
Definition 6: Fuzzy Graph [35] A fuzzy graph G is defined by ordered paired of functions of μ and ρ, and G = (μ, ρ). μ represents a fuzzy subset of V (A finite non-empty set of vertices) and ρ represents a symmetric fuzzy relation on μ, i.e., μ: V → [0, 1] and ρ: V × V → [0, 1] such that: ρ(p,q)≤min(μ(p),ρ(q)),∀p,q∈V.
Definition 7: Complete Fuzzy Graph [35] A fuzzy graph G = (μ, ρ) is called complete fuzzy graph if ρ(p,q)=min(μ(p),ρ(q)),∀p,q∈V.
Definition 8: Strong Fuzzy Graph [35] A fuzzy graph G = (μ, ρ) is called strong fuzzy graph if ρ(p,q)=min(μ(p),ρ(q)),∀p,q∈V.
Definition 9: Complement of a Fuzzy Graph [35] The complement of a fuzzy graph G = (μ, ρ) is a fuzzy graph and it is represented as Gc = (μc, ρc), where μc = μ and ρ(p,q)=min(μ(p),ρ(q))-ρ(p,q),∀p,q∈V.
Definition 10: Picture Fuzzy Graph [35] Suppose P=(V,E) be a graph. A pair D=(S,G) be a PFG where S=(μ¨S,η¨S,ν¨S) is PFS on V and G=(μ¨G,η¨G,ν¨G) is PFS on E⊆V×V. Also, for all u,v∈E μ¨G(u,v)⪯μ¨S(u)∧μ¨S(v),μ¨G(u,v)⪯μ¨S(u)∧μ¨S(v), μ¨G(u,v)⪰μ¨S(u)∨μ¨S(v).
## 5 Picture fuzzy hypersoft graph
In [65], hybrid model of a PFS and a SS is defined. Keeping that notion in mind, we introduce PFHSS as an extension of PFSS, which helps for playing a crucial role in decision-making for multi-attribute characteristics.
Definition 11: Picture Fuzzy Hypersoft Set Suppose m disjoint attribute-valued sets are p1, p2, p3, …, pm then their corresponding m distinct attributes are P1,P2,P3,....,Pm,respectively. And P=P1×P2×P3×....×Pm. A mapping is given by H¨:P→PF(U) H¨(t¨)={⟨μH¨(t¨)(j¨i),ηH¨(t¨)(j¨i),νH¨(t¨)(j¨i)⟩|(j¨i)∈U}foranyt¨∈P then pair (H¨,P) represent PFHSS.
Definition 12: Picture Fuzzy Hypersoft Graph Suppose P=(C,S) be a graph. A pair Y=(U,W) be a PFHSG where U=(μ˘U,η˘U,ν˘U) is PFHSS on C and W=(μ˘W,η˘W,ν˘W) is PFHSS on S⊆U×U, for all m,n∈C μ˘W(m,n)⪯μ˘U(m)∧μ˘U(n), μ˘W(m,n)⪯μ˘U(m)∧μ˘U(n), μ˘W(m,n)⪰μ˘U(m)∨μ˘U(n) and 0⪯μ˘U+η˘U+ν˘U⪯1 Example of Picture Fuzzy Hypersoft Graph Consider Y=(U,W) be a PFHSG such that U={o¨1,o¨2,o¨3,o¨4} and W={o¨1o¨2,o¨1o¨3,o¨1o¨4,o¨2o¨4,o¨3o¨4}. Suppose (H¨,L˜) be PFHSS over U. Let U={o¨1,o¨2,o¨3,o¨4} be four refrigerators. Let R={a1,a2,a3} be parameters set, where each ai stands for Company, Size, and Color be the attributes values respectively, {H1, H2, H3} be attribute values against each ai.
Let H1 = {b11 = Company X, b12 = Company Y, b13 = Company Z } H2 = {b21 = Grey, b22 = White, b23 = Golden} H3 = {b31 = Small} L˜=H1×H2×H3 There are a total of 9 outcomes but for simplicity, only three outcomes have been selected for the analysis. L˜={t¨1=(b11,b21,b31),t¨2=(b12,b31),t¨3=(b12,b22,b31)} (H¨,L˜)={H¨(t¨1)={〈0.7,0.1,0.1〉/o¨1,〈0.3,0.2,0.4〉/o¨2,〈0.1,0.5,0.3〉/o¨3,〈0.4,0.1,0.3〉/o¨4},H¨(t¨2)={〈0.6,0.1,0.2〉/o¨1,〈0.4,0.1,0.3〉/o¨2,〈0.7,0.1,0.1〉/o¨3,〈0.2,0.5,0.2〉/o¨4,},H¨(t¨3)={〈0.2,0.3,0.5〉/o¨1,〈0.1,0.1,0.6〉/o¨2,〈0.2,0.1,0.7〉/o¨3,〈0.8,0.1,0.1〉/o¨4}}
The vertices set of PFHSG presented in Table 1.
**Table 1**
| U | t¨1 | t¨2 | t¨3 |
| --- | --- | --- | --- |
| o¨1 | 〈0.7, 0.1, 0.1〉 | 〈0.6, 0.1, 0.2〉 | 〈0.2, 0.3, 0.5〉 |
| o¨2 | 〈0.3, 0.2, 0.4〉 | 〈0.4, 0.1, 0.3〉 | 〈0.1, 0.1, 0.6〉 |
| o¨3 | 〈0.1, 0.5, 0.3〉 | 〈0.7, 0.1, 0.1〉 | 〈0.2, 0.1, 0.7〉 |
| o¨4 | 〈0.4, 0.1, 0.3〉 | 〈0.2, 0.5, 0.2〉 | 〈0.8, 0.1, 0.1〉 |
The PFHS graphs Y1=(H¨(t¨1),G¨(t¨1)), and Y2=(H¨(t¨2),G¨(t¨2)) are shown in Figs 1 and 2, respectively.
**Fig 1:** *Picture fuzzy hypersoft graph Y1=(H¨(t¨1),G¨(t¨1)).* **Fig 2:** *Picture fuzzy hypersoft graph Y2=(H¨(t¨2),G¨(t¨2)).*
Definition 13: Strong Picture Fuzzy Hypersoft Graph A PFHSS graph Y=(U,W) is said to be strong if μ˘W(m,n)=μ˘U(m)∧μ˘U(n),μ˘W(m,n)=μ˘U(m)∧μ˘U(n),μ˘W(m,n)=μ˘U(m)∨μ˘U(n). Fig 3 represents the above presented structure with its edge set presented in Table 2.
**Fig 3:** *Strong picture fuzzy hypersoft graph.* TABLE_PLACEHOLDER:Table 2 Definition 14: Complete Picture Fuzzy Hypersoft Graph A PFHSS graph Y=(U,W) is said to be complete if an edge lies between every two vertices of Y. μ˘W(m,n)=μ˘U(m)∧μ˘U(n),μ˘W(m,n)=μ˘U(m)∧μ˘U(n),μ˘W(m,n)=μ˘U(m)∨μ˘U(n). Fig 4 represents the above presented structure.
**Fig 4:** *Complete picture fuzzy hypersoft graph.*
Definition 15: Cartesian Product of Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graph. The Cartesian product of two PFHSG Y1=(U1,W1) and Y2=(U2,W2) is denoted Y1×Y2 and defined by (U,W) where U=(μ˘U,η˘U,ν˘U) and W=(μ˘W,η˘W,ν˘W) are two PFHS on V=V1×V2, and E={(l,l2),(l,m2)|l∈V1,l2m2∈E2}∪{(l1,n),(m1,n)|n∈V2,l1m1∈E1} respectively which satisfies the following condition: Consider two PFHSG Y1=(U1,W1) and Y2=(U2,W2) are shown in Figs 5 and 6. The cartesian product of Y1=(U1,W1) and Y2=(U2,W2) is shown in Fig 7.
**Fig 5:** *Y1=(U1,W1)
.* **Fig 6:** *Y2=(U2,W2)
.* **Fig 7:** *Cartesian product of two picture fuzzy hypersoft graphs.*
Definition 16: Composition of Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graphs. The Composition of Y1=(W1,U1) and Y2=(W2,U2) is denoted Y1|Y2| and defined by (W,U), where W=(μ˘W,η˘W,ν˘W) and U=(μ˘U,η˘U,ν˘U) are two PFS on V=V1×V2, and E={(l,l2),(l,m2)|l∈V1,l2m2∈E2}∪{(l1,n),(m1,n)|n∈V2,l1m1∈E1}∪{(l1,l2),(m1,m2)|l2m2∈V2l2≠m2,l1m1∈E1} respectively which satisfy the following conditions: The composition of Y1=(U1,W1) and Y2=(U2,W2) is shown in Fig 8.
**Fig 8:** *Composition of picture fuzzy hypersoft graphs.*
Definition 17: Union of Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graph. The union of two PFGs Y1=(W1,U1) and Y2=(W2,U2) is denoted Y1∪Y2 and defined by (W,U) where W=(μ˘W,η˘W,ν˘W) on V=V1∪V2 and U=(μ˘U,η˘U,ν˘U) on E=E1∪E2.
Consider two PFHSG Y1=(U1,W1) and Y2=(U2,W2) are shown in Figs 9 and 10. The union of Y1=(U1,W1) and Y2=(U2,W2) is shown in Fig 11.
**Fig 9:** *Y1=(U1,W1)
.* **Fig 10:** *Y2=(U2,W2)
.* **Fig 11:** *Union of PFHSG.*
Definition 18: Joint of two Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graphs. The joint of two PFHSGs Y1=(W1,U1) and Y2=(W2,U2) is denoted Y1+Y2 and defined by (W,U) where W=(μ˘W,η˘W,ν˘W) on V=V1∪V2 and U=(μ˘U,η˘U,ν˘U) on E=E1∪E2∪E3.
Consider two PFHSG Y1=(U1,W1) and Y2=(U2,W2) are shown in Figs 12 and 13. The joint of Y1=(U1,W1) and Y2=(U2,W2) is shown in Fig 14.
**Fig 12:** *Y1=(U1,W1)
.* **Fig 13:** *Y2=(U2,W2)
.* **Fig 14:** *Join of PFHSG.*
Definition 19: Joint of Picture Fuzzy Hypersoft Graph Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graph. The direct product of Y1=(W1,U1) and Y2=(W2,U2) is denoted Y1⊗Y2 and defined by (W,U) where W=(μ˘W,η˘W,ν˘W) and U=(μ˘U,η˘U,ν˘U) are two PFS on V=V1×V2, and E={(l1,m1),(l2,m2)|l1m1∈E1,l2m2∈E2} which satisfies the following condition Consider two PFHSG Y1=(U1,W1) and Y2=(U2,W2) are shown in Figs 15 and 16. The direct product of Y1=(U1,W1) and Y2=(U2,W2) is shown in Fig 17.
**Fig 15:** *Y1=(U1,W1)
.* **Fig 16:** *Y2=(U2,W2)
.* **Fig 17:** *Direct product of picture fuzzy hypersoft graphs.*
Definition 20: Direct Product of Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graph. The lexicographic product of Y1=(W1,U1) and Y2=(W2,U2) is denoted Y1⊙Y2 and defined by (W,U) where W=(μ˘W,η˘W,ν˘W) and U=(μ˘U,η˘U,ν˘U) are two PFS on V=V1×V2, and E={(l,l2),(l,m2)|l∈V1,l2m2∈E2}∪{(l1,n),(m1,n)|n∈V2,l1m1∈E1} respectively which satisfy the following conditions: Definition 21: Strong Product of Picture Fuzzy Hypersoft Graphs Suppose Y1⋆=(V1,E1) and Y1⋆=(V2,E2) be two graph. The strong product of Y1=(W1,U1) and Y2=(W2,U2) is denoted (Y1Y2)• and defined by (W,U) where W=(μ˘W,η˘W,ν˘W) and U=(μ˘U,η˘U,ν˘U) are two PFS on V=V1×V2, and E={(l,l2),(l,m2)|l∈V1,l2m2∈E2}∪{(l1,n),(m1,n)|n∈V2,l1m1∈E1}∪{(l1,l2),(m1,m2)|l2m2∈V2l2≠m2,l1m1∈E1} respectively which satisfy the following conditions:
## 6 Application of picture fuzzy hypersoft graphs in decision support analysis
PFHSS is a vital hybrid structure to deal with problems faced in the real world. Studying inconsistent, incomplete, indeterminate, and multi-argument facts is highly beneficial when discussing problems in medical science, engineering, and business studies. Due to its versatile nature and wide range of applications, PFHSS has become an interesting and inventive subject for researchers. So, PFHSG can provide solutions to these kinds of problems. Here, the PFHS graph is used to deal with MADMP, and an algorithm is proposed to outline the thought process that illustrates the model’s working.
Algorithm: Step 1: Calculate the impact coefficient between the attributes K by $ij=μ˘Wij+η˘Wij+ν˘Wij3, $ij={μ˘Wij,η˘Wij,ν˘Wij} is the PFHS graph edge between nodes K for i, $j = 1$, 2, …, n, and $ij=$ji.
Step 2: Find the attribute of the alternative s˘i below: s˘i=(μˇWi,ηˇWi,νˇWi)=13∑$j = 1$nwi(∑$$p \leq 1$$n$pjcip) Step 3: Computation of the score functions using the follwoing equation: score (s˘i)=12[1+μˇWi-2ηˇWi-νˇWi].
Step 4: Selection of the best alternative by ranking the alternatives.
Step 5: Reporting of results.
## 7 Product risk assessment based for optimal design of investment strategies
In this section, numerical examples for the PFHSG MADM problem with picture fuzzy and hypersoft information are used to present the application of the proposed algorithms. An owner of supermarket wants to invest money and buy a variety of products based on numerous factors illustrated in the figure below. Now, a smart investment would be to to buy the products that have highest optimal value in terms of Return on investment, environmental factors, and shelf life of the product etc. Based on these factors, we propose an algorithm based on PFHS graphical structure to find the risk in investment when buying each product while addressing each of the factors addressed in the figure. The problem is illustrated below: Suppose S˘={s1˘=Bakery, s2˘=Butchershop, s3˘=Dairystore, s4˘=Pharmacy} are four measurable alternatives for a businessperson starting a new business. Let E = {a1, a2, a3, a4} be attributes set where each ai stands for Gross margin, Product monthly sales, Customer satisfactions and environmental impact analysis respectively, whose corresponding attribute values are {H1, H2, H3} respectively. In order for the businessperson to efficiently invest in these four alternative investment options, a risk analysis must be done that addresses all the factors listed below to generate the quickest return on investment while also considering factors like the market image of the store and quality of the products.
Let the factors being considered for the risk analysis assessment be: Now, L˜=H1×H2×H3×H4 There are one hundred and eight outcomes but for simplicity, only three outcomes are explored and risk assessment is done on these three outcomes. The edge set of the outcomes is presented in Table 3. L˜={t˘1=(b11,b22,b31,b41),t˘2=(b12,b23,b33,b41),t˘3=(b13,b22,b33,b43)} (H˘,L˜)={H˘(t˘1)={〈0.5,0.1,0.1〉/s˘1,〈0.3,0.2,0.2〉/s˘2,〈0.3,0.2,0.3〉/s˘3,〈0.5,0.1,0.2〉/s˘4},H˘(t˘2)={〈0.5,0.1,0.1〉/s˘1,〈0.3,0.1,0.2〉/s˘2,〈0.8,0.1,0.1〉/s˘3,〈0.4,0.1,0.2〉/s˘4,},H˘(t˘3)={〈0.3,0.2,0.1〉/s˘1,〈0.1,0.1,0.6〉/s˘2,〈0.2,0.1,0.7〉/s˘3,〈0.4,0.1,0.1〉/s˘4}} is picture fuzzy hypersoft set.
**Table 3**
| U | t˘1 | t˘2 | t˘3 |
| --- | --- | --- | --- |
| s˘1 | 〈0.5, 0.1, 0.1〉 | 〈0.5, 0.1, 0.1〉 | 〈0.3, 0.2, 0.1〉 |
| s˘2 | 〈0.3, 0.2, 0.2〉 | 〈0.3, 0.1, 0.2〉 | 〈0.1, 0.1, 0.6〉 |
| s˘3 | 〈0.3, 0.2, 0.3〉 | 〈0.8, 0.1, 0.1〉 | 〈0.2, 0.1, 0.7〉 |
| s˘4 | 〈0.5, 0.1, 0.2〉 | 〈0.4, 0.1, 0.2〉 | 〈0.4, 0.1, 0.1〉 |
Let M = (cij)4×3 be a picture fuzzy hypersoft decision matrix: M=(⟨0.5,0.1,0.1⟩⟨0.5,0.1,0.1⟩⟨0.3,0.2,0.1⟩⟨0.3,0.2,0.2⟩⟨0.3,0.1,0.2⟩⟨0.1,0.1,0.6⟩⟨0.3,0.2,0.3⟩⟨0.8,0.1,0.1⟩⟨0.2,0.1,0.7⟩⟨0.5,0.1,0.2⟩⟨0.4,0.1,0.2⟩⟨0.4,0.1,0.1⟩) Also, a complete graph C=(K,D) represents a relationship between attribute values {t˘1,t˘2,t˘3} shown in Fig 18, where K={t˘1,t˘2,t˘3} and D={e12=(t˘1,t˘2),e13=(t˘1,t˘3),e23=(t˘2,t˘3)} are the vertices and edge sets, respectively. The sets are as follows: {t˘1=〈0.3,0.1,0.4〉, t˘2=〈0.5,0.2,0.2〉, t˘3=〈0.2,0.2,0.2〉} and {e12 = 〈0.2, 0.1, 0.4〉, e13 = 〈0.1, 0.1, 0.5〉, e23 = 〈0.2, 0.2, 0.2〉}.
**Fig 18:** *Picture fuzzy hypersoft graphs.*
The weight vector W of L˜ is given by W=(w1,w2,w3)=(0.38,0.32,0.30).
Step 1: Computation of the impact coefficient between the attributes K: $12=μ˘W12+η˘W12+ν˘W123=0.2+0.1+0.43=0.233=0.483 $13=μ˘W13+η˘W13+ν˘W133=0.1+0.1+0.53=0.233=0.483 $23=μ˘W23+η˘W23+ν˘W233=0.2+0.2+0.23=0.2=0.447
$11=μ˘W11+η˘W11+ν˘W113=1.0+0.0+0.03=0.333=0.577 Step 2: The attributes of the alternative s˘i is calculated below: s˘1=13[w1($11c11+$21c12+$31c13)+w2($12c11+$22c12+$31c13)+w3($13c11+$23c12+$33c13)]=13[0.38((0.577)〈0.5,0.1,0.1〉+(0.483)〈0.5,0.1,0.1〉+(0.483)〈0.3,0.2,0.1〉)+0.32((0.483)〈0.5,0.1,0.1〉+(0.577)〈0.5,0.1,0.1〉+(0.483)〈0.3,0.2,0.1〉)+0.30((0.483)〈0.5,0.1,0.1〉+(0.447)〈0.5,0.1,0.1〉+(0.577)〈0.3,0.2,0.1〉)]=(0.200,0.068,0.051) s˘2=13[w1($11c21+$21c22+$31c23)+w2($12c21+$22c22+$31c23))+w3($13c21+$23c22+$33c23)]=13[0.38((0.577)〈0.3,0.2,0.2〉+(0.483)〈0.3,0.1,0.2〉+(0.483)〈0.1,0.1,0.6〉)+0.32((0.483)〈0.3,0.2,0.2〉+(0.577)〈0.3,0.1,0.2〉+(0.483)〈0.1,0.1,0.6〉)+0.30((0.483)〈0.3,0.2,0.2〉+(0.447)〈0.3,0.1,0.2〉+(0.577)〈0.1,0.1,0.6〉)]=(0.119,0.085,0.153) s˘3=13[w1($11c31+$21c32+$31c33)+w2($12c31+$22c32+$31c33))+w3($13c31+$23c32+$33c33)]=13[0.38((0.577)〈0.3,0.2,0.3〉+(0.483)〈0.8,0.1,0.1〉+(0.483)〈0.2,0.1,0.7〉)+0.32((0.483)〈0.3,0.2,0.3〉+(0.577)〈0.8,0.1,0.1〉+(0.483)〈0.2,0.1,0.7〉)+0.30((0.483)〈0.30.2,0.3〉+(0.447)〈0.8,0.1,0.1〉+(0.577)〈0.2,0.1,0.7〉)]=(0.223,0.068,0.187) s˘4=13[w1($11c41+$21c42+$31c43)+w2($12c41+$22c42+$31c42))+w3($13c41+$23c42+$33c43)]=13[0.38((0.577)〈0.5,0.1,0.2〉+(0.483)〈0.4,0.1,0.2〉+(0.483)〈0.4,0.1,0.1〉)+0.32((0.483)〈0.5,0.1,0.2〉+(0.577)〈0.4,0.1,0.2〉+(0.483)〈0.4,0.1,0.1〉)+0.30((0.483)〈0.5,0.1,0.2〉+(0.447)〈0.4,0.1,0.1〉+(0.577)〈0.4,0.1,0.2〉)]=(0.221,0.051,0.085) Step 3: Computation of Final score functions for ranking: score(s˘1)=12[1+μˇW1-2ηˇW1-νˇW1]=12[1+0.200-2(0.068)-0.051]=0.438 score(s˘2)=12[1+μˇW2-2ηˇW2-νˇW2]=12[1+0.200-2(0.068)-0.051]=0.398 score(s˘3)=12[1+μˇW3-2ηˇW3-νˇW3]=12[1+0.200-2(0.068)-0.051]=0.450 score(s˘4)=12[1+μˇW4-2ηˇW4-νˇW4]=12[1+0.200-2(0.068)-0.051]=0.517 Hence (s˘4) is best choice because score (s˘4)⪰ score (s˘3)⪰ score (s˘1)⪰ score (s˘2). This provides a clear indication that the Pharmacy section of the supermarket is the best for investment as it has the lowest risk for loss of investment while considering the factors listed in Fig 19. Following the *Pharmacy is* the Dairy products section of the store, with the Bakery at the third position and the Meat section at the fourth. This example only describes a minimal example for an explanation but can address the sub-attributes of attributes in a graphical manner which are in most cases neglected (Other than Hypersoft Structures) due to the inability to address all the factors addressed in Table 4. This tool is great for obtaining a graphical depiction for development and solving a decision-making problem. It works great as a decision support system as it has potential applications in just about any field, from medical diagnostic systems to mechanical engineering problems.
**Fig 19:** *Factors considered for the risk assessment for supermarket products.* TABLE_PLACEHOLDER:Table 4
## 8 Comparative analysis
A comparative analysis of the proposed structure is provided in Table 4. Previously available structures in literature can address some attributes using fuzzy parameters like membership and non-membership functions, but they fall short when there are sub-attributes. These are then addressed using Hypersoft structures that can deal with such issues, and the proposed hybrid structure covers the whole spectrum of characteristics allowing for deep and thorough analysis.
## 9 Conclusion
Risk evaluation has always been of great interest for individuals wanting to invest in various businesses, especially in the marketing and product sale centres. A finely detailed evaluation of the risk factor can lead to better returns in terms of investment in a particular business. This presents a MADM problem requiring numerous factors to be addressed simultaneously. Graph theory is an essential tool for solving MADM problems in various kinds of hybrid structures like PFS, PFSS, HS, and PFHSS. Picture fuzzy hypersoft graph is a new concept in the theory of graphs. PFHS graph has been applied to solve problems containing consistent, inconsistent, and multi-argument information. This article introduces basic operations like union, composition, cartesian product, direct product, and joint of PFHS graph. We proposed an algorithm in which a relationship is created among attributes and alternatives in the decision process to obtain the desired result. The structure is then used to address a risk analysis problem for investment distribution for product buying in a supermarket. In the future, we can extend this structure in neutrosophic, spherical and T-spherical hypersoft sets while applying the structures to actual data for real-world applications.
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---
title: Association between interleukin-10 gene polymorphisms (rs1800871, rs1800872,
and rs1800896) and severity of infection in different SARS-CoV-2 variants
authors:
- Sattar Jabbar Abbood Abbood
- Enayat Anvari
- Abolfazl Fateh
journal: Human Genomics
year: 2023
pmcid: PMC9990970
doi: 10.1186/s40246-023-00468-6
license: CC BY 4.0
---
# Association between interleukin-10 gene polymorphisms (rs1800871, rs1800872, and rs1800896) and severity of infection in different SARS-CoV-2 variants
## Abstract
### Background
Polymorphisms in the interleukin-10 (IL10) gene have been linked to the severity of the patients infected with the viral infections. This study aimed to assess if the IL10 gene polymorphisms rs1800871, rs1800872, and rs1800896 were linked to coronavirus disease 19 (COVID-19) mortality in different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in the Iranian population.
### Methods
For genotyping IL10 rs1800871, rs1800872, and rs1800896, this study used the polymerase chain reaction-restriction fragment length polymorphism method in 1,734 recovered and 1,450 deceased patients.
### Results
The obtained finding indicated IL10 rs1800871 CC genotype in the Alpha variant and CT genotype in the Delta variant had a relationship with COVID-19 mortality; however, there was no association between rs1800871 polymorphism and the Omicron BA.5 variant. The COVID-19 mortality rate was associated with IL10 rs1800872 TT genotype in the Alpha and Omicron BA.5 variants and GT in the Alpha and Delta variants. The COVID-19 mortality rate was associated with IL10 rs1800896 GG and AG genotypes in the Delta and Omicron BA.5; nevertheless, there was no association between rs1800896 polymorphism with the Alpha variant. According to the obtained data, the GTA haplotype was the most common of haplotype in different SARS-CoV-2 variants. The TCG haplotype was related to COVID-19 mortality in the Alpha, Delta and Omicron BA.5 variants.
### Conclusion
The IL10 polymorphisms had an impact on COVID-19 infection, and these polymorphisms had different effects in various SARS-CoV-2 variants. To verify the obtained results, further studies should be conducted on various ethnic groups.
## Introduction
The coronavirus disease 2019 (COVID-19) pandemic has been a persistent threat to public health for over a few years. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variations that are more fatal and contagious are a serious worry, especially as causative therapy is still lacking and vaccination coverage rates are lower than expected. Additionally, vaccination-induced immunity is probably only going to last one or two seasons. Identifying populations at high risk of developing severe COVID-19 and putting protective measures in place for those populations is one way to save lives. The COVID-19 mortality toll was the highest in the elderly, obese, male, immunocompromised, tobacco users, chronic disease patients, socioeconomically disadvantaged, black people, and cancer [1, 2]. Additionally, interindividual genetic differences could be a factor in COVID-19 cases that are more severe [3–9].
Recent research has revealed that the amount of inflammatory cytokines is elevated in COVID-19. According to a literature review,, interleukins-2 (IL2), IL6, IL7, IL10, granulocyte colony-stimulating factor (G-CSF), interferon gamma, inducible protein-10, tumor necrosis factor alpha, monocyte chemoattractant protein-1, macrophage inflammatory protein-1 all play important roles in COVID-19 development [10, 11].
The IL-10, known as a pleiotropic cytokine, has strong immunosuppressive and anti-inflammatory effects. Initially, it was thought that IL-10 was produced by T helper 2 cells; it is currently understood that IL-10 is produced by a variety of immune cells with lymphoid and myeloid origins that function in both adaptive and innate immunity [12]. Several studies show that high IL-10 expression levels predict poor outcomes in patients with COVID-19 and appear to be a distinguishing feature of hyperinflammation during severe SARS-CoV-2 infection. The IL-10 is canonically categorized as an anti-inflammatory cytokine and rises dramatically early in the course of the disease [13, 14].
Three polymorphisms, IL10 rs1800871 (− 819 T/C), rs1800872 (− 592 C/A), and rs1800896 (-1082 G/A), in the promoter region of IL10 gene have been studied more to date. Their haplotypes in different populations are related to the low or high expression of IL10 gene. Polymorphisms in the promoter region contribute genetically to interindividual variations in IL10 production. The IL10 rs1800896 (-1082 G/A) polymorphism has been found to be associated with greater IL10 serum levels and an increased risk of developing severe pneumonia [15]. Additionally, the IL10 rs1800872 (592 C > A) polymorphism of the gene causes a considerable reduction in the negative promoter function, changing IL10 transcription and mRNA production [16].
Concerning the efficacy of IL10 in regulating T-cell activity and its effects on viral infections, in this study examined three single-nucleotide polymorphisms (SNPs) in the IL10 promoter (rs1800871, rs1800872, and rs1800896) to determine how host genetic variables affect COVID-19 severity according SARS-CoV-2 variants.
## Patients definition
The current study comprised 3,184 patients with a diagnosis of COVID-19 who were referred to a teaching hospital of Ilam University of Medical Sciences, Ilam, Iran, within November 2020 to February 2022, including 1,734 recovered and 1450 deceased patients. A COVID-19 infection was deemed for all patients as a result of a positive SARS-CoV-2 laboratory test with real-time reverse transcription polymerase chain reaction (rtReal time-PCR) from the nasopharyngeal swabs. Peripheral blood samples from each patient were taken to isolate deoxyribonucleic acid (DNA) and conduct additional genetic studies.
The samples were collected in the three peaks (Alpha, Delta, and Omicron BA.5) from 14,472 positive patients based on the inclusion criteria, namely [1] patients who were willing to participate in the study and had signed a written consent form, [2] all patients who were Iranian with one ethnicity, and [3] patients who did not have any underlying comorbidities diseases, such as kidney, heart, and pulmonary diseases, hypertension, diabetes, obesity, cancer, viral infections (e.g., human immunodeficiency virus and hepatitis B and C viruses), and pregnancy.
All clinical data of patients such as real-time PCR cycle threshold (Ct) values, 25-hydroxyvitamin D, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), complete blood count (CBC), lipid profile (cholesterol, high density lipoprotein, and low density lipoprotein), liver enzymes (aspartate aminotransferase, alkaline phosphatase, and alanine aminotransferase), creatinine and uric acid were extracted from patient files, and these tests were performed when the patient entered the hospital.
According to the World Health Organization guidelines, adult COVID-19 patients were divided into three clinical course categories, mild, moderate, and severe. In this study, subjects with mild/moderate and severe/critical symptoms were considered recovered and deceased patients, respectively.
Patients with mild symptoms include those who have a fever, fatigue, cough, headache, myalgia, and fatigue but do not have dyspnea or pneumonia; patients with moderate symptoms include those who have blood oxygen saturation levels above $93\%$ on room air and evidence of pneumonia based on imaging showing up to $50\%$ lung involvement; patients with severe symptoms include those who have blood oxygen saturation levels below $93\%$ on room air and need supportive oxygen therapy.
## IL10 rs1800871, rs1800872, and rs1800896 genotyping
After genomic DNA isolation of all patients using QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany), IL10 rs1800871, rs1800872, and rs1800896 genotyping was carried out by polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) technique.
The PCR was conducted according to the following conditions: initial denaturation at 95ºC for 5 min, 37 cycles of 95ºC for 30 s, 57 ºC (rs1800896 and rs1800871) and 61ºC (rs1800872) for 40 s, 72ºC for 45 s, and final extension at 72ºC for 10 min. The specific primers for each location were as follows: for IL10 rs1800871 was f-5'-CTCGCCGCAACCCAACTGGC-3' and r-5'-TCTTACCTATCCCTACTTCC-3' and for rs1800872 was f-5'-GGTGAGCACTACCTGACTAGC-3' and r-5'-CCTAGGTCACAGTGACGTGG-3', and for rs1800896 was f-5'-CTCGCCGCAACCCAACTGGC-3' and r-5'-TCTTACCTATCCCTACTTCC-3'. In a 25 ml reaction mixture, RFLP digestion was carried out using RseI (Thermo, USA), RsaI (New England Biolabs, USA), and MnII (New England Biolabs, USA) for the rs1800871, rs1800872, and rs1800896, respectively. The reaction mixture was incubated at 37 °C for 16–18 h before being separated on a $2\%$ agarose gel electrophoresis.
The undigested PCR products with 209-bp for IL10 rs1800871 represented the T allele. The existence of the C allele was established by visualizing two 125- and 84-bp-sized PCR product fragments that had been digested. The C allele was represented by the 412-bp PCR products of rs1800872. Observing two 176- and 236-bp long fragments of the digested PCR result verified the existence of the A allele. Finally, the T allele was represented by the 134-bp undigested PCR product of rs1800896. The G allele’s presence was verified by visualizing two digested PCR product fragments with 101- and 33-bp [17]. Several samples were randomly chosen and sequenced using the Sanger sequencing method to corroborate the PCR–RFLP results.
## Statistical analyses
Statistical analysis was conducted in SPSS version 22.0 software (SPSS, Inc, Chicago, IL, USA). Using appropriate statistical analyses for continuous and discrete data (the Mann–Whitney U test and Chi-square tests), differences in demographic and clinical data between COVID-19 recovered and deceased groups were investigated. The Hardy–*Weinberg equilibrium* (HWE) was investigated using genetic data to evaluate the effectiveness of the genotyping tests. The Chi-squared test was used to compare genotype and allele count distributions among COVID-19 subgroups for each variant. In order to account for confounding variables, including SARS-CoV-2 variants, the effect of each genetic trait on the severity of COVID-19 was assessed by odds ratio (OR) with a $95\%$ confidence interval (CI) using a logistic regression model. The correlation study was performed using the SNPStats program, which also allowed for the determination of the minor allele frequency (MAF), HWE and dominant, over-dominant, co-dominant, and recessive models. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to determine which model suited the data the best. The model with the lowest AIC score was the one that performed the best (http://bioinfo.iconcologia.net/SNPStats). Statistics were considered significant at P-values less than 0.05.
## Demographics and baseline clinical characteristics of COVID-19 patients
According to Table 1, this study included three variants, the Alpha, Delta, and Omicron with 1,022, 1,026, and 1,132 patients, respectively. The Alpha (53.0 ± 12.7) and the Omicron BA.5 (53.7 ± 12.9) variants were both younger than the Delta variant (58.0 ± 11.8). In the Alpha variant, there were 479 ($46.9\%$) male and 543 ($53.1\%$) female. In the Delta variant, there were 546 ($53.2\%$) male and 480 ($46.8\%$) female patients. In the Omicron BA.5 variant, there were also 546 ($53.2\%$) male and 480 ($46.8\%$) female patients. Table 1Comparison of laboratory parameters between SARS-CoV-2 variantsSARS-CoV-2 variantsVariablesAlpha ($$n = 1$$,022)Delta ($$n = 1$$,026)Omicron BA.5 ($$n = 1$$,136)P-valueDeceased/ Improved patients$\frac{479}{543}$ (46.9/$53.1\%$)$\frac{674}{352}$ (65.7/$34.3\%$)$\frac{297}{839}$ (26.1/$73.9\%$) < 0.001*Mean age ± SD53.0 ± 12.758.0 ± 11.853.7 ± 12.90.128Gender (male/female)$\frac{525}{497}$(51.4/$48.6\%$)$\frac{546}{480}$ (53.2/$46.8\%$)$\frac{598}{538}$ (52.6/$47.4\%$)0.692ALT, IU/L (mean ± SD) (Reference range: 5–40)38.5 ± 24.840.8 ± 24.735.8 ± 24.20.001AST, IU/L (mean ± SD) (Reference range: 5–40)34.9 ± 15.534.5 ± 14.031.9 ± 14.4 < 0.001*ALP, IU/L (mean ± SD) (Reference range: up to 306)190.2 ± 84.7188.6 ± 74.0177.2 ± 83.5 < 0.001*Cholesterol, mg/dL (mean ± SD) (Reference range: 50–200)116.1 ± 34.1120.5 ± 40.5123.1 ± 39.4 < 0.001*TG, mg/dL (mean ± SD) (Reference range: 60–165)124.1 ± 54.9121.6 ± 48.8126.9 ± 55.90.245LDL, mg/dL (mean ± SD) (Reference range: up to 150)82.8 ± 45.185.3 ± 45.3104.7 ± 48.3 < 0.001*HDL, mg/dL (mean ± SD) (Reference range: > 40)32.5 ± 11.332.1 ± 11.533.6 ± 11.70.039*WBC, 109/L (mean ± SD) (Reference range: 4000–10,000)7627.3 ± 2843.27599.2 ± 2715.77704.9 ± 2807.70.297CRP, mg/L (mean ± SD) (Reference range: < 10 mg/L Negative)61.6 ± 21.563.9 ± 22.060.2 ± 21.70.122ESR, mm/1st h (mean ± SD) (Reference range: 0–15)50.1 ± 16.052.3 ± 16.049.1 ± 16.10.025FBS, mg/dL (mean ± SD) (Reference range: 70–100)107.1 ± 41.6109.8 ± 43.2106.5 ± 40.70.716Platelets × 1000/cumm (mean ± SD) (Reference range: 140,000–400,000)184 ± 71185 ± 74184 ± 690.994Uric acid, mg/dL (mean ± SD) (Reference range: 3.6–6.8)4.8 ± 1.84.4 ± 1.75.2 ± 1.8 < 0.001*Creatinine, mg/dL (mean ± SD) (Reference range: 0.6–1.4)0.9 ± 0.31.0 ± 0.30.8 ± 0.3 < 0.001*qPCR Ct value20.1 ± 6.417.4 ± 6.121.9 ± 6.0 < 0.001*25-hydroxy vitamin D, ng/mL (mean ± SD) (Sufficiency: 21–150)24.2 ± 12.821.8 ± 10.333.0 ± 13.40.029*IL10 rs1800871 < 0.001*TT271 ($26.5\%$)498 ($48.5\%$)391 ($34.4\%$)CT550 ($53.8\%$)377 ($36.7\%$)619 ($54.5\%$)CC201 ($19.7\%$)151 ($14.8\%$)126 ($11.1\%$)IL10 rs1800872 < 0.001*GG474 ($46.4\%$)532 ($51.9\%$)269 ($23.7\%$)GT396 ($38.7\%$)386 ($37.6\%$)711 ($62.6\%$)TT152 ($14.9\%$)108 ($10.5\%$)156 ($13.7\%$)IL10 rs1800896 < 0.001*AA358 ($35.0\%$)510 ($49.7\%$)575 ($50.6\%$)AG550 ($53.8\%$)338 ($32.9\%$)511 ($45.0\%$)GG114 ($11.2\%$)178 ($17.4\%$)50 ($4.4\%$)ALT Alanine aminotransferase, AST Aspartate aminotransferase, ALP Alkaline phosphatase, TG Triglyceride, LDL Low density lipoprotein, HDL High density lipoprotein, WBC White blood cells, CRP C-reactive protein, ESR Erythrocyte sedimentation rate, FBS Fasting blood glucose, SD Standard deviation, SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2, IL10 interleukins 10. * Statistically significant (< 0.05) The 25-hydroxy vitamin D rate was significantly different between the Alpha, Delta, and Omicron BA.5 variants ($$P \leq 0.029$$) and was (24.2 ± 12.8, 21.8 ± 10.3, and 33.0 ± 13.4), respectively. Compared to the Alpha (20.1 ± 6.4) and Omicron BA.5 (21.9 ± 6.0) variants, the mean qPCR Ct values in the Delta variation (17.4 ± 6.1) were greater ($P \leq 0.001$).
## COVID-19 mortality adjusted by SARS-CoV-2 variants and IL10 polymorphisms rs1800871, rs1800872, and rs1800896
The IL10 rs1800871 CC genotype, compared to other genotypes, was significantly related to COVID-19 mortality. In IL10 rs1800872 and rs1800896 polymorphisms, the patients with TT and GG genotypes had a higher COVID-19 death rate.
Table 2 shows the findings of the inheritance model analysis for IL10 rs1800871, rs1800872, and rs1800896 polymorphisms in patient samples. The Codominant model for all three SNPs with the lowest AIC and BIC in studied patients was the best fitting ones. The IL10 rs1800871 CC genotype was correlated with a higher risk of COVID-19 mortality ($P \leq 0.0001$, OR 3.59, $95\%$ CI 2.82–4.56). In IL10 rs1800872 TT genotype ($P \leq 0.0001$, OR 3.39, $95\%$ CI 2.65–4.35) and GG in rs1800896 ($P \leq 0.0001$, OR 2.65, $95\%$ CI 2.04–3.45) were correlated to a higher risk of COVID-19 mortality. Table 2IL10 gene polymorphisms association with COVID-19 mortality adjusted by SARS- CoV-2 variantsIL10 rs1800871GroupsModelGenotypeRecovered patientsDeceased patientsOR ($95\%$ CI)P-valueAICBICAlleleT2281 ($66.0\%$)1585 ($55.0\%$)––––C1187 ($34.0\%$)1315 ($45.0\%$)––––CodominantT/T716 ($41.2\%$)444 ($30.6\%$)1.00 < 0.0001*3927.23957.6C/T849 ($49.0\%$)697 ($48.1\%$)1.78 (1.50–2.12)C/C169 ($9.8\%$)309 ($21.3\%$)3.59 (2.82–4.56)DominantT/T716 ($41.2\%$)444 ($30.6\%$)1.00 < 0.0001*3963.53987.7C/T- C/C1018 ($58.8\%$)1006 ($69.4\%$)2.10 (1.79–2.48)RecessiveT/T-C/T1565 ($90.2\%$)1141 ($78.7\%$)1.00 < 0.0001*3970.13994.3C/C169 ($9.8\%$)309 ($21.3\%$)2.53 (2.04–3.13)OverdominantT/T-C/C885 ($51.0\%$)753 ($51.9\%$)1.000.0234040.64064.8C/T849 ($49.0\%$)697 ($48.1\%$)1.19 (1.02–1.38)Minor allele frequency (C)0.340.45––––IL10 rs1800872AlleleG2290 ($66.0\%$)1753 ($60.0\%$)––––T1178 ($34.0\%$)1147 ($40.0\%$)––––CodominantG/G703 ($40.5\%$)572 ($39.5\%$)1.00 < 0.0001*3948.03978.3G/T884 ($51.0\%$)609 ($42.0\%$)1.25 (1. 06–1.48)T/T147 ($8.5\%$)269 ($18.6\%$)3.39 (2.65–4.35)DominantG/G703 ($40.5\%$)572 ($39.5\%$)1.00 < 0.0001*4014.94039.1G/T-T/T1031 ($59.5\%$)878 ($60.5\%$)1.56 (1.33–1.83)RecessiveG/G-G/T1587 ($91.5\%$)1181 ($81.5\%$)1.00 < 0.0001*3953.03977.2T/T147 ($8.5\%$)269 ($18.6\%$)2.99 (2.38–3.75)OverdominantG/G-T/T850 ($49.0\%$)841 ($58.0\%$)1.000.214044.24068.45G/T884 ($51.0\%$)609 ($42.0\%$)0.91 (0.78–1.06)Minor allele frequency (T)0.340.40––––IL10 rs1800896AlleleA2530 ($73.0\%$)1755 ($61.0\%$)––––G938 ($27.0\%$)1145 ($39.0\%$)––––CodominantA/A912 ($52.6\%$)531 ($36.6\%$)1.00 < 0.0001*3954.13984.4A/G706 ($40.7\%$)693 ($47.8\%$)1.95 (1. 66–2.29)G/G116 ($6.7\%$)226 ($15.6\%$)2.65 (2.04–3.45)DominantA/A A/G-G/G912 ($52.6\%$)531 ($36.6\%$)1.00 < 0.0001*3957.53981.8822 ($47.4\%$)919 ($63.4\%$)2.07 (1.78–2.42)RecessiveA/A-A/G1618 ($93.3\%$)1224 ($84.4\%$)1.00 < 0.0001*4018.64042.8G/G116 ($6.7\%$)226 ($15.6\%$)1.91 (1.49–2.44)OverdominantA/A-G/G1028 ($59.3\%$)757 ($52.2\%$)1.00 < 0.0001*4007.94032.2A/G706 ($40.7\%$)693 ($47.8\%$)1.61 (1.38–1.88)Minor allele frequency (G)0.270.39––––COVID-19 Coronavirus disease, OR Odds ratios, CI Confidence intervals, IL10 Interleukins 10, AIC Akaike information criterion, BIC Bayesian information criterion, OR Odds ratios, CI Confidence intervals; *Statistically significant (< 0.05) The IL10 rs1800871 ($$P \leq 0.33$$), rs1800872 ($$P \leq 0.54$$), and rs1800896 ($$P \leq 0.94$$) polymorphisms in recovered and deceased patients were compatible with the HWE. The MAF for IL10 rs1800871 (C), rs1800872 (T) and rs1800896 (G) polymorphisms in recovered patients was lower than those in recovered ones.
## IL10 polymorphisms rs1800871, rs1800872, and rs1800896 frequencies in SARS-CoV-2 variants
The results of this study showed that the mortality rate was significantly higher in the Delta variant than in the other two variants ($P \leq 0.001$).
Table 1 lists the frequency of IL10 rs1800871, rs1800872, and rs1800896 genotypes in different SARS-CoV-2 variants. Briefly, in IL10 rs1800871 polymorphism, the frequency of TT, CT, and CC in the Alpha variant was 271 ($26.5\%$), 550 ($53.8\%$), and 201 ($19.7\%$), respectively. In the Delta variant, the frequencies were 498 ($48.5\%$), 377 ($36.7\%$), and 151 ($14.8\%$), respectively. In the Omicron variant, the frequencies were 391 ($34.4\%$), 619 ($54.5\%$), and 126 ($11.1\%$), respectively.
In IL10 rs1800872 polymorphism, the frequency of GG, GT, and TT in the Alpha variant was 474 ($46.4\%$), 396 ($38.7\%$), and 152 ($14.9\%$), respectively. In the Delta variant was 532 ($51.9\%$), 386 ($37.6\%$), and 108 ($10.5\%$) and in the Omicron BA.5 was 269 ($23.7\%$), 711 ($62.6\%$), and 156 ($13.7\%$), respectively.
In IL10 rs1800896 polymorphism, the frequency of AA, AG, and GG in the Alpha variant was 358 ($35.0\%$), 550 ($53.8\%$), and 114 ($11.2\%$), respectively. In the Delta variant was 510 ($49.7\%$), 338 ($32.9\%$), and 178 ($17.4\%$) and in the Omicron BA.5 was 575 ($50.6\%$), 511 ($45.0\%$), and 50 ($4.4\%$), respectively (Table 1).
After adjusting the association of IL10 rs1800871 polymorphism with SARS-CoV-2 variants, the CC genotype (OR 3.92, $95\%$ CI 2.64–5.82) in the Alpha variant and CT genotype (OR 1.32, $95\%$ CI 1.01–1.73) in the Delta variant had a relationship with COVID-19 mortality; however, there was no association between rs1800871 polymorphism with the Omicron BA.5 variant (Table 3).Table 3IL10 rs1800871, rs1800872, and rs1800896 genotypes association with SARS-CoV-2 variantsVariantsrs1800871 GenotypesRecovered patientsDeceased patientsOR ($95\%$ CI)AlphaT/T1601111.00C/T3292210.97 (0.72–1.30)C/C541473.92 (2.64–5.82)DeltaT/T1653331.00C/T1012761.35 (1.01–1.82)C/C8665–Omicron BA.5T/T41911.00C/T29199–C/C3897–Variantsrs1800872 GenotypesRecovered patientsDeceased patientsOR ($95\%$ CI)AlphaG/G2811931.00G/T1991971.44 (1.10–1.89)T/T63892.06 (1.42–2.98)DeltaG/G2243081.00G/T763102.97 (2.19–4.02)T/T52560.78 (0.52–1.19)Omicron BA.5G/G198711.00G/T602102–T/T3212410.81 (6.73–17.36)Variantsrs1800896 GenotypesRecovered patientsDeceased patientsOR ($95\%$ CI)AlphaA/A1821761.00A/G3082420.81 (0.62–1.06)G/G53611.19 (0.78–1.82)DeltaA/A2112991.00A/G1072311.52 (1.14–2.03)G/G341442.99 (1.98–4.52)Omicron BA.5A/A519561.00A/G2912207.01 (5.05–9.71)G/G29216.71 (3.59–12.55)SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2, IL10 Interleukins 10, OR Odds ratios, CI Confidence intervals The COVID-19 mortality rate was associated with IL10 rs1800872 TT genotype in the Alpha (OR 2.06, $95\%$ CI 1.42–2.98) and Omicron BA.4 (OR 10.81, $95\%$ CI 6.73–17.36) variants and GT in the Alpha (OR 1.44, $95\%$ CI 1.10–1.89) and Delta (OR 2.97, $95\%$ CI 2.19–4.02) variants (Table 3).
The COVID-19 mortality rate was associated with IL10 rs1800896 GG genotype in the Delta (OR 1.52, $95\%$ CI 1.14–2.03) and Omicron BA.4 (OR 6.71, $95\%$ CI 3.59–12.55) variants and AG in the Delta (OR 2.99, $95\%$ CI 1.98–4.52) and Omicron BA.4 (OR 7.01, $95\%$ CI 5.05–9.71) variants; however, there was no association between rs1800896 polymorphism with the Alpha variant (Table 3).
According to the obtained data of the current study, the GTA haplotype was the most common of haplotype in different SARS-CoV-2 variants. The TCG haplotype was related to COVID-19 mortality in the Alpha (OR 1.34, $95\%$ CI 1.09–1.65), Delta (OR 1.33, $95\%$ CI 1.06–1.67) and Omicron BA.5 (OR 35.92, $95\%$ CI 21.84–59.07) variants. The likelihood of death in patients with the Omicron BA.5 variant was 35-fold, compared to other variants. The GCA haplotype for the Alpha variant (OR 8.02, $95\%$CI 4.91–13.08) was statistically significant. The TCA haplotype for the Alpha (OR 58.26, $95\%$CI 7.48–79.96) and Omicron BA.5 (OR 19.44, $95\%$CI 11.15–33.87) variants was observed as statistically significant. The TTA and GCG haplotypes were associated with the mortality rate in the Omicron BA.5 (OR 7.08, $95\%$CI 3.73–13.44) and Delta (OR 3.55, $95\%$CI 1.28–9.88) variants, respectively (Table 4).Table 4SARS-CoV-2 variants and IL10 rs1800871, rs1800872, and rs1800896 haplotypesAlphaDeltaOmicronHaplotypesFrequencyOR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)GTA0.54861.001.001.00TCG0.28171.34 (1.09–1.65)1.33 (1.06–1.67)35.92 (21.84–59.07)GCA0.04438.02 (4.91–13.08)––TCA0.041558.26 (7.48–79.96)–19.44 (11.15–33.87)TTA0.0384––7.08 (3.73–13.44)GCG0.0253–3.55 (1.28–9.88)–GTG0.0166–––SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2, IL10 Interleukins 10, OR Odds ratios, CI Confidence intervals
## Discussion
The current study used different SARS-CoV-2 variants to examine the genetic susceptibility of the host to COVID-19 mortality. To ascertain if IL10 rs1800871, rs1800872, and rs1800896 polymorphisms are connected to the vulnerability to COVID-19 mortality according to different SARS-CoV-2 variants, the alleles and their genotypes were investigated.
In patients with COVID-19, the allele C (0.36) for the IL10 rs1800871 as MAF was directly correlated with death. This amount was equal to Asian (0.317), other Asian (0.379), and East Asian (0.293) and was different from other regions in European (0.738), African (0.594), and South Asian (0.585) (https://www.ncbi.nlm.nih.gov/snp/rs1800871). In this study, the MAF for IL10 rs1800871 in recovered patients (0.34) was lower than in the deceased ones (0.45).
In patients with COVID-19, the allele T (0.37) for the IL10 rs1800872 as MAF was directly correlated with death. This amount was equal to Iran, South Asian (0.380), Latin American (0.361), African American (0.407), and European (0.293) and was different from other regions in Asian (0.726), East Asian (0.760), other Asian (0.610) and African (0.408) (https://www.ncbi.nlm.nih.gov/snp/rs1800872). In this study, the MAF for IL10 rs1800872 in recovered patients (0.34) was lower than in the deceased ones (0.40).
The MAF for IL10 rs1800896 (C) was 0.39 that was almost similar to Iran, South Asian (0.290), African (0.331), African American (0.334), Latin American (0.356), and European (0.473); however, it was different from Asian (0.063), East Asian (0.060), and other Asian (0.074) (https://www.ncbi.nlm.nih.gov/snp/rs1800896). In this study, the MAF for IL10 rs1800896 in recovered patients (0.27) was lower than in the deceased ones (0.39).
There have been reports of a link between SNPs in the IL10 gene and respiratory viral infectious diseases; this cytokine is thought to be a critical molecule in COVID-19 development. Due to this issue, the present study offered to examine, for the first time, if the COVID-19 death rate is connected with the polymorphisms rs1800871, rs1800872, and rs1800896 in a cohort of Iranian patients infected with different SARS-CoV-2 variants. These polymorphisms are a member of a collection of haplotypes linked to various amounts of IL10 production [18, 19]. In a study has been shown that Omicron variant showed lower IL-10 concentrations compared to other variants, a notion that can potentially be explained by clinical features of this specific variant [20].
In this study, the COVID-19 mortality rate was associated with the IL10 rs1800896 GG and AG genotypes in the Delta and Omicron BA.4 variants; nevertheless, there was no association between rs1800896 polymorphism with the Alpha variant. Rizvi et al., indicated that AG genotypes was correlated with COVID-19 severity [21]. The G to A polymorphism at rs1800896 controls how the IL10 gene is expressed. It has been reported that individuals with the GG genotype have higher levels of IL10 transcription and circulating levels of IL10 than individuals with the AA genotype [22]. According to a study involving 23 countries, there is a substantial positive connection between the frequency of the rs1800896 AG genotype and the prevalence of COVID-19. The IL10 gene polymorphisms in different populations at the rs1800896 locus revealed that populations in Japan, China, Tunisia, and Mexico frequently have the AA genotype; however, populations in Iran, India, the Netherlands, Finland, Germany, Spain, Czechia, Norway, Poland, the UK, and Brazil frequently have the AG genotype. Only among the Italian population the rs1800896 GG genotype had the highest frequency [23]. Moreover, the IL10 rs1800896 AG genotype was substantially related to death in infections with influenza A/H1N1pdm09 [24]. Due to the increased expression of the IL10 gene, it seems that G allele as MAF compared to A allele plays an important role in the susceptibility to severe COVID-19 infection. However, which allele can play a role in the infection of COVID-19 can depend on various factors such as race.
The rs1800896 AG and GG genotypes are linked to an increased risk of hepatitis B virus (HBV) and can make individuals more vulnerable to it. On the other side, chronic HBV patients have been shown to have elevated levels of IL10, suggesting that those with the rs1800896 G allele are at risk for contracting HBV [25]. Additionally, the rs1800896 GG genotype was correlated with the increased risk of systemic lupus erythematosus [17].
The findings of the present study revealed that the COVID-19 mortality rate was associated with the IL10 rs1800872 TT genotype in Alpha and Omicron variants and GT in Alpha and Delta variants. The GG genotype was observed to play a substantial protective function in preventing COVID-19 severity among patients who carried the rs1800872 polymorphism. The frequency of the GG genotype was higher in mild than in severe COVID-19 individuals, according to a study on the Mexican population. However, the results were not determined to be statistically significant [18, 20]. The IL10 rs1800872 GT genotypes were linked to a higher risk of contracting the influenza A/H3N2 virus. This might be a result of IL-10’s anti-inflammatory properties, which stop the natural killer and T cell activities from having an impact on the intense inflammatory response following the initial infection [26].
The IL10 rs1800872 polymorphism is linked to an increase in the severity of autoimmune and infectious diseases and regulates the transcription and production of IL10. The rs1800872 TT genotype was related to rheumatoid arthritis susceptibility in Iranian patients [27]. Studies from other cultures, such as Hong Kong and China, ruled out the link between this polymorphism and susceptibility and severity of other viral illnesses, such as influenza A/H1N1pdm09 and SARS [28]. The inconsistent findings in these kinds of studies can be addressed from the perspectives of immunogenetics and population genetics by taking into account various human immune responses to viruses and the genetic structure of populations [20].
The IL10 rs1800871 CC genotype in the Alpha variant and CT genotype in the Delta variant had a relationship with COVID-19 mortality; nonetheless, there was no association between the rs1800871 polymorphism with the Omicron BA.5 variant in the current study. In contrast to the present study, a study in Mexico indicated that the IL10 rs1800871 and rs1800872 polymorphisms among 193 COVID-19 patients were not linked to the severity of the disease. Probably, the reason for this difference is the number of examined samples, which in the current study was much more, and another reason could be the difference in ethnicity. The relationship between rs1800871 and other viral infections was shown in HBV infection. It has been demonstrated that the IL10 rs1800871 C allele and CC genotype can increase the risk of HBV infection [29]. In addition, a higher risk of systemic lupus erythematosus in Iranian patients was observed at the IL10 rs1800871 CC genotype [17].
The above-mentioned three SNPs’ impact on COVID-19 susceptibility might be explained by haplotype analysis. According to the obtained data of the current study, the GTA haplotype was the most common of haplotype in different SARS-CoV-2 variants. The TCG haplotype was related to COVID-19 mortality in Alpha, Delta, and Omicron BA.5 variants. In prior reports, the TCG haplotype was observed with increased IL10 production, compared to other haplotypes [30]. In this study, IL10 haplotype distribution indicated that the frequency of the TCG haplotype among the deceased COVID-19 patients in three different SARS-CoV-2 variants was higher than in the recovered subjects.
In addition to the strength of this study in examining these polymorphisms with the death rate of COVID-19 in different variants of SARS-CoV-2, this study included some limitations. The lack of access to healthy individuals who did not have a history of COVID-19 and comparing the results with them was one of the main limitations of the study. Additionally, the results were obtained in one ethnic group, and other ethnic groups living in Iran should be examined to confirm the results. Another limitation of this study was not examining the serum level of IL10 due to a lack of budget.
In conclusion, the IL10 rs1800871, rs1800872, and rs1800896 polymorphism had an impact on COVID-19 infection, and these polymorphisms had different effects on various SARS-CoV-2 variants. To verify the obtained findings, further studies should be conducted on various ethnic groups.
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|
---
title: Weight loss, visit-to-visit body weight variability and cognitive function
in older individuals
authors:
- Michelle H Zonneveld
- Raymond Noordam
- Behnam Sabayan
- David J Stott
- Simon P Mooijaart
- Gerard J Blauw
- J Wouter Jukema
- Naveed Sattar
- Stella Trompet
journal: Age and Ageing
year: 2023
pmcid: PMC9990986
doi: 10.1093/ageing/afac312
license: CC BY 4.0
---
# Weight loss, visit-to-visit body weight variability and cognitive function in older individuals
## Abstract
### Objective
to investigate the association between variability and loss of body weight with subsequent cognitive performance and activities of daily living in older individuals.
### Design
cross-sectional cohort study.
### Setting
PROspective Study of Pravastatin in the Elderly at Risk, multicentre trial with participants from Scotland, Ireland and the Netherlands.
### Subjects
4,309 participants without severe cognitive dysfunction (mean age 75.1 years, standard deviation (SD) = 3.3), at higher risk for cardiovascular disease (CVD).
### Methods
body weight was measured every 3 months for 2.5 years. Weight loss was defined as an average slope across all weight measurements and as ≥$5\%$ decrease in baseline body weight during follow-up. Visit-to-visit variability was defined as the SD of weight measurements (kg) between visits. Four tests of cognitive function were examined: Stroop test, letter-digit coding test (LDCT), immediate and delayed picture-word learning tests. Two measures of daily living activities: Barthel Index (BI) and instrumental activities of daily living (IADL). All tests were examined at month 30.
### Results
both larger body weight variability and loss of ≥$5\%$ of baseline weight were independently associated with worse scores on all cognitive tests, but minimally with BI and IADL. Compared with participants with stable weight, participants with significant weight loss performed 5.83 seconds ($95\%$ CI 3.74; 7.92) slower on the Stroop test, coded 1.72 digits less ($95\%$ CI −2.21; −1.13) on the LDCT and remembered 0.71 pictures less ($95\%$ CI -0.93; −0.48) on the delayed picture-word learning test.
### Conclusion
in older people at higher risk for CVD, weight loss and variability are independent risk-factors for worse cognitive function.
## Introduction
The process of ageing is accompanied by fluctuations in homeostatic processes, resulting in intrinsic intraindividual variability in physiological parameters. For example, variability in weight, including both gaining and losing weight, is associated with significant increases in mortality [1, 2]. Several observational studies have demonstrated that ‘unintentional’ weight loss in older adults is related to increased frailty and functional decline [2–5]. The origin of unintentional weight loss in older adults is often linked to the manifestation of malignancies, but numerous social, behavioural and health factors may also be instrumental [6, 7]. Over $27\%$ of frail older individuals above the age of 65 years experience unintentional weight loss [8], where a specific cause cannot be found in as many as $25\%$ of cases [2].
Recent evidence indicates that unintentional weight loss in older individuals associates with brain atrophy and cognitive impairment, which are known to associate with Alzheimer’s disease [3, 6, 9]. Other markers of unstable homeostasis and intraindividual variability, including variability in systolic blood pressure and low-density lipoprotein cholesterol, have also been associated with worsened cognitive function [10, 11]. We hypothesised that larger variation in body weight, as well as loss of weight, is independently associated with lower cognitive performance, independent of baseline body mass index (BMI) and traditional risk factors. Therefore, in the present study, we investigated the association of 2.5-year body weight loss and variation in body weight with subsequent cognitive performance and activities of daily living in a cohort of older individuals at increased risk of cardiovascular disease (CVD) but without severe cognitive dysfunction at baseline.
## Study design and participants
The data employed in the present study originates from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER). In all, 5,804 men and women aged 70–82 years from three countries (the Netherlands, Scotland and Ireland) were enrolled between December 1997 and May 1999 in a prospective, multicentre randomised trial in order to assess the safety and efficacy of pravastatin in reducing the risk of major vascular events. Participants were eligible for enrolment if they had pre-existing vascular disease or increased risk because of smoking, hypertension or diabetes.
During recruitment, the following exclusion criteria were applied: cognitive impairment (Mini-Mental Score Examination score < 24); history of malignancy within the past 5 years except localised basal cell carcinoma; recent stroke, transient ischemic attack (TIA), myocardial infarction, surgery or amputation for vascular disease ≤6 months before study entry. More details regarding exclusion criteria of PROSPER have been described elsewhere [12, 13]. The PROSPER study was approved by the Medical Ethics Committees of the three collaborating centres and complied with the Declaration of Helsinki. All participants gave written informed consent.
In the present study, the following inclusion criteria were applied: ≥1 out of the four cognitive tests scores at month 30 of follow-up; ≥2 weight measurements recorded between baseline and month 30 of follow-up with a maximum of 11 repetitive measurements.
## Exposure variables
Participants in the PROSPER study were reviewed every 3 months and body weight was measured at each visit, resulting in a maximum of 11 repetitive weight measurements.
From the weight collected at baseline and follow-up visits, we computed two determinants: weight loss and weight variability. Visit-to-visit weight variability was calculated by means of the intraindividual standard deviation (SD) over each individual’s measurements between baseline and every 3 months up to 30 months of follow-up. Weight loss was defined using two methods: first by calculating an average slope across all weight measurements, followed by calculating a delta incorporating only the first and last weight measurements during follow-up. The average slope incorporating all weight assessments during follow-up was computed using a linear mixed model capable of handling missing data during the visits. By fitting a linear-mixed model over the various measurements during follow-up, an average slope of weight change was calculated per participant. Then, the delta of weight change from baseline to month 30 was calculated and defined according to the following classification: weight loss as ≥$5\%$ of body weight decreased from baseline to month 30; weight gain as ≥$5\%$ of weight increased; and stability if within <$5\%$ weight variation between baseline and month 30 of follow-up [2, 6]. Although no universally accepted definition of clinically relevant weight loss exists, previous observational studies have employed this definition [2, 6].
## Cognitive function measurements
Serving as our outcome measurements, cognitive function was assessed face-to-face using four neuropsychological performance tests. More details regarding the tests are described in detail elsewhere [14]. The Stroop colour-word inference test (selective attention) and the letter-digit coding test (LDCT) (processing speed) were used to measure executive functioning [14]. The outcome parameter for the Stroop test was the total number of seconds to complete the third Stroop card. The outcome variable for the LDCT was the total number of correct entries in 60 s. Visual episodic memory was assessed with the 15-Picture Learning test (PLT) testing immediate and delayed recall. The main outcome was the accumulated number of recalled pictures over the three learning trials and the number of pictures recalled after 20 min. Functional status was assessed using the Barthel Index (BI) and instrumental activities of daily living (IADL). BI assesses self-care activities of daily living using 10 items (such as dressing) where a higher score indicates higher independence, with a maximum of 20 points [15]. Similarly, IADL measures activities of daily living using seven items with a maximum of 14 points, but in addition includes the interaction with the social and physical environment [16]. Likewise, here a higher score also higher functional capacity and independence. All outcomes were assessed at month 30 to maximise the availability of outcomes after the measurement of weight variability and weight change.
## Covariates
Covariates were obtained from an extensive medical history using routine care data during a 10-week screening period. The participant’s general practitioner reported on the history of various clinical diseases, including CVD, diabetes mellitus, TIA, stroke and myocardial infarction. The following were evaluated using a medical inventory: number of medications used, use of diuretics and antidepressants, years of education, smoking status, alcohol intake. Systolic and diastolic blood pressure were measured every 3 months [10]. Data on diseases developed during follow-up were ascertained using hospital records and general practitioners’ records, and included: diabetes; non-fatal cardiovascular and coronary events including myocardial infarction, stroke and TIA; hospitalisation because of heart failure; serious non-fatal cancer [13]. These endpoints were classified by an independent committee. Data on these baseline covariates were complete for all participants.
## Statistical analyses
Baseline characteristics of the study participants are reported as mean (SD) or median (interquartile range (IQR)) for continuous variables and number (percentage) for categorical variables. To investigate whether slope and variability were two independent phenotypes, we assessed the correlation with a Pearson’s correlation coefficient. We repeated the analyses with both slope of weight change and weight variability in the same multivariable-adjusted regression model to study whether these phenotypes have independent effects.
The associations of visit-to-visit variability in total weight (in SD), slope of weight change (in kg) and weight loss (in categories) with measures of cognitive function at month 30 were assessed using multivariable-adjusted linear regression models. Weight variability and slope of weight change were analysed as continuous variables and in equally sized thirds, where the lower third of weight variability and the middle third of the slope of weight change were defined as reference categories. For reasons of clinical interpretation, we presented the results on slope of weight change as the difference in cognitive function at month 30 per extra 0.1 kg/month weight loss.
The linear regression analyses were adjusted for covariates based on their biological plausibility as potential confounders. Therefore, in the minimally adjusted model, we included sex, age, country as a three-level variable, mean weight during follow-up, height at baseline and years of education. The fully adjusted model additionally included smoking status, alcohol intake, number of medications used, use of diuretics and antidepressants, and history of CVD, diabetes and myocardial infarction.
Data are reported as the mean multivariable-adjusted difference in outcome between the second and third thirds of weight change in comparison with the reference group, with accompanying $95\%$ confidence interval. For example, ‘weight lost’ and ‘weight gained’ are compared with the reference group ‘stable weight’. All analyses were performed using SPSS Windows version 26 (IBM Corp., 2019). Data from the PROSPER study is not publicly available.
## Sensitivity analyses
In addition to these overall analyses, we performed sensitivity analyses to investigate the robustness of the associations considering different subgroups of the population. First, we performed separate analyses for placebo and pravastatin treatment groups. Next, it has been shown that high blood pressure variability was associated with worse cognitive function [10]. As variability in weight and blood pressure may have a common cause, we additionally adjusted our models for systolic blood pressure variability and mean systolic blood pressure from baseline to month 30. This allows for the separation of effects of weight variability from those originating from blood pressure variability. Systolic blood pressure variability was defined as the intraindividual SD from baseline to month 30, where blood pressure was measured every 3 months, as previously done [10, 17]. Furthermore, we included both weight loss (slope) and visit-to-visit body weight variables in the same multivariable-adjusted linear regression model to test independence of the two phenotypes. Last, we performed analyses excluding individuals who developed any of the following diseases during follow-up, to ensure weight loss did not follow as a result: incident diabetes, non-fatal cancer, non-fatal stroke or TIA, hospitalisation because of heart failure and non-fatal coronary or cardiovascular events.
Adjusting the analyses between weight variability and cognitive function for systolic blood pressure variability did not materially change the results (Table 4). The results were essentially unchanged when stratifying for the treatment groups (Supplementary Tables 2–4). Repeating the analyses after excluding participants with incident disease-states during follow-up also did not materially change the associations (Supplementary Tables 5–7). The associations between continuous weight variability and continuous slope of weight change with cognitive function remained significant when including the two determinants in the same model (Supplementary Table 8).
## Results
After excluding participants with <2 measurements ($$n = 225$$) and participants without cognitive test scores at month 30 ($$n = 1$$,270), 4,309 participants were eligible for inclusion (Table 1). The mean age was 75.1 years (SD = 3.3) and more than half of the study population was female ($$n = 2$$,222, $51.6\%$). Large majority of participants had a history of hypertension ($$n = 2$$,706, $62.8\%$). The mean weight during follow-up was 72.9 kg (SD = 13.3) and participants had a median weight loss of 0.01 kg per month (IQR −0.07; 0.06). Baseline weight characteristics per third of weight loss and weight variability are reported in Supplementary Table 1.
**Table 1**
| Sociodemographics | Sociodemographics.1 | All (n = 4,309) |
| --- | --- | --- |
| Age, year, mean (SD) | Age, year, mean (SD) | 75.1 (3.3) |
| Female, n (%) | Female, n (%) | 2,222 (51.6) |
| Age left school, year, mean (SD) | Age left school, year, mean (SD) | 15.2 (2.1) |
| Current smoker, n (%) | Current smoker, n (%) | 1,079 (25.0) |
| Alcohol intake, unit intake per week, mean (SD) | Alcohol intake, unit intake per week, mean (SD) | 5.30 (9.2) |
| Cardiovascular risk factors | Cardiovascular risk factors | |
| History of CVD, n (%) | History of CVD, n (%) | 1878 (43.6) |
| History of hypertension, n (%) | History of hypertension, n (%) | 2,706 (62.8) |
| History of stroke or TIA, n (%) | History of stroke or TIA, n (%) | 465 (10.8) |
| History of myocardial infarction, n (%) | History of myocardial infarction, n (%) | 560 (13.0) |
| Diabetes mellitus, n (%) | Diabetes mellitus, n (%) | 446 (10.4) |
| Serum cholesterol, mmol/L, mean (SD) | Serum cholesterol, mmol/L, mean (SD) | 5.68 (0.9) |
| Weight during follow-up, kg, mean (SD) | Weight during follow-up, kg, mean (SD) | 72.9 (13.3) |
| Weight change, kg/month, mean (SD) | Weight change, kg/month, mean (SD) | −0.01 (0.1) |
| Lost more than 5% of baseline body weight during follow-up, n (%) | Lost more than 5% of baseline body weight during follow-up, n (%) | 802 (18.6) |
| Gained more than 5% of baseline body weight during follow-up, n (%) | Gained more than 5% of baseline body weight during follow-up, n (%) | 580 (13.5) |
| Number of weight measurements, median (IQR) | Number of weight measurements, median (IQR) | 10 (10; 10) |
| BMI, kg/m2, mean (SD) | BMI, kg/m2, mean (SD) | 26.9 (4.1) |
| Pravastatin treatment, n (%) | Pravastatin treatment, n (%) | 2,146 (49.8) |
| Number of medications, median (IQR) | Number of medications, median (IQR) | 3 (2; 5) |
| Use of diuretics, n (%) | Use of diuretics, n (%) | 1804 (41.9) |
| Cognitive function at month 30 of follow-up | Cognitive function at month 30 of follow-up | |
| Stroop test, s, mean (SD)a | Stroop test, s, mean (SD)a | 64.5 (26.1) |
| LDCT, digits coded, mean (SD)b | LDCT, digits coded, mean (SD)b | 22.9 (7.8) |
| PLTi, pictures remembered, mean (SD)c | PLTi, pictures remembered, mean (SD)c | 9.5 (2.0) |
| PLTd, pictures remembered, mean (SD)c | PLTd, pictures remembered, mean (SD)c | 10.2 (2.9) |
| Barthel, index, mean (SD)d | Barthel, index, mean (SD)d | 19.7 (0.9) |
| IADL, points, mean (SD)d | IADL, points, mean (SD)d | 13.5 (1.3) |
The correlation between continuous weight variability and the continuous slope of weight change was negligible (Pearson’s $r = 0.22$).
## Association between weight loss and cognitive function
Table 2 displays the association of the slope of weight change and cognitive function. After full adjustments, in comparison to the middle third, the lower third of the slope of weight change was associated with a worse performance in all cognitive and functional domains except on the BI (−0.04 points, $95\%$ CI −0.10; 0.03). To illustrate, at month 30 of follow-up, the lower third coded 1.42 ($95\%$ CI −1.98; −0.86) digits less on the LDCT and performed 4.39 s ($95\%$ CI 2.42; 6.37) slower on the Stroop test. On the other hand, in comparison to the middle third, the upper third of the slope of weight change was not significantly associated with cognitive performance. Continuously, the slope of weight change was also associated with worse cognitive function on all tests. Per 0.10 kg/month additional average weight loss, the score on the Stroop test was 1.82 s ($95\%$ CI 1.13; 2.49) slower, 0.70 less ($95\%$ CI −0.90; −0.51) digits were coded on the LDCT and 0.21 less ($95\%$ CI −0.29; −0.14) pictures were remembered on the delayed PLT.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Weight change (slope) | Weight change (slope).1 | Weight change (slope).2 | Weight change (slope).3 |
| --- | --- | --- | --- | --- | --- |
| | | Low third | Middle third | Upper third | Continuousc |
| | | (Nmax = 1,436) | (Nmax = 1,436) | (Nmax = 1,437) | All (Nmax = 4,309) |
| Cognitive test | Cognitive test | Beta (95% CI) | Beta (95% CI) | Beta (95% CI) | Beta (95% CI) |
| Minimally adjusted a | Minimally adjusted a | | | | |
| Stroop, s | Stroop, s | 4.75 (2.84; 6.67) | Ref | 0.34 (−1.52; 2.19) | 1.91 (1.25; 2.57) |
| LDCT, digits coded | LDCT, digits coded | −1.52 (−2.07; −0.97) | Ref | 0.32 (−0.21; 0.86) | −0.69 (−0.88; −0.50) |
| PLTi, pictures remembered | PLTi, pictures remembered | −0.37 (−0.52; −0.23) | Ref | 0.01 (−0.13; 0.15) | −0.15 (−0.20; −0.10) |
| PLTd, pictures remembered | PLTd, pictures remembered | −0.56 (−0.77; −0.36) | Ref | −0.06 (−0.26; 0.14) | −0.21 (−0.28; −0.14) |
| Barthel, index | Barthel, index | −0.04 (−0.10; 0.03) | Ref | 0.00 (−0.06; 0.07) | −0.02 (−0.04; 0.00) |
| IADL, points | IADL, points | −0.11 (−0.20; −0.02) | Ref | −0.07 (−0.16; 0.02) | −0.04 (−0.07; 0.01) |
| Fully adjusted b | Fully adjusted b | | | | |
| Stroop, s | Stroop, s | 4.39 (2.42; 6.37) | Ref | 0.15 (−1.78; 2.06) | 1.82 (1.13; 2.49) |
| LDCT, digits coded | LDCT, digits coded | −1.42 (−1.98; −0.86) | Ref | 0.43 (−0.12; 0.97) | −0.70 (−0.90; −0.51) |
| PLTi, pictures remembered | PLTi, pictures remembered | −0.37 (−0.52; −0.22) | Ref | 0.02 (−0.12; 0.17) | −0.15 (−0.20; −0.10) |
| PLTd, pictures remembered | PLTd, pictures remembered | −0.52 (−0.73; −0.31) | Ref | −0.01 (−0.22; 0.20) | −0.21 (−0.29; −0.14) |
| Barthel, index | Barthel, index | −0.04 (−0.10; 0.03) | Ref | 0.00 (−0.06; 0.07) | −0.02 (−0.05; 0.00) |
| IADL, points | IADL, points | −0.11 (−0.21; −0.02) | Ref | −0.07 (−0.16; 0.02) | −0.04 (−0.07; −0.01) |
Loss of ≥$5\%$ of weight during follow-up was associated with worse performance on all domains, but did not show association with BI and IADL (Table 3 and Figure 1). After full adjustments, in comparison to maintaining stable weight during follow-up, participants who lost ≥$5\%$ of baseline body weight performed 5.83 s ($95\%$ CI 3.74; 7.92) slower on the Stroop test. Furthermore, weight loss was also associated with a worse performance on the LDCT (Beta −1.72 digits coded, $95\%$ CI −2.21; −1.13) and the PLT, both immediate (beta −0.48 pictures remembered, $95\%$ CI −0.64; −0.33) and delayed (beta −0.71 pictures remembered, $95\%$ CI −0.93; −0.48). In comparison to individuals who maintained stable weight, we did not find evidence of a significant association between weight gain during follow-up and cognitive function.
## Association between visit-to-visit body weight variability and cognitive function
The associations of visit-to-visit weight variability and cognitive function are presented in Table 4. After full adjustments, both middle and upper thirds of weight variability, in comparison to the lower third, performed worse on all tests. For example, the middle third (moderate variability) of weight variability was associated with 1.93 s ($95\%$ CI 0.01; 3.86) slower performance on the Stroop test, whereas the upper third (high variability) was associated with 4.52 s ($95\%$ CI 2.52; 6.51) slower performance, in comparison to the lower third (low variability). Likewise, higher weight variability as a continuous variable was associated with worse cognitive function on all domains. Here, a 1-SD higher weight variability was associated with 1.46 s ($95\%$ CI 0.82; 2.09) slower performance on the Stroop test. There were no associations with functional capacity.
**Table 4**
| Unnamed: 0 | Unnamed: 1 | Weight variability, SD | Weight variability, SD.1 | Weight variability, SD.2 | Weight variability, SD.3 |
| --- | --- | --- | --- | --- | --- |
| | | Low third | Middle third | Upper third | Continuous |
| | | (Nmax = 1,436) | (Nmax = 1,436) | (Nmax = 1,437) | All (Nmax = 4,309) |
| Cognitive test | Cognitive test | Beta (95% CI) | Beta (95% CI) | Beta (95% CI) | Beta (95% CI) |
| Minimally adjusted a | Minimally adjusted a | | | | |
| Stroop, s | Stroop, s | Ref | 2.30 (0.42; 4.18) | 6.01 (4.07; 7.95) | 1.98 (1.35; 2.60) |
| LDCT, digits coded | LDCT, digits coded | Ref | −1.42 (−1.96; −0.88) | −2.29 (−2.85; −1.74) | −0.73 (−0.91; −0.55) |
| PLTi, pictures remembered | PLTi, pictures remembered | Ref | −0.21 (−0.35; −0.06) | −0.63 (−0.78; −0.49) | −0.19 (−0.24; −0.14) |
| PLTd, pictures remembered | PLTd, pictures remembered | Ref | −0.21 (−0.35; −0.06) | −0.96 (−1.16; −0.75) | −0.29 (−0.35; −0.22) |
| Barthel, index | Barthel, index | Ref | −0.04 (−0.10; 0.03) | −0.09 (−0.15; −0.02) | −0.06 (−0.08; −0.04) |
| IADL, points | IADL, points | Ref | −0.04 (−0.13; 0.05) | −0.19 (−0.28; −0.10) | −0.10 (−0.13; −0.07) |
| Fully adjusted b | Fully adjusted b | | | | |
| Stroop, s | Stroop, s | Ref | 2.25 (0.31; 4.18) | 5.32 (0.31; 4.18) | 1.72 (1.08; 2.35) |
| LDCT, digits coded | LDCT, digits coded | Ref | −1.36 (−1.92; −0.81) | −2.16 (−2.72; −1.59) | −0.67 (−0.86; −0.49) |
| PLTi, pictures remembered | PLTi, pictures remembered | Ref | −0.19 (−0.33; −0.04) | −0.61 (−0.76; −0.46) | −0.18 (−0.23; −0.13) |
| PLTd, pictures remembered | PLTd, pictures remembered | Ref | −0.20 (−0.41; 0.01) | −0.91 (−1.12; −0.69) | −0.27 (−0.33; −0.20) |
| Barthel, index | Barthel, index | Ref | −0.04 (−0.11; 0.02) | −0.08 (−0.15; −0.02) | −0.05 (−0.08; −0.03) |
| IADL, points | IADL, points | Ref | −0.05 (−0.14; 0.04) | −0.17 (−0.27; −0.08) | −0.09 (−0.12; −0.06) |
| Fully adjusted with systolic blood pressure variability c | Fully adjusted with systolic blood pressure variability c | Fully adjusted with systolic blood pressure variability c | Fully adjusted with systolic blood pressure variability c | | |
| Stroop, s | Stroop, s | Ref | 1.93 (0.01; 3.86) | 4.52 (2.52; 6.51) | 1.46 (0.82; 2.09) |
| LDCT, digits coded | LDCT, digits coded | Ref | −1.28 (−1.83; −0.73) | −1.95 (−2.51; −1.39) | −0.61 (−0.79; −0.43) |
| PLTi, pictures remembered | PLTi, pictures remembered | Ref | −0.17 (−0.31; −0.03) | −0.58 (−0.73; −0.43) | −0.17 (−0.22; −0.12) |
| PLTd, pictures remembered | PLTd, pictures remembered | Ref | −0.18 (−0.39; 0.02) | −0.86 (−1.08; −0.65) | −0.25 (−0.32; −0.19) |
| Barthel, index | Barthel, index | Ref | −0.04 (−0.10; 0.03) | −0.08 (−0.14; −0.01) | −0.05 (−0.07; −0.03) |
| IADL, points | IADL, points | Ref | −0.04 (−0.14; 0.05) | −0.17 (−0.25; −0.06) | −0.08 (−0.11; −0.05) |
## Discussion
In the present study, we investigated the association of 2.5-years variation in weight and loss of weight with subsequent cognitive performance in a cohort of older individuals at increased risk of CVD. Loss of weight and a higher weight variability were independently significantly associated with worse cognition. These findings were consistent in all tested cognitive domains and independent of incident disease-states, use of diuretics or antidepressants, cardiovascular risk-factors. This study found no associations with activities of daily living.
Major strengths of this study are its size with >4,300 older participants, use of multiple consecutive weight measurements and the ability to investigate various cognitive domains. Furthermore, participants were free of dementia at baseline because of the PROSPER exclusion criteria of MMSE scores < 24. A limitation is the lack of information regarding physical activity and intentional weight loss. At the onset of data collection, study participants received health counselling that may have led some participants to intentionally lose weight. Intentional loss of weight has been showed to result in improved cognitive function [18], whereas the present study could not corroborate this observation. Furthermore, reverse causation may also play a role as cognitive impairment can also lead to raised energy expenditure or changes in eating habits [2]. Lack of associations of weight loss or variability with BI and IADL may be because of the ceiling effects.
Current evidence on the association between cognitive decline and variation of weight mainly comes from studies that collected fewer weights measurements (≤3) than the present study (median of 11 measurements, IQR 11; 11) [1, 6, 19, 20]. Furthermore, these studies did not calculate an average slope of weight change during follow-up as done in the present study. In line with our results, these studies demonstrate that greater variation in weight is associated with higher risk of dementia. In the present study, we used a battery of cognitive tests to examine various domains as opposed to solely the diagnosis of dementia [1, 19–21], adding nuance to our findings. Consistent with our findings, these studies suggest that larger variation in weight may function as a marker of risk of early cognitive impairment.
On the other hand, some studies present mixed results. Improved cognitive function following intended weight loss has also been observed amongst older individuals [18, 22, 23]. However, these studies were designed to ‘intentionally’ induce weight loss in participants, whereas in the present study, it is believed that weight loss in the vast majority was ‘unintentional’ and therefore a consequence of other subclinical processes.
Incident disease-states such as cancer are often thought to be the underlying cause of weight loss [2]. In the present study, participants with a recent history of malignancies (<5 years) and cardiovascular events (<6 months) were excluded during recruitment of the original PROSPER trial, and we found that associations did not change after repeating the analyses excluding participants with incident disease-states during follow-up. It is therefore more likely that weight loss in the present study may have resulted from unstable homeostasis rather than because of major disease during the follow-up. We also demonstrated that higher weight variability was associated with worse cognitive function, independent of systolic blood pressure variability. This may suggest that variability in body weight and systolic blood pressure do not share a common cause, and that the current findings are different from what we have previously published [10, 11]. In addition, the two variability variables that are more likely to symbolise different biological pathways are involved in regulating homeostasis.
The biological mechanisms by which weight loss and most notably weight variability is associated with cognitive function are not fully understood. Weight loss can result as a downstream effect of normal ageing as metabolic needs may change [2]. In addition, polypharmacy can cause dentition and absorption issues, altered gastric signals, causing early satiation or loss of appetite. Reverse causality, where weight loss is an early manifestation of dementia [24], could also contribute to our findings. However, weight loss has been shown to precede symptoms of cognitive decline, implying that pathological processes of weight loss could contribute, perhaps indirectly, to cognitive decline [25]. Future long-term investigations are warranted to examine whether maintaining weight stability can effectively decrease the risk of cognitive decline.
In conclusion, we found that in older participants at increased risk for vascular disease, steeper decline in weight and a higher variability in body weight were strongly associated with lower cognitive function in multiple domains. These findings were independent of cardiovascular risk-factors, comorbidities, incident disease-states and independent of each other. Although we did not produce evidence favouring weight change to cause decreased cognitive function, it may represent an early manifestation or signal of cognitive decline.
## Declaration of Conflicts of Interest
N.S. has consulted for and/or received speaker honoraria from Abbott Laboratories, Afimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics and Sanofi; and received grant support paid to his university from AstraZeneca, Boehringer Ingelheim, Novartis and Roche Diagnostics outside the submitted work.
## Declaration of Sources of Funding
M.H.Z. was supported by Young Talent Award from the Netherlands Cardiovascular Research Initiative funded project ENERGISE (CVON2014-02). The original PROSPER clinical trial was funded by an investigator-initiated grant from Bristol-Myers Squibb, USA. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## Ethical Approval
This study was approved by the institutional ethics committees of the three collaborating centres. All participants gave written informed consent.
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|
---
title: Metabolic, fibrotic and splicing pathways are all altered in Emery-Dreifuss
muscular dystrophy spectrum patients to differing degrees
authors:
- Jose I de las Heras
- Vanessa Todorow
- Lejla Krečinić-Balić
- Stefan Hintze
- Rafal Czapiewski
- Shaun Webb
- Benedikt Schoser
- Peter Meinke
- Eric C Schirmer
journal: Human Molecular Genetics
year: 2022
pmcid: PMC9991002
doi: 10.1093/hmg/ddac264
license: CC BY 4.0
---
# Metabolic, fibrotic and splicing pathways are all altered in Emery-Dreifuss muscular dystrophy spectrum patients to differing degrees
## Abstract
Emery-Dreifuss muscular dystrophy (EDMD) is a genetically and clinically variable disorder. Previous attempts to use gene expression changes to find its pathomechanism were unavailing, so we engaged a functional pathway analysis. RNA-Seq was performed on cells from 10 patients diagnosed with an EDMD spectrum disease with different mutations in seven genes. Upon comparing to controls, the pathway analysis revealed that multiple genes involved in fibrosis, metabolism, myogenic signaling and splicing were affected in all patients. Splice variant analysis revealed alterations of muscle-specific variants for several important muscle genes. Deeper analysis of metabolic pathways revealed a reduction in glycolytic and oxidative metabolism and reduced numbers of mitochondria across a larger set of 14 EDMD spectrum patients and 7 controls. Intriguingly, the gene expression signatures segregated the patients into three subgroups whose distinctions could potentially relate to differences in clinical presentation. Finally, differential expression analysis of miRNAs changing in the patients similarly highlighted fibrosis, metabolism and myogenic signaling pathways. This pathway approach revealed a transcriptome profile that can both be used as a template for establishing a biomarker panel for EDMD and direct further investigation into its pathomechanism. Furthermore, the segregation of specific gene changes into distinct groups that appear to correlate with clinical presentation may template development of prognostic biomarkers, though this will first require their testing in a wider set of patients with more clinical information.
## Introduction
Emery-Dreifuss muscular dystrophy (EDMD) is a genetically heterogeneous neuromuscular orphan spectrum disease affecting ~0.3–0.4 in 100 000 people [1], with clinical variability presenting even in family members carrying the same mutation (2–5). EDMD patients present typically in mid to late childhood with early contractures of elbows and Achilles’ tendons and progressive wasting of the lower leg and upper arm muscles. Cardiac involvement is also highly characteristic but tends to appear later in development and quite variably in time, though it tends to be reasonably uniform in the form it takes of cardiac conduction defects and dilated cardiomyopathy [6]. Other features vary considerably in clinical presentation, leading to the usage of ‘Emery-Dreifuss-like syndromes’ [7,8]: patients from the same pedigree can show remarkable phenotypic variation [2]. *The* genetic variability is underscored by several confirmed linked genes and several additional candidate genes, although there are still some cases where no confirmed or candidate disease allele has been identified (9–11). The lack of large pedigrees in combination with its genetic heterogeneity, clinical variability, already some known modifier genes and limited patient numbers makes solving its pathomechanism difficult.
The original genes linked to EDMD, EMD encoding emerin and LMNA encoding lamin A/C, have both cytoskeletal and gene regulation roles leading to strong arguments for either function being responsible for the EDMD pathomechanism [12,13]. The subsequent linking of nesprin and Sun proteins to EDMD [14,15] failed to lend clarity since they function in mechanosignal transduction [16]. However, several recently linked genes have clear roles in genome organization and regulation [10], suggesting that this is the pathomechanism. *These* genes encode proteins that, such as emerin, are nuclear envelope transmembrane proteins (NETs) and seem to function by fine-tuning muscle gene expression by promoting the release of pro-myogenic genes from the nuclear periphery to enhance their activation while concomitantly recruiting metabolism genes (many from the alternative differentiation pathway of adipogenesis) to the nuclear envelope to better repress them (17–19). EDMD mutations were found in five muscle-specific NETs with this genome organization function, PLPP7 (also known as NET39), WFS1, TMEM38A, TMEM201 and TMEM214, and each tested had some specificity in the sets of genes that they target, though there was also some overlap [17]. These studies together with the wide range of lamin and emerin gene regulatory activities led us to the non-traditional hypothesis for the EDMD pathomechanism whereby moderate reductions in many genes could yield the same phenotype as shutting down a central gene of a particular pathway. Accordingly, we considered that searching for uniformity among patients in altered pathways might be more revealing than searching for uniformity in expression changes of particular genes.
The only previous study, to our knowledge, using gene expression changes to identify critical misregulated genes underlying EDMD pathophysiology, focused on the identification of genes altered specifically in EDMD compared with a set of 10 other muscular dystrophies [20]. This study only considered eight total LMNA- or EMD-linked cases of EDMD, but EDMD now has many more genes and modifiers linked to it and, moreover, there is a wider clinical spectrum of EDMD-like phenotypes [11]. Their analysis indicated potential abnormalities in the regulation of cell cycle and myogenic differentiation, associated with perturbations in the pRB/MYOD/LMNA hub, which were consistent with changes in an Emd−/− mouse model [21]. Roughly a fifth each of EDMD mutations occurs in LMNA and EMD while another 5–$6\%$ are collectively caused by four other widely expressed nuclear envelope proteins nesprin 1 (encoded by SYNE1), nesprin 2 (encoded by SYNE2), Sun1 (encoded by SUN1) and FHL1 (encoded by FHL1) (14,15,22–24)). Another approximately $20\%$ of EDMD mutations were accounted for by muscle-specific NETs that regulate muscle-specific genome organization [10]. These include NET39 (encoded by PLPP7), TMEM38A (encoded by TMEM38A), WFS1 (encoded by WFS1), NET5 (encoded by TMEM201) and TMEM214 (encoded by TMEM214) that affect 3D gene positioning with corresponding effects on expression [17,19]. Accordingly, we sought to search for commonly affected pathways from a much wider range of EDMD-linked genes including LMNA, EMD, FHL1, SUN1, SYNE1, PLPP7 and TMEM214 alleles on the expectation that, covering an even wider genetic and clinical spectrum, the most important pathways for EDMD pathophysiology would be highlighted.
