The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels

The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels Chao-Qiang Lai, Laurence Parnell, Yu-Chi Lee, Haihan Zeng, Caren Smith, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1700692/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Although current evidence shows that environmental and lifestyle factors are associated with DNA methylation patterns, mechanisms underlying the relationship between diet and other exposures and epigenetic profiles remain to be fully described. To clarify the unique connections between dietary intake and lifestyle factors on disease risk, we conducted epigenetic mapping of diet and lifestyle habits for plasma triglyceride concentrations (TG) by investigating links between lifestyle, including diet, and methylation marks with TG. Methods: We first conducted an epigenome-wide association study (EWAS) for TG in the Framingham Heart Study Offspring population (n=2,178). We then examined the relationships between dietary and lifestyle-related variables, collected over 13 years, and differential DNA methylation sites (DMSs) associated with the last TG measures (exam 8). Second, we conducted a mediation analysis to evaluate causal relationships between diet-related variables and TG. Results: The EWAS revealed 28 TG-associated DMSs at 19 regions (e.g., ABCG1, CPT1A, DHCR24, GARS, NCORS, PFKFB3, PHGDH, PPP2R2B, RNF145, SARS, SLC1A5, SLC43A1, SLC7A11 SREBF1, TXNIP, ZFHX3 ). After accounting for multiple testing, we identified 427 significant associations (representative of 102 unique associations) between these DMSs and one or more dietary and lifestyle-related variables. The most significant and consistent associations between 11 TG-associated DMSs and diet were alcohol and carbohydrate intake (% total energy), with P -values ranging from 2.89E-04 to 8.37E-70. Mediation analyses demonstrated that alcohol and carbohydrate intake independently affect TG via DMSs as mediators. For seven of the 19 identified DMS regions, higher alcohol intake was associated with lower methylation and higher TG. In contrast, increased carbohydrate intake was associated with higher DNA methylation at two epigenetic loci ( CPT1A and SLC7A11) and lower TG. Conclusions: Our findings imply that TG-associated DMSs reflect dietary intakes that could affect cardiometabolic disease risk via epigenetic changes, specifically through their impact on DNA methylation. epigenetic mapping DNA methylation diet lifestyle triglyceride cardiometabolic disease Figures Figure 1 Figure 2 Background Diet and lifestyle habits affect human health. As environmental exposures to the human genome, and ones of a consistent and habitual nature, dietary intake and lifestyle behaviors modify epigenetic status without changing the genomic DNA sequence, but do affect gene expression and the physiological function of cells and organelles (1, 2). In turn, an altered physiology contributes to the risk of human diseases (2). However, the mechanisms underlying the alteration of epigenetic status through diet and lifestyle exposures are incompletely characterized. Increasing evidence supports that DNA methylation measurements from peripheral blood mononuclear cell (PBMC) DNA are robust and relevant biomarkers of health status, as supported by the strong correlation between methylation age and chronological age (3, 4). DNA methylation age, measured in PBMCs, combined with plasma biomarkers, can accurately predict biological age (5, 6). Furthermore, biological aging measured from PBMCs is associated with diet and lifestyle habits (7). Thus, epigenetic marks measured from the PBMC DNA reflect biological aging and health status. Epigenome-wide association studies have identified many epigenetic marks associated with metabolic and cardiovascular diseases (CVD) (8–10). Epigenetic markers associated with disease risk exhibit altered methylation profiles as a result of specific environmental factors that induced those epigenetic changes (11, 12). Plasma triglyceride (TG) is a causal metabolic risk factor of CVD independent of other risk factors, including low-density cholesterol (LDL-C) (13). Importantly, elevated TG levels are cause for specific dietary and pharmacological intervention (14, 15). In this study, our objective was to map epigenetic marks of diet and lifestyle habits for TG in participants of the Framingham Heart Study. To achieve this, we first identified epigenetic marks associated with TG and then examined the correlation between identified TG-associated epigenetic marks and diet and lifestyle factors longitudinally measured at four time points up to 13 years. Methods The Framingham Heart Study: The Framingham Heart Study, launched in 1948, is a community-based longitudinal population study that recruited participants who self-identified as being of European descent and lived in Framingham, MA (16). In 1971, the original FHS participants’ children and spouses were recruited to establish the Framingham Offspring Study (FOS) (17). Participants of FOS were interviewed and clinically examined about every 4 to 8 years after that. In this study, we used data from participants who took part in one or more of the four examination cycles: exam 5 (1991-1995), exam 6 (1995-1998), exam 7 (1998-2001), and exam 8 (2005-2008) over a mean of 13 years. Only participants who completed diet and health assessment questionnaires and for whom a whole-genome DNA methylation profile was available (see below) were included in this study. These data were requested from dbGaP (https://dbgap.ncbi.nlm.nih.gov, with study accessions: phs000007.v28.p10 and phs000007.v25.p9; downloaded on September 27, 2017). Dietary intake and food grouping Foods and nutrients were derived from the 126-item modified Willett semi-quantitative food frequency questionnaire (FFQ) in the 5 th through 8 th study examinations (1991-2008) (18). Dietary exposures were classified as follows: 1) Daily absolute intake of nutrients/bioactives, including all macronutrients, fiber, vitamins, minerals, and bioactives (i.e., absolute amounts). Macronutrients (i.e., fat, carbohydrate, protein) were further expressed and analyzed as percentages of total energy intake; 2) Individual food items (servings/week or servings/day) as captured by the FFQ (i.e., 129 food items); 3) Food groups whereby individual food items were collapsed into 31 food groups. All dietary and lifestyle variables were summarized in Table S1. Physical activity scores were estimated based on the Paffenbarger questionnaire of the Harvard Alumni Activity Survey (19). Physical activity was not available for Exam 6. Other lifestyle exposures included alcohol intake (grams per day or number of day alcohol drinking per week) and smoking (number of cigarettes per day). Genome-wide DNA methylation Whole-genome DNA methylation analysis was conducted in PBMC DNA from 2446 participants of FOS at exam 8 using Illumina Infinium® HumanMethylation450 BeadChip (San Diego, CA) (20). DNA methylation data were requested from dbGaP (accession: phs000724.v9.p13). The QC processing was applied to the raw IDAT files as described (12). The proportion of the total methylation-specific signal was calculated as a β score to estimate the methylation level at each methylation site. CpG probes with a detection P-value >0.01 and missing sample percentage >1.5%, or 10% of samples without sufficient intensity, were excluded from further analysis, reducing the sample size to 2178. The batch effects across slides and the β scores were normalized using the ComBat function in the ChAMP package in R (21). To adjust for the heterogeneity of cell-type composition in the blood across samples, we calculated principal components (PCs) with β scores of all filtered autosomal DNA methylation sites (DMS) using the prcomp function (v12.12.1). The first 5 PCs were used as covariates to control for heterogeneity of different cell types in all analyses. After QC, 415,202 DMSs remained and were included in this study. Annotation was based on human genome build GRCh37/hg19. Epigenome-wide association study for TG We conducted an epigenome-wide scan for TG using a mixed linear regression model to identify DMSs associated with TG. Log-10 transformed TG was modeled as the dependent variable, DMSs as predictors while controlling for sex and age at exam 8, cell-type heterogeneity, and family relationship as a random effect. The analysis was implemented in the SNP and VARIATION SUITE 8.9.0 (GoldenHelix Inc., Bozeman, MT). A Bonferroni test was applied to correct for multiple testing with epigenome-wide significance at 1.10E-07 (9). The total phenotypic variance of TG explained by identified epigenetic loci was estimated in participants not taking lipid-lowering medication using Multi-Locus Mixed Model while controlling for sex, age, cell-type heterogeneity, and family relationships. Association between TG-associated DMSs and dietary intake and lifestyle factors To identify environmental factors associated with TG-associated DMSs, we conducted environment and epigenetic association analyses with all three categorizations of dietary exposure (Table S1), and with lifestyle factors, measured in each of four exams of FOS. For each DMS, the DNA methylation level was modeled as the dependent variable in a linear mixed model with each dietary intake and lifestyle factor as a predictor while controlling for sex, age at exam 8, cell-type heterogeneity, and family relationship as a random effect. The analyses were conducted in two models: all participants (All) while controlling for lipid-lowering medication and in a sample of participants without lipid-lowering medication (No-lipid med). These association tests were conducted and implemented in the SNP and VARIATION SUITE 8.9.0 (GoldenHelix Inc., Bozeman, MT). For each DMS as a dependent variable, to correct for multiple testing, we estimated the total number of independent variables represented by all dietary intake and lifestyle factors using a correlation matrix method (22). For each of the four exams, all dietary intake predictors were calculated and classified in a similar way (18), with the number of dietary variables ranging from 267 to 391 (Table S1, range is mainly due to the different availability of data on nutrients/bioactives at the various exams). In all cases, the estimated independent factors ranged from 153 to 170. Using Bonferroni adjustment, for each DMS, we corrected for multiple testing with P =0.05/170=0.0003. Mediation analysis As alcohol and carbohydrate intake were the strongest and most consistent exposures associated with TG-associated DMSs across all four exams, mediation analysis was used to examine causal relationships between exposures of alcohol and carbohydrate intake, and TG with DMSs as the mediators. The CAUSALMED procedure in SAS 9.4 (SAS, Cary, NC) was used for the mediation analysis. To be consistent, both alcohol and carbohydrate