Background

African Americans are disproportionally affected by type 2 diabetes (T2D) [1]. The prevalence of T2D among African Americans is nearly twice that of European–ancestry Americans [1]. Various factors such as socio-economic status, genetic predisposition, environmental triggers, as well as lifestyle factors have been found to contribute to the high risk of T2D among African Americans [2, 3]. However, traditional risk factors such as obesity do not fully explain the elevated risk among African Americans compared with European Americans, and it is estimated that genetic variants only explain 17.5% of the phenotypic variance of T2D [4]. Environmental, lifestyle and social determinants are the major players in ethnic differences in health and a better understanding of the mechanisms by which these factors increase T2D risk in African Americans is needed.

Insulin resistance is a major feature of T2D [5], and African Americans have been reported to have a higher degree of insulin resistance relative to European-ancestry Americans [6]. In addition, differences in insulin resistance have been reported between men and women of various ancestries, which may play a role in sex distinctions in T2D prevalence and phenotypes [7, 8]. Given that the onset of T2D is often preceded by many years of increasing insulin resistance, the direct study of insulin resistance can provide insight into the early phases of T2D pathology. Genetic, environmental, and lifestyle factors influence insulin resistance with the epigenome as a potential interface where these factors converge [9]. Epigenetics, which is the study of heritable yet reversible molecular modifications to DNA without altering the DNA sequence, may thus provide new insights into mechanisms underlying insulin resistance in order to improve our understanding of the pathogenesis of insulin resistance [10]. Furthermore, because of the strong effect of environmental and lifestyle factors on the epigenome, studying epigenetics provides an opportunity to improve our understanding of the social pathways underlying health disparities, and the variable and reversible nature of epigenetics provides opportunities for intervention [11]. The best understood and most studied epigenetic modification is DNA methylation, which can modulate gene expression through the binding of methyl groups to CpG dinucleotides in the DNA.

Previous studies among Europeans, European ancestry Americans, Indian Asians, and Mexican Americans have identified several hundred DNA methylation sites associated with insulin resistance [12,13,14,15,16]. However, data are scarce on the association between DNA methylation and insulin resistance among African Americans. African Americans differ from other African ancestry populations in terms of environmental exposures and the amount of non-African genetic admixture (mainly a European ancestry component which is estimated to range from 10 to 20% on average) [17]. Since DNA methylation can be affected by genetic, environmental as well as lifestyle factors, findings from other population groups cannot be extrapolated to African Americans who have dissimilar genetic variation, environmental exposures, and lifestyles.

Hence, we aimed to identify DNA methylation loci associated with insulin resistance among African American men, using data from the Howard University Family Study (HUFS) [18]. Studying DNA methylation changes associated with insulin resistance may contribute to identifying markers of early pathological changes related to T2D.

Results

Participant characteristics

Characteristics of the 136 unrelated African American men without T2D are shown in Table 1. Almost 80% of the participants consumed alcohol, and 73% were current smokers. The mean BMI was 27.2 kg/m2, while their glycolytic markers, such as fasting glucose and insulin levels, were in the normal range. Granulocytes represented about 50% of the immune cells inferred by Houseman et al. [19] and each of the other immune cell types was less than 10% as shown in Table 1. The immune cells showed low correlation with insulin resistance assessed using Homeostatic Model Assessment (HOMA-IR) with an r2 ranging from 0.057 for CD8 + to 0.204 for CD4 + .