## RNA-Seq analysis of EDMD patient cells
We performed RNA-Seq on myotubes differentiated in vitro from myoblasts isolated from 10 unrelated clinically diagnosed EDMD patients with distinct mutations in seven different genes to sample the genetic diversity of EDMD (Fig. 1). Patient mutations were TMEM214 p.R179H, PLPP7/NET39 p.M92K, Sun1 p.G68D/G388S, Nesprin 1 p.S6869*, Emerin p.S58Sfs*1, FHL1 mutations c.688 + 1G > A, p.C224W and p.V280M, Lamin A/C mutation p.T528K and Lamin A mutation p.R571S (this last mutation occurs in an exon absent from the lamin C splice variant). These mutations covered a wide range of clinical phenotypes with the age of onset ranging from early childhood to adult life and associated pathology ranging from no reported contractures to rigid spine (Table 1). Myoblast isolation followed by in vitro differentiation was chosen over isolating mRNA directly from the tissue samples in order to try to capture the earliest changes in gene expression due to the disease mutations i.e. the most likely to initiate the pathomechanism. Differentiating isolated myoblasts cells also reduces tertiary effects and variation from the age of the patients, range in time from onset to when biopsies were taken and differences in biopsy site (Table 1). These patient variables could also affect the efficiency of myotube differentiation; so, to ensure that different percentages of undifferentiated cells in the population did not impact measuring gene expression changes, the myotubes were specifically isolated by short trypsinization thus removing all myoblast contamination (Fig. 1A and Supplementary Material, Fig. S1). To define the baseline for comparison, two age-matched healthy controls were similarly analyzed. Samples from all patients and controls all yielded high-quality reads ranging between 56 and 94 million paired-end reads (Supplementary Material, Table S1).
**Figure 1:** *RNA-Seq for EDMD. (A) Workflow. Muscle biopsies were taken from the regions described in Table 1 and myoblasts recovered. These were then differentiated in vitro into myotubes, and myotubes selectively recovered by partial trypsinization. Myotube RNA was extracted and used for sequencing. Scale bar 20 μm. (B) Number of genes differentially expressed (FDR $5\%$) for each individual patient and for all patients considered together as a single group (denoted as ‘G1’). (C) Heatmap of log2FC values for genes changing expression in EDMD patients compared with healthy controls (G1). Red is upregulated and blue is downregulated. Black indicates no change. The G1 lane is the averaged data across all patients. (D) Dendrogram showing the relationships between patients, which fall into three broad groups. (E) Overlaps with genome-organizing NETs gene targets. The number of genes altered by knockdown of muscle-specific genome-organizing NETs Wfs1, Tmem38a, NET39 or Tmem214 that were also altered in the EDMD patient cells is given. G1 refers to the analysis of all 10 patients as a single group against the healthy controls, while gp1–3 refers to the three subgroups identified. Total numbers of differentially expressed (DE) genes are given for each.* TABLE_PLACEHOLDER:Table 1 First, we compared each individual patient against the controls. Compared with the controls, each individual patient had between 310 and 2651 upregulated genes and between 429 and 2384 downregulated genes with a false discovery rate (FDR) of $5\%$ (Fig. 1B). The large difference in the number of differentially expressed (DE) genes between patients suggested large heterogeneity. When we calculated the intersection of DE genes in all patients, only three genes were similarly downregulated (MTCO1P12, HLA-H, HLA-C), and one upregulated (MYH14) at $5\%$ FDR, indicating a high degree of variation between patients (Supplementary Material, Fig. S2A). MTCO1P12 is a mitochondrially encoded pseudogene that has been reported to be severely downregulated in inflammatory bowel disease, associated with reduced mitochondrial energy production [25]. HLA-C is a member of the MHC class I and is involved in interferon gamma signaling, while HLA-H is a pseudogene derived from HLA-A which may function in autophagy (26–28). Mutations in non-muscle myosin gene MYH14 appear to be associated with hearing loss rather than muscle defects [29,30], although it has also been recently linked to mitochondrial fission defects [31].
The small number of intersecting genes was expected, given the heterogeneity and the number of samples. However, this kind of comparison underestimates the underlying similarity since all it takes is one patient for any given gene to miss the arbitrary $5\%$ FDR cutoff and the gene would not be selected. Because the goal of this study is to identify common features among a sample of EDMD spectrum patients, instead of focusing on individual patients we next compared all patients together as a single group against the controls, and we denoted this analysis as G1 (one single group) throughout the text (Fig. 1). The G1 analysis revealed a set of 1127 DE genes (894 upregulated and 233 downregulated) with a $5\%$ FDR cutoff. Preliminary examination of this set of genes across the 10 patients suggested that they might fall into two or three broadly distinct profiles with a main group comprising half of the patients that includes the patient with the classical emerin mutation. Around $60\%$ of the genes were altered in the same direction in all 10 patients (Fig. 1 and Supplementary Material, Fig. S2B and C). Thus, while the majority of the 1127 genes behave in a similar fashion (albeit with differences in gene expression levels), there are substantial underlying differences and apparent subgroups of patients with more similar expression profiles that might reflect clinical variation in EDMD.
Hierarchical clustering identified one main subgroup comprising half of the patients that includes the EMD mutation, with the remaining patients falling more loosely into two smaller groups (Fig. 1C and D). While it is tempting to suggest these groups may represent separate EDMD spectrum subcategories, the small cohort employed precludes this conclusion. However, we reasoned that the distinction may bear clinical relevance and provide proof of principle that transcriptome analysis could assist clinicians in diagnosis and prognosis of EDMD spectrum patients. The three groupings were: group 1—Emerin p.S58Sfs*1, Tmem214 p.R179H, NET39 p.M92K, Lamin A p.R571S, FHL1 p.C224W; group 2—Sun1 p.G68D/G388S, FHL1 c.688 + 1G > A, FHL1 p.V280M; group 3—Nesprin 1 p.S6869*, Lamin A/C p.T528K (Fig. 1D). The same groups were independently identified using a principal component analysis (PCA) (Supplementary Material, Fig. S2D). Of particular note, both PCA and t-distributed stochastic neighbor embedding analyses revealed that clustering was independent of parameters such as patient gender or age or myotube enrichment differences (Supplementary Material, Fig. S3). Moreover, the several FHL1 and lamin mutations tested segregated into different expression subgroups. At the same time, the more recently identified EDMD mutations in TMEM214 and NET39 segregated with more classic emerin, FHL1 and lamin A mutations, further indicating the likelihood that their genome organizing functions could mediate core EDMD pathophysiology.
The hypothesis that EDMD is a disease of genome organization misregulation is underscored by the fact that $15\%$ of the genes changing expression in EDMD patient cells were altered by knockdown of at least one of the four muscle-specific genome-organizing NETs that we previously tested [17] (Fig. 1E). Interestingly, in that study most of the genes altered by knockdown of NET39, TMEM38A and WFS1 were non-overlapping, while those altered by knockdown of TMEM214 exhibited considerable overlap with the sets altered by each of the other NETs [17]. However, here there were roughly 70 DE genes overlapping with the sets of genes altered by knockdown of each individual NET while the total number of DE genes under the regulation of any of the four NETs was 165, indicating an enrichment in the EDMD DE set for genes influenced by multiple NETs (Fig. 1E). Thus, it is not surprising that NET39 and TMEM214 were both segregated together. Another interesting observation is that the number of NET-regulated genes overlapping with group 1 and group 3 was similar, but much fewer were overlapping for group 2. This suggests that gene misregulation in groups 1 and 3 might be more strongly mediated by the muscle-specific NET-gene tethering complexes than in group 2.
## Functional pathway analysis of gene expression changes in EDMD patient cells
The primary aims of this study were to determine whether a functional pathway analysis would be more effective at revealing the likely underlying EDMD pathomechanism than just looking for uniformly altered genes and, if so, to identify candidate biomarkers from the affected pathways, though these would require subsequent validation due to the limited number of patient cells available for analysis. Before using this approach with our wider set of EDMD alleles, we applied a pathway analysis to the data from the previous microarray study by Bakay and colleagues where just LMNA and EMD mutations were considered [20]. We reanalyzed Bakay’s EDMD data and extracted the subset of DE genes with FDR of $5\%$ (1349 and 1452 upregulated and downregulated genes, respectively). In order to identify enriched functional categories within each set of DE genes, we used g:Profiler [32]. This tool calculates the expected number of genes to be identified for any given functional category by chance and compares it to the number of genes observed. We selected categories that were significantly enriched with an FDR of $5\%$. The resulting list was then summarized by selecting representative classes using a similar approach to Revigo [33], but extended to other functional category databases in addition to gene ontology (GO) terms. Briefly, similarity matrices were generated by calculating pairwise Jaccard similarity indices between categories and applying hierarchical clustering to group together similar functional categories based on the genes identified. Redundancy was then reduced by choosing a representative category from each group.
The functional categories enriched in the set of EDMD-upregulated genes revealed defects in cytokine signaling, organization of the extracellular matrix (ECM), and various signaling pathways important for muscle differentiation and function (e.g. PI3K-Akt, TGF-beta, SMADs). In addition, there was an aberrant upregulation of alternative differentiation pathways, notably adipogenesis but also angiogenesis and osteogenesis. The functions highlighted among the downregulated genes were largely related to metabolism, mitochondrial especially, as well as ribosome biogenesis, muscle contraction and myofibril assembly (Fig. 2A). Applying the same methodology to our wider set of patient alleles highlighted fewer pathways than what we observed in the Bakay EDMD data, an expected outcome for the hypothesis that sampling the wider genetic variation in the disorder might hone in on the most central pathways for the pathomechanism. Among the upregulated categories, neurogenesis and ECM-related functions stood out, as well as MAPK signaling, lipid transport and TAP binding which are linked to interferon-gamma signaling [34]. One category stands out among the downregulated genes: RNA splicing (Fig. 2B). The data above used g:Profiler which is very sensitive to the number of DE genes identified because it looks for statistical overrepresentation of genes belonging to specific functional categories among a set of previously identified DE genes. By contrast, gene set enrichment analysis (GSEA) [35] does not prefilter the data and instead ranks all genes according to the difference in expression between the two conditions tested: controls and EDMD. Next, it determines whether the distribution in the ranked list for any given functional category is random or significantly enriched statistically at either end of the ranked list. This method is especially sensitive for detecting functional categories where many genes are altered by a small amount and does not consider individual gene P-values. Therefore, we also applied GSEA to our data, querying several functional genesets within the Reactome, KEGG and WikiPathways databases, found in the Molecular Signatures Database (MSigDB) [36]. This approach identified a larger set of functional categories that generally expanded on those identified by g:Profiler and matched better what we observed from the Bakay EDMD geneset, with strong links to ECM organization and cytokine signaling that may be relevant to fibrosis, differentiation, metabolism and splicing (Fig. 2C).
**Figure 2:** *Search for functional categories changing in EDMD patients. (A) Box plot of log2FC values for differentially expressed genes within significantly enriched functional categories in EDMD patients analyzed in the Bakay study using g:profiler. (B) Box plot of log2FC values for differentially expressed genes within significantly enriched functional categories in EDMD patients analyzed in this study using g:profiler. (C) Box plot of log2FC values for leading edge genes within significantly enriched functional categories in EDMD patients analyzed in this study using GSEA pathway (C2 geneset collection: canonical pathways). (D) GSEA enrichment plot for mitochondrially encoded genes. GSEA analysis using the C1 geneset (positional) revealed an upregulation of mitochondrially encoded genes. See Supplementary Material, Table S3 for further details.*
An expansion of categories for metabolic functions included specific categories for diabetes mellitus, adipogenesis, white and brown fat differentiation, nitrogen metabolism fatty acid metabolism, retinol metabolism and many others. Similarly, there was an expansion of cytokines supporting inflammation for the fibrotic pathways and proteoglycans and elastin adding to the previous emphasis on collagens for ECM defects. Among the differences between our data and Bakay’s EDMD data, two categories stand out: RNA splicing and calcium signaling, which were only observed in our data. It is unclear how much this reflects using terminally differentiated muscle material versus early stages of differentiation in vitro, or a factor of microarray versus RNAseq analysis. In some cases, this is most likely due to the different transcriptome platform used. For example, applying GSEA to genomic positional genesets revealed near uniform upregulation of all mitochondrially encoded genes (Figs 2D and 3A). This could not be observed on Bakay’s data because the microarrays did not contain probes for mitochondrially encoded genes. The upregulation of mitochondrial transcripts could lead to increased oxidative stress [37]. This finding provides yet another mechanism that could lead to metabolic dysregulation on top of the alterations already indicated by the nuclear genome transcript changes. This further underscored the need to test for actual metabolic deficits in the patient cells themselves as well as to further investigate the other functional pathways highlighted by this analysis.
**Figure 3:** *Metabolism changes confirmed in patients. (A) Heatmap of all mitochondria-encoded genes showing almost uniform upregulation in EDMD patients. (B) Heatmap of nuclear-encoded downregulated genes in Bakay’s study that supported mitochondria function. Downregulation is also generally observed in patients belonging to group 1, but not group 2 or 3. Functional analyses performed in primary patient (n = 12) and control (n = 7) myoblast cultures for (C) glycolysis and basal respiration show a significant reduction of both in EDMD myoblast cultures. (D) Testing for fuel dependency we could not observe any changes (* glucose and fatty acids have been measured; glutamine calculated). (E) ATP production is reduced in EDMD samples. All functional experiments have been repeated in at least three independent experiments. (F) qPCR of mitochondria shows a reduction of mitochondria in EDMD myoblasts. (G) Heatmap of mitophagy genes (GO-BP, GO:0000422, Autophagy of the mitochondrion) that are differentially expressed in at least one of Bakay’s EDMD, G1 analysis (all 10 patients analyzed as a single group) or G3 analysis (each of the three subgroups analyzed separately).*
## Detailed analysis of metabolic pathways uniformly altered in EDMD patients
Since metabolic disruption has been previously reported to affect muscle differentiation/myoblast fusion [38], we decided to investigate this further. While we identified a general upregulation of mitochondrially encoded genes, Bakay’s data showed a downregulation of several classes related to mitochondrial function (Fig. 2A) which was due entirely to nuclear-encoded genes, as there were no mitochondrial genes represented in the microarrays. When we checked the behavior of those genes in our data, we did not observe the same downregulation when considering all 10 patients as a single group (Fig. 3B). However, this is largely due to variability among the patient subgroups identified earlier, suggesting a mechanistic breakdown between them. Group 1 which contained half of our patients, including emerin and lamin A mutations, exhibited the same general downregulation of the nuclear-encoded mitochondrial genes. In contrast, group 2 displayed no alteration in gene expression, while group 3 showed upregulation although this was driven mostly by patient 9 (LMNA) with the other patient in the group, patient 4 (SYNE1), displaying very few changes. While no single gene was uniformly altered in the same direction for all patients, several genes from glycolytic and oxidative metabolism pathways, typically encoding components of mitochondrial complexes, were altered in all tested patients. Other non-mitochondrial metabolic pathways were also altered such as lipid translocation (Fig. 2C and Supplementary Material, Table S3). Interestingly, downregulation of nuclear-encoded mitochondrial genes was also generally observed in other muscular dystrophies included in the study by Bakay and colleagues (Supplementary Material, Fig. S4).
To investigate the relevance of these gene changes to cellular metabolism, we performed real-time metabolic analysis using the Seahorse XFp Extracellular Flux Analyzer. Myoblasts isolated from the above patients plus several additional EDMD patients and controls were tested, so that we had a total of 14 EDMD patients and 8 controls for this analysis (Table 1). Probing for glycolysis, a significant reduction of the extracellular acidification rate in the EDMD samples was observed (Fig. 3C). Next, we investigated mitochondrial function. When testing for basal respiration there was also a significant reduction of the oxygen consumption rate in the EDMD samples (Fig. 3C). There were no significant differences in fuel dependency, but ATP production was considerably reduced in the EDMD samples (Fig. 3D and E). The significant reduction in mitochondrial respiration raised another possibility to investigate that the absolute number of mitochondria might also be down due to problems in mitochondria biogenesis. Therefore, we quantified relative mitochondria numbers by qPCR. This revealed a clear reduction in mitochondria numbers (Fig. 3F), which with the generally elevated mitochondrial genome transcripts would suggest that a reduction in mitochondria numbers resulted in an overcompensation of expression which in turn could have resulted in inhibiting mitochondrial fission and repair. Thus, we also investigated whether genes in pathways associated with mitophagy were altered in the patients. Indeed, multiple mitophagy pathway genes were altered in all patients (Fig. 3G). Although no one individual gene was altered in all the patients, it is worth noting CISD2 is significantly downregulated in most patients. Reduction of CISD2 has been linked to degeneration of skeletal muscles, misregulated Ca2+ homeostasis and abnormalities in mitochondrial morphology in mouse [39], as well as cardiac dysfunction in humans [40].
## Detailed analysis of other pathways uniformly altered in EDMD patients
Several studies suggest that the timing of several aspects of myotube fusion could underlie some of the aberrancies observed in patient muscle [41] and, though it is unclear whether fibrosis drives the pathology or is a consequence of the pathology, fibrosis has been generally observed in EDMD patient biopsies. Contributing to these processes could be several subpathways that fall variously under the larger pathways for ECM/fibrosis, cell cycle regulation and signaling/differentiation (Fig. 4A). As for the metabolic analysis, no individual genes were altered in cells from all patients, but every patient had some genes altered that could affect ECM through changes in collagen deposition (Fig. 4B). For example, 35 out of 46 collagen genes exhibited changes in at least one comparison (Bakay EDMD, G1 or one of the subgroups gp1, gp2 and gp3) and all patients had multiple of these genes altered (Supplementary Material, Fig. S5). Note that it often appears visually that the *Bakay data* in the first column has little change when viewing the cluster analysis, but when looking at the full set of genes listed in the matching supplemental figures there are definitely some genes strongly changing, just not necessarily the same ones. This may be due to differences in the myogenic state of the material studied: while Bakay and colleagues used muscle biopsies containing terminally differentiated muscle fibers, we focused on the earlier stages of myogenesis by in vitro differentiating cultured myoblasts obtained from muscle biopsies. Despite this, it is important to note that while different genes may be affected, most of the same pathways were highlighted in both Bakay’s and our study. Collagens COL6A1, COL6A2, COL6A3 and COL12A1 are linked to Bethlem muscular dystrophy (42–45) and, interestingly, all these collagens were upregulated in group 1 patient cells and downregulated in group 3 patient cells (Supplementary Material, Fig. S5). Matrix metalloproteinases, which participate in the degradation and remodeling of the ECM, were also altered with 13 out of 28 matrix metalloproteinases exhibiting changes in at least one of the comparisons and all patients had multiple of these genes altered (Fig. 4C and Supplementary Material, Fig. S5). Notably the metalloproteinase MMP1 (collagenase I), which has been proposed to resolve fibrotic tissue [46], was downregulated in all but one patient, as well as in Bakay EDMD samples. Likewise, multiple genes associated with fibrosis from FibroAtlas (Fig. 4D and Supplementary Material, Fig. S6) and with inflammation that would support fibrosis such as cytokine (Fig. 4E and Supplementary Material, Fig. S7) and INF-gamma signaling (Fig. 4F and Supplementary Material, Fig. S8) were affected in all patients. In fact, out of 941 genes in FibroAtlas there were 542 altered between all the patients. Heatmaps of gene clusters with similar expression patterns are shown in Figure 4, but more detailed individual panels with all gene names listed are shown in Supplementary Material, Figures S5–11. A few genes that stand out for their functions within the INF-gamma signaling pathway include IRF4 that is a regulator of exercise capacity through the PTG/glycogen pathway [47] and ILB1 that helps maintain muscle glucose homeostasis [48] such that both could also feed into the metabolic pathways altered.
**Figure 4:** *Genes changing expression within functional groups supporting ECM/fibrosis/myotube fusion. (A) Summary of altered functions in EDMD. (B) Heatmap showing gene expression changes for collagens. (C) Heatmap showing gene expression changes for matrix metalloproteinases. (D) Heatmap showing gene expression changes for general proteins involved in fibrosis according to FibroAtlas (http://biokb.ncpsb.org.cn/fibroatlas). (E) Heatmap showing gene expression changes for cytokine signaling proteins (GO-BP, GO:0019221). (F) Heatmap showing gene expression changes for interferon-gamma signaling proteins (WikiPathways, WP619). (G) Heatmap showing downregulation of genes involved in the degradation of cell cycle proteins. GSEA identified downregulation in EDMD patients of REACTOME hsa-1 741 423 (APC-C mediated degradation of cell cycle proteins) and hsa-187 577 (SCF/SKP2 mediated degradation of p27/p21) (Fig. 2C). Both genesets were combined. (H) Heatmap showing gene expression changes for skeletal muscle regeneration proteins (GO-BP, GO:0043403). (I) Heatmap showing gene expression changes for myoblast fusion (GO-BP, GO:0007520). (J) Heatmap showing gene expression changes for myoblast differentiation (GO-BP, GO:0045445). (K) Heatmap showing gene expression changes for specific genes functioning in myotube fusion that are under genome-organizing NET regulation. (L) Heatmap showing the upregulation of many pro-adipogenic genes (Wikipathways, WP236). All heatmaps were generated from differentially expressed genes in at least one of Bakay’s EDMD, G1 analysis (all 10 patients analyzed as a single group) or G3 analysis (each of the three subgroups analyzed separately). K-means unsupervised hierarchical clustering was used to summarize the heatmap into eight clusters of roughly coexpressing genes. Full heatmaps displaying gene names are provided in Supplementary Material, Figures S5–S11. The number of genes per cluster is indicated on the right of each heatmap. Red and blue indicate upregulation and downregulation, respectively.*
Another subpathway critical for myogenesis and the timing and integrity of myotube fusion is cell cycle regulation. Cell cycle defects could lead to spontaneous differentiation and were previously reported in myoblasts from EDMD patients and in tissue culture cell lines expressing emerin carrying EDMD mutations which could lead to depletion of the stem cell population [41,49]. All tested EDMD patients exhibited downregulation of multiple genes involved in the degradation of cell cycle proteins (Figs 2C and 4G, and Supplementary Material, Fig. S8) which could indicate an uncoupling of the joint regulation of cell cycle and myogenesis program [50], for example cells starting to fuse when they should still be dividing or vice versa.
Other pathways in addition to ECM deposition directly associated with myogenic differentiation, myoblast fusion and muscle regeneration were also altered in all patients (Fig. 4H–J and Supplementary Material, Figs S9 and S10), though, again, no single gene in these pathways was altered in the same way in all patients’ cells. Poor differentiation and myotubes with nuclear clustering were observed in differentiated EDMD myoblast cultures [14] and in the mouse C2C12 differentiation system when EDMD-linked NETs were knocked down [17].
Previous work using C2C12 cells identified six genes whose products are required in the early differentiation stages and were under the regulation of muscle-specific genome-organizing NETs [17]. *These* genes (NID1, VCAM1, PTN, HGF, EFNA5 and BDNF) are critical for the timing and integrity of myotube fusion and need to be expressed early in myoblast differentiation but shut down later or they inhibit myogenesis (51–55). All six genes were misregulated in at least five but none were affected in all patients (Fig. 4K). *In* general terms, these genes were upregulated in group 1, downregulated in group 3 and mixed in group 2. All six genes were upregulated in Bakay’s EDMD data, although only NID1 and HGF were statistically significant at $5\%$ FDR. Both were upregulated only in group 1 and downregulated in group 3. PTN showed a similar pattern of expression as HGF although the only statistically significant changes were for upregulation in group 1.
Several myogenic signaling pathways were altered such as MAPK, PI3K, BMP and Notch signaling, and several alternate differentiation pathways were de-repressed such as adipogenesis that could disrupt myotube formation and function (Fig. 2 and Supplementary Material, Table S3). Myogenesis and adipogenesis are two distinct differentiation routes from the same progenitor cells and whichever route is taken the other becomes repressed during normal differentiation [56,57]. We previously showed that knockout of fat- or muscle-specific genome organizing NETs yield de-repression of the alternate differentiation pathway [17,58] and the Collas lab showed that Lamin A/C lipodystrophy point mutations yield de-repression of muscle differentiation genes in adipocytes [59]. We now find here that adipogenesis genes are upregulated in both Bakay’s EDMD data and our data (Fig. 2). This is especially prominent for the five patients in group 1 while group 3 showing strong downregulation of a subset of the same genes and group 2 broadly looking like an intermediate of the other two groups (Fig. 4L and Supplementary Material, Figs S11 and S12), and thus could also contribute to the metabolic defect differences between patients.
## Splicing pathways uniformly altered in EDMD patients yield loss of muscle-specific splice variants
Among the downregulated functional categories, mRNA splicing stood out with many genes uniformly downregulated in all patient samples (Fig. 2B and C and Supplementary Material, Fig. S14). Because of that we decided to investigate various subcategories and we found that there was a striking and uniform upregulation of factors supporting alternative splicing (AS) while constitutive splicing factors involved in spliceosome assembly and cis splicing are downregulated (Fig. 5A and Supplementary Material, Fig. S15). Expression changes of as little as $10\%$ (log2FC > 0.1) have been shown to result in biologically relevant changes for vital proteins like kinases and splicing factors [60]. We thus assume that upregulation and downregulation of whole spliceosome subcomplexes even in low log2FC ranges lead to significant splicing misregulation. Notably, snRNAs of the U1 spliceosomal subcomplex, responsible for 5’ SS recognition, constitute as much as $20\%$ of all downregulated splicing factors (|log2FC| > 0.1, RNU1s and RNVU1s). Interestingly, a similar sharp cut-off between alternative and constitutive splicing has been reported in myotonic dystrophy (DM1/DM2) with similar genes being affected, namely CELFs, MBNLs, NOVA, SMN$\frac{1}{2}$ and SF3A1, among others [61]. DM1 is one of the best studied splicing diseases and shares typical muscular dystrophy symptomatology with EDMD, namely progressive muscle weakness and wasting, cardiac arrhythmia and contractures. Moreover, a number of splicing changes in DM1 and DM2 also occur in other muscular dystrophies [62]. Of note, MBNL3 is 4-fold transcriptionally upregulated in EDMD compared with controls. Its protein product impairs muscle cell differentiation in healthy muscle and thus needs to be downregulated upon differentiation onset [63].
**Figure 5:** *Analysis of splicing defects reveals loss of muscle-specific splice variants. (A) GOchord plot for misregulated splicing factors indicates the primary change is upregulation of alternative splicing and downregulation of constitutive splicing. Plot for gp1 is shown as a representative. DE genes log2FC > |0.15| and P < 0.05. (B) Venn diagrams of alternatively spliced genes predicted by rMATS, DEXSeq and ISA for all samples (G1) and the three subgroups (gp1, gp2 and gp3). Overlap between all three methods indicates genes with exon skipping events that lead to annotated isoform switches. (C) Bar chart for functional pathways reveals an enrichment in altered splice variants for pathways associated with metabolism, gene expression, cytoskeleton organization, DNA repair, proliferation/differentiation, stress, ECM/fibrosis/and sarcomere structure. Number of genes detected displayed as log2. (E) Pie chart of significant AS events with psi > |0.1| in gp1 detected with rMATS, which were used to scan for muscle specific-splice variants, shown as heatmap. AS events, alternative splicing events, SE = exon skipping, MXE = mutually exclusive exons, RI = intron retention, A3SS = alternative 3′ splice site, A5SS = alternative 5′ splice site. (F) Isoform switches as analyzed using isoformSwitchAnalyzer for ZNF880 and TMEM38A. ZNF880 shows the same isoform switch in all groups with preferential use of the shorter isoform that contains the KRAB domain (black), but not the zinc finger domain (blue). TMEM38A shows a clear switch from the muscle isoform to a shorter isoform.*
Next, we performed splicing analysis to determine whether mis-splicing could drive some of the pathway alterations observed in the EDMD samples. For this purpose, we used three different methods: DEXSeq analyses exon usage, rMATS provides information about the five most common AS events and isoform Switch Analyzer (ISA) indicates which splicing events lead to annotated isoform switches. This revealed varying amounts of alternatively spliced genes in all samples and the three subgroups (Fig. 5B). Since every method focuses on a different event type/aspect of splicing, a higher amount of unique than overlapping genes is to be expected. Accordingly, an overlap of all three methods indicates genes with exon skipping events that lead to annotated isoform switches. The number of mis-spliced genes overlapping between the three algorithms was only 1 gene, ZNF880, in G1. In contrast, when analyzing group 1, 2 and 3 separately, each patient grouping had many mis-spliced genes identified by all three algorithms with 18 mis-spliced genes in the intersect for group 1, the group including half of the patients, and as much as 95 in group 3 (Supplementary Material, Table S4). *These* genes include Nesprin 3 (SYNE3), the splicing factor kinase CLK1 and the chromatin regulator HMGN3, all of which are potentially contributing to EDMD, given their functions. All results can be found in Supplementary Material, Table S5. The rMATS analysis includes five AS events: exon skipping (SE), intron retention (RI), mutually exclusive exons (MXE), alternative 3′ splice site (A3SS) and alternative 5′ splice site (A5SS) usage. Using this comprehensive dataset, we searched for AS events that are significantly differentially used |(percent-spliced-in-(psi)-value| > 0.1 and P-value < 0.05, Supplementary Material, Table S5). Comparing AS event inclusion between control and EDMD samples, we find thrice as much intron retention in EDMD, while all other events are similarly included as excluded. We hypothesize that this could be a result of downregulated U1 snRNAs which are necessary for proper spliceosome assembly. Supporting the likely importance of the splicing pathway to the EDMD pathomechanism, pathway analysis on these genes revealed a strong enrichment for pathways associated with metabolism, gene expression and the cytoskeleton (Fig. 5C). Moreover, group 1 and group 3 display an enrichment for myogenesis and muscle contraction. Using a custom-made set of genes either specific or relevant for muscle development and structure (see Materials and Methods section), we then scanned all significant and differential AS events (Fig. 5D and Supplementary Material, Fig. S16). Notably, mis-splicing led to the absence of many muscle-specific splice variants (Fig. 5E), among them vital muscle structural genes like TTN, TNNT3, NEB, ACTA4 and OBSCN as well as developmental regulators of the MEF2 family. Importantly, many of these genes mis-spliced in the EDMD patients are linked to a variety of other muscular dystrophies. For example, TTN, CAPN3, PLEC and SGCA are linked to Limb-Girdle muscular dystrophy (64–69), DMD is linked to Duchenne muscular dystrophy and Becker muscular dystrophy [70,71], and BIN1, TNNT$\frac{2}{3}$ and MBNL1 are mis-spliced in myotonic dystrophy [72] and all of these are mis-spliced and/or have missing muscle-specific splice variants in many of the patients in our cohort.
Intriguingly, one of the mis-spliced genes that also displays an isoform switch in group 3 is TMEM38A that has been linked to EDMD [10]. The altered splicing map for TMEM38A reveals that not only is its expression highly elevated in EDMD patients (log2FC = 2.9) but also that the protein-coding isoform displays a higher usage relative to abundance compared with the non-coding isoform (Fig. 5F) Many other notable mis-spliced genes are involved in myotube fusion such as the previously mentioned NID1 that is under spatial genome positioning control of NET39, another of the genome organizing NETs causative of EDMD. Most compellingly, three mis-spliced genes having to do with myogenesis/myotube fusion had muscle-specific splice variants absent in all patients (G1 rMATS). These were CLCC1 whose loss yields muscle myotonia [73], HLA-A/B that disappears during myogenesis and is linked as a risk factor for idiopathic inflammatory myopathies [74,75], and SMAD2 that shuts down myoblast fusion [76]. The above examples were found in all patients within a particular group, but not always amongst all patients from the study or determined by all algorithms; however, there were also some mis-spliced genes that are potentially even more interesting because they were mis-spliced in all patients and with all three algorithms yielding the same results. One of these was ZNF880. While overall transcript numbers remained similar, the isoform predominantly expressed in control cells, ENST00000422689, is strongly downregulated in group 1 while the shorter isoform, ENST00000600321, is strongly upregulated (Fig. 5F). Interestingly, the dominant isoform in EDMD loses the zinc finger domain (light and dark blue) and is left with the repressive KRAB domain (black). Little is known about ZNF880 except that it has an unclear role in breast and rectal cancer [77,78], and additional experiments are necessary to elucidate its role in EDMD.
## miRNA-Seq analysis of EDMD patient cells
Changes in miRNA levels have been observed in a number of muscular dystrophies and are often used as biomarkers (79–81), but a comprehensive investigation of miRNA levels in EDMD has thus far not been engaged. Thus, the in vitro differentiated EDMD patient cells used for the preceding analysis were also analyzed by miRNA-Seq. We identified 28 differentially expressed miRNAs with some variation among patients (Fig. 6A). We extracted their putative targets from the miRDB database (http://mirdb.org) and selected those targets whose expression changed in the opposite direction of the miRNAs. Pathway analysis revealed misregulation of miRNAs largely associated with the same pathways that were misregulated from the RNA-Seq data, e.g. metabolism, ECM/fibrosis and signaling/differentiation (Fig. 6B and Supplementary Material, Table S6). More specifically, for metabolism 9 of the misregulated miRNA were linked to metabolic functions and with only partial overlap another 9 linked to mitochondria function, for ECM/fibrosis 19 of the misregulated miRNAs were linked to ECM and again with only partial overlap 10 to fibrosis and 13 to cytokines and inflammation. As noted before the ECM category in addition to potentially contributing to fibrosis is also relevant for myotube fusion along with cell cycle regulation that was targeted by 13 misregulated miRNAs and myogenesis that was targeted by 5 miRNAs. Several misregulated miRNAs had functions relating to alternative differentiation pathways with 4 relating to adipogenesis, 12 to neurogenesis, 17 to angiogenesis and for signaling there were 9 misregulated miRNAs affecting MAPK pathways, 8 for Akt signaling, 1 for JAK–STAT signaling, 5 for TGF-beta signaling, 2 for Notch signaling and 3 for TLR signaling. Interestingly, some misregulated miRNAs were also reported as being linked to disease states such as miR-140-3p to dilated cardiomyopathy through its repressive effect on the integrin metalloproteinase gene ADAM17 [82]. As well as working within cells, miRNAs are often detected within a circulating exosomal microvesicle population that can be harvested from blood serum. This makes them especially attractive as potential biomarkers when compared with more invasive biopsies, but a much larger sample size together with more clinical information will be required to clarify these as biomarkers.
**Figure 6:** *miRNA analysis. (A) Heatmap of miRNAs that had altered levels in EDMD patient cells compared with controls. Red indicates upregulated and blue downregulated with intensity according to the log2FC values. (B) Overview of functional categories linked to differentially expressed miRNAs in EDMD. The label size is proportional to the number of DE miRNAs associated with each category.*
## EDMD gene expression signature suggests relationships to other muscular dystrophies
The earlier Bakay study analyzed patient samples from other muscular dystrophies for comparison to EDMD. Several of the disorders show a high degree of pairwise similarity from a transcriptome point of view (Supplementary Material, Fig. S12A). In fact, many of the functional categories misregulated in EDMD are also altered in the same direction in several other muscular dystrophies, although with some differences (Supplementary Material, Fig. S12B). For example, collagens as a general category showed the highest correlations between EDMD and most other muscular disorders with the exception of hereditary spastic paraplegia, suggesting that the ECM is a major player in most muscular dystrophies. In contrast functional categories related to metabolism and mitochondrial function such as glycolysis, oxidative phosphorylation and mitophagy were more uniquely changed, i.e. weaker correlated between EDMD and other muscular disorders. Splicing also showed a weaker correlation with other muscular disorders compared with other muscle-specific functions (Supplementary Material, Fig. S12B). Because of the overall degree of similarity among neuromuscular diseases, and the fact that Bakay et al. used terminally differentiated muscle while our data come from a very early stage of differentiation in vitro, we reasoned that GSEA could be a sensitive method to compare the datasets. To this effect, we reanalyzed Bakay’s data and extracted the DE genes for each of the neuromuscular diseases. We then checked our data for enrichment of those specific genesets. We then plotted the GSEA normalized enrichment score against the –log10(P-value). This way we can visualize positive or negative associations on the x-axis, and the higher on the y-axis the higher the confidence (lower P-values).
When we looked at all patients as a single group (G1), EDMD was the best match, with the highest score and lowest P-value (FDR 0.001), although unsurprisingly a few other diseases came very close, notably LGMD2A and facioscapulohumeral muscular dystrophy (FSHD) (Fig. 7A). This indicates that despite the differences in the individual DE genes between the mature muscle data from Bakay’s EDMD geneset and our early in vitro differentiation geneset, a clear EDMD gene expression signature was displayed in our data. The next best match is Limb-Girdle muscular dystrophy 2A (LGMD2A), which shares some symptomatology with EDMD including muscle wasting, contractures and mild cardiac conduction defects although cardiac involvement is infrequent [83]. The differences in gene signatures that broke down the 10 patients into three EDMD patient subgroups could reflect an underlying cause of clinical disease spectrum or indicate that a group may not be adequately classified as EDMD. Therefore, we performed the same GSEA analysis on each subgroup separately. Group 1, which had both classic emerin and lamin A EDMD mutations, showed an even better match with the Bakay EDMD group which was again very close to LGMD2A but also to DMD, Becker muscular dystrophy (BMD), FSHD and Limb-Girdle muscular dystrophy 2I (LGMD2I) (Fig. 7B). LGMD2B was still separate and closer to juvenile dermatomyositis (JDM). For group 2, none of the diseases matched at $5\%$ FDR, although the Bakay EDMD set remained the most like our set. Interestingly, two diseases exhibited an anti-correlation: DMD and BMD, which are both caused by mutations in the dystrophin gene DMD. In contrast, group 3 appeared to be the most distinct and in many ways opposite to group 1, which is a pattern that was often observed in the functional gene subsets analyzed (Supplementary Material, Figs S5–S11). Group 3 was anti-correlated with EDMD and most of the other muscular dystrophies, while the neurogenic amyotrophic lateral sclerosis appeared as the best match, possibly suggesting a neuronal bias in this group (Fig. 7B). This is further supported by the appearance of axonal neuropathy, ataxia, undergrowth and speech problems in one of the two patients from this group (patient 4, SYNE1; Table 1), while none of the others exhibited any signs of neuropathy.
**Figure 7:** *EDMD is distinct from other MDs. (A) Scatterplots from GSEA analysis comparing Bakay data for several muscular dystrophies to the new patient data from this study (all 10 patients as a single group = G1). On the x-axis, the normalized enriched score (NES) is a measurement of the enrichment of the DE geneset identified in our study compared with each of the diseases in the Bakay study. The y-axis shows the –log10(FDR), which is a measurement of statistical confidence. The gray horizontal line marks the 5% FDR threshold. Muscular dystrophies from the Bakay study are EDMD, Limb-Girdle muscular dystrophy 2A (LGMD2A), Limb-Girdle muscular dystrophy 2B (LGMD2B), Limb-Girdle muscular dystrophy 2I (LGMD2I), fascioscapulohumeral muscular dystrophy (FSHD), Duchenne muscular dystrophy (DMD), Becker muscular dystrophy (BMD), juvenile dermatomyositis (JDM), acute quadriplegic myopathy (AQM), amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP). (B) Same as (A), for each individual patient subgroup. (C) Box plot of log2FC values for differentially expressed genes within significantly enriched functional categories for each patient subgroup compared with the rest, using g:Profiler. (D) Each patient subgroup was relatively more enriched for certain functional pathways than other subgroups, suggesting that treatments for example targeting different metabolic pathways for groups 1 and 3 might partially ameliorate some patient difficulties.*
The relationship of the patient groups segregated by gene signatures to potential differences in clinical presentation is underscored by the functional pathways enriched in each group over the others (Fig. 7C). Group 1 showed a strong enrichment of pathways associated with ECM and fibrosis, such as interferon signaling, TNF signaling, ECM organization, ECM proteoglycans, integrin cell surface interactions, collagen formation and signaling by PDGF all upregulated. Adipogenesis was also particularly promoted in group 1 compared with the others, and cardiac conduction defects were also highlighted. Group 2 was more uniquely associated with Hippo signaling and BMP2-WNT4-FOXO1 pathway and had fewer links to ECM and fibrosis. Group 3 was more uniquely associated with metabolism, particularly upregulation of oxidative phosphorylation, mitochondrial biogenesis, glucagon signaling pathway, gluconeogenesis, glycolysis and gluconeogenesis, metabolism, TP53 regulates metabolic genes and thermogenesis pathways. This would suggest that group 1 pathophysiology may have more characteristics of fibrosis and altered myofibers, while group 2 may have more differentiation or mechanosignaling defects and group 3 more metabolic defects (Fig. 7D).
## Discussion
Attempts to identify the EDMD pathomechanism or clinical biomarkers purely through gene expression signatures are limited because there is too little uniformity in differential gene expression between all patients, although there have been some promising reports using miRNA profiling or detection of cytokines in serum [81,84]. We therefore engaged a functional pathway analysis using in vitro differentiated myotubes derived from 10 unrelated EDMD patients with known mutations in seven EDMD-linked genes. While it is difficult to detect many individual genes that were uniformly changed in all patients, we found many pathways that were affected in all patients. Thus, although different genes may have been targeted in different patients, the same functional pathway would be disrupted and thus yield a pathology with similar clinical features. Many pathways were disrupted when we re-analyzed data from the previously published Bakay study [20] and we postulated that, as they just analyzed mutations in two of the over two dozen genes linked to EDMD, analyzing a larger set of linked genes might narrow down the number of pathways to highlight those most relevant to EDMD pathophysiology. Indeed, when we considered a wider set of patients with mutations in seven different genes the set of affected pathways narrowed to the point that we could identify four likely candidate umbrella pathways.
These four umbrella pathways all make sense for contributing to or even driving the EDMD pathomechanism [85]. Disruption of metabolism pathways was consistent with the significantly reduced glycolysis and mitochondrial respiration output we showed in patient myoblasts compared with controls and it makes sense that this could lead to fatigue, weakness and muscle atrophy. ECM changes and fibrosis pathways are consistent with pathology observed in EDMD and similarly could drive some of the initial pathology and, as fibrosis accumulates, contribute to disease progression. De-repression of genes from alternate differentiation pathways and defects in myogenesis through disrupted signaling pathways and cell cycle regulation could generate aberrant myotubes to yield pathology. Finally, the last disrupted pathway of splicing yields a loss of muscle-specific splice variants that could impact on all three preceding pathways.
There is much scope for intersection between the four highlighted pathways altered in all sampled EDMD patient cells. For example, amongst the de-repressed differentiation pathways was adipogenesis that could also have impact on the metabolism pathway. Even amongst the few genes that were uniformly altered in all patients sampled, though not originally obvious, a more detailed reading of the literature leads to intersections with these pathways. For example, while the MYH14 general upregulation did not make obvious sense for muscle defects since it is not part of the contractile machinery, it has been shown that a mutation in MYH14 disrupts mitochondrial fission in peripheral neuropathy [31]. Thus, MYH14 could potentially feed into the mitochondrial deficits noted in the patient cells. Many of the miRNAs found to be altered in the patients feed into several of these pathways. For example, miR-2392 that is increased in all patients downregulates oxidative phosphorylation in mitochondria [86] but at the same time also is reported to promote inflammation [87]. miR-140 that is up in all groups has roles in fibrosis through collagen regulation [88], is pro-adipogenic [89] and inhibits skeletal muscle glycolysis [90]. miRNAs could also be used potentially prognostically between the different groups as for example miR-146a is upregulated in group 1, unchanged in group 2 and downregulated in group 3. This miRNA has a strong effect on inflammation and has been implicated in fibrosis in the heart [91]. miRNAs show some promise as biomarkers, especially if they could be isolated from circulating exosome vesicles in serum. Several miRNAs have been proposed as markers for lamin A/C-associated muscular dystrophies, targeting functions such as muscle repair through TGF-beta and Wnt signaling. Some of those were also identified in our study, such as miR-335, which plays a role in muscle differentiation [81]. Other miRNAs identified in that study appear to be misregulated only in one subgroup of patients but not others. For example, miR-100 and miR-127-3p are misregulated in group 1 only, while miR-136, miR-376c and miR-502-3p are only misregulated in group 3, and miR-148a is upregulated in group 1 but downregulated in group 3. Because there is so much functional overlap between miRNA targets and the pathways noted from the RNA-Seq analysis, it is unclear to what extent the gene expression changes observed could be indirect from the misregulated miRNAs. Nonetheless, there are four core functions targeted by multiple mechanisms that we argue are likely to be central to the core EDMD pathomechanism. Interestingly, the literature is filled with many examples of mutation or loss of different splicing factors causing muscle defects though no individual mis-spliced gene was identified as mediating these effects. Similarly, in myotonic dystrophy type 1 (DM1) there are many mis-spliced genes thought to contribute to the disease pathology [72]. For example, the splicing factor SRSF1 that is down in most patients is important for neuromuscular junction formation in mice [92]. It has to be noted that while the present study used in-vitro differentiated myotubes, these pathways may cross-talk not just at the level of gene expression in the myotubes themselves, but also through effects determined in vivo by the muscle microenvironment which may vary depending on the activation of inflammatory pathways, for example.
How so many genes become misregulated has not been experimentally proven, but for lamin A/C, emerin, Sun1, nesprin, TMEM214 and PLPP7/NET39, the fact that mutations to all individually yield many hundreds of gene expression changes with considerable overlap strongly suggests that they function in a complex at the nuclear envelope to direct genome organization. Knockdown of Tmem214 and NET39 as well as several other muscle-specific NETs each alters the position and expression of hundreds of genes [17]. Separately it was found that lamins and NETs, including emerin, function together in distinct complexes involved in tethering genes to the nuclear envelope in fibroblasts [93] and in muscle cells [94]. Thus, disruption of emerin, lamin A/C or any other component of these tethering complexes could yield sufficiently similar gene/pathway expression changes to yield the core clinical features of EDMD. We propose that the different muscle-specific NETs give specificity to a complex containing lamin A/C and emerin and that Sun1 and nesprin proteins can indirectly impact on these complexes through mediating mechanosignal transduction and FHL1 in interpreting such signals. Since $15\%$ of all genes changing here were affected by at least one of the muscle-specific genome-organizing NETs that were tested by knockdown, this would provide a core set of genome organization and expression changes to cause the core EDMD pathology. Since the majority of genes affected by each NET tested were unique to that NET with the exception of Tmem214, this could account for other gene expression changes that drive the segregation into subgroups which could in turn contribute to clinical variation. This interpretation is consistent with the numbers of genes changing for mutations in different nuclear envelope proteins (Fig. 1B). That the genome organizing NET mutations yielded fewer genes changing than the lamin A/C mutations may be because lamin A/C mutations disrupt multiple genome tethering complexes and thus affect more genes. The segregation of the two LMNA mutation gene signatures into separate groups might reflect separate complexes for lamin C or each mutation disrupting different sets of complexes. In either case, the extreme differences in lamin mutations gene expression profiles is not entirely surprising as different lamin mutations also exhibited large differences in studies of nuclear mechanics [95]; so this could also impact on mechanosignal transduction. The Sun1 mutation may have affected fewer genes because of redundancy with Sun2 in its mechanosignal transduction function while the Nesprin 1 mutation may have had more genes changing because it is more central to mechanosignal transduction. More work is needed to clarify on all these possibilities.
The FHL1 mutations add another level of complexity to EDMD as there are several splice variants of FHL1 and only the B variant (ENST00000394155) targets to the nuclear envelope [96]. That EDMD is a nuclear envelope disorder is underscored by the fact that none of the FHL1 mutations occur in exons found in the much shorter C variant (ENST00000618438) and the patient 8 mutation p.V280M is in an exon unique to FHL1B. Thus, the nuclear envelope splice variant is the only one that could yield pathology in all patients, though some of the variation could come from one of the patients also expressing the mutant A splice variant (ENST00000543669).
While further work is needed to validate the correlations between the gene expression profile subgroupings and their clinical presentation and disease progression, our finding of such distinct gene expression profiles amongst clinically diagnosed EDMD patients argues that the currently used clinical phenotype spectrum umbrella of the EDMD classification may be too broad and it might be reclassified in more precise subtypes. What is clear is that the original classifications of EDMD subtypes based just on the mutated gene often allow for cases with very dissimilar gene profiles to be classified together, while similar gene signature cases are classified as separate classes. The two mutations in LMNA yielded changes in gene expression profiles that were far more different from one another than the group 1 lamin A mutation gene profile was from the TMEM214, NET39, emerin and FHL1 mutation gene signatures. Similarly, the FHL1 p.C224W mutation yielded greater gene expression differences from the other two FHL1 mutations than it did for the other proteins in group 1. Thus, EDMD might be better classified by similarities in gene expression profiles than by the particular gene mutated and our study shows proof of principle for this, even if the groupings may change slightly once a larger patient cohort can be established and examined. Regardless, these groups have distinctive gene expression and miRNA signatures that could be used as biomarkers both diagnostically and perhaps prognostically. To get to that point will require a more comprehensive modern description of clinical Gestalt phenotypes including e.g. imaging datasets and disease progression timelines deciphering unique groups. Importantly, and regardless of the disease nomenclature, the different pathways we found enriched for in each subgroup could be converted to clinical recommendations based on the much more conserved individual gene expression changes for each subgroup (Fig. 7C). For example, EDMD patients have been considered by some clinicians to be at risk for malignant hyperthermia [97], though a consensus was never achieved. Our data show that the three genes currently associated with malignant hyperthermia (RYR1, CACNA1S and STAC3) are all misregulated in groups 1 and 3, but not group 2 (Supplementary Material, Fig. S17). Thus, checking expression of these genes might indicate whether a patient is likely to be at risk or not. Finally, an additional new aspect coming up from our datasets is that it might be worth further investigating the role of splicing in muscle differentiation because it might be of wider relevance to muscular dystrophy beyond DM and EDMD.
## Patient materials
The sources of patient samples were the Muscle Tissue Culture Collection (MTCC) at the Friedrich-Baur-Institute (Department of Neurology, Ludwig-Maximilians-University, Munich, Germany) and the MRC Centre for Neuromuscular Disorders Biobank (CNDB) in London.
## Ethical approval and consent to participate
All materials were obtained with written informed consent of the donor at the CNDB or the MTCC. Ethical approval of the rare diseases biological samples biobank for research to facilitate pharmacological, gene and cell therapy trials in neuromuscular disorders is covered by REC reference 06/Q$\frac{0406}{33}$ with MTA reference CNMDBBL63 CT-2925/CT-1402, and for this particular study was obtained from the West of Scotland Research Ethics Service (WoSRES) with REC reference 15/WS/0069 and IRAS project ID 177946. The study conduct and design complied with the criteria set by the Declaration of Helsinki.
## Myoblast culture and in vitro differentiation into myotubes
Myoblasts were grown in culture at 37°C and $5\%$ CO2 using a ready to use formulation for skeletal muscle (PELOBiotech #PB-MH-272-0090) and maintained in subconfluent conditions. In order to induce differentiation, the cells were grown to confluency and 24 h later the growth medium replaced with skeletal muscle differentiation medium (Cell Applications #151D-250). The differentiation medium was replaced every other day. Myotubes were selectively harvested after 6 days by partial trypsinization followed by gentle centrifugation (Supplementary Material, Fig. S1). Each differentiation experiment was performed in triplicate. To avoid batch effects, experiments were distributed over several weeks, so that no two samples or replicates were differentiated at the same time. Myotubes were stored in Trizol at –80°C until all samples were collected.
## RNA extraction
Myotubes were stored in Trizol at –80°C until all samples were available. Subsequent steps were performed simultaneously for all samples. Total RNA was extracted from each sample and separated into a high molecular weight fraction (>200 nt, for mRNA-Seq) and a low molecular weight fraction (<200 nt, for miRNA-Seq) with the Qiagen RNeasy (#74134) and miRNeasy (#1038703) kits, according to the manufacturer’s instructions. RNA quality was assessed with a Bioanalyzer (Agilent Technologies), and all samples had an RIN > 7, with an average of 9.4 (Supplementary Material, Table S1).
## mRNA-Seq analysis
Between 3 and 5 μg of total RNA were sent to Admera Health LLC (NJ, USA) for sequencing in paired-end mode, 2x 150 nucleotides, using an Illumina HiSeq 2500 sequencer. The sequencing library was prepared with the NEBNext Ultra II kit, with RiboZero rRNA depletion (NEB #E7103, Illumina #20040526). Between 60 and 90 million paired end reads were obtained from each sample and mapped to the human genome (Hg38) with STAR v2.7.5a [98] using default parameters. Mapping quality was assessed with FastQC v0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc). Sequencing adaptors were removed with trimmomatic v0.35 [99]. Low-quality reads and mitochondrial contaminants were removed, leaving on average 70 million useful reads per sample (Supplementary Material, Table S1). Differential expression analysis was performed in R with DESeq2 v1.32.0 [100] after transcript quantitation with Salmon v1.4.0 [101]. We used an FDR threshold of $5\%$ for differential expression.
## miRNA-Seq analysis
miRNA was sent to RealSeq Biosciences Inc. (SC, USA) for sequencing using an Illumina NextSeq 500 v2 sequencer in single end mode, 1 × 75 nucleotides. The sequencing library was prepared with Somagenics’ Low-bias RealSeq-AC miRNA library kit (#500–00012) and quality assessed by Tapestation (Lab901/Agilent). On average, 5 million good quality reads were obtained per sample. Mapping and quality trimming was performed using the NextFlow nf-core/smrnaseq pipeline (https://nf-co.re/smrnaseq) with default parameters, which summarizes the reads per miRNA using the annotations from mirTop (https://github.com/miRTop/mirtop). Differential expression analysis was performed in R with DESeq2 v1.32.0. We used an FDR threshold of 0.2 for differential expression. Putative miRNA targets were extracted from miRDB (https://mirdb.org/) for each differentially expressed miRNA and their expression compared against the miRNA. We kept as potential targets those genes whose expression changed in the opposite direction of the miRNA.
## Bakay muscular dystrophy dataset analysis
Normalized (MAS5.0) microarray transcriptome data for a panel of 11 muscular dystrophies and healthy controls were downloaded from the Gene Expression Omnibus database (GEO), accession GSE3307 (https://www.ncbi.nlm.nih.gov/geo/). Differential expression analysis comparing each disease to the controls was performed using Limma 3.48.1 [102]. We used an FDR threshold of $5\%$ for differential expression.
## Functional analyses
Functional analyses were performed with g:Profiler [103] and Gene Set Enrichment Analysis (GSEA v4.1.0) [35] tools. g:Profiler was used to determine enriched categories within a set of DE genes, with an FDR of $5\%$ as threshold. GSEA was performed with default parameters, in particular using ‘Signal2Noise’ as ranking metric and ‘meandiv’ normalization mode. Redundancy in category lists was reduced by comparing the similarity between each pair of enriched categories using Jaccard similarity coefficients. Hierarchical clustering (k-means) was then applied to the resulting matrix in order to identify groups of similar functional categories, and a representative from each group chosen. Full unfiltered results are shown in Supplementary Material, Table S3. Tissue-specific gene enrichment analysis was evaluated with TissueEnrich [104].
For the miRNA-Seq experiments, functional analysis was first performed using g:profiler on the set of DE miRNA genes. Then, putative targets for each miRNA were extracted and their expression compared with the relevant miRNA. Putative targets whose expression was not altered in the opposite direction as the miRNA were removed from the list. Significant functions were displayed using Cytoscape v3.8.2 [105], with the size of the functional labels proportional to the number of miRNAs assigned to each function.
## Real-time metabolic measurements
Metabolic measurements on primary human myoblast cultures were performed using a Seahorse XFp Extracellular Flux Analyzer (Agilent Technologies). For this, myoblasts of matched passage number were seeded in XFp Cell Culture Miniplates (103025-100, Agilent Technologies) at a density of 1.5 × 104 cells per well. Cell density was assessed using an automated cell counter (TC20, BioRad). Oxygen consumption rates and extracellular acidification rates were measured using the Mito Stress Test Kit and the Glycolysis Stress Test Kit (Agilent Technologies #103020-100), respectively, according to the manufacturer’s instructions. Samples were measured in triplicates and each measurement was repeated between two and four times. Data were normalized to the number of cells and analyzed for each well.
## Fuel dependency tests
Glucose dependency and fatty acid dependency were determined according the instruction of Agilent Seahorse XF Mito Fuel Flex Test kit (Agilent Technologies #103260-100). The glutamine dependency was determined from the glucose and fatty acid measurements.
## Mitochondrial gene quantification
Reverse transcription of RNA was performed using the QuantiTecT Reverse Transcription Kit (Qiagen #205311) following the manufacturer’s instructions. For the reaction we used the SYBR® Green Master Mix (Bio-Rad #1725150) and samples were run and measured on CFX Connect™ (Bio-Rad). As genome reference gene B2M (FP: 5‘-TGCTGTCTCCATGTTTGATGTATCT-3′; RP: 5‘-TCTCTGCTCCCCACCTCTAAGT-3′) [106]. Primer sequences for the mitochondrial genome were: FP: 5′-TTAACTCCACCATTAGCACC-3′; RP: 5′-GAGGATGGTGGTCAAGGGA-3′ [107]. Samples were analyzed using the delta Ct method.
## Splice site prediction analysis
Raw data was mapped to the human genome assembly GRCh38 (hg38) and sorted by coordinate using STAR 2.7.9a [98] for analysis in DESeq2 [100] and DEXSeq [108], trimmed using an in-built trimming function for rMATS [109] or counted using Kallisto 0.48.0 [110] for isoformSwitchAnalyzer (ISA) [111]. All analyses were performed for G1, gp1, gp2 and gp3 separately. Visualizations were conducted in R version 4.1.2.
DESeq2: Mapped reads were counted using FeatureCounts, then analyzed using DESeq2 [100]. The R package fgsea was used for GSEA, and genes were assigned to biological pathways retrieved from MSigDB v7.5.1 (c5.go.bp.v7.5.1.symbols.gmt, 7658 gene sets, [35]). Splicing pathways and their genes were plotted using GOPlot [112].
DEXseq: Mapped reads were counted using the in-built DEXseq counting function in Python 3.9. Standard DEXseq workflow was followed. Exons with |logFCs| > 1 and P-values < 0.05 were set to be significantly different.
rMATS: Standard workflow was followed, and code was executed in Python 2.7. Results were analyzed in R and set to be significantly differentially spliced with |psi-values| > 0.1 and P-values < 0.05. Pie charts displaying the distribution of event usage were generated for all groups (Supps. something). GO term enrichment analysis was performed using g:profiler2 [113]. All events were searched for muscle-specific genes using a set of 867 genes relevant for muscle system process, development, structure and contraction, combined from GO terms (GO:0003012, GO:0006936, GO:0055001 and GO:0061061).
ISA: Kallisto counts were read into R and standard ISA procedure was followed, including splicing analysis using DEXseq, coding potential using CPC 2.0 [114], domain annotation using HmmerWeb Pfam 35.0 [115], signal peptides using SignalP 5.0 [116] and prediction of intrinsically unstructured proteins using IUPred2A [117]. In-built visualization tools were used for splicing maps.
## Data availability
Bakay et al. muscular dystrophy dataset is available at NCBI GEO with accession GSE3307.
RNA-Seq and miRNA-Seq datasets have been deposited at NCBI GEO with accession GSE204804 and GSE204826, respectively.
Conflict of Interest statement: The authors have no conflicts of interest to declare.
## Funding
This work was supported by Muscular Dystrophy UK (grant number 18GRO-PG24-0248); Medical Research Council (grant number MR/R018073 to E.C.S.); Deutsche Forschungsgemeinschaft (grant number 470092532 to P.M.).
## Authors’ contributions
J.I.H. processed patient samples for RNA- and miRNA-Seq and analyzed the data with assistance from SW. V.T. performed splicing analysis. L.K.-B., S.H. and P.M. performed various metabolic analyses. R.C. helped with generation of figures and critical discussion. B.S. provided patient samples and inspiration. E.C.S. designed the study and wrote the manuscript.
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|
---
title: Preclinical study of experimental burns treated with photobiomodulation and
Human Amniotic Membrane, both isolated and associated
authors:
- Fernanda Cláudia Miranda Amorim
- Emilia Ângela Loschiavo Arisawa
- Luciana Barros Sant’anna
- Ana Beatriz Mendes Rodrigues
- Davidson Ribeiro Costa
journal: Revista Latino-Americana de Enfermagem
year: 2023
pmcid: PMC9991011
doi: 10.1590/1518-8345.5552.3726
license: CC BY 4.0
---
# Preclinical study of experimental burns treated with photobiomodulation and Human Amniotic Membrane, both isolated and associated
## Abstract
### Objective:
to evaluate the effect of photobiomodulation with low-level 660 nm laser alone or associated with Human Amniotic Membrane in the repair of partial-thickness burns in rats.
### Method:
an experimental study conducted with 48 male Wistar rats, randomized into four groups: Control, Human Amniotic Membrane, Low-Level Laser Therapy, and Low-Level Laser Therapy associated with Human Amniotic Membrane. The histopathological characteristics of the skin samples were analyzed 7 and 14 days after the burn. The data obtained were submitted to the Kolmogorov-Smirnov and Mann-Whitney tests.
### Results:
the histological analysis of the burn injuries showed a decrease in inflammation ($p \leq 0.0001$) and an increase in proliferation of fibroblasts ($p \leq 0.0001$) mainly at 7 days in all treatments related to the control group. At 14 days, the greater effectiveness in accelerating the healing process was significant ($p \leq 0.0001$) in the Low-Level Laser Therapy group associated with the Human Amniotic Membrane.
### Conclusion:
the association of photobiomodulation therapies with the Human Amniotic Membrane allowed verifying a reduction in the healing process time of the experimental lesions, stimulating its proposal as a treatment protocol in partial-thickness burns.
## Objetivo:
avaliar o efeito da fotobiomodulação com laser de baixa intensidade 660 nm isoladamente ou associada à membrana amniótica humana no reparo de queimaduras de espessura parcial em ratos.
evaluar el efecto de la fotobiomodulación con láser de baja intensidad 660 nm de sola o combinada con la membrana amniótica humana en la reparación de quemaduras de espesor parcial en ratas.
## Método:
estudo experimental com 48 ratos Wistar machos, randomizados em quatro grupos: Controle, Membrana Amniótica Humana, *Terapia a* Laser de Baixa Intensidade e *Terapia a* Laser de Baixa Intensidade associado à Membrana Amniótica Humana. As características histopatológicas das amostras de pele foram analisadas aos 7 e 14 dias após a queimadura. Os dados obtidos foram submetidos aos testes de Kolmogorov-Smirnov e Mann Whitney.
estudio experimental con 48 ratas Wistar macho, aleatorizadas en cuatro grupos: Control, Membrana Amniótica Humana, Terapia con Láser de Baja Intensidad y Terapia con Láser de Baja *Intensidad combinada* con la Membrana Amniótica Humana. Las características histopatológicas de las muestras de piel fueron analizadas a los 7 y 14 días después de la quemadura. Los datos obtenidos fueron sometidos a las pruebas de Kolmogorov-Smirnov y Mann-Whitney.
## Resultados:
a análise histológica das lesões por queimadura mostrou a diminuição da inflamação ($p \leq 0$,0001) e aumento da proliferação de fibroblastos ($p \leq 0$,0001), principalmente nos 7 dias em todos os tratamentos relacionados ao grupo controle. Aos 14 dias, a maior efetividade na aceleração do processo cicatricial foi significativa ($p \leq 0$,0001) no grupo *Terapia a* Laser de Baixa Intensidade associado à Membrana Amniótica Humana.
el análisis histológico de las lesiones por quemadura mostró una disminución de la inflamación ($p \leq 0$,0001) y un aumento de la proliferación de fibroblastos ($p \leq 0$,0001) principalmente a los 7 días en todos los tratamientos en comparación con el grupo control; a los 14 días, en el grupo de Terapia con Láser de Baja *Intensidad combinada* con la Membrana Amniótica *Humana la* mayor efectividad en la aceleración del proceso de cicatrización fue significativa ($p \leq 0$,0001).
## Conclusão:
a associação das terapias de fotobiomodulação à membrana amniótica humana permitiu comprovar redução no tempo do processo cicatricial das lesões experimentais, estimulando sua proposição como protocolo de tratamento em queimaduras de espessura parcial.
## Conclusión:
la asociación de terapias de fotobiomodulación con la membrana amniótica humana permitió comprobar que hubo una reducción en el tiempo del proceso de cicatrización de lesiones experimentales, lo cual favorece que se proponga como protocolo de tratamiento en quemaduras de espesor parcial.
## Highlights
[1] Association of technology with biomaterials.
[2] Innovation in Tissue Engineering.
[3] Protocol for burn treatments.
## Introduction
Skin burns are injuries caused by heat, radiation, radioactivity, electricity, friction or contact with chemical products. In the world, nearly 180,000 people die every year as a result of this problem, a reality also expressed in the last decade in Brazil by the high in-hospital mortality rate due to this cause 1, 2.
Thermal burns can occur by scalds (hot liquids), contact (hot solids) or flames 1. In addition to being the most prevalent and strenuous, these types of lesions directly impair the phases of an adequate healing process, as they present reduced angiogenesis, sustained inflammation, oxidative stress, increased proteolysis and septicemia as main characteristics 3.
As for depth, burns can be classified as follows: superficial-thickness (first degree), partial-superficial (second degree), deep-superficial (second degree) and full-thickness (third or fourth degree). Histologically, superficial-thickness burns only reach the epidermis; partial superficial-thickness burns reach the epidermis and papillary dermis, but the skin annexes remain intact; deep superficial-thickness burns injure the epidermis and reticular dermis and most of the skin appendages are destroyed; and, in full-thickness burns, the entire epidermis, dermis and appendages of the skin are destroyed (third degree), and may even involve the muscular fascia and/or bone (fourth degree) 4.
The main clinical characteristics of partial superficial-thickness lesions are erythema, phlictenes, humidity, hyperemia, pressure pallor and healing time from 7 to 20 days 4. In this sense, the healing process of burn injuries is complex, as it involves differentiated cells that are activated during the different and overlapping phases of the tissue repair process called inflammation, proliferation and remodeling 5.
The injuries resulting from burns establish challenges in the skin repair process, as the burned area presents characteristics that hinder repair, such as irregular edges and tissue necrosis, in addition to being capable of reaching the epidermis, dermis and deep tissues. The need for hospitalizations and high hospital costs is also highlighted 6.
The multifaceted environment of burn wound healing has stimulated the investigation of innovative therapeutic interventions that enable immediate repair of this problem 7. Thus, defining an appropriate strategy in view of the needs and complexity of burns becomes fundamental for the success of therapeutic treatments in terms of performance and cost. In this sense, biomaterials and new technologies stand out for having general properties capable of inducing different biological responses that can be adapted according to the application 8.
In the context of technologies, photobiomodulation with the use of low-level laser therapy (LLLT) has stood out for favoring wound healing due to its biomodular effects 9 - 11. In contrast, related to biomaterials, the Human Amniotic Membrane (HAM) has been used as a promising alternative, with great potential for application in regenerative medicine for presenting low antigenicity and protection against infections, as well as for acting as a substrate for epithelization 12. Therefore, several studies have clinically evaluated the benefit of HAM as a biological substitute 13 - 15.
Thus, considering the complexity of burn therapy and the need for experimental studies that investigate alternative treatments that favor tissue regeneration in this condition, this study evaluated the effect of LLLT associated with HAM in the repair of superficial partial thickness burns in rats.
## Type of study
This is an experimental research study with a quantitative approach.
## Study locus
The research was conducted at the experimental surgery laboratory of the UNINOVAFAPI University Center, located in the municipality of Teresina (PI), Brazil.
## Study period
Data collection took place from January to March 2019.
## Animals
A total of 48 male rats (*Rattus norvegicus* albinus, Wistar) were studied: 40 days old, weighing 200 ± 50 g, kept in polypropylene cages under aseptic conditions, specific feed with food and water ad libitum, and exposed to a $\frac{12}{12}$-hour light-dark cycle, housed in individual cages.
## Study groups
The animals were randomized and allocated into four groups with twelve animals each, namely: Control (C); animals subjected to experimental burns without treatment; Human Amniotic Membrane (HAM), rats subjected to experimental burns treated with application of HAM fragment; Low-Level Laser Therapy (LLLT), animals subjected to experimental burns treated with LLLT; and LLLT+HAM, animals subjected to experimental burns treated with the association of LLLT and HAM. The animals from all 4 (four) groups were subdivided into 2 (two) subgroups according to experimental times of 7 and 14 days, containing 6 animals in each.
## Data collection
The experimental protocol was developed in five stages: capture of the human placenta, processing of the biomaterial, induction of burns, and application of HAM fragments and LLLT, isolated or associated.
The placentas were collected from two selected parturients, after signing the Free and Informed Consent Form (FICF), subjected to elective cesarean section, with a healthy clinical history, negative serological tests for HIV-1, VDRL, HbsAg and anti-HCV, and gestational age from 37 weeks to 41 weeks and 6 days (full term placenta), according to criteria established in a previous study 16.
The placentas were inspected immediately after removal, placed in a sterile plastic bag, packed at a temperature of 10ºC and transported to the experimental surgery laboratory. The biomaterial was processed in an aseptic environment following the protocols described 16, isolating the HAM that was sectioned into fragments of suitable dimensions for this research (4 x 4 cm) that were used in 24 hours 17.
Initially, the animals were weighed, sedated (Xylazine $2\%$, 0.01 mL/kg and Ketamine $10\%$, 0.005 mL/kg) and their dorsal region was epilated. The experimental burn was induced using a beaker (3 cm in diameter), filled with 50 mL of water heated to 100°C, supported in direct contact with the shaved region skin for 10 seconds, without additional pressure. Subsequently, the lesions were evaluated considering the macroscopic aspects, which included observation of staining (red or pink) and presence of a bubble to characterize the superficial partial thickness burn 4.
The animals from group C received no treatment. In the animals from the HAM and LLLT+HAM groups, HAM fragments were applied immediately after the burn, always with the mesenchymal face in contact with the skin lesion area, exceeding its edges by 1 cm, and fixed with a topical adhesive.
The protocol used in the animals from the LLLT group included laser application. The first applications with laser occurred 30 minutes after burn induction and were repeated at 24-hour intervals. A Laserpulse Ibramed© device (Indústria Brasileira de Equipamentos Médicos - IBRAMED) was used, and the irradiation parameters emnployed in the experiment were the following: wavelength of 660 nm, power of 30 mW, with an irradiation time of 12 seconds per point, contact area of 0.06310 cm2, energy density of 6 J/cm2, continuous pulse parameters, with treatment in a single dose, in a 24-hour interval, and with the animal’s back as anatomical location.
The irradiations were applied punctually at four equidistant points, in the shape of a cross, 1 cm between the edge of the lesion and the irradiation point, with a 90º angle and protection of the laser tip with a sterile transparent film, avoiding possible contamination.
In the HAM+LLLT group, the protocols described for the HAM and LLLT groups were associated, and the laser was applied on the amniotic membrane every 24 hours at both experimental times studied (7 and 14 days).
The animals were euthanized according to the experimental time studied (7 or 14 days) with administration of an overdose of anesthetic (sodium thiopental 100 mg/kg, intraperitoneally). The burned area and surrounding tissue were carefully removed and fixed in neutral buffered formalin ($10\%$).
## Histological techniques
The burned skin area and the surrounding area, including the entire area of the lesion and the edges of adjacent normal tissue (1 cm from the edge), were removed and fixed in $4\%$ buffered formalin for 48 hours and then transferred to a $70\%$ alcohol solution, cleared with xylene and embedded in paraffin. Four longitudinal semi-serial histological sections of 2 to 3 μm were made from each block, spread on glass slides and stained with Hematoxylin and Eosin (H&E) and Picrossirius Red.
## Histological and morphometric assessments
The injured area was evaluated macroscopically after the burn and throughout the experiment considering skin color, presence of blisters and superficial crust formation. The histological sections stained with H&E were scanned using a Leica® DM 2500 microscope coupled to a Leica® DFC 425 camera and the Leica® Application Suite LAS v3.7 program. The images were obtained from the cross sections of four sequential fields of each slide, with the 10X and 40X objectives under a light microscope. To quantify the number of inflammatory cells (neutrophils and macrophages) and fibroblasts (young and adult), the images were analyzed using the ImageJ software, which allowed elaborating a grid and the individual marking of the cell nuclei with the aid of the manual counting tool.
The slides stained with Picrosirius Red were evaluated by digital image analysis to calculate the area occupied by the deposition of collagen types I and III, and photographed with the 10X objective, with a polarized light microscope (Leica® DM 2000) coupled to the camera (Leica® DFC 425). The Image-Pro Plus 4.5 program was used to quantify the percentage of type I and III collagens. When analyzed in association with polarized light, presence of collagen considered the following specifications for identification of the collagen types: collagen type I - yellow-reddish color; and collagen type III - green-whitish color. All histomorphometric analyses were performed blindly.
## Statistical analysis
The data collected were evaluated for the coefficient of variation and sample distribution for determination of the statistical test. The GraphpadPrism V program (GraphPad Software, California, USA) and the Kolmogorov-Smirnov test were used to analyze data distribution. Due to the non-parametric presentation, the Mann-Whitney test was applied in the intragroup analysis. For the comparison between groups, the Kruskal-Wallis test was applied with Dunn’s Multiple Comparison Test powders (multiple comparisons - intergroup analysis). A $95\%$ confidence interval and a $5\%$ significance level ($p \leq 0.05$) were considered. The data are presented as mean ± standard error (of the mean).
## Ethical aspects
This study was approved by the Research Ethics Committee of the University of Vale do Paraíba (2.077.418) for the use of HAM and by the Ethics Committee on Animal Experimentation of the UNINOVAFAPI University Center with No. 005P/V$\frac{2}{2017}$, following the recommendations proposed by Resolution $\frac{446}{2012}$ of the National Health Council (Conselho Nacional de Saúde, CNS),
## Results
The evaluation of the photomicrographs of the histological slides stained with H&E shows progression of wound healing after partial superficial-thickness burns in all groups and experimental times (Figure 1).
Figure 1Photomicrographs showing histopathological changes in partial superficial-thickness burns in rats from groups C, LLLT, HAM and LLLT+HAM at seven and fourteen days. Teresina, PI, Brazil, 2022Lower A (10X) and upper B (40X) magnification. The red arrows represent the blood vessels, the yellow arrows are edema area, the black arrows show inflammatory infiltrates and the orange arrows are fibroblasts.
Use of LLLT associated with HAM significantly decreased the number of inflammatory cells when compared to the other treatment protocols. In the intergroup analysis, it was evidenced that group C presented the highest mean number of inflammatory cells in the periods analyzed. At 14 days there was a statistical difference in the animals from the LLLT+HAM group in relation to the C and HAM groups, with emphasis on the reduction in the number of inflammatory cells in the LLLT+HAM animals (Figure 2).
There was an increase in the number of fibroblasts in the LLLT+HAM group when compared to the C, HAM and LLLT groups in the experimental time of 7 days. At day 14, the animals from the LLLT+HAM and HAM groups presented higher means when compared to the other groups (Figure 2).
Figure 2Effect of photobiomodulation with continuous LLLT (660 nm) applied alone and in association with HAM in the mean count of fibroblasts and inflammatory cells in partial superficial-thickness burns. Teresina, PI, Brazil, 2022Kruskal-Wallis test applied with Dunn’s Multiple Comparison Test powders (multiple comparisons - intergroup analysis). * C = Control Group; †HAM = Human Amniotic Membrane Group; ‡LLLT = Low-Level Laser Therapy Group; §LLLT+HAM = Low-Level Laser Therapy Group associated with Human Amniotic Membrane; ║p = Extremely significant difference ($p \leq 0.0001$); ¶p = Significant difference ($p \leq 0.05$) The intragroup analysis of the inflammatory cells showed that only the LLLT and LLLT+HAM groups evidenced statistically significant differences. In relation to the mean of fibroblasts, statistically significant values were observed in the HAM and LLLT+HAM groups (Table 1).
Table 1Intragroup histopathological analysis of the mean Inflammatory Cell and Fibroblast count (mean ± standard error) after treatment with LLLT (660 nm), HAM and combination of both therapies (LLLT+HAM) 7 and 14 days after partial surface thickness thermal burns in rats ($$n = 48$$). Teresina, PI, Brazil, 2022GroupsInflammatory cells Fibroblasts Experimental times Experimental times 7 days14 daysp*7 days14 daysp*Control 158.4 ± 4.92 168.8 ± 3.64 ns † 85.8 ± 3.05 87.5 ± 2.42 ns † HAM 100.5 ± 2.53 108.8 ± 2.40 ns † 96.2 ± 6.23 105.8 ± 5.31 p‡ LLLT 94.6 ± 2.43 87.5 ± 2.12 p‡ 102.9 ± 2.56 97.04 ± 2.82 ns † LLLT+HAM 99.42 ± 2.451 85.24 ± 2.64 p‡ 134.4 ± 3.30 93.21 ± 3.04 p§ Mann-Whitney test applied for intragroup analysis. * p = Significance level; †ns = Non-significant difference; ‡p = Significant difference ($p \leq 0.05$); §p = Extremely significant difference ($p \leq 0.0001$).
The percentage of collagen types I and III (%) in the periods analyzed did not differ across the experimental groups. However, when compared to group C, there was a slight increase in type III collagen in the LLLT+HAM group on the seventh day, although without statistical significance (Figure 3).
Figure 3Graphical representation of the effect of photobiomodulation with LLLT and amniotic membrane, applied alone or in combination, on the percentage of collagen types III and I in partial superficial-thickness burns in Wistar rats ($$n = 48$$). Teresina, PI, Brazil, 2022Kruskal-Wallis test applied with Dunn’s Multiple Comparison Test powders (multiple comparisons - intergroup analysis).
The evaluation of the photomicrographs of the histological slides stained with picrosirius showed type I and III collagen fibers of the lesions after partial superficial burns in all groups and experimental times (Figure 4).
Figure 4Photomicrographs observed with polarized and non-polarized light showing type I and III collagen fibers in partial superficial-thickness burns in Wistar rats from groups C, HAM, LLLT and LLLT+HAM at seven and fourteen days, 10X objective ($$n = 48$$). Teresina, PI, Brazil, 2022
## Discussion
Using rats in experimental research involving tissue repair in burns has been a frequent practice, especially because their skin composition (epidermis and dermis) is similar to that of human skin, in addition to presenting low cost and reduced healing times. However, morphology of the rodent skin is unique and differs from the architecture of the human skin because it presents low adherence to the underlying structures, presence of the l-gluconolactone enzyme that converts l-gluconogamalactone into vitamin C, wound contraction healing and lower risk of infection 18.
This study investigates the association of photobiomodulation (LLLT) with HAM for the treatment of experimentally induced partial superficial burn injuries in rats. It evaluated tissue repair in these lesions and describes the evolution of the healing process using isolated and combined therapies at experimental times of 7 and 14 days.
Published clinical and experimental studies have used LLLT for the healing of acute and chronic wounds; however, there are few reports of the use of this technology combined with other therapies in partial superficial burns and no study using HAM as an adjunct in treatment 7, 10, 11, 19.
In our research it was verified that, at the experimental times, there was a significant reduction of inflammatory cells in all treated groups in relation to the control group. However, it was found that, although the therapies are effective in reducing inflammation, the combination of the LLLT+HAM therapies was more effective than their isolated use, evidencing the additive effects on modulation of the inflammatory activity that the combined treatment can promote. Considering that the anti-inflammatory effects of the isolated use of these therapies in burns are already described in the literature, our findings evidence that their combined use enhances the inflammatory activity. These findings also suggest that the combined therapy acts harmoniously, that HAM would function as a biological substrate and that it would have its action enhanced from the microenvironment conducive to cell oxygenation, growth and modulation created with the irradiated light.
We discovered that all therapies (both isolated and associated) were effective in reducing inflammatory cells at both experimental times; such findings reveal the importance of their early use so that the tissue repair process occurs without delays. In this sense, a study with induced acute wounds revealed that exacerbated and prolonged inflammation causes harms in the re-epithelialization process by modifying the formation of granulation tissue, with an increase in the possibility of scar formation 20.
It is worth noting that, in the intergroup analysis of the second experimental period, LLLT+HAM was extremely effective when compared to the isolated therapies. In addition to that, LLLT alone was more effective in reducing the mean number of inflammatory cells than the treatment of burns with HAM alone.
Photobiomodulation with LLLT has been used to reduce inflammation, pain and edema, as well as to preserve and restore tissues damaged by the injury. These effects can be achieved using wavelengths between 600 and 1000 nm. In this sense, clinical and experimental studies with partial and total thickness burns have used photobiomodulation with LLLT with a wavelength of 660 nm 21, ratifying choice of this parameter also in our study [5, 19, 22, 23].
Similarly to the results found in our study, modulation of the inflammatory response was evidenced in the healing of skin grafts in rats in a recent study that used the same LLLT irradiation protocol 24. In addition to that and corroborating our findings, the effects with a single LLLT dose have been pointed out in the literature, with acceleration of the inflammatory phase in skin repair among them 22.
It is known that inflammation and angiogenesis are important factors in determining wound healing and that the decrease in inflammation enables an increase in angiogenesis. Thus, the modulation properties of the inflammation and cell proliferation levels are found in research studies with LLLT 25, 26.
In the context of the association of LLLT with other therapies in burn treatments, a study that used this tool combined with medicinal honey obtained results for inflammation and pain attenuation in burn healing and acceleration of the repair process characterized by increased cell proliferation 7, corroborating the same effects of the therapeutic association protocol used in our study.
It is noted that, for the treatment of superficial partial thickness burns, the ideal dressings are those that can preserve heat, provide moisture, avoid contamination by microorganisms, be safe and not adhere to the injury or require frequent exchanges 27. Therefore, HAM stands out for being a biomaterial that has all the listed characteristics 12.
HAM has been applied to acute and chronic wounds, as evidenced by the promising results obtained with the application of this biomaterial in the healing of these lesions due to its properties 28, 29. It is noted that the dressings used with this biomaterial or in association with other products can facilitate proliferation of fibroblasts and contribute to the release of angiogenic factors 30.
In the intergroup analysis context and regarding proliferation of fibroblasts, our results show that LLLT+HAM was effective at both experimental times. It is noted that fibroplasia was benefited by the use of the combined therapies since, already in the initial phase of the experiment, HAM may have modulated the LLLT action, enhancing the cell activation process and, consequently, culminating in early onset of the proliferative phase, characterized by an extremely significant increase of fibroblasts in relation to all other experimental groups. This fact evidences the high capacity to repair the burned tissue when choosing the treatment with the associated therapies.
A number of studies evidence a biological response favoring the tissue repair process by stimulating proliferation of fibroblasts, and the improvement in microcirculation has also been proven in the context of photobiomodulation with LLLT 31 - 33.
The data obtained in our study reinforce the beneficial properties of using LLLT and HAM reported in previous studies in the context of the isolated use of these therapies or their association with other products 7, 30 - 34. However, we showed that the combination of therapies (LLLT and HAM) in burns can yield excellent results due to the sum of the modulating and protective effects in the different tissue repair phases.
Considering the complexity of the healing process in burns, our results are presented as a preclinical phase that encourages expansion of the techniques for evaluating the effects determined by the association of LLLT and HAM, in order to detect more information about the interaction of photobiomodulation with the biomaterial, aiming at a future introduction of this combination therapy in clinical protocols for the treatment of superficial partial thickness burns.
In our study, there was no statistical significance for the percentage of collagen types I and III in the periods analyzed, although in the photomicrographs there was progression in the cell organization process evidenced in the treatment groups. In this sense, we suggest extending the experimental time to contemplate all the dynamics of the repair process. Furthermore, non-measurement of the lesion is pointed out as a study limitation, limiting wound contraction monitoring. In addition to that, immunological markers were not used, which are important evaluators of the tissue and biochemical reactions in the tissue-related repair.
## Conclusion
Our study showed that photobiomodulation with low-level 660 nm laser acts synergistically to the topical application of the Human Amniotic Membrane, constituting an effective therapeutic protocol in the treatment of superficial partial thickness burns. Combination of the therapies enhanced the anti-inflammatory effects and stimulation of cell proliferation, accelerating the tissue repair process.
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4. Abazari M, Ghaffari A, Rashidzadeh H, Badeleh SM, Maleki Y. **A Systematic Review on Classification, Identification, and Healing Process of Burn Wound Healing**. *Int J Low Extrem Wounds* (2020) **11**. DOI: 10.1177/1534734620924857
5. Brassolatti P, Bossini PS, Kido HW, Oliveira MCD, Almeida-Lopes L, Zanardi LM. **Photobiomodulation and bacterial cellulose membrane in the treatment of third-degree burns in rats**. *J Tissue Viability* (2018) **27** 249-256. DOI: 10.1016/j.jtv.2018.10.001
6. Saavedra PA, Brito ES, Areda CA, Escalda PM, Galato D. **Burns in the Brazilian Unified Health System: a review of hospitalization from 2008 to 2017**. *Int J Burns Trauma* (2019) **9** 88-98. PMID: 31777684
7. Yadav A, Verma S, Keshri GK, Gupta A. **Combination of medicinal honey and 904 nm superpulsed laser-mediated photobiomodulation promotes healing and impedes inflammation, pain in full-thickness burn**. *J Photochem Photobiol B* (2018) **186** 152-159. DOI: 10.1016/j.jphotobiol.2018.07.008
8. Murray RZ, West ZE, Cowin AJ, Farrugia BL. **Development and use of biomaterials as wound healing therapies**. *Burns Trauma* (2019) **7**. DOI: 10.1186/s41038-018-0139-7
9. Lamaro-Cardoso A, Bachion MM, Morais JM, Fantinati MS, Milhomem AC, Almeida VL. **Photobiomodulation associated to cellular therapy improve wound healing of experimental full thickness burn wounds in rats**. *J Photochem Photobiol B* (2019) **194** 174-182. DOI: 10.1016/j.jphotobiol.2019.04.003
10. Nilforoushzadeh MA, Kazemikhoo N, Mokmeli S, Zare S, Dahmardehei M, Vaghar Doost R. **An Open-Label Study of Low-Level Laser Therapy Followed by Autologous Fibroblast Transplantation for Healing Grade 3 Burn Wounds in Diabetic Patients**. *J Lasers Med Sci* (2019) **10** S7-S12. DOI: 10.15171/jlms.2019.S2
11. Vaghardoost R, Momeni M, Kazemikhoo N, Mokmeli S, Dahmardehei M, Ansari F. **Effect of low-level laser therapy on the healing process of donor site in patients with grade 3 burn ulcer after skin graft surgery (a randomized clinical trial)**. *Lasers Med Sci* (2018) **33** 603-607. DOI: 10.1007/s10103-017-2430-4
12. Gholipourmalekabadi M, Farhadihosseinabadi B, Faraji M, Nourani MR. **How preparation and preservation procedures affect the properties of amniotic membrane? How safe are the procedures?**. *Burns* (2020) **46** 1254-1271. DOI: 10.1016/j.burns.2019.07.005
13. Raza MS, Asif MU, Abidin ZU, Khalid FA, Ilyas A, Tarar MN. **Glycerol Preserved Amnion: A Viable Source of Biological Dressing for Superficial Partial Thickness Facial Burns**. *J Coll Physicians Surg Pak* (2020) **30** 394-398. DOI: 10.29271/jcpsp.2020.04.394
14. Ahuja N, Jin R, Powers C, Billi A, Bass K. **Dehydrated Human Amnion Chorion Membrane as Treatment for Pediatric Burns**. *Adv Wound Care (New Rochelle)* (2020) **9** 602-611. DOI: 10.1089/wound.2019.0983
15. Lashgari MH, Rostami MHH, Omid Etemad O. **Assessment of outcome of using amniotic membrane enriched with stem cells in scar formation and wound healing in patients with burn wounds**. *Bali Med J* (2019) **8** 41-46. DOI: 10.15562/bmj.v8i1.1223
16. Sant'Anna LB, Cargnoni A, Ressel L, Vanosi G, Parolini O. **Amniotic membrane application reduces liver fibrosis in a bile duct ligation rat model**. *Cell Transplant* (2011) **20** 441-453. DOI: 10.3727/096368910X522252
17. Sant'Anna LB, Hage R, Cardoso MA, Arisawa EA, Cruz MM, Parolini O. **Antifibrotic Effects of Human Amniotic Membrane Transplantation in Established Biliary Fibrosis Induced in Rats**. *Cell Transplant* (2016) 25-25. DOI: 10.3727/096368916X692645
18. Abdullahi A, Amini-Nik S, Jeschke MG. **Animal models in burn research**. *Cell Mol Life Sci* (2014) **71** 3241-3255. DOI: 10.1007/s00018-014-1612-5
19. Allban AAH, Munahi AK, Kadhim A, Alzamili SKN. **Low-Level Laser Therapy (Two Different Wavelengths 660nm and 820nm) Compared with Nigella sativa Oil for Treatment of Burns in Rats**. *J Int Pharma Res* (2019) **46** 346-352
20. Qian LW, Fourcaudot AB, Yamane K, You T, Chan RK, Leung KP. **Exacerbated and prolonged inflammation impairs wound healing and increases scarring**. *Wound Repair Regen* (2016) **24** 26-34. DOI: 10.1111/wrr.12381.