consumption that were normalized to the total energy intake at each exam were treated as the exposure variables, with plasma TG at exam 8 as the outcome variable and 11 DMSs for alcohol intake and 10 DMSs for carbohydrate intake at exam 8 as mediators. The significance threshold was adjusted for multiple tests using Bonferroni correction at P -value = 0.0045 (0.05/11). The total, direct, and indirect effects were estimated via mediation analysis. The natural indirect effect (NIE) measured the effect of alcohol consumption on TG mediated by the DMS, while the natural direct effect (NDE) measured the residual effect not mediated by the DMS. The total effect is the sum of the direct and the indirect effects (23). Mediation analyses were conducted only in participants not taking lipid-lowering drugs to avoid potential confounding resulting from interference of the medication that lower TG levels. Mediation analysis was conducted in all four exams while controlling for sex, age, BMI, physical activity, smoking status, cell-type heterogeneity, medications for type 2 diabetes and hypertension at exam 8 (as DNA methylation was measured at exam 8). Mediation analysis was conducted further for different types of alcoholic drinks (servings/week) in all four exams using the identical models. Results 1) Epigenome-wide association of plasma triglyceride To identify DMSs associated with plasma TG, we conducted an epigenome-wide association study (EWAS) while controlling for sex, age, BMI, family relationship, and cell type heterogeneity. In the FOS cohort of 2178 participants available at exam 8 we identified 28 DMSs significantly associated with plasma TG at the epigenome-wide significance of P ≤1.1E-07 (n=2178, Table 1). Considering the potential confounding effect of lipid-lowering medication on TG, we additionally conducted an EWAS only in 1184 participants not using lipid-lowering medication (Table 1). Only 10 loci associations reached epigenome-wide significance (Table 1). Among the 28 identified loci, individual DMSs accounted for variance in TG ranging from 1.3% to 6.8%. In total, 28 loci accounted for 15.3% of TG phenotypic variance. As some loci were highly correlated, 19 loci were selected to represent the 28 loci based on clusters in the correlation matrix accounting for 15% of TG phenotypic variance. 2) Dietary and lifestyle factors associated with DMS in four exams To characterize and map epigenetic status in relation to environmental exposures, we first examined the association between 19 DMSs and all dietary measures and lifestyle factors in four exams over an average of a 13-year timeframe. For each TG-associated DMS, we examined its association with each of the dietary and lifestyle exposures in two linear mixed models: all participants (All) and participants not taking lipid-lowering medication (No lipid med) while controlling for sex, age at exam 8, family relationship, cell-type heterogeneity, and medications for hypertension and type 2 diabetes in four exams. Figure 1 displays the Manhattan plot of associations between 19 DMSs and all dietary and lifestyle factors measured and estimated at exam 8 for all participants (Fig. 1A, n=1923) and participants not taking lipid-lowering medication (Fig. 1B, n=1041). After correction for multiple testing ( P =0.0003), 35 dietary and lifestyle variables were associated with cg06690548 at SLC7A11 when all participants were included in the analysis (Fig 1A and Table 2). Just 10 of those dietary measures associated with cg06690548 in participants who did not take lipid-lowering medication (Fig. 1B, n=1041). In exam 8 with all participants (All), 16 of 19 DMSs were associated with at least one of the dietary and lifestyle variables (Table 2). Three DMSs (cg19494588, cg27431877, cg02316713) showed few associations with dietary variables, and these could be more sensitive to exposures not analyzed here or not available in this population. Interestingly, across four exams, each DMS showed a similar pattern of association with dietary intake and lifestyle factors (Fig. S1A and S1B for exam 5, Fig. S2A and S2B for exam 6, and Fig. S3A and S3B for exam 7), with exam 8 showing the greatest number of significant associations. A summary of all significant associations for all participants and those not taking lipid medication between each DMS and each dietary and lifestyle variable is presented in Table S2. We observed 427 associations in all participants and 289 in participants not taking lipid-lowering medications summed over four exams, respectively, representing 102 and 74 unique associations between TG-associated DMSs and diet and lifestyle factors (Table 2 and Table S2). Among those associations between DMSs and dietary measures, we found exams 5, 6, and 7 shared respectively 37.2%, 37.2%, and 54.1% of the exam 8 associations for all participants, and 51.0%, 46.9%, and 67.3% for participants not taking lipid medication (Table 2). To define the impact of specific diet and lifestyle habits as exposures that alter epigenetic status, we then ranked dietary and lifestyle measures by summarizing the total number of associations with DMSs over four exams (Table S3). Among all dietary and lifestyle measures, the following dietary measures showed strong associations with 19 DMSs: alcohol intake (g/d), carbohydrate intake (% total energy intake), total sugar intake (g/d), smoking (number of cigarettes per day), vitamin B 1 and B 2 without counting supplements, dairy desserts/ice cream, calcium, animal fat/saturated fat, fat intake, vitamin D, protein intake, accounting for 84.8% of all associations (Table S2 and S3). Eleven DMSs were most strongly associated with alcohol intake across all four exams with P -value varying from 2.89E-4 to 8.37E-70, with individual DMSs accounting for methylation variation ranging from 0.7% to 14.6% (Table 3). Interestingly, among the 10 DMSs that were associated with carbohydrate intake (% total energy intake), nine also were associated with alcohol intake, yet these nine DMS were associated with alcohol and carbohydrate intake in opposite directions (Table 3). 3) Mediation analysis: alcohol and carbohydrate intake on TG Considering the strong association between DMSs with TG and alcohol and carbohydrate consumption, and the opposing direction of the influence of these two dietary factors, we conducted a mediation analysis to examine the potential causal effects of alcohol and carbohydrate intake on plasma TG. Mediation analysis was conducted in all four exams only in those participants not taking lipid-lowering medication to exclude the effect of lipid medication on TG while controlling for covariates (sex, age, BMI, physical activity, smoking status, cell-type heterogeneity, medication for type 2 diabetes and hypertension at exam 8). In Fig. 2A (Table S4), for exam 8 seven DMSs (cg14476101, cg19693031, cg06690548, cg21429551, cg11376147, cg20544516, cg22304262) exhibited significant mediated effects related to alcohol intake (% total energy) on TG. Strikingly, the positive direction of the estimate value remained the same for all six sites across all four exams, whereas the natural indirect effect (NIE) of cg20544516 became insignificant in exams 5 and 6. This suggests that the positive effects of alcohol intake on TG are mediated through seven DMSs at seven genes ( PHGDH, TXNIP, SLC7A11, GARS, SLC43A1, SREBF1, SLC1A5 ). Also, we found that TXNIP -cg19693031, SLC7A11 -cg06690548, GARS -cg21429551, CPT1A- cg00574958 displayed a negative natural direct effect (NDE) on (decreased) TG in exams 7 and 8 (Table S4). To determine if different types of alcohol exert differential mediated effects on TG, we undertook further mediation analysis by four types of alcoholic drinks: beer, red wine, white wine, and liquor (all as servings per week). As shown in Figure S4, beer and liquor showed strong indirect mediated effects (NIE) on (increased) TG through SLC7A11 -cg06690548 over all four exams, and to some extent through cg14476101 and cg21429551 over most of the four exams. Red wine and white wine displayed mediated significant positive effects on TG only via SLC7A11 -cg06690548. Interestingly, as shown in Fig S5, while not significant, red and white wine showed negative non-mediated effects (NDE - not through mediation) on (decreased) TG, whereas beer and liquor showed no trend of such effects on TG. For carbohydrate intake, as percent of total energy, only DMSs cg06690548 and cg00574958 showed significant negative mediation effects on (decreased) TG for all four exams (Fig. 2B, Table S5). This observation suggests that carbohydrate intake shows negative effects as TG through those DMSs at genes ABCG1 and CPT1A . On the other hand, all 10 DMSs show significant positive non-mediated effects (Natural Direct Effect) on TG in exam 8, but not in other exams, except for cg00574958 in exam 7. This observation implies that the non-mediated effects of carbohydrate are not as long-lasting as those mediated effects through DMS as mediators from carbohydrate consumption. To determine if alcohol consumption and carbohydrate intake mediate effects on TG independently of each other, we re-ran the mediation analysis for carbohydrate and alcohol while controlling additionally for alcohol intake or carbohydrate intake, respectively. The results showed that both mediated effects remain significant after mutual adjustment (alcohol consumption or carbohydrate intake reciprocally, data not shown). This underscores that alcohol consumption and carbohydrate intake independently affect TG through the mediators of the epigenetic status of the respective genes. Discussion To characterize the nature of the relationship between epigenetic status and diet and lifestyle for TG, we first identified DMSs associated with TG by conducting an EWAS, then examined the relationship between those TG-associated DMSs and diet and lifestyle habits over a period of ~ 13 years. While there was a trend for more factors associated with TG-epigenetic marks in the last exam than in the earlier exams, several dietary factors showed a consistent correlation with epigenetic marks over all four exams. The most impactful dietary and lifestyle factors include alcohol and carbohydrate intake, total sugar, smoking, vitamins B1 and B2, dairy desserts, calcium, saturated fat, total fat, vitamin D, protein, and sweet baked foods (Table S3). TG is a causal risk factor for CVD (13), in addition to LDL-C. EWAS identified 19 independent DMSs, which accounted for a substantial amount of total TG variation (15%). Over four exams, we observed many associations between the 19 TG-associated DMSs and diet and lifestyle factors, representing 102 of these factors. The strongest and most consistent associations are alcohol and carbohydrate intake, representing 11 of 19 DMSs. Alcohol intake accounts for 13.3% of cg06690548 methylation variation at SLC7A11 . Although high alcohol intake (1–2 drinks/day) was associated