Table 1 Participant characteristics (n = 136)
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Association between DNA methylation and HOMA-IR

Differentially methylated positions (DMPs): Epigenome-wide DMPs for HOMA-IR were identified using linear regression analyses with adjustment for age, alcohol consumption, tobacco smoking, BMI, estimated cell types, and technical effects (hybridization batch and array position). DNA methylation levels of three CpG sites (cg14013695, cg00456326, cg20259981) showed genome-wide significant associations with HOMA-IR at a 5% false discovery rate (FDR) (Table 2; Fig. 1, Additional file 1: Supplementary Fig. S1). Using GapHunter, we confirmed that none of these 3 DMPs had an underlying multimodal distribution. cg14013695 was annotated to the transcription start site (TSS1500) of HOXA5 on chromosome 7. Our results showed that a single unit higher level of HOMA-IR was associated with a 1.6% lower DNA methylation of cg14013695 (FDR = 0.035). The second DMP (cg00456326) was located on chromosome 2 in the TSS1500 of the gene OSR1 and was within the shores of the nearest CpG island. DNA methylation levels of cg00456326 were 2.7% higher for each unit higher level of HOMA-IR (FDR = 0.035). The third genome-wide significant CpG site (cg20259981) was annotated to the 5’UTR of ST18 on chromosome 8 in the open sea. One-unit higher HOMA-IR was associated with 1% lower DNA methylation of cg20259981.

Table 2 Top 10 DMPs associated with insulin resistance in African American men
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Fig. 1
figure 1

Manhattan plot of epigenome-wide P-values for HOMA-IR in African American men. The red line indicates FDR < 0.05

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Differentially methylated regions (DMRs): Our study did not identify any DMRs, defined as three or more CpG sites within the direct vicinity of each other, at a family-wise error rate (FWER) of < 0.05 or FDR < 0.05 using the Bumphunter and DMRcate packages, respectively. However, our topmost DMR identified with Bumphunter (FWER = 0.124) was annotated to the TSS1500 of HOXA5 on chromosome 7, a regulatory region from which our topmost DMP (cg14013695) was also identified. The DMP cg14013695 was situated 900 bp downstream of the DMR, and although cg14013695 was not among the 17 CpG sites that this DMR consisted of the direction of effect was the same. The DMP as well as the 17 CpG sites of the DMR were hypomethylated for each unit increase in HOMA-IR (Table 3, Fig. 2, Additional file 2: Supplementary Table S1).

Table 3 Top 5 differentially methylated regions associated with insulin resistance in African American men
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Fig. 2
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Differentially methylated region for insulin resistance in African Americans annotated to the HOXA5 gene. Beta values for CpG sites in the DMR are provided in Additional file 2: Supplementary Table S2

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Evaluation of our top DMPs in previous EWAS

We searched the literature to determine if our three genome-wide significant DMPs have been reported in previous EWAS. We found that cg14013695 (TSS1500 of HOXA5) was previously reported in an EWAS on insulin resistance among Mexican Americans (Beta = − 0.174, p = 1.49E − 7, FDR = 0.056) [9]. The same DMP has also been reported in relation to oral squamous cell carcinoma, colorectal cancer, and inflammatory bowel disease among others [21]. The other two DMPs -cg00456326 in the TSS1500 of OSR1 and cg20259981 in the TSS1500 of ST18- have been associated with atherosclerosis and Down syndrome and with asthma, respectively. Next, we searched whether genes annotated to our top three DMPs (i.e., HOXA5, OSR1, and ST18) had been reported in previous EWAS. CpG sites annotated to the HOXA5 gene have been associated to 33 different traits, with the most frequently reported associations including depressive disorders (46 associations), Kabuki syndrome (33 associations), and inflammatory bowel disease (23 associations) [21]. The most-reported associations for CpG sites within the OSR1 gene are for breast cancer (6 associations), and atherosclerosis (5 associations). Down syndrome (11 associations) and asthma (7 associations) are most reported for the ST18 gene. With respect to cardiometabolic traits, we found that HOXA5 was reported in an EWAS on obesity in African Americans, while OSR1 was reported in an EWAS on insulin resistance and T2D among European populations [3, 12, 22].