21. Huang YY, Sharma SK, Carroll J, Hamblin MR. **Biphasic dose response in low level light therapy - an update**. *Dose Response* (2011) **9** 602-618. DOI: 10.2203/dose-response.11-009.Hamblin
22. Andrade ALM, Brassolatti P, Luna GF, Parisi JR, Oliveira AML, Frade MAC. **Effect of photobiomodulation associated with cell therapy in the process of cutaneous regeneration in third degree burns in rats**. *J Tissue Eng Regen Med* (2020) **14** 673-683. DOI: 10.1002/term.3028
23. Amadio EM, Marcos RL, Serra AJ, Santos SA, Caires JR, Fernandes GHC. **Effect of photobiomodulation therapy on the proliferation phase and wound healing in rats fed with an experimental hypoproteic diet**. *Lasers Med Sci* (2021) **36** 1427-1435. DOI: 10.1007/s10103-020-03181-1
24. Moreira SH, Pazzini JM, Alvarez JLG, Cassino PC, Bustamante CC, Bernardes FJL. **Evaluation of angiogenesis, inflammation, and healing on irradiated skin graft with low-level laser therapy in rats (Rattus norvegicus albinus wistar)**. *Lasers Med Sci* (2020) **35** 1103-1109. DOI: 10.1007/s10103-019-02917-y
25. Rathnakar B, Rao BS, Prabhu V, Chandra S, Rai S, Rao AC. **Photo-biomodulatory response of low-power laser irradiation on burn tissue repair in mice**. *Lasers Med Sci* (2016) **31** 1741-1750. DOI: 10.1007/s10103-016-2044-2
26. Gupta A, Keshri GK, Yadav A, Gola S, Chauhan S, Salhan AK. **Superpulsed (Ga-As, 904 nm) low-level laser therapy (LLLT) attenuates inflammatory response and enhances healing of burn wounds**. *J Biophotonics* (2015) **8** 489-501. DOI: 10.1002/jbio.201400058
27. **ISBI Practice Guidelines for Burn Care**. *Burns* (2016) **42** 953-1021. DOI: 10.1016/j.burns.2016.05.013
28. Han LG, Zhao QL, Yoshida T, Okabe M, Soko C, Rehman MU. **Differential response of immortalized human amnion mesenchymal and epithelial cells against oxidative stress**. *Free Radic Biol Med* (2019) **135** 79-86. DOI: 10.1016/j.freeradbiomed.2019.02.017
29. Campelo MBD, Santos JAF, Maia ALM, Ferreira DCL, Sant'Anna LB, Oliveira RA. **Effects of the application of the amniotic membrane in the healing process of skin wounds in rats**. *Acta Cir Bras* (2018) **33** 144-155. DOI: 10.1590/s0102-865020180020000006
30. Rahman MS, Islam R, Rana MM, Spitzhorn LS, Rahman MS, Adjaye J. **Characterization of burn wound healing gel prepared from human amniotic membrane and Aloe vera extract**. *BMC Complement Altern Med* (2019) **19** 115-115. DOI: 10.1186/s12906-019-2525-5
31. Ranjbar R, Takhtfooladi MA. **The effects of low level laser therapy on Staphylococcus aureus infected third-degree burns in diabetic rats**. *Acta Cir Bras* (2016) **31** 250-255. DOI: 10.1590/S0102-865020160040000005
32. Hashmi JT, Huang YY, Osmani BZ, Sharma SK, Naeser MA, Hamblin MR. **Role of low-level laser therapy in neurorehabilitation**. *PM R* (2010) **2** S292-S305. DOI: 10.1016/j.pmrj.2010.10.013
33. Ihsan FR. **Low-level laser therapy accelerates collateral circulation and enhances microcirculation**. *Photomed Laser Surg* (2005) **23** 289-294. DOI: 10.1089/pho.2005.23.289
34. Kshersagar J, Kshirsagar R, Desai S, Bohara R, Joshi M. **Decellularized amnion scaffold with activated PRP: a new paradigm dressing material for burn wound healing**. *Cell Tissue Bank* (2018) **19** 423-436. DOI: 10.1007/s10561-018-9688-z
|
---
title: 'Severe COVID-19 outcomes by cardiovascular risk profile in England in 2020:
a population-based cohort study'
authors:
- Charlotte Warren-Gash
- Jennifer A. Davidson
- Helen Strongman
- Emily Herrett
- Liam Smeeth
- Judith Breuer
- Amitava Banerjee
journal: The Lancet Regional Health - Europe
year: 2023
pmcid: PMC9991014
doi: 10.1016/j.lanepe.2023.100604
license: CC BY 4.0
---
# Severe COVID-19 outcomes by cardiovascular risk profile in England in 2020: a population-based cohort study
## Body
Research in contextEvidence before this studyWhile severe outcomes of COVID-19 occur more frequently among individuals with pre-existing health conditions, the role of underlying cardiovascular risk is incompletely understood. We searched PubMed from inception to 11 April 2022 using the terms ((“cardiovascular risk” OR “hypertension”) AND (“COVID-19” OR “SARS-CoV-2”) AND (“severe outcomes” OR “mortality”)). Due to a large number of results, we limited to studies of adults in non-specialist populations and filtered by systematic reviews and meta-analyses. Results were obtained from 28 relevant systematic reviews, many covering overlapping studies. Most included studies were small (100s–1000s of patients) and conducted among hospitalised COVID-19 patients. Cardiovascular disease, and to a lesser extent hypertension, were typically associated with raised risks of severe outcomes and death from COVID-19 in these studies. Later population-based studies show that existing cardiovascular disease and some individual cardiovascular risk factors (diabetes, hypertension) were associated with COVID-19-related deaths. No studies assessed cardiovascular risk using risk prediction tools such as QRISK3 which combine different elements of risk. Added value of this studyThis is, to our knowledge, the only study of COVID-19 outcomes to date to characterize underlying cardiovascular risk profile comprehensively using a validated risk prediction score (QRISK3) in a large population-based cohort. We demonstrate a gradient in the risks of hospitalisation, intensive care unit (ICU) admission, major adverse cardiovascular events (MACE) and mortality by cardiovascular risk level, with the highest incidence of severe outcomes occurring among individuals with existing cardiovascular disease, followed by those at raised cardiovascular risk then those at low risk. In cohorts with confirmed and suspected COVID-19, we show that having an elevated QRISK3 score was associated with a higher risk of all categories of severe outcomes after accounting for sociodemographic, lifestyle and clinical confounders, while hypertension status was associated only with a higher risk of MACE.Implications of all the available evidenceBeing at raised cardiovascular risk, defined by having an elevated QRISK3 score, is associated with severe outcomes after COVID-19. Individuals at raised cardiovascular risk represent an important target for COVID-19 prevention and management, as an addition to the current focus on those with diagnosed cardiovascular disease. Strategies to improve cardiovascular health could also improve outcomes following COVID-19.
## Summary
### Background
While cardiovascular disease (CVD) is a risk factor for severe COVID-19, the association between predicted cardiovascular risk and severe COVID-19 among people without diagnosed CVD is unclear.
### Methods
We carried out historical, population-based cohort studies among adults aged 40–84 years in England using linked data from the Clinical Practice Research Datalink. Individuals were categorized into: existing CVD, raised cardiovascular risk (defined using QRISK3 score ≥$10\%$) and low risk (QRISK3 score <$10\%$) at $\frac{12}{03}$/2020. We described incidence and severe outcomes of COVID-19 (deaths, intensive care unit [ICU] admissions, hospitalisations, major adverse cardiovascular events [MACE]) for each group. Among those with a COVID-19 record to $\frac{31}{12}$/2020, we re-classified cardiovascular risk at infection and assessed the risk of severe outcomes using multivariable Cox regression with complete case analysis. We repeated analyses using hypertension to define raised cardiovascular risk.
### Findings
Among 6,059,055 individuals, 741,913 ($12.2\%$) had established CVD, 1,929,627 ($31.8\%$) had a QRISK3 score ≥$10\%$ and 3,387,515 ($55.9\%$) had a QRISK3 score <$10\%$. Marked gradients were seen in the incidence of all severe COVID-19 outcomes by cardiovascular risk profile. Among those with COVID-19 ($$n = 146$$,760), there was a strong association between raised QRISK3 score and death: adjusted hazard ratio [aHR] 8.77 (7.62–10.10), $$n = 97$$,725, which remained present, though attenuated in age-stratified results. Risks of other outcomes were also higher among those with raised QRISK3 score: aHR 3.66 (3.18–4.21) for ICU admissions, 3.38 (3.22–3.56) for hospitalisations, 5.43 (4.44–6.64) for MACE. When raised cardiovascular risk was redefined by hypertension status, only the association with MACE remained: aHR 1.49 (1.20–1.85), $$n = 57$$,264.
### Interpretation
Individuals without pre-existing CVD but with raised cardiovascular risk (by QRISK3 score) were more likely to experience severe COVID-19 outcomes and should be prioritised for prevention and treatment. Addressing cardiovascular risk factors could improve COVID-19 outcomes.
### Funding
$\frac{10.13039}{501100011950}$BMA Foundation for Medical Research/$\frac{10.13039}{501100000833}$Rosetrees Trust, $\frac{10.13039}{100004440}$Wellcome, $\frac{10.13039}{501100000274}$BHF.
## Evidence before this study
While severe outcomes of COVID-19 occur more frequently among individuals with pre-existing health conditions, the role of underlying cardiovascular risk is incompletely understood. We searched PubMed from inception to 11 April 2022 using the terms ((“cardiovascular risk” OR “hypertension”) AND (“COVID-19” OR “SARS-CoV-2”) AND (“severe outcomes” OR “mortality”)). Due to a large number of results, we limited to studies of adults in non-specialist populations and filtered by systematic reviews and meta-analyses. Results were obtained from 28 relevant systematic reviews, many covering overlapping studies. Most included studies were small (100s–1000s of patients) and conducted among hospitalised COVID-19 patients. Cardiovascular disease, and to a lesser extent hypertension, were typically associated with raised risks of severe outcomes and death from COVID-19 in these studies. Later population-based studies show that existing cardiovascular disease and some individual cardiovascular risk factors (diabetes, hypertension) were associated with COVID-19-related deaths. No studies assessed cardiovascular risk using risk prediction tools such as QRISK3 which combine different elements of risk.
## Added value of this study
This is, to our knowledge, the only study of COVID-19 outcomes to date to characterize underlying cardiovascular risk profile comprehensively using a validated risk prediction score (QRISK3) in a large population-based cohort. We demonstrate a gradient in the risks of hospitalisation, intensive care unit (ICU) admission, major adverse cardiovascular events (MACE) and mortality by cardiovascular risk level, with the highest incidence of severe outcomes occurring among individuals with existing cardiovascular disease, followed by those at raised cardiovascular risk then those at low risk. In cohorts with confirmed and suspected COVID-19, we show that having an elevated QRISK3 score was associated with a higher risk of all categories of severe outcomes after accounting for sociodemographic, lifestyle and clinical confounders, while hypertension status was associated only with a higher risk of MACE.
## Implications of all the available evidence
Being at raised cardiovascular risk, defined by having an elevated QRISK3 score, is associated with severe outcomes after COVID-19. Individuals at raised cardiovascular risk represent an important target for COVID-19 prevention and management, as an addition to the current focus on those with diagnosed cardiovascular disease. Strategies to improve cardiovascular health could also improve outcomes following COVID-19.
## Introduction
By the end of 2020, the COVID-19 pandemic had led to an estimated 3 million deaths worldwide.1 Large, population-based studies show that existing cardiovascular disease (CVD) and some individual cardiovascular risk factors (such as diabetes and hypertension) are associated with COVID-19-related deaths.2, 3, 4 Other studies support associations between CVD or individual risk factors and severe COVID-19 outcomes among hospitalised patients.5, 6, 7 CVD is a component of the QCOVID risk prediction tool which predicts risks of hospitalisation and mortality from COVID-19.8 However, it is unclear how being at raised cardiovascular risk, defined by commonly-used clinical risk prediction tools such as QRISK3, affects severe COVID-19 outcomes among individuals without existing CVD. Such individuals were not considered ‘clinically vulnerable’ in England during the COVID-19 pandemic.9 Cardiovascular complications of COVID-19 have increasingly been recognized: population-based self-controlled case series studies from Scotland,10 Sweden11 and Denmark12 show an early elevation in acute cardiovascular events such as myocardial infarction (MI) and stroke following COVID-19. Similar transient elevations in the risks of MI and stroke occur following other laboratory-confirmed respiratory infections including influenza and Streptococcus pneumoniae.13 Although evidence from before the COVID-19 pandemic showed that such complications are more frequent after respiratory infections among individuals at raised cardiovascular risk,14 this has not been comprehensively investigated for COVID-19.
Population-based studies with detailed cardiovascular risk assessments are needed to assess the burden of acute severe outcomes of COVID-19, including cardiovascular complications, among individuals with differing levels of underlying cardiovascular risk to guide accurate stratified prevention and management. Here we aimed to quantify the incidence and severe outcomes of SARS-CoV-2 infections and to assess the risk of severe COVID-19 outcomes following infection by underlying cardiovascular risk profile among adults in England.
## Data sources
We used the Clinical Practice Research Datalink (CPRD) Aurum15 January 2022 dataset, with individual level linked data from Hospital Episode Statistics Admitted Patient Care (HES APC), Office for National Statistics (ONS) deaths, Second Generation Surveillance System (SGSS) SARS-CoV-2, and COVID-19 Hospitalisations in England Surveillance System (CHESS).16 The CPRD Independent Scientific Advisory Committee (application 20_000135) and the London School of Hygiene and Tropical Medicine (LSHTM) Ethics Committee (application 22717) approved the study. CPRD provided relevant HES, ONS, SGSS and CHESS data for the study population. All code lists are published on LSHTM Data Compass.17
## Incidence study population and follow-up
All individuals aged 40–84 years with at least one year of post-registration time at their primary care practice who are eligible for linkage to HES were eligible for inclusion in our incidence study. Follow-up of individuals started at the latest of; age 40 years, 12 months post-registration, or 12 March 2020, and ended at the earliest of; date of death or outcome of interest, administrative censor (date of leaving the practice or date of last data collection from the practice), or 31 December 2020 (Fig. 1). We started follow-up from 12 March 2020 when daily reporting to CHESS was initiated.18Fig. 1Study design overview with 2020 England COVID-19 timeline.
## Cohort study population and follow up
Our cohort study included individuals with COVID-19. In our main analysis we defined this as laboratory-confirmed SARS-CoV-2, identified using SGSS and CHESS data. All individuals in either of the two datasets were considered to have SARS-CoV-2, with the date of infection taken as the earliest specimen date. In a secondary analysis, we defined COVID as clinically reported COVID-19 (CPRD or HES APC [any diagnostic position] recorded) without laboratory-confirmed SARS-CoV-2. We only considered one infection, the earliest recorded, per individual. Follow-up in the cohort study started at this date and ended at the earliest of the dates set out in our incidence study (Fig. 1). We stratified the study population further in time based on the UK COVID-19 waves (one; 12 March to 16 August and two; 17 August to 31 December), during which different testing practices were in operation.
## Outcomes
Our primary outcome of interest was death attributable to COVID-19. We defined COVID-19 attributable deaths as those coded as U07.1 or U07.2 in ONS data. In a sensitivity analysis we explored broadening our primary outcome of death attributable to COVID-19 to all-cause death which occurred within 28 days of the individual's diagnosis (based on test result among those with laboratory-confirmed SARS-CoV-2 or consultation date for those with clinically reported COVID-19). Our secondary outcomes were hospitalisation due to COVID-19 (defined by COVID-19 in the primary diagnosis field of any episode recorded in HES APC or presence in CHESS dataset), ICU admission due to COVID-19 (defined by ICU admission recorded in CHESS), need for respiratory support due to COVID-19 (defined by mechanical ventilation recorded in CHESS), or major adverse cardiovascular event (MACE [composite of acute coronary syndrome which included myocardial infarction and unstable angina, ischaemic stroke, acute left ventricular failure, or major ventricular arrhythmia recorded in CPRD or HES APC]). These definitions were informed by a systematic review of the validity of cardiovascular event recording in electronic health records.19
## Exposure
Our exposure of interest was cardiovascular risk. First, we identified individuals with established CVD (CPRD Aurum or HES APC recorded) diagnosed before baseline. Among individuals without CVD, we then used QRISK3 score to identify individuals with and without raised cardiovascular risk. Individuals with established CVD were included in our incidence study but excluded from our cohort study.
QRISK3 is a validated UK ten-year risk prediction score for myocardial infarction or stroke based on a combination of known risk factors,20 such as age, sex, ethnicity, socio-economic status, family history of coronary heart disease in a first degree relative aged <60 years, and comorbid health conditions (further outlined in Supplementary methods). We classified individuals as having raised cardiovascular risk (QRISK3 ≥$10\%$) or low cardiovascular risk (QRISK3 <$10\%$) at baseline, based on NICE thresholds for recommending statins for primary prevention.21 *In a* secondary analysis, we further stratified QRISK3 scores into <$10\%$, 10–$20\%$, or ≥$20\%$.
In separate analyses, we redefined raised cardiovascular risk based on hypertension status within the five years before baseline as a pragmatic method to identify individuals at raised cardiovascular risk in settings where QRISK3 is not widely used. We classified hypertension using coded CPRD diagnoses or the most recent to baseline blood pressure (BP) reading with systolic BP of ≥140mmHg or diastolic BP of ≥90mmHg.
## Covariates
Covariates differed depending on the method used to define cardiovascular risk. In analyses where raised cardiovascular risk was defined by QRISK3, we included covariates which were not part of determining the QRISK3 score as detailed in the Supplementary methods. In analyses where hypertension was used to define raised cardiovascular risk, we included covariates accounted for in the QRISK3 algorithm as well as those not included in the algorithm and adjusted for in QRISK3 analysis, as outlined in the Supplementary methods.
## Statistical analysis
We described the baseline characteristics, for both the incidence and cohort study populations, using numbers and percentage for categorical variables and mean with standard deviation or median with interquartile range for continuous variables.
For our incidence study population, stratified by cardiovascular risk, we calculated incidence of the primary outcome of COVID-19 death and secondary outcomes of ICU admission, respiratory support, hospitalisation, and MACE, among the whole population, regardless of COVID-19 status. We then calculated the incidence of SARS-CoV-2 infection and clinically reported COVID-19, as well as our primary and secondary outcomes following laboratory-confirmed SARS-CoV-2 or clinically reported COVID-19. We further stratified results by time according to COVID-19 wave. Additionally, we generated age standardised incidence rates, stratified by sex, using one-year age bands from the ONS mid-year population estimates for 2020.22 Among our cohort study population (those with COVID-19), we used Cox proportional hazards regression finely adjusted for calendar time to generate hazard ratios for the association between cardiovascular risk and each outcome, initially adjusting models in hypertension analysis for age and sex, and then in a full model adjusted for all potential confounders. A complete case-analysis approach was used for multivariable analyses. We reported numbers in unadjusted and full models, compared characteristics of those included and excluded from the complete case analysis and also re-ran unadjusted models in the complete case analysis population. We did not conduct multiple imputation because data in CPRD are unlikely to be missing at random. We examined non-proportionality using Schoenfeld's residuals. In a post-hoc analysis, we stratified QRISK3 results by age group to evaluate the effect of age. There were no individuals aged 75–84 years with a QRISK3 score <$10\%$, so age-stratified results were only generated for age groups of 40–54, 55–64, and 65–74 years. Age, like all risk factors included in the calculation of QRISK3 score, had not been adjusted for in the main QRISK3 analysis as the variable is also considered in the assignment of individual scores. However, in a further post-hoc analysis we additionally adjusted for age given the strong association between age and risk of severe COVID-19 outcomes.2 We conducted all analyses in Stata, version 16.
## Role of the funding source
The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.
## Description of incidence study population
The incidence study population included 6,059,055 individuals aged 40–84 years of age (Fig. 2), $12.2\%$ [741,913] had established CVD and among those without established CVD, $31.9\%$ [1,929,627] had a QRISK3 score ≥$10\%$ and $55.9\%$ [3,387,515] had a QRISK3 score <$10\%$, and $31.1\%$ [1,881,654] had hypertension and $56.7\%$ [3,435,488] had no hypertension. The baseline characteristics of the study population are described in Supplementary Table S1.Fig. 2Study population flow chart.
## Incidence of COVID-19 and severe outcomes
Among all individuals the incidence of COVID-19 death was 1.7 ($95\%$ CI 1.7–1.8) per 1000 with the highest incidence among those with established CVD (7.4 [7.2–7.7] per 1000), followed by those with raised cardiovascular risk (QRISK3 ≥$10\%$; 2.2 [2.1–2.2] and hypertension; 1.4 [1.3–1.5] per 1000), and was lowest among those at low cardiovascular risk (QRISK3 <$10\%$; 0.2 [0.2–0.2] and no hypertension; 0.7 [0.6–0.7] per 1000). The same gradient by cardiovascular risk level was observed for hospitalisations and MACE, and for outcomes among individuals with laboratory-confirmed SARS-CoV-2 and clinically reported COVID-19 (Table 1). Results by COVID-19 wave showed a higher incidence of outcomes of interest in wave 1 compared to the beginning of wave 2 (Supplementary Tables S2 and S3). Employing the sensitivity analysis definition of death (all cause within 28 days of diagnosis), resulted in similar incidence as COVID-19 death among individuals with laboratory-confirmed SARS-CoV-2 and a higher incidence than COVID-19 death among those with clinically reported COVID-19 (Table 1). Age standardized rates, stratified by sex, are shown in Supplementary Table S4. While age standardized rates showed similar trends across the cardiovascular risk levels, these rates were diminished compared to crude estimates. Table 1Number and incidence rates of laboratory-confirmed SARS-CoV-2 and clinically reported COVID-19 and outcomes of interest. AllEstablished CVDQRISK3 scoreHypertensionRaised riskLow riskRaised riskLow riskNRate ($95\%$ CI) per 1000NRate ($95\%$ CI) per 1000NRate ($95\%$ CI) per 1000NRate ($95\%$ CI) per 1000NRate ($95\%$ CI) per 1000NRate ($95\%$ CI) per 1000All individuals6,059,055741,9131,929,6273,387,5151,881,6543,435,488COVID-19 deatha78661.7 (1.7–1.8)41647.4 (7.2–7.7)32032.2 (2.1–2.2)4990.2 (0.2–0.2)20141.4 (1.3–1.5)16880.7 (0.6–0.7)Hospitalisationb28,0136.1 (6.0–6.2)10,88019.4 (19.0–19.8)10,7947.3 (7.2–7.4)63392.5 (2.4–2.6)84815.9 (5.8–6.0)86523.4 (3.3–3.4)Major adverse cardiovascular event71,03515.5 (15.4–15.6)49,31888.0 (87.2–88.8)16,60411.2 (11.0–11.4)51132.0 (2.0–2.1)12,6338.8 (8.6–8.9)90843.5 (3.4–3.6)Laboratory-confirmed SARS-CoV-2174,12938.0 (37.8–38.2)24,77944.2 (43.7–44.8)41,41628.0 (27.7–28.2)107,93442.5 (42.2–42.7)50,85435.3 (35.0–35.6)98,49638.2 (37.9–38.4)COVID-19 deatha647548.9 (47.7–50.1)3493199.9 (193.4–206.6)259782.9 (79.8–86.2)3854.6 (4.2–5.1)166442.5 (40.5–44.6)131817.4 (16.5–18.4)All cause death within 28 days of diagnosis664950.2 (49.0–51.4)3592205.6 (198.9–212.4)266185.0 (81.8–88.3)3964.7 (4.3–5.2)171643.9 (41.8–46.0)134117.7 (16.8–18.7)ICU admissionc202415.3 (14.6–16.0)49928.6 (26.2–31.2)93029.7 (27.8–31.7)5957.1 (6.6–7.7)81120.7 (19.4–22.2)7149.4 (8.8–10.1)Respiratory supportd10848.2 (7.7–8.7)23013.2 (11.6–15.0)52616.8 (15.4–18.3)3283.9 (3.5–4.4)47512.1 (11.1–13.3)3795.0 (4.5–5.5)Hospitalisationb17,893135.2 (133.2–137.2)6555375.1 (366.1–384.3)7047225.0 (219.8–230.3)429151.3 (49.8–52.9)5628143.9 (140.1–147.7)571075.3 (73.4–77.3)Major adverse cardiovascular event225117.0 (16.3–17.7)142281.4 (77.3–85.7)61619.7 (18.2–21.3)2132.5 (2.2–2.9)48612.4 (11.4–13.6)3434.5 (4.1–5.0)Clinically reported COVID-1970,70015.4 (15.3–15.5)13,66824.4 (24.0–24.8)20,24013.7 (13.5–13.9)36,79214.5 (14.3–14.6)21,37414.8 (14.6–15.0)35,65813.8 (13.7–14.0)COVID-19 deatha72313.4 (12.5–14.4)36536.2 (32.6–40.1)31820.7 (18.5–23.1)401.4 (1.0–1.9)17810.8 (9.3–12.5)1806.6 (5.7–7.6)All cause death within 28 days of diagnosis159929.6 (28.2–31.1)82381.5 (76.2–87.3)65742.7 (39.5–46.1)1194.2 (3.5–5.0)37522.8 (20.6–25.2)40114.6 (13.3–16.1)Hospitalisationb369268.4 (66.2–70.6)1336132.4 (125.5–139.7)138589.9 (85.3–94.8)97134.1 (32.0–36.3)112468.2 (64.3–72.3)123244.9 (42.5–47.5)Major adverse cardiovascular event200237.1 (35.5–38.7)1282127.0 (120.3–134.2)56937.0 (34.0–40.1)1515.3 (4.5–6.2)41825.4 (23.0–27.9)30211.0 (9.8–12.3)aAscertained from ONS death certificate data in which the COVID related ICD-10 codes U07.1 or U07.2 were present in the record.bAscertained from presence in CHESS dataset or HES APC record coded with primary diagnosis of U07.1 or U07.2.cAscertained from CHESS records coded with ICU/HDU admission, only available for those with laboratory confirmed SARS-CoV-2.dAscertained from CHESS record coded with use of respiratory support via invasive mechanical ventilation, only available for those with laboratory confirmed SARS-CoV-2.
## Description of cohort study population
After excluding those with established CVD, 146,760 people had laboratory-confirmed SARS-CoV-2 and 56,197 had clinically-reported COVID-19 during our study period (Fig. 2). Among those with laboratory-confirmed SARS-CoV-2, when cardiovascular risk was classified by QRISK3 score, $26.8\%$ [39,295] had raised risk (a score ≥$10\%$) and $73.2\%$ [107,465] had low risk (a score <$10\%$). When hypertension was used to classify cardiovascular risk, $34.0\%$ [49,955] had raised risk (hypertension) and $66.0\%$ [96,805] had low risk (no hypertension). Individuals with laboratory-confirmed SARS-CoV-2 and raised cardiovascular risk (QRISK3 ≥$10\%$ or hypertension) were older and a higher proportion were men. Baseline characteristics of the laboratory-confirmed SARS-CoV-2 study population are shown in Table 2 and of the clinically reported COVID-19 study population in Supplementary Table S5. When compared to individuals with laboratory-confirmed SARS-CoV-2, a higher proportion of those with clinically reported COVID-19 were older, women, less affluent, and lived in London. Table 2Baseline characteristics of the laboratory-confirmed SARS-CoV-2 study population by cardiovascular risk. AllQRISK3 scoreHypertensionRaised riskLow riskRaised riskLow riskN = 146,760N = 39,295N = 107,465N = 49,955N = 96,805Age (years), Mean (SD)a54.0 (10.1)65.3 (9.5)49.9 (6.6)57.7 (10.6)52.2 (9.3)Age group (years)a 40-5484,928 ($57.9\%$)5150 ($13.1\%$)79,778 ($74.2\%$)21,382 ($42.8\%$)63,546 ($65.6\%$) 55-6439,757 ($27.1\%$)13,967 ($35.5\%$)25,790 ($24.0\%$)16,435 ($32.9\%$)23,322 ($24.1\%$) 65-7414,782 ($10.1\%$)12,885 ($32.8\%$)1897 ($1.8\%$)7819 ($15.7\%$)6963 ($7.2\%$) 75-847293 ($5.0\%$)7293 ($18.6\%$)0 ($0.0\%$)4319 ($8.6\%$)2974 ($3.1\%$)Sexa Women80,805 ($55.1\%$)14,316 ($36.4\%$)66,489 ($61.9\%$)24,608 ($49.3\%$)56,197 ($58.1\%$) Men65,955 ($44.9\%$)24,979 ($63.6\%$)40,976 ($38.1\%$)25,347 ($50.7\%$)40,608 ($41.9\%$)Ethnicitya White or not stated104,902 ($71.5\%$)28,657 ($72.9\%$)76,245 ($70.9\%$)36,280 ($72.6\%$)68,622 ($70.9\%$) South Asian12,340 ($8.4\%$)4588 ($11.7\%$)7752 ($7.2\%$)3893 ($7.8\%$)8447 ($8.7\%$) Black3408 ($2.3\%$)519 ($1.3\%$)2889 ($2.7\%$)1378 ($2.8\%$)2030 ($2.1\%$) Mixed/Other11,793 ($8.0\%$)2624 ($6.7\%$)9169 ($8.5\%$)4114 ($8.2\%$)7679 ($7.9\%$) Unknown14,317 ($9.8\%$)2907 ($7.4\%$)11,410 ($10.6\%$)4290 ($8.6\%$)10,027 ($10.4\%$)*Townsend quintilea* 1 (most affluent)28,068 ($19.1\%$)6224 ($15.8\%$)21,844 ($20.3\%$)9175 ($18.4\%$)18,893 ($19.5\%$) 228,488 ($19.4\%$)6968 ($17.7\%$)21,520 ($20.0\%$)9619 ($19.3\%$)18,869 ($19.5\%$) 328,259 ($19.3\%$)7281 ($18.5\%$)20,978 ($19.5\%$)9612 ($19.2\%$)18,647 ($19.3\%$) 428,947 ($19.7\%$)8099 ($20.6\%$)20,848 ($19.4\%$)10,027 ($20.1\%$)18,920 ($19.5\%$) 5 (least affluent)32,940 ($22.4\%$)10,712 ($27.3\%$)22,228 ($20.7\%$)11,505 ($23.0\%$)21,435 ($22.1\%$) Unknown58 ($0.0\%$)11 ($0.0\%$)47 ($0.0\%$)17 ($0.0\%$)41 ($0.0\%$)Region of residence North East6207 ($4.2\%$)1746 ($4.4\%$)4461 ($4.2\%$)2276 ($4.6\%$)3931 ($4.1\%$) North West34,059 ($23.2\%$)9696 ($24.7\%$)24,363 ($22.7\%$)12,364 ($24.8\%$)21,695 ($22.4\%$) Yorkshire and the Humber4908 ($3.3\%$)1328 ($3.4\%$)3580 ($3.3\%$)1718 ($3.4\%$)3190 ($3.3\%$) East Midlands2508 ($1.7\%$)690 ($1.8\%$)1818 ($1.7\%$)897 ($1.8\%$)1611 ($1.7\%$) West Midlands24,071 ($16.4\%$)6916 ($17.6\%$)17,155 ($16.0\%$)8972 ($18.0\%$)15,099 ($15.6\%$) East of England5347 ($3.6\%$)1207 ($3.1\%$)4140 ($3.9\%$)1662 ($3.3\%$)3685 ($3.8\%$) South West32,542 ($22.2\%$)8765 ($22.3\%$)23,777 ($22.1\%$)10,067 ($20.2\%$)22,475 ($23.2\%$) South Central26,156 ($17.8\%$)6050 ($15.4\%$)20,106 ($18.7\%$)8170 ($16.4\%$)17,986 ($18.6\%$) London10,734 ($7.3\%$)2812 ($7.2\%$)7922 ($7.4\%$)3737 ($7.5\%$)6997 ($7.2\%$) Unknown228 ($0.2\%$)85 ($0.2\%$)143 ($0.1\%$)92 ($0.2\%$)136 ($0.1\%$)BMI categorya,b Underweight (<18.5 kg/m2)865 ($0.6\%$)336 ($0.9\%$)529 ($0.5\%$)182 ($0.4\%$)683 ($0.7\%$) Normal (18.5–24.9 kg/m2)25,789 ($17.6\%$)5980 ($15.2\%$)19,809 ($18.4\%$)5727 ($11.5\%$)20,062 ($20.7\%$) Overweight (25.0–29.9 kg/m2)39,501 ($26.9\%$)12,415 ($31.6\%$)27,086 ($25.2\%$)13,618 ($27.3\%$)25,883 ($26.7\%$) Obese (30.0–39.9 kg/m2)34,394 ($23.4\%$)12,652 ($32.2\%$)21,742 ($20.2\%$)16,361 ($32.8\%$)18,033 ($18.6\%$) Severely obese (≥40.0 kg/m2)5558 ($3.8\%$)1985 ($5.1\%$)3573 ($3.3\%$)3153 ($6.3\%$)2405 ($2.5\%$) Unknown40,653 ($27.7\%$)5927 ($15.1\%$)34,726 ($32.3\%$)10,914 ($21.8\%$)29,739 ($30.7\%$)Cholesterol:HDL, Mean (SD)a,b3.8 (1.2)4.0 (1.3)3.7 (1.1)3.8 (1.2)3.7 (1.2)Systolic blood pressure, Mean (SD)a,b,c128.1 (14.6)134.3 (14.4)125.5 (13.8)138.7 (13.5)122.1 (11.3)Smoking statusa,b Non-smoker73,789 ($50.3\%$)18,643 ($47.4\%$)55,146 ($51.3\%$)26,210 ($52.5\%$)47,579 ($49.1\%$) Ex-smoker34,028 ($23.2\%$)12,543 ($31.9\%$)21,485 ($20.0\%$)13,287 ($26.6\%$)20,741 ($21.4\%$) Current smoker12,400 ($8.4\%$)4630 ($11.8\%$)7770 ($7.2\%$)3872 ($7.8\%$)8528 ($8.8\%$) Unknown26,543 ($18.1\%$)3479 ($8.9\%$)23,064 ($21.5\%$)6586 ($13.2\%$)19,957 ($20.6\%$)Alcohol consumptionb No heavy drinking85,406 ($58.2\%$)26,746 ($68.1\%$)58,660 ($54.6\%$)32,332 ($64.7\%$)53,074 ($54.8\%$) Heavy drinking12,319 ($8.4\%$)3760 ($9.6\%$)8559 ($8.0\%$)4512 ($9.0\%$)7807 ($8.1\%$) Unknown49,035 ($33.4\%$)8789 ($22.4\%$)40,246 ($37.5\%$)13,111 ($26.2\%$)35,924 ($37.1\%$)Family history of CHDa13,116 ($8.9\%$)4579 ($11.7\%$)8537 ($7.9\%$)4371 ($8.7\%$)8745 ($9.0\%$)Consultation frequency in prior 12 months, Median (IQR)3 (1–7)6 (2–10)3 (1–6)4 (1–9)3 (1–6)Medication used Regular corticosteroidsa1545 ($1.1\%$)1070 ($2.7\%$)475 ($0.4\%$)790 ($1.6\%$)755 ($0.8\%$) Antihypertensivesa35,232 ($24.0\%$)15,938 ($40.6\%$)19,294 ($18.0\%$)21,361 ($42.8\%$)13,871 ($14.3\%$) Statins19,723 ($13.4\%$)13,931 ($35.5\%$)5792 ($5.4\%$)11,230 ($22.5\%$)8493 ($8.8\%$) Antiplatelets7415 ($5.1\%$)4360 ($11.1\%$)3055 ($2.8\%$)3830 ($7.7\%$)3585 ($3.7\%$) Anticoagulants2720 ($1.9\%$)1761 ($4.5\%$)959 ($0.9\%$)1359 ($2.7\%$)1361 ($1.4\%$)Comorbid conditionAtrial fibrillationa1420 ($1.0\%$)1303 ($3.3\%$)117 ($0.1\%$)745 ($1.5\%$)675 ($0.7\%$)Migrainesa5396 ($3.7\%$)907 ($2.3\%$)4489 ($4.2\%$)1559 ($3.1\%$)3837 ($4.0\%$)Diabetesa12,238 ($8.3\%$)9811 ($25.0\%$)2427 ($2.3\%$)6603 ($13.2\%$)5635 ($5.8\%$)CKD stage 3–5a9294 ($6.3\%$)7013 ($17.9\%$)2281 ($2.1\%$)5482 ($11.0\%$)3812 ($3.9\%$)Chronic liver disease1563 ($1.1\%$)775 ($2.0\%$)788 ($0.7\%$)641 ($1.3\%$)922 ($1.0\%$)Chronic respiratory disease (not asthma)4880 ($3.3\%$)3303 ($8.4\%$)1577 ($1.5\%$)2267 ($4.5\%$)2613 ($2.7\%$)Asthma with recent OCS used7558 ($5.1\%$)2597 ($6.6\%$)4961 ($4.6\%$)3120 ($6.2\%$)4438 ($4.6\%$)Asthma with no recent OCS use14,861 ($10.1\%$)3582 ($9.1\%$)11,279 ($10.5\%$)5056 ($10.1\%$)9805 ($10.1\%$)Severe mental illness/antipsychotic usea1700 ($1.2\%$)956 ($2.4\%$)744 ($0.7\%$)595 ($1.2\%$)1105 ($1.1\%$)Dementia2407 ($1.6\%$)2046 ($5.2\%$)361 ($0.3\%$)1060 ($2.1\%$)1347 ($1.4\%$)Chronic neurological disease1932 ($1.3\%$)1034 ($2.6\%$)898 ($0.8\%$)746 ($1.5\%$)1186 ($1.2\%$)Learning/intellectual disability1014 ($0.7\%$)361 ($0.9\%$)653 ($0.6\%$)292 ($0.6\%$)722 ($0.7\%$)Non-haematological cancer Diagnosed <1 year ago3839 ($2.6\%$)2275 ($5.8\%$)1564 ($1.5\%$)1807 ($3.6\%$)2032 ($2.1\%$) Diagnosed 1–4.9 years ago4554 ($3.1\%$)2085 ($5.3\%$)2469 ($2.3\%$)1878 ($3.8\%$)2676 ($2.8\%$) Diagnosed ≥5 years ago8436 ($5.7\%$)2764 ($7.0\%$)5672 ($5.3\%$)2998 ($6.0\%$)5438 ($5.6\%$)Haematological malignancy Diagnosed <1 year ago558 ($0.4\%$)373 ($0.9\%$)185 ($0.2\%$)246 ($0.5\%$)312 ($0.3\%$) Diagnosed 1–4.9 years ago273 ($0.2\%$)162 ($0.4\%$)111 ($0.1\%$)124 ($0.2\%$)149 ($0.2\%$) Diagnosed ≥5 years ago278 ($0.2\%$)114 ($0.3\%$)164 ($0.2\%$)113 ($0.2\%$)165 ($0.2\%$)Rheumatoid arthritisa1276 ($0.9\%$)648 ($1.6\%$)628 ($0.6\%$)560 ($1.1\%$)716 ($0.7\%$)*Systemic lupus* erythematosusa164 ($0.1\%$)57 ($0.1\%$)107 ($0.1\%$)46 ($0.1\%$)118 ($0.1\%$)HIVa234 ($0.2\%$)56 ($0.1\%$)178 ($0.2\%$)100 ($0.2\%$)134 ($0.1\%$)Immunosuppressione1404 ($1.0\%$)643 ($1.6\%$)761 ($0.7\%$)597 ($1.2\%$)807 ($0.8\%$)Erectile dysfunctiona7183 ($10.9\%$)5293 ($21.2\%$)1890 ($4.6\%$)3556 ($14.0\%$)3627 ($8.9\%$)aIn QRISK3 algorithm, but non-imputed version included here (for smoking status, cholesterol:HDL ratio, systolic BP and BMI).bMost recent measure before baseline. N with missing cholesterol:HDL measurement 55,392 ($37.7\%$).cUsed on hypertension definition. N with missing systolic BP measurement 16,713 ($11.4\%$).dAt least 1 prescription in the 12 months before baseline. Other than corticosteroids which was defined as at least 2 prescriptions prior to baseline with the most recent ≤28 days before baseline.eEver history of solid organ transplant or permanent cellular immune deficiency; history in the 24 months before baseline for aplastic anaemia, bone marrow or stem cell transplant; history in the 12 months before baseline for biologics or other immunosuppressant therapy (excluding corticosteroids), other or unspecified cellular immune deficiency.
## Risk of death after COVID-19
In unadjusted analysis, raised QRISK3 score was associated with a substantial increase in COVID-19 death overall (HR 16.33 [14.61–18.24] $$n = 146$$,760) and in the study population with complete data available (HR 14.95 [13.07–17.10], $$n = 97$$,725) (Supplementary Table S6). After adjustment for non-QRISK3 confounders, the association between QRISK3 score and COVID-19 death attenuated but remained substantial (aHR 8.77 [7.62–10.10]). Characteristics of those included and excluded from the fully-adjusted model are shown in Supplementary Table S7. Among all patients aged 40–54 years, $6.1\%$ had a QRISK3 score ≥$10\%$ but among those who died from COVID-19, $25.4\%$ had a QRISK3 score ≥$10\%$ (Supplementary Table S8). In age-stratified and further age-adjusted analysis, the association between QRISK3 score and COVID-19 death was diminished compared to the main effect estimate but remained statistically significant in all age-group strata (Supplementary Table S9) with an age-adjusted result of 2.91 (2.45–3.45) In comparison, there was no association between hypertension and COVID-19 death (aHR 1.05 [0.94–1.18], $$n = 57$$,264) (Fig. 3). Results for all adjustment factors are shown in Supplementary Fig. S1.Fig. 3Adjusted hazard ratios for raised cardiovascular risk effect on COVID-19 severe outcomes presented separately for a) QRISK3 and b) hypertension from complete case analysis. QRISK3 score hazard ratios are for the effect of a score ≥ $10\%$ with <$10\%$ as the reference ($$n = 97$$,725 for complete case analysis compared to 146,760 for crude model). Hypertension hazard ratios are for the effect of having hypertension with not having hypertension as the reference ($$n = 57$$,264 for complete case analysis). Hypertension models were adjusted for age, sex, ethnicity, socioeconomic status, body-mass index, alcohol consumption, smoking status, total cholesterol: high density lipoprotein cholesterol ratio, family history of coronary heart disease, treatment with corticosteroids, antiplatelets, or anticoagulants, diagnosis of atrial fibrillation, migraine, diabetes, chronic kidney disease stage 3–5, chronic liver disease, chronic lung disease, asthma, severe mental illness, dementia, chronic neurological disease, learning disability, or malignancy, and treatment or diagnosis of a immunosuppressive condition; and QRISK3 models were adjusted for alcohol consumption, treatment with antiplatelets or anticoagulants, diagnosis of chronic liver disease, chronic lung disease, asthma, dementia, chronic neurological disease, learning disability, or malignancy, and treatment or diagnosis of an immunosuppressive condition (which are not included in the QRISK3 algorithm).
## Risk of other severe outcomes after COVID-19
Significant associations were also found for QRISK3 score ≥$10\%$ and the outcomes of ICU admission (aHR 3.66 [3.18–4.21]), respiratory support (aHR 3.73 [3.10–4.49]), hospitalisation (aHR 3.38 [3.22–3.56]), and MACE (aHR 5.43 [4.44–6.64]). There was only a minor reduction in associations after further adjustment for age (Fig. 3). There was no association between hypertension and ICU admission (aHR 1.15 [0.98–1.36]), respiratory support (aHR 1.20 [0.97–1.48]), or hospitalisation (aHR 1.05 [0.99–1.11]) but there was an association between hypertension and MACE (aHR 1.49 [1.20–1.85]).
## Additional analyses
Results between wave 1 and wave 2 were broadly similar for all outcomes (Supplementary Table S10). Similar results were also obtained when COVID-19 was clinically reported rather than laboratory-confirmed (Supplementary Table S11). Further stratification of the QRISK3 score showed a substantially greater risk of COVID-19 death in individuals with a QRISK3 score of ≥$20\%$ (aHR 15.15 [13.05–17.59]) than 10-<$20\%$ (aHR 5.32 [4.54–6.23]) when both were compared to those with a score <$10\%$ (Supplementary Table S12). A similar, though less extreme gradient was observed for the other outcomes.
## Discussion
In this large, population-based cohort study using linked data from England in 2020, we found a striking gradient in the occurrence of severe COVID-19 outcomes by underlying cardiovascular risk profile among people without pre-existing CVD. The risks of death, ICU admission, hospital admission and MACE were all greater among individuals at raised cardiovascular risk measured by QRISK3 score, compared to those at low risk, despite no increase in recorded infections in this group. Associations between raised cardiovascular risk and COVID-19 deaths remained present, though attenuated, when results were stratified by 15-year age-group and further adjusted for age. When cardiovascular risk was measured by hypertension alone, differences were only evident for MACE outcomes. Analysis by pandemic waves revealed similar patterns, although the incidence of severe outcomes was greatest during the first wave.
Our study used linked electronic health record data from primary and secondary, including intensive, care, mortality records and national laboratory surveillance to capture detailed clinical and laboratory data on SARS-CoV-2 infections and outcomes. It is, to our knowledge, the first UK population-based study to assess COVID-19 outcomes using a comprehensive, combined measure of cardiovascular risk, QRISK3, rather than focusing on individual vascular risk factors. Findings from this large, representative cohort should be generalizable to adults in England aged 40–84 years (the upper age for which QRISK3 can be used to assess cardiovascular risk). Our dataset spanned the first and major part of the second wave of the COVID-19 pandemic in England, allowing comparisons of outcomes between waves. Limiting follow up to the end of December 2020 prevented contamination from the emergence of coronavirus variants or widespread roll out of the COVID-19 vaccination programme in England.
Nevertheless, differences in the availability of laboratory PCR testing are likely to have led to differences in the reported incidence of infection between waves: a laboratory-confirmed definition of SARS-CoV-2 lacked sensitivity to identify cases occurring during wave one before mass testing became widely available. It is also possible that some outcomes such as hospitalisation or MACE may have led to in-hospital testing, strengthening the observed association between vascular risk status and severe outcomes in the laboratory-confirmed cohort during the first wave. However, individuals who were at raised cardiovascular risk defined by QRISK3 score had a lower incidence of laboratory-confirmed infection than those at low cardiovascular risk, suggesting that differential in-hospital testing is unlikely to have biased our results. Reasons for the lower rates of laboratory-confirmed infections among individuals at high cardiovascular risk are unclear but may reflect reduced access to community testing e.g. due to shielding. Stratifying by pandemic wave to explore the effect of expanded testing and advances in clinical management of COVID-19 in later time periods revealed similar results to the main analysis. When we compared results for individuals with confirmed SARS-CoV-2 infection to those with clinically diagnosed COVID-19, we also saw similar patterns. Our descriptive analysis of COVID-19 outcomes alone regardless of recorded infection status supported findings from the cohort analysis.
The magnitude of association between cardiovascular risk status and severe outcomes varied by the method used to classify cardiovascular risk. *In* general, classification by QRISK3 produced more exaggerated differences between high and low cardiovascular risk groups than classification by hypertension alone. This is perhaps unsurprising as QRISK3 is a more comprehensive measure of cardiovascular risk, which includes additional comorbidities and socio-demographic components of risk. While age is a major driver of severe COVID-19 outcomes, associations with raised cardiovascular risk remained present in both age-stratified and age-adjusted analyses. Although misclassification of cardiovascular risk status could have occurred due to the documented reductions in GP visits and healthcare-seeking for non-COVID conditions during the pandemic,23 under-recognition of individuals at raised cardiovascular risk would have led to bias towards the null. In addition, our sensitivity analysis in which QRISK3 status was graded more finely into three strata (<$10\%$, 10–$19\%$, $20\%$+), confirmed a gradient of increasing risk of severe outcomes with increasing vascular risk level, which suggests that the main results are robust to any minor exposure misclassification. As QRISK3 scores were developed for the UK population, levels of cardiovascular risk identified in our study population may differ to those in other countries using different risk scores, although results of our hypertension analysis should generalize to other settings.
Residual confounding may also have been present in our study. While we adjusted for a broad range of sociodemographic, lifestyle and clinical confounding factors, some variables are either not measured (such as genetic risk profiles) or are sub-optimally recorded (such as BMI) in EHRs. Nevertheless, population-based self-controlled case series analyses of COVID-19 and thrombotic outcomes, which use within-person comparisons to control implicitly for fixed confounding24 show comparable results to cohort studies,11,25 suggesting that confounding is unlikely to explain our cohort results. Missing data on alcohol consumption reduced numbers for the QRISK3 complete case analysis (as other lifestyle factors were imputed in the QRISK3 algorithm if missing) whereas for hypertension, reduced numbers were driven by missing data on alcohol, smoking, BMI, cholesterol and ethnicity. Nevertheless, complete case analysis gives unbiased results when the chance of being a complete case is independent of outcome after taking covariates into account, even when data are missing not at random.26 Our findings extend those from previous smaller studies of individual cardiovascular risk factors and COVID-19 outcomes,27,28 supporting a strong association between raised cardiovascular risk profile and severe COVID-19 outcomes. While a previous Mendelian randomisation study, which by design avoids reverse causation and most confounding, failed to show an association between some genetically-predicted cardiovascular risk factors (blood pressure, BMI, type 2 diabetes and coronary artery disease) and COVID-19 hospitalisation,29 estimates had wide confidence intervals and did not capture full profiles of either cardiovascular risk or severe COVID-19 outcomes. The bidirectional relationship between cardiovascular risk and COVID-19 shown in our study is consistent with pre-COVID era work on cardiovascular complications of acute respiratory infections, showing a gradient in the risk of complications aligned with underlying cardiovascular risk status.14 Potential mechanisms underlying severe outcomes in COVID-19 include pro-inflammatory, pro-thrombotic and vasoconstrictive effects of SARS-CoV-2-mediated imbalances in ACE-2/RAS signalling.30 It has been suggested that individuals with conditions leading to raised cardiovascular risk are likely to have altered cytokine profiles leading to chronic systemic inflammation, which may have a synergistic effect on disease severity in acute COVID-19.31 A substantial burden of cardiovascular disease has also been demonstrated in survivors of acute COVID-19 at one year.32,33 Understanding the natural history of longer-term cardiovascular and other complications including post-COVID-19 syndrome34 in individuals at raised cardiovascular risk, along with the mechanisms underlying both short and long-term health changes, should be a priority for future research. Future studies could also investigate the role of COVID-19 treatments in modifying or mediating the relationship between raised cardiovascular risk and severe COVID-19. Combining mechanistic research with clinical evidence to improve patient care among those at raised cardiovascular risk is essential to prevent and manage severe outcomes of COVID-19 in this group.35 Our study highlights the need for a continued focus on integrated prevention e.g. combining COVID-19 vaccinations with cardiovascular disease prevention to improve health among those at raised cardiovascular risk.
In conclusion, we showed that individuals at raised cardiovascular risk in England were more likely to die or to experience severe outcomes after COVID-19 than those at low cardiovascular risk, despite not initially being identified as a vulnerable group. Those at raised cardiovascular risk should be considered a priority for targeted prevention and treatment strategies for COVID-19. Addressing cardiovascular risk factors could improve outcomes after COVID-19.
## Contributors
CWG conceptualized the study and obtained funding. CWG, JAD, HS, EH, LS, JB and AB contributed to study design. JAD managed and analysed data, supported by EH, HS and CWG. JAD and CWG drafted the manuscript. All authors reviewed the manuscript, interpreted data and approved the final version for publication.
## Data sharing statement
Data used for the study were obtained from the UK CPRD database under licence from the UK Medicines and Healthcare Products Regulatory Agency. Access to CPRD data is subject to protocol approval via CPRD's Research Data Governance Process (https://cprd.com/data-access). All codelists used for this study are available on LSHTM Data Compass: https://doi.org/10.17037/DATA.00002762. Analytical code is available via GitHub: https://github.com/jenAdavidson/cvrisk_covid_cohort.
## Ethical approval
The CPRD Independent Scientific Advisory Committee (application 20_000135) and the London School of Hygiene and Tropical Medicine (LSHTM) Ethics Committee (application 22717) approved the study.
## Declaration of interests
CWG reports participation in the data safety and monitoring board for the IAMI trial of influenza vaccine for cardiovascular disease (NCT02831608) ending April 2020. JB reports consulting fees from ARCbio, HVivo and GSK and participation in a data safety and monitoring board for the COM Cov trial, Oxford, now ended. AB reports grants from NIHR, AstraZeneca and the British Medical Association and leadership roles as Vice-President, Digital, Marketing, Communications for the British Cardiovascular Society and Senior Advisor to the Emerging Leaders Programme of the World Heart Federation. All other authors report no conflicts.
## Supplementary data
Supplementary Methods S1 and S2, Tables S1–S12, and Fig. S1
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|
---
title: Elevated levels of interleukin-18 are associated with several indices of general
and visceral adiposity and insulin resistance in women with polycystic ovary syndrome
authors:
- Plamena Kabakchieva
- Antoaneta Gateva
- Tsvetelina Velikova
- Tsvetoslav Georgiev
- Kyosuke Yamanishi
- Haruki Okamura
- Zdravko Kamenov
journal: Archives of Endocrinology and Metabolism
year: 2022
pmcid: PMC9991028
doi: 10.20945/2359-3997000000442
license: CC BY 4.0
---
# Elevated levels of interleukin-18 are associated with several indices of general and visceral adiposity and insulin resistance in women with polycystic ovary syndrome
## ABSTRACT
### Objective:
Our aim was to analyze levels of proinflammatory biomarker interleukin-18 (IL-18) in healthy controls and patients with polycystic ovary syndrome (PCOS) focusing on its association with obesity, clinical, hormonal, and metabolic characteristics.
### Subjects and methods:
Fifty-eight patients with PCOS were enrolled in the study fulfilling the *Rotterdam criteria* and were matched for age, body mass index (BMI), and ethnicity with 30 healthy controls. Detailed anthropometric measurements, clinical investigations, hormonal and biochemical tests were obtained between the 3rd and 5th day of a menstrual cycle. A subanalysis of the PCOS group was performed separating patients into several groups according to a waist-to-height ratio (WHtR), insulin resistance (IR), and free androgen index (FAI). Serum IL-18 levels were measured using the ELISA method.
### Results:
Levels of IL-18 were similar between PCOS patients and controls. IL-18 was higher in overweight/obese women compared to normal-weight women when analyzing all participants together and separately PCOS or controls group ($p \leq 0.001$, $p \leq 0.001$, $$p \leq 0.01$$, respectively). Additionally, IL-18 levels were higher in high-WHtR and IR subgroups compared to low-WHtR ($p \leq 0.001$) and non-IR PCOS women ($p \leq 0.001$). PCOS women with high FAI had greater serum IL-18 levels than normal-FAI patients ($$p \leq 0.002$$). Levels of IL-18 correlated positively with most of the anthropometric and metabolic parameters. In multiple linear regression, age, waist circumference, and fasting insulin were independently related factors with IL-18.
### Conclusion:
Elevated levels of IL-18 were related to several indices of general and visceral adiposity and insulin resistance in PCOS.
## INTRODUCTION
Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders among women of reproductive age [1]. PCOS is now perceived as both a reproductive syndrome, presenting clinically with hyperandrogenism, ovulatory dysfunction (including oligo-amenorrhea), polycystic ovaries, and infertility [1], and a metabolic disorder [2]. Most women with PCOS have obesity and insulin resistance (IR) [3,4] that further increases the risk for type 2 diabetes, hypertension, dyslipidemia, nonalcoholic fatty liver disease (NAFLD), and metabolic syndrome [5-7].
All of the aforementioned and associated with PCOS conditions are considered chronic low-grade inflammatory disorders [8] where visceral adipose tissue is thought to play a central role [9]. Being infiltrated with different immune cells (T-cells and macrophages) [10,11], fat tissue additionally produces various pro-inflammatory cytokines related to IR [12].
Interleukin-18 (IL-18) is a cytokine, discovered by Okamura and cols. in the late 20th century and initially was described as an interferon-gamma (IFNγ)-inducing factor [13]. As a potent pro-inflammatory cytokine, elevated IL-18 levels were already observed in many low-grade inflammatory conditions such as obesity, metabolic syndrome [14,15], prediabetes [16], type 2 diabetes, latent autoimmune diabetes of the adults (LADA) [17], hypertension [18], and dyslipidemia [19].
Elevation of IL-18 [20-22] and other inflammatory biomarkers, such as C-reactive protein (CRP) [23,24], tumor necrosis factor (TNF-α), and interleukin-6 [25] was observed as well as in PCOS patients. Nevertheless, the inflammatory nature of PCOS is still debatable because of the great variability of reported findings; thus, there is insufficient evidence to conclusively resolve the issue [26,27]. According to some authors, the independent association between PCOS and low-grade inflammation is not quite certain and it could be analyzed after clear stratification of participants according to body mass index (BMI) or other parameters, using clinically relevant cutoffs [27].
Our research aimed to determine the IL-18 levels in women with PCOS and compare them with healthy controls, to analyze its association with different markers for global and central adiposity, insulin resistance, and hyperandrogenism, and to determine which of those variables independently predict IL-18 levels.
## Study population
Fifty-eight women with PCOS (25.9 ± 5.2 years) were consecutively enrolled in this cross-sectional observational study and matched for age, ethnicity, and BMI with 30 healthy controls (27.6 ± 5.2 years). All the PCOS patients were recruited in the Endocrinology Clinic of University Hospital “Alexandrovska”, Sofia, Bulgaria and met the *Rotterdam criteria* [28].
The control group consisted of healthy volunteers with a regular menstrual cycle without clinical or biochemical hyperandrogenism and a history of infertility. Exclusion criteria for all participants in the study were: pregnancy, hyperprolactinemia, thyroid dysfunction, premature ovarian failure, hypothalamic amenorrhea, congenital adrenal hyperplasia, androgen-producing tumors, Cushing's syndrome or disease, use of oral contraceptive, antiandrogen or insulin-sensitizing drugs (metformin, thiazolidinediones) for the last 3 months. The study protocol was approved by the Research Ethics Committee of the Medical University of Sofia, Bulgaria with approval protocol number $\frac{14}{22.05.2020.}$ Each participant signed written informed consent before recruitment.
## Anthropometric and clinical assessment
Anthropometric and clinical assessment was carried out by the same clinician to avoid an inter-observer error. All participants were evaluated for hirsutism (according to modified Ferriman-Gallwey [mFG] score), acne, alopecia (using the Ludwig visual scale), and acanthosis nigricans (AN). Detailed anthropometric measurements were done including the following parameters: height, weight, BMI (kg/m2), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR).
Obesity was defined as BMI ≥ 30 kg/m2, overweight as BMI between 25 and 29.9 kg/m2, and normal weight as BMI ≤ 24.9 kg/m2 and > 18,5 kg/m2 [29]. Ultrasound pelvic investigation was performed by an experienced sonographer for assessment of polycystic morphology of the ovaries.
## Laboratory tests
After an overnight fasting state, blood samples were obtained for measurement of fasting plasma glucose (FPG), serum immunoreactive insulin (IRI), testosterone, dehydroepiandrosterone sulfate (DHEAS), androstenedione, 17-OH-progesterone, luteinizing hormone (LH), follicle stimulating hormone (FSH), and estradiol between the 3rd and 5th day of spontaneous or progesterone-induced menstrual bleeding. Additionally, fasting serum samples were collected, centrifuged, and stored in a freezer at −80 °C until its examination for assessment of IL-18 and sex hormone-binding globulin (SHBG). A standard oral glucose tolerance test (OGTT) was also performed with measurement of plasma glucose and IRI at 0, 60, and 120 min. The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated using FPG and fasting insulin according to the following equation: HOMA-IR = FPG (mmol/L) x fasting insulin (mU/L)/22.5 [30]. Testosterone was measured by employing electrochemiluminescence immunoassay (ECLIA) by analytics Elecsys 2010 with analytical sensitivity 0.069 nmol/L and CV 1.2-$4.7\%$. SHBG was measured by enzyme-linked immunosorbent assay (ELISA), based on the principle of competitive binding with an analytical sensitivity of 0.23 nmol/L. The free androgen index (FAI) was calculated using the following equation: FAI=Testosterone (nmol/L)/SHBG (nmol/L) × 100.
All other hormonal and biochemical measurements were performed by standard techniques in the referent for Bulgaria Laboratory of the University hospital “Alexandrovska”. Serum IL-18 was measured by ELISA (Medical & Biological Laboratories Co., Ltd, Nagoya, Japan # code No 7620) with a detection limit of 12.5 pg/mL.
## PCOS subgrouping
A detailed subanalysis of the PCOS group was performed separating patients into several groups. According to WHtR, the patient group was divided into high-WHtR (WHtR ≥ 0.5; $$n = 30$$) and low-WHtR (WHtR < 0.5; $$n = 28$$) [31,32]. Further, the PCOS group was divided into PCOS with IR ($$n = 25$$) and without IR ($$n = 33$$). IR was accepted when the homeostatic model assessment of IR (HOMA-IR) > 2.5 and/or peak insulin levels during an OGTT > 100 IU/mL [33]. Finally, patients with PCOS were divided into two androgen groups according to FAI: normal FAI (FAI < 5, $$n = 39$$) and high FAI (FAI ≥ 5, $$n = 19$$) [34].
## Statistical analysis
Statistical analysis was conducted using the SPSS software version 21.0. The normality of data distribution was assessed with the Shapiro-Wilk test. Parametric tests (Independent Samples T-Test) were carried out when data were normally distributed and the hypotheses were presented as the difference between the mean values ± standard deviation (SD). In case of a skewed data distribution, a nonparametric Mann-Whitney U test was used to compare the variables and the hypotheses were presented as the difference between the medians (interquartile range [IQR]). Pearson and Spearman correlation analyses were used for normally and abnormally distributed data, respectively. Multiple regression analysis using a forward (probability for entry ≤ 0.98, probability for removal ≥ 0.99) and enter methods for the introduction of independent variables were performed to identify the main determinants of IL-18 levels among the variables. The sample size was calculated based on the IL-18 measurements from a previous study [35] using Sample Size Calculator Clinical Calc (https://clincalc.com/stats/samplesize.aspx). To obtain a power analysis with alpha 0.05 and power > $80\%$, the sample size resulted in at least 20 participants per group. For all comparisons, $p \leq 0.05$ was chosen as the level of significance at which the null hypothesis was rejected.
## Comparison between PCOS and healthy women
The population in our study consisted of 58 PCOS patients and 30 healthy women who were matched for age, BMI, and ethnicity. Women with PCOS had significantly higher levels of testosterone, DHEAS, androstenedione, 17-OH-progesterone, FAI, and LH/FSH ratio compared to controls. Expectedly, healthy controls were less hirsute than patients. All other clinical, anthropometric, and metabolic parameters were similar between the two groups. IL-18 levels were also similar between patients and healthy controls (Table 1).
**Table 1**
| Variable | PCOS (n = 58) | Controls (n = 30) | P-value |
| --- | --- | --- | --- |
| Age (years) | 25.9 ± 5.2 | 27.6 ± 5.2 | 0.102 |
| Height (cm) | 164 (160; 168) | 165 (160; 169) | 0.798 |
| Weight (kg) | 73 (58; 87) | 70 (59.5; 97.3) | 0.711 |
| BMI (kg/m2) | 27.7 ± 7.3 | 29.3 ± 7.4 | 0.738 |
| WC (cm) | 83.5 (76; 101.3) | 84 (74; 96.8) | 0.672 |
| HC (cm) | 100.5 (92; 110) | 102 (92.8; 110.3) | 0.724 |
| WHR | 0.84 (0.77; 0.96) | 0.81 (0.76; 0.90) | 0.296 |
| WHtR | 50.4 (45.3; 63.6) | 48.7 (44.9; 57.4) | 0.391 |
| AN | 21/58 | 6/30 | 0.147 |
| Acne | 14/58 | 6/30 | 0.791 |
| mFG score | 8 (4; 14) | 3.5 (1; 6.3) | <0.001 |
| FPG (mmol/L) | 5.0 (4.8; 5.3) | 5.05 (4.8; 5.2) | 0.621 |
| OGTT 60' glucose (mmol/L) | 6.8 (5.1; 9.0) | 6.7 (5.1; 8.0) | 0.788 |
| OGTT 120' glucose (mmol/L) | 5.5 (4.8; 6.5) | 5.5 (4.8; 6.3) | 0.788 |
| Fasting IRI (mU/L) | 9.4 (5.9; 16.8) | 9.3 (6.0; 14.4) | 0.996 |
| OGTT 60' IRI (mU/L) | 80.9 (45.8; 125.9) | 61.9 (30.9; 124.3) | 0.379 |
| OGTT 120' IRI (mU/L) | 42.3 (23.4; 85) | 30.1 (17.2; 49) | 0.058 |
| HOMA-IR | 2.15 (1.25; 3.74) | 2.18 (1.28; 3.27) | 0.982 |
| LH (mU/mL) | 6.6 (4.7; 8.5) | 5.8 (4.1; 8.1) | 0.214 |
| FSH (mU/mL) | 5.1 (4.3; 6.1) | 5.5 (4.8; 6.5) | 0.333 |
| LH/FSH ratio | 1.2 (1.0; 1.8) | 1.0 (0.8; 1.2) | 0.023 |
| Estradiol (pmol/L) | 133.7 (96.9; 193.8) | 147.2 (104.8; 190.3) | 0.768 |
| Testosterone (nmol/L) | 1.6 (1.1; 2.0) | 0.9 (0.7; 1.1) | <0.001 |
| DHEAS (mcmol/L) | 8.9 (6.5; 11.6) | 5.5 (4.2; 7.5) | <0.001 |
| Androstenedione (ng/mL) | 4.4 (3.0; 5.5) | 2.3 (1.5; 2.7) | <0.001 |
| 17-OH-progesterone (ng/mL) | 1.6 (1.3; 1.9) | 1.2 (1.0; 1.3) | <0.001 |
| FAI | 3.4 (1.7; 6.8) | 1.5 (1.0;3.1) | 0.002 |
| SHBG (nmol/L) | 45.1 (28.6; 79.1) | 54.0 (28.3; 80.5) | 0.544 |
| IL-18 (pg/mL) | 211.8 (134.6; 308.3) | 249.8 (179.9; 367.1) | 0.081 |
## Comparison between overweight/obese and normal-weight women in the groups
When all participants were taken into account ($$n = 88$$), IL-18 levels were higher in overweight/obese women ($$n = 50$$) compared to normal-weight women ($$n = 38$$) (300.8 [211.4; 357] vs. 177.5 [114.5; 210.9], $p \leq 0.001$) and in both groups separately – control group (313.5 [199.6; 461.6] vs. 202.5 [132.3; 249.8], $$p \leq 0.01$$) and PCOS group (295.4 [223.1; 344.3] vs. 135 [112.3; 192.3], $p \leq 0.001$) (Figure 1). When comparing IL-18 levels between overweight/obese PCOS women and overweight/obese healthy controls, they did not showed significant difference (313.5 [199.6; 41.6] vs. 295.4 [223.1; 344.3], $$p \leq 0.208$$). Similar results were seen when comparing normal-weight women in the two groups (202.5 [132.3; 249.8] vs. 135 [112.3; 192.3], $$p \leq 0.064$$).
**Figure 1:** *Clustering IL-18 levels in the study population. Overweight/obese women had higher serum IL-18 levels than normal-weight women in both healthy and PCOS patients together and separately. No difference in IL-18 levels was found between overweight/obese patients (n = 33) and overweight/obese controls (n = 17) and between normal-weight patients (n = 25) and normal-weight controls (n = 13).*
## Subanalysis in the PCOS group
A detailed analysis in the PCOS group showed that high-WHtR patients had higher levels of IL-18, than low-WHtR participants (296.8 [227.5; 344.1] vs. 140.8 [115.7; 200.8], $p \leq 0.001$) (Figure 2A). IL-18 levels were significantly elevated in PCOS patients with IR, than those without IR (316.3 [237.1; 352.5] vs. 172.5 [119; 211.8], $p \leq 0.001$) (Figure 2B). IL-18 levels were also higher in PCOS women with high FAI than patients with normal FAI (298.3 [214.5; 355.3] vs. 181.1 [129.1; 262.4], $$p \leq 0.002$$) (Figure 2C).
**Figure 2:** *Clustering IL-18 levels in PCOS women. (A) Difference in serum IL-18 levels between low- WHtR (n = 28) and high-WHtR PCOS women (n = 30). (B) Difference in serum IL-18 levels between non-IR (n = 33) and IR (n = 25) PCOS women. (C) Difference in serum IL-18 levels between normal-FAI (n =39) and high-FAI (n = 19) PCOS women.*
## Correlations of IL-18 levels in the patients and controls
IL-18 levels correlated positively with almost all anthropometric and metabolic parameters in the PCOS group, presented in Table 2. In the control group, the serum levels of IL-18 did not show a correlation with hormonal and most of the anthropometric and metabolic parameters. The IL-18 levels correlated weakly and positively with WHtR ($r = 0.381$; $$p \leq 0.038$$) and OGTT 60' IRI ($r = 0.434$; $$p \leq 0.017$$).
**Table 2**
| Variable | r | p |
| --- | --- | --- |
| BMI (kg/m2) | 0.556 | <0.001 |
| Weight (kg) | 0.544 | <0.001 |
| Height (cm) | - 0.089 | 0.505 |
| WC (cm) | 0.598 | <0.001 |
| HC (cm) | 0.506 | <0.001 |
| WHR | 0.420 | 0.001 |
| WHtR | 0.553 | <0.001 |
| FPG (mmol/L) | 0.351 | 0.007 |
| OGTT 60' glucose (mmol/L) | 0.322 | 0.015 |
| OGTT 120' glucose (mmol/L) | 0.227 | 0.087 |
| Fasting IRI (mU/L) | 0.634 | <0.001 |
| OGTT 60' IRI (mU/L) | 0.352 | 0.008 |
| OGTT 120' IRI (mU/L) | 0.356 | 0.006 |
| HOMA-IR | 0.639 | <0.001 |
| mFG score | 0.286 | 0.030 |
| LH (mU/mL) | - 0.169 | 0.206 |
| FSH (mU/mL) | - 0.099 | 0.460 |
| LH/FSH ratio | - 0.102 | 0.447 |
| Estradiol (pmol/L) | 0.073 | 0.584 |
| Testosterone (nmol/L) | 0.055 | 0.680 |
| DHEAS (mcmol/L) | - 0.040 | 0.765 |
| Androstenedione (ng/mL) | - 0.099 | 0.457 |
| 17-OH-progesterone (ng/mL) | 0.010 | 0.940 |
| FAI | 0.232 | 0.080 |
| SHBG (nmol/L) | - 0.240 | 0.070 |
## Multiple linear regression in the PCOS group
Multiple linear regression was carried out to predict IL-18 levels from the reported demographic, anthropometric, and metabolic characteristics in the patient group. Using a forward stepwise regression model, only age, WC, and fasting IRI were selected for further analysis adding explanatory power. These variables significantly predicted IL-18, F[3, 55] = 17.817, $p \leq 0.001$, R2 = 0.497. All three variables added statistical significance to the prediction model, $p \leq 0.05$ (Figure 3).
**Figure 3:** *Scatterplot depicting the relationship between observed and predicted IL-18 values according to a linear regression model with IL-18 as a dependent variable and independent variables – age, WC, and fasting IRI. Approximately 50% of variations in IL-18 could be explained by these three variables.*
## DISCUSSION
Metabolic abnormalities observed in women with PCOS are undoubtedly associated with chronic low-grade inflammation. Is PCOS per se however an inflammatory condition or the reported relationship between PCOS and low-grade inflammation is due to confounding factors? The present study aimed to address this question by comparing levels of IL-18, as a potent proinflammatory biomarker, in PCOS patients and healthy controls. Furthermore, we determined the variables which independently predicted IL-18 levels in PCOS patients.
Our study found no difference in IL-18 levels between PCOS patients and healthy controls. Intentionally, the two groups were matched for age, BMI, and ethnicity that probably predetermined similar values of most metabolic parameters. In contrast, PCOS patients were more hyperandrogenic and therefore more hirsute than controls. Notably, hyperandrogenism is the most important characteristic in the diagnostic criteria of PCOS according to the National Institute of Health [36] and the Androgen Excess and Polycystic Ovary Syndrome Society [37] and one of the three characteristics according to *Rotterdam criteria* [28].
Clinical research exploring the association between PCOS per se and IL-18 reported confusing results. Some of those studies showed that PCOS independently from obesity is linked to increased levels of IL-18 [20-22]. However, Kaya and cols. [ 38] demonstrated that obesity explained IL-18 levels in PCOS women and impacted the significance in difference of the biomarker levels between patients and controls. Those data showed that obese PCOS women and obese healthy controls had similar levels of IL-18. Another study by Lindholm and cols. [ 39] demonstrated a lack of significance in IL-18 levels among the three groups in their analysis: lean PCOS women, overweight PCOS women, and overweight controls. More interestingly, when analyzing amounts of mRNA for inflammatory markers (including IL-18) in adipose tissue, the authors observed no differences between overweight PCOS patients and overweight controls, whereas inflammatory markers were higher compared to lean PCOS patients [39]. A non-causal relation thus was previously suggested with PCOS and IL-18 levels because they both were linked to obesity as a confounding factor. We found similar results in line with recent studies that levels of IL-18 are increased in obese participants [20,22,38,39]. In each of the three analyses (including all participants, healthy controls, or PCOS group, separately), overweight/obese women had significantly higher levels of IL-18 than normal-weight women. Importantly, PCOS status influenced the levels of IL-18 neither in overweight/obese participants nor in lean of them.
To enlighten the relation between inflammation and PCOS, the study was intended to contribute to the current knowledge and look from a different angle, taking into account relevant markers of global and central adiposity, insulin resistance, and hyperandrogenism. Importantly, the results of our study rather support the hypothesis that the association between PCOS per se and elevation of IL-18 levels is dependent on confounding factors such as obesity and insulin resistance which influence the cytokine levels resulting in low-grade inflammation in PCOS. Similar to prior research in the field [35], we confirmed that IL-18 levels were higher in PCOS patients with IR than without IR. In our study, however, greater interest was aroused in the investigation of PCOS patients divided according to WHtR and FAI using clinically relevant cutoffs, which was performed for the first time to the best of our knowledge. WHtR is an effective marker for the assessment of high metabolic and cardiovascular risk profiles [31,32]. On the other hand, androgen excess is associated with a deteriorated metabolic profile [40] and FAI is a relevant parameter for the assessment of hyperandrogenism. The results from our two subanalyses with WHtR and FAI showed that IL-18 levels were significantly higher in patients with visceral obesity and hyperandrogenism. Further studies should assess the role of IL-18 as a predisposing factor for increased cardiovascular and additional metabolic risk among patients with PCOS.
IL-18 levels were correlated positively with several indices for general and visceral adiposity and most of the metabolic parameters associated with IR in the PCOS group. Although the hyperandrogenic PCOS patients had higher IL-18 levels than those with normal androgen levels, only mFG score showed a weak correlation with the interleukin levels. These results and the established link between IL-18 and deteriorated metabolic profile favor an assumption that visceral obesity and insulin resistance predispose to increased inflammation in PCOS patients rather than hyperandrogenism. In the multiple regression analysis, approximately $50\%$ of variances in IL-18 levels were explained by age, WC, and fasting insulinemia.
The limitations of our study include a relatively small number of subjects and its cross-sectional design. Furthermore, we cannot determine if there is a cause-and-effect relationship between IL-18 levels and metabolic and cardiovascular risk in PCOS women. Larger and longitudinal studies are warranted to clarify such a relation.
In conclusion, in our study, levels of IL-18 were similar between patients with PCOS and healthy controls. IL-18 levels were related to several indices of general and visceral adiposity and insulin resistance in the PCOS group where age, waist circumference, and fasting insulinemia most closely explain serum amounts of this proinflammatory biomarker.
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|
---
title: 'Association among gestational diabetes mellitus, periodontitis and prematurity:
a cross-sectional study'
authors:
- Carla Andreotti Damante
- Gerson Aparecido Foratori
- Paula de Oliveira Cunha
- Carlos Antonio Negrato
- Silvia Helena Carvalho Sales-Peres
- Mariana Schutzer Ragghianti Zangrando
- Adriana Campos Passanezi Sant’Ana
journal: Archives of Endocrinology and Metabolism
year: 2022
pmcid: PMC9991029
doi: 10.20945/2359-3997000000435
license: CC BY 4.0
---
# Association among gestational diabetes mellitus, periodontitis and prematurity: a cross-sectional study
## ABSTRACT
### Objective:
Gestational diabetes mellitus (GDM) causes maternal and infant morbidity. Periodontitis is associated with adverse pregnancy outcomes. The aim of this study was to evaluate periodontal status, prematurity and associated factors in pregnant women with and without GDM.
### Subjects and methods:
This observational cross-sectional study included 80 pregnant women with GDM (G1 = 40) and without GDM (G2 = 40). Demographic and socioeconomic status, systemic and periodontal health condition, prematurity and newborns’ birth weight were analyzed. For bivariate analysis, Mann-Whitney U-test, t test and Chi-squared test were used. Binary logistic regression analyzed independent variables for periodontitis and prematurity ($p \leq 0.05$).
### Results:
Patients from G1 presented lower socioeconomic status, higher weight and body mass index (BMI). Prematurity (G1 = $27.5\%$; G2 = $2.5\%$; $p \leq 0.05$) and severe periodontitis percentages (G1 = $22.5\%$; G2 = 0; $$p \leq 0.001$$) were higher in G1 than in G2. Logistic regression analysis showed that household monthly income (OR = 0.65; $95\%$ CI 0.48-0.86; $$p \leq 0.003$$) and maternal BMI (adjusted OR = 1.12; $95\%$ CI 1.01-1.25; $$p \leq 0.028$$) were significant predictors of periodontitis during the third trimester of pregnancy. Presence of GDM remained in the final logistic model related to prematurity (adjusted OR = 14.79; $95\%$ CI 1.80-121.13; $$p \leq 0.012$$).
### Conclusions: Pregnant
women with GDM presented higher severity of periodontitis, lower socioeconomic status, higher overweight/obesity and a 10-fold higher risk of prematurity. Socioeconomic-cultural status and BMI were significant predictors for periodontitis, and GDM was a predictor to prematurity.
## INTRODUCTION
During pregnancy, the increase of progesterone, estrogen and other placental-derived hormones leads to several changes in the levels of immunosuppressants and inflammatory mediators [1].
Gestational diabetes mellitus (GDM) is defined as a condition of glucose intolerance that is first diagnosed during the second or third trimester of pregnancy, in which lower glucose levels are necessary than those for the diagnosis of diabetes unrelated to pregnancy [2]. It is generally associated with obesity, previous diagnosis of GDM, advanced maternal age and family history of diabetes [3-5].
Periodontitis is a chronic inflammatory disease of the periodontium associated with the local presence of bacteria [6]. In this process, bacterial infiltration occurs in the periodontium and the toxins produced locally stimulate a chronic inflammatory response that progressively destroys the periodontal tissues [6]. Pregnant women diagnosed with GDM are more likely to present worst periodontal condition [7]. Likewise, a systematic review with meta-analysis found a significant association between periodontitis and GDM in four cross-sectional studies and two case-control studies. Nevertheless, the case-control studies showed inconsistent data after sensitivity tests [5]. Consequently, current scientific evidence cannot corroborate a positive association between periodontitis and GDM.
GDM is a significant cause of maternal and infant morbidity, including macrosomia and maternal hypertensive disorders [5,8,9]. Scientific literature also highlights an association between periodontitis and adverse pregnancy outcomes, such as preterm birth and low birth weight [10,11].
Considering the lack of evidence regarding the association between GDM and periodontitis and the adverse effects of both conditions on neonates’ health at birth, the aim of this study was to evaluate the periodontal status, prematurity and associated factors in pregnant women with and without GDM. The null hypotheses of this study are: [1] there are no changes in the periodontal parameters of women with GDM; [2] the babies of women with GDM are born within the normal period of gestation (after the 37th gestational week). Alternative hypotheses are: [1] there are changes in the periodontal parameters of women with GDM; [2] these women's babies are born prematurely (before the 37th gestational week).
## SUBJECTS AND METHODS
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used to ensure the accurate reporting of this study [12].
## Ethical aspects
This study received approval from the Ethics Committee on Human Research of Bauru School of Dentistry, University of São Paulo (CAAE 58339416.4.0000.5417). The study was conducted in accordance with the Helsinki Declaration, revised in 2013. All subjects provided written informed consent prior to participating.
## Sample composition
This cross-sectional study was conducted from March 2018 to February 2019. The sample was consecutively recruited by convenience from the public health sector in the city of Bauru, São Paulo, Brazil. This study was composed of 80 patients who were divided into two groups: G1 – pregnant women with GDM ($$n = 40$$) and G2 – pregnant women without GDM ($$n = 40$$). All patients were evaluated regarding their oral health during the third trimester of pregnancy (27th-35th gestational weeks). The presence/absence of GDM was obtained from medical records. The diagnosis of GDM was completed by a 2-hour oral glucose tolerance test performed with 75 grams of glucose, between 24-28th gestation weeks, according to the International Association of Diabetes and Pregnancy Study *Group criteria* [13]. To diagnose GDM, at least one glycemic value ≥92 mg/dL (fasting), ≥180 mg/dL (one hour) and ≥153 mg/dL (two hours) needed to be present [13].
Inclusion criteria for the study were to have had performed the examination of the glycemic curve for the diagnosis of GDM between 24-28th gestational weeks; the absence of systemic diseases; regular gestational follow-up with obstetricians and being in the third trimester of pregnancy. Exclusion criteria were the presence of neuromotor or communication difficulties, use of illicit drugs, consumption of alcohol during pregnancy, smoking, preeclampsia, any severe gestational problem requiring absolute rest, current orthodontic and/or dental treatment or the presence of edentulism.
## Anthropometric measurements
Patients’ weight and height during pregnancy were obtained through an automatic scale (MIC 300PP; Micheletti Ind., São Paulo, São Paulo, Brazil) and stadiometer (2.20; WCS Ind., Curitiba, Paraná, Brazil) located at Bauru School of Dentistry, University of São Paulo. Pre-pregnancy weight and body mass index (BMI) were obtained from medical records. Normal-weight pregnant women were those who had a pre-pregnancy BMI between 18.5 and 24.9 kg/m2. Pregnant women were considered overweight when pre-pregnancy BMI was greater than or equal to 25.0 kg/m2, and obese when pre-pregnancy BMI was at least 30.0 kg/m2 [2,14].
Patients were also classified according to weight gain, as defined by the Institute of Medicine protocol [15]. This classification establishes the recommended weight gain during gestation according to patients’ nutritional status found before pregnancy. If the patient gained more than the highest recommended value, she was classified as presenting excessive weight gain.
## General assessments
Socioeconomic status was assessed according to education level and household monthly income, which were graded as follows: 0, illiteracy; 1, did not complete primary education; 2, completed primary education; 3, did not complete high school; 4, completed high school; 5, did not complete higher education; 6, completed higher education; 7, specialization; 8, master's degree; 9, PhD.
Household monthly income was based in the *Brazilian minimum* wage (MW) (approximately USD 220.00) and categorized in the following levels: level 1 – family receiving up to one MW; level 2 – between 1 and 2 MW; level 3 – between 2 and 3 MW; level 4 – between 3 and 4 MW; level 5 – between 4 and 5 MW; level 6 – family receiving more than 5 MW.
## Periodontal examinations
Oral examinations were conducted by a qualified dentist who was calibrated by a gold standard examiner (kappa intra-examiner = 0.95; $95\%$ confidence interval [CI] = 0.89-0.97; kappa inter-examiner = 0.92; $95\%$ CI = 0.87-0.95). A plain oral mirror n. 05 (Cod. 7503; Duflex/SS White, Juiz de Fora, Minas Gerais, Brazil), a standard Universal North Carolina periodontal clinical probe (QD.320.05; Quinelato, Schobell Ind. Ltda, Rio Claro, São Paulo, Brazil), and a syringe with compressed air were used to examine the oral cavity. All teeth were evaluated, excluding third molars.
Periodontal analysis was performed including probing depth (PD) and clinical attachment loss (CAL). PD was measured from the free gingival margin to the bottom of the periodontal pocket and CAL was measured from the cementoenamel junction to the base of the periodontal pocket [16]. Six sites of each tooth were assessed (mesial, center, distal, both in the buccal and lingual surfaces).
Presence of periodontitis was confirmed if interproximal CAL was present at ≥2 non-adjacent teeth, or buccal CAL ≥3 mm with pocketing >3 mm was detectable at ≥2 teeth. Additionally, CAL could not be ascribed to other causes such as: gingival recession of traumatic origin; cervical dental caries, CAL on the distal aspect of a second molar associated with malposition or extraction of a third molar, endodontic lesion and vertical root fracture [17]. Staging of periodontitis was classified in I, II and III as previously described [17]. Bleeding on probing (BOP) for each assessed site was examined considering the presence or absence of bleeding according to Ainamo and Bay [18].
## Neonates’ data
After labor, patients were contacted in order to obtain neonates’ data. Mothers provided babies’ birth weight and length, as well as type of delivery and date of birth. Data were classified as follows: Low birth weight (LBW) < 2,500 g [19]; insufficient weight at birth (IWB) = 2,500 to 2,999 g [19]; normal birth weight (NBW) = 3,000 to 3,999 g [20]; high birth weight (HBW) or macrosomia > 4,000 g [21]. Prematurity was considered present when the birth occurred before 37th gestational weeks.
Based on gender and gestational age at birth, all children who were born prematurely were classified according to their weight for gestational age. For this purpose, the intrauterine growth curve “International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH21st)” was used [22]. Neonates who presented birth weight below the 10th percentile for their gestational age were considered small for gestational age (SGA); those who presented birth weight above the 90th percentile for their respective gestational age were considered large for gestational age (LGA).
## Statistical analysis
Statistical analysis was performed with IBM SPSS Version 25 (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.). According to previous evidence in the same field [16,23], the power test was calculated considering an intergroup difference between the mean CAL during pregnancy of at least $10\%$, with a standard deviation of $10\%$. Based on the mean CAL and standard deviations of the two groups, an effect size of 0.90 was obtained, resulting in a power of $90.5\%$ with this study's sample size. In addition, for sample size, the Hosmer and Lemeshow protocol for logistic regression analysis was considered [24], which allows the inclusion of 10 cases for each combination of independent variables [25-27]. In this study, dichotomization of periodontitis (0 = no periodontitis; 1 = periodontitis) and babies’ prematurity (0 = no prematurity; 1 = prematurity) was performed for binary logistical regressions, and in these models a maximum of four independent variables were inserted. Thus, the inclusion of 80 patients in the sample was considered acceptable.
Thus, statistical analysis was performed in two steps: 1) bivariate analysis and 2) logistic regression by the stepwise backward (likelihood ratio) method. In bivariate analysis, first the Kolmogorov-Smirnov test was applied to verify the normality of the variables, and the following tests were used: Mann-Whitney U-test, t test and Chi-squared test. Binary logistic regression was performed in order to analyze which independent variables could be related to the presence of periodontitis during the third trimester of pregnancy and to prematurity. The independent variables inserted in the initial logistic regression model regarding the presence of periodontitis during pregnancy were: presence of GDM, maternal BMI and household monthly income (Supplementary file 1). The independent variables inserted in the initial logistic regression model regarding the prematurity were: presence of GDM, presence of periodontitis, maternal BMI and household monthly income (Supplementary file 2). Hosmer-Lemeshow, collinearity, and residual analyses were used to increase the understanding of the logistic regression results. A significance level of $5\%$ was adopted.
## RESULTS
Initially, 54 pregnant women with GDM were consecutively recruited from specific health care centers for pregnant women with diabetes. Among them, four women did not accept to participate in the study, three of them reported to use orthodontic devices and two were smokers during pregnancy. Therefore, the initial screening with oral evaluation was performed in 45 pregnant women with GDM. Only 41 participants showed up on the day of the consultation to take the research exams. The four missing women justified being on absolute rest during pregnancy. One woman was excluded from the sample for having multiple tooth loss. Simultaneously, 45 pregnant women without GDM were consecutively recruited from public health units in Bauru. All of them underwent the initial screening and were scheduled to take the research exam. However, two of them did not show up, and justified being on rest; three were classified as underweight and two women mentioned being under periodontal treatment recent to that period, so they were excluded from the sample. At that time, in order to match the groups, two pregnant women without GDM were recruited. Finally, the sample consisted of 80 pregnant women divided into: G1 - with GDM ($$n = 40$$) and G2 - without GDM ($$n = 40$$). Figure 1 shows the flowchart of the sample composition according to STROBE.
**Figure 1:** *Sample composition.*
Overall, pregnant women from G1 had lower education levels, lower household monthly income ($p \leq 0.0001$), greater pre-pregnancy and pregnancy weight, greater pre-pregnancy and pregnancy body mass index (BMI) and greater weight gain during pregnancy in comparison with patients from G2 (Table 1). Prior to pregnancy, $30\%$ ($$n = 12$$) of women from G1 were classified as being overweight and $40\%$ ($$n = 16$$) as being obese. In contrast, $25\%$ ($$n = 10$$) of women from G2 were considered as being overweight, while $12.5\%$ ($$n = 5$$) were obese. A higher prevalence of excessive weight gain was found in patients from G1 than in those from G2 ($$p \leq 0.0009$$) (Table 1).
**Table 1**
| Variables | G1 (n = 40) | G2 (n = 40) | p |
| --- | --- | --- | --- |
| Maternal age (years) | 32.5 [24.5-36.5] | 30 [27.5-33] | 0.335* |
| Education level* | 3.5 [1–4] | 6 [4–6] | <0.0001 * |
| Household monthly income# | 2 [1–3] | 5.5 [4–6] | <0.0001 * |
| Pre-pregnancy weight (kg) | 75.64 ± 17.52 | 65.12 ± 11.14 | 0.002 † |
| Pre-pregnancy BMI (kg/m2) | 28.77 [24.12-32.95] | 24.19 [21.96-27.77] | 0.002 * |
| Weight during pregnancy (kg) | 83.78 ± 15.41 | 73.10 ± 10.74 | 0.0006 † |
| BMI during pregnancy (kg/m2) | 32.21 ± 6.13 | 27.90 ± 3.94 | 0.0003 † |
| Weight gain during pregnancy – n (%) | | | 0.0009 ‡ |
| Normal | 26 (65%) | 38 (95%) | |
| High | 14 (35%) | 2 (5%) | |
There was no intergroup difference regarding bleeding on probing ($$p \leq 0.796$$), nonetheless, patients from G1 showed higher PD ($p \leq 0.0001$) and CAL ($$p \leq 0.0001$$) values. Moreover, $65\%$ ($$n = 26$$) of patients from G1 were diagnosed with periodontitis, being $22.5\%$ ($$n = 9$$) classified as stage III. In contrast, $32.5\%$ ($$n = 13$$) of patients from G2 were diagnosed with stage II periodontitis and $67.5\%$ ($$n = 27$$) presented no periodontitis (Table 2). Additionally, of those 28 women in G1 categorized as overweight/obese, 20 had periodontitis (2, 10 and 8 in the stages I, II and III of periodontitis, respectively), whilst of those 15 women from G2 categorized as overweight/obese, nine of them had periodontitis (all in the stage II of periodontitis).
**Table 2**
| Variables | Variables.1 | G1 (n = 40) | G2 (n = 40) | p |
| --- | --- | --- | --- | --- |
| PD (mm) | PD (mm) | 2.28 ± 0.49 | 1.90 ± 0.24 | <0.0001 † |
| CAL (mm) | CAL (mm) | 2.27 [2.00-2.65] | 1.96 [1.86-2.08] | 0.0001 * |
| BOP (%) | BOP (%) | 24.99 ± 21.04 | 25.99 ± 12.46 | 0.796† |
| Periodontitis severity – n (%) | Periodontitis severity – n (%) | | | |
| | No | 14 (35%) | 27 (67.5%) | |
| | Stage I | 2 (5%) | 0 | 0.001 ‡ |
| | Stage II | 15 (37.5%) | 13 (32.5%) | |
| | Stage III | 9 (22.5%) | 0 | |
Regarding neonates’ characteristics, the presence of GDM did not influence birth length, weight and type of delivery. However, women from G1 presented higher prevalence of premature babies ($$p \leq 0.001$$) (Table 3). Of the 11 children who were born prematurely to women from G1, $54.54\%$ ($$n = 6$$) were considered LGA according to INTERGROWTH21st [22]. The only baby who was born prematurely in G2 had adequate weight for the gestational age at birth.
**Table 3**
| Variables | Variables.1 | G1 (n = 40) | G2 (n = 40) | p |
| --- | --- | --- | --- | --- |
| Childbirth height (cm) | Childbirth height (cm) | 48 [46.75-49.75] | 49 [47–50] | 0.193* |
| Childbirth weight (kg) | Childbirth weight (kg) | 3.42 ± 0.65 | 3.24 ± 0.39 | 0.137† |
| Delivery type – n (%) | Delivery type – n (%) | | | |
| | Cesarean | 33 (82.5%) | 30 (75%) | 0.415‡ |
| Prematurity – n (%) | Prematurity – n (%) | | | |
| | Yes | 11 (27.5%) | 1 (2.5%) | 0.001 ‡ |
Binary logistic regression was performed to verify the independent predictors of periodontitis (0 = no periodontitis; 1 = periodontitis) in the studied population (Supplementary file 1). Collinearity analysis showed that all the independent variables inserted in the regression models showed values of tolerance greater than 0.10 and Variance Inflation Factor (VIF) values lower than 10 (VIF < 2). Household monthly income and maternal BMI remained related to periodontitis [X²[2] = 22.44; $p \leq 0.0001$; Negelkerke's R² = 0.326] in the final logistic regression model. The final model's overall accuracy was $75\%$. Hosmer and *Lemeshow analysis* indicated a Chi-square for the final model of 4.32 for 8 degrees of freedom ($$p \leq 0.827$$). Both household monthly income (adjusted OR = 0.65, $95\%$ CI 0.48-0.86, $$p \leq 0.003$$) and maternal BMI (adjusted OR = 1.12, $95\%$ CI 1.01-1.25, $$p \leq 0.028$$) were significantly associated with periodontitis. Household monthly income presented as a negative coefficient, indicating that the lower the household monthly income, the higher was the frequency of periodontitis.