with increased TG (24, 25), other studies have indicated that alcohol intake is associated with increased HDL and decreased TG, and increased risk of hypertension, coronary heart disease, and myocardial infarction (26). Lifetime average consumption of alcohol is positively associated with accelerated biological aging, as estimated by GrimAge (27). Our study found that 13 of 19 DMSs were associated with alcohol intake. From mediation analysis, the results further support that the effects of alcohol intake increased TG via differential DNA methylation of seven DMSs at PHGDH, TXNIP, SLC7A11, GARS, SLC43A1, SREBF1 , and SLC1A5 . Different types of alcoholic drinks, notably (beer, red wine, white wine, and liquor,) all showed consistent mediated effects on TG through CpG methylation at SLC7A11 . Although the amount of total alcohol intake decreased from exam 5 to exam 8 (28), all 11 DMSs exhibited a solid and consistent association with alcohol intake across the four exams (Table 3 ). Alcohol could have a cumulative effect on DMSs from exam 5 to exam 8 (Table 3 ), but this remains to be unequivocally illustrated. The high consumption of alcohol affecting risk of CVD, myocardial infarction, and aging could be confounded by unhealthy lifestyle choices such as smoking. Nevertheless, our results suggest that alcohol and carbohydrate intakes, and smoking are the most critical lifestyle factors acting epigenetically to modulate TG. Alcohol is a more energy dense nutrient than carbohydrate. In this study, our results indicated that alcohol intake and carbohydrate intake exhibited opposite effects on TG through epigenetic mechanisms. Alcohol intake was strongly associated with nine DMSs across four exams. Mediation analysis implied that alcohol intake was associated with increased TG through seven DMSs in seven gene regions ( SARS, PHGDH, TXNIP, SLC7A11, GARS, SLC43A1, CPT1A, SREBF1, SLC1A5 ) as mediators. On the contrary, carbohydrate intake was strongly correlated with six of the same DMSs (excluding PHGDH ), but in the opposite direction (Table 3 ). Mediation results support that carbohydrate shows negative effects on (decreased) TG through two DMSs (cg00574958 and cg06690548 as mediators. In a prior study using data from two cohorts, we demonstrated with mediation analysis that carbohydrate intake induces CPT1A methylation at cg00574958, and observed negative indirect effects on (decreased) BMI, glucose, hypertension, TG, type 2 diabetes, and metabolic syndrome, and this then reduces the risk of metabolic diseases (11). In addition, that research observed that CPT1A mRNA expression was negatively associated with carbohydrate intake. From a mechanistic perspective, male C57BL/6J mice fed an ethanol-containing diet exhibited higher levels of liver TG, indicating hepatic steatosis and, interestingly, altered diurnal oscillations of core clock genes in the liver but not in the suprachiasmatic nucleus, compared to control mice (29). These chrono-disruptions in the liver propagated to specific clock-controlled genes and several metabolic genes, including Cpt1a (29). The prominent findings in the current analysis are the consistent associations between TG-associated DMSs and alcohol, with alcohol acting as the mediator to affect TG. Many of those same DMSs have been observed as associated with alcohol and diseases consequential to heavy drinking. For example, a recently published EWAS identified the same CpG sites noted here in SLC7A11 , SLC43A1 , and PHGDH , with a different CpG observed in SLC1A5 , all associated with alcohol consumption (30). The top EWAS probe cg06690548, mapped to cystine/glutamate transporter SLC7A11 , was replicated in the second cohort of alcohol use disorders (AUD) and control participants showing strong hypomethylation in AUD ( P < 10 –17 ). Importantly, it was observed that decreased methylation at cg06690548 in SLC7A11 was consistently associated with clinical measures, including increased heavy drinking days. Additionally, hypomethylation at cg06690548 was associated with elevated total cholesterol and TG levels (30). Regarding PHGDH , encoding phosphoglycerate dehydrogenase, increased lipid accumulation and reduced NAD + activity were seen in mouse Phgdh -knockout primary hepatocytes incubated with free fatty acids, effects that were reversed upon Phgdh overexpression, including reduced hepatic TG accumulation (31). SLC1A5 is known as a transporter of alanine, serine, and cysteine but transports glutamine in a Na+-dependent manner in the liver (32). A comparison of rats fed a high-alcohol diet either supplemented with glutamine (at 0.84%) or not indicated that hepatic fat deposition, inflammation, altered liver function, and hyperammonemia in the glutamine group were all attenuated (33). Parallel to the stress that alcohol intake places on the hepatic biological clocks are oxidative stress in the liver and its induction of TXNIP (34). In cultured hepatocytes and mouse livers, alcohol exposure inhibited the expression of FoxO1 , identified as a transcriptional regulator for microRNA MIR148A , which is a direct inhibitor of TXNIP expression (35). Furthermore, hepatocytes treated with ethanol exhibited TXNIP overexpression and activation of the NLRP3 inflammasome and caspase-1-mediated pyroptosis (35). Similarly, it was reported that exposure of the liver to high levels of alcohol results in reduced capacity to methylate proteins and DNA, as observed with protein phosphatase PP2A. Reduced action of this phosphatase permits phosphorylation and nuclear exclusion of FoxO1, leading to increased expression of TXNIP, which caused hepatic lipid accumulation (36). Lastly, numerous reports connect lipogenesis and glucose metabolism regulator SREBF1 and its encoded proteins to the effects of alcohol, for example (37). In sum, our results examined in the context of these previous reports clearly show that the observed associations between methylation levels at specific CpGs and outcomes related to metabolic diseases can be strongly mediated by various exposures. Thus, EWAS must consider the impact that dietary and other lifestyle exposures impart on those CpGs that are sensitive to such in ways that manifest as altered risk of disease. Importantly, the dietary assessment of the FOS cohort from exams 5 to 8 over 13 years uses data from four standardized exams (18), making their use in such analyses as presented here a distinct advantage. This study examined all dietary intakes measured at four different time points. Several key foods, like alcohol, carbohydrate, ice cream, and sugar, plus smoking, all show consistent correlation with these DMSs across four exams. However, there was a trend for alcohol consumption, sugar intake, and smoking exposure to decrease from exam 5 to exam 8 (18). This study is not without its limitations. One of those is the measurements of epigenetic status were performed in PBMCs, which may not be the optimal tissue for epigenetic signals of diet and lifestyle habits as related to TG. Yet DNA methylation measured from blood DNA can accurately predict biological age (3), which is associated with environmental exposure (7). Second, the loci described here are from the study population alone, and are not to be considered as general-use biomarkers of exposure to alcoholic drinks or other dietary factors, as equating the methylation status at specific loci with exposure to alcohol would be unethical (38). In addition, while there is no replication of these results in another cohort, the associations between TG-associated DMSs at exam 8 and diet and lifestyle habits were observed in four exams over 13 years. Although that consistency strengthens the findings, it must be recognized that such epigenetic marks of diet and lifestyle could be specific to given environments and populations. Hence, the conclusions based on findings from the current study must be interpreted with caution. Conclusions This study mapped epigenetic signatures of diet and lifestyle habits for TG in this free-living population. Our results indicate that dietary factors of alcohol and carbohydrate are associated with specific DNA methylation markers and could mediate the observed associations between diet and cardiometabolic risk factors. Abbreviations TG: plasma triglyceride concentrations; EWAS: epigenome-wide association study; DMSs: DNA methylation sites; PBMC: peripheral blood mononuclear cell; CVD: cardiovascular diseases; NDE: natural direct effect; NIE: natural indirect effect; LDL-C: low-density cholesterol; AUD: alcohol use disorders. Declarations Ethics approval and consent of participants The Institutional Review Board (IRB) at the Tuft University approved all research included in this study (IRB-MODCR-03-11513). Consent for publication Not applicable Availability of data and materials The controlled access datasets were analyzed in this study. These data are available and can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov) under the accession numbers phs000007.v25.p9, phs000007.v28.p10, phs000342.v18.p11, phs000724.v9.p13., phs000492.v2. Conflict of Interest The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions The authors contributions were as follows: study concept and design: C-QL and JMO; data acquisition, data analysis and results interpretation: C-QL, HHZ, Y-CL, NMM, LDP, CAS; drafting of the manuscript: C-QL, LDP; funding and supervision: JMO; and all authors: reviewed, edited, made intellectual contributions to the manuscript and approved the final manuscript. Funding This research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA. Acknowledgments The authors would like to thank all participants in the study. References Aguilera O, Fernandez AF, Munoz A, Fraga MF. Epigenetics and environment: a complex relationship. J Appl Physiol (1985). 2010;109(1):243-51. Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. Nature. 2019;571(7766):489-99. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. Issa JP. Aging and epigenetic drift: a vicious cycle. J Clin Invest. 2014;124(1):24-9. 