Transferability analysis of CpG sites identified in other populations

We tested the transferability of a total of 855 CpG sites reported in five previous EWAS in Mexican Americans, Indian Asians, Europeans, and European ancestry Americans (Additional file 2: Supplementary Tables S2 and S3) [12,13,14,15,16]. Analysis of the previously reported DMPs was performed using linear regression models similar to those employed in the main analysis. A total of 848 unique CpG sites remained after the removal of 7 CpG sites duplicated across studies. Exclusion of an additional 118 probes that did not meet quality-control thresholds in our data resulted in 730 candidate DMPS included in the final linear regression analysis (Additional file 2: Supplementary Table S4). We found that in our sample of African American men, only cg14013695 (TSS1500 of HOXA5) was statistically significant at a Bonferroni-corrected p-value of 6.8E − 05 (i.e., p = 0.05/730; Additional file 2: Supplementary Table S4). We also assessed whether DMPs identified in other populations were statistically significant in our main analyses. We found that 47 (6.4%) DMPs (including cg14013695) were statistically significant at p < 0.05 (Additional file 2: Supplementary Tables S5 and S6). The majority of the replicated DMPs (40/47; 93%) were from the study by Apron et al.[12]. While these DMPs reached significance in the replication, we were not able to check for consistency in direction of effect for many DMPs as effect sizes were not reported for these [12]. However, for the three DMPs whose effect sizes were reported (cg14013695, cg17126947, cg22065733), the direction of effect was consistent.

Biological pathways and chromatin state enrichment

We conducted enrichment analyses using the clusterProfiler package and the EWAS Toolkit in order to assess whether our top 100 DMPs enriched to particular biological pathways at a 5% FDR. The clusterProfiler Gene Ontology (GO) results showed that CpG sites annotated to HOXA5, OSR1, HOXA1, PAX8, HIPK1, and HOXA6 were enriched to embryonic organ morphogenesis and embryonic organ development (FDR = 0.030). The clusterProfiler KEGG pathway analysis did not yield any enriched pathways. Furthermore, clusterProfiler enrichment analysis for chromatin state, CpG Islands and gene position showed that top DMPs were enriched to the flanking active TSSs (P-value = 0.026) and enhancers (P-value = 0.002), were within CpG Islands (P-value = 0.03) and were within the TSS1500 (P-value = 0.05) (Additional file 2: Supplementary Table S7a). Enrichment analysis using the EWAS Toolkit did not reveal any GO or KEGG enriched pathways at an FDR < 0.05. The EWAS Toolkit chromatin state analysis showed enrichment to the flaking active TSSs and enhancers in white blood cells and skeletal muscle cells (Additional file 2: Supplementary Table S7b).

DNA methylation at top loci and gene expression

In order to gain insight into the relationship between DNA methylation differences and gene expression, we assessed the correlation between our top three DMPs and gene expression in white blood cells using the IMETHYL database [23]. We found that high DNA methylation levels of cg14013695 (TSS1500 of HOXA5) were associated with low expression of HOXA5 (Fragments Per Kilobase of transcript per Million mapped reads; FPKM = − 0.11 ± 0.39). Low DNA methylation of cg00456326 (TSS1500 of OSR1) was associated with decreased expression of OSR1 (FPKM = − 0.64 ± 0.18). Such data for cg20259981 (TSS1500 of ST18) were not available in the IMETHYL database (Table 4). Querying the EWAS Toolkit showed that cg14013695 was positively correlated with HOXA5 expression in liver (R2 = 0.332, P-value = 1.32E − 11) and kidney (R2 = 0.124, P-value = 0.001) tissue and that cg20259981 was positively correlated with expression of ST18 in brain (R2 = 0.119, P-value = 0.004) and testis tissue (R2 = 0.434, P-value = 1.48E − 7).