The binary logistic regression was performed to verify the independent predictors of babies’ prematurity (0 = no prematurity; 1 = prematurity) (Supplementary file 2). Collinearity analysis showed that all the independent variables inserted in the regression models showed values of tolerance greater than 0.10 and VIF < 2. Presence of GDM remained in the final logistic model associated with prematurity (adjusted OR = 14.79, $95\%$ CI 1.80–121.13, $$p \leq 0.012$$). The final model [X²[1] = 11.22; $$p \leq 0.001$$; Negelkerke's R² = 0.22] showed an overall accuracy of $85\%$.
## DISCUSSION
This study evaluated the periodontal status and the related factors in pregnant women with and without GDM, and their association with the neonates’ health outcomes. Our main findings suggest that GDM is associated with higher prevalence and severity of periodontitis. Moreover, pregnant women with GDM had a higher percentage of premature babies, despite the babies being born with normal anthropometric parameters.
GDM is associated with advanced maternal age and with the increase in prevalence of obesity found among pregnant women worldwide, representing an important economic burden for the public health care system [28]. In this study, there was no difference between groups regarding maternal age, however, $70\%$ of pregnant women from G1 were considered as being overweight or obese (Table 1). This can be explained by the fact that the presence of excessive adiposity is associated with visceral accumulation of adipose tissue, which directly contributes to insulin resistance [29].
Another factor that may be associated with the high prevalence of GDM is excessive gestational weight gain [30]. In this study, $35\%$ of patients from G1 ($$n = 14$$) presented excessive gestational weight gain, whilst only $5\%$ from G2 presented the same condition (Table 1). We hypothesized that excessive intake of caloric food and abundant nutritional availability during pregnancy, associated with an increase in insulin resistance, can cause an impairment on the glucose metabolism, leading to the onset of GDM [16]. These authors also found an association between maternal overweight, excessive gestational weight gain and an increased prevalence of GDM during the second trimester of pregnancy [16].
The association of these risk factors with adverse outcomes is mediated by the contextual variables of each subject. Therefore, socioeconomic and cultural conditions play an important role in the aforementioned outcomes. In this study, patients from G1 showed lower education level and household monthly income (Table 1). Consequently, it is expected that low access to information and to regular health services, as well as poor eating habits and lack of physical activity could result in a higher prevalence of overweight [16] and excessive weight gain during pregnancy [23], which, in turn, are associated with the presence of GDM.
In the present study, patients from G1 had poorer periodontal condition with higher PD and CAL. Our results are in accordance with those from Xiong and cols. demonstrating a higher percentage of periodontitis among patients with GDM [31]. Our study showed that $22\%$ of patients from G1 presented severe periodontitis, whilst for G2, $67.5\%$ had no periodontitis and $32.5\%$ had mild periodontitis (Table 2). Women with GDM may be at higher risk of developing more severe periodontal disease than those without GDM, even after delivery [32]. Özçaka and cols. observed higher periodontitis rates, plaque accumulation and BOP in patients with GDM compared to those without GDM [33]. As aforementioned, there was no intergroup difference for maternal age in this present study. Nonetheless, it is important to point out that periodontal disease is also mediated by patients’ socioeconomic-cultural condition, since low household monthly income and educational level result in inadequate oral hygiene habits and low access to oral health care [34]. In this study, the independent variables better associated with the occurrence of maternal periodontitis through the logistic regression model were household monthly income (OR = 0.651; $$p \leq 0.003$$) and high BMI (OR = 1.129; $$p \leq 0.028$$) (Supplementary file 1).
The association between periodontal disease and GDM remains unclear and may be misinterpreted due to a variety of confounding factors. There is a hypothesis suggesting that the levels of hyperglycemia found in GDM may be too mild and too short to have a significant effect on gingival tissues and cause periodontitis [31]. Some studies [31-33] suggest that this hypothesis is either not plausible or there might be other factors influencing the worst periodontal status in patients with GDM. Indeed, it is possible that periodontitis can be an etiological factor for GDM instead of a consequence of this condition, since the chronic subclinical inflammation of periodontitis induces local host immune responses and causes transient bacteremia, which may affect the systemic health [31]. Maternal gingival inflammation may result in insulin resistance [35], which could exacerbate the physiological insulin resistance during pregnancy, leading to an impairment on glucose tolerance and finally to GDM [33].
A recent study showed that crevicular fluid concentrations of matrix metalloproteinases 8 and 9 (MMP-8 and MMP-9) were increased since the beginning of pregnancy in patients with GDM [36]. This increase of inflammatory mediators is also observed in severe periodontitis [36]. Moreover, bacterial load may contribute to the worsening of periodontal status, given that there is an association between the severity of periodontitis and higher counts of *Porphyromonas gingivalis* and *Prevotella intermedia* in patients with GDM [37].
Obesity also negatively influences the periodontal condition of individuals. Recent studies showed a higher prevalence of periodontitis in overweight pregnant women [16,25-27]. It is important to consider that obesity and periodontitis share common modulating factors, such as low socioeconomic and cultural status. In addition, the adipose tissue of overweight and obese individuals secretes inflammatory mediators, which in turn cause an exacerbated inflammatory response in the whole body [38]. Therefore, even with a small amount of biofilm on the teeth, there is an exacerbated inflammation in the periodontal tissues of overweight individuals, becoming even more intense due to high levels of estrogen and progesterone found in pregnant women [16]. In this study, 20 pregnant women from G1 had both overweight/obesity and periodontitis ($$n = 2$$, $$n = 10$$ and $$n = 8$$ in the stages I, II and III of periodontitis, respectively). In contrast, nine of women from G2 had both overweight/obesity and periodontitis (all of them in the stage II of periodontitis). The high prevalence of pregnant women with overweight/obesity and periodontitis in both groups reflected that maternal BMI remained in the final logistic regression model related to the presence of periodontitis.
There is no consensus regarding the association between GDM, obesity, maternal periodontitis and prematurity. As stated above, there is an association between GDM, obesity and periodontitis. Moreover, medical studies claim that both obesity and GDM are associated with macrosomia due to insulin resistance which results in an increased availability of glucose to the fetus. These elevated levels of glucose cross the placenta, and contribute to fetal hyperinsulinemia and accelerated fetal growth, with babies generally presenting above normal size and weight [39]. Also, elevated levels of triglycerides are found in pregnant women with insulin resistance, which are cleaved into smaller molecules and transferred to the fetal circulation, resulting in greater energy input to the fetus [39]. In contrast, in literature there is plausible evidence that periodontitis is associated with prematurity and low birth weight [40]. The presence of Gram-negative periodontopathogens may impair the development of the fetus directly or indirectly. Periodontal bacteria may lodge in the placenta through the bloodstream, or can indirectly mediate inflammation through cytokines that are released in periodontal tissues. Hence, bacteria prevent adequate absorption of nutrients by the fetuses and stimulate early contractions, with the possibility of premature rupture of membranes (PROM), resulting in prematurity and low birth weight [40].
Jesuino and cols. demonstrated that children who were born from women with excessive gestational weight gain had above normal weight, considering their z-score parameters [23]. On the other hand, Foratori-Junior and cols. demonstrated an association between maternal overweight, periodontitis and low birth weight [26]. Yet, the association of GDM with prematurity is still unclear. Our results showed that $27.5\%$ of patients from G1 presented preterm birth, whilst only $2.5\%$ from G2 showed the same condition (Table 3). Although there was no intergroup difference regarding the infants’ weight at birth, children who were born premature were categorized more frequently as LGA, according to the INTERGROWTH21st growth curve [22], which takes into account sex, birth weight and gestational week of birth.
Adverse pregnancy outcomes are associated with periodontitis, with an odds-ratio ranging from 1.10 to 20 [41]. In addition, there is an association between pre-pregnancy BMI and perinatal outcomes [42]. As a variety of factors may be related to preterm birth, the binary logistic regression was performed to verify which independent variable could be a predictor of prematurity. The presence of GDM, and not periodontitis, was related to this outcome, but with a high range of $95\%$ confidence interval, which may be explained by the limitation of this study in respect of the small sample size (OR = 14.79; $95\%$ CI 1.80-121.13; $$p \leq 0.012$$) (Supplementary file 2). We hypothesized that periodontitis did not remain in the final logistic model associated with prematurity in this study due to the small sample size and numerous confounding factors, considering the high prevalence of women with both GDM and overweight/obesity, which might be inversely associated with prematurity.
Our study has some limitations. The cross-sectional design of the study makes it impossible to infer causality (cause and effect) of the study outcomes. The association tests performed in this study using logistic regression models showed low values of odds-ratio, which can be explained by the sample size. As aforementioned, prospective cohorts with a larger number of patients (preferably population-based studies) evaluating the association of the same outcomes are necessary to be more representative in order to extrapolate data to other populations. Moreover, the recruitment of the sample by convenience from public health service in Bauru also is a limitation of this study. Finally, some laboratory analyzes at molecular levels are necessary to better understand the systemic diseases’ effect on periodontium. Consequently, future studies should perform analyses evaluating glycemic control and its relationship with inflammatory mediators in saliva and plasma.
Despite the limitations, our study sheds some light regarding the association between GDM, periodontitis and prematurity. Thus, the authors call the attention of doctors and dentists to the importance of the transdisciplinary and holistic approach of the pregnant woman in order to offer prevention and treatment for these patients and, consequently, improve the health of their children.
In conclusion, pregnant women with GDM presented lower socioeconomic status, higher prevalence of overweight/obesity and higher prevalence and severity of periodontitis. Women's socioeconomic-cultural status and BMI were the factors associated with periodontitis during pregnancy, whilst GDM was the factor associated with pre-term labor.
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|
---
title: When do we need to suspect maturity onset diabetes of the young in patients
with type 2 diabetes mellitus?
authors:
- Özlem Üstay
- Esra Arslan Ateş
- Tugçe Apaydin
- Onur Elbasan
- Hamza Polat
- Gizem Günhan
- Ceyda Dinçer
- Lamia Şeker
- Ayşegül Yabacı
- Ahmet Ilter Güney
- Dilek Gogas Yavuz
journal: Archives of Endocrinology and Metabolism
year: 2022
pmcid: PMC9991031
doi: 10.20945/2359-3997000000431
license: CC BY 4.0
---
# When do we need to suspect maturity onset diabetes of the young in patients with type 2 diabetes mellitus?
## ABSTRACT
### Objetivo:
Maturity onset diabetes of the young (MODY) patients have clinical heterogeneity as shown by many studies. Thus, often it is misdiagnosed to type 1 or type 2 diabetes(T2DM). The aim of this study is to evaluate MODY mutations in adult T2DM patients suspicious in terms of MODY, and to show clinical and laboratory differences between these two situations.
### Subjects and methods:
In this study, we analyzed 72 type 2 diabetic patients and their relatives (35F/37M) who had been suspected for MODY and referred to genetic department for mutation analysis. *The* gene mutations for MODY have been assessed in the laboratory of Marmara *University* genetics. Totally 67 (32F/35M; median age 36.1) diabetic patients were analyzed for 7 MODY mutations. Twelve patients who have uncertain mutation (VUS) were excluded from study for further evaluation. MODY(+) (n:30) patients and T2DM patients (n:25) were compared for clinical and laboratory parameters.
### Results:
In MODY(+) subjects, mutations in GCK (MODY 2) (n:12; $40\%$) were the most common followed by HNF4A (MODY 1) (n:4; $13.3\%$). Diabetes diagnosis age was younger in MODY(+) group but not statistically significant. Sixty-six percent of MODY(+) subjects had diabetes history at 3-consecutive generations in their family compared with $28\%$ of T2DM patients statistically significant (p:0.006). Gender, BMI, C-peptide, HbA1c, lipid parameters, creatinine, GFR, microalbuminuria, vitamin D and calcium were not statistically different between the groups.
### Conclusion:
According to present study results, MODY mutation positivity is most probable in young autoantibody [-] diabetic patients diagnosed before 30 years of age, who have first degree family history of diabetes.
## INTRODUCTION
Type 2 diabetes mellitus (T2DM) incidence has been increasing in Turkey, as well as globally, and has begun to be seen at much earlier ages. Some of these patients have a genetic disposition for diabetes, just like their relatives. Maturity onset diabetes of the young (MODY) is an autosomal dominant inherited, common monogenic form of diabetes [1, 2]. Incidence of MODY varies by region ranging from $2\%$ to $5\%$ of all diabetic patients [1, 3]. MODY is caused by over 800 mutations in 14 different MODY-related genes [4 - 8]. The most common mutations are found in GCK (glucokinase), HNF1A (hepatocyte nuclear factor-1alpha) and HNF4A (hepatocyte nuclear factor-4alpha) [2, 9].
Many studies have shown the clinical heterogeneity of MODY patients [10 - 13]. Thus, MODY is often misdiagnosed as type 1 or type 2 diabetes. The clinical diagnosis of MODY is based on young onset (before the age of 25), presence of diabetes in at least 3 consecutive generations, absence of β-cell autoantibodies, and relatively preserved endogenous insulin secretion, according to some studies [14, 15]. Since MODY is diagnosed at an early age, it is often confused with early-onset T2DM. Early-onset T2DM is recognized as a special kind of type 2 diabetes which was diagnosed at a young age (30-45 years) with various genetic tendencies. Although type 2 diabetes is a disease related to aging, the prevalence of early-onset T2DM in adults has increased globally [16]. Due to overlapping clinical features, distinguishing MODY from early-onset T2DM is often difficult. The molecular diagnosis and classification of MODY patients are essential for a correct treatment decision and in the judgment of the prognosis. Genetic testing is highly specific and sensitive; therefore, it represents the gold standard for diagnosing MODY. However, genetic testing is an expensive procedure which renders it an inaccessible tool for diagnosing MODY. Thus, careful consideration is required when determining which patients should be tested; and considerable efforts have been made to investigate nongenetic or clinical markers in order to facilitate the differential diagnosis of MODY.
Studies have shown that GCK mutations are common in Turkish pediatric cohorts, which represent one-quarter of MODY cases of all diabetics in childhood [17]. The pediatric population usually has a higher risk of type 1 diabetes mellitus (T1DM), and their clinical presentation is quite different in young patients from that of T2DM patients. Thus, it is relatively easy to differentiate these two diseases in pediatric groups. However, insufficient number of studies have been conducted related to the clinical differences between MODY and early-onset T2DM in adult populations. Moreover, contradictory data about the most common MODY mutation types appears in recent literature. GCK and HNF1A mutations are known to be the most common mutations, especially in Europe and North America [2, 9, 18]. According to the limited data available in Turkey, HNF1A is regarded as the most common mutation in MODY patients, however there is insufficient information about the clinical characteristics of early-onset T2DM patients and differences from MODY [19].
The aim of this study was to evaluate MODY mutations in adult T2DM patients who were suspected of MODY, and to show clinical and laboratory differences between these two types of diabetes.
## SUBJECTS AND METHODS
In this study, we analyzed 72 T2DM patients and their relatives (35 F/37 M), followed at the endocrinology out-patient clinic of Marmara University Hospital, who had been suspected of MODY and referred to the genetic department for mutation analysis. Although the clinical diagnosis of MODY is usually based-on young onset before the age of 25, presence of diabetes in at least 3 consecutive generations, absence of β-cell autoantibodies (anti-GAD antibody, anti-islet cell antibody), and relatively preserved endogenous insulin secretion, the specific clinical criteria for MODY diagnosis are still not very clear. In our study, we enrolled all patients referred to the genetic department for MODY mutation, thus not all the patients met all the clinical criteria for MODY diagnosis described in the literature.
The study was approved by the local ethics committee of Marmara University School of Medicine (protocol number 09.2020.01).
All patients were invited to the endocrinology outpatient clinic and were evaluated for age, duration of diabetes, age at diagnosis, micro and macro complications of diabetes, family history of diabetes, drug usage (type and exposure time), body mass index (BMI), and blood pressure; and were tested for fasting plasma glucose, fasting serum C-peptide level, glycosylated hemoglobin (HbA1c), lipid profile (total, LDL-cholesterol, HDL-cholesterol and triglycerides), islet cell autoantibodies (ICAs), glutamic acid decarboxylase (GAD) autoantibodies and insulin antibodies (IAAs).
Positive results for GAD autoantibodies appeared in 5 out of 72 patients ($7.7\%$). Only one of them had a mutation for MODY, and all of these 5 patients were diagnosed with latent autoimmune diabetes of adults (LADA).
*The* gene mutations for MODY were assessed in the Marmara *University* genetics laboratory. In total, 67 (32 F/35 M; median age 36.1) diabetes patients were analyzed for 7 MODY mutations. They were categorized according to the pathogenicity identification as pathogenic MODY (+), variant of uncertain significance (VUS), and without any mutation (T2DM). Fifteen patients who had VUS mutations were excluded from the study for further evaluation. MODY (+) patients ($$n = 27$$) and T2DM patients ($$n = 25$$) were compared in terms of clinical and laboratory parameters.
## Genetic analysis
All patients were informed in person and their written consent was obtained. Genomic DNA was isolated from peripheral blood leucocytes using the QIAamp DNA Blood Mini QIAcube Kit (Qiagen, Hilden, Germany), according to the manufacturer’s protocols. All coding exons and exon-intron boundaries of seven genes that were associated with MODY (KCNJ11, ABCC8, INS, GCK, HNF4A, HNF1A, HNF1B) were amplified using the Multiplicom MODY MasterDx (Agilent, CA, USA) kit. Prepared library was sequenced on the Illumina Miseq platform (Illumina Inc., San Diego, CA, USA). The data were analyzed by the Sophia DDM data analysis software. In order to call variants, sequencing data was aligned to human reference genome, hg19. After amplifying targeted regions using designed primers, Sanger sequencing on ABI Prism 3500 Genetic Analyzer (Thermo Fisher Scientific, MA USA) was performed for the confirmation of the detected variants and segregation analysis. Novel variations were classified according to the American College of Medical Genetics and *Genomics criteria* [20]. Mutation Taster, The Sorting Intolerant from Tolerant (SIFT) and deleterious annotation of genetic variants using neural networks (DANN) were used for computational pathogenicity prediction [21]. The data of minor allele frequencies of variants were obtained from GnomAD [22].
## Statistical analysis
The distribution of the data was examined using the Shapiro-Wilk test. Normally distributed data between the two groups were compared with an independent samples t test, and non-normally distributed data between the two groups were compared with the Mann-Whitney U test. The difference between categorical variables was examined with Pearson’s X2 test, and Fisher’s exact test. The descriptive statistics of the data are presented as mean and standard deviation, median (min-max), and n (%). All statistical analyses were conducted in the IBM SPSS Statistics 22.0 program with a significance level of 0.05.
## RESULTS
We evaluated 67 patients who were eligible for this study design. Twenty-seven of these patients ($40.3\%$) were found to have pathogenic mutations grouped as MODY (+), and 15 were found to have VUS mutations ($22.4\%$) and were excluded from the study. In MODY (+) subjects, mutations in GCK (MODY 2) were the most common followed by HNF4A (MODY 1). All pathogenic and likely pathogenic mutation types in the mutation-positive group were given in detail in Table 1 [23 - 33].
**Table 1**
| Unnamed: 0 | Gene | Transcript | Variation | Variation Type | Status | dbSNP | ClinVar | Classification ACMG | HGMD | References |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | GCK | NM_000162 | c.214G>A (p.Gly72Arg) | Missense | PR | rs193922289 | P | P | CM023383 | Lehto et al ., 1999 ( 23 ) |
| 2 | GCK | NM_000162 | c.506A>G (p.Lys169Arg) | Missense | PR | - | - | LP | CM141531 | Flanagan et al ., 2014 ( 24 ) |
| 3 | GCK | NM_000162 | c.572G>A (p.Arg191Gln) | Missense | PR | rs886042610 | LP/VUS | LP | CM012120 | Massa et al ., 2001 ( 25 ) |
| 4 | GCK | NM_000162 | c.775G>A (p.Ala259Thr) | Missense | PR | rs1375656631 | P | P | CM980894 | Hattersley et al ., 1998 ( 26 ) |
| 5 | GCK | NM_000162 | c.898G>A (p.Glu300Lys) | Missense | PR | rs1255911887 | - | LP | CM930305 | Froguel et al ., 1993 ( 27 ) |
| 6 | GCK | NM_000162 | c.943C>T (p.Leu315Phe) | Missense | PR | - | - | LP | CM064013 | Vits et al ., 2006 ( 28 ) |
| 7 | HNF1A | NM_000545 | c.392G>A (p.Arg131Gln) | Missense | PR | rs753998395 | P | LP | CM961361 | Yamagata et al ., 1996( 29 ) |
| 8 | HNF1B | NM_000458 | c.1390G>C (p.Gly464Arg) | Missense | Novel | - | - | LP | - | - |
| 9 | HNF4A | NM_001030003 | c.110T>C (p.Leu37Pro) | Missense | Novel | - | - | LP | - | - |
| 11 | HNF4A | NM_001030003 | c.1097C>G (p.Pro366Arg) | Missense | PR | rs193922469 | LP | LP | - | - |
| 12 | ABCC8 | NM_000352 | c.1616A>G (p.Tyr539Cys) | Missense | PR | rs193922397 | LP | LP | - | - |
| 13 | ABCC8 | NM_000352 | c.4055G>A (p.Arg1352His) | Missense | PR | rs28936370 | P | P | CM042667 | Magge et al ., 2004 ( 30 ) |
| 14 | ABCC8 | NM_000352 | c.4306C>T (p.Arg1436*) | Nonsense | PR | rs193922402 | P | P | CM112832 | Powell et al ., 2011 ( 31 ) |
| 15 | ABCC8 | NM_000352 | c.4631G>C (p.Ser1544Thr) | Missense | Novel | - | - | LP | - | - |
| 16 | KCNJ11 | NM_000525 | c.841C>T (p.Leu281Phe) | Missense | Novel | - | - | LP | - | - |
| 17 | KCNJ11 | NM_000525 | c.1019C>A (p.Pro340His) | Missense | PR | - | - | LP | CM144523 | Mohnike et al ., 2014 ( 33 ) |
| 18 | KCNJ11 | NM_000525 | c.1084G>A (Ala362Thr) | Missense | PR | rs755839409 | - | LP | CM182438 | Mohan et al ., 2018 ( 32 ) |
We detected four novel variations which were predicted to be likely pathogenic according to the ACMG guidelines [20]. The novel likely pathogenic variants and pathogenicity evaluation details were presented in Table 2. Related variations were segregated with the disease in three families. However, the patient carrying the HNF4A c.110T>C (p.Leu37Pro) variation declared that no family members were diagnosed with MODY, hence we were not able to screen the parents for MODY.
**Table 2**
| Unnamed: 0 | Gene (Transcript ID) | Variation | Family History | Segregation | Mutation Taster | SIFT | DANN Score | GnomAD | Pathogenicity |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | HNF1B (NM_000458) | c.1390G>C (p.Gly464Arg) | + | Compatible | Disease Causing | Damaging | 0.9986 | 0 | LP |
| 2 | HNF4A (NM_001030003) | c.110T>C (p.Leu37Pro) | - | | Disease Causing | Damaging | 0.9964 | 0 | LP |
| 3 | ABCC8 (NM_000352) | c.4631G>C (p.Ser1544Thr) | + | Compatible | Disease Causing | Tolerated | 0.9672 | 0 | LP |
| 4 | KCNJ11 (NM_000525) | c.841C>T (p.Leu281Phe) | + | Compatible | Disease Causing | Tolerated | 0.9729 | 0 | LP |
In MODY (+) subjects, GCK mutations (MODY 2) ($$n = 12$$; $44.4\%$) were the most common followed by HNF4A mutations (MODY 1) ($$n = 3$$; $11.1\%$). When we compared MODY (+) ($$n = 27$$) and T2DM ($$n = 25$$) groups according to the laboratory parameters, no differences between the groups were found. C-peptides, HbA1c, lipid parameters, creatinine, glomerular filtration rate (GFR), microalbuminuria, vitamin D, and calcium were not statistically different between the groups (Table 3).
**Table 3**
| Unnamed: 0 | MODY (n:27) | T2DM (n:25) | p-value |
| --- | --- | --- | --- |
| C-peptide | 1.60(0.01-3.18) | 1.78(0.65-4.78) | 0.145 |
| FPG (mg/dL) | 112(82-366) | 109(74-329) | 0.44 |
| HbA1c (%) | 6.70(5.8-10.5) | 7(5.4-14.6) | 0.962 |
| LDL | 101(38-201) | 115(83-212) | 0.068 |
| HDL | 49(26-65) | 45(27-68) | 0.283 |
| Trig | 91(32-558) | 120(37-381) | 0.18 |
| U acid | 4.70(2.80-6.40) | 4.80(1.80-7.56) | 0.705 |
| Crea | 0.61(0.21-0.96) | 0.69(0.36-2.63) | 0.118 |
| GFR | 143.79(55.56-315) | 114.26(62.03-1725) | 0.213 |
| Microalbuminuria | 43(32-52) | 43(37-52) | 0.842 |
| Ca | 9.40(8.3-10.3) | 9.50(8.8-10.8) | 0.355 |
| P | 3.20(2-5) | 3.40(2-4.6) | 0.573 |
| PTH | 39.13(18.90-83.27) | 38.53(10.11-58.13) | 0.848 |
| Vitamin D | 16.82(7.03-43) | 15.54(4.72-48.77) | 0.952 |
Regarding demographic parameters and diabetes history, median age was 36.1 years, and there appeared to be no difference between the groups. BMI and gender were similar between the groups (Table 4). Diabetes diagnosis age was younger in the MODY (+) group, but the difference was not statistically significant. Sixty-six percent of MODY (+) subjects had diabetes history for 3 consecutive generations in their family, compared to $32\%$ of T2DM patients, which was statistically significant ($$p \leq 0.008$$). The groups were similar in terms of diabetes complications. Pharmacologic treatment modalities were not very different between the groups, but we found that MODY (+) patients tended to start insulin therapy at a younger age than the negative group-did; thus, their insulin duration time was longer than that of the negative group, although their insulin doses were lower than the T2DM group. We evaluated all patients according to concomitant comorbidities such as hypertension, hyperlipidemia, and hepatosteatosis. More patients in the T2DM group had hypertension ($40\%$) than in the MODY (+) group ($7.7\%$), which was statistically significant ($$p \leq 0.007$$). Other comorbidities were similar between the groups (Table 4).
**Table 4**
| Unnamed: 0 | Unnamed: 1 | MODY (n:27) | T2DM (n:25) | p-value |
| --- | --- | --- | --- | --- |
| Gender (F/M) | Gender (F/M) | Gender (F/M) | Gender (F/M) | Gender (F/M) |
| | Female | 11 (40.7%) | 14 (56%) | 0.271 |
| | Male | 16 (59.3%) | 11 (44%) | 0.271 |
| Age (years) | Age (years) | 34.22 ± 16.26 | 40.96 ± 11.77 | 0.092 |
| BMI (kg/m2) | BMI (kg/m2) | 28.64 (20.3-44.1) | 27.63 (20.36-52.49) | 0.624 |
| DM diagnosis age | DM diagnosis age | 28.11 ± 16.41 | 33.40 ± 11.69 | 0.185 |
| 3 generation DM | 3 generation DM | 18 (66.6%) | 8 (32%) | 0.008 |
| Retinopathy (n) | Retinopathy (n) | 0 | 3 (12%) | 1 |
| Nephropathy (n) | Nephropathy (n) | 4 (15.4%) | 6 (24%) | 0.499 |
| Neuropathy (n) | Neuropathy (n) | 2 (7.7%) | 4 (16%) | 0.419 |
| OAD | OAD | 1(1-3) | 1(1-3) | 0.725 |
| OAD duration (years) | OAD duration (years) | 6.50(1-23) | 3(1-20) | 0.138 |
| Insulin starting age (years) | Insulin starting age (years) | 21.82 ± 10.74 | 32.43 ± 10.54 | 0.021 |
| Insulin duration (years) | Insulin duration (years) | 11(2-23) | 5(1-16) | 0.231 |
| <0.5 IU/kg insulin requirement (n) | <0.5 IU/kg insulin requirement (n) | 17 (73.9%) | 16 (69.6%) | 0.743 |
| Hypertension (n) | Hypertension (n) | 2 (7.7%) | 10 (40%) | 0.007 |
| Hepatosteatosis (n) | Hepatosteatosis (n) | 9 (34.6%) | 8 (32%) | 0.843 |
| Hyperlipidemia (n) | Hyperlipidemia (n) | 3 (11.5%) | 7 (28%) | 0.173 |
## DISCUSSION
In this study, we analyzed the accuracy and parallelism of our mutation-requesting criteria with the presence of mutations among adult diabetic patients, and also compared the clinical and laboratory results of mutation (+) and [-] groups. We found MODY mutation positivity in $40.3\%$ of all study subjects and the most common mutation was GCK ($44.4\%$) in our study population. The frequency of MODY in T2DM patients varies by country, with $21\%$ in the USA, $27\%$ in the UK, $31\%$ in Norway, $39\%$ in the Netherlands, $19\%$ in Japan, and to our knowledge, $29\%$ in the Turkish pediatric population [17, 34]. MODY subtype frequencies vary by region. Haliloglu and cols. [ 17] showed that the most common MODY mutation is GCK in pediatric diabetic patients in Turkey (almost $25\%$), whereas in the SEARCH study HNF1A mutation was found to be the most common MODY subtype among the young diabetic population in the USA [10]. Almost all studies about MODY were performed in pediatric groups, and the most common MODY subtype was usually the GCK mutation in European countries, higher than in Asian countries such as Japan and Korea [35, 36]. In our study, pathologic GCK mutations were the most common, followed by HNF4A mutations in the adult MODY (+) group; similar to what was found in the pediatric population in Turkey. These results are consistent with the distribution in other Southern European populations [15, 34, 37].
Genetic testing is the gold standard for diagnosing MODY and it can be utilized for planning a treatment strategy according to the mutation type. However, genetic testing is often expensive and widely accessible. Thus, careful consideration is required when determining which patients need to undergo genetic testing. According to the results of some studies, most of which were conducted in childhood diabetes patients, some criteria were established, and genetic analysis was recommended for patients who met these criteria. Shields and cols. [ 3] developed a prediction model to determine the probability of MODY in patients with young-onset diabetes. They predicted that positive C-peptides and negative autoantibodies were strongly suggestive of MODY compared to T1DM. On the other hand, they claimed that the presence of insulin resistance and high BMI could be clinical markers for T2DM.
MODY is a genetically heterogeneous disease and to date 14 genes (GCK, HNF1A, HNF4A, HNF1B, INS, KCNJ11, ABCC8, PDX1, NEUROD1, KLF11, CEL, PAX4, BLK and APPL1) were associated with MODY. However, GCK and HNF1A mutations are detected in approximately half of the MODY patients [25]. We detected four novel variations which were predicted to be likely pathogenic according to the ACMG guidelines. None of these four novel variations were reported in population studies (GnomAD) and they were not present in 200 Turkish control chromosomes. The novel likely pathogenic variants were analyzed using in silico analysis tools stated in Table 2. Family members were screened for the detected variations and it was shown that related variations co-segregated with the disease in three families. However, the patient carrying the HNF4A c.110T>C (p.Leu37Pro) variation declared that no family members were diagnosed as MODY and we were not able to screen the parents for MODY. In total, 180 variations were reported in The Human Gene Mutation Database Professional in HNF4A gene of which most are missense variations. There are several studies reporting molecular findings in MODY cases in Turkish or other populations. However, it is still possible to encounter novel variations due to high allelic heterogeneity in MODY [26, 27].
Few studies have compared adult T2DM patients with MODY patients in terms of clinical and laboratory parameters. Chambers and cols. [ 38] compared 75 MODY [-] and 22 MODY (+) patients. The positive group had a lower HbA1c, and their family history of diabetes was significantly longer; most of them had not undergone any pharmacological treatment. According to these findings, they reported that MODY could be suspected in youth diabetes patients with negative antibodies and preserved C-peptides. Zhang and cols. [ 11] reported that MODY(+) Chinese patients were younger at diagnosis, and had a longer duration of diabetes, higher fasting plasma glucose, lower C-peptides, lower BMI, lower HOMA and lower triglycerides compared with early-onset T2DM patients. A recent study on 263 Japanese patients [35] showed that mutation-positive patients had a lower BMI and insulin resistance compared to mutation-negative diabetics; and were also younger at the time of the diagnosis. Based on previous studies, Jang [39] suggests MODY genetic analysis in adult diabetic patients if they were diagnosed before the age of 30, if β-cell antibodies are negative, and if they have a family history of diabetes and BMI ≤ 30 kg/m2 without insulin resistance.
In our study, family history was the most significant distinguishing feature among the clinical MODY diagnosis criteria. MODY (+) subjects had a significantly long diabetes history of 3 consecutive generations in their family ($66\%$) compared to T2DM patients ($32\%$) ($$p \leq 0.008$$). Diabetes diagnosis age was younger in the MODY (+) group compared to the mutation-negative group, but this difference was not statistically significant ($$p \leq 0.092$$). Since most patients with the GCK mutation can continue without treatment for many years, the age of diagnosis may be advanced. In our study, we have found that the age of diagnosis of the MODY (+) group was higher than expected. This might be caused by the presence of GCK mutation in the majority of the patients and the fact that some of these patients were incidentally diagnosed by genetic analysis when their first degree relatives were diagnosed with mutation-positive T2DM. Contrary to other studies, we found no significant differences in C-peptides, BMI, or HbA1c between the groups. Similarly, in another study from Korea [36] comparing 23 mutation-positive patients with 17 early-onset T2DM patients in terms of clinical and metabolic profiles, no differences were found for age at diagnosis, BMI, C-peptides, and fasting and postprandial glucose levels. Interestingly, we found that mutation-positive patients start insulin therapy at younger ages than the T2DM patients. On the other hand, we had expected that GCK mutation-positive patients would not need any treatment for a long time. This could be because other mutations may cause uncontrolled aggressive hyperglycemia. In our study population, hypertension was the unique comorbidity accompanying to diabetes in mutation-negative patients. Although microvascular complication frequency was quite higher in T2DM patients, there were no statistically differences between the groups according to diabetes complications, duration of diabetes, or insulin usage.
All these study results show that previously suggested diagnostic criteria for MODY [10, 40, 41] might not be sufficient to predict MODY patients. According to current studies, C-peptide level seems to be more of an important criterion for T1DM. No difference was found in our patient group in terms of HbA1c and BMI; thus, they might not be sufficient criteria to predict MODY alone. According to the present study results, MODY mutation positivity is most probable in young autoantibody negative diabetes patients diagnosed before 30 years of age; who have a first-degree family history of diabetes. Screening for MODY would be an appropriate approach in young patients who typically do not fit the T2DM profile and have a first-degree family history of diabetes.
One limitation of our study was the small number of patients because we only recruited patients who were suspected of MODY and who were referred for genetic analysis. Therefore, we did not define specific inclusion criteria for the patients. Another limitation of our study was that all existing mutations could not be examined. We screened for the single nucleotide variations (SNVs) and small deletions or insertions in only 7 of these genes’ coding regions. We were not able to exclude the copy number variations (CNVs), variations in noncoding regions of these genes and the rest of the MODY related genes in MODY [-] group.
In conclusion, according to the results of this study, it would be meaningful to investigate MODY mutations in T2DM patients who were diagnosed before the age of 30, who have a family history of diabetes in their first-degree relatives, and who are autoantibody negative. Other parameters are not very valuable for screening MODY mutation in every young diabetic patient.
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|
---
title: Number of teeth lost on diet quality and glycemic control in patients with
type 2 diabetes mellitus
authors:
- Danieli Londero da Silveira
- Laura Emanuelle da Rosa Carlos Monteiro
- Christofer da Silva Christofoli
- Beatriz D. Schaan
- Gabriela Heiden Telo
journal: Archives of Endocrinology and Metabolism
year: 2022
pmcid: PMC9991037
doi: 10.20945/2359-3997000000429
license: CC BY 4.0
---
# Number of teeth lost on diet quality and glycemic control in patients with type 2 diabetes mellitus
## ABSTRACT
### Objectives:
To describe the oral health profile and evaluate the impact of tooth loss on diet quality and glycemic control among 66 patients with type 2 diabetes (T2DM) treated in an endocrinology outpatient clinic at a teaching hospital.
### Materials and methods:
Questionnaires about diabetes self-care (SDSCA), masticatory ability, diet quality, anxiety level about dental treatment, and oral health were applied. Laboratory tests were retrieved from medical records or newly collected samples.
### Results:
The presence of fewer than 21 teeth was associated with an unsatisfactory self-perceived masticatory ability ($r = 0.44$; $$p \leq 0.007$$). Most participants reported not having received guidance on oral health from their endocrinologists ($81.8\%$) and having had the last visit to the dentist 2 years or more before the study ($36.8\%$). The mean HbA1c level in the group with fewer than 21 teeth was comparable to that in the group with functional dentition (8.9 ± 1.5 and 8.7 ± $1.6\%$, respectively; $$p \leq 0.60$$).
### Conclusion:
Adults with T2DM have a high prevalence of tooth loss and lack of information about oral hygiene care. Our results reinforce the need for more effective communication between medical and dental care teams.
## INTRODUCTION
Achievement of glycemic control with the adoption of a healthy diet, along with maintenance of adequate body weight and control of serum lipids and blood pressure, is one of the main pillars of the management of type 2 diabetes mellitus (T2DM) [1].Robust evidence demonstrates that adherence to self-care in diabetes enables and enhances therapeutic success, mediating satisfactory results through a reduction in cardiovascular risk and improvement in metabolic control, quality of life, symptoms of anxiety, and depression [2].
The prioritization of consumption of foods that are fresh instead of those that are rich in fat, sodium, and sugar contributes to maintaining metabolic control [3]. Poor oral health, represented by partial or total tooth loss, is associated with a higher probability of masticatory difficulty [4]. One of the consequences of this complication is a preference for foods based on consistency [4], which in turn can compromise the individual’s nutritional status and general health, considering the low nutritional value of some of these foods [5].
A follow-up by a professional multidisciplinary team can help prevent these changes in eating patterns due to abnormal masticatory ability, but this prevention requires strong adherence and regular dental care. Data suggest that expanding coverage of periodontal treatment among patients with T2DM has the potential to prevent tooth loss in more than $30\%$ of the cases [6]. Encouraging patients with T2DM and poor oral health conditions to receive periodontal treatment could improve oral health conditions and reduce costs related to the treatment of diabetes and its complications [6]. However, numerous interferences limit the patients’ access to treatment, including socioeconomic and educational factors. These factors determine an individual’s behavior and health perception [7].
Another factor that can impact oral health is anxiety. This feeling can be fueled by situations related to dental care, which cause apprehension and discomfort, culminating in avoidance of care and aggravating the oral condition [8]. A study reported that anxiety about dental treatment might even impact the quality of life of these patients [9]. Only a few studies in the literature have evaluated oral health and the impact of dental anxiety on the use of dental services, diet quality, and glycemic control in patients with diabetes. The aim of the present study is to evaluate the impact of oral health and the number of teeth on the quality of diet and, consequently, on the glycemic control of patients with T2DM.
## MATERIALS AND METHODS
Cross-sectional study including patients with a previous diagnosis of T2DM, aged 18 years or older, following up at the endocrinology outpatient clinic at a teaching hospital in southern Brazil from August 2017 to July 2018. Patients with cognitive disorders that prevented the understanding of the research proposal were excluded. The sample was chosen randomly among patients seen in the period described and recruited using the electronic medical record system of Hospital de Clínicas de Porto Alegre (HCPA).
This study followed the principles of Resolution $\frac{466}{2012}$ and was approved by the Research Ethics Committee on Humans of HCPA under CAAE number 70321717.2.0000.5327. All participants received information about the research objectives and procedures and agreed to participate by signing an informed consent form.
The sample size was calculated considering a margin of error of $0.01\%$, $95\%$ confidence levels, and bilateral correlation coefficient test (r value -0.46), resulting in a calculated sample of 65 individuals with T2DM. The manuscript was prepared according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations.
## Study variables
To assess diabetes self-care, we applied the Summary of Diabetes Self-Care Activities Measure (SDSCA) questionnaire [10]. The questionnaire has 18 questions covering the following domains: general diet, specific diet, exercise, medication taking, blood glucose testing, foot care, and smoking.
To assess the perception of the individuals about their oral health and the impact of oral health on their quality of life, we used the index “chewing ability”, adopted in epidemiological surveys [11]. This index consists of five questions about the ability to chew or bite certain types of food and the possible answers were “yes” or “no”. This scale was assigned a score ranging from zero to five, were then classified as having a deficient (score 0 to 3) or satisfactory mastication (score 4 to 5).
For assessment of the participants’ dietary intake, quantitative data were obtained regarding the frequency of food consumption using the Food Frequency Questionnaire – Porto Alegre (FFQ – Porto Alegre) [12] validated for populations of adolescents, adults, and older adults in southern Brazil. The data for the calculations were obtained from the Centesimal Composition of Food table of the Brazilian Institute of Geography and Statistics (IBGE) [13] or from the Brazilian Table of Food Composition (TACO) [14], according to availability.
Data were collected from electronic medical records regarding the following aspects: age, sex, ethnicity, education level, diabetes duration, medications used, and complications associated with diabetes. The social-demographic, and economic profile of the patients were evaluated according to the socioeconomic classification criteria of the Brazilian Association of Research Companies (ABEP) [15]. To assess the participant’s anxiety level regarding dental treatment, we used the Corah’s Dental Anxiety Scale [16].
Data related to the individual’s oral health were assessed using the Individual Oral Health Questionnaire 2013 by the National Health Survey (PNS) in partnership with IBGE [17]. The original version of the questionnaire consists of 18 items evaluating some oral health variables and we added to the original version two questions related to the receipt of guidance regarding oral health by the dentist or endocrinologist to assess the multidisciplinary interaction between these two specialties.
The dental examination recorded the number and distribution of natural teeth, missing dental groups, presence and identification of the type of dental prosthesis and information related to the alteration of salivary perception and taste sensitivity, xerostomia and presence of cavitated carious lesions. We used the CPO-D Index to evaluate tooth loss. Considering the 28 teeth present in the dental arches (excluding the third molars), the variable “number of teeth” was dichotomized, and individuals with fewer than 21 teeth in the mouth were classified as having “non-functional dentition,” while those with at least 21 teeth were considered to have “functional dentition,” as defined by the World Health Organization (WHO) [18].
The results of glycated hemoglobin (HbA1c) performed up to three months from the date of the interview were collected from electronic medical records. Those without HbA1c measurement during this period had blood collected for this purpose regardless of fasting status. Levels of HbA1c were measured by high-performance liquid chromatography (HPLC). To assess the nutritional status of the study population, the participants’ body mass index (BMI) was calculated based on weight and height data obtained from medical records.
## Statistical analysis
The analysis was performed using SPSS, version 18.0 (IBM, Armonk, NY, USA). Descriptive data with normal distribution were presented as mean and standard deviation, and nonparametric data as median, percentile, or frequency. The Shapiro-Wilk test was applied to analyze the normality of the values referring to the SDSCA items.
Student’s t test was used to compare self-perceived masticatory ability, degree of anxiety about dental care, HbA1c, and diabetes self-care with the variable “number of teeth present”. This test was also used to compare the glycemic control between groups that reported having or not having difficulty in eating and among those with preference for solid or liquid/pureed foods, considering a significance level of 0,05.
The chi-square test was used for categorical variables, while the Spearman test was used to analyze the correlation between the variable “number of teeth present” and self-perceived masticatory ability, degree of anxiety, glycemic control, and items in the T2DM treatment adherence questionnaire and diet quality.
Data regarding the adherence of study participants to the items of the Diabetes Self-Care Activities Measure (SDSCA) questionnaire were expressed as median (interquartile range 25-75) in days per week for self-care activities in the previous 7 days.
## RESULTS
A total of 618 potentially eligible patients were identified from August 2017 to July 2018; of these, 108 were recruited. Overall, 32 individuals did not sign the consent form (mostly due to lack of time to respond to the questionnaires) and 10 were excluded. The reasons for exclusion were failure to attend the scheduled interview ($$n = 6$$), questionnaire interruption due to fear of being late for the medical consultation ($$n = 2$$), and family members not knowing whether the presence of the subjects in the research could be confirmed ($$n = 2$$), yielding a final sample of 66 patients with T2DM.
The final sample comprised adult patients aged 59.7 ± 10.2 years, mostly women ($54.5\%$) and white ($66.7\%$), with a family income of up to 3 minimum wages ($54.5\%$) and complete elementary school education level ($44\%$) (Table 1).
**Table 1**
| Variables | Variables.1 | N | % (Mean ± SD) |
| --- | --- | --- | --- |
| Sex (% women) | Sex (% women) | 36 | 54.5 |
| Age (≥50 years) | Age (≥50 years) | 55 | 83.3 |
| Ethnicity (% white) | Ethnicity (% white) | 44 | 66.7 |
| Education (% complete elementary school) | Education (% complete elementary school) | 29 | 44 |
| Family income (up to 3 minimum wages) | Family income (up to 3 minimum wages) | 36 | 54.5 |
| Age at diabetes diagnosis (years) | Age at diabetes diagnosis (years) | | 42.9 ± 10.7 |
| Diabetes duration (years) | Diabetes duration (years) | | 17.6 ± 9.2 |
| Active or previous smoker | Active or previous smoker | 41 | 62.1 |
| HbA1c ≥8.5 (%)* | HbA1c ≥8.5 (%)* | 35 | 53 |
| Diabetes complications | Diabetes complications | 57 | 86.4 |
| | Retinopathy | 26 | 39.4 |
| | Nephropathy | 18 | 27.3 |
| | Neuropathy | 13 | 19.7 |
| Cardiovascular diseases** | Cardiovascular diseases** | 36 | 54.5 |
| BMI (kg/m²)*** | BMI (kg/m²)*** | | 32.1 ± 6.5 |
| Normal weight | Normal weight | 7 | 10.7 |
| Overweight or obesity | Overweight or obesity | 59 | 89.3 |
| Use of medications | Use of medications | Use of medications | Use of medications |
| | Statins | 54 | 81.8 |
| | Metformin | 52 | 78.8 |
| | Rapid-acting insulin | 31 | 47.0 |
| | Short-acting insulin | 56 | 84.8 |
The mean diabetes duration was 17.6 ± 9.2 years, and the mean age at diagnosis was 42.9 ± 10.7 years. As for the occurrence of comorbidities, there was a high prevalence of overweight and obesity, with a mean BMI of 32.1 ± 6.5 kg/m2, and a high mean HbA1c, and a high mean HbA1c level (8.9 ± $1.5\%$), showing poor glycemic control. Complications of diabetes were observed in $86.4\%$ of the participants. About $71\%$ of the patients reported no practice of any type of regular physical activity in the week prior to the interview (Table S1).
**Table S1**
| Markers | None | 1x | 2x | 3x | 4x | 5x | 6x | 7x |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Markers | % | % | % | % | % | % | % | % |
| Healthy diet | 15 | 1.5 | 5 | 12 | 6 | 18 | 1.5 | 41 |
| Professional food guidance | 12 | 0 | 5 | 12 | 13 | 20 | 3 | 35 |
| Blood glucose monitoring | 17 | 11 | 3 | 8 | 3 | 1 | 0 | 57 |
| Physical activity (at least 30 minutes) | 71 | 3 | 0 | 5 | 1.5 | 3 | 1.5 | 15 |
| Specific physical exercise | 74 | 1.5 | 5 | 3 | 1.5 | 1.5 | 1.5 | 12 |
Regarding the dental conditions of the participants (Table 2), the first tooth loss occurred at an early age (26.9 ± 11.8 years). Only $21.2\%$ of the respondents received information from their dentists about the importance of oral health care in their T2DM treatment, and only $18.2\%$ received information from their endocrinologists about the importance of glycemic control on diseases related to the oral cavity. On clinical examination, frequent use of dental prostheses ($65.2\%$) was verified as a result of the high rate of bilateral tooth loss ($93.9\%$), and the main reason for the last appointment was due to problems related to maladaptation or fracture of the tooth dental prosthesis ($37.9\%$). Most patients reported a frequency of visits to the dentist at intervals of 2 years or more ($36.8\%$) and care center in the private system ($60.6\%$). Given the possibility of delay in seeking dental care due to anxiety or fear concerning dental treatment, moderate to extreme anxiety about dental treatment was observed in $24.3\%$ of the patients.
**Table 2**
| Variables | Variables.1 | N | % (Mean ± SD) |
| --- | --- | --- | --- |
| Number of teeth | Number of teeth | Number of teeth | Number of teeth |
| | Edentulous | 15 | 22.7 |
| | <21 teeth | 34 | 51.5 |
| | ≥21 teeth | 17 | 25.8 |
| Use of dental prosthesis | Use of dental prosthesis | Use of dental prosthesis | Use of dental prosthesis |
| | Total | 18 | 27.3 |
| | Removable partial | 18 | 27.3 |
| | Fixed/implant | 2 | 3.0 |
| | Total + removable partial | 5 | 7.6 |
| Missing dental group | Missing dental group | Missing dental group | Missing dental group |
| | Incisors | 43 | 65.2 |
| | Canines | 33 | 50.0 |
| | Premolars | 60 | 90.9 |
| | Molar | 64 | 97.0 |
| First tooth loss (years) | First tooth loss (years) | | 26.9 ± 11.8 |
| Bilateral tooth loss | Bilateral tooth loss | 62 | 93.9 |
| Presence of cavitated carious lesions | Presence of cavitated carious lesions | 21 | 31.8 |
| Xerostomia | Xerostomia | 37 | 56.1 |
| Salivary change | Salivary change | 23 | 34.8 |
| Taste sensitivity | Taste sensitivity | 11 | 16.7 |
| Frequency of oral hygiene | Frequency of oral hygiene | Frequency of oral hygiene | Frequency of oral hygiene |
| | Two times or more a day | 61 | 92.5 |
| | Does not use dental floss | 51 | 77.3 |
| Brush change (less than 3 months) | Brush change (less than 3 months) | 29 | 44.0 |
| Perception of oral health (bad) | Perception of oral health (bad) | 20 | 30.3 |
| Degree of difficulty in feeding | Degree of difficulty in feeding | Degree of difficulty in feeding | Degree of difficulty in feeding |
| | No pain or slight difficulty | 44 | 66.7 |
| | Regular to very intense | 22 | 33.3 |
| Last visit to the dentist (2 years or more) | Last visit to the dentist (2 years or more) | 24 | 36.8 |
| Preference for some type of food | Preference for some type of food | Preference for some type of food | Preference for some type of food |
| | Solid | 3 | 4.5 |
| | Liquid/pureed | 27 | 40.9 |
| Gingival bleeding | Gingival bleeding | 17 | 25.8 |
| Tooth mobility | Tooth mobility | 17 | 25.8 |
Although almost all participants ($92.5\%$) reported performing oral hygiene two or more times a day, the use of dental floss was largely neglected. The prevalence of cavity carious lesions was low ($31.8\%$). However, this rate may have been underestimated, since the study was carried out on an outpatient setting without equipment to differentiate carious lesions in terms of activity (active or inactive) and extension (enamel or dentin injury). Other oral manifestations commonly observed in patients with T2DM were detected in the study, namely, xerostomia ($56.1\%$) and changes in salivary appearance ($34.8\%$) and taste sensitivity ($16.7\%$).
Still regarding the dental characteristics of the participants, due to the reduced number of teeth, only a few patients ($4.5\%$) had preference for solid foods. On the other hand, most of them ($66.7\%$) reported mild difficulty in eating or no pain. Most participants ($64\%$) reported consuming fruits and/or vegetables daily (Table S2). A comparison between the groups that reported having versus not having feeding difficulties showed no statistical difference in HbA1c values (9.2 vs. $8.6\%$, respectively; $$p \leq 0.126$$).
**Table S2**
| Items | None | 1x | 2x | 3x | 4x | 5x | 6x | 7x |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Items | % | % | % | % | % | % | % | % |
| Fruits and/or vegetables | 5 | 3 | 1 | 9 | 6 | 9 | 3 | 64 |
| Red meat or foods with whole milk and dairy products | 3 | 9 | 14 | 9 | 8 | 3 | 3 | 51 |
| Sweets | 46 | 29 | 12 | 6 | 0 | 1 | 0 | 6 |
When the preference for the type of food was evaluated, patients who reported difficulty in eating solid foods had a higher mean HbA1c value than those reporting difficulty in eating liquid/pureed foods, although this result was also not significant (10.2 vs. $9.1\%$, respectively, $$p \leq 0.152$$). In the correlation analysis, the number of teeth was positively and strongly associated with masticatory ability ($$p \leq 0.001$$; $r = 0.44$), but a negative correlation was observed between the number of teeth and consumption of five or more servings of fruits and/or vegetables in the previous week ($$p \leq 0.001$$; r = -0.39). These data are shown in Table 3.
**Table 3**
| Variables | r | P value |
| --- | --- | --- |
| Masticatory ability | 0.44** | 0.001 |
| Degree of anxiety | - 0.12 | 0.314 |
| HbA1c | - 0.02 | 0.827 |
| SDSCA – Follow a healthy diet | - 0.12 | 0.324 |
| SDSCA – Follow food guidelines | 0.06 | 0.599 |
| SDSCA – Eat five or more servings of fruits and/or vegetables | - 0.39** | 0.001 |
| SDSCA – Eat red meat and/or whole milk products | 0.05 | 0.685 |
| SDSCA – Eat sweets | - 0.07 | 0.565 |
| SDSCA – Perform physical activities for at least 30 minutes | 0.01 | 0.926 |
| SDSCA – Perform specific physical activities (walking, swimming, etc.) | 0.02 | 0.871 |
| SDSCA – Assess blood glucose | 0.06 | 0.603 |
| SDSCA – Assess blood glucose the recommended number of times | 0.14 | 0.26 |
Stratified analyses according to the number of teeth, shown in Figure 1, pointed out to a direct relationship between fewer teeth (<21 teeth) and increased prevalence of unsatisfactory self-perceived masticatory ability ($$p \leq 0.007$$). More than half of the patients ($51.5\%$) had an unsatisfactory masticatory ability (Table S3). However, the mean HbA1c between groups with fewer versus more than 21 teeth was similar (8.9 vs. $8.7\%$, respectively, $$p \leq 0.597$$).
**Figure 1:** *Comparison between groups of patients with fewer and more than 21 teeth – Porto Alegre, RS ($$n = 66$$).* TABLE_PLACEHOLDER:Table S3 Table S4 shows that the median energy consumption (in kcal/day) of the sample was lower among individuals with fewer than 21 teeth (1949.4 kcal) compared with those with more than 21 teeth (2041.8 kcal), although this difference was not significant ($$p \leq 0.73$$). The SDSCA results showed lower adherence to the items “consumption of sweets” (25th-75th interquartile range [IQR] 0-2), “performing physical activities for at least 30 minutes” (25th-75th IQR 0-3), and “performing specific physical activities” (25th-75th IQR 0-1.2), but greater adherence to items related to “fruit and/or vegetable consumption” in the control of T2DM (25th-75th IQ 4.7-7) in patients with fewer than 21 teeth. The data obtained are shown in Table S5.
## DISCUSSION
This study aimed to describe the oral health profile perceived by patients with diabetes, evaluate the impact of tooth loss on diet quality and glycemic control, and provide information on the use of primary dental care for patients with T2DM treated at a teaching hospital in southern Brazil. A very low prevalence of dental care was identified among the patients with T2DM evaluated, despite a high prevalence of missing teeth and unsatisfactory chewing ability.
Most participants had low education level and family income, a finding similar to other studies. According to the authors, the lower the educational level, the greater the dissatisfaction with chewing and the greater the prevalence of negative impact on oral health, lifestyle, access to services, and information on health care [20].
Despite available care offered by the Unified Health System (SUS), access to dental services in our study (characterized by the dental care report) was $60\%$ in the private system. Oral health care is still considered an important challenge for the SUS network [21], despite the availability of several specific programs [22]. This is reflected in lower participation of dentists in the public health care system, and access to this care often remains conditioned on out-of-pocket payment. In this study, the age of the patient when the first tooth loss occurred was quite early (26.9 ± 11.8 years), and about a third of the sample had their last visit to the dentist 2 years or more before the study interview. Prevention of tooth loss, especially at a premature age, requires frequent visits to the dentist and greater awareness by the patient regarding the necessary care to maintain oral health. On average, the patients lost teeth before receiving a diagnosis of T2DM, so other factors seem to have been associated with tooth loss. Social inequalities in the occurrence of oral problems related to tooth loss have been identified in Brazil [23]. Some authors state that this type of inequality profile may be due to inequities related to access to dental services, increasing the prevalence of oral problems [24].
From the results found in the present study, there seems to be little concern about oral health among the participants and lack of information on the importance and impact of oral hygiene care on the quality of life and general health of these patients. These findings call for better integration between endocrinologists and dentists in guiding patients with T2DM on the need to maintain adequate glycemic control and oral hygiene in order to minimize the associated risks and increase therapeutic success [25]. Patients with T2DM without adequate control have multiple associated oral manifestations [6]. Thus, this population was expected to have more regular visits to the dentist and at shorter intervals. Sousa and cols. [ 26] stressed the need for improved dialogue between dentistry and medicine, pointing out to an approach focused on the principles of integrality.
Tooth brushing along with regular and correct flossing promote effective control of the supragingival dental biofilm and, consequently, interproximal caries and gingivitis [27]. However, the introduction of these measures does not significantly improve the indices of plaque and gingivitis if the individual does not know how to perform these tasks satisfactorily [28]. As noted by other authors [27], daily tooth brushing was well accepted in our study, but few individuals used dental floss regularly. The prevalence of edentulism in our sample ($22.7\%$) was similar to that found by Huang and cols. in 2013 ($26\%$) [29], but the prevalence of cavitated carious lesions was low ($31.8\%$). This finding may be related to the method used in the study to evaluate carious disease, i.e., performed on an outpatient setting and without equipment to allow for the differentiation of caries lesions in terms of activity (active or inactive) and extension (enamel or dentin injury).
Most participants had fewer than 21 teeth and bilateral tooth loss, which became evident mainly during later dental follow-up evaluations. Tooth loss may be more common in adults with T2DM [6] due to increased exposure to periodontal disease, the most important oral complication and sixth in prevalence among all classic complications of diabetes mellitus [30]. According to the literature, these data are strongly associated with the degree of glycemic control, diabetes duration, patient’s age, and presence of associated medical complications [31].
Despite being a self-reported item, masticatory ability was reported as unsatisfactory by more than half of the sample ($51.5\%$). Also, the prevalence of poor masticatory ability was higher than that reported by Figueiredo and cols. [ 32]. Our results showed that the group of patients reporting difficulty in eating solid foods tended to have a higher mean HbA1c level than the group of patients with difficulty in eating liquid or pureed foods, although this result did not reach statistical significance (10.2 vs. $9.1\%$, respectively, $$p \leq 0.152$$). Considering that the mean HbA1c difference between the groups was about $1\%$, the lack of association may have been due to a lack of statistical power. About $40\%$ of the patients evaluated had a preference for eating liquid or pureed foods. Although these data vary between studies, they are somewhat concerning, as difficulty or dissatisfaction with chewing can lead to dietary restrictions and, consequently, interfere with glycemic control, causing a negative impact on the quality of life of an individual [3]. In the current study, the mean HbA1c between the groups with fewer versus more than 21 teeth was similar (8.9 vs. $8.7\%$, respectively, $$p \leq 0.597$$), and it is not possible to establish a relationship between tooth loss and diabetes metabolic control. The cross-sectional design and the reduced sample size may have limited the identification of these results. However, in a population-based study conducted in Germany, poorly controlled diabetes was associated with an average increase in periodontal attachment loss and increased risk of future tooth loss compared with normal glycemic control [33].
In our sample, half of the patients with T2DM ($53\%$) were outside the goals of glycemic control proposed by the Brazilian Diabetes Society [3] and most patients ($86.4\%$) were at risk for metabolic complications associated with diabetes. The high prevalence of physical inactivity ($71\%$) found in our study is noteworthy. Due to the benefits attributed to the practice of physical exercise, such as improvement in nutritional status, insulin sensitivity, and glucose tolerance favoring glycemic control, physical activity should be encouraged in patients with T2DM [2].
In a recent study that evaluated the relationship between oral health, insulin resistance and resistance training in rats, it was observed that periodontal disease promoted a decrease in insulin sensitivity, due to the release of inflammatory mediators, such as the tumor necrosis factor-α (TNF -α). However, resistance training promoted an improvement in insulin sensitivity in rats with periodontal disease [34]. Several authors have already established this bidirectional relationship between diabetes and periodontal diseases. At the same time that the systemic complications of T2DM promote changes in periodontal conditions [35], glycemic homeostasis can be affected by periodontal disease [35,36], as they increase the inflammatory cytokines responsible for insulin resistance [37]. In addition, periodontal treatment has been shown to reduce TNF-α levels and improve glycemic control in patients with T2DM [38].
With regard to the consumption of foods considered unhealthy and, therefore, to be consumed at the most once a week, we observed a high frequency of adequate consumption of sweet foods, despite the inadequate metabolic control observed in our patients. Almost half of the sample ($46\%$) had not consumed sweets in the previous week before the interview. Additionally, we observed frequent consumption in the previous week of fruits and vegetables – foods that are considered indispensable for a healthy diet pattern [4]. These findings differ from data reported by Kobayashi and cols. [ 39] and Lima and cols. [ 40], who demonstrated an inadequate diet based on excessive consumption of these foods.
Despite the importance of these results, we must emphasize that this study has some limitations. The design has limitations inherent to cross-sectional studies resulting from atemporal monitoring. The sample was selected from patients receiving care at a single center and with a low socioeconomic level. The instrument used to collect dietary data (FFQ) also has limitations, since it relies on the respondent’s memory and does not provide information regarding the type of fibers and processing and consistency of foods, which are important factors interfering in the glycemic index. However, studies like the present one investigating the impact of tooth loss on the quality of diet and glycemic control in patients with T2DM are scarce in the literature and contribute to identifying the oral health profile of these patients and promoting strategies to prevent complications and promote health.
Although recommendations for primary treatment of T2DM include attention to oral health, the percentage of patients receiving dental care was low. When treatment was required, the patients often sought private care. The prevalence of tooth loss in patients with T2DM was high and oral changes were frequent, suggesting a need for greater promotion of oral health care and more effective dialogue between dentists and endocrinologists. In conclusion, the findings of the present study indicate that in patients with T2DM it is essential to promote oral health care through regular visits to the dentist and frequent examinations for prevention of tooth loss, increase awareness to the impact of oral health conditions on quality of life and general health, and improve the connection between diabetes and dental care services within the public and private care networks.
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|
---
title: The A allele of the rs759853 single nucleotide polymorphism in the AKR1B1 gene
confers risk for diabetic kidney disease in patients with type 2 diabetes from a
Brazilian population
authors:
- Cristine Dieter
- Natália Emerim Lemos
- Nathalia Rodrigues de Faria Corrêa
- Felipe Mateus Pellenz
- Luís Henrique Canani
- Daisy Crispim
- Andrea Carla Bauer
journal: Archives of Endocrinology and Metabolism
year: 2022
pmcid: PMC9991038
doi: 10.20945/2359-3997000000432
license: CC BY 4.0
---
# The A allele of the rs759853 single nucleotide polymorphism in the AKR1B1 gene confers risk for diabetic kidney disease in patients with type 2 diabetes from a Brazilian population
## ABSTRACT
### Objective:
The AKR1B1 gene encodes an enzyme that catalyzes the reduction of glucose into sorbitol. Chronic hyperglycemia in patients with diabetes mellitus (DM) leads to increased AKR1B1 affinity for glucose and, consequently, sorbitol accumulation. Elevated sorbitol increases oxidative stress, which is one of the main pathways related to chronic complications of diabetes, including diabetic kidney disease (DKD). Accordingly, some studies have suggested the rs759853 polymorphism in the AKR1B1 gene is associated with DKD; however, findings are still contradictory. The aim was to investigate the association of the rs759853 polymorphism in the AKR1B1 gene and DKD.
### Materials and methods:
The sample comprised 695 patients with type 2 DM (T2DM) and DKD (cases) and 310 patients with T2DM of more than 10 years’ duration, but no DKD (controls). The polymorphism was genotyped by real-time PCR.
### Results:
Allelic and genotype frequencies of this polymorphism did not differ significantly between groups. However, the A/A genotype was associated with risk for DKD after adjustment for gender, triglycerides, BMI, presence of hypertension and diabetic retinopathy, and duration of DM, under both recessive ($$P \leq 0.048$$) and additive ($$P \leq 0.037$$) inheritance models.
### Conclusion:
Our data suggest an association between the AKR1B1 rs759853A/A genotype and risk for DKD in Brazilians T2DM patients.
## INTRODUCTION
Diabetic kidney disease (DKD) is an important microvascular complication that affects around $40\%$ of all patients with diabetes mellitus (DM), and is the leading cause of end-stage renal disease in individuals on renal replacement therapy. Moreover, patients with DKD have increased cardiovascular mortality compared to patients with DM without this complication [1,2]. DKD is defined clinically by presence of albuminuria and/or a gradual decline in the glomerular filtration rate (GFR) [3]. Known risk factors for DKD are long-lasting hyperglycemia, arterial hypertension, dyslipidemia, and genetic polymorphisms [1,4].
Aldo-keto reductase family 1 member B (AKR1B1), also known as aldose reductase, belongs to the aldo/keto reductase superfamily and is the first enzyme of the polyol pathway, catalyzing the reduction of glucose into sorbitol using NADPH as a cofactor [reviewed in [5,6]]. This reaction is the rate-limiting step of the polyol pathway. Under chronic hyperglycemia in patients with DM, AKR1B1 affinity for glucose is high, leading to sorbitol accumulation and increased consumption of NADPH, thus reducing the available amount of this cofactor to be used in other metabolic processes, such as production of nitric oxide [5,6]. Moreover, sorbitol accumulation changes cellular membrane osmotic pressure and triggers oxidative stress, long thought to be one of the main causative mechanisms of DM and its chronic complications. In the kidneys, it may trigger dysfunction and, consequently, DKD [5-7].
In this context, some studies have demonstrated an association between single nucleotide polymorphisms (SNPs) in the AKR1B1 gene and chronic complications of DM, including DKD [8-12]. The rs759853 G/A SNP is located in the promoter region of ARK1B1 gene and has been studied in several populations regarding a purported association with DKD. A meta-analysis performed by Cui and cols. [ 8] included nine case-control or cohort studies that investigated the associated between the rs759853 SNP and DKD, and showed this SNP was associated with risk for DKD in patients with type 1 DM (T1DM) or type 2 DM (T2DM) [OR = 1.52, $95\%$ CI (1.26-1.84), $P \leq 0.0001$, for the dominant model]. However, considering that none of these studies used the current criteria for classifying kidney disease [13] and no study has been conducted in the Brazilian population, we performed a case-control study to investigate whether the AKR1B1 rs759853 SNP is associated with DKD in patients from Southern Brazil with T2DM.
## Sample profile and clinical and laboratory analyses
This case-control study was conducted in accordance with the STROBE and STREGA guidelines [14,15]. The sample consisted of 1,005 unrelated patients with T2DM recruited from Hospital de Clínicas de Porto Alegre and Grupo Hospitalar Conceição (Porto Alegre, Rio Grande do Sul, Brazil) between 2002 and 2013, as previously described in detail [16].
T2DM was diagnosed following American Diabetes Association guidelines [17], while DKD was diagnosed based on KDIGO guidelines [13], using urinary albumin excretion (UAE) and estimated GFR (eGFR). Patients were divided into two groups according to renal function: 1) non-DKD controls ($$n = 310$$): patients with T2DM of ≥10 years’ duration and without any degree of DKD (UAE <30 mg/g and eGFR ≥60 mL/min/1.73 m²; and 2) DKD cases ($$n = 695$$): patients with moderate (UAE 30-300 mg/g and/or eGFR 30-59 mL/min/1.73 m²) or severe DKD (UAE >300 mg/g and/or eGFR 1-29 mL/min/1.73 m²). All subjects included in the study self-declared their ethnicity as “White”.
A standard form was applied to collect data on age, age at T2DM diagnosis, T2DM duration, and drug treatment, and all subjects underwent clinical and laboratory evaluations, as reported elsewhere [18]. Concisely, patients were weighed barefoot, wearing outdoor clothes, and their height was recorded. Body mass index (BMI) was calculated as weight (kg)/height (meters)2. Fasting serum and plasma samples were collected for laboratory measurements. Fasting glucose levels were measured using the glucose oxidase method. Glycated hemoglobin (HbA1c) quantification was performed using different methodologies; values were traceable to the Diabetes Control and Complications Trial (DCCT) [19]. The Jaffé reaction was used for creatinine measurement. Total plasma cholesterol, HDL cholesterol, and triglycerides levels were measured using enzymatic methods, and UAE was quantified by immunoturbidimetry [20]. EGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [21].
The protocol of this study was approved by the Research Ethics Committee of Hospital de Clínicas de Porto Alegre (CAAE number: 97779318700005327), and all individuals gave assent and written informed consent prior to inclusion in the study.
## Genotyping of the rs759853 SNP on the AKR1B1 gene
DNA was extracted from blood leukocytes using a salting-out method [22]. The rs759853 (G/A) SNP in the AKR1B1 gene was genotyped by allele discrimination-real time PCR technique using a Human TaqMan SNP Genotyping Assay (Thermo Fisher Scientific, Foster City, CA, USA) specific for this SNP (assay ID = C_2795303_10). Real-time PCRs were conducted in 384-well plates (5µL total volume), using 2 ng of DNA, TaqMan ProAmp Mastermix 1× (Thermo Fisher Scientific), and TaqMan SNP Genotyping Assay 1×. Plates were then placed in the ViiA7 Real-Time PCR System (Thermo Fisher Scientific) and heated at 95 °C (10 minutes), which was followed by 50 cycles of 95 °C (15 seconds) and 62 °C (90 seconds).
## Statistical analyses
Allele frequencies were counted directly, and deviations from Hardy-*Weinberg equilibrium* (HWE) were checked using the chi-square (χ2) test. Genotype and allele frequencies were compared between groups using χ2 tests. Moreover, genotypes were compared between case and control groups considering different inheritance models, categorized accordingly to a previous study [23]. Clinical and laboratory variables were compared between groups using Student’s t tests or χ2 tests, as suitable. Categorical data are shown as percentages. Normal distributions of continuous characteristics were evaluated using Kolmogorov-Smirnov and Shapiro-Wilk tests. Those variables with normal distribution are shown as mean ± SD or percentage, while those characteristics with a skewed distribution were log-transformed before analyses and are shown as median (interquartile range).
The size of association between the rs759853 genotypes and DKD was calculated using OR with $95\%$ CI. Multivariate logistic regression analyses were used to evaluate whether the AKR1B1 SNP was independently associated with DKD while adjusting for confounding factors. To select the confounding factors to be included in the multivariate model, we chose those variables with $P \leq 0.250$ on univariate analysis or those with a relevant biological association with DKD. Statistical analyses were done in PASW Statistics 18.0 software (SPSS, Chicago, IL), and P values < 0.05 were considered significant.
Sample size was calculated in the OpenEpi website (www.openepi.com), using frequencies from a previous meta-analysis that evaluated the association of this SNP with DKD in Caucasian individuals with T2DM (minor allele frequency = 0.27 and OR = 1.6) [8]. Therefore, the calculated sample size was 568 individuals in the case group and 256 individuals in the control group.
## Sample description
The main characteristics of non-DKD patients (controls) and DKD cases are shown in Table 1. The median eGFR (mL/min per 1.73 m2) was 82.0 (70.0-92.0) in the non-DKD group and 46.0 (20.0-63.0) in patients with DKD ($P \leq 0.0001$), while the median UAE (mg/g) was 5.1 (3.0-10.9) in controls and 77.7 (23.9-349.3) in the DKD group ($P \leq 0.0001$). As expected, arterial hypertension and diabetic retinopathy (DR) were more prevalent in the DKD group ($P \leq 0.0001$). Also, HDL cholesterol was lower and triglyceride levels were higher in DKD patients compared to non-DKD patients ($P \leq 0.0001$ for both). Males comprised $53.7\%$ of the cases and $36.2\%$ of the control group ($P \leq 0.0001$).
**Table 1**
| Characteristic | Non-DKD patients (n = 310) | DKD cases (n = 695) | P * |
| --- | --- | --- | --- |
| Age (years) | 67.4 ± 10.5 | 66.9 ± 11.3 | 0.530 |
| Gender (% male) | 36.2 | 53.7 | <0.0001 |
| BMI (kg/m²) | 28.8 ± 5.2 | 28.7 ± 5.1 | 0.970 |
| HbA1c (%) | 7.4 ± 1.6 | 7.6 ± 2.0 | 0.126 |
| Hypertension (%) | 81.2 | 90.0 | <0.0001 |
| Age at DM diagnosis (years) | 47.0 ± 10.4 | 47.8 ± 11.1 | 0.282 |
| T2DM duration (years) | 20.3 ± 8.3 | 18.9 ± 10.6 | 0.024 |
| Total cholesterol (mg/dL) | 194.6 ± 47.0 | 194.6 ± 51.0 | >0.999 |
| Triglycerides (mg/dL) | 133.5 (96.2-186.0) | 158.5 (111.0-240.7) | <0.0001 |
| HDL cholesterol (mg/dL) | 47.4 ± 12.4 | 42.9 ± 11.9 | <0.0001 |
| LDL cholesterol (mg/dL) | 114.2 ± 42.9 | 114.1 ± 45.3 | 0.994 |
| Creatinine (µg/dL) | 0.8 (0.7 -0.9) | 1.3 (1.0-2.8) | <0.0001 |
| eGFR (mL/min per 1.73 m2) | 82.0 (70.0-92.0) | 46.0 (20.0-63.0) | - |
| UAE (mg/g) | 5.1 (3.0-10.9) | 77.7 (23.9-349.3) | - |
| DR (%) | 43.6 | 61.8 | <0.0001 |
## Genotype and allele frequencies in DKD patients and controls
Genotype and allele frequencies of the rs759853 (G/A) SNP in the AKR1B1 gene in cases with DKD and controls without this complication are described in Table 2. Genotype frequencies of this SNP were consistent with the HWE in the control group ($$P \leq 0.817$$) and did not differ significantly between cases and controls ($$P \leq 0.410$$). Additionally, the minor allele frequency (A) of the rs759853 SNP did not differ between groups ($39\%$ in cases vs. $37\%$ in controls, $$P \leq 0.327$$). However, on multivariate analysis, the A/A genotype was significantly associated with risk for DKD [OR = 1.630, $95\%$ CI (1.025-2.618), $$P \leq 0.039$$], adjusting for gender, triglycerides, BMI, presence of hypertension, DR, and duration of DM (Table 2). Accordingly, the A/A genotype remained associated with risk for DKD under both recessive [OR 1.548, $95\%$ CI (1.004-2.388), $$P \leq 0.048$$] and additive [OR = 1.659, $95\%$ CI (1.030-2.672), $$P \leq 0.037$$] inheritance models, adjusting for the same above-mentioned variables.
**Table 2**
| Unnamed: 0 | Non-DKD patients (n = 310) | DKD cases (n = 695) | P * | Adjusted OR (95% CI) / P† |
| --- | --- | --- | --- | --- |
| Genotype | Genotype | Genotype | Genotype | Genotype |
| G/G | 127 (41.0) | 275 (39.6) | 0.410 | 1 |
| G/A | 137 (44.2) | 293 (42.2) | | 1.115 (0.786-1.583)/ 0.541 |
| A/A | 46 (14.8) | 127 (18.2) | | 1.630 (1.025-2.618)/ 0.039 |
| Allele | Allele | Allele | Allele | Allele |
| G | 0.63 | 0.61 | 0.327 | - |
| A | 0.37 | 0.39 | | |
| Recessive model | Recessive model | Recessive model | Recessive model | Recessive model |
| G/G + G/A | 264 (85.2) | 568 (81.7) | 0.214 | 1 |
| A/A | 46 (14.8) | 127 (18.3) | | 1.548 (1.004-2.388)/ 0.048 |
| Additive model | Additive model | Additive model | Additive model | Additive model |
| G/G | 127 (73.4) | 275 (68.4) | 0.271 | 1 |
| A/A | 46 (26.6) | 127 (31.6) | | 1.659 (1.030-2.672)/ 0.037 |
| Dominant model | Dominant model | Dominant model | Dominant model | Dominant model |
| G/G | 127 (41.0) | 275 (39.6) | 0.727 | 1 |
| G/A + A/A | 183 (59.0) | 420 (60.4) | | 1.247 (0.901-1.726)/ 0.184 |
Exploratory analyses were performed to compare clinical and laboratory characteristics between T2DM patients stratified by presence of the rs759853 A/A genotype under the recessive model (Table 3). Mean age, HbA1c, age at diagnosis, duration of DM, total cholesterol, triglycerides, HDL, LDL, and UAE did not differ between patients carrying the A/A genotype and those with the G/G + G/A genotype ($P \leq 0.050$). Additionally, frequencies of male sex, hypertension, and DR did not differ between groups ($P \leq 0.050$).
**Table 3**
| Characteristic | G/G + G/A (n = 832) | A/A (n = 173) | P * |
| --- | --- | --- | --- |
| Age (years) | 67.1 ± 11.0 | 67.1 ± 11.3 | 0.931 |
| Gender (% male) | 47.8 | 50.9 | 0.511 |
| BMI (kg/m²) | 28.6 ± 5.0 | 29.3 ± 5.5 | 0.162 |
| HbA1c (%) | 7.6 ± 1.9 | 7.5 ± 2.0 | 0.523 |
| Hypertension (%) | 87.4 | 87.0 | 0.969 |
| Age at T2DM diagnosis (years) | 47.5 ± 10.7 | 47.8 ± 11.8 | 0.705 |
| DM duration (years) | 19.4 ± 9.9 | 19.1 ± 10.4 | 0.681 |
| Total cholesterol (mg/dL) | 194.5 ± 49.7 | 195.2 ± 50.2 | 0.882 |
| Triglycerides (mg/dL) | 149.0 (105.0-221.2) | 151.5 (109.7-225.2) | 0.858 |
| HDL cholesterol (mg/dL) | 44.4 ± 12.3 | 44.0 ± 12.0 | 0.663 |
| LDL cholesterol (mg/dL) | 114.4 ± 44.8 | 112.8 ± 43.2 | 0.69 |
| Creatinine (µg/dL) | 1.1 (0.8-1.8) | 1.0 (0.8-1.5) | 0.417 |
| eGFR (mL/min per 1.73 m2) | 60.0 (32.0-82.0) | 60.0 (40.5-82.5) | 0.246 |
| UAE (mg/g) | 22.0 (5.0-142.0) | 32.8 (5.5-143.5) | 0.482 |
| DR (%) | 57.0 | 51.2 | 0.213 |
## DISCUSSION
AKR1B1 acts on the polyol pathway by catalyzing the reduction of glucose to sorbitol. Under hyperglycemic environments, this pathway leads to intracellular buildup of sorbitol, causing tissue damage – as observed in the microvascular complications of diabetes, including DKD [24]. Hence, AKR1B1 polymorphisms have been associated with DM and its complications. The rs759853 SNP in the promoter region of the AKR1B1 gene has been the most studied SNP in this gene regarding DKD [8]. Therefore, we sought to analyze the association between the rs759853 SNP and susceptibility to DKD in a Southern Brazilian population. Our results show the A/A genotype was associated with risk for DKD. This is the first study to replicate the association between the rs759853 SNP and DKD in a Latin American population, and using both UAE and eGFR measurements for DKD classification.
In agreement with our results, other studies have demonstrated the association of rs759853 SNP with risk for DKD in different populations [9-12,25-27]. Neamat-Allah and cols. [ 9] reported the association of the G/A + A/A genotypes with risk for DKD in T1DM and T2DM patients from England and Ireland. In the same line, in American Caucasians with T1DM, the A/A genotype was reported as a risk factor for DKD, with the A allele frequency being higher in those with DKD compared to those without this complication ($41.2\%$ vs. $32.9\%$, $$P \leq 0.014$$) [10]. The A/A genotype frequency of this SNP was also higher in Japanese T2DM patients with DKD compared to normoalbuminuric controls, and was associated with risk for DKD after adjustment for covariables (OR = 4.3; $95\%$ CI 1.1-6.0) [25]. In a prospective cohort comprising 1,074 Chinese T2DM patients, those who developed cardiorenal complications over 8 years of follow-up had a higher frequency of the G/A + A/A genotypes ($44\%$ vs. $35\%$, $$P \leq 0.008$$) and A allele ($27\%$ vs. $22\%$, $$P \leq 0.026$$) in comparison to those who did not develop any complication [12]. In contrast, no association was found between this SNP and DKD in another sample of Chinese T2DM patients [26]. Cui and cols. [ 8] performed a meta-analysis of nine case-control or cohort studies (totaling 4,735 T1DM and T2DM patients) that investigated the association between the rs759853 SNP and DKD, and showed significant associations between this SNP and susceptibility to DKD in both T1DM and T2DM groups, under different inheritance models. Moreover, this association remained in T2DM patients stratified by ethnicity (Caucasians and Asian patients). On the other hand, no association was observed between this SNP and progression of DKD [8].
The rs759853 SNP in AKR1B1 has also been investigated regarding its association with DR; however, findings are still controversial [28,29]. Kaur and Vanita [28] analyzed 926 North Indian T2DM patients and reported an association between the A/A genotype and risk for DR (OR = 1.61, $95\%$ CI 1.39-2.28). In contrast, a study comprising 268 Chinese T2DM patients found no significant difference in rs759853 genotypes between patients with and without DR ($$P \leq 0.400$$) [29]. Furthermore, Cao and cols. [ 30] showed in a meta-analysis of 21 publications that rs759853 was not associated with DR. Interestingly, after subgroup analysis by DM type, this SNP conferred protection against DR onset in patients with T1DM (additive model: OR = 0.33, $95\%$ CI 0.17-0.67; dominant model: OR = 0.49, $95\%$ CI 0.36-0.68; recessive model: OR = 0.48, $95\%$ CI 0.28-0.83) [30]. Of note, in the present study, DR was included as a covariate in the logistic regression analyses.
AKR1B1 gene and protein expressions have also been studied in diabetic patients [31,32]. Hodgkinson and cols. [ 33] cultured peripheral blood mononuclear cells from DM patients with high glucose for 5 days and showed that AKRKB1 mRNA levels were higher in those cells collected from DKD patients compared to non-DKD patients and healthy subjects. Lewko and cols. [ 34] reported that AKR1B1 gene and protein expressions were elevated in mouse podocytes cultured with high glucose compared to cells cultured under normal glucose concentration. In kidneys from patients with and without DM, AKR1B1 activity was higher in glomeruli and small arteries of those patients with DKD compared to the non-DKD group [35]. Interestingly, a recent study found hypomethylation of the AKR1B1 gene in T2DM DKD cases compared to non-DKD patients [36]. Moreover, AKR1B1 methylation levels were negatively correlated with UAE levels in DKD patients [36].
Some aspects may have influenced the findings of the present study. First, we cannot exclude a population stratification bias when investigating our samples, as only White individuals enrolled in the study. Second, we cannot rule out the occurrence of type II error during the statistical analyses. Even though we had more than $80\%$ power (α = 0.05) to detect an OR ≥ 1.6 for DKD risk, we cannot rule out the possibility that the AKR1B1 rs759853A allele could be associated with DKD at lower ORs. Third, we found an association between this SNP and risk for DKD only after adjusting for covariates; however, this analysis is extremely important, since DKD is a multifactorial disease, caused either by activation of glucose-dependent pathways as well as by the presence of hypertension and obesity in patients with T2DM.
In conclusion, our study indicates that the A/A genotype of rs759853 SNP in the AKR1B1 gene is a risk factor for DKD in a Southern Brazilian population. This association has also been demonstrated in other populations of different ethnic origins and is biologically plausible, considering the involvement of AKR1B1 in the polyol pathway and its relation with DM and its complications.
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|
---
title: Room-temperature crystallography reveals altered binding of small-molecule
fragments to PTP1B
authors:
- Tamar Skaist Mehlman
- Justin T Biel
- Syeda Maryam Azeem
- Elliot R Nelson
- Sakib Hossain
- Louise Dunnett
- Neil G Paterson
- Alice Douangamath
- Romain Talon
- Danny Axford
- Helen Orins
- Frank von Delft
- Daniel A Keedy
journal: eLife
year: 2023
pmcid: PMC9991056
doi: 10.7554/eLife.84632
license: CC BY 4.0
---
# Room-temperature crystallography reveals altered binding of small-molecule fragments to PTP1B
## Abstract
Much of our current understanding of how small-molecule ligands interact with proteins stems from X-ray crystal structures determined at cryogenic (cryo) temperature. For proteins alone, room-temperature (RT) crystallography can reveal previously hidden, biologically relevant alternate conformations. However, less is understood about how RT crystallography may impact the conformational landscapes of protein-ligand complexes. Previously, we showed that small-molecule fragments cluster in putative allosteric sites using a cryo crystallographic screen of the therapeutic target PTP1B (Keedy et al., 2018). Here, we have performed two RT crystallographic screens of PTP1B using many of the same fragments, representing the largest RT crystallographic screens of a diverse library of ligands to date, and enabling a direct interrogation of the effect of data collection temperature on protein-ligand interactions. We show that at RT, fewer ligands bind, and often more weakly – but with a variety of temperature-dependent differences, including unique binding poses, changes in solvation, new binding sites, and distinct protein allosteric conformational responses. Overall, this work suggests that the vast body of existing cryo-temperature protein-ligand structures may provide an incomplete picture, and highlights the potential of RT crystallography to help complete this picture by revealing distinct conformational modes of protein-ligand systems. Our results may inspire future use of RT crystallography to interrogate the roles of protein-ligand conformational ensembles in biological function.
## Introduction
Of the ~150,000 protein crystal structures in the public Protein Data Bank (PDB) (Berman et al., 2000), ~122,000 (~$81\%$) have a non-polymer ligand modeled, and many thousands more reside in private pharmaceutical company databases. However, of the trove of public protein-ligand crystal structures, the vast majority (~$94\%$) with temperature annotations were determined at cryogenic (cryo) temperature (≤200 K), typically after the protein crystals were flash-cooled in liquid nitrogen. By contrast, only a small minority (~$6\%$) were determined at elevated temperatures (>200 K; of these, mostly >277 K or 0°C). This statistic is unnerving in light of the fact that, for proteins, cryo crystallography distorts protein conformational ensembles (Keedy et al., 2014), whereas room-temperature (RT) crystallography reveals distinct protein conformational heterogeneity, including alternate conformations of side chains and backbone segments, that better aligns with solution data and is in some cases more relevant to biological function (Fraser et al., 2009; Fraser et al., 2011; Fenwick et al., 2014; Keedy et al., 2015).
In contrast to proteins, relatively little is known about how crystallographic temperature affects protein-ligand interactions. Past studies that focused on individual compounds or small sets of related/congeneric compounds have offered tantalizing hints, with RT resulting in shifted binding poses (Maeki et al., 2020; Bradford et al., 2021; Gildea et al., 2021; Milano et al., 2022), binding at a different site (Fischer et al., 2015), and even a change of crystal symmetry (Gildea et al., 2021). *In* general, RT crystallography of protein-ligand complexes is increasingly accessible, thanks to advances in methodology (Fischer, 2021) including serial crystallography (Milano et al., 2022), even with as few as 1000 images, at synchrotrons (Weinert et al., 2017) or X-ray free electron lasers (Moreno-Chicano et al., 2019). Other RT approaches are also emerging, including in situ crystallography (Sanchez-Weatherby et al., 2019; Lieske et al., 2019), in some cases using crystallization plates pre-coated with dry compounds (Gelin et al., 2015; Teplitsky et al., 2015), as well as microfluidics (Maeki et al., 2020; Sui et al., 2021).
Despite this promising foundation, a central question remains: how frequently, and in what ways, does temperature affect protein-ligand structural interactions? To our knowledge, this question has not yet been addressed using a sufficiently large library of chemically diverse ligands. This gap is a significant obstacle toward a thorough understanding of how ligand and protein conformational heterogeneity interplay with one another to control biologically important phenomena such as enzyme catalysis and allosteric regulation. It also limits the potential of structure-based drug design (SBDD), given that cryo temperature is reported to degrade the utility of crystal structures for computational docking and binding free energy calculations (Bradford et al., 2021).
An emerging high-throughput approach to identifying protein-ligand hits is crystallographic small-molecule fragment screening, in which hundreds to thousands of ‘fragments’' of drug-like small molecules are subjected to high-throughput crystal soaking and structure determination with a protein of interest. For example, a recent crystallographic fragment screen of the SARS-CoV-2 coronavirus’s main protease (Mpro) (Douangamath et al., 2020) provided dozens of starting points for crowd-sourcing the design of potent new small-molecule inhibitor candidates (Achdout et al., 2022), complementing another crystallographic screen of Mpro using repurposed drug molecules (Günther et al., 2021).
Previously, several authors of the current study performed a crystallographic fragment screen of the archetypal protein tyrosine phosphatase, PTP1B (also known as PTPN1) (Keedy et al., 2018), a highly validated therapeutic target for diabetes (Elchebly et al., 1999), cancer (Krishnan et al., 2014), and neurological disorders (Krishnan et al., 2015) that has also been deemed ‘undruggable’ (Zhang, 2017; Mullard, 2018). That fragment screen produced X-ray datasets for 1627 unique fragments, of which 110 were clearly resolved in electron density maps at 12 fragment-binding sites scattered across the surface of PTP1B. Of the top three fragment-binding ‘hotspots’, one was previously validated with a non-covalent allosteric small-molecule inhibitor (Wiesmann et al., 2004), and another was validated with a new covalent allosteric inhibitor inspired by the fragment hits (Keedy et al., 2018), thus highlighting fragment screening as a valuable tool for discovering allosteric footholds in proteins (Krojer et al., 2020). Importantly, however, all previously reported crystallographic fragment screens, including those mentioned above, were conducted at cryo temperature.
To elucidate the role of temperature in dictating protein-ligand interactions, here we have explored the use of large-scale crystallographic small-molecule fragment screening at RT. Specifically, we have performed RT crystallographic fragment screens of PTP1B with many of the same fragments used for the previous cryo screen (https://elifesciences.org/articles/36307), thereby allowing direct inferences regarding the effects of temperature. Our work uses 143 unique, chemically diverse ligands which, to our knowledge, is several-fold (4–5×) more than any previous RT crystallographic study. Moreover, we have used two complementary strategies for RT data collection. The two screens were performed with different diffraction data collection approaches, at different times, and with distinct but partially overlapping sets of fragments – so together they ensure that our overall conclusions are robust.
In both RT fragment screens, we observe that fewer fragments bind, and on average more weakly (with lower occupancy). However, many fragments that bind at RT do so with a variety of temperature-dependent differences, including unique binding poses, changes in solvation, totally new binding sites, and even distinct protein allosteric conformational responses (Cui et al., 2017; Choy et al., 2017; Keedy et al., 2018; Hjortness et al., 2018; Hongdusit et al., 2020; Torgeson et al., 2022) to ligand binding. Serendipitously, we also identify a fragment that binds covalently to a key lysine in the allosteric 197 site (Keedy et al., 2018), providing an intriguing new foothold for further allosteric inhibitor development.
Overall, this work provides new insights into ligandability and allostery in the important therapeutic target enzyme PTP1B. More broadly, it highlights the limitations of relying solely on cryo crystallography and the relative advantages of RT crystallography for elucidating interactions between ligands and proteins, with implications for a wide range of applications including SBDD.
## Two crystallographic fragment screens at RT
This work centers on two RT crystallographic screens of PTP1B: single-crystal (hereafter abbreviated as ‘1-xtal’) and in situ. For both these two new RT screens and the prior cryo screen (Keedy et al., 2018), the procedures were identical for crystallization and crystal soaking with small-molecule fragments. However, the procedures differed in their approaches to crystal harvesting and diffraction data collection. In the previous cryo screen, the fragment-soaked crystals were harvested by hand with nylon loops, cryo-cooled in liquid nitrogen, and subjected to X-ray diffraction under a traditional cryo gas stream. In the new 1-xtal RT screen, the fragment-soaked crystals were harvested by hand with nylon loops, enclosed in plastic capillaries to prevent dehydration, and subjected to X-ray diffraction at ambient temperature. In the new in situ RT screen, the unharvested fragment-soaked crystals, still in the mother liquor solution in the crystallization plates, were subjected directly to X-ray diffraction at ambient temperature. See Materials and methods for further details about the experimental procedures. As outlined below, the crystallographic data and hit rates were similar for both RT screens, suggesting that the alternative protocols did not significantly impact the overall results.