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Epigenetic variants associated with fasting plasma triglyceride of the Framingham Heart Study at Exam 8 All participants (n=2178)* Participants no_lipid_med (n=1184) # DMS Chr Position Genes P-Value Beta Beta SE Variance explained P-Value Beta Beta SE Variance explained cg17901584 1 55353706 DHCR24 3.29E-15 -0.598 0.075 2.7% 5.58E-10 -0.657 0.105 3.1% cg03725309 1 109757585 SARS 5.12E-09 -0.697 0.119 1.5% 5.65E-06 -0.695 0.153 1.7% cg16246545 1 120255941 PHGDH 6.13E-12 -0.520 0.075 2.1% 9.75E-07 -0.508 0.103 2.0% cg14476101 1 120255992 PHGDH 7.05E-15 -0.445 0.057 2.7% 1.17E-07 -0.420 0.079 2.3% cg19693031 1 145441552 TXNIP 1.52E-16 -0.576 0.069 3.0% 7.64E-06 -0.440 0.098 1.6% cg06690548 4 139162808 SLC7A11 1.38E-11 -0.371 0.055 2.0% 2.57E-09 -0.514 0.086 2.9% cg19494588 5 146195103 PPP2R2B 6.67E-10 -0.371 0.060 1.7% 1.00E-05 -0.351 0.079 1.6% cg26403843 5 158634085 RNF145 9.61E-09 0.393 0.068 1.5% 5.27E-05 0.378 0.093 1.3% cg21429551 7 30635762 GARS 4.09E-11 -0.366 0.055 1.9% 1.01E-09 -0.466 0.076 3.0% cg03068497 7 30635838 GARS 7.89E-09 -0.297 0.051 1.5% 7.61E-09 -0.411 0.071 2.7% cg19390658 7 30636176 GARS 4.52E-10 -0.415 0.066 1.7% 1.19E-11 -0.603 0.088 3.7% cg05014727 10 6214016 PFKFB3 3.32E-08 -0.474 0.086 1.3% 4.16E-03 -0.321 0.112 0.7% cg26262157 10 6214079 PFKFB3 8.12E-08 -0.470 0.087 1.3% 5.51E-05 -0.455 0.112 1.3% cg07504977 10 102131012 intergenic 2.04E-12 0.608 0.086 2.2% 2.64E-05 0.503 0.119 1.4% cg11376147 11 57261198 SLC43A1 8.43E-13 -1.440 0.200 2.3% 2.21E-08 -1.494 0.265 2.5% cg00574958 11 68607622 CPT1A 3.98E-25 -2.561 0.244 4.7% 9.43E-16 -2.875 0.353 5.2% cg09737197 11 68607675 CPT1A 8.55E-11 -1.034 0.158 1.9% 2.04E-05 -0.906 0.212 1.5% cg17058475 11 68607737 CPT1A 3.81E-23 -1.796 0.179 4.3% 1.86E-10 -1.507 0.234 3.3% cg27431877 12 124911924 NCOR2 1.04E-07 0.834 0.156 1.3% 2.17E-02 0.481 0.209 0.4% cg07434438 16 72961899 ZFHX3 4.20E-08 -0.599 0.109 1.3% 1.42E-03 -0.466 0.146 0.8% cg20544516 17 17717183 SREBF1;MIR33B 7.85E-11 0.982 0.150 1.9% 7.57E-05 0.774 0.195 1.3% cg08129017 17 17728660 SREBF1 3.11E-09 0.576 0.097 1.5% 1.72E-05 0.547 0.127 1.5% cg11024682 17 17730094 SREBF1 1.27E-09 0.682 0.112 1.6% 7.39E-03 0.393 0.146 0.6% cg22304262 19 47287778 SLC1A5 1.37E-08 -0.499 0.088 1.4% 2.72E-05 -0.500 0.119 1.4% cg02316713 21 43619559 ABCG1 1.52E-08 0.609 0.107 1.4% 2.16E-04 0.538 0.145 1.1% cg27243685 21 43642366 ABCG1 2.51E-14 0.925 0.121 2.6% 3.24E-09 0.949 0.159 2.8% cg00222799 21 43655464 ABCG1 1.72E-10 0.588 0.092 1.8% 6.01E-04 0.423 0.123 1.0% cg06500161 21 43656587 ABCG1 1.23E-35 1.355 0.107 6.7% 7.79E-14 1.071 0.142 4.5% *All participants adjusted for age, sex, family relationship, cell heterogeneity, and medications for lipid lowering, hypertension, diabetes. # Participants not use lipid lowering medication adjusted for age, sex, family relationship, cell heterogeneity, and medication for hypertension and diabetes. Table 2. Numbers of dietary and lifestyle measures that were associated with each of 19 TG-associated epigenetic variants in four exams of the Framingham Heart Study Exam 5 Exam 6 Exam 7 Exam 8 DMS All (n=1800) No lipid med (n=1710) All(n=1985) No lipid med (n=1746) All (N=2014) No lipid med (N=1618) All (N=1923) No lipid med (N=1041) Total Total - No lipid med cg17901584 0 0 5 5 3 5 10 1 18 11 cg03725309 9 9 23 18 8 5 9 0 49 32 cg14476101 7 8 10 8 9 7 10 5 36 28 cg19693031 4 3 4 3 10 11 35 10 53 27 cg06690548 15 12 14 13 22 18 19 9 70 52 cg19494588 0 0 0 1 0 0 0 0 0 1 cg26403843 1 1 0 0 1 1 1 1 3 3 cg21429551 8 8 8 6 9 8 5 3 30 25 cg26262157 0 0 0 0 0 0 1 0 1 0 cg07504977 0 1 2 1 5 1 11 3 18 6 cg11376147 6 7 7 6 9 9 7 0 29 22 cg00574958 10 9 5 6 17 8 23 10 55 33 cg27431877 0 0 0 0 0 0 0 0 0 0 cg07434438 0 0 0 0 0 0 2 1 2 1 cg20544516 0 0 2 2 3 3 3 3 8 8 cg08129017 7 8 4 4 5 2 2 0 18 14 cg22304262 7 7 6 6 7 4 6 3 26 20 cg02316713 0 0 0 0 1 0 0 0 1 0 cg06500161 1 3 1 1 4 2 4 0 10 6 Total 75 76 91 80 113 84 148 49 427 289 Table 3. 12 Epigenetic variants associated with alcohol consumption or/and carbohydrate intake across four exams in FHS. Alcohol intake (grams/day) Carbohydrate intake (% total energy) Exam Marker P-Value Beta Beta SE Variance Explained P-Value Beta Beta SE Variance Explained 5 cg03725309 5.10E-11 -0.00037 0.00006 2.4% 0.004 0.00030 0.00010 0.5% cg14476101 7.43E-23 -0.00112 0.00011 5.3% 3.01E-08 0.00116 0.00021 1.7% cg19693031 1.13E-07 -0.00050 0.00009 1.6% 0.054 0.00033 0.00017 0.2% cg06690548 2.10E-56 -0.00188 0.00011 13.1% 2.92E-18 0.00193 0.00022 4.2% cg21429551 1.09E-19 -0.00109 0.00012 4.5% 1.87E-06 0.00105 0.00022 1.3% cg07504977 0.024 0.00018 0.00008 0.3% 1.27E-03 -0.00046 0.00014 0.6% cg11376147 7.61E-13 -0.00024 0.00003 2.8% 8.37E-07 0.00030 0.00006 1.4% cg00574958 5.19E-06 -0.00013 0.00003 1.2% 1.47E-05 0.00022 0.00005 1.0% cg20544516 0.009 0.00012 0.00004 0.4% 0.334 -0.00008 0.00008 0.1% cg08129017 1.09E-08 0.00039 0.00007 1.8% 0.045 -0.00025 0.00012 0.2% cg22304262 8.08E-16 -0.00058 0.00007 3.6% 1.35E-05 0.00058 0.00013 1.1% cg06500161 0.034 0.00013 0.00006 0.3% 0.004 -0.00032 0.00011 0.5% 6 cg03725309 5.25E-11 -0.00036 0.00005 2.2% 1.89E-03 0.00030 0.00010 0.5% cg14476101 3.93E-29 -0.00126 0.00011 6.2% 5.11E-09 0.00116 0.00020 1.7% cg19693031 7.14E-13 -0.00066 0.00009 2.6% 3.03E-04 0.00058 0.00016 0.7% cg06690548 8.37E-70 -0.00205 0.00011 14.6% 9.30E-21 0.00195 0.00021 4.3% cg21429551 1.02E-19 -0.00107 0.00012 4.1% 3.55E-05 0.00085 0.00021 0.9% cg07504977 0.007 0.00021 0.00008 0.4% 5.24E-04 -0.00047 0.00013 0.6% cg11376147 9.84E-16 -0.00026 0.00003 3.2% 4.68E-07 0.00029 0.00006 1.3% cg00574958 2.89E-04 -0.00010 0.00003 0.7% 3.03E-07 0.00024 0.00005 1.3% cg20544516 2.44E-05 0.00018 0.00004 0.9% 0.029 -0.00017 0.00008 0.2% cg08129017 3.64E-06 0.00031 0.00007 1.1% 0.002 -0.00037 0.00012 0.5% cg22304262 4.61E-14 -0.00056 0.00007 2.8% 2.12E-03 0.00040 0.00013 0.5% cg06500161 1.26E-03 0.00019 0.00006 0.5% 1.59E-02 -0.00025 0.00011 0.3% 7 cg03725309 1.22E-12 -0.00038 0.00005 2.5% 5.82E-05 0.00036 0.00009 0.8% cg14476101 2.89E-32 -0.00131 0.00011 6.8% 2.47E-09 0.00113 0.00019 1.8% cg19693031 5.43E-13 -0.00066 0.00009 2.6% 1.25E-05 0.00067 0.00015 0.9% cg06690548 4.69E-66 -0.00195 0.00011 13.7% 8.00E-23 0.00192 0.00019 4.7% cg21429551 8.43E-18 -0.00100 0.00011 3.6% 3.46E-09 0.00116 0.00019 1.7% cg07504977 0.012 0.00019 0.00008 0.3% 3.23E-06 -0.00059 0.00013 1.1% cg11376147 8.89E-16 -0.00026 0.00003 3.2% 2.06E-07 0.00028 0.00005 1.3% cg00574958 7.54E-06 -0.00012 0.00003 1.0% 3.01E-11 0.00030 0.00004 2.2% cg20544516 2.74E-05 0.00018 0.00004 0.9% 0.007 -0.00020 0.00007 0.4% cg08129017 1.05E-05 0.00030 0.00007 1.0% 0.088 -0.00019 0.00011 0.1% cg22304262 1.31E-11 -0.00050 0.00007 2.3% 5.90E-05 0.00050 0.00012 0.8% cg06500161 1.01E-04 0.00023 0.00006 0.8% 1.06E-06 -0.00048 0.00010 1.2% 8 cg03725309 1.68E-09 -0.00031 0.00005 1.9% 4.71E-05 0.00039 0.00009 0.9% cg14476101 1.01E-27 -0.00118 0.00011 6.1% 5.43E-07 0.00099 0.00020 1.3% cg19693031 1.17E-13 -0.00065 0.00009 2.9% 1.85E-04 0.00060 0.00016 0.7% cg06690548 8.39E-61 -0.00183 0.00011 13.3% 3.20E-15 0.00163 0.00021 3.2% cg21429551 9.17E-12 -0.00077 0.00011 2.4% 0.007 0.00055 0.00021 0.4% cg07504977 0.047 0.00015 0.00007 0.2% 1.48E-06 -0.00064 0.00013 1.2% cg11376147 2.74E-15 -0.00025 0.00003 3.2% 1.23E-05 0.00025 0.00006 1.0% cg00574958 5.12E-06 -0.00012 0.00003 1.1% 3.13E-09 0.00028 0.00005 1.8% cg20544516 2.15E-05 0.00018 0.00004 0.9% 0.002 -0.00023 0.00008 0.5% cg08129017 7.41E-06 0.00029 0.00007 1.1% 0.002 -0.00037 0.00012 0.5% cg22304262 1.06E-14 -0.00055 0.00007 3.1% 0.017 0.00031 0.00013 0.3% cg06500161 0.008 0.00015 0.00006 0.4% 3.46E-06 -0.00047 0.00010 1.1% Additional Declarations No competing interests reported. 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Association between 19 DMSs and dietary intake and lifestyle factors at exam 8. All participants (n=1919) were examined while controlling the association tests for sex, age at exam 8, cell-type, family relationship, and medications for lipid-lowering, hypertension, and diabetes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e1B\u003c/strong\u003e. Association between 19 DMSs and dietary intake and lifestyle factors at exam 8. Association tests included participants (n=1041) not taking lipid-lowering medication while controlling for sex, age at exam 8, cell-type, family relationship, and medications for hypertension and diabetes.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1700692/v1/e177f38d0100b510586434d8.jpg"},{"id":22242205,"identity":"bce73b50-632c-4fd0-bd3e-3e6de0c13555","added_by":"auto","created_at":"2022-06-03 22:13:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of alcohol consumption (A) and carbohydrate intake (B) on plasma TG via epigenetic mediators.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Indirect effects of alcohol intake (% total energy) on TG through 11 DMSs as mediators were estimated for four exams (exam 5 – 8) using mediation analysis in participants not taking lipid-lowering medication while controlling for covariates (sex, age and BMI, physical activity, smoking status, cell-type heterogeneity, medication for type 2 diabetes and hypertension at exam 8). \u003cstrong\u003e(B)\u003c/strong\u003e Indirect effects of carbohydrate intake (% total energy intake) on TG through 10 DMSs as mediators were estimated for four exams (exam 5 – 8) using mediation analysis in participants not taking lipid-lowering medication while controlling for covariates (sex, age and BMI, physical activity, smoking status, cell-type heterogeneity, medication for type 2 diabetes and hypertension at exam 8). Orange and blue bars indicate significant and non-significant mediation effects of carbohydrate intake on TG after correction for multiple testing, respectively.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1700692/v1/5c99830d45dfacc263a278cc.jpg"},{"id":26445882,"identity":"06c8338f-4138-4846-8bce-4788ee6ae7e8","added_by":"auto","created_at":"2022-09-14 10:44:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":762031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1700692/v1/0af7cf76-2cb3-4db4-b284-147ef866fabb.pdf"},{"id":22242208,"identity":"4bc91bb4-ffd3-4bb3-a5f2-3a857d138013","added_by":"auto","created_at":"2022-06-03 22:13:34","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1463355,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-1700692/v1/0319a8bdbe29143ffa715b28.pptx"},{"id":22242207,"identity":"6e384981-7d16-4b9a-a90c-60724b10e08a","added_by":"auto","created_at":"2022-06-03 22:13:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":104642,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1700692/v1/0b38450c8a0be0c0bddc1911.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eDiet and lifestyle habits affect human health. As environmental exposures to the human genome, and ones of a consistent and habitual nature, dietary intake and lifestyle behaviors modify epigenetic status without changing the genomic DNA sequence, but do affect gene expression and the physiological function of cells and organelles (1, 2). In turn, an altered physiology contributes to the risk of human diseases (2). However, the mechanisms underlying the alteration of epigenetic status through diet and lifestyle exposures are incompletely characterized. Increasing evidence supports that DNA methylation measurements from peripheral blood mononuclear cell (PBMC) DNA are robust and relevant biomarkers of health status, as supported by the strong correlation between methylation age and chronological age (3, 4). DNA methylation age, measured in PBMCs, combined with plasma biomarkers, can accurately predict biological age (5, 6). Furthermore, biological aging measured from PBMCs is associated with diet and lifestyle habits (7). Thus, epigenetic marks measured from the PBMC DNA reflect biological aging and health status.