Table 4 Relationship between DNA methylation and gene expression as reported in the IMETHYL database
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Assessment of gene function related to insulin resistance

We assessed whether genes annotated to our top three DMPs were linked to insulin resistance traits in the GWAS catalog, EWAS catalog, and GeneCards. In the GWAS catalog, we found out that genetic variants annotated to HOXA5, OSR1 and ST18 were not independently related to insulin resistance traits (Additional file 2: Supplementary Table S8). However, several genes located in the 500 kilobases (kb) vicinity of HOXA5 (i.e., HOXA11-AS, HOXA11, HOXA-AS3, HOXA3, HOTTIP, HOXA6) and ST18 (i.e., RB1CC1) were associated with BMI adjusted waist circumference and BMI adjusted waist-to-hip ratio (Additional file 2: Supplementary Table S8). In the EWAS catalog, we found that aberrant DNA methylation in HOXA5 (including cg14013695) was associated with pancreatic ductal adenocarcinoma and gene expression in the liver (Additional file 2: Supplementary Table S9A). Several genes in the 500 kb vicinity of HOXA5 (i.e., HOXA3, HOXA9, HOXA7, HOXA1, EVX1) were also associated with high density lipoprotein (HDL) cholesterol efflux capacity, BMI, and pancreatic ductal adenocarcinoma (Additional file 2: Supplementary Table S9B). The genes ST18 and OSR1, as well genes in their 500 kb vicinity, were not associated with any insulin resistance traits.

Discussion

In this EWAS of insulin resistance, we have identified three DMPs (i.e., cg14013695, cg00456326, cg20259981) associated with insulin resistance among African American men at a 5% FDR. Two of these DMPs—i.e., cg00456326 (OSR1) and cg20259981 (ST18)—have not been reported in other populations. In addition, we successfully replicated another 46 DMPs, which were previously identified in other populations. To our knowledge, this is the first EWAS on insulin resistance among African Americans.

Differential methylation of our top DMP, cg14013695 (HOXA5) seems to play a role in the pathogenesis of insulin resistance across populations. This DMP has previously been reported in an insulin resistance EWAS among Mexican Americans [16]. In our sample of African Americans as well as in Mexican Americans, cg14013695 was hypomethylated for higher levels of insulin resistance. The CpG site cg14013695 is located in the transcription start sites of HOXA5 where lower DNA methylation can increase gene expression [24, 25]. We found that decreased DNA methylation of cg14013695 leads to increased expression of the HOXA5 gene in blood cells, but to decreased expression in kidney and liver [23]. The HOXA5 gene located on chromosome 7 is a transcription factor that is involved in regulating human embryonic development and adult stem cell differentiation [26]. More importantly, it has been shown that HOXA5 is highly expressed in the adipose tissue and plays an active role in regulating adipocyte functions, including differentiation and body fat distribution [27]. Methylation levels of the HOXA5 promoter region, 600 bp upstream of the transcription start site, were found significantly increased in preadipocytes of first-degree relatives of T2D subjects compared with subjects with no family history of T2D [28]. A similar inverse association between methylation and expression level was found for this promotor region as for the transcription start site in which the cg14013695 is located [28]. Functional studies have further shown that changes in HOXA5 expression in adipocytes play a role in chronic inflammation through M2 macrophage polarization and the eIF2α/PERK signaling pathways [29]. Besides the functional studies, GWAS have also found associations between genes in the 500 kb vicinity of HOXA5 (i.e., HOXA11-AS, HOXA11, HOXA-AS3, HOXA3, HOTTIP, HOXA6) and BMI adjusted waist-to-hip ratio, as well as BMI-adjusted waist circumference [30]. Likewise, EWAS have also found associations between aberrant DNA methylation in HOXA5 and pancreatic ductal adenocarcinoma, as well as gene expression in the liver [31]. Moreover, DNA methylation alterations in genes within 500 kb vicinity of HOXA5 (i.e., HOXA3, HOXA9, HOXA7, HOXA1, EVX1) have been associated with HDL cholesterol efflux capacity and with BMI [31]. This evidence clearly points to a link between DNA methylation in HOXA5 and the pathogenesis of insulin resistance. Furthermore, successful replication of this CpG site reported in Mexican Americans in our study could point to the common pathology of insulin resistance between African Americans and Mexican Americans.