For each RT screen, we used small-molecule fragments that were used previously for the cryo temperature crystallographic screen (Keedy et al., 2018) (see Materials and methods). Fragments were chosen from two categories: [1] cryo-hits which bound to the protein in the previous cryo screen, and [2] cryo-non-hits which were soaked into crystals but did not bind in the previous cryo screen. The 1-xtal RT screen used 59 cryo-hits and 51 cryo-non-hits, whereas the in situ RT screen used 48 cryo-hits and 32 cryo-non-hits. The fragment sets for the two screens were partially overlapping and complementary, with 23 fragments in common, of which 20 were cryo-hits and 3 were cryo-non-hits.
The fragment-soaked and control dataset for both screens are totaled and categorized in Table 1. Unless noted otherwise, the unique fragment datasets plus DMSO datasets for each screen were used for all subsequent analyses. As the two screens had 23 fragments in common, there were a total of 86+80–23=143 unique fragments overall across both RT screens.
**Table 1.**
| Unnamed: 0 | 1-xtal | In situ |
| --- | --- | --- |
| Total raw X-ray datasets collected | 269 | 111 |
| Processed datasets with unique fragments | 86 | 80 |
| Cryo-hit | 38 | 48 |
| Cryo-non-hit | 48 | 32 |
| DMSO (negative control) | 7 | 20 |
The data were high resolution for both RT screens (Figure 1): the average resolution was 2.30 Å for 1-xtal and 1.99 Å for in situ, as compared to 2.10 Å for the previous cryo screen (Keedy et al., 2018). The slightly lower resolution of the 1-xtal data may be due to some degree of radiation damage, which was largely avoided by the in situ strategy (see Materials and methods). As outlined below, the results of the two screens are broadly very similar, and indeed identical for several fragments used in both screens (Figure 4—figure supplement 4), suggesting that radiation damage with the 1-xtal data was not a major factor in dictating our overall results. Additionally, a visual inspection of all the 1-xtal RT hits featured in this paper did not show any signs of local radiation damage (see Materials and methods).
**Figure 1.:** *Resolution distributions from room-temperature (RT) crystallographic screens.Histogram of X-ray resolutions of datasets soaked with DMSO (green), cryo-hits compounds (blue), or cryo-non-hits (red), collected at RT via (A) 1-xtal or (B) in situ data collection techniques.*
## Identifying fragment-binding hits
Using these high-resolution datasets, we identified low-occupancy protein fragment-binding events using the PanDDA algorithm (Pearce et al., 2017a) and manual inspection and modeling (see Materials and methods). For the fragments that bound to PTP1B at cryo (cryo-hits) (Keedy et al., 2018), we then examined how many bound to PTP1B at RT. The initial hit rates from the event maps automatically generated by PanDDA were surprisingly low: $\frac{12}{38}$ ($32\%$) for 1-xtal and $\frac{7}{48}$ ($15\%$) for in situ. Additionally, for cryo-non-hits, PanDDA revealed only two binding events; both were for the same fragment in the same dataset (vide infra).
To identify hits that may have been missed by the automated PanDDA event identification algorithm, we manually generated RT event maps with the cryo value for 1-BDC, a quantity within PanDDA that is directly related to ligand-binding occupancy (Pearce et al., 2017a) (see Materials and methods). With this approach, we found five new binding events: three for the 1-xtal datasets and two for in situ datasets. This brought the new totals to $\frac{15}{38}$ ($39\%$) for 1-xtal and $\frac{9}{48}$ ($19\%$) for in situ, still fairly low hit rates.
This observation prompted us to reexamine how the many partial datasets or ‘wedges’' obtained from in situ crystallography are assembled into complete datasets for use in subsequent steps including map calculation and PanDDA modeling (see Materials and methods). Recently, a new software called cluster4x was unveiled for pre-clustering X-ray datasets in the space of differences in structure factor amplitudes and/or Cα positions (Ginn, 2020). When applied to our past cryo PTP1B screen (Keedy et al., 2018), cluster4x identified previously unrecognized binding events (Ginn, 2020). The RT datasets are more isomorphous than the past cryo datasets (Figure 1—figure supplement 1). Nevertheless, to enhance isomorphism for our in situ screen, here we used cluster4x to pre-cluster in situ wedges (Figure 1—figure supplement 2) before merging within three main clustersthat are partially overlapping but qualitatively distinct from each other. We then assembled sets of similar wedges into complete datasets (see Material and methods) for input to PanDDA. This pre-clustering protocol resulted in five additional hits that were not previously observed with the all-wedges datasets, bringing the total RT hit rate for cryo-hits up from $\frac{9}{48}$ ($19\%$) to $\frac{14}{48}$ ($29\%$) for in situ.
Given the final cryo-hit reproduction rates of $39\%$ and $29\%$ for the RT screens, we investigated whether temperature affected the binding occupancy, or percent of unit cells in the crystal with a fragment bound. As an accessible proxy for occupancy, we examined PanDDA 1-BDC values. Many fragments have lower occupancy at RT than at cryo (Figure 2). This trend holds for cryo-hits that bind to the cryo site at RT either with the same pose (blue points in Figure 2) or with a new pose (orange points in Figure 2) (see Table 2).
**Figure 2.:** *Fragment-binding occupancies are different and often lower at room temperature (RT).1-BDC (a proxy for occupancy) is plotted for each binding event observed in either of two RT screens vs. in the previous cryogenic (cryo) screen. For two datasets, two binding events for the same fragment in the same structure are included as separate points. See Table 2 for definitions of pose categories. Those that did not show binding at RT are in gray along the x-axis. In some additional cases, RT event maps were calculated using the cryo 1-BDC to identify bound ligands at RT; these cases would sit artificially on the diagonal, and are not shown here.* TABLE_PLACEHOLDER:Table 2.
## Distribution of fragment hits at RT
Between our two RT screens, we have 15+14 = 29 new RT events with small-molecule fragments. These fragments fall into several categories based on the binding site, binding pose, and conformational response by the binding site (Table 2, Table 2—source data 1).
The RT fragment hits are bound at sites distributed throughout PTP1B, including at least one at the active site and at all three allosteric sites previously highlighted by the cryo screen (Keedy et al., 2018): the 197 site, BB site, and L16 site (Figure 3—figure supplement 1). Many of these sites have RT hits in both the 1-xtal and in situ screens (Figure 3—figure supplement 1), confirming the success of both screens. Notably, in all of these four key sites, one or more fragments bind differently from cryo – either binding with a new pose at RT or binding to this site only at RT and not cryo (Figure 3).
**Figure 3.:** *Fragments have a similar distribution across protein sites but different binding modes at room temperature (RT).Overview of fragments bound across PTP1B at RT, colored by RT pose compared to cryogenic (cryo) pose: same site, same pose (blue); same site, new pose (orange); new site (red). See Table 2 for more details on the definitions of these classifications. Also highlighted are the active-site WPD loop (red), P loop (yellow), 197 allosteric site (green), BB allosteric site (orange), and L16 allosteric site (purple) (Keedy et al., 2018). The protein is shown in its open conformation with the WPD loop and L16 in the open state. The α7 helix is not shown since it is disordered when the protein is in the open state, which is favored at higher temperatures (Keedy et al., 2018). α7 does become ordered in one RT fragment-bound structure, but is not shown here.*
## Similar binding for many cryo-hits at RT
We next turned our attention to the precise binding poses of cryo-hit fragments at RT. Of the cryo-hits that also bind at RT, most do so with a similar pose (Figure 4, Table 2): 9 cases for 1-xtal (Figure 4—figure supplement 1) and 7 for in situ (Figure 4—figure supplement 2). Many of these are concentrated in two sites on the non-allosteric front side of the protein (Figure 3—figure supplement 1) that were also highly populated in the cryo screen. Additionally, some fragments are double-represented due to the overlap between the two screens. Notably, of the three fragments with binding events in both the 1-xtal and in situ screens, all three bind similarly in both RT screens (Figure 4—figure supplement 4), suggesting the RT results are reproducible and reliable.
**Figure 4.:** *Fragments that bind similarly at room (RT) vs. cryogenic (cryo) temperatures.For each dataset, the RT PanDDA event map is in red (contour levels below), the RT model is in red (waters in red), and the corresponding cryo model is in blue (waters in purple). Datasets are named as follows: x####=RT in situ, z####=RT 1-xtal, y####=cryo. (A–C) in situ. (D–F) 1-xtal. (A) RT: x0224 (2.0 σ), cryo: y0118. (B) RT: x0285 (1.5 σ), cryo: y0772. (C) RT: x0262 (1.5 σ), cryo: y1656. (D) RT: z0007 (2.0 σ), cryo: y1710. (E) RT: z0015 (1.8 σ), cryo: y1554. (F) RT: z0025 (1.5 σ), cryo: y1294. This figure contains selected examples of fragments that bind similarly at RT vs. cryo; for all examples, see Figure 4—figure supplement 1 for 1-xtal and Figure 4—figure supplement 2 for in situ. For examples with no RT density for the cryo ligand using the cryo 1-BDC, see Figure 4—figure supplement 3.*
Although all of the aforementioned fragments themselves bind with the same pose at RT vs. cryo, in some cases water molecules around them differ with temperature. In a few examples, clear event map density is present for a water at RT but not at cryo (Figure 4C, Figure 4—figure supplement 5A–B) or vice versa (Figure 4—figure supplement 5C), even when varying event map contour levels. Therefore, even when ligand binding is similar, the solvation layer around the ligand can change at cryo vs. RT.
## New binding poses at RT
Some cryo-hit fragments bind in the same site at RT, but with a quite different pose. In one striking example, the fragment binds with the central ring in the same position at RT vs. cryo, but with substantially different positions for the two substituent groups (Figure 5). The in-plane chlorine and out-of-plane methylamine group are clearly defined in the respective event maps: the RT density is incompatible with the cryo model, and vice versa. Notably, this fragment binds in the allosteric L16 site (Figure 3), which was first reported alongside the original cryo fragment screen for PTP1B and highlighted as a promising target for small-molecule allosteric inhibitor development (Keedy et al., 2018).
**Figure 5.:** *Temperature-dependent ligand conformational heterogeneity.(A) In the room temperature (RT) dataset (x0227), the RT event map contoured at 2 σ (red) matches the RT model (red) rather than the cryogenic (cryo) model (blue) for both substituent groups of the ring. (B) In the cryo dataset (y0071), the cryo event map contoured at 1.2 σ (blue) matches the cryo model rather than the RT model.*
Another example features alternate ligand conformations that coexist in the same site, but only at one temperature. The RT event map suggests a pose with the carbonyl pointed one direction, toward Arg238 (Figure 6A). However, at cryo, this fragment was previously modeled with the carbonyl rotated by a 180° flip, enabling a water-bridged H-bond (Figure 6B). The RT event map has weak evidence at best for the flipped cryo conformation (Figure 6A). By contrast, the cryo event map has significant evidence for both conformations (Figure 6B). This observation is akin to other examples in which a ligand exhibits conformational heterogeneity in a single X-ray dataset (van Zundert et al., 2018). Here, however, the ligand conformational heterogeneity is temperature-dependent, enabling cross-pollination of conformations across temperatures to improve modeling (Keedy, 2019; Bradford et al., 2021).
**Figure 6.:** *Temperature modulates fragment pose and solvation within the same site.(A) In the room temperature (RT) dataset (x0260), the RT event map contoured at 1.6 σ (red) matches the RT model (red), but shows little evidence for the cryogenic (cryo) model (y0180, blue). (B) In the corresponding cryo dataset, the cryo event map contoured at 1.6 σ (blue) matches both the cryo model (blue) and the RT model. Notably, only at cryo does the event map include density for a water molecule (purple ball) next to the fragment carbonyl group and well positioned for a hydrogen bond (pale green dashed line) with the cryo fragment pose.*
A pair of other examples also feature fragments with distinct poses that are differentially stabilized at RT vs. cryo. In each of these two related examples, the RT event density is clear that the fragment binds with its longer substituent well ordered and pointed underneath the active-site WPD loop, which closes over the fragment (Figure 7A and B, left panels). At cryo, the loop still closes over the fragment, and the core of the fragment is in a similar location. However, the cryo event density is inconsistent with the RT pose – instead, the longer substituent seems to protrude toward solution (Figure 7A and B, right panels). For one of these fragments, a new ordered water molecule at cryo displaces the RT ligand pose (Figure 7B).
**Figure 7.:** *Room-temperature (RT) fragment pose is flipped compared to the cryogenic (cryo) pose.(A) Left: RT density (red) 1.5 σ, z0055 (red); y0884 (blue). Right: cryo density (blue) 1 σ, z0055 (red); y0884 (blue). (B) Left: RT density (red) 2 σ, x0256 (red); y0650 (blue). Right: cryo density (blue) 0.8 σ, x0256 (red); y0650 (blue) (not previously deposited to the PDB). Density is linked at RT (dashed box), consistent with the fragment pose, but is cut off at cryo, even at lower contour. There is little to no density for the open state of the WPD loop (not shown).*
## New binding sites at RT
Beyond just differences within the same binding site, temperature can also modulate ligand binding more dramatically, even altering what protein site the ligand binds to. In a first example, the fragment binds to the allosteric BB site (Wiesmann et al., 2004; Keedy et al., 2018) at cryo (Figure 8A and C), but there is no event density at RT. Instead, there is strong fragment-binding event density at a different site nearly 40 Å away (Figure 8A and B). The RT event density supports subtle protein shifts in the new binding site to accommodate the new fragment-binding event (Figure 8B). By contrast, in the cryo-binding site, the RT protein conformation would clash with the cryo pose, disallowing binding at RT.
**Figure 8.:** *Fragments bind at new sites only at room temperature (RT).(A–C) First example. (A) The two sites are ~38 Å away from one another. (B) In the RT dataset (z0042), the RT event map, calculated with 1-BDC of 0.36 and contoured at 1.5 σ (red), supports a bound fragment in the RT model (red) at a new site while the cryogenic (cryo) model (y1525) (blue) has no bound fragment. (C) By contrast, the RT event map (same contour) does not show any density for the cryo model (blue) from the previous cryo dataset (y1525). (D–F) Second example. (D) The two sites are ~46 Å away from one another. (E) The RT event map contoured at 1.75 σ (red) (same contour) does not support the cryo model (blue) from the previous cryo dataset (y0572). (F) By contrast, at a new site the RT event map (same contour) supports a bound fragment in the RT model (x0225) (red). The cryo model has no bound fragment.*
In additional examples, elevated temperature dissipates what seem to be cryo-binding artifacts. In the first such example, at cryo the fragment binds with an artifactual stacking arrangement involving three copies of the fragment (Figure 8D and E), but at RT there is no event density (automated or custom) for this stacking. This result suggests that temperature can modulate protein-ligand energy landscapes significantly, in this case by disfavoring enthalpically favorable stacking at higher temperature. Moreover, at RT, new event density for a single copy of this fragment appears at a distal site (Figure 8F) that is over 45 Å away from the cryo site (Figure 8D). Cryo event density at the new site was too weak to justify modeling a bound fragment (Keedy et al., 2018). Thus, the cryo-binding site is unique to cryo and the RT-binding site is unique to RT. In fact, this is the only case in which a fragment binds at RT to a new site that was not previously thought to bind any fragments at cryo (although later computational reanalysis did discover one previously undetected adjacent cryo-hit in this area; Ginn, 2020). A Tris buffer molecule also fortuitously binds in the same location in another published structure (PDB ID 4y14), although it is held in place by a distinct crystal contact due to that structure’s space group.
In a related but distinct case, a fragment previously bound at cryo with a seemingly similar artifactual stacking arrangement, this time involving two copies of the fragment (Figure 8—figure supplement 1A). However, at RT the entire stack does not disappear – instead, one copy remains bound (Figure 8—figure supplement 1B). At cryo, this latter copy was slightly more ordered than the other, based on event map strength. Thus, elevated temperature is sufficient to displace the more weakly bound copy, but not the more tightly bound one.
In a final, somewhat more complicated example, a fragment previously bound at three distal sites at cryo. At RT the fragment binds to only one of the cryo sites, in a nearly identical pose. In the other cryo sites, RT-binding events were not readily detected, either automatically by PanDDA or in RT event maps calculated at the cryo events’ 1-BDC values. More strikingly, at RT the fragment now binds to an additional new site (Figure 8—figure supplement 2A and B) that is over 40 Å away from any of the three cryo sites (Figure 8—figure supplement 2C). Although fragment binding was clear in cryo event maps at the three cryo sites, cryo density was unconvincing at the RT site; therefore, no binding event was detectable at this new site at cryo. Thus, as with the examples above (Figure 8), this fragment binds uniquely to a new site at RT.
## New covalent binding events to lysines
In addition to the fragments that switch binding sites at RT as detailed above, one fragment binds only in our RT datasets – and in an unexpected fashion. In RT event maps, we observe strong event density at/near both the allosteric 197 and L16 sites (Keedy et al., 2018). Surprisingly, at each site, the event density is contiguous with the side chains of a nearby lysine residue (Figure 9), consistent with covalent binding by the isatin-based fragment. First, at the allosteric L16 site, the fragment binds covalently to Lys237 (part of the eponymous Loop 16) – although it binds adjacent to the L16 pocket itself, nearer to the allosteric BB site (Figure 9C). Second, at the allosteric 197 site site, the fragment binds covalently to Lys197 with a pose that is strikingly similar to that of a covalent allosteric inhibitor tethered to a K197C mutant (Keedy et al., 2018; Figure 9A).
**Figure 9.:** *Unanticipated covalent adducts at previously reported allosteric sites only at room temperature (RT).(A) RT structure with the fragment covalently bound to both K197 and K237 (z0048, red), aligned with cryogenic (cryo) structure with a previously reported allosteric inhibitor covalently bound to K197C (6b95, green). (B) Fragment bound to K197 at the 197 allosteric site, with RT event density at 1.5 σ. (C) Fragment bound to K237 at the L16 allosteric site, with RT event density at 1.5 σ.*
The distal active-site P loop and substrate-binding loop adopt new conformations that are similar to those observed when the catalytic Cys215 is oxidized (van Montfort et al., 2003), although it is unclear whether Cys215 is oxidized in our RT event map. These conformations were not observed with the K197C-tethered allosteric inhibitor (Figure 9—figure supplement 1).
This fragment is a cryo-non-hit, meaning it demonstrably did not bind at cryo despite a high-resolution cryo dataset (1.89 Å, y1159). Indeed, it is the only cryo-non-hit to bind in either RT screen. This cryo-non-hit was chemically dissimilar to all previous cryo-hits: the most similar cryo-hit has a low Tanimoto score relative to this RT fragment (0.36, y1703) and does not bind near the RT sites. It is possible that the crystal for the cryo dataset was insufficiently soaked with this compound, or that the new RT-binding events seen here are due to additional chemical changes to the compound in DMSO solvent over time that altered its reactivity toward lysines. As expected for fragments due to their weak binding affinities, this molecule does not inhibit PTP1B with an in vitro activity assay (Keedy et al., 2018) (data not shown). However, our observations here raise the hope that optimized versions of this compound, particularly driven by fragment linking of the K197C-targeted compound and this new fragment (Figure 9A), could yield potent allosteric inhibitors for wildtype (WT) PTP1B, without need for mutation to a cysteine.
## Unique protein conformational responses at RT
Temperature does not only affect fragment binding to the protein – it can also affect the protein’s conformational response to fragment binding. With both screens, we observe protein conformational responses that are preferentially localized to the key allosteric sites that were identified in our previous study as being inherently linked to the active site (Keedy et al., 2018).
The C-terminal end of the α6 helix forms part of the allosteric L16 site (Keedy et al., 2018). At cryo, fragments in this site that intercalate below the α6 helix push it further in the direction of α7, the BB site, and the rest of the allosteric network (Keedy et al., 2018). At RT, structures with two of these fragments (Figure 4—figure supplement 2G, Figure 5) show that they affect the position of α6 similarly at RT vs. cryo (Figure 10—video 1); perhaps surprisingly, this remains true despite one fragment exhibiting a 180° pose flip (Figure 5).
However, in the nearby allosteric BB site (Wiesmann et al., 2004), the α6 helix is differentially ordered upon binding of a fragment at RT vs. cryo (Figure 10). Although the fragment binds in the same pose at RT and cryo, an entire additional helical turn of α6 is ordered at RT. This example illustrates that temperature can modulate not only the positions of protein structural elements during ligand binding, but also their relative order vs. disorder.
**Figure 10.:** *Temperature-dependent ordering of an α-helix augmenting a fragment-binding site.(A) In the BB allosteric site, the room temperature (RT) density, x0222 (red); contoured at 1.25 σ, is consistent with an extended and more ordered α6 helix (dashed box). (B) In contrast, the cryogenic (cryo) density, y0205 (blue); contoured at 1.75 σ, becomes disordered and therefore the α6 helix is not modeled as extended as in the RT model (dashed box). (C) Overlay of the two models showing the fragment pose is extremely similar whereas the RT helix is extended and more ordered (dashed box).*
Elsewhere on the contiguous allosteric back face of PTP1B, in the 197 site (Keedy et al., 2018), a fragment binds with a similar pose at cryo and RT (Figure 11, Figure 11—figure supplement 1). When this fragment binds at cryo, the protein globally remains in its default open state (Figure 11). However, at RT, the allosteric L16 site closes, and the active-site WPD loop partially closes (Figure 11). Notably, this fragment binds in the same position as a previously reported covalently tethered allosteric inhibitor (Keedy et al., 2018; Figure 11—figure supplement 2; see also Figure 9). Thus, RT allows for distinct protein conformational redistributions in response to fragment binding in allosteric sites.
**Figure 11.:** *Allosteric protein responses at key sites seen only at room temperature (RT).Although the fragment binds in a similar manner and in the same allosteric site (the 197 site) in both the RT model (z0032) (red) and the cryogenic (cryo) model (y1763) (blue), the protein response is different between the two temperatures. At cryo, the protein retains the default open conformation, with loop 16 in the L16 site open and the WPD loop also open. Alternatively, at RT, the L16 site is fully closed, while the WPD loop exhibits alternate conformations with the loop both open and closed. The α7 helix (not shown) remains disordered in both temperatures.*
## Discussion
Cryo X-ray crystallography is the predominant experimental method for deriving insights into protein-ligand structures, but the effects of cryo temperature on protein-ligand binding are poorly understood. To fill this critical gap, here we report a large set of RT crystal structures of the dynamic enzyme PTP1B in complex with diverse small-molecule fragments, and present a detailed comparison with the corresponding cryo temperature structures. Our data suggest that temperature can significantly affect the occupancy, pose, and even location of small-molecule binding to proteins in crystal structures. Moreover, we show that temperature can modulate protein conformational responses to ligand binding, leading to new insights into allosteric networks.
Although only 29–$39\%$ of the fragments that previously bound at cryo temperature (Keedy et al., 2018) also bound here at RT, several lines of evidence suggest this is predominantly due to the difference in data collection temperature, as opposed to, for example, variability in experimental steps. First, the RT hit rates for cryo-hits were similar for our two RT screens, which were performed with different techniques (single-crystal and in situ) by largely different sets of people at different times. Second, we monitored log files from the acoustic droplet ejection instrument used for soaking (Collins et al., 2017) and excluded any crystals that may not have been soaked correctly. Third, in multiple RT datasets, a cryo-hit fragment demonstrably no longer binds at the original site but does bind at a different site (Figure 8), confirming the crystals were soaked correctly. Fourth, even when cryo-hit fragments are observed in RT electron density event maps, we observe a trend of lower occupancies at RT (Figure 2). We conclude that the large temperature difference between cryo and RT (>178 K) underlies the observed changes in binding. This is in accord with recent studies in which only 0 of 9 (Guven et al., 2021) and 5 of 30 (Gildea et al., 2021) cryo-hit ligands were seen to bind at RT, and in which lower occupancies were seen at RT than at cryo for <10 ligands (Bradford et al., 2021).
When fragments do bind at RT, they often do so differently than at cryo, in a variety of ways (Figure 4—figure supplement 5, Figure 5, Figure 6, Figure 8, Figure 8—figure supplement 2). How does higher diffraction temperature cause such significant changes in protein-ligand binding? We speculate that after crystal soaking (which occurs at ambient temperature), all cryo-hit fragments are initially bound, but in many cases only loosely, with high B-factors that render them invisible from RT diffraction data. During cryocooling, the ligand B-factors (i.e. temperature factors) then drop rapidly on a faster timescale than overall crystal cooling (Halle, 2004), with many becoming sufficiently well ordered to be observable in cryo density (at least with PanDDA). Relatedly, it is unclear why some fragments bind at both cryo and RT, but with a different pose or binding site: they have similar molecular weights and numbers of rotatable bonds, yet are more hydrophobic and have more interactions with the binding site (Table 2—source data 2). To more deeply interrogate the complex relationship between temperature, cooling kinetics, and protein-ligand conformational ensembles, additional experiments are planned using mechanically controlled variable cryocooling rate (Warkentin et al., 2006) and variable crystal size. Future studies can also explore the degree to which the conclusions drawn here for small-molecule fragments can be extrapolated to larger, drug-sized ligands.
Another relevant consideration is the expected variability of cryo structures, as a baseline for differences between RT vs. cryo structures. Previous work has shown that cryo crystal structures of proteins have greater inherent variability than do RT structures, presumably due to idiosyncratic crystal cryocooling kinetics (Keedy et al., 2014). However, despite growing interest in crystallographic fragment screening, no work has examined replicates of many fragment-soaked cryo crystal structures to establish the impact of crystal variability on details of fragment binding such as pose. One study using fragment screens with two different crystal forms of the same protein showed that most fragments did not bind in both crystal forms, and of those that did, only two of five bound in the same site with the same pose (Schuller et al., 2021); however, this is a different situation from repeats of the same fragment in the same crystal form. Another study showed that crystallographically refined occupancies of ligands approach saturation at ~15 min of soaking time (Cole et al., 2014) however, our soaking times were many hours (Keedy et al., 2018), so this should not be a significant source of variability in our datasets. The PanDDA algorithm seeks to overcome (typically cryo) dataset variability by averaging to establish a reliable ground state density estimate for the purposes of identifying hits, yet individual hits may still have idiosyncratic features. Overall, future studies focused on fragment (and larger ligand) reproducibility in terms of binding occupancy, site, and pose at cryo temperature would be useful contributions to the field.
Even when ligand binding is similar at RT vs. cryo, the protein response can differ. One case involves essentially complete closing of the allosteric L16 site, but only partial closing (~$50\%$) of the active-site WPD loop (Figure 11) – in contrast to the previous paradigm in which the WPD loop and allosteric sites are precisely conformationally coupled (Keedy et al., 2018). Similar (de)coupling was also seen recently with serial synchrotron crystallography of apo PTP1B (Sharma et al., 2023). Thus, RT crystallography can add important nuance to our understanding of allosteric mechanisms in PTPs (Choy et al., 2017; Cui et al., 2017; Hjortness et al., 2018) and likely other proteins.
Our results here provide several insights that can aid future development of allosteric small-molecule modulators for PTP1B, a highly validated but ‘undruggable’ (Zhang, 2017; Mullard, 2018) therapeutic target. First, we observe new conformations for fragments on both sides of Loop 16 of the allosteric L16 site (Figure 5, Figure 6), offering unique footholds for structure-based inhibitor design of allosteric inhibitors. This local ligand heterogeneity, combined with the malleability of the adjacent α6 helix (Figure 10, Figure 10—video 1) and varying levels of apparent coupling between the L16 and active sites (Figure 11), argue for additional studies to decipher how different ligands in this region may selectively perturb the conformations of remote sites to allosterically control PTP1B function.
Second, one new RT fragment-binding site reported here was not previously shown to bind any fragments at cryo (Keedy et al., 2018) although additional clustering did identify one adjacent cryo-hit (Ginn, 2020), thus offering a new ligand-binding foothold. Coincidentally, the corresponding site in the paralog SHP2 has been successfully targeted with small-molecule allosteric inhibitors that stabilize a regulatory domain interface in the auto-inhibited state (Chen et al., 2016; LaRochelle et al., 2018). Although PTP1B lacks this additional regulatory domain, our data suggest future studies to explore whether it may nonetheless harbor latent allosteric capabilities that stem from this region within the catalytic domain.
Third, we observe a fragment covalently bound to Lys197 of the allosteric 197 site, with a similar pose as our previously reported allosteric inhibitor that was covalently tethered to an engineered K197C mutant (Keedy et al., 2018; Figure 9). This unexpected result opens new doors to design a covalent allosteric inhibitor targeting WT PTP1B, inspired by other success stories of progressing covalent fragment hits (Miller et al., 2013; Resnick et al., 2019). The potential of targeting the allosteric 197 site of PTP1B is further reinforced by our new finding that fragment binding in this site (Figure 11—figure supplement 1) can elicit allosteric conformational responses at RT that were masked at cryo (Figure 11).
Altogether, we observe RT fragments bound in a variety of sites in PTP1B with potential for enabling downstream allosteric drug design. We see fragments bound in all three previously reported surface allosteric sites in the PTP1B catalytic domain: the BB site (Wiesmann et al., 2004), the 197 site (Keedy et al., 2018), and the L16 site (Keedy et al., 2018). The BB site is also thought to be near a secondary binding site for a second class of allosteric inhibitors for PTP1B, four example, MSI-1436, which primarily targets a different site in the disordered C-terminus (Krishnan et al., 2014). In addition to these three surface allosteric sites in the catalytic domain, we also see fragments bound in the active-site pocket (Pedersen et al., 2004). Notably, in all four of these key sites, we observe fragments that either adopt different poses at RT vs. cryo, or were not previously bound in that site at all at cryo (Figure 3). Such novel ligand poses in sites that are known to harbor allosteric capability offer promising new routes for fragment-based drug design (Krojer et al., 2020). This could be done either by ‘growing’ existing inhibitors by attaching moieties similar to fragment poses, or by designing new inhibitors ‘from scratch’ by identifying compounds that combine the (new) poses of multiple fragments in a site (Gahbauer et al., 2023). Fragment poses for these designs could derive from previous cryo structures and/or our new RT structures; the merits of combining multiple such sources of poses remain to be explored. Fragment-based design strategies could be used to develop non-covalent allosteric modulators or, in the case of the 197 site as mentioned above, covalent allosteric modulators of the WT enzyme (Figure 9). In addition to the fragments at previously established binding sites in PTP1B, as noted above we also see a fragment bound at a new site at RT: the N-terminal α1’-α2’ helical bundle, corresponding to an allosteric inhibitor binding site in SHP2 (Chen et al., 2016; LaRochelle et al., 2018). This site was not bound by any fragments in the previous cryo screen (Keedy et al., 2018), making this new fragment a potentially valuable starting point for exploring the possible allosteric capabilities of this relatively underexplored region of the PTP1B catalytic domain tertiary structure.
It is instructive to consider the results reported here in light of the growing (and exciting) trend toward leveraging artificial intelligence and machine learning to address central problems in structural biology and biophysics. Most famously, the AI/ML algorithm AlphaFold 2 (Jumper et al., 2021) (and to a lesser extent RoseTTAfold; Baek et al., 2021) made a quantum leap in protein structure prediction accuracy. More relevant to the work reported here, AI/ML is being used to great effect for SBDD and computational chemistry, including protein-ligand docking (Corso et al., 2022) and ligand design (Wallach et al., 2015). Importantly, all of these methods rely on training data in the form of experimental protein structures from the PDB, the vast majority of which are cryo temperature crystal structures. For structure prediction, this temperature distribution undoubtedly introduces bias into the predicted models, likely favoring well-packed states that preclude functionally required conformational heterogeneity. For drug design, it may favor protein-ligand interactions that overweight enthalpic considerations and underweight entropic ones, feature inaccurate solvation environments, or suggest artificially rigid proteins. The full implications of these biases remain to be clarified (Bradford et al., 2021). RT crystal structures of protein-ligand interactions have the potential to ameliorate or bypass the limitations of cryo structures for training AI/ML methods. The number of structures reported here is insufficient to explore such ideas; it also remains unclear how useful weakly binding fragments may be for training AI/ML methods aimed at larger compounds. Nevertheless, our findings that protein-ligand interactions often differ from how they appear in cryo crystal structures prompts important questions as the age of AI/ML continues to rapidly unfold.
Overall, our work highlights the value and accessibility of RT crystallographic ligand screening for providing unique insights into protein-ligand interactions, particularly for allosteric sites (Krojer et al., 2020). More broadly, by using temperature as a readily accessible experimental knob, this study speaks to the potential of a multitemperature crystallography strategy, including excursions to higher temperatures in the physiological regime (Doukov et al., 2020; Otten et al., 2020; Ebrahim et al., 2022), for elucidating fundamental connections between molecular structure, heterogeneity, and function (Keedy, 2019).
## Materials and methods
**Key resources table**
| Reagent type (species) or resource | Designation | Source or reference | Additional information |
| --- | --- | --- | --- |
| Peptide, recombinant protein | Human PTP1B recombinant protein | This paper | Purified from Escherichia coli BL21 cells |
| Software, algorithm | PanDDA software | PanDDA (https://pandda.bitbucket.io/) | Version 0.2.14 |
## Protein expression
All experiments used the same PTP1B construct as was used previously: residues 1–321, WT* (C32S/C92V double mutation), in the pET24b vector carrying a kanamycin resistance gene (Keedy et al., 2018). Expression and purification were also performed as previously described (Keedy et al., 2018). PTP1B was transformed into BL21 *Escherichia coli* competent cells. The cultures were grown overnight in a 5 mL LB media containing 35 mg/L (final) kanamycin at 37°C shaking continuously at 150 rpm. Next, this overnight culture was used to inoculate 1 L LB media containing 35 mg/L (final) kanamycin. This culture was grown until the optical density at 600 nm (OD600) reached between 0.6 and 0.8. PTP1B expression was then immediately induced by adding IPTG to 100 µM (final) and incubating for about 18–20 hr at 18°C shaking continuously at 200–250 rpm. The culture was then pelleted by centrifugation, the supernatant discarded, and the cell pellets (‘cellets’) harvested and stored at –80°C for subsequent purification.
## Protein purification
On the day of purification, each cellet was retrieved from –80°C and thawed on ice in 45 mL of lysis buffer (100 mM MES pH 6.5, 1 mM EDTA, freshly added 1 mM DTT) and a dissolved Pierce Protease Inhibitor Tablet. The cells were resuspended using a tabletop vortex. The homogenous cell suspension was then subjected to sonication using a Branson Digital Sonifier, with the probe submerged halfway into the suspension for about 20 min (10 s on/off) with $50\%$ amplitude. The lysed cells were then subjected to centrifugation at 4°C, and the supernatant was filtered using 0.22 µm syringe filters and loaded onto an SP FF $\frac{16}{10}$ cation exchange column, pre-equilibrated in lysis buffer, in an ÄKTA Pure purification system (GE Healthcare Life Sciences). The protein was eluted as 5 mL fractions using a linear gradient of lysis buffer from 0 to 1 M NaCl. PTP1B eluted at approximately 200 mM NaCl per the UV detector and SDS-PAGE analysis. The PTP1B fractions were pooled together and concentrated to 3 mL volume, then applied to a Superdex 75 (GE Healthcare Life Sciences) size exclusion column pre-equilibrated in crystallization buffer (10 mM Tris pH 7.5, 0.2 mM EDTA, 25 mM NaCl, 3 mM freshly added DTT). PTP1B eluted as a single peak, with high purity per SDS-PAGE analysis. The purified PTP1B protein was then concentrated to 40 mg/mL and used for crystallization.
## Protein crystallization
The PTP1B crystallization conditions used here were similar to those described previously (Keedy et al., 2018). 40 mg/mL protein in crystallization buffer was mixed with well solution (0.1 M HEPES pH 7.5, 0.3 M magnesium acetate, $13.5\%$ PEG 8000, $2\%$ ethanol, and 1 mM beta-mercaptoethanol) and seed stock in a 135:135:30 nL protein:well:seed ratio. Glycerol was not included. Seed stocks were prepared using Hampton Seed Bead tools with previously grown crystals. Drops were set using a TTP Labtech Mosquito device in 96-well sitting-drop crystallization trays. For the single-crystal screen, both MiTeGen In-Situ-1 and MRC SwissCi trays were used. For the in situ crystallographic screen, MiTeGen In-Situ-1 trays were used. Crystals appeared within about 3 days, and grew to maximum size within about 1 week. Crystals grew to dimensions of approximately 100×20×20 μm3 up to approximately 500×100×100 μm3.
## Fragment selection
For the 1-xtal screen, we used fragments from the Maybridge 1000 fragment library (Maybridge Ro3 core set), the Edelris Keymical fragment library, and the Diamond Light Source in-house fragment library (DSPL) (Cox et al., 2016). For cryo-hits, we included 59 fragments that bound to several different sites of interest at cryo. For cryo-non-hits, we included 51 fragments that spanned the range of highly similar to dissimilar as compared to the previous cryo-hits.
For the in situ screen, we used fragments from the DSi-Poised (DSiP) library, which is a new version of the DSPL that contains many of the same fragments. For cryo-hits, we included all cryo-hits that were available in the DSiP library, as well as 12 cryo-hits we had previously purchased, for a total of 48 molecules. For cryo-non-hits, we included the 50 fragments in the DSiP library that were most similar to any previous cryo-hit. For both screens, similarity between fragments was assessed based on Tanimoto scores calculated using RDKit, 2022, topological fingerprints.
Some fragments that were cryo-non-hits in our original cryo screen (Keedy et al., 2018) were subsequently identified as cryo-hits using the new cluster4x method for computational clustering method (Ginn, 2020). Here, for both screens, we considered such fragments to be cryo-hits. This corresponded to three fragments for 1-xtal and one fragment for in situ. However, no RT-binding events were seen for any of these newly identified cryo-hits.
## Crystal soaking
For each screen, crystals were soaked with small-molecule fragments using an Echo acoustic droplet ejection liquid handler and a database mapping individual fragments to individual crystals, as described (Collins et al., 2017). For the in situ screen, anywhere from one to five wells were soaked with a given fragment, depending on the number of crystals per well.
Two strategies were used to confirm that fragments were successfully soaked into the crystallization drops. First, for both screens, log files for the acoustic droplet ejection device were inspected, and any wells with suspicious entries or errors were excluded. Second, for the in situ screen, optical images of the drops after soaking were visually inspected, and any wells that did not clearly feature a second adjacent drop corresponding to the fragment in DMSO were excluded.
## X-ray diffraction
For the 1-xtal screen, harvested crystals on size-matched nylon loops were enclosed in plastic capillaries containing ~10 µL of well solution and sealed with vacuum grease, and these samples were mounted onto the goniometer at Diamond Light Source beamline i03. Most datasets were collected with 180° of rotation over 1800 images with 0.1° oscillations with 0.05 s exposures. Some datasets near the end of the data collection shift were lowered to collect only 120° of crystal rotation, as smaller crystals sometimes did not appear to survive the full 180° dose. The X-ray beam was attenuated to $4.5\%$ transmission for a flux of ~4.5e11 ph/s with a 50×20 or 80×20 μm2 beam profile at a wavelength of 0.97625 Å. Temperature was controlled at 278 K using an Oxford Cryostream (800 Series).
For the in situ screen, crystallization trays were mounted onto the goniometer at Diamond Light Source beamline i24 for diffraction data collection. Partial datasets (wedges) were collected with up to 36° of rotation over 360 images with 0.1° oscillations with 0.03 s exposures. For each fragment, anywhere from 2 to 24 (average: 7) wedges were collected. In some cases, wedges for the same fragment derived from different crystals in the same well; in other cases, wedges for the same fragment derived from crystals in different wells soaked with the same fragment. The X-ray beam was attenuated to $1.5\%$ transmission for a flux of ~4.5e10 ph/s with a 50×50 μm2 beam profile at a wavelength of 0.96874 Å. Temperature was controlled by pointing a cryostream set to 277 K at the in situ tray mounted on the goniometer. Temperature was confirmed to be ~22°C (~295 K) by a handheld thermometer held by the tray.
Translational/vector data collection was not used for either screen. Whereas cryo datasets were previously named y#### (y for ‘cryo’), RT datasets here were named x#### for the in situ screen and z#### for the 1-xtal screen.
## X-ray data processing
For the 1-xtal screen, datasets were reduced using XDS (Kabsch, 2010). The frames that were used to process the datasets were manually chosen to exclude frames where the number of detected spots dipped below around 20, commonly due to the crystal rotating out of the beam, the crystal reaching the end of its lifetime due to radiation damage, or when the diffraction quality dropped as a result of the dimensions of the crystal. Multiple datasets were merged only if they derived from the same crystal. Resolution cutoffs were chosen to ensure the following statistics in the highest resolution bin: an <I/σ(I)> of 1.0 or higher, a completeness of $90\%$ or higher, and a CC$\frac{1}{2}$ of at least $50\%$. The resolutions of individual datasets were not held to be identical, and the cutoff for each dataset was chosen to be the point at which the reflections from the highest resolution bin made the statistics of that bin better, or kept the same for <I/σ(I)>, CC$\frac{1}{2}$ and completeness. Datasets shared a common set of Rfree flags and a common reference dataset to ensure consistent data indexing due to the space group of the crystal form, P 31 2 1. The final datasets were reasonably high resolution (Figure 1A).
For the in situ screen, individual wedges were first reduced using Dials (Winter et al., 2018). All frames were included. Resolution cutoffs for individual wedges were chosen automatically by Dials (Winter et al., 2018). Next, multiple wedges for the same fragment, regardless of crystal or well, were merged using xia2.multiplex (Gildea et al., 2021). In some cases the unit cell (89.6, 89.6, 106.2, 90, 90, 120) and/or the space group (P 31 2 1) was flagged in the xia2.multiplex input. Additionally, for some datasets the final merging step had to be done separately with dials.merge. For DMSO, anywhere from two to six wedges were merged. DMSO wedges were usually grouped by crystallization well, but in some cases were combined across wells to improve statistics. The final datasets were high-resolution (Figure 1B).
To check for global radiation damage, we used RADDOSE-3D to calculate the predicted average diffraction weighted dose (ADWD) for each dataset (Bury et al., 2018). For the in situ screen, predicted ADWD was ~0.03–0.04 MGy, depending on estimated crystal size, using the up-to-36° wedges. For the 1-xtal screen, predicted ADWD was ~3.2–7.4 MGy, depending on estimated crystal size and beam size, using the full 180° datasets. Thus the in situ data are well below the estimated RT limit of ~0.4 MGy (Fischer, 2021). The 1-xtal data are above the quoted RT limit (yet below the cryo limit of ~20–30 MGy; Owen et al., 2006); this was ameliorated for individual datasets by cutting later frames with reduced average intensities, as noted above. Additionally, we inspected 2Fo-Fc electron density maps for the individual 1-xtal RT hits featured here and observed no signatures of local radiation damage such as decarboxylation of Asp/Glu side chains, whether near the cryo and/or RT fragment-binding site(s) or elsewhere in the protein.
For an alternative data processing pipeline for the in situ data, the cluster4x algorithm (Ginn, 2020) was used to pre-cluster in situ wedges before merging with xia2.multiplex. First, the P 31 2 1 indexing hand for approximately half the wedges was changed using the Pointless (Evans, 2006) utility from CCP4 (Winn et al., 2011) to achieve consistency. Then, the wedges were clustered in real space. The resulting three clusters were partially overlapping in this space, and datasets were visually/manually assigned to these clusters. For each cluster, xia2.multiplex was used as described above, and separate PanDDa runs were performed as described below.
For each dataset, we used the Dimple utility from CCP4 (Winn et al., 2011) for phasing and initial refinement. Dimple was run with molecular replacement (flag: -M0) for the first dataset only, and only with downstream refinement steps (flag: -M1) for all other datasets. Additional flags were included to obtain a consistent set of Rfree reflections (--free-r-flags, --freecolumn R-free-flags). For both screens, the same structural model was used for Dimple, based on a high-resolution DMSO-soaked in situ merged dataset. This model reflects the predominant global open state of PTP1B, with the α7 helix unmodeled and the C-terminus of the α6 helix modeled with partial occupancy (Keedy et al., 2018).
## PanDDA modeling and refinement
For both the 1-xtal and in situ screens, PanDDA (Pearce et al., 2017a) version 0.2.14 was used. The pandda.analyse command was used with the minimum build datasets set to 20.
In addition to the automatic PanDDA analysis, for each dataset for which PanDDA did not show an event, we did a manual BDC scan from 1-BDC values of 0–0.9 as well as generating custom maps at the 1-BDC value that corresponded to the cryo 1-BDC. We saw five events with this manual inspection that PanDDA missed at the corresponding cryo 1-BDC. We used the automatically generated event maps throughout the manuscript, unless otherwise noted that a manually calculated event map is used.
Fragments and associated protein changes were modeled using pandda.inspect in Coot. Waters were kept the same between the unbound and bound models, except where the PanDDA event map indicated a shift, deletion, or an addition of a new water. Ligand restraints files were calculated with eLBOW (Moriarty et al., 2009). We aimed to keep the RT models similar to the cryo models except when the RT map argued otherwise, so that modeled differences were due to temperature.
For the in situ datasets in this manuscript, we report all hits derived from the all-wedges datasets, plus a small number of distinct hits from the pre-clustered datasets as noted where appropriate.
Because ligands are not fully occupied, to prepare for refinement we must use an ensemble of bound state plus unbound, that is, ground state for refinement (Pearce et al., 2017b). *We* generated such an ensemble model with pandda.export. We then added hydrogens with Phenix ReadySet! Restraints, both between multi-state occupancy groups and between local alternate locations, were generated using giant.make_restraint scripts from PanDDA 1.0.0. The argument ‘MAKE HOUT Yes’ was added to the Refmac restraint file to ensure the Hydrogens were preserved.
For refinement of fragment-bound ensemble models, the published protocol for post-PanDDA refinement for deposition (Pearce et al., 2017b) was used, including the giant.quick_refine scripts from PanDDA 1.0.0 and the program Refmac (Murshudov et al., 2011). For a few examples, the script was rerun if the ligand was refined to a total occupancy greater than 1. Additionally, some hydrogens refined to 0 occupancy so they were manually edited to match the remainder of its residue. Refined bound-state models were then re-extracted using giant.split_conformations.
In addition to fragment-bound models, a ground-state model was refined for each screen, using the model used for MR previously and the highest-resolution DMSO dataset per screen.
## Funding Information
This paper was supported by the following grants:
## Data availability
Bound state-models, structure factors, PanDDA event maps, and traditional maps (2Fo-Fc and Fo-Fc) for all fragment-bound structures are available in the Protein Data Bank under the following PDB ID accession codes: 7FQM, 7FQN, 7FQO, 7FQP, 7FQQ, 7FQR, 7FQS, 7FQT, 7FQU, 7FQV, 7FQW, 7FQX, 7FQY, 7FQZ, 7FRF, 7FRG, 7FRH, 7FRI, 7FRJ, 7FRK, 7FRL, 7FRM, 7FRN, 7FRO, 7FRP, 7FRQ, 7FRR. For each screen, a ground-state (unbound) model is also available, along with structure factors for all datasets involved in the respective screen, under the following PDB ID accession codes: 7FRE (1-xtal), 7FRS (in-situ), 7FRT (in-situ, cluster 1), 7FRU (in-situ, cluster 2). In addition, we provide a Zenodo directory containing our full PanDDA run directories, bound-state models, event maps, identifying information for all fragments, and related details at https://doi.org/10.5281/zenodo.7255364.
The following datasets were generated: MehlmanT BielJ AzeemSM NelsonER OrinsH HossainS DunnettLE TalonR AxfordD von DelftF KeedyDA PatersonNG DouangamathA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOPL000619a7FQM MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000497a7FQN MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000523a7FQO
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000505a7FQP MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000611a7FQQ MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000666a7FQR MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOOA000555a7FQS
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOMB000293a7FQT MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMSOA000470b7FQU MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with XST00000847b7FQV MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOCR000171b7FQW
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOPL000601a7FQX MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOPL000278a7FQY MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOMB000203a7FQZ MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with FMOPL000089a7FRF
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z312226417FRG MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z28564347627FRH MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z3213182267FRI MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z28564347707FRJ
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z308201607FRK MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z28564349177FRL MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z5097564727FRM MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z9154929907FRN
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z7447547227FRO MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with XST00000245b7FRP MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with XST00000217b7FRQ MehlmanT BielJ AzeemSM NelsonER HossainD DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA RCSB Protein Data Bank2022PanDDA analysis group deposition -- Crystal structure of PTP1B in complex with Z28564349067FRR
MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA 2022PanDDA analysis group deposition -- Crystal structure of PTP1B after initial refinement with no ligand modeledRCSB Protein Data Bank7FRE MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA 2022PanDDA analysis group deposition of ground-state model of PTP1BRCSB Protein Data Bank7FRS MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA 2022PanDDA analysis group deposition of ground-state model of PTP1B, using pre-clustering, cluster 1RCSB Protein Data Bank7FRT MehlmanT BielJ AzeemSM NelsonER HossainS DunnettLE PatersonNG DouangamathA TalonR AxfordD OrinsH von DelftF KeedyDA 2022PanDDA analysis group deposition of ground-state model of PTP1B, using pre-clustering, cluster 2RCSB Protein Data Bank7FRU
Skaist MehlmanT BielJT KeedyDA 2022PanDDA analysis of PTP1B re-screened against fragment libraries at RTZenodo10.5281/zenodo.7255364
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|
---
title: 'The Relationship between Pinguecula and Diabetes Mellitus: A Comparative Cross-Sectional
Study'
authors:
- Hisham M. Jammal
- Mohammed Abu-Ameerh
- Jamila G. Hiasat
- Sara Issa
- Muawyah Al Bdour
journal: Journal of Ophthalmology
year: 2023
pmcid: PMC9991467
doi: 10.1155/2023/9060495
license: CC BY 4.0
---
# The Relationship between Pinguecula and Diabetes Mellitus: A Comparative Cross-Sectional Study
## Abstract
### Purpose
To assess the relationship between diabetes mellitus (DM) and the presence of pinguecula and to identify other risk factors associated with pinguecula in patients attending the eye clinic at two tertiary university hospitals in Jordan.
### Methods
This was a comparative cross-sectional hospital-based study of 241 consecutive patients (122 patients with DM and 119 patients with no diabetes). All patients underwent complete ophthalmic examination, and data were collected regarding age, sex, occupational activity, presence and grade of pinguecula, glycosylated hemoglobin (HbA1c), and presence of diabetic retinopathy.
### Results
The mean (standard deviation, SD) ages of the DM and non-DM groups were 59.5 (10.8) years and 59.0 (11.6) years (p-value = 0.729), respectively. There was no significant difference in the prevalence of pinguecula between the diabetic and nondiabetic groups ($66.4\%$ vs. $66.5\%$, $$p \leq 0.998$$). Multivariate logistic regression analysis revealed that only outdoor occupational activity (OR = 5.16, $95\%$ CI: 1.98–13.44, $$p \leq 0.001$$) was associated with increased prevalence of pinguecula. DM was not significantly associated with pinguecula (OR = 0.96, $95\%$ confidence interval (CI): 0.55–1.67, $$p \leq 0.873$$). Neither age nor sex were significantly associated with pinguecula (p-value = 0.808, p-value = 0.390), respectively.
### Conclusion
DM was not significantly associated with the development of pinguecula in this Jordanian population. The prevalence of pinguecula was significantly associated with an outdoor occupational activity.
## 1. Introduction
Pinguecula is a common benign and degenerative condition of the bulbar conjunctiva that is characterized by an elevated yellowish lesion on either side of the cornea [1]. Histologically, the lesion demonstrates elastotic changes in collagen fibers resulting in the formation of basophilic subepithelial tissue [1]. The exact pathogenesis is still incompletely understood; most studies associate pinguecula with increasing age and sunlight exposure [2–6]. Other less clear associations include alcohol intake [4], dry eye syndrome [7], and contact lens wear [8, 9].
To further explore the molecular basis of the relation between ultraviolet exposure and pinguecula, Kaji et al. [ 10] detected significant amounts of advanced glycation end-products (AGEs) in pinguecula surgical specimens compared to specimens of conjunctiva without pinguecula; they attributed this to local ultraviolet (UV) irradiation and decreased antioxidant activities. Diabetes mellitus (DM) exerts systemic complications by producing AGEs [11] and may thus be a potential factor in the development of pinguecula. Some corneal manifestations of DM may be attributed to the accumulation of AGEs in corneal basement membranes [12]. Mimura et al. [ 13] suggested that diabetes may be associated with the development of pinguecula. To the best of our knowledge, no further studies were conducted to describe this association. This study aimed to assess the relationship between DM and pinguecula in patients attending the eye clinic at two tertiary university hospitals in Jordan.
## 2.1. Study Design
A comparative cross-sectional hospital-based study was conducted in a group of 122 consecutive patients with DM and a group of 119 patients with no DM, age-matched by group, who attended the ophthalmology clinic at two tertiary teaching hospitals in Jordan (Jordan University Hospital and King Abdullah University Hospital). All patients were Jordanians of Arab ethnicity.
In the DM group, DM was diagnosed if patients were currently treated with oral hypoglycemic agents, with or without insulin, and if they fulfilled the American Diabetic Association (ADA) Guidelines diagnostic criteria [14]. In the non-DM group, all patients had glycosylated hemoglobin (HbA1c) <$5.8\%$. Those with a history of ocular surgery involving the ocular surface or nasolacrimal apparatus, eyelid abnormalities (e.g., entropion and trichiasis), acute conjunctivitis, recent use of topical ophthalmic medication (e.g., steroids), and history of wearing contact lenses were excluded.
The study was approved by the Ethics Committee for Medical Research at the Jordan University Hospital and the University of Jordan. It adhered to the tenets of the Declaration of Helsinki. Informed consent for participation was obtained from all participants.
## 2.2. Data Collection
Demographic and clinical data including age, sex, occupational activity, and presence and duration of DM were collected using a structured data collection form. The outdoor occupational activity was considered if ≥4 hours of work was outdoors. All patients underwent a complete ophthalmic examination including visual acuity testing, slit lamp examination, and dilated fundus examination. Data on the presence, severity, laterality, and location of pinguecula in affected eyes, and the presence and severity of diabetic retinopathy were recorded.
Pinguecula was diagnosed when present in either eye. The severity of pinguecula was graded according to a scheme based on slit lamp examination findings (Table 1) [13].
## 2.3. Statistical Analysis
Data were analyzed using Statistical Package for Social Sciences (SPSS) version 23 (SPSS Inc., Chicago, IL, USA). Frequencies and percentages were calculated for categorical data and Pearson's chi-square test was used to assess the association between the presence of pinguecula and DM and other categorical variables. The independent samples t-test was used to compare means between the two groups. The association between DM (independent variable) and pinguecula (dependent variable) was analyzed using binary logistic regression to adjust for possible factors. A p-value of <0.05 was considered statistically significant.
## 3.1. Baseline Characteristics
There were 122 patients ($50.6\%$) with DM and 119 ($49.4\%$) without diabetes. The baseline characteristics of the two study groups are shown in Table 2. The mean (SD) ages of the DM and non-DM groups were 59.5 (10.8) years and 59.0 (11.6) years, which were not significantly different (p-value = 0.729). Differences in sex and outdoor/indoor occupation between the two groups were not significant (p-value = 0.219 and p-value = 0.423, respectively). The mean (SD) duration of DM in the DM group was 12.3 (7.1) years (range, 1–32 years).
## 3.2. Prevalence of Pinguecula
The prevalence of pinguecula among patients with and without DM were $66.4\%$ and $65.5\%$, respectively and were not significantly different (p-value = 0.998). In the DM group, there was no significant association between the presence of pinguecula and the stage of diabetic retinopathy ($$p \leq 0.285$$), the duration of DM ($$p \leq 0.647$$), or the level of HbA1c ($$p \leq 0.160$$).
Among patients with a pinguecula, no association between the presence of diabetes and severity or laterality (p-value = 0.094 and p-value = 0.313, respectively) of the pinguecula was found. Table 2 summarizes the differences between patients with DM and patients with no DM.
## 3.3. The Multivariate Analysis of the Association between DM and Pinguecula
In multivariate logistic regression, the association between DM and pinguecula was tested after adjusting for sex, occupational activity, and age (Table 3). After adjusting for important variables in the model, diabetes was not significantly associated with pinguecula (odds ratio (OR) = 0.96, $95\%$ confidence interval (CI): 0.55–1.67, $$p \leq 0.873$$). The only variable significantly associated with pinguecula was outdoor occupational activity (OR = 5.16, $95\%$ CI: 1.98–13.44, $$p \leq 0.001$$).
## 4. Discussion
Few studies assessed the prevalence and risk factors for pinguecula and found a wide range of 11.3–$75.6\%$ prevalence rates around the world, reflecting the heterogeneity in the study designs (population versus hospital-based), age groups, population characteristics (e.g., social, environmental, ethnic, and geographic factors) related to UV radiation levels [3, 4, 15–20].
None of the population-based studies on the prevalence of pinguecula that included DM in the assessment found a relationship between the two [3, 4, 15]. However, results from the hospital-based study by Mimura et al. [ 13] suggested that DM may be associated with the development of pinguecula, possibly due to deposition of AGEs, diabetic microvascular damage of conjunctival blood vessels, and diabetes-related eye dryness. We could not find a significant association between diabetes and the presence of pinguecula, nor between the severity of pinguecula and diabetes. The lack of association between pinguecula and duration of DM, HbA1c levels, and the stage of diabetic retinopathy among patients with DM in the current study may further support the lack of association between DM and pinguecula. However, the effect of ethnic and genetic differences between the two populations should not be ignored, as racial and ethnic differences in diabetic complications and comorbidities do exist globally [21].
Most of the literature on the prevalence of pinguecula reports that pinguecula is significantly associated with age and outdoor occupational activity, both of which may indicate chronic UV light exposure. Data from the present study revealed a $66\%$ prevalence of pinguecula among all study participants. This rate approximates those in Australia ($69.5\%$) [20] and in two studies from Iran ($56.2\%$ and $61.0\%$ in 40–59 years and 40–64 years age groups, respectively) [16, 19]. Interestingly, Norn [22] reported a much higher prevalence rate of $90\%$ in the Red Sea territory of Jordan among a sample of 127 subjects, aged ≥10 years. The included subjects resided in the Red Sea port of Aqaba in Jordan, known to have a higher solar UV index than the rest of the country, and nearly half of the study subjects were soldiers, who were more likely to spend time outdoors. In addition, Norn [22] found that the percentage of pinguecula increased within the 10-year age groups of 10, 20, 30, and 40 years but leveled out after 40 years, which is similar to the roughly constant prevalence rates over the 10-year age groups above 40 years found in our study. To explain this stability, it is possible that exposure to UV radiation in sunlight initiates the formation of pinguecula at younger ages which increases in prevalence over time. Then, at older ages, subjects who did not develop pinguecula earlier may continue to remain unaffected, as they become less exposed to sunlight owing to retirement, sedentary lifestyle, or preference for indoor occupations. A high level of UV exposure in childhood is a risk factor for high rates of UV-related ocular disease in adulthood, according to Seki et al. [ 23], who reported a pinguecula prevalence rate of $29.1\%$ among 227 children living in rural Tanzania.
In the current study, outdoor occupational activity was the only significant independent factor associated with pinguecula. This is consistent with findings from other studies [3, 4, 15, 19, 22]. The similar rates of pinguecula observed in countries located at different latitudes with different UV radiation levels indicate that environmental, ethnic, and genetic factors may interact with or modify the mechanism by which UV exposure contributes to the development of pinguecula.
In the present study, sex was not found to be significantly related to pinguecula after adjusting for other factors, contrary to most studies that found an association between the male sex and pinguecula [4, 16, 17, 19, 20]; we attributed this to how males tend to be more involved in outdoor occupations than females.
Some study limitations should be acknowledged. This was a hospital-based and cross-sectional study, which may suffer from selection bias. A longitudinal and cohort study design may help to better describe the association between DM and pinguecula. In addition, the use of sunglasses as protective measures against UV radiation was not evaluated.
In summary, no relationship between DM and pinguecula was found among Jordanians. The presence of pinguecula was associated with an outdoor occupational activity.
## Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
## Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
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|
---
title: The Impact of Complete Revascularization in Symptomatic Severe Left Ventricular
Dysfunction between Coronary Artery Bypass Graft and Percutaneous Coronary Intervention
authors:
- Hsiu-Yu Fang
- Yen-Nan Fang
- Yin-Chia Chen
- Jiunn-Jye Sheu
- Wei-Chieh Lee
journal: Cardiology Research and Practice
year: 2023
pmcid: PMC9991473
doi: 10.1155/2023/9226722
license: CC BY 4.0
---
# The Impact of Complete Revascularization in Symptomatic Severe Left Ventricular Dysfunction between Coronary Artery Bypass Graft and Percutaneous Coronary Intervention
## Abstract
### Objective
The study aimed to compare the clinical outcomes between the patients receiving coronary artery bypass surgery (CABG) or percutaneous coronary intervention (PCI) for the patients with symptomatic severe left ventricular (LV) dysfunction and coronary artery disease (CAD).
### Methods
Between February 2007 and February 2020, a total of 745 patients who received coronary artery angiography for reduced LV ejection fraction (LVEF) < $40\%$ and symptomatic New York Heart Association (NYHA) functional class ≥ 3 were recruited. The patients ($$n = 236$$) who were diagnosed with dilated cardiomyopathy or valvular heart disease without coronary artery stenosis, those with prior history of CABG or valvular surgery ($$n = 59$$), those who presented ST-segment elevated myocardial infarction (STEMI), those with a CAD and SYNTAX score of ≦ 22 ($$n = 175$$), those who received emergent CABG for coronary perforation ($$n = 3$$), and those who had NYHA class ≦ 2 ($$n = 65$$) were excluded. Finally, 116 patients with reduced LVEF and those who had a SYNTAX score >22, who received CABG ($$n = 47$$) and PCI ($$n = 69$$), were recruited for this study.
### Results
There was no significant difference in the incidence values of in-hospital course and those of in-hospital mortality, acute kidney injury, and postprocedural hemodialysis. There was no significant difference in the 1-yearfollow-up of recurrent MI, revascularization, or stroke between the groups. The 1-year heart failure (HF) hospitalization rate was significantly lower in the CABG group than in all patients of the PCI group ($13.2\%$ vs. $33.3\%$; $$p \leq 0.035$$); however, there was no significant difference in the same variable between the CABG group and the complete revascularization subgroup ($13.2\%$ vs. $28.2\%$; $$p \leq 0.160$$). The revascularization index (RI) was significantly higher in the CABG group than in all patients of the PCI group or complete revascularization subgroup (0.93 ± 0.12 vs. 0.71 ± 0.25; $p \leq 0.001$) and (0.93 ± 0.12 vs. 0.86 ± 0.13; $$p \leq 0.019$$). The 3-year HF hospitalization rate was significantly lower in the CABG group than in all patients of the PCI group ($16.2\%$ vs. $42.2\%$; $$p \leq 0.008$$); however, there was no difference in the same variable between the CABG group and the complete revascularization subgroup ($16.2\%$ vs. $35.1\%$; $$p \leq 0.109$$).
### Conclusions
In patients with symptomatic (NYHA class ≥ 3) severe LV dysfunction and CAD, CABG brought less HF admission when compared to patients in the PCI group, but this did not differ when compared to the complete revascularization subgroup. Therefore, an extensive revascularization, achieved by CABG or PCI, is associated with a lower HF hospitalization rate during the 3-yearfollow-up period in such populations.
## 1. Introduction
Coronary artery disease (CAD) is one of the leading causes of severe left ventricular (LV) dysfunction and mortality in recent years [1–3]. With the improvement of healthcare in CAD, the treatment strategies are changing day after day [4]. According to the reports from the American Heart Association, both the annual rate of death due to CAD in the USA and the incidence of LV dysfunction caused by CAD were increasing [5]. This fact made us pay more attention to this combination of severe LV dysfunction and CAD.
Current American and European guidelines do not provide precise recommendations for revascularization strategies in patients with CAD and severe LV dysfunction [6, 7]. Nevertheless, the exclusion of patients with severe LV dysfunction from the clinical trial made the optimal revascularization of these patients still controversial [8–10]. Some cohort studies had concluded that coronary artery bypass surgery (CABG) was associated with a lower mortality rate and a lower major adverse cardiac event (MACE) rate than that with percutaneous coronary intervention (PCI) in patients with severe LV dysfunction CAD [11, 12]. However, the evidence was not strong enough to conclude the result of revascularization strategies. Recently, one meta-analysis showed that among the patients with severe LV dysfunction, CABG resulted in a lower mortality rate and an increased risk of stroke [13]. Surprisingly, none of these cohort studies or meta-analyses stressed on patients' clinical conditions such as hemodynamic status, New York Heart Association (NYHA) functional class, or SYNTAX score. This retrospective study aimed to explore the optimal strategy for patients with symptomatic severe LV dysfunction with NYHA class ≥3 and CAD.
## 2. Materials and Methods
The study population belonged to the HF registry of Kaohsiung Chang Gung Memorial Hospital. [ 14] This study was approved by the ethical committees of the institutional review board of Chang Gung Memorial Hospital (202100931B0) and the National Institution of Health of Taiwan.
## 2.1. Patients and Groups
We retrospectively enrolled 745 patients who received coronary artery angiography for reduced LV ejection fraction (LVEF) < $40\%$ between February 2007 and February 2020 (Figure 1). The patients ($$n = 236$$) who were diagnosed with dilated cardiomyopathy or valvular heart disease without coronary artery stenosis, those with prior history of CABG or valvular surgery ($$n = 59$$), those with ST-segment elevated myocardial infarction, those with CAD and SYNTAX scores of ≦22 ($$n = 175$$), those who underwent emergency CABG for coronary perforation ($$n = 3$$), and those with NYHA class ≦2 ($$n = 65$$) were excluded. There were 116 patients with reduced LVEF and SYNTAX scores of >22 who received CABG ($$n = 47$$) and PCI ($$n = 69$$) who were enrolled into this study. The Institutional Review Committee on Human Research at our institution approved the study protocol.
## 2.2. Definitions
Our myocardial infarction (MI) definitions followed the most recent universal definition of MI [15]. Symptomatic heart failure (HF) was defined, according to the NYHA classification, as being in a class of ≥3. Revascularization was defined as any repeat PCI in a target vessel or coronary artery bypass graft (CABG) in a target vessel for lesions with a stenosis of ≧$70\%$. Cardiovascular (CV) mortality was defined as death due to MI, cardiac arrhythmia, or HF. All-cause mortality was defined as death from any cause. Recurrent MI was defined as acute MI occurring 1 month after the index MI. Complete revascularization was defined as the absence of any angiographic significant stenosis (>$70\%$) in all three epicardial coronary arteries with a diameter of at least 2.5 mm after PCI.
## 2.3. Study Endpoints
The primary composite endpoints of our study were any recurrent MIs, revascularization, sudden death/ventricular arrhythmia, HF hospitalization, stroke or CV mortalities, and all-cause mortality rate during the 1- and 3-yearfollow-up periods. The secondary composite endpoints included all in-hospital events, such as in-hospital mortality, acute kidney injury, and postprocedural hemodialysis (HD).
## 2.4. Statistical Analysis
Data were expressed as the mean ± standard deviation for continuous variables or as counts and percentages for categorical variables. Continuous variables were compared using the independent sample t-test or the Mann–Whitney U test. Categorical variables were compared using the chi-square test. Univariate and multivariate cox regression analyses were performed to identify the associations relating to the 1-year CV mortality rate. Correlations between variables were expressed as hazard ratios with $95\%$ confidence intervals. The Kaplan–Meier curves were created to illustrate the 1-year CV mortality data in each of the groups. All statistical analyses were performed using SPSS 22.0 (IBM. Corp., Armonk, NY). A p value of <0.05 was considered statistically significant.
## 3.1. Baseline Characteristics
Table 1 presents the baseline characteristics of the study participants. There were no significant differences between the two groups in terms of demographic characteristics, including age, sex, serum creatinine level, and body mass index. There was also no significant difference in the rates of comorbidities, such as hypertension, diabetes mellitus, peripheral artery occlusive disease, chronic obstructive pulmonary disease, current smoking status, chronic kidney disease more than stage 3, and a prior MI history of >90 days between the two groups. The proportion of participants in the CABG group with the end-stage renal disease was significantly higher than that in all patients in the PCI group ($21.3\%$ vs. $7.2\%$; $$p \leq 0.046$$); however, the difference in the same parameter between the CABG group and the complete revascularization subgroup was not statistically significant ($21.3\%$ vs. $11.9\%$; $$p \leq 0.270$$). There were also significantly more patients with a prior history of PCI in the CABG group than in all patients in the PCI group ($55.3\%$ vs. $13.0\%$; $p \leq 0.001$) and the complete revascularization subgroup ($55.3\%$ vs. $11.9\%$; $p \leq 0.001$). The major clinical presentation was acute coronary syndrome ($89.4\%$ vs. $76.8\%$ vs. $85.7\%$) other than stable angina or HF.
The surgical risk and coronary complexity parameters such as the New EuroScore II were higher in the CABG group than in all patients of the PCI group or the complete revascularization subgroup (9.2 ± 7.2 vs. 3.6 ± 2.0; $p \leq 0.001$) or (9.2 ± 7.2 vs. 3.5 ± 1.7; $p \leq 0.001$). The SYNTAX score was also higher in the CABG group than in all patients of the PCI group or the complete revascularization subgroup (40.4 ± 10.5 vs. 35.9 ± 8.2; $$p \leq 0.010$$) or (40.4 ± 10.5 vs. 35.4 ± 8.0; $$p \leq 0.014$$). Further analysis revealed no significant difference in coronary complexity parameters such as SYNTAX score > 33, left main disease, multivessel disease, two-vessel disease, three-vessel disease, or chronic total occlusion (CTO) between the groups.
The complete revascularization parameter was assessed by the British Cardiovascular Intervention Society's myocardial jeopardy score (BCIS-JS) [16, 17] before and after PCI. The revascularization index was calculated by (preBCIS-JS-postBCIS-JS)/(preBCIS-JS). The BCIS-JS before the procedure was also significantly higher in the CABG group than in all patients of the PCI group or the complete revascularization subgroup (11.8 ± 0.6 vs. 10.6 ± 1.7; $p \leq 0.001$) and (11.8 ± 0.6 vs. 10.4 ± 3.0; $p \leq 0.001$). The BCIS-JS after the procedure was also significantly higher in the CABG group than in all patients of the PCI group (0.9 ± 0.4 vs. 3.1 ± 2.8; $p \leq 0.001$), but no significant difference was observed in the CABG group and the complete revascularization subgroup (0.9 ± 0.4 vs. 1.4 ± 1.3; $$p \leq 0.064$$). Both the revascularization index (RI) was higher than 0.67 in all groups but still significantly higher in the CABG group than in all patients of the PCI group or the complete revascularization subgroup (0.93 ± 0.12 vs. 0.71 ± 0.25; $p \leq 0.001$) and (0.93 ± 0.12 vs. 0.86 ± 0.13; $$p \leq 0.019$$).
There was also no significant difference in the need for mechanical support before, during, and after the procedure ($34.0\%$ vs. $36.2\%$, $$p \leq 0.845$$; $34.0\%$ vs. $31.0\%$, $$p \leq 0.823$$). Most of the timing of mechanical support was before the procedure and maintained 24 hours after the procedure. The type of mechanical support such as extracorporeal membrane oxygenation (ECMO) ($0\%$ vs. $4.3\%$, $$p \leq 0.271$$; $0\%$ vs. $7.1\%$, $$p \leq 0.101$$) and intraaortic balloon pump (IABP) ($34.0\%$ vs. $36.2\%$, $$p \leq 0.845$$, $34.0\%$ vs. $31.0\%$, $$p \leq 0.823$$) did not show differences in both the groups. There was no Impella used in the study groups.
There was no significant difference in the rate of use of ACEI/ARB/ARNi and MRA between the groups. However, the rate of use of beta-blockers was significantly lower in the CABG group than in all patients of the PCI group ($62.2\%$ vs. $85.3\%$; $$p \leq 0.007$$) or the complete revascularization subgroup ($62.2\%$ vs. $87.8\%$; $$p \leq 0.007$$). Also, the rate of use of furosemide was also significantly lower in the CABG group than in all patients of the PCI group ($31.9\%$ vs. $59.4\%$; $$p \leq 0.005$$) or the complete revascularization subgroup ($31.9\%$ vs. $50.0\%$; $$p \leq 0.090$$). There was no significant difference in the mean follow-up duration between the groups.
## 3.2. Echocardiographic Parameters
Table 2 demonstrates the echocardiography parameters at the baseline and follow-up. There were no significant differences in baseline echocardiography parameters such as LA dimension, LVEF, LVEF < $30\%$, LVEDV, LVESV, AR grade > 2, MR grade > 2, TR grade > 2, and TRPG between the CABG group and all patients of the PCI group. Among the follow-up echocardiography parameters, there were no significant differences in LA dimension, LVEF, LVEF > $50\%$, LVEF > $40\%$, LVEDV, LVESV, AR grade > 2, MR grade > 2, TR grade > 2, and TRPG between the CABG group and all patients in the PCI group. There were also no significant differences in a reduction in LVEDV of > $10\%$ or a reduction in LVESV of > $10\%$ between the CABG group and in all patients of the PCI group. There were no significant differences in the improvements in LVEF or mean LVEF > $10\%$ between the CABG group and in all patients of the PCI group and the complete revascularization subgroup (14.7 ± 15.5 vs. 15.8 ± 17.3 vs. 18.9 ± 16.6) and ($62.8\%$ vs. $64.3\%$ vs. $69.7\%$).
## 3.3. Clinical Outcomes
Table 3 illustrates the long-term clinical outcomes of groups. There were no significant differences in the incidences of in-hospital course and in-hospital mortality, acute kidney injury (AKI), and postprocedural HD. There were no differences in primary composite endpoints at 1-year or 3-yearfollow-up comparing CABG vs. all patients in the PCI group and the complete revascularization subgroup ($29.8\%$ vs. $42.0\%$, $$p \leq 0.240$$; $29.8\%$ vs. $40.5\%$, $$p \leq 0.374$$) ($42.6\%$ vs. $53.6\%$, $$p \leq 0.262$$; $42.6\%$ vs. $52.4\%$, $$p \leq 0.399$$). There was no statistically significant difference in the 1-yearfollow-up between the groups of recurrent MI, revascularization, or stroke, and they were similar; however, the 1-year HF hospitalization rate was significantly lower in the CABG group than in all patients of the PCI group ($13.2\%$ vs. $33.3\%$; $$p \leq 0.035$$); however, the difference in the same parameter between the CABG group and the complete revascularization subgroup was not statistically significant ($13.2\%$ vs. $28.2\%$; $$p \leq 0.160$$). The incidences of sudden death/ventricular arrhythmia, CV mortality, and all-cause mortality were higher in the CABG group; however, the differences were not statistically significant.
There was no statistically significant difference in the 3-yearfollow-up between the recurrent MI, revascularization, and stroke groups. The 3-year HF hospitalization rate was significantly lower in the CABG group than in all patients of the PCI group ($16.2\%$ vs. $42.2\%$; $$p \leq 0.008$$); however, the difference in the same parameter between the CABG group and the complete revascularization subgroup was not statistically significant ($16.2\%$ vs. $35.1\%$; $$p \leq 0.109$$). The incidences of sudden death/ventricular arrhythmia, CV mortality, and all-cause mortality were higher in the CABG group, although the differences were not statistically significant.
## 3.4. Kaplan–Meier Curves Comparing the 1- and 3-YearAll-Cause Mortality Rates between CABG and PCI
Figure 2 shows the Kaplan–Meier curve illustrating the differences in the 1- and 3-yearall-cause mortality rates between the groups. There was no significant difference in the 1-yearall-cause mortality rate between the CABG group and in all patients of the PCI group ($$p \leq 0.197$$) and between the CABG group and the complete revascularization subgroup ($$p \leq 0.631$$). There was no significant difference in the 3-yearall-cause mortality rate between the CABG group and in all patients of the PCI group ($$p \leq 0.298$$) and between the CABG group and the complete revascularization subgroup ($$p \leq 0.741$$).
## 3.5. Kaplan–Meier Curves Comparing 1- and 3-Year HF Hospitalization Rates between CABG and PCI
Figure 3 shows the Kaplan–Meier curve illustrating the difference in the 1- and 3-year HF hospitalization rates between the groups. There was some trend lower but without a statistically significant difference in the 1-year HF hospitalization rate between the CABG group and in all patients of the PCI group ($$p \leq 0.061$$) or between the CABG group and the complete revascularization subgroup ($$p \leq 0.129$$). There was also some trend lower but without a statistically significant difference in the 3-year HF hospitalization rate between the CABG group and in all patients of the PCI group ($$p \leq 0.050$$) and between the CABG group and the complete revascularization subgroup ($$p \leq 0.144$$).
## 4. Discussion
The most common cause of severe LV dysfunction is coronary artery disease. Despite numerous studies made, atherogenesis and its progression leading to endothelial injury remain multifactorial which include smoking, hypertensive trigger, and oxidative stress. One of the most definite progressions of atherosclerosis is coronary artery calcification. Coronary artery calcification has been used for staging coronary atherosclerosis. The mechanism of coronary artery calcification may lie in the release of apoptotic/necrotic bodies, the release of matrix vesicles, and the differentiation of pericytes during the plaque calcification process [18]. Survivors after adequate treatment of acute coronary syndrome and myocardial infarction may develop severe LV dysfunction due to irreversible myocyte loss with scar formation, hibernating myocardium, or adverse cardiac remodeling [19].
The meta-analysis results of patients with severe LV dysfunction and CAD who were treated with CABG had a higher risk of stroke during the short-termfollow-up; however, they had a lower risk of death, MI, and repeat revascularization than those treated with PCI during the long-termfollow-up [13]. Randomized controlled trials on revascularization strategies in patients with severe LV dysfunction CAD are rare, except for the Heart Failure Revascularization Trial [20] and Surgical Treatment for Ischemic Heart Failure Extension Study [21]. Both of these randomized controlled trials were not set up by comparing CABG and PCI in patients with severe LV dysfunction. Conversely, most randomized trials only compared CABG with PCI in patients whose LV function was not severely depressed.
The NYHA classification has served as a fundamental tool for the risk stratification of HF and for determining the clinical trial eligibility and candidacy for devices and drugs. Recently, one systemic review showed that the NYHA system poorly discriminated against patients with HF across the spectrum of functional impairment [22]. However, other studies showed that the NYHA classification, along with other comorbidities, still might be helpful in identifying a subgroup of implantable cardioverter defibrillator carriers with poor prognoses and a higher risk of CV death [23]. To the best of our knowledge, there is no existing study that compares CABG and PCI in patients with symptomatic severe LV dysfunction CAD.
Current evidence from randomized trials suggests that the risk of mortality and MI does not differ significantly among various revascularization strategies for multivessel CAD. A meta-analysis showed that complete revascularization of multivessel CAD was associated with a reduction in the rate of MACE due to the reduction in emergency revascularization [24]. However, the importance of complete revascularization had been addressed only to reduce repeat revascularization and subsequent major adverse cardiac and cerebral events in patients with LV dysfunction but not to reduce the long-term mortality rate [25]. One of the most important reasons that complete revascularization cannot achieve long-term clinical outcome improvement is the variety of complete revascularization definitions. In the COMPLETE trial [26], the definition of complete revascularization was on the completeness of nonculprit lesion PCI which is defined as the vessel which is larger than 2.5 mm with angiographic more than $70\%$ stenosis or with 50 to $69\%$ stenosis but a fractional flow reserve (FFR) of less than 0.80. The DANAMI-3-PRIMULTI trial [27] also defined complete revascularization as revascularization of all coronary lesions which are greater than $50\%$ in diameter stenosis and larger than 2.0 mm in diameter which was confirmed by FFR. Another reason that complete revascularization cannot achieve long-term clinical outcome improvement is because the extensive revascularization is not well defined. The British Cardiovascular Intervention Society's myocardial jeopardy score (BCIS-JS) was developed to define the completeness of coronary revascularization [16]. Revascularization index (RI) was defined as (pre-BCIS-JS-postBCIS-JS)/(pre-BCIS-JS) which showed that RI < 0.67 is associated with a higher mortality rate. In our study, the RIs were significantly lower in all patients of the PCI group and the complete revascularization subgroup compared with the CABG group suggesting that the degree of extensive revascularization is still different. In our study, the long-term mortality rate did not differ significantly in CABG, all patients in the PCI group, or the complete revascularization subgroup, and even in patients with symptomatic (NYHA class more than 3) and severe LV dysfunction who had less HF admission due to the differences in complete revascularization.
Elective insertion of the hemodynamic support device in complex high-risk indicated patients (CHIP) may be reasonable as an adjunct to PCI or CABG which was a class IIb recommendation according to guidelines [28]. BCIS-1 study showed that in patients with severe ischemic cardiomyopathy treated with PCI, elective IABP use during PCI was associated with a $34\%$ relative reduction in all-cause mortality compared with unsupported PCI [29]. The evidence of prophylactic ECMO in CHIP PCI was insufficient which also showed benefits in case series results [30, 31]. PROTECT II study showed that in 452 symptomatic patients with complex three-vessel coronary artery disease or unprotected left main coronary artery disease and severe depressed left ventricular function underwent nonemergent PCI, although the 30-day incidence of major adverse events was not different between IABP and Impella 2.5, but improved outcomes were observed for Impella 2.5 group at 90 days with significant maintenance of mean arterial pressure during PCI [32, 33]. Roma-Verona registry also demonstrated that the Impella-protected PCI could achieve more complete revascularization with a higher revascularization index with significant improvement in the left ventricular ejection fraction and survival [34]. The Impella-supported intervention also showed its benefit not only in CHIP PCI but also in cardiogenic shock patients in the IMP-IT registry [35]. This evidence provides us more confidence in extensive revascularization under mechanical support such as IABP, ECMO, or Impella. However, bleeding complications under mechanical support are common. One of the most leading causes of bleeding complications was the cleavage of von Willebrand factor (VWF) when using ECMO or left ventricular assist devices (LVADs). The basic VWF monomer contains 2050 amino acids making the protein sensitive to changes in fluid flow and shear stress. LVADs have been associated with an increased bleeding risk, commonly attributed to hydrodynamic changes and shear stress induced by continuous flow across the devices, which augmented VWF cleavage also seen in severe LV dysfunction patients [36]. These made one-stage surgery such as CABG instead of prolonging the use of mechanical support devices more suitable for clinical decision-making [37].
## 4.1. Limitations
Our study had several limitations. First, we enrolled patients from 2007 to 2020 due to the limited number of patients, which represented selection bias. Second, the PCI and CABG were all operator-dependent procedures that influenced the true clinical outcome. Third, there was no analysis between drug-eluting stents or bare-metal stents in the PCI group.
## 5. Conclusions
In patients with symptomatic (NYHA class ≥ 3) severe LV dysfunction and CAD, CABG brought less HF admission when compared to patients in the PCI group but did not differ when compared to the complete revascularization subgroup. Therefore, extensive revascularization, achieved by CABG or PCI, is associated with a lower HF hospitalization rate during a 3-yearfollow-up period in such populations.
## Data Availability
The data used to support the findings of the study can be obtained from the corresponding author upon request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Hsiu-Yu Fang wrote the manuscript. Yin-Chia Chen, Yen-Nan Fang, Jiunn-Jye Sheu, and Wei-Chieh Lee collected the data. Wei-Chieh Lee wrote the final manuscript. Jiunn-Jye Sheu and Wei-Chieh Lee equally contributed to the study.
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|
---
title: Trimethylamine-N-Oxide Promotes High-Glucose-Induced Dysfunction and NLRP3
Inflammasome Activation in Retinal Microvascular Endothelial Cells
authors:
- Lidan Xue
- Lili Huang
- Yajing Tian
- Xin Cao
- Yu Song
journal: Journal of Ophthalmology
year: 2023
pmcid: PMC9991475
doi: 10.1155/2023/8224752
license: CC BY 4.0
---
# Trimethylamine-N-Oxide Promotes High-Glucose-Induced Dysfunction and NLRP3 Inflammasome Activation in Retinal Microvascular Endothelial Cells
## Abstract
### Introduction
Along with blood glucose levels, diabetic retinopathy (DR) development also involves endogenous risk factors, such as trimethylamine-N-oxide (TMAO), a product of intestinal flora metabolic disorder, which exacerbates diabetic microvascular complications. However, the effect of TMAO on retinal cells under high-glucose conditions remains unclear. Therefore, this study examined the effects of TMAO on high-glucose-induced retinal dysfunction in the context of NLRP3 inflammasome activation, which is involved in DR.
### Materials and Methods
TMAO was assessed in the serum and aqueous humor of patients using ELISA. Human retinal microvascular endothelial cells (HRMECs) were treated for 72 h as follows: NG (normal glucose, D-glucose 5.5 mM), NG + TMAO (5 μM), HG (high glucose, D-glucose 30 mM), and HG + TMAO (5 μM). The CCK8 assay was then used to assess cell proliferation; wound healing, cell migration, and tube formation assays were used to verify changes in cell phenotype. ZO-1 expression was determined using immunofluorescence and western blotting. Reactive oxygen species (ROS) formation was assessed using DCFH-DA. NLRP3 inflammasome complex activation was determined using a western blot.
### Results
The serum and aqueous humor from patients with PDR contained higher levels of TMAO compared to patients with nontype 2 diabetes (Control), non-DR (NDR), and non-PDR (NPDR). TMAO showed significant acceleration of high-glucose-induced cell proliferation, wound healing, cell migration, and tube formation. ZO-1 expression decreased remarkably with the combined action of TMAO and a high glucose compared to either treatment alone. TMAO also promoted high-glucose-activated NLRP3 inflammasome complex.
### Conclusion
The combination of TMAO and high-glucose results in increased levels of ROS and NLRP3 inflammasome complex activation in HRMECs, leading to exacerbated retinal dysfunction and barrier failure. Thus, TMAO can accelerate PDR occurrence and development, thus indicating the need for early fundus monitoring in diabetic patients with intestinal flora disorders.
## 1. Introduction
Diabetic retinopathy (DR) is a common microvascular disorder complicated by diabetes and is now the leading cause of blindness and visual impairment among the working-age population [1]. With DR progression, it can be divided into nonproliferative DR (NPDR) and proliferative DR (PDR). NPDR usually demonstrates microaneurysms, intraretinal microvascular abnormalities (IRMA), increased vessel permeability, and hard and soft exudations. Blood flow changes, loss of pericytes, broken tight junctions of endothelial cells, and pachynsis of capillaries in the endothelial basement membrane form the pathological basis of NPDR [2]. The core process of PDR is neovascularization, with gradual ischemia and hypoxia [3]. Oxidative stress and inflammatory reactions play significant roles in pathological processes throughout the development of diabetic retinal vasculopathy [4]. Anti-VEGF targeting has been widely used as an effective intervention in patients with PDR and diabetic macular edema. Studies on the Joslin 50-year Medalist cohort, comprising patients suffering from insulin-dependent diabetes for 50 years or more, have indicated no significant correlation between the severity of diabetic retinopathy and the level of blood glucose control [5]. This suggests that endogenous risk factors and protective factors other than glucose levels may be involved in the occurrence and development of DR.
Trimethylamine N-oxide (TMAO) is an amine oxide that can be induced by gut dysbiosis [6]. Choline, betaine, and carnitine, which originate from animal dietary components, are converted into trimethylamine (TMA) under the influence of the gut microbiota [7]. TMA is transported into the liver through the portal circulation, where it is oxidized to TMAO by flavin-containing monooxygenase 3 (FMO3) [8]. Several studies have shown that most cardiovascular diseases (CVD) and renal diseases are closely related to TMAO [9]. A recent review indicated that gut dysbiosis and the development of type 2 diabetes (T2DM) as well as the related retinal, neurological, and renal microvascular complications went hand in hand [10]. TMAO might play a key role in diabetic cardiomyopathy (DCM) through the pathways of inflammation, connexin remodeling, the increase of calcium ions (Ca2+), myocardial fibrosis, and so on [11, 12]. In addition, TMAO could activate renal inflammation, oxidative stress, fibrosis, and endothelial dysfunction in the pathogenesis of diabetic kidney disease (DKD) [13, 14]. A clinical study demonstrated that elevated TMAO concentrations in plasma were associated with increased incidence and severity of DR in T2DM [15]. However, the specific mechanism of TMAO as a risk factor for DR and its effect on retinal cells under the action of high glucose remain unclear.
The NLRP3 inflammasome has been found to be upregulated in the retinal proliferative membranes of PDR patients as well as in in vitro and in vivo DR models [16]. Once inflammatory stimuli are sensed, activated NLRP3 binds the adaptor protein ASC, which contains a pyrin domain (PYD) and caspase recruitment domain (CARD), to recruit and cleave caspase-1. This triggers the downstream reaction, resulting in interleukin-1β (IL-1β) release [17]. Considering the role of TMAO as a risk element in DR, it is important to examine whether TMAO can enhance NLRP3 inflammasome activation in DR. In hyperglycemia, dysfunction and activation of the NLRP3 inflammasome are found in retinal microvascular endothelial cells (RMECs) [18].
Therefore, in this study, we examined the mechanism by which TMAO is involved in the dysfunction and activation of NLRP3 inflammasomes in RMECs under a high-glucose environment.
## 2.1. Serum and Aqueous Humor Sampling and Quantitative TMAO Analyses
Participants with or without type 2 diabetes who had cataract extraction and participants with PDR and type 2 diabetes who underwent vitrectomy at the Second Affiliated Hospital of Nantong University were recruited. This study followed the guidelines of the Declaration of Helsinki. Sampling was carried out with the informed consent of patients and approval from the hospital ethics committee. The samples were stored at −80°C. The concentrations of TMAO in serum and aqueous humor samples were determined using a human TMAO ELISA kit (JINGME, JiangSu, China), according to the manufacturer's protocol; the results were determined at 450 nm using a microplate reader (BIOTEK, USA). The TMAO concentrations were calculated based on standard concentrations and expressed as ppm (mg/L).
## 2.2. Cell Culture and Treatments
A human retinal microvascular endothelial cell line (HRMEC) was purchased from the BeNa Culture Collection (Beina Chuanglian Biotechnology Institute, Beijing, China). Cells (passages 3–6) were cultured in D-glucose (5.5 mM)-containing DMEM (HyClone, UTAH, USA) containing $5\%$ fetal bovine serum (FBS) at 37°C ($95\%$ air, $5\%$ CO2). When the cells reached confluence after seeding, the medium was replaced as follows: NG (Normal Glucose, D-glucose 5.5 mM), OSM (Osmotic, Mannitol 24.5 mM, and D-glucose 5.5 mM), NG (Normal Glucose, D-glucose 5.5 mM) + TMAO (5 μM, Sigma, Japan), HG (High Glucose, D-glucose 30 mM), and HG (High Glucose, D-glucose 30 mM) + TMAO (5 μM) in DMEM with $5\%$ FBS for 72 h before the experiments.
## 2.3. CCK8 Assay
HRMECs were seeded into 96-wells plates (5000 cells/well) in D-glucose (5.5 mM) DMEM medium with $5\%$ FBS for 24 hours, and then treated with D-glucose (5.5 mM) + TMAO (0 μM), D-glucose (30 mM)) + TMAO (0 μM), D-glucose (30 mM) + TMAO (1.25 μM), D-glucose (30 mM) + TMAO (2.5 μM), D-glucose (30 mM) + TMAO (5 μM), D-glucose (30 mM) + TMAO (10 μM), D-glucose (30 mM) + TMAO (20 μM), D-glucose (30 mM) + TMAO (50 μM), and D-glucose (30 mM) + TMAO (100 μM). At the indicated time points (24 h, 48 h, and 72 h), CCK-8 solution (10 μL/well, Proteintech, IL, USA) was added to the wells and incubated at 37°C for 2 h. Absorbance was detected at a wavelength of 450 nm using a microplate reader.