\u003c/p\u003e \u003cp\u003eEpigenome-wide association studies have identified many epigenetic marks associated with metabolic and cardiovascular diseases (CVD) (8\u0026ndash;10). Epigenetic markers associated with disease risk exhibit altered methylation profiles as a result of specific environmental factors that induced those epigenetic changes (11, 12). Plasma triglyceride (TG) is a causal metabolic risk factor of CVD independent of other risk factors, including low-density cholesterol (LDL-C) (13). Importantly, elevated TG levels are cause for specific dietary and pharmacological intervention (14, 15). In this study, our objective was to map epigenetic marks of diet and lifestyle habits for TG in participants of the Framingham Heart Study. To achieve this, we first identified epigenetic marks associated with TG and then examined the correlation between identified TG-associated epigenetic marks and diet and lifestyle factors longitudinally measured at four time points up to 13 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eThe Framingham Heart Study:\u0026nbsp;\u003c/strong\u003eThe Framingham Heart Study, launched in 1948, is a community-based longitudinal population study that recruited participants who self-identified as being of European descent and lived in Framingham, MA (16).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn 1971, the original FHS participants\u0026rsquo; children and spouses were recruited to establish the Framingham Offspring Study (FOS) (17). Participants of FOS were interviewed and clinically examined about every 4 to 8 years after that. In this study, we used data from participants who took part in one or more of the four examination cycles: exam 5 (1991-1995), exam 6 (1995-1998), exam 7 (1998-2001), and exam 8 (2005-2008) over a mean of 13 years. Only participants who completed diet and health assessment questionnaires and for whom a whole-genome DNA methylation profile was available (see below) were included in this study. These data were requested from dbGaP (https://dbgap.ncbi.nlm.nih.gov, with study accessions: phs000007.v28.p10 and phs000007.v25.p9; downloaded on September 27, 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary intake and food grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFoods and nutrients were derived from the 126-item modified Willett semi-quantitative food frequency questionnaire (FFQ) in the 5\u003csup\u003eth\u003c/sup\u003e through 8\u003csup\u003eth\u003c/sup\u003e study examinations (1991-2008) (18). Dietary exposures were classified as follows: 1) Daily absolute intake of nutrients/bioactives, including all macronutrients, fiber, vitamins, minerals, and bioactives (i.e., absolute amounts). Macronutrients (i.e., fat, carbohydrate, protein) were further expressed and analyzed as percentages of total energy intake; 2) Individual food items (servings/week or servings/day) as captured by the FFQ (i.e., 129 food items); 3) Food groups whereby individual food items were collapsed into 31 food groups. All dietary and lifestyle variables were summarized in Table S1. Physical activity scores were estimated based on the Paffenbarger questionnaire of the Harvard Alumni Activity Survey (19). Physical activity was not available for Exam 6. Other lifestyle exposures included alcohol intake (grams per day or number of day alcohol drinking per week) and smoking (number of cigarettes per day).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide DNA methylation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole-genome DNA methylation analysis was conducted in PBMC DNA from 2446 participants of FOS at exam 8 using Illumina Infinium\u0026reg; HumanMethylation450 BeadChip (San Diego, CA) (20). DNA methylation data were requested from dbGaP (accession: phs000724.v9.p13). The QC processing was applied to the raw IDAT files as described (12). The proportion of the total methylation-specific signal was calculated as a \u0026beta; score to estimate the methylation level at each methylation site. CpG probes with a detection P-value \u0026gt;0.01 and missing sample percentage \u0026gt;1.5%, or 10% of samples without sufficient intensity, were excluded from further analysis, reducing the sample size to 2178. The batch effects across slides and the \u0026beta; scores were normalized using the ComBat function in the ChAMP package in R (21). To adjust for the heterogeneity of cell-type composition in the blood across samples, we calculated principal components (PCs) with \u0026beta; scores of all filtered autosomal DNA methylation sites (DMS) using the prcomp function (v12.12.1). The first 5 PCs were used as covariates to control for heterogeneity of different cell types in all analyses. After QC, 415,202 DMSs remained and were included in this study. Annotation was based on human genome build GRCh37/hg19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpigenome-wide association study for TG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted an epigenome-wide scan for TG using a mixed linear regression model to identify DMSs associated with TG. Log-10 transformed TG was modeled as the dependent variable, DMSs as predictors while controlling for sex and age at exam 8, cell-type heterogeneity, and family relationship as a random effect. The analysis was implemented in the SNP and VARIATION SUITE 8.9.0 (GoldenHelix Inc., Bozeman, MT). A Bonferroni test was applied to correct for multiple testing with epigenome-wide significance at 1.10E-07 (9). The total phenotypic variance of TG explained by identified epigenetic loci was estimated in participants not taking lipid-lowering medication using Multi-Locus Mixed Model while controlling for sex, age, cell-type heterogeneity, and family relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between TG-associated DMSs and dietary intake and lifestyle factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify environmental factors associated with TG-associated DMSs, we conducted environment and epigenetic association analyses with all three categorizations of dietary exposure (Table S1), and with lifestyle factors, measured in each of four exams of FOS. For each DMS, the DNA methylation level was modeled as the dependent variable in a linear mixed model with each dietary intake and lifestyle factor as a predictor while controlling for sex, age at exam 8, cell-type heterogeneity, and family relationship as a random effect. The analyses were conducted in two models: all participants (All) while controlling for lipid-lowering medication and in a sample of participants without lipid-lowering medication (No-lipid med). These association tests were conducted and implemented in the SNP and VARIATION SUITE 8.9.0 (GoldenHelix Inc., Bozeman, MT).\u003c/p\u003e\n\u003cp\u003eFor each DMS as a dependent variable, to correct for multiple testing, we estimated the total number of independent variables represented by all dietary intake and lifestyle factors using a correlation matrix method (22). For each of the four exams, all dietary intake predictors were calculated and classified in a similar way (18), with the number of dietary variables ranging from 267 to 391 (Table S1, range is mainly due to the different availability of data on nutrients/bioactives at the various exams). In all cases, the estimated independent factors ranged from 153 to 170. Using Bonferroni adjustment, for each DMS, we corrected for multiple testing with \u003cem\u003eP\u003c/em\u003e=0.05/170=0.0003.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs alcohol and carbohydrate intake were the strongest and most consistent exposures associated with TG-associated DMSs across all four exams, mediation analysis was used to examine causal relationships between exposures of alcohol and carbohydrate intake, and TG with DMSs as the mediators. The CAUSALMED procedure in SAS 9.4 (SAS, Cary, NC) was used for the mediation analysis. To be consistent, both alcohol and carbohydrate consumption that were normalized to the total energy intake at each exam were treated as the exposure variables, with plasma TG at exam 8 as the outcome variable and 11 DMSs for alcohol intake and 10 DMSs for carbohydrate intake at exam 8 as mediators. The significance threshold was adjusted for multiple tests using Bonferroni correction at \u003cem\u003eP\u003c/em\u003e-value = 0.0045 (0.05/11). The total, direct, and indirect effects were estimated via mediation analysis. The natural indirect effect (NIE) measured the effect of alcohol consumption on TG mediated by the DMS, while the natural direct effect (NDE) measured the residual effect not mediated by the DMS. The total effect is the sum of the direct and the indirect effects (23). Mediation analyses were conducted only in participants not taking lipid-lowering drugs to avoid potential confounding resulting from interference of the medication that lower TG levels. Mediation analysis was conducted in all four exams while controlling for sex, age, BMI, physical activity, smoking status, cell-type heterogeneity, medications for type 2 diabetes and hypertension at exam 8 (as DNA methylation was measured at exam 8). Mediation analysis was conducted further for different types of alcoholic drinks (servings/week) in all four exams using the identical models.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1) Epigenome-wide association of plasma triglyceride\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify DMSs associated with plasma TG, we conducted an epigenome-wide association study (EWAS) while controlling for sex, age, BMI, family relationship, and cell type heterogeneity. In the FOS cohort of 2178 participants available at exam 8 we identified 28 DMSs significantly associated with plasma TG at the epigenome-wide significance of \u003cem\u003eP\u003c/em\u003e \u0026le;1.1E-07 (n=2178, Table 1). Considering the potential confounding effect of lipid-lowering medication on TG, we additionally conducted an EWAS only in 1184 participants not using lipid-lowering medication (Table 1). Only 10 loci associations reached epigenome-wide significance (Table 1). Among the 28 identified loci, individual DMSs accounted for variance in TG ranging from 1.3% to 6.8%. In total, 28 loci accounted for 15.3% of TG phenotypic variance. As some loci were highly correlated, 19 loci were selected to represent the 28 loci based on clusters in the correlation matrix accounting for 15% of TG phenotypic variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) Dietary and lifestyle factors associated with DMS in four exams\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize and map epigenetic status in relation to environmental exposures, we first examined the association between 19 DMSs and all dietary measures and lifestyle factors in four exams over an average of a 13-year timeframe. For each TG-associated DMS, we examined its association with each of the dietary and lifestyle exposures in two linear mixed models: all participants (All) and participants not taking lipid-lowering medication (No lipid med) while controlling for sex, age at exam 8, family relationship, cell-type heterogeneity, and medications for hypertension and type 2 diabetes in four exams.\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the Manhattan plot of associations between 19 DMSs and all dietary and lifestyle factors measured and estimated at exam 8 for all participants (Fig. 1A, n=1923) and participants not taking lipid-lowering medication (Fig. 1B, n=1041). After correction for multiple testing (\u003cem\u003eP\u003c/em\u003e=0.0003), 35 dietary and lifestyle variables were associated with cg06690548 at \u003cem\u003eSLC7A11\u003c/em\u003e when all participants were included in the analysis (Fig 1A and Table 2). Just 10 of those dietary measures associated with cg06690548 in participants who did not take lipid-lowering medication (Fig. 1B, n=1041).\u003c/p\u003e\n\u003cp\u003eIn exam 8 with all participants (All), 16 of 19 DMSs were associated with at least one of the dietary and lifestyle variables (Table 2). Three DMSs (cg19494588, cg27431877, cg02316713) showed few associations with dietary variables, and these could be more sensitive to exposures not analyzed here or not available in this population. Interestingly, across four exams, each DMS showed a similar pattern of association with dietary intake and lifestyle factors (Fig. S1A and S1B for exam 5, Fig. S2A and S2B for exam 6, and Fig. S3A and S3B for exam 7), with exam 8 showing the greatest number of significant associations. A summary of all significant associations for all participants and those not taking lipid medication between each DMS and each dietary and lifestyle variable is presented in Table S2. We observed 427 associations in all participants and 289 in participants not taking lipid-lowering medications summed over four exams, respectively, representing 102 and 74 unique associations between TG-associated DMSs and diet and lifestyle factors (Table 2 and Table S2). Among those associations between DMSs and dietary measures, we found exams 5, 6, and 7 shared respectively 37.2%, 37.2%, and 54.1% of the exam 8 associations for all participants, and 51.0%, 46.9%, and 67.3% for participants not taking lipid medication (Table 2).\u003c/p\u003e\n\u003cp\u003eTo define the impact of specific diet and lifestyle habits as exposures that alter epigenetic status, we then ranked dietary and lifestyle measures by summarizing the total number of associations with DMSs over four exams (Table S3). Among all dietary and lifestyle measures, the following dietary measures showed strong associations with 19 DMSs: alcohol intake (g/d), carbohydrate intake (% total energy intake), total sugar intake (g/d), smoking (number of cigarettes per day), vitamin B\u003csub\u003e1\u003c/sub\u003e and B\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ewithout counting supplements, dairy desserts/ice cream, calcium, animal fat/saturated fat, fat intake, vitamin D, protein intake, accounting for 84.8% of all associations (Table S2 and S3).\u003c/p\u003e\n\u003cp\u003eEleven DMSs were most strongly associated with alcohol intake across all four exams with \u003cem\u003eP\u003c/em\u003e-value varying from 2.89E-4 to 8.37E-70, with individual DMSs accounting for methylation variation ranging from 0.7% to 14.6% (Table 3). Interestingly, among the 10 DMSs that were associated with carbohydrate intake (% total energy intake), nine also were associated with alcohol intake, yet these nine DMS were associated with alcohol and carbohydrate intake in opposite directions (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Mediation analysis: alcohol and carbohydrate intake on TG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the strong association between DMSs with TG and alcohol and carbohydrate consumption, and the opposing direction of the influence of these two dietary factors, we conducted a mediation analysis to examine the potential causal effects of alcohol and carbohydrate intake on plasma TG. Mediation analysis was conducted in all four exams only in those participants not taking lipid-lowering medication to exclude the effect of lipid medication on TG while controlling for covariates (sex, age, BMI, physical activity, smoking status, cell-type heterogeneity, medication for type 2 diabetes and hypertension at exam 8). In Fig. 2A (Table S4), for exam 8 seven DMSs (cg14476101, cg19693031, cg06690548, cg21429551, cg11376147, cg20544516, cg22304262) exhibited significant mediated effects related to alcohol intake (% total energy) on TG. Strikingly, the positive direction of the estimate value remained the same for all six sites across all four exams, whereas the natural indirect effect (NIE) of cg20544516 became insignificant in exams 5 and 6. This suggests that the positive effects of alcohol intake on TG are mediated through seven DMSs at seven genes (\u003cem\u003ePHGDH, TXNIP, SLC7A11, GARS, SLC43A1, SREBF1, SLC1A5\u003c/em\u003e). Also, we found that \u003cem\u003eTXNIP\u003c/em\u003e-cg19693031, \u003cem\u003eSLC7A11\u003c/em\u003e-cg06690548, \u003cem\u003eGARS\u003c/em\u003e-cg21429551, \u003cem\u003eCPT1A-\u003c/em\u003ecg00574958 displayed a negative natural direct effect (NDE) on (decreased) TG in exams 7 and 8 (Table S4).\u003c/p\u003e\n\u003cp\u003eTo determine if different types of alcohol exert differential mediated effects on TG, we undertook further mediation analysis by four types of alcoholic drinks: beer, red wine, white wine, and liquor (all as servings per week). As shown in Figure S4, beer and liquor showed strong indirect mediated effects (NIE) on (increased) TG through \u003cem\u003eSLC7A11\u003c/em\u003e-cg06690548 over all four exams, and to some extent through cg14476101 and cg21429551 over most of the four exams. Red wine and white wine displayed mediated significant positive effects on TG only via \u003cem\u003eSLC7A11\u003c/em\u003e-cg06690548. Interestingly, as shown in Fig S5, while not significant, red and white wine showed negative non-mediated effects (NDE - not through mediation) on (decreased) TG, whereas beer and liquor showed no trend of such effects on TG.\u003c/p\u003e\n\u003cp\u003eFor carbohydrate intake, as percent of total energy, only DMSs cg06690548 and cg00574958 showed significant negative mediation effects on (decreased) TG for all four exams (Fig. 2B, Table S5). This observation suggests that carbohydrate intake shows negative effects as TG through those DMSs at genes \u003cem\u003eABCG1\u003c/em\u003e and \u003cem\u003eCPT1A\u003c/em\u003e. On the other hand, all 10 DMSs show significant positive non-mediated effects (Natural Direct Effect) on TG in exam 8, but not in other exams, except for cg00574958 in exam 7. This observation implies that the non-mediated effects of carbohydrate are not as long-lasting as those mediated effects through DMS as mediators from carbohydrate consumption.\u003c/p\u003e\n\u003cp\u003eTo determine if alcohol consumption and carbohydrate intake mediate effects on TG independently of each other, we re-ran the mediation analysis for carbohydrate and alcohol while controlling additionally for alcohol intake or carbohydrate intake, respectively. The results showed that both mediated effects remain significant after mutual adjustment (alcohol consumption or carbohydrate intake reciprocally, data not shown). This underscores that alcohol consumption and carbohydrate intake independently affect TG through the mediators of the epigenetic status of the respective genes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo characterize the nature of the relationship between epigenetic status and diet and lifestyle for TG, we first identified DMSs associated with TG by conducting an EWAS, then examined the relationship between those TG-associated DMSs and diet and lifestyle habits over a period of ~\u0026thinsp;13 years. While there was a trend for more factors associated with TG-epigenetic marks in the last exam than in the earlier exams, several dietary factors showed a consistent correlation with epigenetic marks over all four exams. The most impactful dietary and lifestyle factors include alcohol and carbohydrate intake, total sugar, smoking, vitamins B1 and B2, dairy desserts, calcium, saturated fat, total fat, vitamin D, protein, and sweet baked foods (Table S3).\u003c/p\u003e \u003cp\u003eTG is a causal risk factor for CVD (13), in addition to LDL-C. EWAS identified 19 independent DMSs, which accounted for a substantial amount of total TG variation (15%). Over four exams, we observed many associations between the 19 TG-associated DMSs and diet and lifestyle factors, representing 102 of these factors. The strongest and most consistent associations are alcohol and carbohydrate intake, representing 11 of 19 DMSs. Alcohol intake accounts for 13.3% of cg06690548 methylation variation at \u003cem\u003eSLC7A11\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAlthough high alcohol intake (1\u0026ndash;2 drinks/day) was associated with increased TG (24, 25), other studies have indicated that alcohol intake is associated with increased HDL and decreased TG, and increased risk of hypertension, coronary heart disease, and myocardial infarction (26). Lifetime average consumption of alcohol is positively associated with accelerated biological aging, as estimated by GrimAge (27). Our study found that 13 of 19 DMSs were associated with alcohol intake. From mediation analysis, the results further support that the effects of alcohol intake increased TG via differential DNA methylation of seven DMSs at \u003cem\u003ePHGDH, TXNIP, SLC7A11, GARS, SLC43A1, SREBF1\u003c/em\u003e, and \u003cem\u003eSLC1A5\u003c/em\u003e. Different types of alcoholic drinks, notably (beer, red wine, white wine, and liquor,) all showed consistent mediated effects on TG through CpG methylation at \u003cem\u003eSLC7A11\u003c/em\u003e. Although the amount of total alcohol intake decreased from exam 5 to exam 8 (28), all 11 DMSs exhibited a solid and consistent association with alcohol intake across the four exams (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Alcohol could have a cumulative effect on DMSs from exam 5 to exam 8 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), but this remains to be unequivocally illustrated. The high consumption of alcohol affecting risk of CVD, myocardial infarction, and aging could be confounded by unhealthy lifestyle choices such as smoking. Nevertheless, our results suggest that alcohol and carbohydrate intakes, and smoking are the most critical lifestyle factors acting epigenetically to modulate TG.