## 2.4. ROS Assessment
HRMECs were seeded into a 6-well plate and treated as needed. Cells were then loaded with DCFH-DA (10 μM, Beyotime Biotech, Shanghai, China) in serum-free medium and incubated for 20 minutes at 37°C. After washing 3 times with phosphate buffered saline, the images were captured under an inverted fluorescence microscope (ECLIPSE Ti2, NIKON, Japan). Fluorescent intensity was analyzed using Image J software.
## 2.5. Cell Migration and Healing Assays
For migration assays, migration chambers were placed in a 24-well plate and pretreated HRMECs were seeded into the upper chamber. Then, $4\%$ paraformaldehyde was used to fix the cells, and they were incubated for 12 h. Cells were then stained with crystal violet and counted under a microscope as follows: For wound healing assays, HRMECs were incubated on a 6-well plate until confluence and then scratched with sterile 200 μL pipette tips. The wells were then viewed under an inverted microscope (TS2, NIKON, Japan) and the wound healing percentage was calculated after treatment for 24 h, 48 h, and 72 h. The scratch areas were calculated by Image J software, and then the wound healing areas were obtained by calculating the D-value between the scratch areas of 24, 48, and 72 hours and the initial scratch area of this group. The wound healing percentages were the ratios of the wound healing areas to the initial scratch area of the group.
## 2.6. Tube Formation Assay
Matrigel (356234, Corning, NY, USA) was used to coat a 96-well plate (50 μL/L), which was subsequently incubated at 37°C for 45 minutes to solidify. Pretreated cell suspensions were then seeded on the solidified Matrigel (20,000 cells/well). After incubation at 37°C for 6 h, the tube formation of HRMECs was observed using an inverted microscope. The tube length was analyzed using Image J software.
## 2.7. Immunofluorescence Analysis for ZO-1
After treatment, HRMECs were fixed using $4\%$ paraformaldehyde for 15 minutes and then blocked with $1\%$ BSA in PBS for 2 h at 37°C. The cells were then incubated overnight at 4°C with anti-ZO-1 (1: 400; Cell Signaling), followed by incubation with red-labeled antirabbit secondary antibody(1: 500, Invitrogen, CA, USA) for 2 h at room temperature. The cells were sealed with antifade mounting medium containing DAPI (Beyotime Biotech, Shanghai, China) and scanned under a fluorescence microscope (ECLIPSE NI-E, NIKON, Japan). Fluorescent intensity was analyzed using Image J software.
## 2.8. Western Blot
Treated cells were lysed in RIPA containing protease inhibitor (PMSF, 1: 100) on ice for 30 minutes. The cell lysate was then centrifuged at 12000 rpm at 4°C for 15 min. The supernatant was then transferred to new centrifuge tubes. The protein concentrations were determined using a BCA kit (Beyotime Biotech, Shanghai, China); equal amounts (40 μg) of protein samples were separated using SDS-PAGE and then transferred to polyvinylidene fluoride (PVDF) membranes. The membranes were blocked with $5\%$ skimmed milk powder in TBST for 2 h. The membranes were probed overnight at 4°C with anti-NLPR3 (1: 1000, Abcam, UK), anti-ASC (1: 1000, Proteintech, IL, USA), anti-Caspase1 (1: 1000, Proteintech, IL, USA), and anti-IL-1β antibodies (1: 1000, Proteintech, IL, USA). Further, the membranes were incubated with HRP-conjugated secondary antibodies(1: 2000, Proteintech, IL, USA) for 2 h at room temperature. The antibody binding was detected using the enhanced chemiluminescence (ECL) detection kit (Beyotime Biotech, Shanghai, China).
## 2.9. Statistical Analysis
Statistical analysis was performed using GraphPad Prism version 8.0 and SPSS 23. The data are presented as mean ± standard deviation (SD). A one-way ANOVA followed by Tukey's post hoc test was used to examine the significant differences between and within different groups. The Kruskal−Wallis test was used for nonnormally distributed data. To compare the differences in clinical characteristics between the four groups, we used the Wilcoxon and McNemar tests as appropriate. The level of statistical significance was set at $P \leq 0.05.$
## 3.1. TMAO Showed Higher Levels in Proliferative Diabetic Retinopathy (PDR)
We assessed the concentration of TMAO in the samples from four groups of participating patients: control group (CT): cataract without diabetes; non-DR group (NDR): cataract with diabetes but not diabetic retinopathy; non-PDR group (NPDR): cataract with nonproliferative diabetic retinopathy; and PDR group (PDR): patients with proliferative diabetic retinopathy who underwent vitrectomy. Each group contained 5 patients, and the clinical characteristics of the participants were shown in Table 1. No significant differences in age, gender, or diabetes duration among the four groups are displayed in Table 1. Compared with the other three groups, the PDR group showed significantly higher expression of TMAO in both serum (Figure 1(a)) and aqueous humor samples (Figure 1(b)). However, there was no statistically significant difference among the other three groups. This suggests that TMAO may accelerate the progression of diabetic retinopathy and aggravate its condition.
## 3.2. TMAO Promotes Cell Proliferation Induced by High Glucose in Human Retinal Microvascular Endothelial Cells
To investigate the effect of TMAO in HRMECs treated with high glucose (HG, D-glucose 30 mM), the cells were treated with NG (normal glucose, D-glucose 5.5 mM) + TMAO (0 μM) or HG + TMAO (0, 1.25, 2.5, 5, 10, 20, 50, or 100 μM) for 72 hours. The CCK8 assay was then used to determine the cell proliferation based on the OD 450 value. As shown in Figure 2(a), TMAO at 2.5, 5, 10, and 20 μM could promote the cell proliferation induced by HG, and only HG + TMAO (5 or 10 μM) showed statistically significant differences compared with NG + TMAO (0 μM) at 72 h; cell proliferation was also better increased with TMAO 5 μM than 10 μM. Therefore, 5 μM TMAO was used in the subsequent experiments. We continued to compare the cell proliferation in the four different groups; as shown in Figure 2(b), the cell proliferation in NG + TMAO (5 μM), HG, and HG + TMAO (5 μM) was significantly increased compared with that in NG at 72 h. Moreover, the combination of TMAO and HG could accelerate cell proliferation compared with that induced by HG or TMAO alone. Overall, high-glucose-induced proliferation of HRMECs can be increased by TMAO.
## 3.3. TMAO Enhances High-Glucose-Induced Wound Healing, Migration, and Vascular Tube Formation of Human Retinal Microvascular Endothelial Cells
In our previous research, we detected that osmotic pressure had no effect on the migration and tube formation of HRMECs [19]. Thus, we did not conduct osmotic controls in this experiment. Both TMAO and high glucose can individually promote the wound healing of HRMECs, but when combined, the healing process could be accelerated further (Figures 3(a) and 3(b)). To assess cell migration, we used the Transwell chamber and found that the number of migrating cells was significantly increased when HRMECs were cultured with HG and TMAO together, compared with the individual HG and TMAO treatments (Figures 3(c) and 3(d)). Tube formation was also more striking after costimulation with TMAO and high glucose (Figures 3(e) and 3(f)). These results indicate that overexpression of TMAO accelerates neovascularization under high glucose conditions.
## 3.4. TMAO Enhances High-Glucose-Induced Degradation of the Tight Junction Protein ZO-1 in HRMECs
We then examined ZO-1 expression by HRMECs after different treatments for 72 h. As results illustrated, OSM (mannitol 24.5 mM and D-glucose 5.5 mM) exposure produced no effect on the fluorescence and protein expression of ZO-1 compared to NG (D-glucose 5.5 mM) and HG (D-glucose 30 mM) (Figures 4(a)–4(d)). Red fluorescence intensity of ZO-1 decreased significantly upon combined stimulation with TMAO and high glucose compared to that with either treatment alone (Figures 4(e) and 4(g)). Western blot analysis further confirmed a more serious decrease in ZO-1 protein levels with the joint action of TMAO and high glucose (Figures 4(f) and 4(h)). ZO-1 protein is a component of the intercellular tight junctions. When tight junctions are disintegrated, ZO-1 expression decreases. Our results thus indicate that TMAO may accelerate the destruction of tight junctions induced by high glucose in HRMECs. This may suggest that endothelial integrity could be decreased and high glucose-induced vascular leakage could be aggravated by TMAO.
## 3.5. TMAO Can Increase High Glucose-Induced ROS
The intracellular ROS levels after 72 h of different stimulations were found to be significantly upregulated by TMAO and high glucose individually, but were further increased by the combination of TMAO and high glucose (Figures 5(a) and 5(b)). Previous studies have demonstrated that upregulation of oxidative stress can lead to inflammation in the DR. These results indicate that TMAO can enhance high glucose-induced ROS, suggesting that the inflammation induced by high glucose can also be exacerbated by exposure to TMAO.
## 3.6. TMAO Can Enhance High-Glucose-Induced NLRP3 Inflammasome Signaling Activation
High glucose can induce inflammasome activation, which plays an important role in the occurrence and development of DR [20]. In this study, we investigated whether TMAO could enhance the NLRP3 inflammasome activation induced by high-glucose (Figure 5(c)). The protein expression levels of NLRP3 (Figure 5(d)), ASC (Figure 5(e)), Caspase-1 (Figure 5(f)), cleaved-Caspase-1 (Figure 5(g)), IL-1 (Figure 5(h)), and Pro-IL-1β (Figure 5(i)) were found to be increased by the combined treatment with TMAO and high glucose compared to that induced by either TMAO or high glucose alone. Thus, TMAO may accelerate the occurrence of DR by increasing the inflammasome activation induced by high glucose.
## 4. Discussion
We found that the TMAO content in serum and aqueous humor were both much higher in the PDR group than in the CT, NDR, and NPDR groups. Our study aimed to confirm whether TMAO can accelerate high-glucose-induced dysfunction and NLRP3 inflammasome activation in HRMECs to accelerate the pathogenesis of DR. We found that TMAO can enhance the proliferation, wound healing, migration, tube formation, and degradation of intercellular tight junctions induced by high glucose in HRMECs. Thus, TMAO could accelerate high glucose-caused neovascularization and vascular leakage. Interestingly, we found that a 5 μM concentration of TMAO combined with high-glucose guided greater cell proliferation than 10 μM as well as higher concentrations. On the one hand, it was reported that TMAO could activate oxidative stress to induce pyroptosis in the vascular endothelial cells [21] or apoptosis in renal cells [22]. On the other hand, apoptosis in the pancreatic acinar cells has been investigated and could occur after treatment with TMAO via ER stress [23]. So, with the increase in TMAO concentration, we guess that pyroptosis or apoptosis may occur in the HRMECs. In that case, 5 μM concentration of TMAO could induce greater cell proliferation than higher concentration of TMAO in HRMECs. We demonstrated that TMAO can enhance the high-glucose-induced occurrence of oxidative stress and NLRP3 inflammasome complex activation. Pathological neovascularization is known to play a key role in the development of DR [24]. Oxidative stress and NLRP3 inflammasome complex activation, including the subsequent chronic inflammation, are also crucial pathogenic processes in DR [25, 26]. Gut dysbiosis has been indicated to be associated with the progression of diabetic microvascular complications, including DR, and TMAO overexpression can be induced by gut dysbiosis [10]. Based on these findings, TMAO overexpression in the retina can accelerate the occurrence and development of DR. The plasma levels of TMAO are reported to be associated with the incidence rate and severity of DR [15]. Our experimental results were consistent with these clinical findings. However, we did not further investigate additional signaling pathways for the combined action of TMAO and high glucose in HRMECs; therefore, we attempted to conjecture the related mechanisms based on previous studies on DR and another diabetic vascular complication associated with TMAO.
The PKC (protein kinase C) pathway plays a key role in the oxidative stress of DR [27]. Hyperglycemia increases the synthesis of diacylglycerol (DAG) through glycolysis, which is an agonist of PKC [28]. PKC can enhance active NADPH oxidase and regulate the assembly and activation of NOX 2 and NOX 4 isoforms, which can further increase the level of oxidative stress in retinal cells [29]. PKC activity can also increase NF-κB phosphorylation, which can increase inflammation, activation of the NLRP3 inflammasome, and loss of ZO-1 and Claudin-1, thus damaging the blood –retinal barrier [30–32]. TMAO has also been found to activate PKC in human umbilical vein endothelial cells (HUVECs) to induce monocyte adhesion [33]. TMAO may thus enhance high-glucose-induced oxidative stress through the PKC pathway.
The NLRP3 inflammasome can also be activated by several other pathways, including toll-like-receptor-4 (TLR4) [34], p38 [35], Nrf2 [36, 37], ROS [38], PKR [39], and prostaglandin E [40, 41], in retinal cells. In cardiovascular disease, TMAO has been indicated to activate the NLRP3 inflammasome through ROS induction [41]. Therefore, we consider that TMAO can promote ROS to further accelerate high-glucose-induced activation of the NLRP3 inflammasome.
Based on our findings, TMAO can accelerate the high-glucose-induced destruction of tight junctions; however, TMAO itself can activate the NLRP3 inflammasome and induce dysfunction in HRMECs. It is widely reported that TMAO can increase the risk of cardiovascular disease [42, 43]. Disorders of the gut microbial ecosystem are reported to increase the risk of cardiovascular disease (CVD), atherosclerosis, diabetes, and stroke [44, 45]. As products of intestinal dysbacteriosis, TMAO may thus be a risk factor for ocular fundus diseases; thus, regular examination of the ocular fundus is necessary, especially in patients with diabetes.
## 5. Conclusion
In conclusion, our study confirmed higher levels of TMAO in patients with PDR. We further demonstrated that TMAO can promote NLRP3 inflammasome activation and HRMEC dysfunction induced by high glucose. TMAO overexpression may thus accelerate the course of DR and loss of vision. Therefore, fundus monitoring needs to be conducted as early as possible in diabetic patients with intestinal flora disorders. But this study also has some limitations, such as the fact that no animal experiments were conducted to further verify this study and the number of clinical participants was small. In the future, we will recruit more participants in our research and conduct more experiments to explore the mechanism of TMAO with DR.
## Data Availability
All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.
## Ethical Approval
All experiments in this study were approved by the Ethics Committee of the Second Affiliated Hospital of Nantong University (2021KYG045). This study followed the guidelines of the Declaration of Helsinki. Sampling was carried out after written informed consents were signed by patients.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Conception and study design were done by LDX; data acquisition was performed by XC; data analysis was done by YJT; manuscript drafting was done by LLH; manuscript revising was done by YS. All authors have read and approved the final version of this manuscript to be published. Lidan Xue and Lili Huang contributed equally to this work.
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|
---
title: TNF-α Enhances the Therapeutic Effects of MenSC-Derived Small Extracellular
Vesicles on Inflammatory Bowel Disease through Macrophage Polarization by miR-24-3p
authors:
- Huikang Xu
- Jiamin Fu
- Lijun Chen
- Sining Zhou
- Yangxin Fang
- Qi Zhang
- Xin Chen
- Li Yuan
- Yifei Li
- Zhenyu Xu
- Charlie Xiang
journal: Stem Cells International
year: 2023
pmcid: PMC9991477
doi: 10.1155/2023/2988907
license: CC BY 4.0
---
# TNF-α Enhances the Therapeutic Effects of MenSC-Derived Small Extracellular Vesicles on Inflammatory Bowel Disease through Macrophage Polarization by miR-24-3p
## Abstract
Human menstrual blood-derived mesenchymal stem cells (MenSCs) and their secreted small extracellular vesicles (EVs) had been proven to relieve inflammation, tissue damage, and fibrosis in various organs. The microenvironment induced by inflammatory cytokines can promote mesenchymal stem cells (MSCs) to secrete more substances (including EVs) that could regulate inflammation. Inflammatory bowel disease (IBD) is a chronic idiopathic intestinal inflammation, the etiology and mechanism of which are unclear. At present, the existing therapeutic methods are ineffective for many patients and have obvious side effects. Hence, we explored the role of tumor necrosis factor α- (TNF-α-) pretreated MenSC-derived small EV (MenSCs-sEVTNF-α) in a mouse model of dextran sulfate sodium- (DSS-) induced colitis, expecting to find better therapeutic alterations. In this research, the small EVs of MenSCs were obtained by ultracentrifugation. MicroRNAs of small EVs derived from MenSCs before and after TNF-α treatment were sequenced, and the differential microRNAs were analyzed by bioinformatics. The small EVs secreted by TNF-α-stimulating MenSCs were more effective in colonic mice than those secreted directly by MenSCs, as evidenced by the results of histopathology analysis of colonic tissue, immunohistochemistry for tight junction proteins, and enzyme-linked immunosorbent assay (ELISA) for cytokine expression profiles in vivo. The process of MenSCs-sEVTNF-α relieving colonic inflammation was accompanied by the polarization of M2 macrophages in the colon and miR-24-3p upregulation in small EVs. In vitro, both MenSC-derived sEV (MenSCs-sEV) and MenSCs-sEVTNF-α reduced the expression of proinflammatory cytokines, and MenSCs-sEVTNF-α can increase the portion of M2 macrophages. In conclusion, after TNF-α stimulation, the expression of miR-24-3p in small EVs derived from MenSCs was upregulated. MiR-24-3p was proved to target and downregulate interferon regulatory factor 1 (IRF1) expression in the murine colon and then promoted the polarization of M2 macrophages. The polarization of M2 macrophages in colonic tissues then reduced the damage caused by hyperinflammation.
## 1. Introduction
Inflammatory bowel disease (IBD) is a nonspecific and refractory chronic intestinal disease, which is also known as green cancer because of its prolonged course and serious decline in patients' quality of life [1–3]. Abdominal pain, diarrhea, hematochezia, and weight loss are the primary clinical symptoms of patients with this disease. The incidence of IBD had increased dramatically in some developing countries in Asia [4]. The number of IBD patients in China was also increasing year by year, and it had been reported that it could reach 150,000 in 2025 [5]. A retrospective study of IBD published in the Lancet in 2017 showed that the incidence in the United States and Europe had reached a stable level, while non-Western countries were experiencing a significant increase in new cases [6]. The direct mechanism of IBD pathogenesis was unclear, but environments, genetics, and intestinal microbiota had been reported to be involved [7, 8]. The uncertain pathology and mechanisms of the disease led to limited research into the remedy of IBD. Traditional therapies, including immunosuppressive agents and biological agents, were prone to develop therapeutic drug resistance, with less significant efficacy and obvious side effects [9]. Anti-tumor necrosis factor (TNF) agents are the most advanced treatment method nowadays, but it is ineffective for up to $40\%$ of patients [10]. Therefore, there is an urgent need to find out a more appropriate alternative therapy.
Mesenchymal stem cell (MSC) injection is a new treatment for IBD, and some researchers had carried out clinical trials of MSC administration for Crohn's disease [11]. The therapeutic effect of MSCs for inflammatory diseases relies on two main aspects: one is that MSCs enter the body and home to the damaged site and differentiate into damaged cells [12]; the other is that MSCs regulate inflammation by secreting extracellular vesicles (EVs) [13]. However, it is difficult for MSCs to survive in tissues for a long time after entering the human body, and their ability to differentiate into colonic tissues is minimal [14]. Thus, we hypothesized that MSCs relieved colitis mainly by secreting certain anti-inflammatory or prorepair substances in EVs.
MSCs and their derived EVs played a functional role in the treatment of inflammatory diseases in previous studies [15–21]. The umbilical cord, bone marrow, and adipose-derived MSC-exosomes had been shown to have efficacy on IBD, with different therapeutic mechanisms [22–24]. Exosomes derived from MSCs can play a similar role in immunomodulation and injury repair as MSCs. Exosomes are small vesicles between 30 and 200 nm in diameter, rich in protein and complex ribonucleic acid (RNA) [25]. Minimal information for studies of extracellular vesicles 2018 (MISEV2018) defined EVs as all particles naturally secreted by cells, including exosomes released by late endosomes. They have a bilayer structure and are not self-replicating, and small EVs are defined as vesicles that are less than 200 nm in size [26]. The exosomes mentioned in previous studies in 2018 mostly were small EVs. EVs have the advantages of small size, direct action, and easy storage, and they do not face safety problems such as pulmonary embolism caused by cell therapy [27, 28]. In addition, many studies had demonstrated that EVs from MSCs exchange information with other cells through the small RNAs in them and further alter gene expression [29–32]. The small RNAs in these studies were mainly microRNAs, which are noncoding single-stranded RNA molecules with a length of about 22 nucleotides encoded by endogenous genes [33, 34]. Human menstrual blood-derived mesenchymal stem cells (MenSCs) are derived from the female endometrium and excreted from the female body through menstrual blood. They are characterized by strong self-replication and directional differentiation and low immunogenicity. Because of its low immunogenicity, it rarely arose immune rejection when it is used as allogeneic cell therapy. The advantages of MenSCs include easy access, high cell proliferation rates, and no ethical issues [35–37]. In previous studies, MenSCs and their small EVs can alleviate diseases such as tumors, pulmonary fibrosis, and liver failure [37–40]. Researchers had shown that MSCs regulate inflammation bidirectionally and inhibit inflammation only in the context of inflammation [41, 42]. Trace amounts of proinflammatory cytokines, such as interferon γ (IFN-γ) and TNF-α, contributed to the activation of anti-inflammatory effects of MSCs [43, 44].
When the colon tissue is damaged, macrophages play a significant role in destroying foreign microorganisms, removing necrotic tissues and debris, and tissue remodeling and regeneration [2]. Macrophages in the inflammatory environment include the “classical-activated” (M1) macrophage phenotype and the “alternating-activated” (M2) macrophage phenotype, to achieve a balance between promoting inflammation and inhibiting inflammation in tissues and organs. MSC-derived exosomes can regulate the inflammatory process by promoting M2 macrophage polarization and further balance the excessive inflammation to reduce tissue damage [43, 45, 46].
In this study, we found that TNF-α-stimulated MenSCs promoted the production of miR-24-3p in the small EVs. MiR-24-3p in TNF-α stimulated MenSC-derived small EVs (MenSCs-sEV TNF-α) downregulated the interferon regulatory factor 1 (IRF1) expression and promoted M2 macrophages activation, thus alleviating colonic inflammation.
## 2.1. Animals
All animal experimental operations had been approved by the Ethics Review Committee of the Experimental Animal Center of Zhejiang University, and the ethical approval number is ZJU20210240. For this in vivo experiment, male C57BL/6 mice, aged 6–8 weeks, were purchased from the SLAC company (Shanghai, China). All animals were raised in the animal center at 24°C and $60\%$ humidity, with sufficient feed and water. In this study, we strived to reduce the suffering of animals and minimize the number of animals used. In all animal experiments, the number of mice in each group was 6 ($$n = 6$$).
In the acute colitis model, mice were orally administrated with $5\%$ dextran sulfate sodium (DSS) (YEASEN, China, MW: 36000-50000) for 7 consecutive days, and the clinical symptoms (diarrhea, bloody stool, and weight loss) of mice were monitored by disease activity index (DAI) every day. On day 2 of modeling, mice were injected with small EVs (200 μg) intraperitoneally, and on day 7, $5\%$DSS will be replaced with ordinary drinking water. On day 9, all acute colitis mice were euthanized to obtain the colon and serum for subsequent experiments.
The methods for chronic modeling were described previously [23]. In the chronic colitis model, the mice were raised with $3\%$ DSS for 1 week, followed by normal water for 1 week, during which time the feed was normal, and then, the above was repeated in one round for a total of 28 days.
After the mice were euthanized, the data of inflammatory infiltration in the colon of the mice were obtained by hematoxylin and eosin (H&E) staining and myeloperoxidase (MPO) activity detection. DAI and H&E staining analysis scoring rules had been reported previously [22]. The scoring rules in this study are shown in Tables 1 and 2.
## 2.2. Intestinal Permeability Test
The intestinal permeability assay in mice is to test intestinal epithelial integrity. The concentration of FITC-dextran was 25 mg/ml, and the dosage of mice was 0.6 mg/g. On day 7 of the acute DSS-colitis model in mice, FITC-dextran was administered to mice by gavage on an empty stomach and mice were sacrificed 4 hours later to obtain the serum. FITC fluorescence can be detected in the mouse serum. Fluorescence intensity is directly proportional to intestinal permeability, with stronger fluorescence indicating more severe intestinal damage.
## 2.3. Isolation and Culture of MenSCs
MenSCs were provided by the Innovative Precision Medicine (IPM) Group, and the specific process of obtaining cells was as above [37, 47, 48]. In brief, menstrual blood samples were collected from healthy young women ($$n = 3$$) aged 20 to 30 years during menstruation using Divacup (Kitchener, ON). Fresh menstrual blood samples should not be stored for more than 72 hours in a storage solution containing kanamycin sulfate, cefadroxil, vancomycin hydrochloride, amphotericin B, gentamicin sulfate, and heparin in a 4°C refrigerator. Stem cells in menstrual blood were obtained by density gradient centrifugation with Ficoll-Hypaque (DAKEWE, China). The isolated interlayer cells were cultured in Minimum Essential Medium α (MEMα) (Gibco, USA) adding $15\%$ Australian fetal bovine serum (FBS). MenSCs were completely digested with $0.25\%$ trypsin-EDTA (Fisher Scientific, USA) for 5 min, then neutralized with complete medium, and centrifuged to complete subculture. Passages 5-8 (p5-p8) of MenSCs can be used for collection of small EVs.
## 2.4. Identification of MenSCs
To verify the multidirectional differentiation potential of MenSCs, we induced the cells into osteoblastic, adipogenic, and chondrogenic cells for 20 to 30 days [37]. After the differentiation of the cells to a certain extent, they were treated with the corresponding stains and finally photographed with a microscope (Olympus, Japan). The surface molecules of MenSCs were detected by flow cytometry (ACEA NovoCyte, ACEA Biosciences, USA).
## 2.5. The Isolation of MenSC-Derived Small EVs
As mentioned above, small EVs were isolated from the cell culture supernatants of MenSCs by ultracentrifugation [38]. After the passage of MenSCs to P5-P8, the cells adhered to the wall overnight and were cultured with or without 20 ng/ml TNF-α to $80\%$–$90\%$ fusion degree. Then, the MenSCs were cultured in a pure MEMα medium for 48 h. The collected cell supernatants of MenSCs were subjected to low-speed centrifugation several times, 300 g for 10 minutes, 2000 g for 20 minutes, and 10000 g for 30 minutes. During the above centrifugation process, the precipitate was discarded; in other words, dead cells and cell debris in the supernatant were removed. Then, the supernatant obtained in the previous step was filtered with a 0.22 μm filter (Millipore, USA). The filtrate was centrifuged twice at 100,000 g for 70 min and finally obtained the small EVs. For the following Western blotting assays of small EVs, the protein in exosomes was lysed with 50 μl RIPA lysate (Beyotime Biotechnology, China), and the protein was quantified with a BCA kit (Beyotime Biotechnology, China). The microRNA in small EVs was isolated by microRNA extraction kit (Qiagen, USA), and the relative expression of miR-24-3p was measured by quantitative polymerase chain reaction (q-PCR).
## 2.6. Identification of MenSC-Derived Small EVs
The small EVs were identified by transmission electron microscopy (TEM) (Thermo FEI, Czech Republic) observation, nanoparticle tracking analysis, and Western blotting. For TEM identification, small EVs were first fixed with $2\%$ paraformaldehyde solution and then 5 μl of EV suspension was added to the Formvar-carbon copper grid. After draining excess liquid from the copper mesh, it was stained with $1\%$ uranyl acetate. Immediately afterward, the copper mesh was allowed to stand for 5 minutes at room temperature and then washed with deionized distilled water. After drying, the sample was observed under the transmission electron microscope. The particle size distribution and concentration of small EVs were tested by Nanosight NS 500 (malvin, England). After lysing MenSCs and small EVs with RIPA lysate, the amount of protein was corrected with a BCA kit to ensure that the quality of each group of samples was 10 μg. The Western blotting experiments of GAPDH, CD63, CD81, and TSG101 were then carried out.
## 2.7. Internalization of MenSC-Derived Small EVs
By labeling small EVs in vitro to clarify the location of small EVs in macrophages, we further explored the possible mechanisms of small EV action. MenSCs-derived small EVs (MenSCs-sEV) and MenSCs-sEVTNF-α were stained with red fluorescent dye PKH26 (Sigma-Aldrich, USA), as described above [49]. Briefly, we added 1 μl PKH26 to 50 μl Dilution C and 10 μg exosomes to 50 μl Dilution C in another tube. They were then mixed and allowed to stand at room temperature for 5 min. The staining was then terminated using 500 μl of EVs-depleted FBS, and the small EVs were isolated again by ultracentrifugation. The mouse leukemic monocyte/macrophage cell line RAW264.7 was cultured on a dedicated cell slide for 24-well plates and PKH26-labeled small EVs were cocultured with the cells when they reached $70\%$ fusion. After 48 hours, the cells were fixed with a $4\%$ paraformaldehyde solution and stained with FITC-phalloidin and DAPI, respectively. The slides were sealed with an antifluorescence quenching sealing tablet and observed with an FV3000-OSR microscope (Olympus, Japan).
## 2.8. Cell Transfection
Mimics and inhibitors of miR-24-3p were synthesized by RiboBio Corporation (Guangzhou, China). IRF1 small interfering RNAs (siRNAs) and IRF1 overexpressed plasmids were synthesized by Sangon Biotech Corporation (Shanghai, China). The Lipofectamine™ 3000 transfection reagent (Thermo Fisher, USA) was used to transfect plasmids, microRNA mimics, microRNA inhibitors, and siRNAs. The specific steps were following the instructions provided. The transfection efficiency was measured by qPCR or Western blotting.
## 2.9. Dual-Luciferase Reporter Gene Assay
The wild-type (WT) and mutant-type (MUT) 3′-UTR of IRF1 mRNA were synthesized by Oligobio Biotech Corporation (Beijing, China). This corporation also synthesized the recombinant pmirGLO plasmid containing the WT sequence or MUT sequence. Human embryonic kidney cells (HEK293T, ATCC) were cultured in an incubator at 37°C with 12-well plates. When the fusion degree reached $60\%$, 1 μg/ml plasmids and 50 nM miR-24-3p mimics were cotransfected with serum-free Dulbecco's modified *Eagle medium* (DMEM) (Thermo Fisher, USA) and the Lipofectamine™ 3000 transfection reagent. After 48 h, cells were lysed with lysate from the luciferase reporter gene assay kit (Promega, USA). According to the instructions, the cell lysates were transferred to a white plate. After the addition of LAR II and Stop & Glo reagents (Thermo, USA), the substances in the well plates were quantified twice by the microplate reader and calculated.
## 2.10. Quantitative PCR and Western Blotting
Total RNA isolation and purification of Raw264.7 cells were performed with the aid of the RNeasy mini kit (Qiagen, USA), and all operations were completed according to the kit instructions (Qiagen, USA). Quantitative analysis was accomplished using the -2ΔΔCt method [50]. All primers (including miRNA) used for quantitative real-time PCR are presented in Table S1. As previously described, the specific experimental operations of reverse transcription, qPCR, protein extraction, and Western blotting were performed [37, 39]. Before Western blotting assays, the protein concentrations were measured using the BCA kit (Beyotime, China) to equalize the protein concentrations among the groups. Then, 4× loading buffer was added to the protein and they were boiled in a metal bath at 100°C for 10 min. Finally, the ECL luminescent solution (Bio-Rad, USA) was dropped on the PVDF membrane, and the band of protein could be observed by the ChemiDoc Touch Imaging System (Bio-Rad, USA).
## 2.11. Enzyme-Linked Immunosorbent Assay (ELISA) for Cytokine Analysis
The distal colon of each group was weighed and placed in liquid nitrogen for rapid cryopreservation. The colon samples were thoroughly ground in a homogenizer, and the supernatant of tissue homogenate was collected after centrifugation. The levels of TNF-α, interleukin(IL)-6, IL-10, IL-1β, and IFN-γ in the colonic tissues of mice were measured by the ELISA kit (FANKEW, China). The specific steps of the assay were carried out following the manufacturer's instructions. The absorbance of the sample and standard wells in the ELISA plate was detected at 450 nm with the microplate reader (Biorad, USA).
## 2.12. Isolation of Mononuclear Cells from Lamina Propria of the Colon
After the mice were euthanized, the colon was removed and cut into 1 cm segments. The epithelial digestive fluid (HBSS+5 mM EDTA+1 mM DTT), lamina propria digestive fluid (Collagenase D+DNase I+Dispase II+ PBS), $40\%$/$80\%$ Percoll were prepared in advance. The segments of the colon were first digested several times with epithelial digestive fluid, and they were digested again with lamina propria digestive fluid after the epithelial cells were discarded. Colon tissue fragments from the lamina collected after digestion were filtered through a 40 μm cell sieve, and all obtained cells were subjected to a density gradient centrifugation of $40\%$/$80\%$ Percoll. Further, the white interlaminar cells were collected and centrifuged, and the cell viability should be greater than $95\%$ by the Trypan blue test.
## 2.13. Clearance of Colon Macrophages
To investigate the role of macrophages in MenSCs-sEVTNF-α in IBD mice, we used chlorophosphate liposomes (Clop-lipo) (Formumax, USA) to remove macrophages in animal experiments. Two days before DSS was added to the drinking water of mice, 200 μl Clop-lipo was intraperitoneally injected into mice, and the same dose of PBS-lipo was used as a control. After one week, the proportion of macrophages in lamina propria mononuclear cells was measured by flow cytometry.
## 2.14. Immunohistochemistry (IHC) Analysis
In order to explore the effect of different treatments on the integrity of mouse colon epithelium, IHC staining of zonula occludens- (ZO-) 1, ZO-2, and occludin was performed on every mouse colon. The colon tissues were formalin-fixed and paraffin-embedded. Then, they were cut into 5 μm paraffin sections with a paraffin slicing machine. After the sections were placed in 0.01 mol/l (PH7.8) citric acid buffer for antigen repair, antibodies against ZO-1, ZO-2, and occludin were diluted according to the instructions and then dropped onto the sections for overnight incubation. In the final step, second antibodies was added the next day; then, the sections were mounted with resin after diaminobenzidine and hematoxylin staining.
## 2.15. Statistics
GraphPad Prism 8.0.2 (San Diego, CA) was used to statistically analyze the data differences between groups. In Graphpad Prism software, the Agostino-Pearson method was used to detect the normality of experimental data. When $p \leq 0.1$, the data is normally distributed. The data in this study conform to a random and normal distribution with uniform variance. Therefore, the t-test was used for comparison between the two groups. One-way ANOVA was used for multigroup comparison, and both of them are parametric tests. A p value lower than 0.05 was considered as a significant difference, which means that the data was meaningful. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, and ∗∗∗$p \leq 0.001.$
## 3.1. Identification of MenSCs and Small EVs
MenSCs were identified by optical images of cells, surface phenotype, and trilineage differentiation staining. The results of flow cytometry showed that CD29, CD73, CD90, and CD105 molecules were highly expressed on the surface of MenSCs, while HLA-DR, CD34, CD45, and CD117 molecules were extremely low or almost absent (Figure 1(a)). Microscopically, MenSCs appeared spindle-shaped and fibrous (Figure 1(b)). The pluripotency of MenSCs can be evaluated by the tri-lineage differentiation assay. When MenSCs were cultured in a specific induction medium for 21~30 days, alizarin red staining-positive (osteogenic differentiation), oil red O-stained lipid droplets (adipogenic differentiation), and alcian blue-stained chondrocytes (chondrogenic differentiation) could be observed (Figure 1(b)). Through TEM, the morphology of MenSCs-sEV and MenSCs-sEVTNF-α can be visually seen, which are oval bilayer lipid vesicles less than 200 nm in diameter (Figure 1(c)). In the nanoparticle tracking analysis, the mean diameter of MenSCs-sEV was 166.8 nm, while the mean diameter of MenSCs-sEVTNF-α was 163.9 nm (Figure 1(d)). In Western blotting, CD63, CD81, and TSG101 showed clear bands in MenSCs-sEV and MenSCs-sEVTNF-α, while these molecules showed weak or no bands in their corresponding MSCs (Figure 1(e)).
## 3.2. TNF-α-Pretreated MenSC-Derived Small EVs Alleviated DSS-Induced Acute IBD
On the day following oral administration of $5\%$ DSS, mice developed diarrhea, and the intraperitoneal injection of small EVs can alleviate the subsequent clinical signs compared to the DSS+PBS group. The clinical symptoms of the mice were recorded as DAI. Statistics were made to visualize the severity of the disease, in which we found that the efficacy of MenSCs-sEVTNF-α was better than MenSCs-sEV (Figure 2(a)). Daily body weight monitoring showed that weight loss in all groups except the control group, and the loss of weight in the DSS+MenSCs-sEVTNF-α group was less than that in the other groups (Figure 2(b)). Excessive neutrophils can also damage the tissue. The MPO activity was used to reflect the level of neutrophil infiltration. After small EV therapy, we found that MPO activity decreased (Figure 2(c)) and colon length did not shorten as much as the untreated group (Figure 2(d)). As can be seen from the direct view of the colon, the overall colon of the DSS+PBS group was red, with increased permeability, thin, easily damaged, loosen colon contents, and severe shortening, while the small EV-treated groups, including MenSCs-sEV and MenSCs-sEVTNF-α group, had better morphology (Figure 2(e)). H&E staining showed that DSS caused incomplete colonic structure, loss of crypt, structural destruction, and infiltration of inflammatory cells. Compared with the DSS+PBS group and the DSS+MenSCs-sEV group, the DSS+MenSCs-sEVTNF-α group had less damage and fewer leukocytes in the colon tissue (Figure 2(f)). Further, we conducted a semiquantitative analysis of the results of H&E staining with a double-blind situation, and the results were presented by pathological scores (Figure 2(f)). The degree of colonic damage is positively correlated with the pathological score. After colitis mice were treated with small EVs, the pathological scores of the murine colon decreased significantly. Given the above, MenSC-derived small EVs can alleviate colonic inflammation in mice, and the efficacy of MenSCs-sEVTNF-α was superior to MenSCs-sEV.
## 3.3. MenSCs-sEVTNF-α Can Affect the Colonic Inflammatory Cytokines and Intestinal Epithelial Integrity
Disorders of inflammatory mediators and intestinal barrier dysfunction are two salient characteristics of IBD [51–53]. In the ELISA results, the expression of TNF-α, IFN-γ, IL-1β, and IL-6 was downregulated and IL-10 expression was upregulated in the mouse colon after intraperitoneal injection of MenSCs-sEV or MenSCs-sEVTNF-α (Figure 3(a)). To explore the integrity of intestinal epithelium, we investigated the expressions of the colonic tight junction proteins ZO-1, ZO-2, and occludin using immunohistochemistry, as well as the intestinal permeability test. During the intestinal permeability test, the fluorescence intensity of FITC in the serum reflects the amount of dextran in the serum that infiltrates from the colon. High intestinal permeability indicates that after injury the intestinal epithelium is not intact. We found that the FITC fluorescence value in the serum of mice of the control group was the minimum and that in the DSS+MenSCs-sEVTNF-α group was significantly less than the other two groups (Figure 3(b)). Further, we detected the content and distribution of tight junction proteins ZO-1, ZO-2, and occludin in the colon of mice by immunohistochemistry assays. These three tight junction proteins were distributed more in the colon of the control group and DSS+MenSCs-sEVTNF-α group, while the other two groups were less. The expression of ZO-1 and ZO-2 in DSS+MenSCs-sEV group was significantly higher than that in the DSS+PBS group, but there was no difference in the occludin expression between the two groups (Figure 3(c)).
## 3.4. MenSCs-sEVTNF-α Alleviated DSS-Induced Chronic IBD In Vivo
To explore the role of MenSCs-sEV TNF-α in chronic and recurrent IBD, we orally administered $3\%$ DSS to mice for two weeks discontinuously. And we found that mice in each group experienced repeated hematochezia, diarrhea, and weight loss. Mice injected with small EVs twice (day 7 and 16) had better clinical performance (Figure 4(a)). In addition, the colon length of mice with MenSCs-sEVTNF-α injections on day 7 and day 16 was not significantly shortened, but that of mice without injection and only injecting once was clearly shortened (Figure 4(b)). ELISA showed that the levels of TNF-α, IFN-γ, IL-1β, and IL-6 were decreased in the mouse colon with intraperitoneal injections of MenSCs-sEVTNF-α on both day 7 and day 16. Unexpectedly, the IL-10 level appeared to be higher in a single dose than in two (Figure 4(c)). In addition, the MPO activity of the mouse colon was also detected in the same way as the acute modeling process. After two rounds of treatment, the degree of neutrophil infiltration in the colon was slight, which was consistent with ELISA results (Figure 4(d)). In all animal experiments, the number of mice in each group was 6 ($$n = 6$$) at first. However, in the process of chronic modeling, because of the strong toxicity of DSS, two mice in the DSS group were euthanized in the experiment for poor conditions. In addition, one mouse in the DSS+MenSCs-sEVTNF-α at day 7 group died during the second disease peak due to intolerance to DSS.
H&E staining indicated that the colon epithelium of the two-dose mice was intact, with normal crypt and goblet cell structure and only a small number of inflammatory cell infiltration. In the colon of mice treated with small EVs only on day 7, fossa damage and inflammatory cells infiltration were evident (Figure 4(e)). *In* general, the mice showed a course of exacerbation, remission, and relapse during chronic IBD modeling. The efficacy of injecting twice during IBD exacerbation and relapse was better than injecting once.
## 3.5. MenSCs-sEVTNF-α Increased the Proportion of M2 Phenotype of Macrophages In Vitro
The small EV intraperitoneal injection caused variation in the expression of inflammatory cytokines in the colon of mice. And these inflammatory cytokines, which are mainly secreted by macrophages, are key factors mediating the progression of IBD. As a result, MenSCs-sEVTNF-α may alleviate colonic inflammation by altering macrophage functions. To study whether MenSCs-sEVTNF-α could promote the polarization of M2 macrophages in vitro, MenSCs-sEVTNF-α (100 μg/ml) was added to Raw 264.7 cell culture. First, Raw 264.7 cells were stimulated with 100 ng/ml LPS to expose macrophages to an inflammatory microenvironment. After LPS stimulation, Raw264.7 cells were mainly M1 macrophages, while the number of M2 macrophages accounted for less than $2\%$. Then after 48 hours of coculture with MenSCs-sEVTNF-α, flow cytometry showed that the proportion of CD206+ cells (M2 macrophages) exceeded $10\%$ (Figure 5(a)). To some extent, cell lines may not fully represent the reality of living cells in the tissue. In order to enhance the credibility of the results, we isolated mouse bone marrow-derived macrophages (BMM), then treated them with LPS and MenSCs-sEVTNF-α, and finally obtained similar results (Figure 5(b)). To further explore the levels of inflammatory factors under different treatments, we detected the relative expression levels of TNF-α, IL-1β, IFN-γ, IL-17, iNOS, and Arg-1 in Raw264.7 cells by qPCR. Compared with the LPS+PBS group, MenSCs-sEVTNF-α and MenSCs-sEV decreased the levels of proinflammatory cytokines in Raw264.7 cells, and IL-1β and iNOS expressions in the MenSCs-sEVTNF-α group were lower than those in the MenSCs-sEV-treated group (Figure 5(c)). In Raw264.7 macrophages, MenSCs-sEVTNF-α can downregulate the expression of iNOS and upregulate Arg1, indicating that MenSCs-sEVTNF-α may convert M1 macrophages into M2 macrophages. To further figure out how small EVs affect macrophages, we labeled small EVs with PKH26 and observed the position of the red fluorescence of PKH26 under the confocal microscope. PKH26 red dots could be seen in the cytoplasm of macrophages by immunofluorescence staining after PKH26-labeled small EVs treating macrophages (Figure 5(d)). It is concluded above that small EVs were located in the cytoplasm after phagocytosis by macrophages, furtherly altering the secretion of inflammatory cytokines in macrophages.
## 3.6. MenSCs-sEVTNF-α Elevates the Ratio of M2 Macrophages in the Colon of Mice with DSS-Colitis
We had previously proved that MenSCs-sEVTNF-α can increase the number of M2 macrophages in vitro and further explored whether the same phenomenon can be observed in vivo. In a mouse model of acute IBD, the colons of euthanized mice at day 9 were removed to perform ELISA for iNOS and Arg-1. The results showed that Arg-1 expression was upregulated and iNOS expression was downregulated in the colon of mice treated with MenSCs-sEVTNF-α (Figure 6(a)). Flow cytometry showed that after oral DSS treatment, the proportion of PKH26+ mononuclear cells and M2 macrophages in the lamina propria was dramatically higher than that in the NC group (Figure 6(b)). In the immunofluorescence assay of colonic tissue, the relative fluorescence intensity of Arg-1 of the MenSCs-sEVTNF-α group were obviously higher than other two groups, and the positive sites of Arg1 were identical to PKH26 (Figure 6(c)). This demonstrated that macrophages that absorbed MenSCs-sEVTNF-α could be converted into M2 macrophages.
Both in vivo and in vitro, MenSCs-sEVTNF-α can cause significant changes in macrophages, so we speculated that MenSCs-sEVTNF-α alleviates colonic inflammation mainly through macrophages. To verify the important role of macrophages in this process, we tried to eliminate macrophages in mice with chlorophosphate liposomes (clop-lipo) (FormuMax, USA) in an acute IBD mouse model. After a one-time intraperitoneal injection of 250 μl clop-lipo, cells from the bone marrow of mice were collected and detected by flow cytometry 7 days later. The results of flow cytometry showed that the ratio of F$\frac{4}{80}$+ cells decreased by about $80\%$, which proved that clop-lipo successfully eliminated most macrophages in mice (Figure S1E). In the acute IBD model, MenSCs-sEVTNF-α could not affect the colitis phenotype after intraperitoneal injection of clop-lipo (Figure S1A, B, C, D).
## 3.7. TNF-α Induces Changes in MicroRNAs in Small EVs Derived from MenSCs
Sequencing of MenSCs-sEV and MenSCs-sEVTNF-α was performed on Illumina HiSeqTM 2500 platform by Guangdong RiboBio Biotechnology Co. Ltd. The differential expression results of sequencing were presented in Table S2. Functional heatmaps were used to show microRNA expression differences through hierarchical clustering analysis. Through the difference ratio (|log2(Fold Change)| > 1) and the significant level (p value < 0.05) of these two indicators, we picked out the differential expression microRNAs. Hsa-miR-708-5p, hsa-miR-24-3p, and so on were significantly upregulated in exosomes secreted by MenSCs after TNF-α stimulating, while hsa-miR-365a-5p, hsa-miR-490-5p, and so on were significantly downregulated (Figure 7(a)). Differential microRNA expression was reflected by microRNA differential expression scatterplot and microRNA differential expression volcano map between samples: there were 70 differential microRNAs, 38 of which were upregulated and 32 downregulated (Figures 7(d) and 7(e)). Enrichment analysis showed that the differential expression of microRNAs was related to specific viral and bacterial infection, endocytosis, PI3K-AKT signaling pathways, and signaling pathways that regulate stem cell pluripotency (Figure 7(b)). TNF-α changed the function of small EVs of MenSCs, and differentially expressed microRNAs from TNF-α-MenSCs participated in different biological processes. Through the analysis of differential microRNAs and their downstream target proteins, we found that only the downstream of miR-24-3p was closely related to inflammation relief and macrophage function alterations, so we only made further research on miR-24-3p.
In order to verify the sequencing results, we extracted the small EVs from the supernatants of MenSCs which were treated with TNF-α for 0, 1, 2, 3, and 4 days and used qPCR to obtain the relative expression of miR-24-3p. The results showed that the longer the MenSCs were exposed to TNF-α, the higher the content of miR-24-3p in MenSCs-sEVTNF-α would be (Figure 7(c)). TNF-α concentration can affect the cell viability of MenSCs. MenSCs treated with different concentrations of TNF-α were quantified by Cell Counting Kit 8 (CCK8). After being treated with 50 ng/ml TNF-α for 48 h, the number of MenSCs was significantly decreased (Figure 7(f)). Hence, 20 ng/ml TNF-α is a safe concentration for promoting MenSCs to secret anti-inflammatory small EVs.
## 3.8. MiR-24-3p in MenSCs-sEVTNF-α Converts M1 to M2 by Downregulating IRF1
Previous studies had shown that IRF1 was closely related to macrophage polarization [54–57]. Moreover, the website TargetScan Human 7.1 predicted that there might be two binding sites between IRF1 and miR-24-3p. In the presence or absence of miR-24-3p overexpression (OE), the WT and MUT 3′-UTR of IRF1 site 1 were used for luciferase reporter gene detection. The binding of miR-24-3p with 3′-UTR WT resulted in the decrease of luciferase activity, while the 3′-UTR MUT could not bind to miR-24-3p, the luciferase activity remained unchanged. It proved that site 1 was the only binding site between miR-24-3p and IRF1 (Figure 8(a)). In vivo, intraperitoneal injection of MenSCs-sEVTNF-α downregulated the expression of IRF1 in the colon of mice, which was verified by western blotting (Figure 8(c)). In vitro, the Western blotting assay also confirmed that MenSCs-sEVTNF-α can make M1 marker iNOS downregulate and M2 marker Arg1 upregulate in RAW264.7 cells (Figure 8(d)).
The mechanism of MenSCs-sEVTNF-α transforming macrophages from M1 to M2 was demonstrated by flow cytometry and Western blotting analysis in vitro. When flow cytometry was performed on Raw264.7 cells with different treatments, we found that LPS stimulation significantly increased the proportion of M1 macrophages, while MenSCs-sEVTNF-α addition decreased the number of M1 and increased M2 proportion. The effect of MenSCs-sEVTNF-α in RAW264.7 was the same as that of the miR-24-3p mimic, both of which could increase M2 macrophages by more than $20\%$. We found that OE of IRF1 counteracted the macrophage polarization-promoting effect of miR-24-3p, which demonstrates that miR-24-3p worked only by binding IRF1 mRNA in Raw264.7 (Figure 8(b)). In the Western blotting assay, when MenSCs-sEVTNF-α was added to Raw264.7 or miR-24-3p mimic was transfected, the M2 marker was upregulated and the M1 was downregulated. When microRNA was overexpressed (miR-24-3p mimic transfection), the downstream target protein IRF1 was also decreased, and vice versa. Knockdown (si-IRF1) or OE of IRF1 can partially rescue the effects of miR-24-3p mimic or inhibitor. In addition, after miR-24-3p was inhibited, MenSCs-sEVTNF-α would no longer promote the polarization of macrophages (Figure 8(f)). *In* general, miR-24-3p affected M2 production by interfering with the expression IRF1. The influence of si-IRF1 and IRF1 OE on the expression of IRF1 was verified by Western blotting (Figure 8(e)).
## 4. Discussion
Menstrual blood, as a kind of metabolic waste of the female body, can cure illnesses because it contains MenSCs. In this study, MenSCs were derived from menstruating women between 20 and 30 years old. The previous report showed that the cellular status of MenSCs changes with the age of donors. MenSCs from older donors (>40 years old) have weak long-term passage ability. EVs of MSCs derived from the bone marrow and umbilical cord had been proven to have alleviating effects on IBD. Exosomes of MSCs from the bone marrow relieved IBD through macrophage, which is related to metallothionein-2 in exosomes [23]. The exosomes of umbilical cord-derived MSCs cured IBD through microRNA, which is related to miR-326 and the NF-κB pathway [58]. The functions and characteristics of MSC and their secreted EVs from different sources (the bone marrow, adipose, umbilical cords, etc.) are generally the same, but there are some differences in the therapeutic mechanisms for diseases [59]. Since MenSCs-sEV was not as effective as MenSCs-sEVTNF-α on DSS-colitis, the mechanism of MenSCs-sEVTNF-α in alleviating colitis was mainly investigated in this study. MenSCs are a new type of MSCs with abundant sources. The application of MenSCs in the treatment of IBD and its possible mechanism are the highlights of this study. The weakness of this study is that we focused too much on macrophages and ignored the possible role of other immune-related cells, such as lymphocytes, colonic epithelial cells, and neutrophils. We need to find other targets of sEV in the colon next. In addition, M2 macrophages can be further classified into M2a, M2b, M2c, and M2d cells. Exosomes derived from M2b macrophages have been reported to alleviate IBD [60], so we also need to further explore whether it is M2b macrophages that engulf sEV and which roles other types of M2 macrophages play.
In many studies, MSCs and MSC-derived EVs had the same therapeutic effect on the same disease, which led many researchers to speculate that MSCs may play the therapeutic effect on diseases by secreting EVs [13, 61, 62]. The advantages of small EVs are their small size, more direct action, better therapeutic effect, and the ability to freely shuttle through the blood circulation without being trapped in capillaries [20]. Besides, previous researchers reported that EVs secreted by MSCs derived from the umbilical cord and bone marrow would not produce rejection in mice [17, 63]. In animal experiments, mice were treated with EVs by intraperitoneal injection. One of the reasons is that bubble generation during intravenous injections can lead to certain mortality in mice. The second reason is that drugs administrated intravenously are mainly in the lungs and liver, which is poorly absorbed by the colon [37].
IBD is a chronic and progressive intestinal inflammatory disease. TNF-α is a proinflammatory cytokine. In the disease process of IBD, TNF-α expression is substantially upregulated in the colon. Nowadays, many researchers tend to use TNF-α inhibitors to treat IBD [64–66]. Preconditioning MenSCs with TNF-α was used to mimic the inflammatory environment in the gut, forcing MenSCs to secrete substances to inhibit TNF-α. Previous investigators pretreated MenSCs with TNF-α and IFN-γ, and they verified that this pretreatment enhances the immunomodulatory capacity of MenSCs-derived EVs by sequencing and cellular experiments [67]. In addition, we also found that MenSCs-sEVTNF-α decreased TNF-α expression both in vivo and in vitro. TNF-α is generally secreted by M1 macrophages, which helps macrophages to destroy microorganisms and phagocytose necrotic cells when the tissue is damaged by the invasion [2, 68, 69]. Macrophages, as a natural immune barrier, kill exogenous microorganisms and cause damage to adjacent tissues. Hyperinflammation caused by M1 macrophages is a crucial cause of IBD, and the transformation of M1 macrophages into M2 macrophages can alleviate inflammation in the colon [60, 70–72]. Research shows that exosomes are quickly cleared by the blood system whether they are injected intraperitoneally, intravenously, or subcutaneously [73]. Small EV clearance appeared to be regulated in part by the innate immune response and may be mediated by complementary conditioning [74]. Macrophages of the endothelial reticular system can rapidly phagocytose EVs, regardless of the mode of administration. Both adipose-derived and bone marrow-derived MSCs can promote the production of M2 macrophages by secreting small EVs, thereby alleviating inflammation [45, 75, 76]. IRFs are closely associated with the maturation, formation, phenotypic transformation, and phenotypic polarization of macrophages. In the IRF family, IRF1, IRF5, and IRF8 promote the production of proinflammatory macrophage M1, while IRF3 and IRF4 promote the production of regulatory macrophage M2. The regulatory effect of IRF2 changes with context [54]. In the resting state, the amount of IRF1 in macrophages is very low. Under the stimulation of the inflammatory environment, the content of IRF1 will increase, accompanied by an increase in the number of M1 macrophages [77]. As a result, IRF1 is an important target for MenSCs-sEVTNF-α to influence the polarization of M2 macrophages.
## 5. Conclusion
Small EVs derived from TNF-α-pretreated MenSCs can relieve colonic inflammation. TNF-α can upregulate miR-24-3p in MenSCs-sEVTNF-α, and the microRNA can transform macrophages from M1 to M2 to relieve inflammation by binding to downstream IRF1 in DSS-induced IBD mice.
## Data Availability
All meaningful data of this study are presented in the results and supplements of this paper. More detailed data can be obtained by reasonably requesting the corresponding author.
## Additional Points
ARRIVE guidelines statement. The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
## Ethical Approval
All the operations of animal experiments, the numbers of animals, and the ethics of the living environment have been approved by the Experimental Animal Center of Zhejiang University, and the ethical approval number is ZJU20210240.
## Conflicts of Interest
The authors declare that they have no conflict of interest.
## Authors' Contributions
Huikang Xu, Jiamin Fu, and Lijun Chen contributed equally to this work. Charlie Xiang designed the research ideas of this study. Huikang Xu, Jiamin Fu, and Lijun Chen performed most of the experiments and wrote the manuscripts. Sining Zhou, Yangxin Fang, and Qi Zhang completed a portion of the experiments with valuable recommendations. Xin Chen, Yuan Li, Yifei Li, and Zhenyu Xu analyzed the data. Huikang Xu, Jiamin Fu, and Lijun Chen are equally the first authors.
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|
---
title: Proinflammatory Cytokines Trigger the Onset of Retinal Abnormalities and Metabolic
Dysregulation in a Hyperglycemic Mouse Model
authors:
- Gaganashree Shivashankar
- Julie C. Lim
- Monica L. Acosta
journal: Journal of Ophthalmology
year: 2023
pmcid: PMC9991478
doi: 10.1155/2023/7893104
license: CC BY 4.0
---
# Proinflammatory Cytokines Trigger the Onset of Retinal Abnormalities and Metabolic Dysregulation in a Hyperglycemic Mouse Model
## Abstract
### Purpose
Recent evidence has shown that retinal inflammation is a key player in diabetic retinopathy (DR) pathogenesis. To further understand and validate the metabolic biomarkers of DR, we investigated the effect of intravitreal proinflammatory cytokines on the retinal structure, function, and metabolism in an in vivo hyperglycemic mouse model.
### Methods
C57Bl/6 mice were rendered hyperglycemic within one week of administration of a single high-dose intraperitoneal injection of streptozotocin, while control mice received vehicle injection. After confirming hyperglycemia, the mice received an intravitreal injection of either proinflammatory cytokines (TNF-α and IL-1β) or vehicle. Similarly, control mice received an intravitreal injection of either proinflammatory cytokines or vehicle. The retinal structure was evaluated using fundus imaging and optical coherence tomography, and retinal function was assessed using a focal electroretinogram (ERG), two days after cytokine injection. Retinas were collected for biochemical analysis to determine key metabolite levels and enzymatic activities.
### Results
Hyperglycemic mice intraocularly injected with cytokines developed visible retinal vascular damage and intravitreal and intraretinal hyper-reflective spots two days after the cytokines injection. These mice also developed a significant functional deficit with reduced a-wave and b-wave amplitudes of the ERG at high light intensities compared to control mice. Furthermore, metabolic disruption was evident in these mice, with significantly higher retinal glucose, lactate, ATP, and glutamine levels and a significant reduction in glutamate levels compared with control mice. Minimal or no metabolic changes were observed in hyperglycemic mice without intraocular cytokines or in control mice with intraocular cytokines at 2 days post hyperglycemia.
### Conclusions
Proinflammatory cytokines accelerated the development of vascular damage in the eyes of hyperglycemic mice. Significant changes were observed in retinal structure, function, and metabolic homeostasis. These findings support the idea that with the onset of inflammation in DR, there is a deficit in metabolism. Therefore, early intervention to prevent inflammation-induced retinal changes in diabetic patients may improve the disease outcome.
## 1. Introduction
Diabetic retinopathy (DR) is a progressive sight-threatening complication of diabetes that is clinically classified into early nonproliferative and late proliferative stages [1]. The current treatments halt the vascular damage in the late stage of DR. However, to prevent disease progression before the onset of significant visual dysfunction, novel treatment strategies should target the earlier molecular events that lead to vascular damage [2, 3].
Currently, the understanding is that not only hyperglycemia but also concomitant retinal inflammation is the major driving factors in the development of vascular abnormalities in DR [4–6]. A recent study from our group found that proinflammatory cytokines promote the development of severe vascular and functional abnormalities including vessel dilation, vessel beading, increased vessel tortuosity, and retinal oedema in the nonobese diabetic mice [7]. This demonstrated that the retina is susceptible to both hyperglycemia and proinflammatory conditions, affecting various interlinked metabolic pathways with a direct detrimental effect on retinal homeostasis [8]. In fact, previous studies have reported that the hyperglycemic retina undergoes apoptosis [9, 10], blood-retina barrier breakdown [11], activation of NF-κB [10], and increased proliferation of microglial cells [12] only after inflammation becomes apparent.
Although current treatments such as panretinal photocoagulation, focal laser treatments, and intravitreal injection of anti-vascular endothelial growth factor (VEGF) target the late-stage sight-threatening vascular problems of DR, there is a high chance of reoccurrence of the disease [13–15]. Given that there has been significant advancement in an artificial intelligence-based system to detect clinical referable DR [16], our research contribution is the evaluation of early-stage disease to assess the consequences of retinal inflammation on retinal energy demand and on key metabolic pathways, which could be therapeutically modulated to delay the onset of vascular problems. Therefore, in this study, we investigated the morphological, functional, and biochemical outcomes of retinal inflammation in early streptozotocin (STZ)-induced diabetic mice with inflammation in the eye.
## 2.1. Animals
All animal experiments were approved by the University of Auckland Animal Ethics Committee (AEC 2205) and were conducted in accordance with the Association for Research in Vision and Ophthalmology (ARVO) statement on the use of animals in research. Six- to seven-week-old male C57BL/6 mice from the Vernon Jansen Unit, the vertebrate containment facility at the University of Auckland, were used in this study. Mice were bred and housed in standard cages under normal light-dark cycle conditions (12-h light (174 lux) and 12-h dark (<62 lux)) and had access to normal food and water ad libitum.
## 2.2. Development of the STZ-Induced Hyperglycemic Mouse Model
Baseline body weight and nonfasting blood glucose levels were measured before the induction of hyperglycemia. Body size and glucose measurements were obtained from eight mice that received a single 150 mg/kg body weight intraperitoneal injection of STZ prepared in 0.1 M sodium citrate as previously described [17]. Eight control mice received a sham intraperitoneal injection of 0.1 M sodium citrate. Nonfasting blood glucose levels were measured one week after administering STZ and repeated for three consecutive days to confirm hyperglycemia using a tail prick test. All blood glucose measurements were done consistently between 9.00 and 10.00 am. Mice with blood glucose levels higher than 15 mmol/L were considered hyperglycemic [18]. Blood glucose readings higher than the upper limit of detection (27.8 mmol/L) of the blood glucose meter (Freestyle Optium H Glucometer, UK) were recorded at 27.8 mmol/L for data analysis.
Although measurement of glycated hemoglobin level (HbA1c) is routinely used as an indicator to assess long-term glycaemic control, previous studies have reported that STZ-induced diabetic animals show a small, nonsignificant increase in HbA1C levels after 1 week of STZ injection [19, 20]. As the present study aims to investigate the early changes in the retina, it was unlikely that HbA1C levels would be significantly elevated after 1 week of STZ administration. Therefore, sustained hyperglycemic was confirmed by measuring blood glucose levels and HbA1C level was not evaluated in this study.
## 2.3. Intravitreal Injection of Proinflammatory Cytokines
An intravitreal injection of a proinflammatory cytokine cocktail was performed, as previously described [7]. The STZ or vehicle injection protocol was repeated to create control and hyperglycemic mice with or without intravitreal cytokines. Briefly, a group of hyperglycemic mice ($$n = 6$$) were intravitreally injected in both eyes with 500 ng/ml each of proinflammatory cytokine TNF-α (#RMTNFAI, Thermo Fisher Scientific, Waltham, MA), and 500 ng/ml IL-1β (#RMIL1BI; Thermo Fisher Scientific) in a total volume of 1 μl (hyperglycemic mice with intraocular cytokines), while another group of six hyperglycemic mice received a vehicle (0.1 M PBS) intravitreal injection into both eyes (hyperglycemic group). Control mice received an intravitreal injection of proinflammatory cytokines ($$n = 6$$ control mice with intraocular cytokines) or a vehicle injection ($$n = 6$$ control mice, 12 eyes). Mugisho et al. [ 2018] observed that nonobese NOD diabetic mice develop clinical signs of DR as early as two days after intravitreal cytokine injection. Therefore, in this study, we investigated the structural, functional, and metabolic changes in the STZ-induced hyperglycemic mouse retina after two days of cytokine injection.
## 2.4. Fundus Imaging and Spectral Domain Optical Coherence Tomography (SD-OCT) Imaging
Fundus and SD-OCT imaging of the mouse retina was performed using the Micron IV imaging system as described previously [21, 22] to identify the appearance of clinical signs of DR including vessel beading and vessel tortuosity. Briefly, mice were anaesthetised by an intraperitoneal injection of ketamine (75 mg/kg body weight; Parnell Technologies, New Zealand) and domitor (0.5 mg/kg body weight; Zoetis, New Zealand). The pupils were dilated using $1\%$ tropicamide (Bausch and Lomb New Zealand Ltd, New Zealand) and the cornea was maintained hydrated with $1\%$ Poly Gel® lubricating eye gel (Alcon, Switzerland). The animals were then placed on a heating pad set at 37°C to maintain body temperature for the duration of anaesthesia. Eyes that developed cloudiness of the lens during anaesthesia were not possible to be analysed in the fundus and OCT studies. The fundus camera was carefully advanced towards the cornea until it came into contact with the poly gel. The Micron IV in-built StreamPix 6 software was used to visualise and record the retinal fundus. The retinal image was centered on the optic nerve head and blood vessel appearance was assessed. Vessel tortuosity was identified by the abnormal twists and turns of the blood vessels, and vessel beading was the alternating areas of blood vessel constriction that gave a “beaded appearance” to the blood vessels.
For the SD-OCT imaging, an ultrabroadband (160 nm) superluminescent diode centered at 830 nm was used as the light source to capture an image of 1024 pixels per A-scan with 1.8 μm longitudinal resolutions. The horizontal line B-scan had 2 μm axial resolutions. Each OCT scan consisted of twenty averaged B-scan images acquired at approximately one-disc diameter from the optic nerve head in the superior and inferior retinal quadrants. Images were analysed using ImageJ software (National Institute of Health, Maryland, USA) to evaluate retinal layer thickness.
## 2.5. Focal Electroretinography (ERG)
Retinal function was assessed using the image-guided focal ERG attachment of the Micron IV imaging system [23]. Briefly, mice were dark-adapted overnight and were handled under dim red-light illumination (λmax = 650 nm). For the ERG recording, mice were anaesthetised by an intraperitoneal injection of ketamine (75 mg/kg body weight) and domitor (0.5 mg/kg body weight); the pupils were dilated using $1\%$ tropicamide and the cornea was maintained hydrated with $1\%$ Poly Gel® lubricating eye gel. The animal was positioned on the heating pad and a subdermal ground platinum electrode was inserted into the base of the tail and the reference platinum electrode was inserted under the scalp skin at the midpoint between the eyes. The objective lens containing the recording gold electrode was advanced towards the cornea and the retinal image under red-light illumination guided the area used to record the ERG.
A recording area of 0.75 mm in diameter was consistently chosen within the central superior retina, approximately one-disc diameter away from the optic nerve head. The ERG recordings were in response to white-light flashes from a light emitting diode (LED) source (5 millisecond duration) and were recorded using the LabScribeERG 3 software (Phoenix Research Labs). The light intensity used to elicit the ERG response ranged from −0.40 to 3.20 log candela seconds per square meter (log cd s/m2), with 20 sweeps and 10 second interval for −0.4 to 1.40 log cd s/m2, 10 sweeps and a 20 second interval for 2.00 log cd s/m2, 3 sweeps and a 60 second interval for 2.60 log cd s/m2, and 2 sweep and 2 second interval for 3.20 log cd s/m2. Multiple individual responses from each sweep were averaged to obtain an improved signal-to-noise ratio [24]. One eye (left or right) per animal was used in the study. The ERG responses were recorded from the superior retina as this was the quadrant mostly associated with retinal thinning during DR [25–28]. The a-wave, b-wave, and implicit time as well as summed OP response were measured using the Micron IV software.
## 2.6. Biochemical Assays
For these biochemical assays data was collected from one or both eyes per experimental group. Retinal metabolites were measured using commercially available kits: glucose (Glucose-Glo™ Assay, #J6021, Promega, Madison, USA), lactate (Lactate-Glo™ Assay, #J5021, Promega), glutamate-glutamine (Glutamine/Glutamate-Glo™ Assay, #J8021, Promega) and adapted to analysis of the retina according to the manufacturer's protocol. Briefly, the retinal glutamate and glutamine levels were measured by incubating the sample with and without glutaminase for 40 minutes, followed by a 60-minute incubation with the supplier's glutamate detection reagent in 1: 1 ratio. The luminescence signal generated by the assay was recorded using the EnSpire® Multimode Plate Reader (Perkin Elmer, Massachusetts, USA). A glutamate standard curve was used to determine the concentration of glutamate and glutamine in the retinal supernatant. The retinal glucose and lactate levels were determined similarly, wherein the sample was incubated with the respective detection reagent and the luminescence signal was recorded. Glucose, lactate, glutamate, and glutamine levels are reported as nmol/mg protein.
ATP levels were measured using the Adenosine 5′-triphosphate (ATP) Bioluminescent Assay Kit (#FLAA, Sigma–Aldrich, Missouri, USA) according to the manufacturer's protocol. Briefly, a freshly prepared ATP assay mix was added to the retinal supernatant and the luminescence signal was recorded immediately. The ATP levels are reported as pmol/mg protein.
## 2.7. Enzymatic Activities in the Retina
Enzymatic activity was evaluated using retinal samples from six eyes per animal group. Glutamine synthetase (GS) activity was evaluated using the method employed in a previous study [29]. Briefly, the reaction mixture was prepared with 50 mM Imidazole-HCl buffer (pH 7.1), 7.6 mM ATP, 1.0 mM phosphoenolpyruvate, 50 mM MgCl2, 10 mM KCl, 40 mM NH4Cl2, 0.35 mM NADH, 0.1 M monosodium glutamate, 25 μg of pyruvate kinase, and 50 μg of lactate dehydrogenase in a volume of 1.0 ml. To eliminate traces of ADP and pyruvate, the reaction mixture was equilibrated at 30°C for 10 minutes. The retinal samples were added to the reaction mixture in a 1: 1 ratio and the rate of change in NADH absorbance was measured at 340 nm, 37°C using the EnSpire® Multimode Plate Reader for 10 minutes. The specific enzyme activity of GS was normalised to total retinal protein concentration and expressed as µmoles per minute per milligram protein.
The glyceraldehyde 3-phosphate dehydrogenase (GAPDH) activity Assay kit (#ab204732, Abcam, Australia) was used to evaluate GAPDH enzyme activity. In this assay, GAPDH catalyses the conversion of glyceraldehyde-3-phosphate to 1,3-bisphosphate glycerate resulting in a stoichiometric NADH generation, which further reacts with the developer to form a coloured product with an absorbance maximum at 450 nm. The retina was homogenised in GAPDH assay buffer and centrifuged at 10,000 × g for 5 minutes at 4°C to remove any cellular debris. The retinal supernatant was added to the reaction mix containing GAPDH assay buffer, GAPDH developer, and GAPDH substrate in a 1: 1 ratio and the colorimetric change was measured kinetically every 5 minutes, for a total of 20 minutes at 450 nm using a plate reader. A NADH standard curve was generated to calculate the specific GAPDH activity. GAPDH activity was expressed as nmoles per minute per milligram of protein.
## 2.8. Statistical Analysis
Normality of biochemical data distribution was confirmed using the Shapiro–Wilk normality test and QQ plots (see appendix). The statistical significance for focal ERGs was determined using two-way ANOVA and a post hoc Dunnett's multiple comparison tests. Statistical significance for the biochemical assays was determined using one-way ANOVA and a post hoc Dunnett's multiple comparison tests. The added effect of cytokines over high glucose conditions alone was evaluated by an unpaired t-test. A p value of less than 0.05 was considered statistically significant. The statistical analysis was conducted using six-eight eyes per animal group. The “n” value in the figure legend indicates individual retinas, with at least six per experimental group. All statistical analysis was performed using GraphPad Prism 8.
As both eyes from the same animal were used for some experiments, sensitivity analysis using the generalised estimating equations was performed to confirm the outcome obtained through traditional statistics. This analysis was performed using IBM SPSS statistics software (version 29).
## 3.1. STZ-Induced Hyperglycemic Mice had Reduced Body Weight and Elevated Blood Glucose Levels within One Week
Body weight and nonfasting blood glucose levels were determined before and after STZ injection (Figure 1). One week after STZ administration, there was a significant $9\%$ ($$p \leq 0.006$$) decrease in body weight in all STZ-injected animals and nonfasting blood glucose levels were significantly elevated by an average of $188\%$ ($$p \leq 0.0002$$), ranging from 18.6 to 27.8 mmol/L in hyperglycemic mice compared to control mice (7.8 to 10.1 mmol/L).
## 3.2. Proinflammatory Cytokines-Induced Vascular Changes in the Hyperglycemic Mouse Retina
Fundus examination was performed two days after the intravitreal injection, and the appearance of the retinal vasculature was assessed (Figure 2). Control mice (Figure 2(a)), control mice with intraocular cytokines (Figure 2(b)) as well as hyperglycemic mice without intraocular cytokines (Figure 2(c)) did not show signs of blood vessel damage or changes in the retinal vasculature. However, intravitreal injection of cytokines to hyperglycemic mice resulted in blood vessel tortuosity in three out of ten eyes and blood vessel beading in five out of ten eyes two days after the intravitreal injection (Figure 2(d)).
## 3.3. Proinflammatory Cytokines-Induced Retinal and Vitreous Hyper-Reflective Spots in Hyperglycemic Mice
SD-OCT imaging was performed to evaluate structural changes in the retina two days after intravitreal injection (Figure 3). The OCT scans showed that both hyperglycemic mice with and without intraocular cytokines developed distinct hyper-reflective spots 2 days after the intravitreal injection. Two out of eight eyes ($25\%$) of the hyperglycemic mice without intraocular cytokines and seven out of ten eyes ($70\%$) of the hyperglycemic mice with intraocular cytokines developed small (less than 20 μm) hyper-reflective spots, while six out of ten eyes ($60\%$) of the hyperglycemic mice with intraocular cytokines developed large (greater than 50 μm) hyper-reflective spots (Figure 3). These hyper-reflective spots were located near the outer plexiform layer and within the inner nuclear layer. Furthermore, three out of ten eyes ($30\%$) of the hyperglycemic mice with intraocular cytokines developed vitreal hyper-reflective spots (Figure 3(g)), which was not observed in any of the hyperglycemic mice without intraocular cytokines. Intravitreal cytokine injection did not induce any retinal abnormalities in control mice. Retinal layer thickness measurements confirmed no retinal thinning in any of the groups compared to control mice (Supplementary Figure 2).
## 3.4. Proinflammatory Cytokines Caused Retinal Functional Deficit in Hyperglycemic Mice
Focal ERG response was recorded to evaluate retinal function two days after intravitreal injection. Hyperglycemic mice with and without cytokines had a significant reduction in the ERG a-wave and b-wave amplitudes compared to control mice. The a-wave amplitude of hyperglycemic mice without cytokines was not reduced significantly except at light intensity 3.2 log cd s/m2 (two-way ANOVA, $$p \leq 0.033$$, Figure 4(a)) and the b-wave amplitude was not reduced except at light intensity 2.6 log cd s/m2 ($$p \leq 0.030$$, Figures 5(c) and 4(b)). Whereas, intravitreal injection of proinflammatory cytokines to hyperglycemic mice significantly further reduced the a-wave amplitude at light intensities 2.6 ($$p \leq 0.001$$) and 3.2 log cd s/m2 ($$p \leq 0.0007$$, Figures 5(d) and 4(a)) and reduced the b-wave amplitude at light intensities 2.0 log cd s/m2 ($$p \leq 0.032$$), 2.6 log cd s/m2 ($$p \leq 0.001$$) and 3.2 log cd s/m2 ($$p \leq 0.002$$, Figures 5(d) and 4(b)) compared to control mice. No significant changes were observed in the a-wave (Figure 4(c)) and b-wave (Figure 4(d)) implicit times in all conditions.
There were no statistically significant differences in the summed OP response in any experimental mouse group compared to control mice (Figure 4(e)), although the individual OP response appeared to be reduced in hyperglycemic mice injected with proinflammatory cytokines.
## 3.5. Proinflammatory Cytokines Trigger a Progressive Loss of Metabolic Homeostasis in Hyperglycemic Mice
The metabolic status of the DR retina was assessed by evaluating key metabolite levels two days postintravitreal injection. Retinal glucose levels were elevated by $81\%$ ($$p \leq 0.003$$) in hyperglycemic mice without cytokines compared with control mice, and those with intraocular cytokines had a significantly elevated retinal glucose of $158\%$ ($p \leq 0.0001$), lactate levels by $243\%$ ($p \leq 0.0001$), ATP by $97\%$ ($p \leq 0.0001$), glutamine levels by $54\%$ ($$p \leq 0.004$$), and reduced glutamate levels by $37\%$ ($$p \leq 0.011$$) compared to control mice (Figure 6). Intravitreal cytokine injection to hyperglycemic mice further increased glucose levels by $42\%$ ($$p \leq 0.005$$), lactate levels by $134\%$ ($$p \leq 0.0002$$), ATP levels by $45\%$ ($$p \leq 0.025$$), glutamine levels by $27\%$ ($$p \leq 0.029$$) above the levels observed in hyperglycemic mice without cytokines.
Hyperglycemic mice with intraocular cytokines showed significantly reduced GAPDH activity by $20\%$ ($$p \leq 0.031$$), while GS activity remained unchanged compared to control mice. However, intravitreal injection of cytokines to hyperglycemic mice caused a significant decrease in GS activity by $24\%$ ($$p \leq 0.004$$) compared to hyperglycemic mice without cytokines (Figure 7).
## 4. Discussion
In this study, it was confirmed that proinflammatory cytokines trigger the development of vascular and retinal abnormalities and metabolic dysregulation characteristic of DR in a STZ-induced hyperglycemic mouse model. The STZ-induced diabetic mouse model is one of the most widely used diabetic models [30], and significant DR-related retinal and vascular signs were evident once exposed to a proinflammatory environment. In the STZ mouse model, proinflammatory cytokines were delivered as a single injection, and combined with hyperglycemia, this single dose of proinflammatory cytokines leads to ocular vascular damage within two days. Hyperglycemic mice did not develop any severe ocular vascular abnormalities for the duration of this study and confirmed that STZ-induced diabetic mice rarely develop serious vascular abnormalities until 6 to 12 month posthyperglycemia [31–33]. The effects of STZ-induced hyperglycemia are seen after 6 months, when retinal physiological and biochemical changes are observed [33]. However, the intravitreal administration of proinflammatory cytokines to hyperglycemic mice accelerated the onset of changes, and resulted in vessel beading, increased vessel tortuosity, and retinal and vitreal hyper-reflective spots within two days. These vascular changes are considered early indicators of microvascular damage in DR [34] and were in line with previous findings from Mugisho et al. [ 2018] [7], wherein intravitreal cytokine injection to nonobese diabetic (NOD) mice triggered the development of severe vascular abnormalities within one week. The development of retinal and vitreal hyper-reflective spots is in concurrence with previous findings in patients with early DR [35, 36] and in the nonobese NOD DR mouse model [7] and DR rat model [37]. These previous studies have shown that intraretinal hyper-reflective spots in the inner retina could be associated with microaneurysms and macroaneurysms based on their size [37, 38], while other studies have demonstrated that the appearance of hyper-reflective spots in the inner nuclear layer is a definitive marker of vascular abnormality [35, 38]. Although we did not collect histological sections for the hyper-reflective spots in this model, hyper-reflective spots were found to be associated with activated microglial cells that are responsible for mediating the early inflammatory response in DR [35, 39–41]. Moreover, the precise location of these hyper-reflective spots in the retina can be predictive of disease progression, as microglial cells tend to migrate from the inner retina towards the photoreceptors with time in the diabetic retina [35]. Although less frequent, vitreal hyper-reflective spots are indicative of a more severe form of DR and are commonly reported in patients with severe proliferative DR [42]. These vitreal hyper-reflective spots are considered infiltrating macrophages that contribute to endothelial cell damage and eventually cause vascular breakdown in DR [43–45]. Hence, low-grade, subclinical inflammation seems to be a critical factor for the development of vascular lesions in DR [7, 43].
In addition to vascular and retinal abnormalities, intravitreal injection of cytokines triggered retinal functional deficits in hyperglycemic mice. Our findings were consistent with previous reports [46, 47] that reduced ERG a-wave and b-wave amplitudes indicative of impairment of photoreceptor and inner retinal function were early events in DR pathology. Although photoreceptors are generally not considered affected in DR (most likely due to the substantial distance from the retinal vasculature), recent reports have found that they are important mediators of oxidative stress and inflammation in DR [48, 49]. In fact, it was observed that patients with retinitis pigmentosa, a rare genetic disorder causing photoreceptor degeneration, were less likely to develop DR associated retinal complications in diabetic patients [50, 51]. Moreover, in addition to Müller cells and microglia, photoreceptors were found to be major contributors of oxidative stress and inflammation in the retina during DR [48] and were found to be apoptotic in STZ-induced diabetic rats [52]. Hence, early metabolic dysregulation in the photoreceptors and within the downstream inner retinal neurons [53] are the likely cause of the reduced a-wave and b-wave amplitudes in DR. However, in spite of the reduced a-wave and b-wave responses, the summed OPs remained unaffected in these mice, implying that the inhibitory feedback pathway initiated in the inner retina by the amacrine cells remained unaffected [54]. However, we hypothesize that significant change in OP amplitude becomes apparent with increasing DR duration.
Altered retinal function is often indicative of an underlying metabolic imbalance [55, 56]. Our previous studies have confirmed that co-exposure of mouse retinal explants to hyperglycemia and proinflammatory cytokines causes biochemical and neurochemical changes in retinal neurons and Müller cells, wherein the metabolite levels of glucose, lactate, ATP, and glutamate are altered, in addition to the re-distribution of glutamate and glutamine within the inner retinal neurons [57, 58]. As expected, hyperglycemic mice with intraocular cytokines showed altered glucose, lactate, ATP, glutamate, and glutamine levels, suggesting that cytokines can trigger metabolic dysregulation in hyperglycemic mice within two days. In addition, we also found that the activities of GAPDH and GS were altered in hyperglycemic mice with intraocular cytokines, and these enzymes are critical in regulating glucose and glutamate-regulating pathways [58]. This implies that inflammation could be the causative factor of early functional deficits in DR. We observed the same outcome in an in vitro model of high glucose plus cytokines [58], where lactate accumulation may also be mediated by pyruvate recycling when GAPDH activity is reduced [59, 60] or shunting through AGE pathways [61]. There is evidence of complete oxidative degradation of glutamate to form pyruvate and then lactate via a nonglycolytic route [62]. In support of this, previous studies have reported that metabolic pathways including glycolysis [63–65] and glutamate pathways [56], alter retinal function. These early functional deficits were also reported in diabetic patients with no apparent DR signs and without any significant visual dysfunction, suggesting that functional deficits may precede the manifestation of clinical signs of DR but are more likely to be a strong indicator of DR severity and the magnitude of functional loss [66]. Although retinal thinning is a clinical marker of neurodegeneration and is an early occurrence in DR [28, 67], no changes in the retinal layer thickness at both time points were observed, suggesting that functional and metabolic changes in DR may precede the occurrence of neurodegeneration.
Low glutamate and high glutamine levels observed in our model were not due to elevated glutamine synthetase activity. In such a scenario, glutamate hypersensitivity (a cause for retinal damage) is possible as overall decrease in glutamate reflects a rapid decrease in its synthesis from carbon dioxide, rather than from glutamine as described by Gowda et al. [ 68] in diabetic conditions. Our previous studies showed this decrease in glutamate in bipolar cells [58] and confirmed the decrease in glutamate and increase in glutamine. We have also shown that despite low glutamate levels, there are specific retinal areas of glutamate accumulation in Müller endfeet, and this would be the areas of glutamate hypersensitivity in DR. Thus, we think that glutamine levels reflect the state of the diabetic retina, but we do not have evidence that glutamine accumulation is damaging the retina.
Rajagopal et al. [ 2016] have shown that development and progression of DR in fat-fed mice are also associated with hyperglycemia and inflammation. Functional ocular deficits were characterised by electroretinographic dysfunction observed at beginning of 6 months due to glucose intolerance with microvascular disease appearing at 12 months. Interestingly, inflammasome activation was reported at 3 months, before the development of systemic glucose intolerance, electroretinographic defects, or microvascular disease. These results reinforce our suggestion that disease in the diabetic environment may progress through inflammatory stages long before the development of vascular lesions [69].
Therefore, the findings from our study emphasise the importance of early intervention targeting mediators of inflammation to slow down or prevent the progression of DR. In support of this, recent studies have shown that metabolic inhibitors such as the polymethoxylated flavone Niboletin [70] and a Chinese herbal formula, Shuangdan Mingmu capsule [71], promote the upregulation of GAPDH activity in DR, while resveratrol treatment [72] prevents GS downregulation in DR. These metabolic inhibitors could potentially prevent blood-retina barrier breakdown, oxidative stress-induced apoptosis of pericytes, and glutamate excitotoxicity in DR. Moreover, immunological therapy is fast gaining popularity to treat retinal inflammation in DR [73, 74], and drugs of particular interest are the Connexin-43 hemichannel inhibitors. Previous study from our group has shown that Connexin-43 hemichannel blocker mitigates retinal inflammation by effectively blocking the NLRP3 inflammasome pathway in an in vivo mouse model of DR [37, 75, 76].
In conclusion, this study is consistent with the hypothesis that proinflammatory cytokines aggravate the early morphological, functional, and metabolic imbalance in hyperglycemic mice and opens up the opportunity for a wider array of possible therapies concomitantly targeting inflammation and early metabolic dysregulation in DR. Moreover, these findings suggest that early visual dysfunction precedes retinal neurodegeneration and the appearance of severe vascular pathology in DR and are indicative of alerted retinal bioenergetics. Therefore, we have compiled evidence to suggest that it is crucial to control retinal inflammation in diabetics to prevent or delay the rapid worsening of retinal metabolism, which may trigger functional changes and may progress with time if not treated early on.
## Data Availability
The data supporting the findings of this study are included in the figure and supplementary data.
## Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
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title: Investigating genotype–phenotype relationship of extreme neuropathic pain disorders
in a UK national cohort
authors:
- Andreas C Themistocleous
- Georgios Baskozos
- Iulia Blesneac
- Maddalena Comini
- Karyn Megy
- Sam Chong
- Sri V V Deevi
- Lionel Ginsberg
- David Gosal
- Robert D M Hadden
- Rita Horvath
- Mohamed Mahdi-Rogers
- Adnan Manzur
- Rutendo Mapeta
- Andrew Marshall
- Emma Matthews
- Mark I McCarthy
- Mary M Reilly
- Tara Renton
- Andrew S C Rice
- Tom A Vale
- Natalie van Zuydam
- Suellen M Walker
- Christopher Geoffrey Woods
- David L H Bennett
journal: Brain Communications
year: 2023
pmcid: PMC9991512
doi: 10.1093/braincomms/fcad037
license: CC BY 4.0
---
# Investigating genotype–phenotype relationship of extreme neuropathic pain disorders in a UK national cohort
## Abstract
The aims of our study were to use whole genome sequencing in a cross-sectional cohort of patients to identify new variants in genes implicated in neuropathic pain, to determine the prevalence of known pathogenic variants and to understand the relationship between pathogenic variants and clinical presentation. Patients with extreme neuropathic pain phenotypes (both sensory loss and gain) were recruited from secondary care clinics in the UK and underwent whole genome sequencing as part of the National Institute for Health and Care Research Bioresource Rare Diseases project. A multidisciplinary team assessed the pathogenicity of rare variants in genes previously known to cause neuropathic pain disorders and exploratory analysis of research candidate genes was completed. Association testing for genes carrying rare variants was completed using the gene-wise approach of the combined burden and variance-component test SKAT-O. Patch clamp analysis was performed on transfected HEK293T cells for research candidate variants of genes encoding ion channels. The results include the following: (i) Medically actionable variants were found in $12\%$ of study participants (205 recruited), including known pathogenic variants: SCN9A(ENST00000409672.1): c.2544T>C, p.Ile848Thr that causes inherited erythromelalgia, and SPTLC1(ENST00000262554.2):c.340T>G, p.Cys133Tr variant that causes hereditary sensory neuropathy type-1. ( ii) Clinically relevant variants were most common in voltage-gated sodium channels (Nav). ( iii) SCN9A(ENST00000409672.1):c.554G>A, pArg185His variant was more common in non-freezing cold injury participants than controls and causes a gain of function of NaV1.7 after cooling (the environmental trigger for non-freezing cold injury). ( iv) Rare variant association testing showed a significant difference in distribution for genes NGF, KIF1A, SCN8A, TRPM8, KIF1A, TRPA1 and the regulatory regions of genes SCN11A, FLVCR1, KIF1A and SCN9A between European participants with neuropathic pain and controls. ( v) The TRPA1(ENST00000262209.4):c.515C>T, p.Ala172Val variant identified in participants with episodic somatic pain disorder demonstrated gain-of-channel function to agonist stimulation. Whole genome sequencing identified clinically relevant variants in over $10\%$ of participants with extreme neuropathic pain phenotypes. The majority of these variants were found in ion channels. *Combining* genetic analysis with functional validation can lead to a better understanding as to how rare variants in ion channels lead to sensory neuron hyper-excitability, and how cold, as an environmental trigger, interacts with the gain-of-function NaV1.7 p.Arg185His variant. Our findings highlight the role of ion channel variants in the pathogenesis of extreme neuropathic pain disorders, likely mediated through changes in sensory neuron excitability and interaction with environmental triggers.
Themistocleous et al. report that whole genome sequencing revealed clinically relevant variants in $12\%$ of participants with extreme neuropathic pain disorders. The majority of variants were in ion channels, including new phenotype associations (SCN9A p.Arg185His and non-freezing cold injury) and novel gain-of-function variants (TRPA1 p.Ala172Val and episodic pain).
## Graphical Abstract
Graphical abstract
## Introduction
Neuropathic pain occurs as a consequence of a disease or lesion in the somatosensory nervous system.1 It affects 6.9–$10\%$ of the general population2 and has a harmful impact on physical health, psychological health and quality of life.3 Understanding the role of genetic factors in neuropathic pain may reveal new pathophysiological mechanisms and is under-explored.4,5 Extreme pain phenotypes, caused by rare high-impact genetic mutations, offer insight into fundamental neurobiological mechanisms of pain.6 The phenotypes can range from congenital insensitivity to pain7 to enhanced pain perception. Different types of mutations in the same gene can cause a spectrum of phenotypes. For example, bi-allelic loss of function mutations in SCN9A, the gene encoding the voltage-gated sodium channel (Nav) 1.7 and which is highly expressed in peripheral sensory neurons,8 causes congenital insensitivity to pain.9 In contrast, monoallelic gain-of-function variants in the same gene is associated with pain disorders, which are inherited in a Mendelian fashion. These include inherited erythromelalgia10 and paroxysmal extreme pain disorder.11 Variants in genes causing Mendelian pain disorders may act as risk factors for common acquired neuropathic pain disorders. For example, SCN9A variants are implicated in more common neuropathic pain disorders such as idiopathic small fibre neuropathy12 and painful diabetic neuropathy.13 The SCN9A NM_002977.3:c.3448C>T, p.Arg1150Trp variant modulates risk and severity of pain across different chronic pain disorders.14 An environmental trigger that interacts with genes may cause neuropathic pain as some variants are common in the general population. Identification of such gene variants is important in diagnosis, genetic counselling and treatment enabling a stratified approach, such as the use of sodium channel blockers (e.g. lacosamide) in patients with small fibre neuropathy and gain-of-function Nav 1.7 variants.15 In some cases, such as inherited erythromelalgia, a personalized management approach can be used.16,17 Furthermore, there are rare inherited conditions where direct treatment can arrest progression, such as Fabry’s disease and hereditary transthyretin amyloidosis. In these diseases neuropathic pain is often the first symptom, the disease will progress if untreated and timely genetic diagnosis is essential to initiate appropriate treatment.18 The National Institute for Health and Care Research (NIHR) BioResource Rare Disease project applied whole genome sequencing (WGS) to a range of rare diseases, including neuropathic pain disorders.19 The aims of our study were to aid the genetic diagnosis of patients with extreme neuropathic pain phenotypes, to determine the prevalence of gene variants associated with neuropathic pain disorders and to understand how the functional changes caused by gene variants relate to clinical presentation and neuropathic pain (Supplementary Fig. 1).