\u003c/p\u003e \u003cp\u003eAlcohol is a more energy dense nutrient than carbohydrate. In this study, our results indicated that alcohol intake and carbohydrate intake exhibited opposite effects on TG through epigenetic mechanisms. Alcohol intake was strongly associated with nine DMSs across four exams. Mediation analysis implied that alcohol intake was associated with increased TG through seven DMSs in seven gene regions (\u003cem\u003eSARS, PHGDH, TXNIP, SLC7A11, GARS, SLC43A1, CPT1A, SREBF1, SLC1A5\u003c/em\u003e) as mediators. On the contrary, carbohydrate intake was strongly correlated with six of the same DMSs (excluding \u003cem\u003ePHGDH\u003c/em\u003e), but in the opposite direction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Mediation results support that carbohydrate shows negative effects on (decreased) TG through two DMSs (cg00574958 and cg06690548 as mediators. In a prior study using data from two cohorts, we demonstrated with mediation analysis that carbohydrate intake induces \u003cem\u003eCPT1A\u003c/em\u003e methylation at cg00574958, and observed negative indirect effects on (decreased) BMI, glucose, hypertension, TG, type 2 diabetes, and metabolic syndrome, and this then reduces the risk of metabolic diseases (11). In addition, that research observed that \u003cem\u003eCPT1A\u003c/em\u003e mRNA expression was negatively associated with carbohydrate intake.\u003c/p\u003e \u003cp\u003eFrom a mechanistic perspective, male C57BL/6J mice fed an ethanol-containing diet exhibited higher levels of liver TG, indicating hepatic steatosis and, interestingly, altered diurnal oscillations of core clock genes in the liver but not in the suprachiasmatic nucleus, compared to control mice (29). These chrono-disruptions in the liver propagated to specific clock-controlled genes and several metabolic genes, including \u003cem\u003eCpt1a\u003c/em\u003e (29). The prominent findings in the current analysis are the consistent associations between TG-associated DMSs and alcohol, with alcohol acting as the mediator to affect TG. Many of those same DMSs have been observed as associated with alcohol and diseases consequential to heavy drinking. For example, a recently published EWAS identified the same CpG sites noted here in \u003cem\u003eSLC7A11\u003c/em\u003e, \u003cem\u003eSLC43A1\u003c/em\u003e, and \u003cem\u003ePHGDH\u003c/em\u003e, with a different CpG observed in \u003cem\u003eSLC1A5\u003c/em\u003e, all associated with alcohol consumption (30). The top EWAS probe cg06690548, mapped to cystine/glutamate transporter \u003cem\u003eSLC7A11\u003c/em\u003e, was replicated in the second cohort of alcohol use disorders (AUD) and control participants showing strong hypomethylation in AUD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026ndash;17\u003c/sup\u003e). Importantly, it was observed that decreased methylation at cg06690548 in \u003cem\u003eSLC7A11\u003c/em\u003e was consistently associated with clinical measures, including increased heavy drinking days. Additionally, hypomethylation at cg06690548 was associated with elevated total cholesterol and TG levels (30). Regarding \u003cem\u003ePHGDH\u003c/em\u003e, encoding phosphoglycerate dehydrogenase, increased lipid accumulation and reduced NAD\u0026thinsp;+\u0026thinsp;activity were seen in mouse \u003cem\u003ePhgdh\u003c/em\u003e-knockout primary hepatocytes incubated with free fatty acids, effects that were reversed upon \u003cem\u003ePhgdh\u003c/em\u003e overexpression, including reduced hepatic TG accumulation (31). SLC1A5 is known as a transporter of alanine, serine, and cysteine but transports glutamine in a Na+-dependent manner in the liver (32). A comparison of rats fed a high-alcohol diet either supplemented with glutamine (at 0.84%) or not indicated that hepatic fat deposition, inflammation, altered liver function, and hyperammonemia in the glutamine group were all attenuated (33).\u003c/p\u003e \u003cp\u003eParallel to the stress that alcohol intake places on the hepatic biological clocks are oxidative stress in the liver and its induction of \u003cem\u003eTXNIP\u003c/em\u003e (34). In cultured hepatocytes and mouse livers, alcohol exposure inhibited the expression of \u003cem\u003eFoxO1\u003c/em\u003e, identified as a transcriptional regulator for microRNA \u003cem\u003eMIR148A\u003c/em\u003e, which is a direct inhibitor of \u003cem\u003eTXNIP\u003c/em\u003e expression (35). Furthermore, hepatocytes treated with ethanol exhibited \u003cem\u003eTXNIP\u003c/em\u003e overexpression and activation of the NLRP3 inflammasome and caspase-1-mediated pyroptosis (35). Similarly, it was reported that exposure of the liver to high levels of alcohol results in reduced capacity to methylate proteins and DNA, as observed with protein phosphatase PP2A. Reduced action of this phosphatase permits phosphorylation and nuclear exclusion of FoxO1, leading to increased expression of TXNIP, which caused hepatic lipid accumulation (36). Lastly, numerous reports connect lipogenesis and glucose metabolism regulator \u003cem\u003eSREBF1\u003c/em\u003e and its encoded proteins to the effects of alcohol, for example (37). In sum, our results examined in the context of these previous reports clearly show that the observed associations between methylation levels at specific CpGs and outcomes related to metabolic diseases can be strongly mediated by various exposures. Thus, EWAS must consider the impact that dietary and other lifestyle exposures impart on those CpGs that are sensitive to such in ways that manifest as altered risk of disease.\u003c/p\u003e \u003cp\u003eImportantly, the dietary assessment of the FOS cohort from exams 5 to 8 over 13 years uses data from four standardized exams (18), making their use in such analyses as presented here a distinct advantage. This study examined all dietary intakes measured at four different time points. Several key foods, like alcohol, carbohydrate, ice cream, and sugar, plus smoking, all show consistent correlation with these DMSs across four exams. However, there was a trend for alcohol consumption, sugar intake, and smoking exposure to decrease from exam 5 to exam 8 (18).\u003c/p\u003e \u003cp\u003eThis study is not without its limitations. One of those is the measurements of epigenetic status were performed in PBMCs, which may not be the optimal tissue for epigenetic signals of diet and lifestyle habits as related to TG. Yet DNA methylation measured from blood DNA can accurately predict biological age (3), which is associated with environmental exposure (7). Second, the loci described here are from the study population alone, and are not to be considered as general-use biomarkers of exposure to alcoholic drinks or other dietary factors, as equating the methylation status at specific loci with exposure to alcohol would be unethical (38). In addition, while there is no replication of these results in another cohort, the associations between TG-associated DMSs at exam 8 and diet and lifestyle habits were observed in four exams over 13 years. Although that consistency strengthens the findings, it must be recognized that such epigenetic marks of diet and lifestyle could be specific to given environments and populations. Hence, the conclusions based on findings from the current study must be interpreted with caution.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study mapped epigenetic signatures of diet and lifestyle habits for TG in this free-living population. Our results indicate that dietary factors of alcohol and carbohydrate are associated with specific DNA methylation markers and could mediate the observed associations between diet and cardiometabolic risk factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTG: plasma triglyceride concentrations; EWAS: epigenome-wide association study; DMSs: DNA methylation sites; PBMC: peripheral blood mononuclear cell; CVD: cardiovascular diseases; NDE: natural direct effect; NIE: natural indirect effect; LDL-C: low-density cholesterol; AUD: alcohol use disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board (IRB) at the Tuft University approved all research included in this study (IRB-MODCR-03-11513).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe controlled access datasets were analyzed in this study. These data are available and can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov) under the accession numbers phs000007.v25.p9, phs000007.v28.p10, phs000342.v18.p11, phs000724.v9.p13., phs000492.v2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors contributions were as follows: study concept and design: C-QL and JMO; data acquisition, data analysis and results interpretation: C-QL, HHZ, Y-CL, NMM, LDP, CAS; drafting of the manuscript: C-QL, LDP; funding and supervision: JMO; and all authors: reviewed, edited, made intellectual contributions to the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilera O, Fernandez AF, Munoz A, Fraga MF. Epigenetics and environment: a complex relationship. J Appl Physiol (1985). 2010;109(1):243-51.\u003c/li\u003e\n\u003cli\u003eCavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. Nature. 2019;571(7766):489-99.\u003c/li\u003e\n\u003cli\u003eHorvath S. 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Clin Epigenetics. 2022;14(1):44.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eEpigenetic variants associated with fasting plasma triglyceride of the Framingham Heart Study at Exam 8\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"32.67015706806283%\"\u003e\n \u003cp\u003eAll participants (n=2178)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"30.89005235602094%\"\u003e\n \u003cp\u003eParticipants no_lipid_med (n=1184)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003eDMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.366492146596858%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.958115183246074%\"\u003e\n \u003cp\u003eBeta SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.643979057591623%\"\u003e\n \u003cp\u003eVariance explained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.947643979057592%\"\u003e\n \u003cp\u003eBeta SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.539267015706806%\"\u003e\n \u003cp\u003eVariance explained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003ecg17901584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003e55353706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003eDHCR24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.366492146596858%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.29E-15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e-0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.958115183246074%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.643979057591623%\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.58E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e-0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.947643979057592%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.539267015706806%\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003ecg03725309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003e109757585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003eSARS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.366492146596858%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.12E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.539267015706806%\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003ecg00222799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003e43655464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003eABCG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.366492146596858%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.72E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.958115183246074%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.643979057591623%\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e6.01E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.947643979057592%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.539267015706806%\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.109947643979057%\"\u003e\n \u003cp\u003ecg06500161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.549738219895288%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.481675392670157%\"\u003e\n \u003cp\u003e43656587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.413612565445026%\"\u003e\n \u003cp\u003eABCG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.366492146596858%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23E-35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e1.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.958115183246074%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.643979057591623%\"\u003e\n \u003cp\u003e6.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.79E-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.701570680628272%\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.947643979057592%\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.539267015706806%\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e*All participants adjusted for age, sex, family relationship, cell heterogeneity, and medications for lipid lowering, hypertension, diabetes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u003csup\u003e#\u003c/sup\u003eParticipants not use lipid lowering medication adjusted for age, sex, family relationship, cell heterogeneity, and medication for hypertension and diabetes.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003eNumbers of dietary and lifestyle measures that were associated with each of 19 TG-associated epigenetic variants in four exams of the Framingham Heart Study\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.390609390609391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"19.38061938061938%\"\u003e\n \u003cp\u003eExam 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"18.581418581418582%\"\u003e\n \u003cp\u003eExam 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"17.08291708291708%\"\u003e\n \u003cp\u003eExam 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"18.181818181818183%\"\u003e\n \u003cp\u003eExam 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.495504495504496%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.492507492507492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003eDMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003eAll \u0026nbsp;(n=1800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003eNo lipid med (n=1710)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003eAll(n=1985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003eNo lipid med (n=1746)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003eAll (N=2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003eNo lipid med (N=1618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003eAll (N=1923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003eNo lipid med (N=1041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003eTotal - No lipid med\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg17901584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg03725309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg14476101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg19693031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg06690548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg19494588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg26403843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg21429551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg26262157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg07504977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg11376147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg00574958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg27431877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg07434438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg20544516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg08129017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg22304262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg02316713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003ecg06500161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.9%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.5%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.1%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.4%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.5%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.2%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.5%\"\u003e\n \u003cp\u003e427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.5%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e 12 Epigenetic variants associated with alcohol consumption or/and carbohydrate intake across four exams in FHS.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"39.173789173789174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol intake (grams/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"39.173789173789174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrate intake (% total energy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003eExam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003eBeta SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003eVariance Explained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003eBeta SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003eVariance Explained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg03725309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.10E-11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg14476101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.43E-23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.01E-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg19693031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg06690548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.10E-56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e13.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.92E-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg21429551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09E-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.87E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg07504977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.00018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n \u003cp\u003e1.27E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.974358974358974%\"\u003e\n \u003cp\u003e-0.00046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.116809116809117%\"\u003e\n \u003cp\u003e0.00014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.256410256410257%\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"6.695156695156695%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.393162393162394%\"\u003e\n \u003cp\u003ecg11376147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.826210826210826%\"\u003e\n 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"epigenetic mapping, DNA methylation, diet, lifestyle, triglyceride, cardiometabolic disease","lastPublishedDoi":"10.21203/rs.3.rs-1700692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1700692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Although current evidence shows that environmental and lifestyle factors are associated with DNA methylation patterns, mechanisms underlying the relationship between diet and other exposures and epigenetic profiles remain to be fully described. To clarify the unique connections between dietary intake and lifestyle factors on disease risk, we conducted epigenetic mapping of diet and lifestyle habits for plasma triglyceride concentrations (TG) by investigating links between lifestyle, including diet, and methylation marks with TG. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe first conducted an epigenome-wide association study (EWAS) for TG in the Framingham Heart Study Offspring population (n=2,178). We then examined\u0026nbsp;the relationships between dietary and lifestyle-related variables, collected over 13 years, and differential DNA methylation sites (DMSs) associated with the last TG measures (exam 8). Second, we conducted a mediation analysis to evaluate causal relationships between diet-related variables and TG. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The EWAS revealed 28 TG-associated DMSs at 19 regions (e.g., \u003cem\u003eABCG1, CPT1A, DHCR24, GARS, NCORS, PFKFB3, PHGDH, PPP2R2B, RNF145, SARS, SLC1A5, SLC43A1, SLC7A11 SREBF1, TXNIP, ZFHX3\u003c/em\u003e). After accounting for multiple testing, we identified 427 significant associations (representative of 102 unique associations) between these DMSs and one or more dietary and lifestyle-related variables. The most significant and consistent associations\u0026nbsp;between 11 TG-associated DMSs and diet were alcohol and carbohydrate intake (% total energy), with \u003cem\u003eP\u003c/em\u003e-values ranging from 2.89E-04 to 8.37E-70. Mediation analyses demonstrated that alcohol and carbohydrate intake independently affect TG via DMSs as mediators. For seven of the 19 identified DMS regions, higher alcohol intake was associated with lower methylation and higher TG. In contrast, increased carbohydrate intake was associated with higher DNA methylation at two epigenetic loci (\u003cem\u003eCPT1A and SLC7A11)\u003c/em\u003e and lower TG. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our findings imply that TG-associated DMSs reflect dietary intakes that could affect cardiometabolic disease risk via epigenetic changes, specifically through their impact on DNA methylation.\u003c/p\u003e","manuscriptTitle":"The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-03 22:13:29","doi":"10.21203/rs.3.rs-1700692/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66858404-05b8-4b0a-9834-b263eb055f9c","owner":[],"postedDate":"June 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-14T10:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-03 22:13:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1700692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1700692","identity":"rs-1700692","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}