## Recruitment and clinical phenotyping of participants
We recruited patients with extreme neuropathic pain phenotypes, both sensory loss and gain, from secondary care clinics in the UK, located in Oxford, London, Salford and Newcastle. Study participants with a history of lifestyle-altering sensory disorder, either pain or loss of sensation, for greater than 3 months were invited to participate. The criteria for clinical case definitions are shown in Table 1.19 We excluded patients with a known underlying genetic cause of chronic pain, e.g. Fabry’s disease and SCN9A congenital erythromelalgia (genetic pre-screening for these disorders was not mandatory), pregnancy, coincident major psychiatric disorders, poor or no English language skills, patients with documented central nervous system lesions, or patients with insufficient mental capacity to provide informed consent or to complete phenotyping. Description of the clinical phenotyping can be found in the summary NIHR Bioresource paper by Turro et al.19 and is briefly described here.
**Table 1**
| NPD | Diagnostic criteria | Associated phenotypes | Likely inheritance | Known gene (OMIM #) | PMID reference |
| --- | --- | --- | --- | --- | --- |
| Congenital insensitivity to pain (including hereditary sensory and autonomic neuropathy type IV, V, VII) | Inability to perceive painful stimuliOther somatosensory modalities may be impaired but the predominant clinical presentation is loss of pain sensibility | AnosmiaAutonomic dysfunctionAnhidrosisIntellectual impairment | AD, AR | SCN9A (243000), NGF (608654), NTRK1 (256800), SCN11A (615548), PRDM12 (616488), MPV17 (256810), CLTCL1 (601273) | 17167479, 17470132, 17597096, 23596073, 19304393, 14976160, 8696348, 24036948, 26005867, 185990, 26068709 |
| Hereditary sensory and autonomic neuropathy type I, II, III | Progressive neuropathies where the presenting or predominant feature is altered sensory function | Autonomic features and motor nerve involvement | AD, AR, X-linked | SPLTLC1 (162400), SPTLC2 (605713), WNK1 (201300), RAB7 (600882), IKBKAP (223900), FAM134B (613115), KIF1A (614213), ATL1 (613708), ATL3 (615632), CCT5 (256840) | 11242114, 20920666, 15060842, 12545426, 8102296, 19838196, 21820098, 21194679, 24459106, 16399879 |
| Erythromelalgia | Pain and erythema of the extremities which is exacerbated by warming and relieved by cooling. Initially episodic but may become persistent | Onset by age 20 | AD | SCN9A (133020) | 14985375 |
| Familial episodic pain syndrome | Severe episodic pain usually localized to the trunk and limbs with no structural cause. Triggers include cold environment, exercise and fasting | Onset usually in childhoodPossible family history | AD | TRPA1 (615040), SCN11A (615552) | 20547126, 24207120 |
| Small fibre neuropathy | Probable—symptoms in hands and feet consistent with small fibre dysfunction (pain and altered temperature sensibility), clinical signs of small fibre damage (reduced pinprick sensitivity and ability to discriminate warm/cool) and normal nerve conduction studiesDefinite—symptoms in hands and feet, clinical signs of small fibre damage, normal nerve conduction studies, and altered intra-epidermal nerve fibre density at the ankle and/or abnormal quantitative sensory testing of thermal thresholds at the foot | Autonomic features | AD | SCN9A (133020), SCN10A (615551), SCN11A (615552) | 21698661, 23115331, 24207120 |
| Post-traumatic neuropathy | Traumatic nerve injury with clinical evidence of nerve injury in the neuroanatomical distribution of neuropathic pain | | | | |
| Neuropathic pain NOS | Pain with a distinct neuroanatomically plausible distribution; however, no evidence of nerve injury found on clinical examination or specialized investigations | | | | |
Study participants attended a single appointment that included a clinical assessment, screening for neuropathic pain and specialized investigations to investigate distal symmetrical polyneuropathy. A detailed medical and drug history was taken, followed by a structured upper and lower limb neurological examination to detect clinical signs of distal symmetrical polyneuropathy.20,21 DN4 questionnaire was used as a screening tool for neuropathic pain.22 Confirmatory tests included nerve conduction studies,23 skin biopsy for intra-epidermal nerve fibre density24,25 and thermal thresholds in the area of neuropathic pain.26 Study participants’ pain was assessed and graded (Supplementary Fig. 2) according to published guidelines.27 Whole-blood samples were collected and sent to the NIHR BioResource laboratory in Cambridge. A detailed description of the DNA sequencing, WGS data-processing pipeline and identification of relevant gene variants can be found in Turro et al.19 and relevant aspects are summarized below.
All participants provided written informed consent in accordance with the Declaration of Helsinki. The study was approved by the East of England Cambridge South national research ethics committee (REC) reference 13/EE/0325.
## Analysis plan
Genetic analysis was completed in two parts. The first analysis was to identify variants of clinical relevance in known pain genes., grouping rare variants in genes for all neuropathic pain phenotypes considering both a targeted panel of pain genes and their promoters and all genes in the human genome that carried rare variants. Two ion channel variants were selected for electrophysiological analysis to investigate their functional impact (Supplementary Fig. 1).
## Gene list and transcript selection
A list of genes separated into three tiers were curated at the time of recruitment in 2015 (Tables 2–4) and updated in 2021 (Table 5). The division was based on the strength of evidence for the gene being linked to neuropathic pain.19 Only Tier 1 genes (Table 2) were discussed in the multidisciplinary team meetings and considered for clinical reporting.
## Variant filtering to identify variants of clinical relevance
Variants of the 14 Tier 1 neuropathic pain disorders genes were prioritized based on
## Variant interpretation in multidisciplinary teams meeting
Prioritized variants were assessed by a multidisciplinary team.19 Pathogenicity assignment was ascribed according to guidelines of The American College of Medical Genetics.28 Variants were classified as pathogenic, likely pathogenic, variant of uncertain significance (VUS), likely benign or benign. Clinically relevant variants were those deemed medically actionable, i.e. variants that could result in specific, defined medical recommendations, and were reported to the referring clinician.
Pathogenic and likely pathogenic variants were deemed clinically relevant.
VUS were deemed clinically relevant if VUS that did not meet the above criteria, likely benign and benign variants were not reported.
## Group-wise rare variant association testing
Genetic association testing in genes carrying rare variants was carried out using the gene-wise approach of the combined burden and variance-component test SKAT-O.29,30 The SKAT-O test combines a standard gene burden test that maximizes power under the assumption that all rare variants collapsed in a specific gene region are causal and acting in the same direction towards the phenotype and the sequence kernel association that calculates the weighted sum of squares of the variant score statistics and thus is more robust to the presence of variants with opposing effects. The combined SKAT-O test calculates a linear combination of the burden and variant-component test. Parametric bootstrap was used to resample residuals under the null model. The same resampled bootstrap phenotypes, considering covariates, were used for each gene. The weighting of the linear combination is optimized from the data itself. The Rho statistic indicates the weighting of each test and gives an indication of the causality and directionality of the variants’ effects, with rho = 1 reducing SKAT-O to a burden test (high percentage of causality in the same direction) and a rho = 0 to a SKAT (causal and non-causal variants with opposing directions). Age, sex and the three first Principal Components of genetic variation were used as covariates. Only unrelated individuals were used in the group-wise rare variant analysis. Analysis was done per ethnicity and neuropathic pain clinical phenotype.
## Data filtering and pre-process
Only participants with neuropathic pain were included for gene-wise rare variant association testing. In total 39 participants were excluded from the original cohort of 205 for the SKAT-O analysis (8 with no neuropathic pain; 2 age not available; 7 sequencing by Genomics England not available as mapped to Grch38 genome assembly and joint genotyping not conducted; 4 as only founders from families included). Exclusions were also based on ethnicity. We only considered 128 Europeans and 38 Africans with neuropathic pain as cases, 4 South Asians and 14 people of ‘Other’ ethnicity were not included. Unrelated individuals from the NIHR Bioresource Rare Diseases cohort, not recruited as a part of neuropathic pain disorders nor the Neurodevelopmental Disorders cohort, were included as controls. Analysis for individuals with European and African ethnic origin was separated. The sample size used was 128 Europeans and 38 Africans as cases versus 5945 and 154 as ethnically matched controls, respectively.
VCF files were normalized and left aligned, and multi-allelic single nucleotide polymorphisms (SNPs) were broken down to bi-allelic using BCF-tools. Only SNPs and indels that were rare in gnomAD (MAF < 0.001), not common in the whole NIHR Bioresources Rare Diseases cohort (MAF < 0.05), and had MAF < 0.05 in cases and controls combined for each phenotype were considered. Variants should have passed the quality filters in the joint genotyping calls and be of high quality with a call rate > $99\%$, following HWE equilibrium ($P \leq 0.05$). Alleles should have a high average base call depth (>10) and average genotype quality (>20). Regulatory regions for Tier 1–3 genes were downloaded using the GeneHancer resource form GeneCards. We then selected regions annotated as promoters or promoters/enhancers for the Tier 1–3 genes and collapsed in one group associated with the respective gene. For the panel of Tier 1–3 genes and their promoters, we considered variants with ‘modifier’, ‘moderate’ and ‘high’ impact effects. We further selected variants that were Nonsynonymous or have the EPACTS functional annotations: Essential_Splice_Site, Normal_Splice_Site, Start_Loss, Stop_Loss, Stop_Gain, 5′ UTR and 3′ UTR. For the whole gene set (all genes in the human genome), we only considered variants of ‘moderate’ or ‘high’ Variant Effect Predictor impact on protein-coding genes. Manta and Canvas software packages were used to detect deletions of >50 bp.19 Due to the limited sample size and multiple sub-phenotypes, we focused on protein-coding genes and high- and moderate-impact variants for the whole gene set analysis.
Separate analysis was completed for 36 Tier 1–3 genes (72 groups with their respective promoters); functionally validated variants in 3 voltage-gated sodium channels SCN9A, SCN10A and SCN11A8; and the whole gene set (∼20 000 genes).
## Plasmids and site-directed mutagenesis
Human NaV1.7 cDNA was cloned into a modified pcDNA3 expression vector containing downstream IRES and dsRED2 sequences (SCN9A-IRES-DsRED). Human β1 and β2 subunits were cloned into pIRES2-AcGFP (SCN1B-IRES-SCN2B-IRES-eGFP).9 Human TRPA1 cDNA was cloned into a modified pcDNA3 expression vector containing downstream IRES and dsRED2 sequences (TRPA1-IRES-DsRED).9 The mutations p.Arg185His, p.Ala172Val and p.Asn855Ser were introduced using QuickChange II XL site-directed mutagenesis kit (Agilent). The clones were sequenced by standard methods.
## HEK293T cell culture and transfection
Human embryonic kidney HEK-293 T cells were grown in a Dulbecco’s modified Eagle’s culture medium (DMEM/F-12, Invitrogen) containing $10\%$ foetal bovine serum and maintained under standard conditions at 37°C in a humidified atmosphere containing $5\%$ CO2. For the study of the SCN9A(ENST00000409672.1):c.554G>A, p.Arg185Hisvariant, cells were transfected using the jetPEI™ transfection reagent (Polyplus-transfection Inc.) with either WT or mutant Nav1.7 channel combined with β1 and β2 subunits (2:1 ratio). For the study of TRPA1(ENST00000262209.4):c.515C>T, p.Ala172Val and TRPA1(NM_007332.3):c.2564A>G, p.Asn855Ser variants, cells were co-transfected with pMaxGFP (Amaxa) and either human TRPA1 wild type (WT) or human TRPA1- p.Ala172Val/p.Asn855Ser at a ratio of 1:5 to facilitate visualization of positively transfected cells. The total amount of plasmid DNA transfected was 1.2 μg/μl per 35 mm dish. pMaxGFP positive cells were used as control. Cells were used 36–72 h after transfection. Experiments were performed at room temperature and repeated on three or more separate transfections.
## Electrophysiology
Voltage clamp experiments were performed on transfected HEK293T cells. Whole-cell patch clamp recordings were conducted using an Axopatch 200B Amplifier, the Digidata 1550B Low Noise Data Acquisition System and the pClamp10.6 software (Molecular Devices). Data were filtered at 5kHz and digitized at 20kHz. Capacity transients were cancelled and series resistance compensated at 70–$90\%$ in all experiments. Cells were continuously superfused with an extracellular solution or agonist-containing solutions through a common outlet. Standard extracellular solutions for the patch clamp experiments were used to study the respective SCN9A13 and TRPA131,32 variants.
## Electrophysiology study of SCN9A p.Arg185His
The extracellular solutions contained (in mM): 140 NaCl,3 KCl, 1 CaCl2, 1 MgCl2, 10 HEPES, pH 7.3 with NaOH (adjusted to 320 mOsm/l with glucose). Patch pipettes were filled with an internal solution containing (in mM) 140 CsF, 10 NaCl, 1 EGTA, 10 HEPES, pH 7.3 with CsOH (adjusted to 310 mOsm/l with glucose) and had a typical resistance of 2–3 MΩ. Leak currents were subtracted using a P/5 protocol, applied after the test pulse. A holding potential of −100 mV and an intersweep interval of 10 s was used for all the protocols. Measurements were done at 10°C, 20°C and 30°C.
## Electrophysiology studies of TRPA1 p.Ala172Val and p.Asn855Ser variants
To study the effects of agonist desensitization on channel activation, the extracellular solution contained (in mM): 127 NaCl, 3 KCl, 1 MgCl2, 10 HEPES, 2.5 CaCl2 and 10 glucose, pH 7.4 with NaOH. Osmolarity was adjusted to 310 mOsm/l with glucose. The intracellular solution contained (in mM): 135 KCl, 2 MgCl2, 2 MgATP, 5 EGTA and 10 HEPES, pH 7.4 with CsOH. Osmolarity was adjusted to 300 mOsm/l with glucose.31 To study the effects of intracellular calcium in channel modulation, the extracellular solution contained (in mM): 140 NaCl, 4 KCl, 2 CaCl2, 1 MgCl2, 10 HEPES, pH 7.4 with NaOH. Osmolarity was adjusted to 310 mOsm/l with glucose. The intracellular solution contained (in mM): 130 KCl, 8 NaCl, 2 EGTA, 1 MgCl2, 1 CaCl2, 4 MgATP, 0.4 Na2GTP, pH 7.4 with CsOH. Osmolarity was adjusted 300 mOsm/l with glucose.
To investigate voltage dependence, currents were recorded during a voltage-step protocol consisting of 400 ms voltage steps to test potentials ranging from −100 to +180 mV, followed by a final invariant step to −75 mV (400 ms) to measure tail currents. The holding potential was set at −0 mV.
The voltage-dependence activation of hTRPA1 p.Ala172Val in response to mustard oil [Allyl isothiocyanate (AITC) a TRPA1 electrophilic agonist] and Menthol (non-electrophilic agonist of TRPA1) was measured. These recordings were performed in a calcium-containing extracellular solution, to preserve agonist desensitization properties.33 Perfusion with TRPA1 agonists was performed through a custom-made gravity perfusion system. Immediately after establishing the whole-cell configuration, perfusion was switched to extracellular solution for 2 min before beginning the voltage clamp recording. AITC (Sigma 377430) was dissolved in DMSO (Sigma D2650), and Menthol (Sigma M2772) in ethanol. The maximum final concentration of either DMSO or ethanol did not exceed $0.1\%$. The effect of TRPA1 agonists on current–voltage curves was measured with a two-voltage-step protocol, as described above. Voltage ramps ranging from −100 to +100 mV for 500 ms, every 5 s, were applied to elucidate the temporal activation of hTRPA1 p.Ala172Val in response to AITC. In this case, the holding potential was set at −70 mV.
Current–voltage curves (I–V curves) were fitted using a combined Boltzmann and linear ohmic relationship: I/Imax = Gmax (Vm−Erev)/(1 + exp(V$\frac{1}{2}$−Vm)/k). Normalized conductance–voltage curves (activation curves) were fitted with a Boltzmann equation G/Gmax = 1/(1 + exp(V$\frac{1}{2}$−Vm)/K), where G was calculated as follows G = I/(Vm−Erev). Steady-state fast inactivation curves were fitted with IT/ITmax = 1/(1 + exp−(V$\frac{1}{2}$−Vm)/k). Tail current-derived voltage-activation curves were fitted to the Boltzmann equation: IT/IT (Max) = 1/(1 + exp[(Vm−V1⁄2)/k]). In all the equations, V$\frac{1}{2}$ represents the half-activation and half-inactivation membrane potentials; *Vm is* the membrane potential, Erev the reversal potential, k the slope factor, G the conductance and IT the current at a given Vm; Gmax and ITmax are the maximum conductance and current, respectively; *Rin is* the fraction of channels that are resistant to slow inactivation. Statistical significance was set at $$P \leq 0.05$$ for group comparisons.
## Statistical analysis
Electrophysiology data are presented as mean ± SEM. Statistical analysis for group comparisons included two-way ANOVA (temperature and genotype as a categorical variables) for p.Arg185His and one-way ANOVA (genotype as categorical variable) for p.Ala172Val with Sidak’s multiple comparison test. Statistically significant differences were defined as $P \leq 0.05.$ Frequencies of individual genetic variants were compared across groups using a two-tailed Fisher’s exact test. Significance for gene-wise associations was set to the Bonferroni-adjusted 0.01 threshold for the number of genes considered. Unadjusted P-values are reported alongside the significance threshold.
## Study participants
A total of 205 study participants with extreme phenotypes were included (Fig. 1). Age ranged from 4.8 to 84.3 years, and 115 ($56.1\%$) participants were men and 90 ($43.9\%$) were women. In total, 38 participants were recruited of African descent. The majority of participants ($90.2\%$) satisfied the criteria for probable or definite chronic neuropathic pain. Participants with possible neuropathic pain ($5.9\%$) included those with ‘neuropathic-type’ pain (burning, stabbing, electric-like shocks, dysaesthesias) in a neuroanatomically plausible distribution, but with no evidence of nerve injury on clinical examination or specialized investigations. Examples include those with episodic pain syndromes or those with burning pain of the hands and feet with a normal clinical examination and investigations. A group of participants (8, $3.9\%$) did not experience neuropathic pain, including participants with loss of pain sensation.
**Figure 1:** *Flow diagram outlining neuropathic pain grading, study participant recruitment and summary of study participant clinical phenotype. Ten participants were excluded due to either samples not received, quality control failures or gender discrepancies. †Included cases of longstanding progressive neuropathies where the presenting or predominant feature altered sensory function and an underlying cause could not be identified. ¶Include cases of post herpetic neuralgia, episodic pain syndromes, neuropathic pain with plausible neuroanatomical distribution but no abnormalities on examination and specialized tests, leprosy, hereditary neuralgic amyotrophy, type 1 complex regional pain syndrome, Noonan syndrome, injury to left arm (these cases were included due to severe pain which was in excess of the inciting injury). *Genes selected for electrophysiological characterization. NOS, not otherwise specified.*
## Gene variants reported—Tier 1 gene analysis
After multidisciplinary team discussion, 26 ($12.0\%$) gene variants were categorized as clinically relevant: 3 pathogenic ($1.4\%$), 2 likely pathogenic ($0.9\%$) and 21 VUS deemed relevant for reporting ($9.7\%$). Apart from three participants who declined consent for feedback on genetic testing, all variants were reported to the referring clinician. The results are summarized in Table 6 and Supplementary Table 1.
**Table 6**
| No | Clinical phenotype | Neuropathic pain grading | Gene | Nucleotide change | Amino acid change | Assigned pathogenicity |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | Erythromelalgia | Definite | SCN9A | c.2543T>C | p.Ile848Thr | Pathogenic |
| 2 | Erythromelalgia | Definite | SCN9A | c.2543T>C | p.Ile848Thr | Pathogenic |
| 3 | Sensorimotor neuropathy | Probable | SPTLC1 | c.399T>G | p.Cys133Trp | Pathogenic |
| 4 | Small fibre neuropathy | Definite | SCN10A | c.4984G>A | p.Gly1662Ser | Likely pathogenic |
| 5 | Small fibre neuropathy | Definite | SCN11A | c.4628G>A | p.Cys1543Tyr | Likely pathogenic |
| 6 | Painful sensory neuropathy | Definite | SPTLC2 | c.886A>C | p.Ile296Leu | VUS |
| 7 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 8 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 9 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 10 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 11 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 12 | Non-freezing cold injury | Definite | SCN9A | c.554G>A | p.Arg185His | VUS |
| 13 | Small fibre neuropathy | Definite | SCN9A | c.4612T>C | p.Trp1538Arg | VUS |
| 14 | Small fibre neuropathy | Definite | SCN9A | c.1445A>G | p.Lys482Arg | VUS |
| 15 | Small fibre neuropathy | Probable | SCN9A | c.2215A>G | p.Ile739Val | VUS |
| 16 | Small fibre neuropathy | Definite | SCN9A | c.2215A>G | p.Ile739Val | VUS |
| 17 | Painful sensory neuropathy | Probable | SCN9A | c.2215A>G | p.Ile739Val | VUS |
| 18 | Sensorimotor neuropathy | Definite | SCN9A | c.2215A>G | p.Ile739Val | VUS |
| 19 | Small fibre neuropathy | Definite | SCN9A | c.4982A>G | p.Glu1661Gly | VUS |
| 20 | Traumatic neuropathy | Probable | SCN9A | c.554G>A | p.Arg185His | VUS |
| 21 | Painful Sensory neuropathy | Definite | SCN10A | c.3445G>A | p.Pro1149Met | VUS |
| 22 | Small fibre neuropathy | Definite | SCN10A | c.2428G>T | p.Gly810Trp | VUS |
| 23 | Painful sensory neuropathy | Definite | SCN10A | c.2737G>A | p.Ala913Thr | VUS |
| 24 | Erythromelalgia | Probable | SCN10A | c.968A>G | p.Tyr323Cys | VUS |
| 25 | Small fibre neuropathy | Definite | SCN11A | c.2471A>G | p.Glu824Gly | VUS |
| 26 | Episodic pain | Possible | SCN11A | c.1730C>T | p.Pro577Leu | VUS |
Three participants were diagnosed with pathogenic mutations. Two sisters with severe erythromelalgia, present since childhood, have the same pathogenic variant in SCN9A p.Ile848Thr. This variant is not present in control populations, is reported in several inherited erythromelalgia pedigrees and causes gain of function through a hyperpolarizing shift in the voltage dependence of activation.10,34 A pathogenic variant in SPTLC1 p.Cys133Trp was identified in a participant diagnosed with a painful sensorimotor neuropathy and is the commonest pathogenic SPTLC1 mutation causing hereditary sensory neuropathy type-1 identified in UK patients.35 Two participants diagnosed with small fibre neuropathy carried likely pathogenic variants, SCN10A p.Gly1662Ser and SCN11A p.Cys1543Tyr. This conclusion was based on criteria available at the time of interpretation, low frequency in genetic databases, location in the important region of the channel, high probability of affecting channel function and previous description in small fibre neuropathy12; however, we note that this variant is classified as likely benign in ClinVar. The remaining 21 variants reported were classified as VUS, due to our conservative approach in assigning pathogenicity.
Of the 40 participants recruited with non-freezing cold injury, six participants ($7.5\%$ allele frequency) carry the SCN9A p.Arg185His variant, which is associated with small fibre neuropathy.12,36 The six participants were all of the African descent (the majority from Ghana). Chronic non-freezing cold injury is an acquired painful sensory neuropathy observed almost exclusively in soldiers.37 Soldiers of African descent are disproportionately affected when compared to Caucasian soldiers. The allele frequency of p.Arg185His variant is <$1\%$ in gnomAD ($1.4\%$ in the African/African-American population); 165 individuals carried the allele in the general gnomAD population, 125 of which were Africans (166 allele counts, 1 was homozygote). Within NIHR Bioresource controls of African ancestry, p.Arg185His allele frequency was 0.0065 (2 out 104 participants,$1.3\%$). In our cohort (Fig. 2A), the allele was significantly more common (6 out of the 38 participants) compared with both the general gnomAD population (Fisher’s Exact test, $$P \leq 1.4$$*10e−7, OR = 28.17, $95\%$ CI [9.98, 65.45]), the gnomAD African population (Fisher’s Exact test, $$P \leq 0.001$$, OR = 5.66, $95\%$ CI [2, 13.03]) and NIHR Bioresource African controls (Fisher’s Exact test, $$P \leq 7.6$$*10e−4, OR = 13.68, $95\%$ CI [2.41, 143.26]).
**Figure 2:** *Description and biophysical characterization of NaV1.7 channel p.Arg185His variant, which shows gain of function only at lower temperatures. (A) p.Arg185His is more common in non-freezing cold injury than control populations. (B) Schematic of NaV1.7 channel topology. R185H is represented with a red dot. (C) Normalized peak current–voltage relationship curves for the WT at 20°C (black squares, V1/2 = −30.7 ± 1.3, k = 4.9 ± 0.4, n = 15), WT at 10°C (grey dots, V1/2 = −26.5 ± 2, k = 6.6 ± 0.6, n = 14), R185H at 20°C (red triangles, V1/2 = −31.7 ± 1.7, k = 5 ± 0.5, n = 15), R185H at 10°C (blue diamonds, V1/2 = −23.8 ± 1.8, k = 6.7 ± 0.5, n = 13). R185H was not significantly different when compared with WT (P > 0.05, two-way ANOVA with Sidak’s multiple comparison test, temperature and genotype are categorical variables). Currents elicited from a holding potential of −100 mV to different test pulse potentials (50 ms) ranging from −80 to 40 mV in 5 mV increments. A holding potential of −100 mV and an intersweep interval of 10 s was used for all the protocols. (D) Steady-state fast inactivation curves for the WT at 20°C (black squares, V1/2= −85.8 ± 1.8, k = 7.5 ± 0.6, n = 14), WT at 10°C (grey dots, V1/2 = −80 ± 1.8, k = 13.7 ± 0.6, n = 13), R185H at 20°C (red triangles, V1/2= −86.9 ± 1.5, k = 7.6 ± 0.3, n = 14), R185H at 10°C (blue diamonds, V1/2= −73.1 ± 2.4, k = 14.7 ± 0.6, n = 13). R185H significantly different when compared with WT at 10°C (P < 0.05, two-way ANOVA with Sidak’s multiple comparison test, temperature and genotype are categorical variables). Currents elicited with test pulses to −10 mV following 500 ms inactivating prepulses. (E) Open-state fast inactivation kinetics for the WT at 20°C (black squares, n = 15), WT at 10°C (grey dots, n = 14), R185H at 20°C (red triangles, n = 15), R185H at 10°C (blue diamonds, n = 13). (F) Normalized peak current–voltage relationship curves for the WT at 20°C (black squares, V1/2 = −30.7 ± 1.3, k = 4.9 ± 0.4, n = 15), WT at 30°C (light blue stars, V1/2 = −34 ± 1, k = 5.2 ± 2.8, n = 12), R185H at 20°C (red triangles, V1/2 = −31.7 ± 1.7, k = 5 ± 0.5, n = 15), R185H at 30°C (pink asterisks, V1/2 = −33.6 ± 2, k = 4.8 ± 0.5, n = 10). Currents elicited from a holding potential of −100 mV to different test pulse potentials (50 ms) ranging from −80 to 40 mV in 5 mV increments. (G) Steady-state fast inactivation curves for the WT at 20°C (black squares, V1/2 = −85.8 ± 1.8, k = 7.5 ± 0.6, n = 14), WT at 30°C (light blue stars, V1/2 = −86.3 ± 2.1, k = 7.5 ± 0.3, n = 12), R185H at 20°C (red triangles, V1/2 = −86.9 ± 1.5, k = 7.6 ± 0.3, n = 14), R185H at 30°C (pink asterisks, V1/2 = −85.9 ± 2.4, k = 9 ± 1.5, n = 9). Currents elicited with test pulses to −10 mV following 500 ms inactivating prepulses. (H) Open-state fast inactivation kinetics for WT at 20°C (black squares, n = 15), WT at 30°C (light blue stars, n = 12), p.Arg185His at 20°C (red triangles, n = 15), R185H at 30°C (pink asterisks, n = 10). NFCI, non-freezing cold injury; NPD, neuropathic pain disorders; WT, wild type. Data are presented as mean ± SEM. Statistical analysis for group comparisons—two-way ANOVA with Sidak’s multiple comparison test (*statistically significant differences, P < 0.05).*
The variant, p.Arg185His, results in an amino acid substitution in the linker between D1/S2 and D1/S3, which lies within a voltage sensing domain of Nav1.7 (Fig. 2B). This residue is highly conserved across all voltage-gated sodium channels. Functional studies have shown that p.Arg185His variant does not alter channel gating properties at room temperature; however, it was associated with enhanced resurgent currents and increases excitability when expressed in dorsal root ganglion neurons.36 Intronic variants and the detection of deletions were included in the gene-level analysis, and none were deemed medically actionable or of interest.
## The impact of SCN9A p.Arg185His variant on NaV1.7 channel function is temperature dependent
Non-freezing cold injury is caused by cold environmental exposure and neuropathic pain is worsened by further cold exposure. We hypothesized that there may be an interaction between NaV1.7 and an environmental trigger, such that cooling magnifies the effect of p.Arg185Hisvariant on channel function. WT and p.Arg185His NaV1.7 channels were expressed in combination with β1 and β2 subunits in HEK293T cells and NaV1.7 mediated currents recorded by whole-cell patch clamp (Fig. 2C–H). Changing the temperature from 20°C to 10°C did not significantly affect the half-activation potential of p.Arg185His (Fig. 2C), but significantly shifted the half-inactivation potential for steady-state fast inactivation of p.Arg185His mutant compared to WT (Fig. 2D). Channel kinetics were slower at 10°C compared with 20°C with similar effects for WT and p.Arg185His (Fig. 2E). We also wanted to assess channel behaviour at higher temperatures (30°C). Increasing the temperature from 20°C to 30°C did not affect I–V curve or the steady-state inactivation of the WT or the p.Arg185His (Fig. 2F and G). Faster inactivation kinetics were observed at 30°C and the change was similar for WT and p.Arg185His (Fig. 2H).
In conclusion, the p.Arg185His mutant exhibited a depolarizing shift of half-inactivation potentials at 10°C but not at 20°C or 30°C, which is a change in channel gating consistent with a gain of function only at lower temperatures.
## Gene-wise rare variant association testing results
When comparing European participants against controls, gene-wise, rare variant association testing for Tiers 1–3 genes identified six genes and regulatory regions of four genes with a significant difference in rare variant distribution (Table 7). In total, 177 018 genomic loci were called in Tier 1–3 genes, 132 605 SNPs (93 295 were singletons), 4059 insertions and 8498 deletions (8859 out of the 12 557 were singletons). Variants per loci rate was 0.000046 in priority genes regions. Heterozygosity to Homozygosity ratio was 0.96, 1.35 for SNPs and 0.29 for INDELS. For European participants, gene-wise rare variant association varied according to clinical phenotype (Supplementary Table 2), and 20 genes and 24 regulatory regions reached Bonferroni-adjusted significance for at least 1 phenotype. For African participants, several genes showed similar associations with post-traumatic neuropathy, small fibre neuropathy and non-freezing cold injury (Supplementary Table 2).
**Table 7**
| Genomic coordinates and the HGNC gene symbol for gene models | Fraction with rare variants | Number of variants considered | P-value | Rho |
| --- | --- | --- | --- | --- |
| Europeans | Europeans | Europeans | Europeans | Europeans |
| Tier 1 pain genes | Tier 1 pain genes | Tier 1 pain genes | Tier 1 pain genes | Tier 1 pain genes |
| All neuropathic pain (n = 126) versus controls (n = 5096) | All neuropathic pain (n = 126) versus controls (n = 5096) | All neuropathic pain (n = 126) versus controls (n = 5096) | All neuropathic pain (n = 126) versus controls (n = 5096) | All neuropathic pain (n = 126) versus controls (n = 5096) |
| 1:115828713-115836247_NGF | 0.0026346 | 14 | 2.053e−08 | 0 |
| 3:38387651-39150434_SCN11A_promoter | 0.0037214 | 186 | 1.7809e−06 | 0 |
| Tier 2–3 pain genes | Tier 2–3 pain genes | Tier 2–3 pain genes | Tier 2–3 pain genes | Tier 2–3 pain genes |
| All neuropathic pain versus controls | All neuropathic pain versus controls | All neuropathic pain versus controls | All neuropathic pain versus controls | All neuropathic pain versus controls |
| 1:212733740-213037331_FLVCR1_promoter | 0.07838 | 704 | 1.0404e−05 | 0 |
| 2:241653459-241759532_KIF1A | 0.038202 | 180 | 8.74e−08 | 0 |
| 2:241757820-241808205_KIF1A_promoter | 0.011691 | 182 | 1.5858e−06 | 0 |
| 12:52056606-52201160_SCN8A | 0.0097151 | 44 | 5.0888e−08 | 0 |
| 2:234835229-234916724_TRPM8 | 0.017948 | 90 | 2.9788e−06 | 0 |
| Tier 1-3 pain genes | Tier 1-3 pain genes | Tier 1-3 pain genes | Tier 1-3 pain genes | Tier 1-3 pain genes |
| Probable/definite neuropathic pain versus controls | Probable/definite neuropathic pain versus controls | Probable/definite neuropathic pain versus controls | Probable/definite neuropathic pain versus controls | Probable/definite neuropathic pain versus controls |
| 1:212733740-213037331_FLVCR1_promoter | 0.078509 | 704 | 2.0274e−07 | 0 |
| 2:241656788-241737150_KIF1A | 0.038265 | 180 | 2.7235e−09 | 0 |
| 2:241757820-241808205_KIF1A_promoter | 0.01171 | 182 | 2.4975e−07 | 0 |
| 1:115828713-115836247_NGF | 0.002639 | 14 | 2.6429e−09 | 0 |
| 12:52056606-52201160_SCN8A | 0.0097312 | 44 | 1.5018e−09 | 0 |
| 2:167227721-167351474_SCN9A_promoter | 0.037605 | 461 | 2.3805e−05 | 0 |
| 8:72935185-72987631_TRPA1 | 0.015009 | 73 | 5.2482e−05 | 0 |
| 2:234835229-234916724_TRPM8 | 0.017978 | 90 | 2.825e−07 | 0 |
| SCN9A, SCN10A, SCN11A functionally validated variants | SCN9A, SCN10A, SCN11A functionally validated variants | SCN9A, SCN10A, SCN11A functionally validated variants | SCN9A, SCN10A, SCN11A functionally validated variants | SCN9A, SCN10A, SCN11A functionally validated variants |
| 3:38739727-38793804_SCN10A (Small fibre neuropathy) | 0.0013329 | 2 | 0.00016219 | 0 |
For the groups of functionally validated voltage-gated sodium channel variants, a significant association of SCN10A with small fibre neuropathy in Europeans was found. This was driven by two gain-of-function variants, SCN10A(ENST00000449082.2):c.4984G>A, p.Gly1662S ((Allele frequency 0.0039 in Neuropathic Pain and 0.0005 in controls) and ENST00000449082.2:c.1661T>C, p.Leu554Pro (Allele frequency 0 in Neuropathic Pain and 0.00008 in controls) (Table 7). These variants are reported in patients with small fibre neuropathy, cause a gain of function of the NaV1.8 channel and enhance dorsal root ganglion neuron excitability.38,39 However, both variants were characterized as likely benign in ClinVar and their allele frequencies in gnomAD suggest that they cannot be the only cause of neuropathic pain. No significant associations were found for SCN9A, nor SCN11A.
For the whole gene set analysis, 146 genes reached Bonferroni corrected significance in Europeans with neuropathic pain when compared to controls (Supplementary Table 3). Four of the genes, KIF1A, KCNQ5, KCNK4 and NOS2 are linked to human neuropathic pain.
## The TRPA1 p.Ala172Val variant is associated with episodic pain and gain of channel function
Variants in Tier 2 and 3 genes were filtered (Tables 3–5), using the same approach as for Tier 1 genes to identify those that may be pathogenic. A rare TRPA1 variant, p.Ala172Val (gnomAD allele frequency 0.0003), was identified in a participant who suffers from episodic widespread chronic pain with neuropathic characteristics particularly affecting the trunk. Clinical phenotype was similar to a neuropathic pain syndrome associated with a TRPA1 channelopathy (p.Asn855Ser variant)32; however, the participant’s pain was not precipitated by physiological stressors. MRI of the brain and spine, nerve conduction studies and skin biopsy of the lower leg were within age- and gender-appropriate reference ranges. The participant’s child, carrying the same variant, was similarly affected by chronic abdominal and pelvic pain with normal investigations. In silico tools, Polyphen and SIFT scores, predicted p.Ala172Val to be deleterious. The variant is moderately conserved across species (Fig. 3A), is situated within the ankyrin repeat domain of the protein (Fig. 3B) and is a non-polar to non-polar amino acid exchange (Grantham score 64). Based on the rarity of the variant, clinical phenotype consistent with the TRPA1 Familial Episodic Pain Syndrome, positive family history and in silico analysis showing the variant to be in the poorly understood N-terminal ankyrin repeats, the TRPA1 p.Ala172Val variant was prioritized for functional studies. These were compared to the TRPA1 variant p.Asn855Ser which is the only variant previously linked to this disorder.
**Figure 3:** *Description and biophysical characterization of hTRPA1 p.Ala172Val variant, which shows gain of function in response to AITC. (A) p.Ala172Val variant: *Alanine is* substituted with a valine in the fourth ankyrin repeat domain of the channel. The residue is moderately conserved across different mammalian species. (B) Schematic of TRPA1 channel topology. p.Ala172Val variant is represented with a blue dot. Voltage-dependence activation of hTRPA1 p.Ala172Val and p.Asn855Ser was measured in response to AITC. Current–voltage curves were measured with a two-voltage-step protocol (voltage ramps ranging from −100 to + 100 mV for 500 ms, every 5 s). Holding potential was set at −70 mV. Application of 25 µM of AITC shows enhanced activity of p.Ala172Val variant in response to 25 μM AITC in the presence of extracellular calcium alone (C–G). (C) Current density, assessed using a two-voltage-step protocol, was significantly different between WT and variant channels as quantified in (D) and (E). (D) Outward currents (+100 mV, WT = 21.12 ± 2.46 pA/pF, p.Arg185His = 56.92 ± 12.52 pA/pF, p.Asn855Ser = 38.12 ± 10.77 pA/pF) were significantly increased for p.Ala172Val ($P \leq 0.005$). (E) Inward currents (−100 mV, WT = −6.12 ± 0.63 pA/pF, p.Arg185His = −8.48 ± 1.37 pA/pF, p.Asn855Ser = −13.48 ± 2.40 pA/pF) were significantly increased for p.N885S ($P \leq 0.005$). (F) Tail current analysis showed a significant shift in half-maximal activation potential for both p.Ala172Val ($$n = 9$$, V$\frac{1}{2}$ = 35.55 ± 5.45 mV, and p.Asn855Ser, V$\frac{1}{2}$ = 37.03 ± 8.28 mV, $$n = 12$$, when compared with WT $$n = 13$$, V$\frac{1}{2}$ = 59.16 ± 5.25 mV; $P \leq 0.05$). Slopes of the voltage-activationcurve (i.e. voltage sensitivity) ($k = 49.38$ ± 2.03 mV WT, $k = 43.29$ ± 2.63 mV A172V, $k = 45.20$ ± 2.36 mV; $$P \leq 0.18$$) were not significantly different. (G) Current–voltage relationships were tested with a voltage-ramp protocol in which voltage changes at a steady rate and the resulting current is recorded. After administration of 25μM (arrow) averaged currents from voltage ramps were extrapolated and at +90 mV showed a 3-fold increase in current densities at positive potentials after the application of 25 µM AITC (WT = 85.13 ± 23.30 mV, p.Ala172Val = 250.4 ± 62.39 mV, p.Asn855Ser = 256.1 ± 54.90 mV; $P \leq 0.05$). Insert shows example tracing. WT, wild type. Data are presented as mean ± SEM. Statistical analysis for group comparisons—one-way ANOVA with Sidak’s multiple comparison test (*statistically significant differences, $P \leq 0.05$).* TABLE_PLACEHOLDER:Table 3 The biophysical properties of WT, p.Ala172Val and p.Asn855Ser hTRPA1 were compared (Supplementary Fig. 3). In HEK293T transfected cells, TRPA1 current–voltage relationship and half-maximal activation for WT, p.Ala172Val and p.Asn855Ser were not statistically different. Under control conditions, WT, p.Ala172Val and p.Asn855Ser current traces showed sustained outward rectification at positive potentials, consistent with an underlying voltage dependence of channel gating. As current density did not differ between WT and the variants, under control conditions, it is unlikely that channel trafficking is affected in p.Ala172Val and p.Asn855Ser channels.
Voltage-dependence activation of hTRPA1 p.Ala172Val and p.Asn855Ser was measured in response to mustard oil (AITC, a TRPA1 electrophilic agonist) and Menthol (non-electrophilic agonist of TRPA1). In response to 25 µM AITC, in the presence of extracellular calcium, p.Ala172Val showed a pronounced linearization of the current–voltage relationship compared to WT in response to a two-voltage-step protocol. A steeper activation curve was observed at positive potentials for both p.Ala172Val and p.Asn855Ser compared with WT (Fig. 3C). Currents were significantly increased at positive potentials for p.Ala172Val (Fig. 3D) and at negative potentials for p.Asn855Ser (Fig. 3E). Analysis of tail currents demonstrated a significant leftward shift of voltage dependence of channel activation in the presence of AITC for both variants (Fig. 3F); however, the slope of the voltage-activation curve did not significantly change. Current–voltage relationships, tested with a voltage-ramp protocol, showed an increase in current densities at positive potentials after the application of 25 µM AITC (Fig. 3G). In summary, these findings show an enhanced response of p.Ala172Val to 25 µM AITC, suggesting a gain-of-function behaviour of this variant, under agonist stimulation.
Application of 100 µM Menthol did not significantly change current density nor voltage sensitivity when applied to p.Ala172Val or p.Asn855Ser in the presence of extracellular calcium (Supplementary Fig. 4). However, in the presence of both extracellular and intracellular calcium, significant changes were observed (Fig. 4A–D). Both p.Ala172Val and p.Asn855Ser channels showed an increase in outward currents and voltage sensitivity in response to 100 µM Menthol.
**Figure 4:** *hTRPA1p.Ala172Val variant shows enhanced activity after the application of 100 µm of menthol in the presence of intracellular and extracellular calcium. Current–voltage curves were measured with a two-voltage-step protocol (voltage ramps ranging from −100 to + 100 mV for 500 ms, every 5 s; holding potential was set at −70 mV). (A) Current density, assessed using a two-voltage-step protocol, in WT and variant channels were significantly different as quantified in (D) and (E). (B) Outward currents were enhanced for both p.Ala172Val and p.Asn855Ser (+100 mV, WT = 21.96 ± 3.64 mV, p.Ala172Val = 39.94 ± 5.80 mV, and p.Asn855Ser = 70.07 ± 21.71 mV; P < 0.05). (C) Inward currents were not statistically different (−100 mV, WT = −8.29 ± 1.97 mV, −6.52 ± 0.83 mV p.Ala712Val, and p.Asn855Ser = −5.72 ± 0.94 mV; P = 0.58, and P = 0.40, respectively). (D) Half-maximal activation potential was significantly shifted leftward for p.Ala172Val and p.Asn855Ser (V1/2, WT = 61.83 ± 7.37 mV n = 8, p.Ala172Val = 36.41 ± 7.58 mV n = 9, p.Asn855Ser = 33.60 ± 6.67 mV n = 6). Voltage sensitivity was significantly altered (slope factor k = 41.51 ± 2.33 mV WT; 40.21 ± 2.98 mV p.Arg185His; 39.32 ± 3.03 mV p.Asn855Ser; P < 0.05). WT, wild type. Data are presented as mean ± SEM. Statistical analysis for group comparisons—one-way ANOVA with Sidak’s multiple comparison test (*statistically significant differences, P < 0.05).*
In summary, the p.Ala172Val variant confers gain-of-function properties on TRPA1 channel in response to the agonists AITC and Menthol. For the latter, the effect was dependent on intracellular calcium.
## Discussion
In this study, we applied WGS to a cohort of patients with neuropathic pain disorders. We carried out two analyses. First, we identified medically actionable variants and second, we carried out group-wise association tests at the gene level for rare variants. The diverse phenotypes ranged from congenital insensitivity to pain to painful neuropathy. Clinically relevant findings in genes associated with pain were reported in $12\%$ of participants. The majority of clinically relevant variants were in voltage-gated sodium channels. We made new genotype–phenotype associations, such as the NaV1.7 p.Arg185His variant which was more frequent in Africans with non-freezing cold injury. We provide new mechanistic insights showing that p.Arg185His interacts with cold, causing a gain of function in the NaV1.7 gating properties. The gain of function of p.Ala172Val in TRPA1 in response to agonists extends our knowledge of painful TRPA1 channelopathies.
The study of genetic neuropathic pain disorders poses challenges in describing clinical phenotype and assigning pathogenicity to associated variants. We used the gold standard grading system, of the Neuropathic Pain Special Interest Group of IASP.27 Such an approach works well in disorders where there is structural injury to sensory neurons. For example, in painful distal symmetrical polyneuropathy, pain and sensory signs are found in a neuroanatomically plausible distribution (meeting probable criteria), and specialized investigations confirm a lesion of the somatosensory nervous system (meeting definite criteria). However, the majority of Mendelian pain disorders are sensory neuron ion channelopathies, in which pain is episodic, with no structural injury to sensory neurons. For example, in Inherited Erythromelalgia (NaV1.7) or Familial Episodic Pain Syndrome (TRPA1, NaV1.9), sensory examination (between pain episodes), neurophysiology and cutaneous innervation are normal. Careful attention to clinical history is, therefore, essential.
We identified medically actionable variants in $12\%$ of the participants. In our cohort, five participants carry pathogenic or likely pathogenic variants. Four pathogenic variants were in voltage-gated sodium channels, which can impact patient care. For example, a pair of sisters with Inherited Erythromelalgia carry the pathogenic SCN9A p.Ile848Thrwith autosomal dominant pattern of inheritance. Only a minority of patients with Inherited Erythromelalgia possess SCN9A mutations.40 Identification of a genetic cause for erythromelalgia means family genetic counselling and preferential treatment with non-selective sodium channel blockers,41 which is not the standard treatment for other causes of neuropathic pain.42 The family may also access future treatments such as selective sodium channel blockers.43 A further 20 participants carry VUS in voltage-gated sodium channels that were deemed clinically relevant. Ascribing pathogenicity to ion channel variants is difficult. The majority are relatively common, exhibit subtle channel gain-of-function effects and likely interact with environmental factors. For example, in our cohort the SCN9A(ENST00000409672.1): c.2215A>G, p.Ile739Valvariant was found in four participants with painful neuropathy and described previously as pathogenic.36 However, p.Ile739Valis common in the general population ($0.2\%$ allele frequency), while the prevalence of small fibre neuropathy is rare (∼50 per 100 00044). It is unlikely that this variant is fully penetrant (thus pathogenic); but may act as a risk factor. Nevertheless, it is important to identify such variants for treatment selection, because Lacosamide (a sodium channel blocker) has shown efficacy in a clinical trial of patients with small fibre neuropathy and rare SCN9A45 variants. Voltage clamp analyses can add valuable experimental evidence to the pathogenicity assessment that cannot be replaced by in silico prediction tools, as in silico analysis is not comprehensive and variant’s effect prediction software are likely to underestimate the impact of the gain-of-function effects. However, recent advances in ion channel modelling have improved outcomes.46 WGS is now integrated into the UK national health system (see https://www.genomicsengland.co.uk/) and is now considered the standard of care across the UK.47 As of 2022 all testing at NHS genomic centres for painful neuropathies and channelopathies is via WGS. The panel application ‘Inherited neuropathies or pain disorder v1.36’ was informed by the NIHR Bioresource (100 000 genomes), our functional studies and includes all the tier 1 pain genes in this paper and TRPA1. Such an approach will identify many more new variants and the findings of our study will be of direct relevance to practitioners assessing patients with neuropathic pain. Understanding how these novel variants relate to clinical phenotype presents significant challenges for clinicians. The combined expertise of our multidisciplinary team meetings was vital in the interpretation of variant pathogenicity. We note that the proportion of cases in which we have found a pathogenic variant that explains the clinical pain phenotype was low ($2.4\%$), although in a higher proportion (a further $10\%$) the finding was clinically actionable. In many cases, this was VUS in which the relationship to the clinical phenotype could not be causally established, but could still have implications for treatment and so was reported. The low yield in solving cases at a diagnostic level partly represents the fact that neuropathic pain is monogenic in only a minority of cases. In many cases it is likely to arise from gene–environment interaction and/or multiple genes. To try and improve diagnostic yield in the future we could enrich analysis pipelines (for instance to include analysis of repeat expansions in the WGS data), expand co-segregation analysis and also functional analysis. It is not possible to test all novel variants with current patch clamp technology. Automated prediction of pathogenicity and higher throughput functional assays should be a priority, and important next steps in the integration of WGS into healthcare.
We were able to extend genotype–phenotype associations of SCN9A. The SCN9A p.Arg185His variant was more common in study participants with non-freezing cold injury. p.Arg185His is associated with neuropathic pain disorders, such as small fibre neuropathy12 and painful diabetic neuropathy.13 Non-freezing cold injury is a chronic neuropathic pain disorder caused by an acquired sensory neuropathy37 after prolonged exposure to cold and wet environments. Further cold exposure intensifies the neuropathic pain in a similar manner to cold allodynia after platinum-based chemotherapy.48 The mechanism for this cold allodynia is unknown. We show that p.Arg185His displays gain-of-function characteristics at lower temperatures. This occurs through a shift of fast inactivation at 10°C, but not at 30°C or room temperature (20°C). Pathogenic SCN9A variants that cause inherited erythromelalgia demonstrate temperature sensitivity at biophysical and clinical levels such that warming intensifies pain and cooling is analgesic.49 A link, therefore, exists between the biophysical properties of NaV1.7 channel, clinical phenotype and temperature sensitivity. An increase of p.Arg185His variant excitability on cooling may contribute to cold allodynia in a sub-group of non-freezing cold injury patients. Another consideration is that increased activity of p.Arg185His at cold temperatures could injure nerves through calcium dependant excitotoxicity.50,51 In summary, p.Arg185His may contribute to clinical phenotype or increase the risk for cold-induced neuropathic pain, but we cannot conclude that is causative in isolation.
Bevimed is an inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison and was used in an analysis of all cohorts of NIHR BioResource Rare Diseases.19 Under different modes of inheritance there was strong evidence for 95 genes and 29 binary disease tags (cases versus controls). No diseases from our cohort were among the associations with strong evidence. We used SKAT-O in our cohort. Group-wise association tests can increase the power to detect associations between rare alleles and phenotypes by aggregating variant counts on the gene level that can have an impact on gene function. Neuropathic pain can arise as a complex effect of rare variants with causal effects in the same direction, which the burden test is best powered to detect, or effects with different levels of causality and direction, which the sequence kernel association test is best powered to detect. We used an approach that maximizes test power by considering the combination of both the burden and sequence kernel association tests. In testing for associations for the whole gene set in Europeans with neuropathic pain, 146 genes reached statistical significance. These are gene-wise results showing a significant association of a configuration of rare alleles grouped at the gene level with certain neuropathic pain phenotypes. In most of the associations, the variant-component test was better powered. This is indicated by the low Rho statistics empirically calculated from the data and is suggestive of the presence of variants with different values of causality and opposing direction of effects towards the phenotype, i.e. both protective and deleterious variants present and driving the association. We focused on bi-allelic variants and indels of high quality, with moderate to high impact on protein-coding genes, as we had a moderate sample size and did not want to diminish statistical power after Bonferroni correction. *The* genes KCNK4, KCNQ5 and NOS2 were significantly associated with neuropathic pain after gene-wise rare variant association testing. These have not been previously linked to human pain at a genetic level, but preclinical models have implicated these genes in pain pathogenesis.52-54KIF1A, a Tier 2-3 priority gene also reached genome-wide significance. The association with neuropathic pain would require replication in independent cohorts and functional studies.
We undertook an analysis focusing on genes known or likely to be implicated in neuropathic pain, and we, therefore, used more relaxed criteria in filtering rare variants, including modifier, moderate and high-impact variants. We also note that our in silico variant effect predictions are limited by their tendency to underestimate gain-of-function effects. Our analysis showed an association of five Tier 2 genes, including TRPA1, with neuropathic pain. TRPA1 is a calcium-permeable non-selective cation channel that acts as a sensor of noxious external stimuli, such as mustard oil (AITC) and Menthol. The p.Asn855Ser TRPA1 variant is associated with familial episodic pain syndrome characterized by truncal pain triggered by physiological stress or exercise.32 We identified a study participant with a similar clinical phenotype of truncal pain who carried the p.Ala172Val TRPA1 variant. p.Ala172Val is a missense mutation in the fourth domain of the Ankyrin region. Our in vitro electrophysiological studies show that TRPA1 p.Ala172Val variant causes a gain of function in response to agonist stimulation and provides evidence for Ca2+-mediated channel activation through the Ankyrin repeat domains of TRPA1 channel.
TRPA1 consists of a large intracellular NH2 and COOH termini, with the NH2 terminus containing an elongated ankyrin repeat domain which is highly conserved. Human TRPA1 consists of 16 ankyrin repeat domains that connect to the transmembrane domains via a linker region. The p.Ala172Val variant did not change the biophysical properties of TRPA1 channel in the naïve state, but did enhance responses to AITC and Menthol. Current density was larger for AITC when compared with Menthol. Due to differences in their electrophilic nature, agonists vary in their ability to covalently (AITC) or noncovalently (Menthol) modify the channel upon binding. This difference in channel binding might underlie different agonist responses. The p.Asn855Ser and p.Ala172Val variants increase activation in response to agonists, although with distinct impacts on the biophysical properties of the channel. Intrinsic differences due to the positions of the variants within the channel (p.Asn855Ser is in the transmembrane domain S4), might underlie the differences observed. Our data show that the ankyrin repeat domain is important in TRPA1 channel activation and gating functions. The p.Ala172Val variant is reported at a heterozygous frequency of 0.0003 in gnomAD and individuals over 70 years of age are reportedly healthy so it is unlikely to be fully penetrant. It is more likely to act as a risk factor and contribute to the expression of neuropathic pain by altering the functional properties of nociceptive afferents; however, the drivers for channel activation (environmental versus endogenous ligands) are not known.
In our study, we identified clinically relevant variants in $12\%$ of the participants, with an impact on clinical care and treatment. The majority of these variants are located in ion channels which are enriched in nociceptors and environmental triggers (such as cold in the case of non-freezing cold injury) may interact and enhance the gain-of-function impact of such variants.
## Supplementary material
Supplementary material is available at Brain Communications online.
## Funding
This research was funded in whole, or in part, by the Wellcome Trust (Grant numbers: 109915/Z/15/Z, 083259, 202747/Z/16/Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
D.L.H.B., A.C.T. and A.S.C.R. are members of the DOLORisk consortium funded by the European Commission Horizon 2020 (ID633491), which received funding from European Union’s Seventh Framework Program (FP$\frac{7}{2007}$-2013). D.L.H.B. and A.C.T. are members of the International Diabetic Neuropathy Consortium (IDNC) research programme, which is supported by a Novo Nordisk Foundation Challenge Programme grant (Grant number NNF14OC0011633). D.L.H.B. is a Wellcome Investigator (202747/Z/16/Z and 223149/Z/21/Z). This work was supported by the Wellcome Trust through a Strategic Award to the London Pain Consortium (ref. no. 083259). D.L.H.B. and A.C.T. are members of the PAINSTORM consortium funded by UK Research and Innovation (UKRI) and Versus Arthritis. A.C.T. is supported by Academy of Medical Sciences Starter Grant SGL022\1086 and is an Honorary Senior Research Fellow and Carnegie-Wits Diaspora Fellow at the Brain Function Research Group, School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. R.H. is a Wellcome Trust Investigator (109915/Z/15/Z), who receives support from the Medical Research Council (UK) (MR/N$\frac{025431}{1}$ and MR/V$\frac{009346}{1}$), the European Research Council [309548], the Newton Fund (UK/Turkey, MR/N$\frac{027302}{1}$), the Addenbrookes Charitable Trust (G100142), the Evelyn Trust, the Stoneygate Trust, the Lily Foundation and Medical Research Council (MRC) strategic award to establish an International Centre for Genomic Medicine in Neuromuscular Diseases (ICGNMD, MR/S$\frac{005021}{1}$). M.M.R. acknowledges funding from the Medical Research Council (MRC MR/S$\frac{005021}{1}$), the National Institutes of Neurological Diseases and Stroke and Office of Rare Diseases (U54NS065712 and 1UOINS109403-01 and R21TROO3034), Muscular Dystrophy Association (MDA510281) and the Charcot Marie Tooth Association (CMTA). This research was supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). D.L.B and A.C.T were funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC).
## Competing interests
D.L.H.B. has acted as a consultant on behalf of Oxford Innovation for Amgen, Bristows, LatigoBio, GSK, Ionis, Lilly, Olipass, Orion, Regeneron and Theranexus on behalf of Oxford University Innovation over the last 2 years. He has received research funding from Lilly. He has received an industrial partnership grant from the BBSRC and AstraZeneca.
ASCR declares the following interests occurring in the last 24 months: undertakes consultancy and advisory board work for Imperial College Consultants that included remunerated work for Abide, Confo, Vertex, Pharmanovo, Lateral, Novartis, Mundipharma, Orion, Shanghai SIMR Biotech Asahi Kasei, Toray & Theranexis; owner of share options in Spinifex Pharmaceuticals from which personal benefit accrued upon the acquisition of Spinifex by Novartis in July 2015 (final payment was made in 2019); named as an inventor on patents: Rice A.S.C., Vandevoorde S. and Lambert D.M Methods using N-(2-propenyl)hexadecanamide and related amides to relieve pain. WO $\frac{2005}{079771}$ and Okuse K. et al. Methods of treating pain by inhibition of vgf activity EP13702262.0/WO2013 110945; Member Joint Committee on Vaccine and Immunisation—varicella sub-committee; Analgesic Clinical Trial Translation: Innovations, Opportunities, and Networks (ACTTION) steering committee member; Medicines and Healthcare Products Regulatory Agency (MHRA), Commission on Human Medicines—Neurology, Pain & Psychiatry Expert Advisory Group.
## Data availability
The genotype and phenotype data can be accessed by application to the NIHR BioResource Data Access Committee at [email protected] or by application to Genomics England Limited following the procedure outlined at https://www.genomicsengland.co.uk/research.
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|
---
title: 'A longer time spent at childcare is associated with lower diet quality among
children aged 5–6 years, but not those aged 1.5–2 and 3–4 years: Dietary Observation
and Nutrient intake for Good health Research in Japanese young children (DONGuRI)
study'
authors:
- Yui Yoshii
- Kentaro Murakami
- Keiko Asakura
- Shizuko Masayasu
- Satoshi Sasaki
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991545
doi: 10.1017/S1368980020003286
license: CC BY 4.0
---
# A longer time spent at childcare is associated with lower diet quality among children aged 5–6 years, but not those aged 1.5–2 and 3–4 years: Dietary Observation and Nutrient intake for Good health Research in Japanese young children (DONGuRI) study
## Body
Childcare fulfils many roles, such as supporting the labour force participation and providing opportunities to foster the well-being and cognitive and social-emotional development of children. As a result, providing more and better childcare has become a policy priority in most of the Organisation for Economic Co-operation and Development countries[1]. In 2017, 35 % of children aged 0–2 years and 87·2 % of children aged 3–5 years enroled in childcare. Children aged 0–2 years spend 29·7 h per week at childcare on average across Organisation for Economic Co-operation and Development countries (data not available for those aged 3–5 years)[2].
Japanese preschool childcare is generally healthy, and the prevalence of overweight among preschool children was estimated at 13·4 %[3]. In 2017, 29·6 % of children aged 0–2 years and 91·4 % of children aged 3–5 years enroled in childcare[2]. In Japan, there are two kinds of institutions for childcare: authorised childcare centres and non-registered childcare centres[4]. Authorised childcare centres are defined as child welfare facilities according to Article 35 of the Child Welfare Law. These centres meet the state’s standards, which means the centres must follow the guideline regarding their school meals[5]. Non-registered childcare centres are defined as childcare facilities other than authorised childcare centres[4]. Childcare centres are for children aged 0 to 6 years whose guardians are not able to take care of them due to work or other reasons. Childcare centres are typically divided into home-based and centre-based childcare in Japan. Home-based childcare is childcare provided by family welfare personnel who look after children under three in homes when parents cannot provide care, and it is one form of non-registered childcare centres[4]. Centre-based childcare is childcare facilities regardless of authorisation other than home-based childcare.
According to previous studies, most childcare centres taking care of children for over 8 h daily provided meals and snacks to children and covered around half of the children’s daily nutrient intake(6–8). Thus, childcare environment may have a strong influence on children’s dietary intake[9,10]. Although there has been a growing concern that maternal employment could have adverse effects on children’s eating habits and health outcomes(11–14), high-quality childcare could counteract these adverse effects[15]. The childcare environment has been shown to positively affect children’s consumption of healthy foods; more servings of fruit, vegetables and low-fat dairy and fewer servings of high-fat, high-sugar foods and sugary drinks were consumed at childcare than at home[9,16].
However, there is a broad consensus that childcare from too early and for too long can be damaging to child growth and development[17]. A follow-up study showed that the more hours children spent at childcare, the more problem behaviours and conflicts with adults they displayed[18]. Another cross-sectional study suggested that the amount of time spent at childcare was associated with disorganised attachment to fathers[19].
Not only cognitive and social-emotional development and attachment but also optimal nutrient intake is vital during the growth and development of toddlers and preschool-age children. To our knowledge, however, only a few studies have focused on the association between the amount of time spent at childcare and children’s dietary intake[20,21]. Bollella et al. compared children with 2–3 h/d of childcare and children with 5–6 h/d of childcare[20], while Garemo et al. compared children with <40 h/week of childcare and children with 40 h or more/week of childcare[21]. These studies indicate the need to examine the influence of longer time spent at childcare on children’s dietary intake. However, the settings of these studies do not match the current Japanese situation. In Japan, normal childcare hours is 8 h a day for childcare centres. For childcare centres, longer childcare services within 11 h are possible[22] depending on parents’ work status or other situations of the family. In the present study, we investigated the association between the amount of time spent at childcare and diet quality among Japanese children aged 1·5–6 years.
## Abstract
### Objective:
To examine the association between the amount of time spent at childcare and diet quality in 668 Japanese children aged 1·5–6 years.
### Design:
A cross-sectional design was used. Dietary information was collected using dietary records (1 d for children aged 1·5–2 years and 2 d for children aged 3–6 years). Diet quality was assessed by counting the number of nutrients not meeting the Japanese Dietary Reference Intakes (DRI). Each child’s guardian reported the average amount of time spent at childcare per d for the previous 1 month.
### Setting:
In total, 315 childcare centres located in twenty-four areas in Japan.
### Participants:
In total, 753 children aged 1·5–6 years who attend childcare facilities.
### Results:
After adjustment for potential confounders, OR for the low diet quality (≥ 5 of twenty nutrients not meeting DRI) in long (≥10 h/d) v. medium (8–10 h/d) childcare hours was 4·81 (95 % CI 1·96, 11·8) among children aged 5–6 years. There was no significant association in children aged 1·5–2 and 3–4 years.
### Conclusion:
This study showed that long time spent at childcare was strongly associated with low diet quality among children aged 5–6 years, but not those aged 1·5–2 and 3–4 years. More research is needed to clarify different associations in each age group.
## Study setting and participants
This analysis was based on the data obtained from the DONGuRI (Dietary Observation and Nutrient intake for Good health Research in Japanese young children) study, a nationwide cross-sectional study. The primary purpose was to describe the dietary and lifestyle characteristics of the children and investigate the associations between these characteristics and health status. A detailed description of the study design and survey procedure has been published elsewhere[23,24]. Briefly, data collection was conducted between October and December 2015 with 753 children aged 1·5–6 years from 315 childcare centres. In Japan, children aged 6 years as of April 2nd start to go to elementary school. Thus, some children aged 6 years at October who included in this study did not go to elementary school at that time. All the childcare centres participated in this study are centre-based childcare, and 98 % of them are authorised childcare centres. Participants undergoing diet therapy as ordered by a doctor or dietitian at the time of the study, having particular dietary habits (such as vegetarianism), planning to move elsewhere before March 2016 or having guardians who are dietitians or medical doctors were excluded from the study. Recruitment was done based on the feasibility of the study and the willingness of the participants and guardians; participation was completely voluntary. Of the 1066 children recruited, 753 from 315 childcare centres agreed to participate (response rate = 70·6 %) (Fig. 1). After excluding the children without questionnaire-based data, anthropometric measurements, or dietary record (DR) data, to ensure a useful analysis, we restricted our analysis to 718 children who were mainly fed by their mothers as very few children were fed by someone other than their mothers. We then excluded 50 children with missing information on any of the variables of interest. The sample for the final analysis composed of 668 children.
Fig. 1Eligibility for and participation in the present analysis (DONGuRI† study). †DONGuRI: Dietary Observation and Nutrient intake for Good hearth Research In Japanese young children. ‡Two boys and two girls aged 3, 4, 5 and 6 years as well as eight boys and eight girls aged; 1·5 to <3 years in each prefecture
## Dietary assessment
Dietary information was collected using 1-d DR on a weekday (with lunch at childcare) for children aged 1·5–2 years and using non-consecutive 3-d DR, including two weekdays (with lunch at childcare) and one weekend day (without lunch at childcare) for children aged 3–6 years. A detailed description of the DR has been published elsewhere[23,24]. Briefly, the research dietitian orally explained the recording procedure to the guardians. The research dietitians were responsible for the recording of dietary intake at childcare centres, while the guardians were responsible for the recording of dietary intake outside the childcare centres including home. Both research dietitians and guardians were asked to conduct DR (including weighing of foods) using the similar manner. The purpose of DR was to assess total dietary intake. In the present study, we restricted our analysis to weekdays to investigate the association between the amount of time spent at childcare and children’s dietary intake.
We intended to consider only the nutrient intake from foods and beverages, following the guidelines of the Japanese Dietary Reference Intakes (DRI)[25]. To compare the dietary intakes reported in the DR and the corresponding DRI values[25], we adjusted the reported dietary intakes to the energy-adjusted intakes on the assumption that each participant consumed his/her estimated energy requirement rather than his/her reported energy[26,27]. The calculation method was as follows: energy-adjusted nutrient intake (unit/d) = reported nutrient intake (unit/d)/reported energy intake (kJ/d) × estimated energy requirement (estimated energy requirement, kJ/d). Estimated energy requirement was calculated using sex- and age-specific equations published in the USA/Canada DRI, based on sex, age, height and weight and physical activity[28]. For this calculation, we assumed a ‘low-active’ level of physical activity (i.e. 1·4 ≤ Physical Activity Level < 1·6)[28], owing to the lack of an objective measure of physical activity.
## Determination of diet quality
Adequacy of each nutrient was assessed using the method reported in the previous study(29–31), which was determined by comparing nutrient levels with each dietary reference value in the Japanese DRI[25]. In the Japanese DRI, the different types of reference values are set according to their purposes. The tentative dietary goal for preventing lifestyle-related diseases (DG) is set for preventing non-communicable diseases, and the estimated average requirement is set for avoiding the insufficiency of nutrients. To assess the overall diet quality of each child, we counted the number of nutrients that did not meet the DRI among six nutrients in the DG and fourteen nutrients in the estimated average requirement. The ranges of the number of nutrients that did not meet the DRIs were 0–20. Low diet quality was defined as the highest tertile category of the number of nutrients not meeting the DRI.
For six nutrients (fat, SFA, carbohydrates, dietary fibre, Na and K) in the DG, the intake levels outside the range of the corresponding DG values were considered as not meeting the DRI. DG of dietary fibre and K were not available for children aged 1–5 years, and the DG of SFA was not available for children aged 1–6 years. Thus, we determined tentative DG for these nutrients according to the procedure reported in a previous study[24]. For fourteen nutrients (protein, thiamine, riboflavin, niacin, folate, vitamins A, B-6, B-12 and C, Ca, Mg, Zn, Fe and Cu) in the estimated average requirement, the intake levels below the estimated average requirement were considered as not meeting the DRI using the cut-point method[25]. For nine nutrients (n-6 PUFA, n-3 PUFA, vitamins D, E, K, pantothenic acid, K, P and Mn) with adequate intake, the inadequacy of intake could not be determined even if their intake levels were less than the adequate intake[25,32].
## Assessment of basic and lifestyle characteristics
All the information was obtained using questionnaires designed for this study. The BMI (BMI; kg/m2) of each child was calculated from the measured body height and weight. Children’s weight status was defined according to the age- and sex-specific BMI (calculated as kg/m2) cut-offs given by the International Obesity Task Force, which correspond to an adult BMI of < 18·5 for underweight, ≥ 18·5 to < 25 for normal and ≥ 25 for overweight and obese individuals[33]. Childcare hours were defined as the average amount of time spent at childcare per d, not including the commuting time, and categorised as short (<8 h/d), medium (8–10 h/d) or long (≥10 h/d). The data of the timing and length of each meal were obtained from the DR. Sleep duration was defined as the sum of daytime naps at childcare centres (reported by research dietitians or the staff of childcare centres) and night-time sleep (reported by the guardians) and categorised as < 11 or ≥ 11 h/d for children aged 1·5–2 years, < 10 or ≥ 10 h/d for children aged 3–5 years and < 9 or ≥ 9 h/d for children aged 6 years according to the recommendations of the American Academy of Sleep Medicine[34]. Outdoor playtime was defined as the duration of outdoor playtime at childcare centres on weekdays (reported by the research dietitians or the staff of childcare centres) and weekend days (reported by the guardians). The number of weekdays ($\frac{5}{7}$) and weekend days ($\frac{2}{7}$) per week was calculated. Children’s screen time (defined as the amount of time watching TV and playing video games), for weekdays and weekend days, was reported separately by their guardians after calculating the number of weekdays and weekend days. The guardians also reported the frequency of breakfast (almost never, 1 time/week, 2–3 times/week, 4–6 times/week or almost every day) and eating out-of-home food (never, 1 time/month, 2–3 times/month, 1 time/week, 2–3 times/week, 4–6 times/week or almost every day).
The guardians were also asked to report the characteristics of the mothers (age, height and weight, educational level, occupation, working hours and cooking hours), the fathers (educational level and occupation) and the households (number of family members under one roof, family makeup and annual income). Mothers’ weight status was defined based on the BMI recommended by the WHO: underweight (< 18·5 kg/m2), normal (≥ 18·5 to < 25 kg/m2) and overweight and obese (≥ 25 kg/m2)[35]. Guardians were asked to select their occupation from one of the following in the baseline questionnaire: security, farming/forestry/fishery, transportation, labour service, sales, service, office work, professional, management and unemployed. Those included in the study were categorised into four occupational groups: [1] manual (security, farming/forestry/fishery, transportation and labour services); [2] sales and service; [3] office work and [4] professional and management[36]. Annual household income was adjusted by household size and composition; household size was taken into account using weights of the modified Organization for Economic Cooperation and Development equivalence scale: the respondent, 1; other adults, 0·5 and children, 0·3[37]. Adjusted annual household income was categorised into approximate tertiles: low (≤1 900 000 yen/year), middle (>1 900 000 to <2 800 000 yen/year) and high (≥2 800 000 yen/year).
## Statistical analysis
All statistical analyses were performed using the R statistical package (version 3·6·1, R Foundation for Statistical Computing) for each age group separately (1·5–2, 3–4 and 5–6 years old). All reported P values are two-tailed, with a P value < 0·05 considered statistically significant. Descriptive data are shown as means and standard deviations for continuous variables and numbers and percentages of participants for categorical variables. χ 2 test was used to examine the difference in the prevalence of participants not meeting the DRI across the three categories of childcare hours. Further, a comparison of the number of nutrients not meeting the DRI and the dietary intake of each food group across the three categories of childcare hours was carried out using ANOVA, followed by the Tukey test for multiple comparisons. Also, a comparison of the dietary intake of each food group both at childcare and at home across the three categories of childcare hours was carried out using ANOVA separately. A comparison of the basic and lifestyle characteristics of children, their parents and households across the three categories of childcare hours was carried out using the χ 2 test. These characteristics were selected as possible factors associated with the diet quality of children on the basis of previous studies(23,38–46). Multivariate-adjusted OR as well as crude OR and 95 % CI for the low diet quality for each category of childcare hours were calculated using logistic regression analysis. The medium category of childcare hours was used as a reference category. Potential confounding factors considered in this analysis were those showing significant differences among the three categories of childcare hours.
## Results
Of the 668 children included in the analysis, 50·7 % (n 339) were boys. The mean time spent at childcare was 8·67 (sd = 1·17), 8·59 (sd = 1·12) and 8·80 (sd = 1·16) h/d for children aged 1·5–2, 3–4 and 5–6 years, respectively. The mean number of nutrients not meeting the DRI was 4·78 (sd, 2·50), 3·69 (sd, 1·75) and 4·07 (sd, 2·08) for children aged 1·5–2, 3–4 and 5–6 years, respectively. There was no significant association between childcare hours and the prevalence of nutrients not meeting the DRI for any nutrient, except for Ca in children aged 3–4 years and for dietary fibre, vitamin C and Fe in children aged 5–6 years. Although the number of nutrients not meeting the DRI among children aged 5–6 years with long childcare hours was significantly larger than that among children with medium childcare hours, there was no such significant difference in other age groups (Table 1). At the level of foods, children with short childcare hours consumed significantly lower intake of vegetables than children with medium childcare hours among children aged 1·5–2 years. Children with short childcare hours consumed significantly higher intake of confectionary than childcare with long childcare hours among children aged 1·5–2 years. Among children aged 3–4 years, children with long childcare hours consumed significant higher intake of cereals than childcare with short childcare hours (Table 2). The intake of sugar-sweetened beverages not at childcare, but home seemed to be inversely associated with childcare hours among all age groups, although it was not significant. In children aged 1·5–2 years and 3–4 years, the intake of cereals at childcare associated with childcare hours (Table S1–S3).
Table 1The prevalence of nutrients not meeting the Dietary Reference Intakes (DRI) across three categories of childcare hours by age groupChildren aged 1·5–2 years (n 330)Children aged 3–4 years (n 176)Children aged 5–6 years (n 172)Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † (n 73)(n 185)(n 72)(n 42)(n 104)(n 30)(n 28)(n 95)(n 39) n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ n ‡ %§ Nutrients with DG Fat3041·17641·12838·90·951842·93836·5826·70·371450·04042·12461·50·12 SFA6284·915282·26083·30·863992·99490·42790·00·882485·78488·43794·90·42 Carbohydrates2027·44624·91419·40·51716·71413·526·70·45414·31717·9717·90·90 Dietary fibre4156·28445·43447·20·292150·04038·51136·70·38828·63637·92461·50·01* Na4460·312366·55069·40·483378·69086·52376·70·312796·48488·43282·10·20 K2027·44323·21723·60·7712·454·8413·30·12517·91313·7820·50·59Nutrients with EAR Protein00·000·000·000·000·000·000·000·000·0 Thiamin2432·94423·81825·00·32716·72423·1826·70·57517·92930·51435·90·27 Riboflavin1216·4158·11013·90·1200·021·913·30·5400·044·225·10·50 Niacin1419·22915·779·70·2700·087·713·30·1427·111·125·10·18 Folate11·400·000·00·1700·000·000·000·000·000·0 Vitamin A2939·76937·33548·60·25511·91817·3723·30·4427·166·3717·90·10 Vitamin B6 811·094·934·20·1400·000·000·000·033·237·70·24 Vitamin B12 11·410·511·40·7300·000·000·000·000·000·0 Vitamin C2230·13619·51419·40·1512·432·926·70·5513·644·2717·90·02* Ca2534·25027·02737·50·21921·43533·71550·00·04*1035·72829·51846·20·18 Mg00·000·000·000·000·000·000·000·000·0 Zn79·684·345·60·2600·000·000·000·000·000·0 Fe3142·55630·32433·30·17819·01413·5723·30·38310·788·41230·8<0·01** Cu00·000·000·000·000·000·000·000·000·0Number of not-meeting DRI nutrients (mean, sd)5·362·704·552·374·812·550·063·551·433·701·723·872·240·753·75A 1·623·76A 2·035·05B 2·24<0·01**DG, tentative dietary goal for preventing lifestyle-related disease; EAR, estimated average requirement.* $P \leq 0.05$; **$P \leq 0.01.$†The difference in the proportion of participants not meeting the DRI was examined using Pearson’s χ 2 test; the difference in the number of nutrients not meeting the DRI was examined using ANOVA followed by Tukey test for multiple comparisons. Different superscript capital letters indicate significant differences.‡The number of participants not meeting the DRI. Each energy-adjusted nutrient intake estimated by the dietary record was compared with each corresponding energy-adjusted DRI value (unit/d), using the cut-point method according to the Japanese DRI, 2015[25]. Energy-adjusted nutrient intake except for fat, SFA and carbohydrates was calculated according to the following equation: nutrient intake (unit/d) = reported nutrient intake (unit/d)/reported energy intake (kJ/d) × estimated energy requirement (kJ/d).§The proportion of participants not meeting the DRI.
Table 2Dietary intake† of each food group (g/4184 kJ) across three categories of childcare hours by age groupChildren aged 1·5–2 years (n 330)Children aged 3–4 years (n 176)Children aged 5–6 years (n 172)Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P ‡ Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P ‡ Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P ‡ (n 73)(n 185)(n 72)(n 42)(n 104)(n 30)(n 28)(n 95)(n 39)Food groups§ Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Cereals19247·919452·620756·30·14181A 28·6198AB 41·0205B 47·40·02*18738·220138·619938·60·26Potatoes24·221·928·628·222·123·40·1527·321·628·519·329·619·20·8928·516·129·218·326·520·00·75Sugars6·556·706·135·864·924·180·196·643·666·995·266·123·820·668·30A 5·305·76A 4·077·45A 7·120·04*Pulses and nuts35·237·635·439·535·237·21·0030·424·926·219·029·318·80·4921·112·225·617·725·215·30·43Vegetables‖ 113A 54133B 61125AB 590·04*1264512741127470·991213912639108350·06Fruits69·662·977·757·283·153·20·3664·044·464·241·180·855·90·1869·138·560·045·257·640·80·53Fruit and vegetable juice2·814·12·19·32·510·60·903·08·93·415·21·68·50·803·48·62·58·71·44·80·57Fish23·520·624·421·429·421·80·1731·419·925·417·222·118·30·0731·820·226·818·026·017·00·37Meats34·422·535·828·931·021·40·4239·317·241·318·543·216·10·6440·319·644·822·444·714·90·58Eggs16·715·817·117·815·719·10·8416·712·120·914·519·315·20·2720·614·021·514·421·416·20·96Milk215922171182091230·88193100166811691050·241287013173127630·94Confectionaries28·8A 29·022·6AB 28·116·5B 26·40·03*22·821·821·423·720·015·70·8729·435·225·724·324·422·60·73Sugar-sweetened beverages27·952·622·553·213·138·10·2024·653·420·040·115·027·40·6327·837·816·331·912·723·90·14Seasonings76·466·479·765·176·560·60·9066·553·482·354·162·548·20·1064·537·782·053·092·676·30·14Other foods†† 2031312241592492210·262561592331402601650·552741502641522751810·92* $P \leq 0.05$; **$P \leq 0.01.$†Energy-adjusted dietary intake of each food group was calculated according to the following equation: dietary intake (unit/d) = reported nutrient intake (unit/d)/reported energy intake (kJ/d) × 4184 kJ.‡The difference in dietary intake for each food group was examined using ANOVA followed by Tukey test for multiple comparisons. Different superscript capital letters indicate significant differences.§Food groups were defined based on the culinary usage and the similarity of nutrient profiles of the foods, mainly according to the Standard Table of Food Composition in Japan[48,49].‖Including mushrooms and seaweeds.¶Consisting of soda, sports drinks, fruits drinks (other than $100\%$ fruit juice), milk beverages and pre-sweetened tea and coffee.††Consisting of fat and oil, alcoholic beverages (added during cooking or processing), unsweetened tea and coffee and readymade meals.
To determine the potential confounding factors between childcare hours and diet quality, the association between childcare hours and the basic and lifestyle characteristics of children, their parents and households was examined (Table 3). In all age groups, longer maternal working hours were associated with longer childcare hours. Among children aged 1·5–2 years and 5–6 years, later dinner time was associated with longer childcare hours. Among children aged 1·5–2 years and 3–4 years, maternal occupation was associated with childcare hours and more frequent children’s eating out was associated with longer childcare hours.
Table 3Associations between basic and lifestyle characteristics and childcare hours by age groupChildren aged 1·5–2 years (n 330)Children aged 3–4 years (n 176)Children aged 5–6 years (n 172)Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † Short (<8 h/d)Medium (8–10 h/d)Long (≥10 h/d) P † n % n % n % n % n % n % n % n % n %Children’s characteristics Children’s sex Boy3547·98847·63548·60·992252·44745·22066·70·111450·04547·42359·00·47 Girl3852·19752·43751·42047·65754·81033·31450·05052·61641·0 Children’s age 1 (or 3 or 5) year old2838·48043·23447·20·602252·45149·01446·70·881657·14648·42256·40·62 2 (or 4 or 6) years old4460·310556·83852·82047·65351·01653·31242·94850·51743·6 Children’s weight status‡ Underweight811·0179·256·90·8124·81211·5413·30·66310·777·425·10·89 Normal6082·215081·16286·13890·58480·82480·02278·67578·93384·6 Overweight56·8189·756·924·887·726·7310·71313·7410·3Maternal characteristics Maternal age <30 years old1520·53116·81013·90·9224·898·726·70·4127·144·225·10·22 ≥30 to <35 years old2432·97238·93041·72047·63432·7723·31035·72324·2410·3 ≥35 to <40 years old2737·06434·62433·31433·34139·41653·3932·13637·91435·9 ≥40 years old79·6189·7811·1614·32019·2516·7725·03233·71948·7 Maternal weight status§ Underweight1520·53116·81318·10·531126·21817·326·70·31310·788·4615·40·23 Normal5372·613573·05677·82866·77875·02686·72485·77578·93282·1 Overweight56·81910·334·237·187·726·713·61212·612·6 Maternal education level Junior high school or high school2128·83317·81115·30·14819·01514·4413·30·69932·12526·3615·40·44 Junior college or vocational school2939·79048·63041·71740·55451·91756·7932·14042·11641·0 College graduates and higher2331·56233·53143·11740·53533·7930·01035·73031·61743·6 Maternal occupation Manual‖ 912·3158·156·90·01*716·71110·600·00·05*725·01515·825·10·09 Sales and service2230·13518·979·7819·01514·426·7517·91313·725·1 Office work1419·26434·62129·21228·62120·21240·0828·62425·31128·2 Professional and management2838·47138·43954·21535·75754·81653·3828·64345·32461·5 Maternal working hours ≤25 h/week3852·14423·856·9<0·01**2559·53432·7310·0<0·01**2071·43132·6512·8<0·01** >25 to ≤35 h/week1824·75127·61318·1511·92625·0310·0621·42021·1410·3 >35 to ≤40 h/week1013·75630·32838·9614·32322·1826·713·62930·51435·9 >40 h/week79·63418·42636·1614·32120·21653·313·61515·81641·0Paternal characteristics Paternal education level Junior high school or high school2331·57339·52230·60·43921·44139·4930·00·16932·13536·81333·30·86 Junior college or vocational school1723·33720·01318·11331·02726·0516·7517·92122·1717·9 College graduates and higher3243·87339·53751·42047·63634·61550·01450·03840·01948·7 Paternal occupation Manual‖ 2230·17138·41825·00·01*1126·23230·8826·70·84828·63334·7923·10·54 Sales and service1419·23217·3811·1614·31716·3413·3310·777·4410·3 Office work34·12312·41419·4819·0109·6413·3414·31414·71128·2 Professional and management3446·65931·93244·41740·54543·31446·71346·44143·21538·5Households’ characteristics Number of siblings 01824·77037·83345·80·09614·32221·21136·70·07414·31212·6512·80·87 13649·37842·23244·42764·34644·21446·71967·95456·82256·4 21520·53116·856·9921·43230·8516·7414·32425·31128·2 ≥345·563·222·800·043·800·013·655·312·6 Number of adults under one roof 122·752·711·40·3600·011·000·0<0·01**13·644·225·10·30 25778·114880·06691·73378·68682·72273·32175·07275·83692·3 345·563·211·412·432·9723·327·155·300·0 ≥41013·72614·145·6819·01413·513·3414·31414·712·6Annual household income Low3547·95429·21318·1<0·01**1126·23533·7310·00·06828·62526·3615·40·59 Middle1926·07741·62940·31740·52826·91033·31035·72930·51333·3 High1926·05429·23041·71433·34139·41756·71035·74143·22051·3Lifestyle characteristics Children’s breakfast frequency Not everyday11·494·934·20·4324·821·926·70·3913·622·100·00·54 Everyday7298·617695·16995·84095·210298·12893·32796·49397·939100 Children’s eating out frequency <1 meal/week6386·316589·25576·40·03*3992·98682·72170·00·04*2485·77983·23487·20·83 ≥1 meal/week1013·72010·81723·637·11817·3930·0414·31616·8512·8 Children’s sleep duration†† Insufficient1216·43619·52230·60·0812·498·7620·00·04*414·31212·6923·10·31 Sufficient6183·614980·55069·44197·69591·32480·02485·78387·43076·9 Children’s outdoor playtime <60 min/d2027·46133·02636·10·72921·42019·2826·70·8913·666·3512·80·02* ≥60 to <90 min/d2635·66133·02636·11126·23028·8930·01139·31313·7717·9 ≥90 min/d2737·06334·12027·82252·45451·91343·31657·17680·02769·2 Children’s screen time <30 min/d3243·88646·54461·10·061228·63331·7723·30·6727·12930·51128·20·04* ≥30 min/d4156·29953·52838·93071·47168·32376·72692·96669·52871·8 Maternal cooking time <7 h/d1723·35027·02027·80·87511·91918·3723·30·63414·31920·0820·50·88 ≥7 to <10 h/day2432·95027·02230·62047·64038·51343·3932·13233·71538·5 ≥10 h/d3243·88545·93041·71740·54543·31033·31553·64446·31641·0 Children’s dinner starting time Before 19:004865·812265·93143·1<0·01**2866·75451·91446·70·172278·65052·61230·8<0·01** After 19:002534·26334·14156·91433·35048·11653·3621·44547·42769·2* $P \leq 0.05$; **$P \leq 0.01.$†Using Pearson’s χ 2 test.‡Defined according to the International Obesity Task Force age- and sex-specific BMI (calculated as kg/m2) cut-offs, which correspond to an adult BMI of <18.5 for underweight, ≥18.5 to <25 for normal and ≥25 for overweight and obese individuals[33].§Defined based on the BMI recommended by the WHO: underweight (<18.5 kg/m2), normal (≥18.5 to <25 kg/m2) and overweight and obese (≥25 kg/m2)[35].‖Manual includes security, farming/forestry/fishery, transportation and labour services.¶Adjusted by household size and composition; household size was taken into account using weights of the modified OECD equivalence scale: the respondent, 1; other adults, 0.5 and children, 0.3[37] and categorised across tertiles (<1.9 million yen/year for low, ≥1.9 to <2.8 million yen/year for middle and ≥2.8 million yen/year for high).††Defined according to the recommendations of American Academy of Sleep Medicine[34]: <11, <10 and <9 h/day for insufficient for children aged 1.5–2, 3–5 and 6 years, respectively.
The association between childcare hours and diet quality is shown in Table 4. Multivariate OR for the low diet quality in the long v. medium childcare hours was 4·81 (95 % CI 1·96, 11·8) among children aged 5–6 years. Conversely, among children aged 1·5–2 years, multivariate OR for the low diet quality in the short v. medium childcare hours was 1·79 (95 % CI 0·96, 3·32), although it was NS ($$P \leq 0$$·07). There was no significant association in children aged 3–4 years.
Table 4Associations between childcare hours and the low diet quality* by age groupChildcare hoursPrevalenceHigh and medium diet quality† Low diet quality‡ Crude modelMultivariate model§ n % n %OR95 % CIOR95 % CIChildren aged 1·5–2 years old Short (<8 h/d)4257·53142·51·500·862·621·790·963·32 Medium (8–10 h/d)12467·06133·01·00Ref1·00Ref Long (≥10 h/d)4866·72433·31·020·571·810·980·531·84Children aged 3–4 years old Short (<8 h/d)3378·6921·40·640·281·500·630·251·59 Medium (8–10 h/d)7370·23129·81·00Ref1·00Ref Long (≥10 h/d)2066·71033·31·180·493·801·190·403·58Children aged 5–6 years old Short (<8 h/d)2175·0725·00·930·35, 2·460·720·232·22 Medium (8–10 h/d)7073·72526·31·00Ref1·00Ref Long (≥10 h/d)1641·02359·04·021·84, 8·824·811·9611·8Ref, reference.*Defined as the highest tertile category of the number of nutrients not meeting the Dietary Reference Intakes (DRI) (≥6 nutrients not meeting the DRI for children aged 1.5–2 years and ≥5 nutrients not meeting the DRI for children aged 3–4 and 5–6 years).†Defined as the lowest and middle tertile category of the number of nutrients not-meeting the DRI (<6 nutrients not meeting the DRI for children aged 1.5–2 years and <5 nutrients not meeting the DRI for children aged 3–4 and 5–6 years).‡For children aged 1.5–2 years, adjustment was made for maternal occupation (manual, sales and service, office work and professional and management), maternal working hours (≤25, >25 to ≤35, >35 to ≤40 or >40 h/week), paternal occupation (manual, sales and service, office work and professional and management), annual household income (low, middle or high), children’s eating out frequency (<1 or ≥1 meal/week) and children’s dinner starting time (before 19:00 or after 19:00). For children aged 3–4 years, adjustment was made for maternal occupation (manual, sales and service, office work and professional and management), maternal working hours (≤25, >25 to ≤35, >35 to ≤40 or >40 h/week), number of adults under one roof (1, 2, 3 or ≥4), children’s sleeping duration (insufficient or sufficient) and children’s eating out frequency (<1 or ≥1 meal/week). For children aged 5–6 years, adjustment was made for maternal working hours (≤25, >25 to ≤35, >35 to ≤40 or >40 h/week), children’s outdoor playtime (<60, ≥60 to <90 or ≥90 min/d), children’s screen time (<30 or ≥30 min/d) and children’s dinner starting time (before 19:00 or after 19:00).
## Discussion
To our knowledge, this is the first study to demonstrate that long childcare hours are associated with low diet quality, but this association was only observed among children aged 5–6 years. This finding suggests the necessity for different targeted approaches by age group to improve the diet quality in children.
There have only been two previous studies comparing the nutrient intake of young children by the amount of time spent at childcare[20,21]. One of these studies, conducted in the USA, showed that children who spent more hours at childcare had higher intakes of Ca and vitamins A, E and B12 [20]. The other study, conducted in Sweden, reported that children who spent more hours at childcare had higher intake of protein and fat and lower intake of sucrose[21]. These results suggested that spending more hours at childcare tends to be associated with higher diet quality, which is consistent with our findings in children aged 1·5–2 years. One potential explanation may be the well-regulated environment at childcare. In the present study, children with short childcare hours consumed significantly lower intake of vegetables than children with medium childcare hours and significantly higher intake of confectionary than childcare with long childcare hours among children aged 1·5–2 years. Although it was not significant, the intake of sugar-sweetened beverages was inversely associated with childcare hours. These findings were in line with a previous study[10]. The intake of sugar-sweetened beverages not at childcare but home seemed to be inversely associated with childcare hours. Also, children with short childcare hours seemed to consume lower intake of vegetables than children with medium childcare hours not at childcare but home. This could be because the eating environment, especially for sugar-sweetened beverages and vegetables, was well regulated at childcare compared with home, especially in children aged 1·5–2 years.
Conversely, in the present study, children aged 5–6 years with long childcare hours had lower diet quality compared with children with medium childcare hours. This finding is discrepant with the previous studies[20,21], in which longer childcare hours were associated with higher diet quality. This discrepancy might be because of the difference in the range of childcare hours. Although the USA study compared children with 2–3 h of childcare and children with 5–6 h of childcare, there were only seven children (of 668) in the present analysis who spent <6 h/d at childcare. Additionally, almost 80 % of the children included in the present analysis spent 8 or more h/d, at childcare, while in the Swedish study, only 9 % of the children spent >40 h/week at childcare (which is equivalent to more than 8 h/d). The underlying reasons for the association between long childcare hours and low diet quality in children aged 5–6 years are still unclear, but one potential explanation may be lifestyle. Longer childcare hours were associated with later dinner. A previous study suggested that a delayed lifestyle (i.e. delayed bedtime, waking time and mealtimes) was correlated with diet-related mental symptoms in children such as fear of new food including eating only familiar food and having preferences regarding food or restlessness during meals[38]. These factors might be associated with children’s diet quality. In the present study, the intake of vegetables was inversely associated with childcare hours, and the intake of seasonings was associated with childcare hours, although both were not significant. Children with long childcare hours consumed lower intake of vegetables at home but not at childcare than children with short and medium childcare hours among children aged 5–6 years, although it was not significant. Also, children with long childcare hours consumed higher intake of seasonings at home but not at childcare than children with short and medium childcare hours among children aged 5–6 years, although it was not significant. However, after adjustment for time of dinner, the association between long childcare hours and low diet quality remained. Thus, lifestyle alone is not likely to explain the inverse association between childcare hours and diet quality in children aged 5–6 years. To compare the diet quality of dinners at earlier times v. later times should be examined in future.
The strengths of this study are examining the diet quality among children with a different distribution of childcare hours as compared with the previous studies[20,21], detailed observation of nutrient intake by DR and considering potential confounding factors such as Socioeconomic Status, working hours and other lifestyle factors. However, this study also had several limitations. First, our participants were not a representative sample of the general population but rather volunteers, and thus the guardians of our participants were possibly more health conscious than the average person. The mean height and weight of children were, however, reasonably comparable with those observed in a national representative sample[47]. Second, our analysis was based on only 1-day (for children aged 1·5–2 years) and 2-day data (for children aged 3–6 years) of dietary intake, which may not reflect habitual intake. Additionally, because of the difference of the dietary recording days, a direct comparison of dietary intake among three age groups should be avoided. Moreover, the number of nutrients not meeting the DRI was simply added to evaluate diet quality, although contribution to diet quality might differ across nutrients. The evidence to determine a weighting coefficient of each nutrient is insufficient at present. Further, the reliability of the DRI of individual nutrients is dependent on the state of the science for each nutrient[25]. Some degree of misclassification of participants by diet quality is, therefore, unavoidable. Third, the accuracy of data on the characteristics and lifestyles of the study participants obtained from the questionnaires remains unknown, even though the accuracy of data on the time spent at childcare is critical for this study. In any case, further research should be conducted with detailed information about time allocation of children and their families as well as a more accurate diet scoring system.
In conclusion, this cross-sectional study showed an inverse association between childcare hours and diet quality in children aged 5–6 years but not in other age groups. These results contribute to the limited evidence base supporting the proposition that long hours spent at childcare could damage healthy child development. More research is needed to specify the underlying mechanisms or different influences in each age group. Although providing more childcare has become a policy priority in most Organisation for Economic Co-operation and Development countries to support the labour force participation of mothers, it is also important to consider the potential side effects of prolonged childcare for healthy growth and development of children.
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|
---
title: 'Food purchase behaviour in a Finnish population: patterns, carbon footprints
and expenditures'
authors:
- Jelena Meinilä
- Hanna Hartikainen
- Hanna L Tuomisto
- Liisa Uusitalo
- Henna Vepsäläinen
- Merja Saarinen
- Satu Kinnunen
- Elviira Lehto
- Hannu Saarijärvi
- Juha-Matti Katajajuuri
- Maijaliisa Erkkola
- Jaakko Nevalainen
- Mikael Fogelholm
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991547
doi: 10.1017/S1368980022001707
license: CC BY 4.0
---
# Food purchase behaviour in a Finnish population: patterns, carbon footprints and expenditures
## Body
Reducing health risks caused by an unhealthy diet (CHD, type 2 diabetes and cancer (WHO 2013)) and reducing the carbon footprint of food consumption require changes in food consumption patterns[1] which in turn might require changes in food prices[2,3].
Several studies based on theoretical models suggest that changing dietary habits could reduce the carbon footprint of a diet by up to 50–80 %[4,5]. Comparisons between diets such as omnivorous, vegetarian and vegan diets only partially reflect the current reality in Western societies, where the proportions of vegetarians and vegans are still low(6–8). Furthermore, we do not know exactly what alternative diets are taking shape and what they contain. To support the climate change mitigation goals and to monitor the effects of any dietary change that is already under way, it is essential to know the carbon footprint of the current food consumption patterns beyond the rarely followed dietary patterns such as vegetarian or vegan, or national averages. In a few studies assessing real-life food consumption patterns, the differences between the carbon footprints of common self-selected dietary patterns have varied from negligible to major[9,10].
To make healthy and environmentally sustainable food available to all, reasonable pricing is important: it can make sustainable food consumption possible for households with low incomes. Several studies suggest that healthy food is more expensive than unhealthy food(11–14). On the other hand, the prices of legumes and grains, considered both healthy and climate-friendly, can be substantially less expensive per kJ than meat[15]. Our previous study showed that plant-based protein sources were bought, on average, for a cheaper price than meat[16]. In addition, in a US study, vegetarians spent less money on food purchases than meat eaters[17]. To make healthy and sustainable foods more attractive to consumers, raising the prices of unhealthy and environmentally unsustainable foods such as red and processed meat could also be a solution[2,18].
Food retailers’ customer loyalty card data provide a unique tool for gaining insights into dietary patterns. We have previously shown that food purchase data are a valid instrument for ranking consumers according to their self-reported food[19] and beer[20] consumption. In this study, the detailed data on purchased product groups over a 1-year period enabled an objective assessment of food expenditure and the allocation of carbon footprints for large sets of product groups on a household level. This automatically accumulating data enabled relatively easy access to unusually large food purchase datasets. Thus, the aim of this study was to identify food purchase patterns by using customer loyalty card data and to study their contribution to the carbon footprints of and expenditure on total food purchases.
## Abstract
### Objective:
To identify food purchase patterns and to assess their carbon footprint and expenditure.
### Design:
Cross-sectional.
### Setting:
Purchase patterns were identified by factor analysis from the annual purchases of 3435 product groups. The associations between purchase patterns and the total purchases’ carbon footprints (based on life-cycle assessment) and expenditure were analysed using linear regression and adjusted for nutritional energy content of the purchases.
### Participants:
Loyalty card holders (n 22 860) of the largest food retailer in Finland.
### Results:
Eight patterns explained 55 % of the variation in food purchases. The Animal-based pattern made the greatest contribution to the annual carbon footprint, followed by the Easy-cooking, and Ready-to-eat patterns. High-energy, Traditional and Plant-based patterns made the smallest contribution to the carbon footprint of the purchases. Animal-based, Ready-to-eat, Plant-based and High-energy patterns made the greatest contribution, whereas the Traditional and Easy-cooking patterns made the smallest contribution to food expenditure. Carbon footprint per euros spent increased with stronger adherence to the Traditional, Animal-based and Easy-cooking patterns.
### Conclusions:
The Animal-based, Ready-to-eat and High-energy patterns were associated with relatively high expenditure on food, suggesting no economic barrier to a potential shift towards a plant-based diet for consumers adherent to those patterns. Strong adherence to the Traditional pattern resulted in a low energy-adjusted carbon footprint but high carbon footprint per euro. This suggests a preference for cheap nutritional energy rather than environment-conscious purchase behaviour. Whether a shift towards a plant-based pattern would be affordable for those with more traditional and cheaper purchase patterns requires more research.
## Recruitment
This study utilises large-scale loyalty card data from the largest grocery chain in Finland (S Group)[21]. The S Group sells groceries through five retail chains, which are convenience stores, supermarkets, hypermarkets, and one upper-market concept with an extended focus on high-quality and special products. The selection of food items varies between chains, from only a few thousand to over 20 000 items. The retail chains follow an ‘Everyday, low-pricing’ strategy (as opposed to a ‘high–low pricing’ strategy). At the time of the data collection [2018], 2·4 million households in Finland held the S Group’s customer loyalty card, which accounted for 88 % of all Finnish households. Loyalty card holders across Finland received an invitation to the study by email if they had given permission to be approached for research purposes, and if they were aged 18 years or above (Fig. 1). Those who gave their consent for the use of their purchase data for research purposes received an invitation to respond to an additional electronic questionnaire with complementary data on, for example, household structure and income[22].
Fig. 1Participant flow
## Study sample and participants
Initial food purchase data were obtained from n 47 066 participants[22]. This study comprises data from the year 2018. In the questionnaire, the participants were asked to assess their degree of loyalty to the retailer (i.e. the proportion of purchases from the retailer’s stores of the total food purchases of the household). Only those who had a self-reported degree of loyalty of at least 61 % (i.e. participants who made a large proportion of their food purchases from the food retailer) and who made at least 50 kg of purchases during 2018 were included in the analysis. Our previous analyses showed that purchases associated more strongly with dietary intake among the most loyal (degree of loyalty >60 %) customers[19,20] and therefore build a more complete picture of relative food purchases[22]. In this study, we used purchase data aggregated to annual consumption in both volume (kg) and expenditure (€).
## Background data
The retailer’s database provided data on the sex and age of the participants. In the additional questionnaire, the participants reported their number of household members and how many of these were aged 0–6, 7–17, 18–24, 25–64 and 65 years or older. We combined the data on these two questions into a family structure variable that consisted of five categories: single-adult households, one adult and a child/children, two adults, two adults and a child/children, or other (households with three or more adults and households with an unknown family structure). The participants reported their loyalty level to the retailer by choosing from the options of <20 %, 21–40 %, 41–60 %, 61–80 % and >80 %, but as explained in the previous section, only participants in the upper two categories (61–80 %, >80 %) were included in this study.
The participants selected the monthly income of their household from five predefined categories ranging from household income less than 1500 €/month to 9000 €/month or more. Dividing the income (here, the mean of each income category) by the square root of the household size produced the monthly household income (OECD square root scale). This income is thus presented in five categories (less than 1000 €/month, 1000–1999 €/month, 2000–2999 €/month, 3000–3999 €/month and 4000 €/month or more).
## Carbon footprint assessment
The food retailer originally had 4234 different product groups for their products (Fig. 2). A total of 3435 product groups were assigned a carbon footprint (kg CO2-equivalent), using 1 kg of food purchased in retail as the functional unit. As carbon footprint values are not available for all the product groups, indicator products were chosen to represent the 3435 product groups, meaning that one indicator product represented several product groups. Based on the available and suitable LCA studies, we used about 100 different indicator products. As an example of the indicator product approach, all fruits were assigned the same carbon footprint, which was estimated on the basis of the weighted average of the carbon footprints of the five most sold fruits – more than 80 % of the fruits sold. Thus, the carbon footprint of an indicator product stems from carbon footprints of several products. The method for calculating the carbon footprints of the indicator products is described in detail elsewhere (Hartikainen, Heusala, Harrison, Katajajuuri and Silvenius, unpublished results).
Fig. 2Food item flow The main life cycle phases of the indicator products until retail were included in the system boundaries of the carbon footprint assessment, which comprised the production of inputs to agriculture, agricultural primary production, food processing, packaging, storage (before retail) and transportation. Food waste, land use changes and changes in soil carbon stocks were excluded due to a lack of data.
Data for producing the carbon footprints of the indicator products consisted of a database for food products sold in Finland, compiled by the Natural Resources Institute Finland. The database contains 170 scientific studies of the carbon footprint assessment of food products, and Natural Resources Institute Finland’s LCA database is supplemented with expert estimates. For most of the indicator products, the carbon footprints were medians of the carbon footprints available in data sources. For ready-to-eat meals and beef, additional modifications were made. We determined the meal’s category on the basis of the recipes of the retailer’s most sold meals, using the available carbon footprints for the ingredients. The ‘beef’ category was determined by calculating the carbon footprints of, on the one hand, combined milk and beef production, and on the other hand, suckler beef production from the literature (medians of results from chosen studies), based on how much of the beef was sourced from the combined and suckler beef production systems[23]. The data for storing, according to the three options of dry, cold and frozen, were from the EcoInvent database[24], and the data for packaging were from Plastics Europe[25], the European Aluminium Association[26], the World Steel Association[27] and the FEFCO[28]. The distance between the producer country and the logistics centres needed for the assessment of emissions from transportation were calculated based on six main production areas: [1] Finland; [2] other Nordic countries and Estonia; [3] the rest of Europe; [4] America; [5] Africa and the Middle East; and [6] Asia. The emission factors for transportation were taken from the Lipasto database[29], except for the trans-oceanic container ship, which was from the Ecoinvent database[24].
After the carbon footprints of the indicator products had been determined for the retailer’s product groups, the purchase volume of each loyalty card holder for each product group (kg) was multiplied by the corresponding carbon footprint (kg CO2-eq) to obtain customers’ product group-specific and total-purchase carbon footprints. The sum of the carbon footprints of all the product groups represents the carbon footprint of the total food purchases per person.
## Nutritional energy content of the purchases
Throughout the text, energy refers to the nutritional energy content of the food purchases (not, e.g., energy utilised in food production). The energy content of 1 kg of each product group (e.g. cucumber, skimmed milk and vegetarian lasagna) was derived from the nutrition calculation software on www.fineli.fi. This webpage utilises the food composition database Fineli which is maintained by the Finnish Institute for Health and Welfare. The purchase volume (kg) of each product group was multiplied by the energy content per 1 kg of the group to obtain the absolute energy content of the purchase. The energy contents of all the purchased product groups were summed to obtain the annual energy content of the total purchases.
## Grouping of food purchase data for factor analysis
For factor analysis, a major regrouping was conducted, based on the purpose of use (combined fresh vegetables such as cucumber, tomato etc., as fresh vegetables; soya milk, soya yoghurt, oat milk, oat yoghurt etc., as different plant-based dairy alternatives, etc.). The aggregation of food groups was restricted to a level that enabled differentiation on the basis of nutritional content and carbon footprint. This was driven by the differences in nutrient content that are relevant for public health in Finland and that are reflected in the food-based dietary guidelines of the Nordic Nutrition Recommendations (e.g. separating high-fibre from low-fibre breads and high-fat from low-fat dairy), the degree of processing (e.g. separating fresh potato from frozen potato) and the carbon footprints of the product groups (e.g. separating meat types such as beef, pork and poultry) (see online Supplemental Table 1). Examples of the aggregated food groups and product groups included were as follows: ‘skimmed milk and sour milk’: regular, low-lactose, and lactose-free skimmed milk and skimmed sour milk, and ‘sugar-sweetened beverages’: soft drinks, energy drinks, juices, ice teas and seasonal drinks. Product groups that were not relevant to overall diet quality (e.g. tea, bottled water, chewing gum and spices) or product groups with very low purchases (e.g. game, reindeer and horse meat) were excluded. The final number of food groups to be used in factor analysis was 56.
## Statistical methods
Participants’ characteristics are presented as means and standard deviations, or frequencies and percentages.
To estimate the total household food purchases, we multiplied the volume of total purchases (kg) by the inverse of the self-reported degree of loyalty, for which we used the midpoints of the category intervals. Deviation from normal distribution, detected by visual inspection of their empirical distributions, led to the logarithmic transformation of the food group variables, the energy content of the total food purchases, the carbon footprint (CO2-eq. values) of and expenditure (€) on total food purchases and the carbon footprint to expenditure ratio. Before the subsequent factor analysis on food purchases measured in kilograms, we performed a 98 % winsorisation of the food group variables to diminish the effect of outliers, that is, outliers below the 1st percentile and above the 99th percentile were truncated into the 1st and 99th percentile, respectively.
Bartlett’s test of sphericity (χ2[1540] = 567 540, $P \leq 0$·001) suggested the appropriateness of the factor analysis and the Kaiser–Meyer–Olkin test (KMO = 0·96) indicated good sampling adequacy. Food purchase patterns were derived from principal component analysis, based on the correlation matrix of food groups[30]. The number of principal components was decided by simultaneously examining the scree plot (see online Supplemental Fig. 1), the Kaiser criterion (eigenvalue >1), the percentage of explained variation (our aim was >50 %) and the interpretability of the factors. We chose eight components and used an orthogonal varimax rotation to produce the final factors, from which we then identified and named the food purchase patterns. All the participants were assigned standardised factor scores to represent food purchase patterns, that is, weighted combinations of the purchased food groups. The pattern scores showed how strongly empirically derived purchase patterns (and the food groups defining it) were reflected in a participant’s shopping basket; the higher the score, the stronger the adherence to the purchase pattern. As customary in nutrition research, the patterns were named based on a feature that was common for the food groups that had high loadings for the factor and that separated the factor from the other factors.
The associations between the food purchase patterns and carbon footprint or expenditure were analysed using linear regression analysis with purchase pattern scores as explanatory variables and log-transformed carbon footprints or log-transformed expenditure as the response variable. The model had one purchase pattern at a time and the log-transformed energy content of the total purchases as explanatory variables, meaning that each pattern was analysed separately. Thus, as the analyses were adjusted for the (log-) energy content, the regression coefficients can be interpreted as the difference between the log-carbon footprint or log-expenditure of two individuals with the same energy content of the total purchases but a unit’s (sd) difference in their purchase pattern. To illustrate the magnitude of the effects of the patterns in a more perceivable manner, we calculated the estimated carbon footprint and expenditure in the lowest and highest thirds and in the lowest and highest 10 % of the pattern scores of each pattern using the regression equation: where Y is either carbon footprint or expenditure, α is the intercept term, β 1 equals the regression coefficient of the pattern score, Q equals the mean of the pattern score in a given quantile (lowest third, highest third, lowest 10 % or highest 10 %) and β 2 equals the regression coefficient of the log-transformed energy content at its mean value (T).
The associations between the patterns and the ratio of carbon footprint (kg CO2-eq.) to expenditure (€) were analysed using simple regression analysis, of which the log-transformed carbon footprint:expenditure ratio was the outcome and one pattern at a time an explanatory variable. The patterns that could not be considered overall dietary patterns, namely Skimmed milk and margarine and Alcohol, were excluded from further analyses.
To gain further insights into the association between purchase patterns and carbon footprint and expenditure, we calculated [1] the individual food groups’ sum of the annual carbon footprint (kg CO2-eq.), [ 2] the percentage of each food group’s carbon footprint of the annual total, [3] the sum of annual expenditure (€) on the individual food groups, [4] the percentage of expenditure on each food group of the total annual expenditure, and [5] the ratio of the annual carbon footprint and the annual expenditure (see online Supplemental Table 2).
## Sensitivity analyses
To investigate the sensitivity of different decisions to the result of the factor analysis, we conducted factor analysis with several different choices: [1] included only participants with an ≥80 % degree of loyalty; [2] no winsorisation of the food variables before factor analysis and [3] 5 % winsorisation of the food variables before factor analysis. The results of these factor analyses were similar to the one presented; the same patterns with similar explained variations were identified. Therefore, these results are not shown.
## Results
The majority of the participants were women (66 %) (Table 1). A two-adult household was the most common family structure (34 %), followed by single-adult households (25 %), and two adults with a child/children (23 %). The majority of the households fell into the scaled monthly income range of 2000–2999 € or 3000–3999 € (29 % and 23 %, respectively), and the majority (61 %) bought 81–100 % of all of their food purchases from the retailer.
Table 1Background characteristics of participants (n 22 860) n %Sex Men774533·9 Women15 11566·1AgeMean sd 47·915·2 n %Family structure Single-adult households571725·0 One adult and a child/children10404·5 Two adults787534·4 Two adults and a child/children527223·1 Other17077·5 Missing12495·5Scaled household income (€/month)* Less than 100019678·6 1000–1999339814·9 2000–2999663329·0 3000–3999514722·5 4000 or more423718·5 Missing14786·5Percentage of purchases from retailer 61–80 %897839·3 81–100 %13 88260·7MedianIQRCO2 of total food purchases from S Group (kg CO2-eq/year)27501676, 4339Total purchase volume (kg/year)733463, 1129Energy content of the total purchases (MJ/year)42442721, 6506Expenditure on food (€/year)31412015, 4691*Income (here the mean of each income category) divided by the square root of the household size produced (OECD square root scale).
## Purchase patterns
Eight factors were derived, which explained altogether 55 % of the variation of the fifty-six food groups (Fig. 3). In descending order of explained variation, the patterns were named Traditional (11·0 % of variation), High-energy (9·7 %), Plant-based (8·0 %), Animal-based (8·0 %), Ready-to-eat (5·7 %), Easy-cooking (3·9 %), Skimmed milk and margarine (3·7 %) and Alcohol (3·3 %). Figure 3 shows the food group loadings in each of the patterns.
Fig. 3Illustration of rotated principal components’ loading matrix of food purchase patterns. The values in the tiles represent the largest factor loading within each pattern. The percentages of explained variances for the factors are in parenthesis after the pattern names under the x-axis
## Carbon footprint of the purchases and association with purchase patterns
An investigation of the food groups behind the patterns showed that 12 % of the total carbon footprint of the purchases originated from beef and processed beef and 9 % from cheese (see online Supplemental Table 2). Both food groups were strongly loaded in the Animal-based pattern (factor loading for beef and processed beef 0·65). The third largest food group that contributed to the total carbon footprint was fresh vegetables (6 %), which loaded strongly in the Plant-based (factor loading 0·61) and Animal-based (0·51) patterns. In contrast, peas, beans and lentils, which loaded strongly (0·73) in the Plant-based pattern, made only a small contribution to the total carbon footprint (0·4 %). All the meat food groups together contributed to 29 % of the total carbon footprint, all the dairy food groups to 28 %, and all the vegetables, fruits and berries to 12 % of the total carbon footprint.
When adjusted for the energy content of the annual purchases, the difference in the carbon footprint (log-kg CO2-eq.) of 1 sd difference in pattern scores was the largest for the Animal-based pattern (β 0·134, 95 % CI (0·132, 0·137)), followed by Easy-cooking (β 0·039, 95 % CI (0·036, 0·042)) and Ready-to-eat (β 0·016, 95 % CI (0·013, 0·019)) (Table 2). For the High-energy (β −0·032, 95 % CI (−0·035, −0·029)), Traditional (β −0·036, 95 % CI (−0·039, −0·032)) and Plant-based patterns (β −0·047, 95 % CI (−0·050, −0·044)), the relationship was inverse; a 1 sd higher Plant-based score was associated with a significant decrease in the carbon footprint of the total purchases. In other words, the Animal-based, Easy-cooking and Ready-to-eat patterns were positively associated, and the Plant-based, Traditional and High-energy patterns were inversely associated with the carbon footprint of the total purchases.
Table 2Regression coefficients (β) and 95 % CI for association between food purchase patterns and log-transformed annual carbon footprint with energy from the purchases (MJ) at its annual mean level, and predicted carbon footprint (kg CO2-eq/year) in the lowest (T1) and highest thirds (T3), and lowest (D1) and highest deciles (D10) of each purchase patternPattern β 95 % CID1T1T3D10LowerUpperAnimal-based0·1340·1320·1372119222529703218Easy-cooking0·0390·0360·0422437247126912761Ready-to-eat0·0160·0130·0192529253926252663High energy−0·032−0·035−0·0292701267124882437Traditional−0·036−0·039−0·0322710268024772415Plant-based−0·047−0·05−0·0442743270624462349 The comparison of the highest 10 % of Animal-based v. Plant-based revealed a + 869 kg CO2-eq annual difference, which means a 27 % lower annual food purchase carbon footprint among those strongly adhering to the Plant-based pattern than among those strongly adhering to the Animal-based pattern. Those with the highest 10 % of Easy-cooking and Ready-to-eat scores had a 14 % and 17 % smaller carbon footprint, respectively, than those in the highest 10 % of the Animal-based scores, but Easy-cooking and Ready-to-eat had a 17 % and 13 % larger carbon footprint, respectively, than those with the highest 10 % of the Plant-based scores. The carbon footprint of those in the highest 10 % of High energy and in the highest 10 % of Traditional was close to that of the Plant-based pattern, only 4 % and 3 % higher, respectively.
## Food expenditure and association with purchase patterns
An investigation of the food groups behind the patterns showed that of the food groups, expenditure was highest on cheese (7 %), fresh vegetables (7 %), fruits and berries (6 %), alcohol beverages (6 %), and yoghurt (5 %) (see online Supplemental Table 2). Cheese and yoghurt were strongly loaded in the Animal-based pattern, whereas fresh vegetables, and fruits and berries were strongly loaded in the Plant-based and Animal-based patterns. Peas, beans and lentils made up only 0·65 % of the total food expenditure.
When adjusted for the annual energy content of the purchases, all the purchase patterns were associated with the total expenditure on food (log- €) (Table 3). The regression indicated a positive correlation for all patterns except those of Traditional (β −0·115, 95 % CI (−0·120, −0·111)) or Easy-cooking (β −0·029, 95 % CI (−0·033, −0·025)), for which the correlations were inverse. The change in the expenditure on food purchases by a 1 sd increase in the food purchase pattern score (adjusted for total energy content of the purchases) was the largest and inverse in the Traditional pattern and the second, third, and fourth largest in the Animal-based (β 0·064, 95 % CI (0·059, 0·068)), Ready-to-eat (β 0·063, 95 % CI (0·059, 0·066)) and Plant-based (β 0·055, 95 % CI (0·051, 0·059)) patterns, but in the opposite direction to that of Traditional. Comparison of the highest 10 % of the Traditional v. Animal-based patterns revealed a + 886 € annual difference between the patterns, which means a 27 % lower annual expenditure among those in the highest 10 % of the Traditional pattern v. those in the highest 10 % of the Animal-based pattern. The difference between those in the highest 10 % of Traditional and Plant-based was similar.
Table 3Regression coefficients and 95 % CI for association between food purchase patterns and log-transformed annual expenditure on food (€) with energy from the purchases (MJ) at its annual mean level, and predicted expenditure (€) in the lowest (T1) and highest thirds (T3), and lowest (D1) and highest deciles (D10) of each purchase patternPattern β 95 % CID1T1T3D10LowerUpperAnimal-based0·0640·0590·0682685274831513273Ready-to-eat0·0630·0590·0662730277031643353Plant-based0·0550·0510·0592742278631343285High energy0·0290·0240·0332828285530423098Easy-cooking−0·029−0·033−0·0253072304128552801Traditional−0·115−0·12−0·1113458333625882387
## Relationship between carbon footprints and expenditures
When the food groups behind the patterns were examined separately, the largest carbon footprint per euro was for beef and processed beef (2·6), followed by pork and beef mixes (2·1), skimmed milk and sour milk (1·3), butter and butter–oil mixes (1·2), and semi-skimmed milk and sour milk (1·1) (see online Supplemental Table 2). In contrast, peas, beans and lentils had small carbon footprints per euro (0·34). Pork and beef mixes, both milk food groups, and butter–oil mixes loaded strongly in the Traditional pattern.
When adjusted for the energy content of the annual purchases, total expenditure (log-€) was positively associated with total carbon footprint (log-kg CO2-eq., β: 0·36, 95 % CI (0·35, 0·37)). Figure 4 shows the relationships between the patterns and the ratio of carbon footprint and expenditure. Stronger adherence to the Traditional (β: 0·048, 95 % CI (0·047, 0·050)), Animal-based (β: 0·042, 95 % CI (0·040, 0·044)) and Easy-cooking (β: 0·037, 95 % CI (0·036, 0·039)) patterns were associated with a higher carbon footprint per spent euro, whereas stronger adherence to the High-energy (β: −0·006, 95 % CI (−0·007, −0·004)), Ready-to-eat (β: −0·011, 95 % CI (−0·013, −0·010)) and Plant-based (β: −0·029, 95 % CI (−0·030, −0·027)) patterns were associated with a lower carbon footprint per spent euro.
Fig. 4Relationship between the purchase patterns and the log-transformed ratio of carbon footprint (kg CO2-eq.) and expenditure (€)
## Discussion
We identified eight food purchase patterns, from which we further analysed the six that explained most of the variation. Of all the patterns, the Animal-based explained the carbon footprint of total food purchases the most, that is, the purchases of those strongly adhering to the Animal-based had the largest carbon footprint, followed by Easy-cooking and Ready-to-eat patterns. As expected, the purchases of those who adhered strongly to the Plant-based pattern had the smallest carbon footprint. Those who adhered strongly to the Animal-based, Ready-to-eat and Plant-based patterns spent the most money on food, whereas those who adhered strongly to the Traditional pattern spent the least money on food. Stronger adherence to the Traditional, Animal-based and Easy-cooking patterns was associated with a larger carbon footprint per euro spent. This was because these patterns had high loadings of animal-based food groups, which had high carbon footprint to expenditure ratios.
Prior research has used alternative methods such as bar-code scanning to study purchase patterns(31–33), and only one has used customer loyalty card data[34]. Because loyalty card data accumulate without any effort required from the participant and is provided by the retailer instead of the customer/participant, they are possibly more objective than data collected by participants using bar-code scanning. Automated accumulation of data without much effort from researchers or participants enables data from a larger number of participants than data collected by participants using bar-code scanning. A detailed comparison of our study and previous studies of purchase patterns per se is not feasible because the countries of these studies have different food cultures (Finland, UK, Germany and USA), the analysed food groups consist of different foods and the analytical methods are different. However, one German[33] and one US study[31] found patterns similar to our Traditional pattern, characterised by high loadings for vegetables, fruits, potatoes, high-fat milk, and high-fat meat, and the US study[31] found a pattern characterised by ready-to-eat meals, and a pattern characterised by sweets, snacks and deserts. These patterns resembled our Ready-to-eat and High-energy patterns, respectively.
A few studies have analysed carbon footprints by using data-driven food consumption patterns, but not actual purchase data[10,35,36]. Comparing our study to somewhat similar studies is challenging because of differing food consumption assessment methods (dietary intake v. food purchases), differing covariates in the models (e.g. energy adjustment), varying LCA methodologies, the individual method choices of assessing CO2-eq values (allocations, system boundaries, etc.), and different food production conditions and practices. All these lead to the studies having different carbon footprints. It is also important to note that unlike dietary intake data, food purchase data also include foods that end up in household food waste (an advantage when studying the environmental impacts of food consumption). The direction of the results of the most similar study to ours[35], however, resembled the direction of ours: a food consumption pattern characterised by a high consumption of meat was associated with a larger carbon footprint than the patterns characterised by less consumption of meat. In the same study, the carbon footprints of the patterns characterised by less meat consumption, such as a plant-based healthy pattern (Lebanese-Mediterranean pattern) and a pattern with high loadings for high-energy/low-nutrient foods, were not fundamentally different to each other. This was similar to our result showing that the carbon footprints of High-energy and Plant-based patterns differed only a little.
The small size of the carbon footprint related to strong adherence to the High-energy pattern, which explained the variation the second most, can be explained by its high loadings for only plant-based foods. These foods, however, were typical ultra-processed foods[37], which are high in sugar, saturated fat and energy, and low in fibre and micronutrients[38,39]. The consumption of ultra-processed foods is associated with obesity and other non-communicable diseases(40–42), although it has not been conclusively shown that these adverse health effects are due to ultra-processing per se. Thus, despite a small carbon footprint, a High-energy pattern is not recommendable as a sustainable alternative to patterns with a large carbon footprint.
The relative increase in the Ready-to-eat score was associated with only a moderate increase in the carbon footprint of total food purchases. This was probably because the Ready-to-eat pattern is a mixture of animal- and plant-based foods (vegetarian, red meat, poultry and fish), and in Finland, the red meat alternatives of ready-to-eat meals usually contain relatively small quantities of meat. Data on the carbon footprints of ready-to-eat meals are scarce, however, and the results of earlier studies vary. In a Finnish study, ready-to-eat meals had a smaller carbon footprint than home-cooked equivalents, owing to raw material selection in ready-to-eat meals[43]. In contrast, in a UK study, ready-to-eat meals had a greater carbon footprint than equivalent home-cooked meals, mainly due to higher waste production during the processing phase[44]. Our data on ready-to-eat meals are based on the scarce available LCA data, which is why any interpretation of the carbon footprint of Ready-to-eat pattern requires caution. More data on the carbon footprints of ready-to-eat meals are clearly required.
According to nutrition recommendations, the food groups to be consumed the most are fruits, vegetables and high-fibre grains[45]. Furthermore, given the climate mitigation goals, consumption and purchases of plant-based products should be much more common, particularly if it leads to a reduction in meat consumption. Previous studies suggest that a plant-based sustainable diet is not affordable for everyone, especially in low- and middle-income countries[46,47]. Compared to other food groups, fruits and vegetables were expensive, whereas starchy staple foods (e.g. wheat flour, potatoes and rice) were the least expensive in all regions of the world[46]. Previous studies, however, have not extensively investigated the expenditure on food of those adhering to plant-based consumption patterns in developed countries.
In terms of reducing the carbon footprints of the households in our study sample, a shift from Animal-based and Easy-cooking patterns towards Plant-based pattern would be beneficial. Those adhering strongly to the Animal-based pattern (highest 10 % of the total pattern score) spent similar amount of money on food as those adhering strongly to the Plant-based pattern, which suggests a lack of economic barrier for a necessary shift towards a plant-based pattern. Those adhering strongly to Easy-cooking spent 484 €/year less on food than those adhering strongly to the Plant-based pattern. The carbon footprint of those adhering strongly to the Traditional pattern was not much larger than that of those strongly adhering to the Plant-based pattern, and more plant-based food choices would improve the nutritive value of their purchases. The difference between the expenditures of those adhering strongly to the Traditional and those adhering strongly to the Plant-based pattern was great – 898 €/year. However, it is worth noting that purchase data are based on actual expenditures. They do not represent the cheapest or most expensive selections. Therefore, cheaper, healthy plant-based food baskets are probably available. What they would contain and on what terms they would appeal to consumers requires more research. Thus, our results suggest that a shift from the Animal-based to the Plant-based pattern should not be considered as an economic issue. An economic barrier to shifting from the Traditional and Easy-cooking to a healthy plant-based pattern would be an important research topic.
Those strongly adhering to High-energy foods had a relatively high expenditure on food. Because of the unhealthy characteristics of ultra-processed foods, the health authorities usually consider ultra-processed foods, which are typically considered cheap, a threat to the health of the lowest income households in particular[30]. Our results suggest that the expenditure on food of at least those who adhered strongly to the High-energy pattern was near the average among the loyalty card holders. High palatability, affordability, convenience (often sold as ready-to-consume) and effective marketing may also increase the purchases of High-energy foods among those with higher expenditure. Even more so than High-energy, the Ready-to-eat pattern was not associated with low expenditure on food, which suggests that aiming for low expenditure is not the main motive of purchasing ready-to-eat meals. Our results thus suggest that a shift from Ready-to-eat pattern to a Plant-based pattern might not be an economic issue. This is supported by a previous study, which showed that convenience was an important food motive among Finnish consumers, especially among younger individuals and households with adults and children[48]. Based on these results, one way to acknowledge the convenience motive but to improve healthiness and reduce the carbon footprint of food consumption could be to increase the availability of healthy plant-based ready-to-eat meals with small carbon footprints.
In our analyses of the carbon footprint per euro in relation to the patterns, the most important results were those of the Traditional and Ready-to-eat patterns. Although those adhering strongly to the Traditional pattern had a lower carbon footprint than the other patterns, their carbon footprint per euro was high. This may indicate primarily cheap-energy-driven rather than environment-conscious purchase behaviour, resulting in an unintentional outcome of a smaller carbon footprint than among the others for the same total energy content. For those strongly adhering to the Ready-to-eat, the carbon footprint per euro was somewhat low, which is logical because they had an average carbon footprint but high expenditure on food. The carbon footprints per euro in relation to the other patterns were mostly in line with the energy-adjusted carbon footprints related to the patterns; those with a high carbon footprint, Animal-based and Easy-cooking, also had a high carbon footprint per euro and those with a low carbon footprint, Plant-based and High-energy, had a low carbon footprint per euro.
This study has several strengths. The potential of customer loyalty card data for investigating food purchase behaviour patterns has remained largely unexplored. Unlike self-reported food consumption data, customer loyalty card data do not suffer from recall bias or under- and over-reporting. Extensive purchase data are obtainable without substantially burdening participants or researchers. As the data covered an extensive time period, they were more accurate regarding habitual consumption and the inclusion of the seasonal variation of food consumption. We have also previously shown that food purchase data are a valid instrument for ranking consumers according to their self-reported food[19] and beer[20] consumption. Our data included detailed information on thousands of product groups, which enabled regrouping based on the principles most appropriate for the purpose. No studies have analysed carbon footprints or the expenditure of data-driven food purchase patterns. Conclusively, our study is among the first to display how customer loyalty card data can be used to identify and assess purchase patterns and the associated carbon footprints and expenditure.
Some uncertainties regarding the data are worth discussing. Even though most of the purchases were bought from the retailer in question, some foods may have been bought from different retailers, especially by those who reported buying only 61–80 % of their groceries from the retailer. However, our sensitivity analysis showed that the purchase patterns were similar when only those who bought ≥81 % of their food from the retailer were included in the factor analysis. This is in line with our previous finding that the proportions of food groups purchased were very similar among customers with high loyalty[22], and that their purchases reflect the loyalty card holder’s dietary intake[19]. To estimate the similarity of the purchase data with the purchases of the general Finnish population, the average annual expenditure on groceries and non-alcoholic beverages in Finland was 2916 €, and on alcohol and cigarettes 578 € in 2016[49]. These figures are not completely comparable to our expenditure data (median 3141 €/year) because the food expenditure in our data was only that of the primary card holder, because only alcoholic beverages with ≤5·5 % of alcohol are available in grocery stores in Finland, because our study data did not include cigarettes, and because of inflation (0·72 % from 2016 to 2018). These figures are, however, of somewhat similar magnitude.
Finally, the study also had some limitations. The sample was selected as those who bought most of their grocery shopping from the retailer of the present study and excluded those who preferred other retailers. The customers of different retailers may have different background or purchase profiles. As we have shown earlier, the purchase sample differed slightly from that of the general Finnish population; there were more women, individuals with higher education, and employed individuals, and less individuals aged under 30 years and over 70 years, as well as retired individuals[22]. It is therefore possible that the purchase patterns specific only to men, to those with lower education, unemployed, and to those aged under 30 or over 70, may not have been identified by the factor analysis. In the indicator product approach, some categories contained versatile food items, which increases the uncertainty related to the carbon footprint estimates. For instance, carbon footprints for ready-to-eat meals should be considered as rough estimates. However, the indicator product approach enabled reasonably sophisticated estimates, and the results of the study can be considered robust estimates of the relative differences concerning carbon footprint of the purchase patterns. The carbon footprints of product groups did not include the customer phase, which could have a significant impact on the carbon footprints of the purchases due to, for example, transportation from stores to homes or cooking methods. It should be noted that we only covered carbon footprints, and hence other highly relevant environmental impacts of food purchase patterns (e.g. biodiversity, water footprint, eutrophication and acidification) were not assessed, although all environmental impacts should be considered together when planning actions, due to their potential trade-offs.
## Conclusions
The carbon footprint was the greatest in those with strong adherence to the Animal-based and the lowest in those with strong adherence to the Plant-based pattern. The finding that strong adherence to the Traditional pattern resulted in a low energy-adjusted carbon footprint but high carbon footprint per euro suggests primarily cheap-energy-driven rather than environment-conscious purchase behaviour. The High-energy and Ready-to-eat patterns, which were both associated with moderate carbon footprints, associated with high expenditure, suggesting motives other than aiming for minimising expenditure. Because those adhering strongly to the Animal-based and the Plant-based patterns spent nearly equivalent amounts of money on food, a shift towards a recommended plant-based purchase pattern would probably not be an economic issue for those strongly adhering to the Animal-based pattern. The characteristics of affordable, healthy, plant-based purchase patterns that would be appealing to those who spend less money on food require further research.
## Conflicts of interest:
Both the research group and the retailer signed a contract on data transfer, ensuring the independence of the research and scientific publishing from business interests. HV received a fee from the S Group. The collaboration included offering professional advice to influencers and writing a blog post about the interpretation of the nutrition calculator in S Group’s mobile app. MF is a member of the S Group’s Advisory Board for Societal Responsibility. This membership involves no compensation. The authors declare no other relationships or activities that could appear to have influenced the present work. The authors declare that they have no other competing interests.
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|
---
title: Household and child food insecurity and CVD risk factors in lower-income adolescents
aged 12–17 years from the National Health and Nutrition Examination Survey (NHANES)
2007–2016
authors:
- Aarohee P Fulay
- Kelsey A Vercammen
- Alyssa J Moran
- Eric B Rimm
- Cindy W Leung
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991548
doi: 10.1017/S1368980021002652
license: CC BY 4.0
---
# Household and child food insecurity and CVD risk factors in lower-income adolescents aged 12–17 years from the National Health and Nutrition Examination Survey (NHANES) 2007–2016
## Body
Food insecurity is defined as inadequate consistent access to sufficient and nutritious food[1]. 2019 estimates indicate that 10·5 % of American households[2] report experiencing food insecurity at least some time during the year. In adults, food insecurity has been associated with overweight/obesity(3–5), metabolic syndrome[6], diabetes[7], hypertension[7,8] and CVD risk factors(9–12). Previously, we found that adults with very low food security had 2·36-fold greater odds of elevated 10-year predicted CVD risk compared with food secure counterparts[10]. Additionally, Ford et al. found that adults aged 30–59 years with very low food security had a higher prevalence of elevated predicted 10-year CVD risk compared with food secure adults (prevalence ratio = 2·38)[11]. Meanwhile, Seligman et al. showed that low-income food-insecure adults were more likely to experience elevated blood pressure and lipid levels compared with food secure adults[12].
Research on the association of food insecurity with CVD risk factors, such as obesity, metabolic syndrome, dyslipidaemia and blood pressure, among adolescents is inconsistent. One review noted emerging evidence for an association between food insecurity and obesity in adolescents[3], while another stated that reported associations have ranged from positive to null or inverse[13]. Meanwhile, Parker et al. found no associations between food insecurity and the presence of the metabolic syndrome in adolescents from National Health and Nutrition Examination Survey (NHANES) 1999–2006[6].
One of the potential reasons for inconsistency of evidence in adolescents could be the use of household food insecurity as a proxy for adolescents’ experience of food insecurity. Most research in adolescents that assesses the association between food insecurity and CVD risk factors uses household food security as the main predictor(14–16), while emerging evidence is showing that it would be beneficial to use child food insecurity[17] when looking at associations as well. Therefore, additional research comparing the associations between household and child food security status and CVD risk factors is needed.
This study examines the associations between household and child food insecurity status and CVD risk factors in 2876 lower-income adolescents aged 12–17 years from the NHANES[18] cycles 2007–2016. This is the first paper that we are aware of that assesses the association between food insecurity and multiple CVD risk factors in adolescents using both household and child food insecurity.
## Abstract
### Objective:
Household food insecurity is associated with CVD risk factors in low-income adults, but research on these associations among adolescents is inconsistent. This study investigates whether household and child food insecurity is associated with CVD risk factors in lower-income adolescents.
### Design:
Cross-sectional. Multivariable linear regression assessed the association between household and child food security and CVD risk factors. Household and child food security was measured using the US Food Security Survey Module. The analyses were adjusted for adolescent’s age, sex, race/ethnicity, smoking status, physical activity and sedentary time, as well as household income and the head-of-household’s education and marital status.
### Setting:
The USA.
### Participants:
The sample was comprised of 2876 adolescents, aged 12–17 years, with household incomes at or below 300 % federal poverty line from the National Health and Nutrition Examination Survey cycles 2007–2016.
### Results:
The weighted prevalence of household food insecurity in the analytic sample was 33·4 %, and the weighted prevalence of child food insecurity was 17·4 %. After multivariable adjustment, there were no significant associations between household and child food insecurity and BMI-for-age Z-score, systolic and diastolic blood pressure, HDL-cholesterol, total cholesterol, fasting TAG, fasting LDL-cholesterol and fasting plasma glucose.
### Conclusions:
Despite observed associations in adults, household food insecurity was not associated with CVD risk factors in a national sample of lower-income adolescents. Child food insecurity was also not associated with CVD risk factors. More research should be conducted to confirm these associations.
## Study population
Data were obtained from NHANES cycles 2007–2016. We limited our analyses to this time frame because physical activity was measured more consistently from the 2007–2008 cycle onward[18]. NHANES is a nationally representative, multi-stage, cluster-sampled continuous survey where data are released in 2-year cycles[18]. NHANES participants provide demographics, dietary, examination, laboratory and questionnaire data on topics including household food security status. A subset of NHANES participants also provide fasting laboratory data including plasma glucose, LDL and TAG. The study population was limited to lower-income (300 % federal poverty line (FPL) or below) adolescent (aged 12–17 years) NHANES participants with data on the exposures, outcomes and covariates. We restricted our sample to lower-income adolescents to limit confounding by income. For the fasting subsample, individuals were included if they reported fasting for 9–24 h.
## Household and child food security
Household food security data were obtained from the US Food Security Survey Module (eighteen questions for households with children) which was answered by an adult member of the household[18,19]. Based on the survey responses, household food security was categorised into full food security, marginal food security, low food security and very low food security[18,19]. Low food security and very low food security were grouped to form a food-insecure category. Child food insecurity, which measures household food security for children aged 17 and under, was also based on the US Food Security Survey Module[18,19]. Eight questions specific to the food security status of children in the household were answered by an adult household member and are categorised as ‘full or marginal food security’, ‘marginal food security’, ‘low food security’ and ‘very low food security’ as per United States Department of Agriculture protocols[18,19]. We also collapsed low and very low into one food-insecure group for this variable.
## Outcomes
NHANES collects physical examination and laboratory data from participants in the mobile examination centre[18]. Systolic blood pressure, diastolic blood pressure, weight and height are measured during the mobile examination centre examination. HDL-cholesterol, total cholesterol, fasting TAG, fasting LDL-cholesterol and fasting plasma glucose are obtained from blood samples taken during the mobile examination centre examination. For diastolic blood pressure, zero values were recoded to missing due to biological implausibility. For both systolic and diastolic blood pressure, we averaged three blood pressure readings to achieve a mean blood pressure measurement for each measure. BMI-for-age Z-score was calculated using the Centers for Disease Control and Prevention SAS Program for 2000 Growth Charts for children and adolescents using weight, height, sex and age data[20].
## Covariates
Adolescent characteristics were reported by the adolescents and included age, sex, race/ethnicity (included in our analyses as a proxy for systemic societal racial/ethnic inequities), vigorous recreational activity, moderate recreational activity, sedentary time and smoking behaviour[18]. Race/ethnicity categories were non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic and other race/ethnicity. Vigorous recreational activity and moderate recreational activity were recoded as binary yes/no variables[10]. If an adolescent participated in ≥10 continuous min of vigorous recreational activity in an average week, they were considered to engage in vigorous recreational activity. Similarly, if they participated in ≥10 continuous min of moderate recreational activity in an average week, they were considered to engage in moderate recreational activity. For sedentary time, participants were asked to provide a total value for minutes of sitting, using vehicular transport, and other passive activities in an average day. Minutes of sedentary activity were then recoded into a binary variable such that low sedentary activity was considered less than or equal to 6 h, and high sedentary activity was considered more than 6 h. Smoking behaviour was recoded as a binary ever/never variable such that the response ‘I have never smoked, not even a puff’ response to the question ‘About how many cigarettes have you smoked in your entire life?’ was coded as ‘no’ and other responses, ranging from ‘1 or more puffs but never a whole cigarette’ to ‘100 or more cigarettes’ was coded as ‘yes’. Household characteristics were reported by the household respondent (i.e. the adult member of the household that responded to the NHANES survey and served as the head-of-household for survey purposes) and included household income-to-poverty ratio and the household respondent’s education level and marital status[18]. Household income-to-poverty ratio is an NHANES variable that is constructed by dividing household income by the Department of Health and Human Services poverty guidelines to generate a ratio that ranges from zero to five (top-coded)[18], although in our sample, we restricted to individuals with a household income-to-poverty ratio of zero to three to capture lower-income individuals at 300 % FPL and below. Household respondent’s education level was recoded into two groups – less than high school graduate and high school graduate or more. Marital status was recoded to married/partnered or single (including widowed, divorced, separated and never married).
## Statistical methods
Data from survey cycles 2007–2008, 2009–2010, 2011–2012, 2013–2014 and 2015–2016 were combined to create a population of 5075 adolescents aged 12–17 years. Individuals were excluded for missing data on household food security status (n 68) or child food security status (n 80), household respondent education (n 187), household respondent marital status (n 99), household income-to-poverty ratio (n 449), vigorous recreational activity (n 308), moderate recreational activity (n 309), sedentary activity (n 336) and smoking (n 455). Additionally, due to varying missingness in the outcome variables, we constructed independent domains for each outcome that restricted for missingness on that particular outcome. Therefore, we excluded individuals with missingness for BMI-for-age Z-score (n 196), systolic blood pressure (n 483), diastolic blood pressure (n 649), HDL-cholesterol (n 731) or total cholesterol (n 731), respectively, for each outcome analysis. Similarly, out of the 2161 adolescents aged 12–17 years who reported fasting for 9–24 h, individuals were excluded for missing data on fasting TAG (n 272), fasting LDL-cholesterol (n 274) or fasting plasma glucose (n 247) for each outcome analysis. When using household food security, the sample consisted of 2876 adolescents aged 12–17 years. When using child food security, the sample consisted of 2872 adolescents. For each outcome analysis, the corresponding sample size can be found in Tables 3 and 4. For all analyses, we assumed that all data were missing at random. Characteristics of the study sample were similar to the sample excluded for missing data except for the variables of race/ethnicity, vigorous recreational activity and sedentary activity (online supplementary material, Appendix Table 1).
We conducted simple linear regressions to assess associations between continuous covariates and food insecurity and χ 2 tests for categorical variables. Multivariable linear regressions using weighted survey procedures were used to assess the association between household food insecurity status and each CVD risk factor. The survey linear regression procedures used robust variances to account for the potential non-normality of the outcome variable. As described above, we conducted available case analyses due to varying missingness in the outcome variables. Mobile examination centre weights[18] were used in analyses with the outcomes of BMI-for-age Z-score, systolic blood pressure, diastolic blood pressure, total cholesterol and HDL-cholesterol. Fasting weights[18] were used for analyses with the outcomes of fasting TAG, LDL-cholesterol and plasma glucose. All 2-year survey weights were recalculated to represent the 10-year time period of 2007–2016. Adolescent age, sex, race/ethnicity, smoking, vigorous recreational activity, moderate recreational activity, sedentary time, household respondent’s education level, marital status and household income-to-poverty ratio were included as covariates in the models. We also performed the same multiple linear regressions with child food insecurity status as the main predictor. Finally, we ran sensitivity analyses with 200 % FPL (rather than 300 % FPL) as the threshold for inclusion in the sample (online supplementary material, Appendix Tables 2 and 3). All analyses were conducted using SAS Version 9.4 (SAS Institute Inc.). Statistical significance was determined at the α = 0·05 level.
## Results
At the household level, the weighted prevalence of marginal food security was 14·5 % and the weighted prevalence of food insecurity was 33·4 %. At the household child level, the weighted prevalence of marginal food security was 8·3 % and the weighted prevalence of food insecurity was 17·4 %. Compared with food secure adolescents, marginally food secure and food-insecure adolescents were more likely to be non-Hispanic Black, Mexican American and other Hispanic race/ethnicity, engage in smoking behaviour and less likely to engage in moderate recreational activity ($P \leq 0$·05) (Table 1). Marginally food-secure and food-insecure households with adolescents were less likely to have a head-of-household with a high school education or higher and that was married/partnered, as well as come from a household with a higher income-to-poverty ratio compared with food secure households ($P \leq 0$·001). Analyses conducted with child food insecurity status as the main predictor variable showed similar associations with race/ethnicity, education, marital status and income (Table 2).
Table 1Characteristics of 2876 lower-income (300 % FPL or below) adolescents aged 12–17 years in NHANES 2007–2016† HH full food security (n 1316)Weighted % or SE‡ HH marginal food security (n 481)Weighted % or SE‡ HH food insecurity (n 1079)Weighted % or SE‡ P § Female64452·025954·351048·80·31Race/ethnicity|| < 0·001* Non-Hispanic White35854·99237·323138·6 Non-Hispanic Black33214·815223·729319·9 Mexican American31615·212620·833824·2 Other Hispanic1597·47111·91359·9 Other1517·8406·3827·4Vigorous recreational activity in typical week82261·327758·365961·50·66Moderate recreational activity in typical week67357·224452·752650·30·03* Low sedentary activity28220·410819·526222·60·46Smoking25022·18719·327927·60·01* Age (years), mean (se)14·530·0614·310·1014·440·060·14HH respondent education ≥ high school grad92076·030466·865965·1< 0·001* HH respondent married/partnered86670·429361·259254·4< 0·001* HH income-to-poverty ratio, mean (se)1·700·031·250·061·160·04< 0·001* BMI-for-age Z-score0·680·050·710·070·780·050·31Systolic blood pressure108·430·40108·530·47109·110·390·45Diastolic blood pressure59·740·5459·270·6759·360·410·78Fasting TAG78·352·2672·832·7378·413·040·28Fasting glucose95·110·4994·110·6195·450·690·21FPL, federal poverty line; NHANES, National Health and Nutrition Examination Survey; HH, household.*Statistically significant estimates at α = 0·05 are indicated.†Numbers may not sum to group totals due to missing data.‡All percentages are weighted.§Chi-square tests were used for sex, race/ethnicity, vigorous recreational activity, moderate recreational activity, sedentary time, smoking, HH respondent education and HH respondent marital status. Simple linear regressions were used for age and income-to-poverty ratio.||Empty cells for each race/ethnicity P-value are intentional.
Table 2Characteristics of 2872 lower-income (300 % FPL or below) adolescents aged 12–17 years in NHANES 2007–2016† HH child full or marginal food security (n 2020)Weighted % or SE‡ HH child marginal food security (n 298)Weighted % or SE‡ HH child food insecurity (n 554)Weighted % or SE‡ P § Female101452·513548·126347·70·16Race/ethnicity|| 0·001* Non-Hispanic White49550·15132·113540·7 Non-Hispanic Black52916·59225·015319·8 Mexican American53617·97519·916923·3 Other Hispanic2658·74312·3567·7 Other1956·83710·7418·5Vigorous recreational activity in typical week123860·918360·033561·40·96Moderate recreational activity in typical week100454·915553·328051·80·54Low sedentary activity44220·57422·013522·80·49Smoking39622·37427·014627·40·07Age (years), mean (se)14·470·0514·50·1014·470·060·95HH respondent education ≥ high school grad134272·319671·834164·90·04* HH respondent married/partnered129067·517357·928750·5< 0·001* HH income-to-poverty ratio, mean (se)1·560·031·190·061·120·05< 0·001* BMI-for-age Z-score0·680·040·720·080·850·070·07Systolic blood pressure108·490·29109·470·62109·090·540·32Diastolic blood pressure59·590·4359·30·8859·460·500·94Fasting TAG76·761·9676·534·7582·173·810·38Fasting glucose94·80·3796·212·2195·860·810·35FPL, federal poverty line; NHANES, National Health and Nutrition Examination Survey; HH, household.*Statistically significant estimates at α = 0·05 are indicated.†Numbers may not sum to group totals due to missing data.‡All percentages are weighted.§Chi-square tests were used for sex, race/ethnicity, vigorous recreational activity, moderate recreational activity, sedentary time, smoking, HH respondent education and HH respondent marital status. Simple linear regressions were used for age and income-to-poverty ratio.||Empty cells for each race/ethnicity P-value are intentional.
At the household level, there were no significant associations between marginal food security or food insecurity and BMI-for-age Z-score, systolic and diastolic blood pressure, HDL-cholesterol, total cholesterol, fasting TAG, fasting LDL-cholesterol or fasting plasma glucose (Table 3). At the household child level, there were also no significant associations between marginal food security or food insecurity and CVD risk factors (Table 4). In our sensitivity analyses using 200 % FPL (rather than 300 % FPL) as a threshold, we also found no associations (online supplementary material, Appendix Tables 2 and 3).
Table 3Multivariable-adjusted associations between household food insecurity and CVD risk factors in lower-income (300 % FPL or below) adolescents aged 12–17 years in NHANES 2007–2016* n † Full food securityMarginal food security beta95 % CIFood insecurity beta95 % CIBMI-for-age Z-score2876Ref.−0·04−0·20, 0·130·04−0·09, 0·17Systolic blood pressure (mmHg)2753Ref.0·16−1·15, 1·460·47−0·64, 1·58Diastolic blood pressure (mmHg)2650Ref.−0·17−1·79, 1·44−0·09−1·05, 0·88HDL-cholesterol (mg/dl)2633Ref.0·68−1·02, 2·38−0·01−1·44, 1·42Total cholesterol (mg/dl)2633Ref.0·29−3·26, 3·83−1·69−5·08, 1·71Fasting TAG (mg/dl)1162Ref.−4·56−11·84, 2·72−0·79−8·02, 6·44Fasting LDL-cholesterol (mg/dl)1161Ref.0·86−3·76, 5·48−1·21−5·34, 2·92Fasting plasma glucose (mg/dl)1179Ref.−1·29−2·86, 0·280·16−1·37, 1·69FPL, federal poverty line; NHANES, National Health and Nutrition Examination Survey.*Models adjusted for adolescent age, sex, race/ethnicity, vigorous recreational activity, moderate recreational activity, smoking, sedentary time, household respondent education, marital status and income.†Due to varying missingness in the outcome variables, we conducted an available case analysis; therefore, for each outcome, we included cases that had data on the exposure, covariates and the specific outcome of interest. For this reason, our n’s for each outcome differ slightly and are listed in the corresponding rows.
Table 4Multivariable-adjusted associations between household child food insecurity and CVD risk factors in lower-income (300 % FPL or below) adolescents aged 12–17 years in NHANES 2007–2016* n † Full or marginal food securityMarginal food security beta95 % CIFood insecurity beta95 % CIBMI-for-age Z-score2872Ref.−0·01−0·18, 0·170·14−0·01, 0·28Systolic blood pressure (mmHg)2749Ref.0·59−0·72, 1·910·18−1·07, 1·43Diastolic blood pressure (mmHg)2646Ref.−0·22−1·93, 1·50−0·09−1·25, 1·07HDL-cholesterol (mg/dl)2629Ref.0·31−1·55, 2·17−0·14−1·73, 1·44Total cholesterol (mg/dl)2629Ref.−1·11−5·47, 3·25−1·07−4·69, 2·55Fasting TAG (mg/dl)1161Ref.2·71−6·59, 12·023·81−4·61, 12·24Fasting LDL-cholesterol (mg/dl)1160Ref.−0·52−5·21, 4·17−0·18−4·62, 4·26Fasting plasma glucose (mg/dl)1178Ref.1·50−3·01, 6·020·97−0·52, 2·47FPL, federal poverty line; NHANES, National Health and Nutrition Examination Survey.*Models adjusted for adolescent age, sex, race/ethnicity, vigorous recreational activity, moderate recreational activity, smoking, sedentary time, household respondent education, marital status and income.†Due to varying missingness in the outcome variables, we conducted an available case analysis; therefore, for each outcome, we included cases that had data on the exposure, covariates and the specific outcome of interest. For this reason, our n’s for each outcome differ slightly and are listed in the corresponding rows.
## Discussion
Household food insecurity was not associated with CVD risk factors in a national sample of lower-income adolescents aged 12–17 years from NHANES cycles 2007–2016. Analyses assessing the association between child food insecurity and CVD risk factors also showed no significant associations.
Some of our findings align with previous studies of low-income adolescent populations. Similar to the lack of association observed between household food security and BMI Z-score, Gundersen et al. found no association between household food insecurity (as reported by the household respondent) and obesity, using measures of BMI, body fat, waist circumference, triceps skinfold thickness and trunk fat mass, in low-income children and adolescents aged 8–17 years[14]. Tester et al. found that low-income adolescents with low or very low household food security (as reported by the household respondent) did not have an increased risk of dyslipidaemia compared with those that were food secure, similar to the lack of association observed between food insecurity and total and HDL-cholesterol in the present study[15]. However, their study showed that marginal food security was associated with higher odds of elevated TAG, TAG to HDL ratio and apoB levels[15]. A possible reason that Tester et al. found significant associations for marginal food insecurity could have been their use of adolescent-specific clinical cut-offs to categorise lipid values into elevated and non-elevated categories[15].
On the other hand, some studies have found associations for food insecurity and CVD risk factors in adolescents[16,21]. For example, South et al. investigated the association between food insecurity and blood pressure in children and adolescents aged 8–17 years, and found that both household and child food insecurity (as reported by the household respondent) were associated with elevated blood pressure in this population[21]. Our results might differ from South et al. because they looked at children aged 8–11 years in addition to adolescents, and associations in children and adolescents could be dissimilar. Holben et al. also found that household marginal food security and/or food insecurity (as reported by the household respondent) was associated with increased central adiposity, overweight, obesity and lower HDL levels compared with full food security in adolescents aged 12–18 years[16]. Holben et al. used NHANES 1999–2006 data while we used 2007–2016 data, and therefore, the associations might differ due to differences in the underlying population in those time periods. Finally, another reason our results might differ from these studies could be that we examined lower-income adolescents to limit confounding by income rather than a nationally representative sample that includes higher-income adolescents.
An important factor to consider is the measurement of food insecurity. Research has shown that child-reported food insecurity measures are predictive of dietary quality outcomes[22,23], and that parent-reported food insecurity might not accurately capture the experience of food insecurity among children and adolescents[24]. Additionally, previous research by Jun et al. has found slightly different associations for outcomes when using child food security status compared with household food security status[17]. Therefore, it is possible that child food security status or child-reported food security status might serve as a better predictor than household food security. For that reason, we also examined child food insecurity as a predictor variable in our analyses but found no associations. It is possible that neither household nor child food security status, which are both reported by the household respondent in NHANES[18], accurately captures the experience of adolescent food insecurity, and this could be a factor in the mixed evidence for food insecurity and CVD risk in adolescents. Assessing associations using both household and child food insecurity is a critical first step in determining the accuracy and prognostic capabilities of food insecurity measures for adolescents; it is also important for future research to look at associations between food insecurity and CVD risk factors through child-reported measures as well.
Another potential reason that there seems to be evidence of an association between food insecurity and CVD risk factors in adults but not in adolescents could be dietary quality. Dietary quality has been associated with both CVD and food insecurity, and therefore, may be a mediator of the association between food insecurity and CVD risk factors. In low-income adults, research has found associations between food insecurity and dietary quality[25]. Meanwhile, in adolescents, research on the association between food insecurity and dietary quality is more limited and inconsistent[17,22,23,26]. Thus, although diet quality was beyond the scope of the current analysis, differences in dietary quality associations in adults and adolescents might explain why we see associations between food insecurity and CVD risk factors in adult populations and inconsistent evidence in adolescents.
The limitations of this study include the cross-sectional study design which lessens our ability to establish a causal relationship between food insecurity and CVD risk factors. However, it is unlikely that high CVD risk in an adolescent population would cause food insecurity, and therefore, lack of temporality and reverse causality should not be a major issue. Additionally, our measures of food insecurity are not specific to the individual adolescent but rather are at the household level[18], which could make the food insecurity variables less accurate in capturing adolescents’ individual experiences. Furthermore, the measures of food insecurity in NHANES are meant to capture food insecurity status over the past year[18,19], and although these measures are validated[18,19], it is unclear how associations with CVD risk factors might shift if food insecurity status changes over time. We also cannot rule out unmeasured confounding from external factors, such as pubertal status, neighbourhood effects and food environment, as a possibility. For example, pubertal status as measured through Tanner stage[27] could confound the relationship between food insecurity and some CVD risk factors in adolescents. However, we did not control for Tanner stage nor neighbourhood effects and food environment because variables to measure these constructs were not available in NHANES. In addition, our covariates of vigorous recreational activity, moderate recreational activity, sedentary activity, smoking and household income-to-poverty ratio could be particularly susceptible to measurement errors and/or reporting bias. That being said, NHANES has strict protocols for survey administration, data entry and data cleaning, so bias and errors are minimised. We also adjusted for potential confounders to minimise confounding bias, although residual confounding may remain. Finally, we assumed that all missing data were missing at random, and our sample differed from the sample excluded for missing data on the variables of race/ethnicity, vigorous recreational activity and sedentary activity; however, NHANES takes survey non-response into account with its survey weighting so that this issue is minimised[18]. Despite these limitations, our study is unique in its investigation of a large, recent and national sample of lower-income adolescents across multiple CVD risk factors using robust analyses and adjusted for numerous socio-demographic and health covariates. To the best of our knowledge, this is also the first study to investigate both household and child food security status in association with multiple CVD risk factors in adolescents.
According to 2019 data, food insecurity affects approximately one out of ten American households[2], and that number has continued to grow due to the recent COVID-19 pandemic[28]. In our sample, approximately one-third of lower-income adolescents were food insecure, and about one out of seven were marginally food secure. Though food insecurity has been associated with CVD risk factors in adults[10,11], we found no association between household or child food insecurity and the CVD risk factors of BMI-for-age Z-score, systolic and diastolic blood pressure, HDL-cholesterol, total cholesterol, fasting TAG, fasting LDL-cholesterol and fasting plasma glucose. However, food insecurity is highly prevalent in low-income adolescents and may still be associated with adverse outcomes[29]. Although we found no significant associations between household or child food insecurity and CVD risk factors, other researchers have indicated that food insecurity, overweight/obesity[30] and dyslipidaemia[15] can co-occur in low-income adolescent populations, even if not causally linked. Therefore, these are still critical issues to be studied and addressed through public health programmes and policies.
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|
---
title: The impact of a healthy checkout intervention on fruit and vegetable ‘micro-pack’
purchases in New Mexico
authors:
- Stephanie Rogus
- Joanne Guthrie
- Mihai Niculescu
- Lina Xu
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991551
doi: 10.1017/S1368980022002026
license: CC BY 4.0
---
# The impact of a healthy checkout intervention on fruit and vegetable ‘micro-pack’ purchases in New Mexico
## Body
Dietary patterns that follow the Dietary Guidelines for Americans are associated with lower risk of chronic disease[1,2]. The Dietary Guidelines for Americans recommend consumption of nutrient-dense foods and beverages and a dietary pattern consisting of wholegrains, lean meats and vegetable protein, low-fat dairy products and fruits and vegetables[1]. However, American diets continue to fall short of Dietary Guidelines for Americans recommendations, particularly for fruit and vegetable consumption[3,4]. While most Americans fail to consume recommended amounts of fruits and vegetables, diets of low-income Americans participating in federal food assistance programmes are of particular concern. The Supplemental Nutrition Assistance Program (SNAP) provides low-income Americans with funds that can be used to purchase foods in most grocery stores; in 2020, 39·9 million people participated in SNAP each month[5]. Although benefits are based on a Thrifty Food Plan (TFP) that is designed to provide enough funds to meet dietary recommendations, participants buy fewer servings of fruits and vegetables than recommended and report purchasing fewer fruits and vegetables compared with low-income and higher-income non-participating Americans[3]. The TFP assumes SNAP households should spend 40 % of their benefits on fruit and vegetables[6],1 but a 2016 Food and Nutrition Service study of purchases indicated they actually spend less than 15 % of their benefits on these foods[8,9]. SNAP is the largest food assistance programme serving low-income Americans; however, a second programme, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), also provides food benefits to low-income pregnant, postpartum and breastfeeding women as well as infants and children under 5 years of age. This programme served 6·2 million people in an average month in 2020[5]. Unlike SNAP, which provides funds that can be used to purchase almost all foods sold in supermarkets and grocery stores, WIC funds the purchase of specific foods chosen to meet the needs of its target population, for example, milk, eggs, iron-rich cereals and whole grains.
Updates to food assistance programmes have been made in recent years to encourage the consumption of fruits and vegetables by recipients. In 2009, WIC added cash value benefits (CVB) to be used specifically for the purchase of fruits and vegetables, and the United States Department of Agriculture (USDA) has funded a number of projects that provide incentives to SNAP participants for purchasing fruits and vegetables(10–12). In 2021, after the previously cited studies of fruit and vegetable purchasing by SNAP participants were conducted, the TFP was revised, with the result that benefit levels were increased[7,13]. The new TFP assumes 38 % of benefits should be allocated to fruits and vegetables. Given that the change in benefit level has just occurred, there is no information on whether SNAP shoppers will respond to the higher benefit levels by purchasing more fruits and vegetables. Simulations of likely purchasing changes in response to increased benefits suggest SNAP households may purchase more fruits and vegetables, but the estimated changes would not be large enough to assure that most households would meet recommendations[14,15]. Other preferences, such as a desire for convenience, may compete for use of the food dollar[16], so strategies to encourage fruit and vegetable purchasing and make it more salient to consumers may still be valuable. Both WIC and SNAP include nutrition education components that promote fruit and vegetable consumption.
Despite these efforts, fruit and vegetable purchasing by low-income consumers continues to lag, and some efforts do not seem to be achieving their full potential. For example, research examining WIC CVB redemption in several states has found that recipients redeem about 70 % of the benefits and an evaluation of the Food Insecurity Nutrition Incentive Program (now called the Gus Schumacher Nutrition Incentive Program or GusNIP), a grant-funded programme operated by the USDA that is designed to incentivise purchase of healthy foods such as fruits and vegetables, found that recipients redeem 82 % of their benefits(11,17–19). Point of purchase interventions has been proposed as a mechanism to increase overall produce purchases by food assistance recipients and the general public[20,21].
Supermarket interventions have the potential to increase purchases of fruits and vegetables as the majority of household food is acquired from these outlets in the USA(21–23). Consumers report that product variety and packaging, price, promotion and display location influence their purchasing decisions, and research suggests that manipulating these aspects of the in-store marketing environment can encourage healthier food purchases[20,24]. Behavioural economics theory suggests that consumers are not always rational decision-makers; psychological influences play a role in food choice, which can lead consumers to value short-term preferences, like taste, and to choose products with high visibility or attractiveness when they are feeling tired, rushed, distracted or hungry[26]. The behavioural economics concept of cognitive overload is experienced in stores due to the sheer number of food products available, time constraints of shoppers and distractions like shopping with children[21]. Healthier foods placed at checkout aisle end-caps can address time constraints by signalling convenience and address attention constraints by signalling prominence[21]. All customers must pass through checkout, and often wait in line, so low-cost, healthy items displayed at checkout may increase their attractiveness and encourage shoppers to purchase them[21]. For low-income shoppers in particular, items such as fruit and vegetable micro-packs, or plastic-wrapped packages of one or more fresh fruits or vegetables displayed on a rack at the checkout aisle, priced at around $1 may be appealing.
Studies examining the stocking policies of supermarkets have found that stores with consistent policies about replacing unhealthy items with healthier items at checkout were associated with fewer purchases of unhealthy items[27,28]; however, few studies have tested healthy checkout interventions in real-world settings. In these healthy checkout studies, researchers replaced less healthy items with healthier items, added healthier items to the current selection or removed less healthy items without replacing them with healthier items at one or more checkout aisles in a store. Interventions have ranged in duration from 4 d to 6 months, substituting unhealthy items like candy and soda with fresh fruits and vegetables, dried fruit, cereal bars, nuts, dried fish and bottled water or removing unhealthy items altogether. Five studies added healthy items to checkout aisles and found that sales of healthier items increased(21,29–31), but there was no reduction in the sales of less healthy items(29–31). Three studies substituted unhealthy foods with healthier options at checkout aisles and reported mixed results. Sigurdsson et al. found an increase in healthy food sales and decrease in unhealthy food sales[33], whereas Huitink et al. found that participants purchased fewer of the healthy items at checkout, suggesting that they did not substitute less healthy items with healthier ones[31]. Adjoian et al. reported that a higher percentage of customers using the healthy checkout purchased healthy items compared with customers using the standard checkout; however, only 4 % of customers bought anything at checkout, so the impact of the healthy checkout aisle was likely limited[34]. Vogel et al. reported increased purchases of fruits and vegetables when unhealthy foods were removed from the checkout aisle and produce was placed near the entrance of stores[35].
Only seven healthy checkout interventions have included fresh produce, and of those, five were experimental or quasi-experimental studies that included control stores or checkout aisles. Two of the five placed healthy products on a rack that was added to the checkout aisle[21,30], two replaced the entire product selection at checkout with healthy items for one or more aisles[29,34] and one only removed less healthy products from checkout aisles[37]. Three of the five studies reported increased sales of fresh fruits and/or vegetables[21,29,35], whereas the other two could not report on changes in produce sales. One did not collect data on pre-intervention sales of fruits and vegetables and could not conclude anything about changes in produce purchases[30] and one only examined changes in the purchase of healthy items overall, which included fresh and dried produce, granola bars, nuts, bottled water and other healthy items[34]. This study reported increased purchases of healthy items among shoppers who went through the healthy checkout aisle, noting that fresh and packaged fruit were the most purchased healthy product[34]. Of the three studies that reported increased fresh produce purchases, one reported an increase in the purchase of carrot snack packs (out of five total healthier items) but no increase in fresh fruit purchases[29], the second found that fresh produce micro-pack sales increased while overall sales stayed constant, suggesting that the micro-packs increased fruit and vegetable purchases[21] and the third reported improvements in dietary quality among their female participants in addition to storewide decreases in unhealthy food sales and increases in fruit and vegetable sales[35]. Payne et al. also examined sales of produce micro-packs purchased using SNAP, finding increased purchases of micro-packs and an increase in the micro-packs’ share of SNAP spending[21]. They concluded that fruit and vegetable micro-packs can replace purchases of other foods for SNAP recipients[21]. Although low-income shoppers may not purchase certain snacks at checkout due to their relative expense compared with multi-pack snacks throughout the store[30], these studies suggest that offering low-cost produce micro-packs at checkout may be a promising strategy for increasing produce purchases of low-income shoppers and encouraging full redemption of benefits specifically targeting fruit and vegetable purchasing, such as the WIC CVB, though more studies testing such interventions are needed.
More research is needed to determine the effectiveness of various in-store strategies, including healthy checkouts, particularly for low-income consumers[20,36]. Of the food retail interventions conducted, most did not include control or comparison groups, were not experimental and included subjective outcome measure data such as self-reported purchases[36]. Most studies also did not examine the sustained effects of the intervention over time; less than 20 % of studies analysed intervention effects beyond 3 months[36].
This study tests a healthy checkout intervention whereby low-cost fresh fruit and vegetable micro-packs were sold and promoted at checkout aisle end-caps. The purpose of this study is to examine the impact of a healthy checkout intervention on fruit and vegetable micro-pack purchases across stores of a regional grocery chain in New Mexico that serves a low-income customer base. This research extends previous research by testing the intervention over a longer time period, including an objective outcome measure and including more intervention and control stores.
## Abstract
### Objective:
Produce sold as plastic-wrapped packs of two to four individual items (i.e., produce micro-packs) that are low cost and placed at checkout may appeal to shoppers with budget constraints and provide a second chance to purchase items available elsewhere in the store. This study examined the impact of an intervention that placed produce micro-packs at checkout and promoted them in grocery stores across New Mexico, USA.
### Design:
This quasi-experimental study placed produce micro-packs at checkout end-caps in thirteen stores (group 1), with eight stores serving as controls (group 2) from 1 July 2019 through 31 January 2020 (first phase). The intervention was extended to group 2 stores from 1 February 2020 through 30 June 2020 (second phase). Cashiers were directed to upsell the micro-packs to Special Supplemental Nutrition Program for Women, Infants, and Children recipients who had unspent cash value benefits for produce purchases.
### Setting:
Twenty-one grocery stores across New Mexico.
### Participants:
Twenty-one produce items sold as micro-packs in stores from July 2019 through June 2020.
### Results:
A random effects model showed that the daily sales of micro-packs increased by 47 % during each intervention period. Group 2 stores had lower sales than group 1 stores during the first phase of the intervention. Once extended to group 2 stores, sales of micro-packs in those stores increased and sales in group 1 stores continued at the higher level.
### Conclusions:
Placing produce micro-packs at checkout may increase produce sales and support health promotion efforts by public and private stakeholders.
## Study design
This research was part of a larger intervention aimed at increasing the redemption of WIC CVB. In partnership with a regional grocery store chain, the intervention included placing fruit and vegetable micro-packs on racks at checkout aisles and changing the software in store registers to notify cashiers when WIC recipients had additional money left on their CVB. Before the intervention began, cashiers were trained to provide information to recipients on the amount left on their CVB and to upsell the micro-packs. However, subsequent training was not provided to any new cashiers that may have been hired, and consistency of cashier upselling was not monitored during the intervention. Therefore, the frequency of cashier upselling may have declined over time.
Fruit and vegetable micro-packs were sold as plastic-wrapped packs of two to four individual fresh fruits and vegetables. Micro-packs were already being sold in the produce aisle of each store prior to the intervention, and available micro-packs were taken from that aisle for the intervention. Micro-packs continued to be sold in the produce section during the intervention. The micro-packs were added to checkout aisles and did not displace the other products typically placed at checkout. Twenty-one different fruits and vegetables were sold as micro-packs for $0·20 to $2·79 each (Table 1).
Table 1Fruit and vegetable micro-pack type, pack size and retail priceFruit/vegetablePack sizeRetail priceRoma tomatoes3$0·99Anaheim pepper2$0·99Red delicious apple2$0·99Avocado2$2·79Green bell pepper2$0·99Grapefruit2$0·99Green beans2$0·99Jalapeno4$0·99Lemon3$0·99Lime3$0·99Nectarine3$2·39White onion2$0·99Yellow onion2$0·99Orange2$0·99Peach3$2·39Plum4$2·39Potato2$0·99Mexican squash2$0·99Yellow squash2$0·99Sweet potato2$0·99Banana1$0·20 The aim of the intervention was to nudge consumers to purchase more fruits and vegetables by: [1] encouraging income-constrained consumers who may limit their fruit and vegetable purchasing while shopping over fear of overspending to purchase produce if they have money leftover at checkout and [2] encouraging impulse or unplanned purchases of produce by increasing their visibility at checkout[37].
The intervention began on 1 July 2019 in thirteen stores (group 1) in New Mexico, with eight stores serving as controls (group 2). Group 1 and group 2 stores were selected in consultation with the retailer and are located in areas throughout the state, with group 1 stores located in the north, south, east, central and northwestern part of the state and group 2 stores located in the north, south, central and east. Additionally, the majority of stores participating in the intervention serve a low-income clientele; over 75 % of intervention and 60 % of control stores are located in census tracts categorised as ‘low-income’ by the United States Department of Agriculture’s Economic Research Service (USDA-ERS) (Table 2).
Table 2Low-income population and location of group 1 and group 2 stores* Census tract low-income population (%)Low-income census tract† LocationGroup 1 stores 136YesNorth, central 234NoSoutheast 362YesEast, central 40NoNorth, central 576YesNorthwest 660YesNorthwest 752YesNorth, central 841YesSouth, central 969YesSouth, central 1034NoSouth, central 1149YesNorth, central 1268YesEast, central 1333YesCentralGroup 2 stores 10NoNorth, central 232NoSoutheast 338NoNortheast 457YesSouth, central 559YesCentral 639YesNorth, central 742YesNortheast 853YesCentral*Table created using the USDA-ERS food access research atlas.†A low-income census tract is defined by the ERS as ‘tracts with a poverty rate of 20 % or higher, or tracts with a median family income less than 80 % of median family income for the state or metropolitan area’[38].
The first phase of the intervention ran for 7 months in group 1 stores (through 31 January 2020). At that time, the second phase of the intervention was initiated where the intervention was extended to the eight group 2 stores (through 30 June 2020), while continuing in group 1 stores. Daily sales, in dollars, of each micro-pack by store were obtained from the retailer from 1 March 2019 through 30 June 2020.
## Statistical analysis
A random effects model was estimated to examine the change in average daily sales per store of the micro-packs in group 1 and group 2 stores during the intervention periods. The model included an indicator variable for store type (group 1 or group 2) and intervention time period (pre-intervention, intervention phase 1 and intervention phase 2), and an interaction between the two. The marginal means were then estimated for group 1 and group 2 stores during each time period.
The analysis was conducted using R version 4.0.2, and differences were determined to be statistically significant if the P-value was below 0·05[39].
## Results
In order to focus the analysis on stores in low-income areas, the two stores with 0 % low-income population were excluded from the analysis. Figure 1 shows the daily sales of all micro-packs at group 1 and group 2 stores, averaged across intervention phases. Sales in group 1 stores increased from baseline following both intervention periods. Sales in group 2 stores did not increase following the first phase of the intervention but did increase following the second phase, when they also participated in the intervention.
Fig. 1Average per-store daily sales of micro-packs for group 1 and group 2 stores across intervention phases., Group 1;, Group 2 The results of the random effects model showed that sales increased during both phases of the intervention.2 Daily micro-pack sales (per store) in group 1 stores significantly increased by 47 % ($P \leq 0$·0001) from baseline (i.e. pre-intervention levels of micro-pack sales in group 1) during the first phase of the intervention, whereas sales in group 2 stores decreased by 3·76 % from baseline, though this change was insignificant (Table 3). During the second phase of the intervention when it was extended to group 2 stores, daily micro-pack sales (per store) in group 1 and group 2 stores increased significantly by 47·2 % ($P \leq 0$·0001) and 44·21 % ($P \leq 0$·0001) from baseline, respectively. The differences-in-differences change in average sales in group 1 stores during the first phase of the intervention was significantly higher compared with group 2 stores (+50·76 %, $P \leq 0$·0001). For group 2 stores, the change in average sales (+2·99 %) during the second phase of the intervention (v. baseline) was not significantly higher compared with group 1 stores. Taken together, these results demonstrate that sales increased in all stores after the intervention was implemented in those stores.
Calculating the marginal mean sales of group 1 and group 2 stores from the random effects model showed that group 1 stores had significantly higher sales of micro-packs during the first ($19·15, 95 % CI14·85, 23·4) and second ($19·18, 95 % CI 14·88, 23·5) phases of the intervention compared with the pre-intervention period in those stores ($13·03, 95 % CI8·71, 17·3) (Table 4). Group 2 stores did not have significantly higher sales during the first phase of the intervention ($8·30, 95 % CI 2·61, 14·0) compared with the pre-intervention period in those stores ($8·79, 95 % CI 3·09, 14·5) but did have higher sales during the second phase of the intervention ($14·55, 95 % CI 8·86, 20·2) compared with both the pre-intervention and first phase of the intervention (see Table 4).
Table 3Percentage sales relative to pre-intervention in group 1 and group 2 stores (baseline = 100 %)* Dependent variableGroupPre-intervention (% of baseline for group 1 stores)Phase 1 intervention (% of baseline for group 1 stores)Difference % P Difference-in-differences % P Daily micro-pack sales per storeGroup 1100·00147·0047·00 $P \leq 0$·000150·76 $P \leq 0$·0001Group 267·4663·70−3·76 $$P \leq 0$$·26Dependent variableGroupPre-intervention (% of baseline for group 1 stores)Phase 2 intervention (% of baseline for group 1 stores)DifferenceDifference-in-differencesDaily micro-pack sales per storeGroup 1100·00147·2047·20 $P \leq 0$·00012·99 $$P \leq 0$$·51Group 267·46111·6744·21 $P \leq 0$·0001*The statistical analyses are based on sales (not percentages) and percentages shown in tables were calculated relative to the pre-intervention sales in group 1 stores (baseline).
Table 4Average daily micro-pack sales per store by intervention phase, in USDVariableEstimate95 % CIGroup 1Pre-intervention (no intervention)$13·038·71, 17·3Intervention phase 1 (active intervention group)$19·1514·85, 23·4Intervention phase 2 (active intervention group)$19·1814·88, 23·5Group 2Pre-intervention (no intervention)$8·793·09, 14·5Intervention phase 1 (no intervention)$8·302·61, 14·0Intervention phase 2 (active intervention group)$14·558·86, 20·2
## Discussion
Results of this study showed that the total daily sales of fruit and vegetable micro-packs increased following the first phase of the intervention in group 1 stores. Sales also increased in group 2 stores during the second phase of the intervention when they no longer acted as a control group and participated in the intervention. The increase in micro-pack sales in group 1 stores remained significantly higher than the pre-intervention period during the second phase of the intervention, demonstrating a sustained effect of the intervention in those stores. These results are similar to other studies that have examined the impact of healthy checkout interventions, which found that healthy purchases increased during the intervention period(21,29–35).
Few studies have tested healthy checkout interventions that include fresh produce. Of those that included a control group, all were conducted over a relatively short timeframe – from 2 weeks to 6 months[21,29,30,34,35]. Three reported increases in produce purchases, with one finding vegetables[29], one finding fruits and vegetables[37] and one finding fruit and vegetable micro-packs increased in sales[21]. The latter study is the only study that has exclusively tested a produce micro-pack intervention. It was conducted over 1 month in three stores (one control and two intervention stores) and reported between 80 and 300 % increase in sales of micro-packs in the intervention stores compared with control stores during the intervention period[21]. Our results align with this study, and the longer period of time over which this study was conducted allowed for the examination of the 7-month intervention and longer-term effects of the intervention through 1 year.
Although the increase in average per-store daily sales in intervention and control stores during the intervention – ranging from $5·76 to $6·15 – may seem small, other studies, including those that offer cash incentives, show relatively small increases in fruit and vegetable purchasing[40]. However, these small changes may be meaningful: a recent study examining the increase in spending needed by SNAP recipients in order to satisfy dietary recommendations for fruit and vegetable consumption found that recipients do not need to spend a lot more money in order to meet recommendations while increasing their produce variety[8]. Using simulation for a four-person household that is receiving the maximum monthly SNAP benefits, they show that produce recommendations cannot be met by spending 25 % of food dollars on fruits and vegetables. In order to meet recommendations and increase variety, recipients need to increase their expenditures to 30 % and ideally 40 %. This means spending $8·37 and $24·12 more per household, respectively, than Americans are currently spending on produce[8]. Further, SNAP households tend to overspend in categories such as fat, oils and sweets, which can be reduced and money can be shifted to fruits and vegetables[9,21].
With the increasing awareness of the relationship between diet-related disease, low income and food insecurity, policy interest in nutrition security has grown. Nutrition security refers to a focus on consistent access to affordable healthy foods and beverages that may help prevent and treat disease[41]. Aligning with this focus is increasing interest in promoting purchases of produce in particular by lowering a household’s cost of purchasing produce through expansion of existing programmes like the WIC CVB and supporting new programmes such as GusNIP, bonus bucks and produce prescription programmes[42,43]. The recent update to the TFP will permanently increase SNAP benefits, increasing the purchasing power of recipients and potentially supporting additional fruit and vegetable purchases[7]. However, for these programmes to succeed in promoting healthy diets, purchasing behaviour must also change[44,45]. An evaluation of the Healthy Incentive Pilot that provided financial bonuses to SNAP participants for fruit and vegetable purchasing found evidence suggesting that informational or promotional aspects of the programme were important contributors to success.[40] USDA supports nutrition education targeted to SNAP participants and other low-income individuals through its SNAP-Ed programme[46]. SNAP-*Ed is* encouraged to work in a variety of community settings, including supermarkets and grocery stores that serve large numbers of low-income consumers[46]. These findings may be of interest to SNAP-Ed and other nutrition education programmes that include a focus on encouraging purchase of fruits and vegetables. Nutrition promotion programmes focused on supermarket interventions have been investigated in a wide range of nations, including Australia, the United Kingdom (UK), Norway, Canada, Japan and the Netherlands[47]; therefore, replication in other settings could be of interest. In fact, the UK recently announced restrictions on the placement of foods high in sugar, fat and salt at checkouts and other prominent locations in medium and large food retailers, including supermarkets[48]. The potential success of this policy was demonstrated by a study that removed chocolate confectionery from prominent locations in stores, including the store entrance and aisle end-caps. The study found that the seasonal increase in confectionery sales was attenuated in intervention stores compared with control stores, which resulted in significant reductions in total energy and fat purchases[49].
Nudges are often encouraged as a low-cost strategy, but it is important to consider the feasibility and sustainability of these approaches. In the case of this project, retailers assumed the cost of packaging and placement of items, making it potentially feasible to implement more widely, assuming continued retailer interest and support. This intervention added healthy items to the checkout aisle without removing any less-healthy alternatives, which may have affected the impact of the intervention but may also be a more sustainable alternative for retailers. Fruits and vegetables placed at checkout may compete with other products that incur slotting fees, making retailers possibly less willing to modify their current checkout selection. However, some food assistance benefits and incentives, such as WIC CVB and fruit and vegetable purchasing incentives or ‘bonus bucks’ offered through some programmes[12], can only be used to purchase produce. This restriction may incentivise retailers to consider adding produce to the checkout aisle, particularly because these products have high profit margins compared with other product categories[50]. As of June 2022, almost 3 years after these data were collected, the stores that implemented this intervention were still offering the micro-packs at checkout, indicating the potential for long-term acceptance. This type of intervention could appeal to retailers to nudge purchasing of fruits and vegetables and as a demonstration of corporate social responsibility.
Healthy checkout interventions may be particularly effective because of their impact on impulse purchases. The convenience and prominence of items at checkout are attractive to customers, and offering low-cost, healthier options provides healthier alternatives for impulse purchases and signals a discount to shoppers[21]. For shoppers using food assistance benefits, these items offer a second chance to fully redeem their benefits.
The strengths of this study are that it was quasi-experimental and included control stores, it included an objective outcome measure and it was conducted over 12 months, which enabled the sustained effects of the intervention to be captured. To our knowledge, published studies have not examined the impact of a healthy checkout intervention beyond 6 months and this study provides evidence of sustained effects of these interventions. This study also included produce that is low cost for consumers or most commonly purchased[8,9]; twelve out of twenty-one of the fruits and vegetables fell into either of these categories. The limitations of this study are that consumption was not captured and data were not available on the form of payment for purchases of the micro-packs – WIC, SNAP or cash – making it impossible to assess to what extent it affected participant use of programme benefits. Second, because this was an intervention that combined placement of low-priced micro-packs at the checkout aisle and promotion of them (through cashier upselling), it is unclear whether micro-pack placement, price or promotion had a greater impact on purchases. Third, the promotion of the micro-packs may not have been sustained over the study period due to no follow-up training or monitoring to ensure that cashiers continued to promote the micro-packs to WIC recipients. Although the regression results showed that, overall, there was a sustained increase in micro-pack sales in intervention stores during the second intervention period, it is worth noting that stores varied in their sales of micro-packs over the study period, with some stores showing a drop in sales a few months after each intervention period. Further research may help identify strategies to maintain interest over time, perhaps by incorporating occasional additional promotional strategies to refresh the message. Fourth, data were only provided on micro-pack sales, not overall produce sales, so it is unclear whether the micro-packs increased fruit and vegetable purchases as customers may have shifted their current produce purchases to the micro-packs without buying more produce overall. Future research is needed to further assess sustained impacts of various types of healthy checkout interventions on the purchase and consumption of produce. Future research is also needed to differentiate the impact of various intervention strategies – such as product, placement, promotion and price – on produce purchases.
## Conflicts of interest:
There are no conflicts of interest.
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