The DMP association in the promoter region of OSR1 (cg00456326) may also play a role in cardiometabolic traits across populations. This DMP has previously been found hypermethylated in postmortem obtained atherosclerotic portions of human aortas compared with donor-matched nonatherosclerotic portions of human aortas from samples in Spain [32]. The CpG site cg00456326 is located in the TSS1500 of OSR1 where the higher DNA methylation level we observed per unit increase in HOMA-IR is likely to lead to decreased expression of the OSR1 gene. The OSR1 gene codes for a transcription factor that plays a role in the regulation of embryonic heart and urogenital development [33]. This gene has also been reported as a tumor suppressor in renal cell carcinomas and gastric cancer [34]. In functional studies, administration of insulin was associated with an increase in phosphorylation of OSR1, which in turn increased sodium reabsorption in the kidneys via the Nacl cotransporter (NCC) [35]. Further studies are needed to better understand the potential role of the OSR1 gene in insulin resistance in African Americans.

Differential methylation of DMP cg20259981 may play a role in insulin action through effects on expression of the ST18 gene. The observed 1% more hypomethylation at the CpG site in the 5’UTR of ST18 for each unit increase in HOMA-IR can be hypothesized to increase expression of ST18, since lower DNA methylation in the gene body usually leads to increased gene expression [24, 25]. The gene ST18 is a tumor suppressor gene, which was originally characterized as the third member of the neural zinc finger transcription factor family [36]. The ST18 gene is highly expressed in the pancreatic islets and represents a novel transcriptional mediator of lipotoxicity and cytokine-induced β-cell death in the pancreas [36]. Furthermore, ST18 deletion can significantly reduce cellular insulin levels and increase β cell apoptosis [37]. In addition to these functional studies, GWAS have also found associations between genes in the 500 kb vicinity of ST18 (i.e., RB1CC1) and BMI-adjusted waist circumference, as well as BMI-adjusted waist-to-hip ratio.

We also noted in pathway enrichment analyses (gene ontology) that CpG sites annotated to HOXA1, HOXA5, HOXA6, OSR1, PAX8, and HIPK1 enriched to embryonic organ morphogenesis. All six genes are homeobox genes and are directly involved in the formation of many body structures during early embryonic development [38]. Recent work has provided evidence that homeobox genes continue to be regionally expressed in adult tissues [38]. Enrichment of this pathway in our study could point to the fact that DNA methylation is associated with the remodeling of body components (regions/structures) that are critical for insulin resistance during stem cell differentiation. For example, HOXA5 expression is associated with adipose tissue remodeling [39], while OSR1 expression is associated with remodeling of sodium transporters in the kidneys [35], and PAX expression is associated with enhanced differentiation of insulin-producing cells [40].

We successfully replicated a total of 47 (6.4%) HOMA-IR associated DMPs that were previously identified in other populations. This means that the majority of DMPs reported in other populations did not replicate in our sample of African American men and that transferability of DMPs for insulin resistance across populations is low. This finding corroborates with previous studies which showed that DNA methylation varies strongly by population group/ethnicity due to differences in genetic ancestry and environmental exposures [41]. Given the numerous environmental exposures, including lifestyle factors, that can have a substantial effect on DNA methylation [42], we suspect the lack of transferability to be mainly due to these factors. Hence, DNA methylation findings should not be extrapolated to populations of different ancestry or in different environmental contexts. Importantly, African Americans are not a homogenous group and are exposed to a wide variety of environmental and social determinants, which may affect the epigenome. As such, our study, which is the first on African Americans, is an important first step because it provides the much-needed literature on the African American population, but more EWAS studies among African Americans reflecting diversity in environments and exposures are needed.

The main strengths of this study are that it represents the first EWAS for insulin resistance in African Americans (a previously understudied population) and our top three DMP-IR associations are supported by evidence from GWAS, EWAS, and functional studies. Nonetheless, there are several limitations. Firstly, insulin resistance was calculated using a HOMA calculator, while a hyperinsulinemic euglycemic clamp and intravenous glucose tolerance test are considered the gold standard. Nevertheless, validation studies have found good correlation between euglycemic clamp and HOMA-IR (r = 0.8) [43], making it a widely used tool in epidemiologic studies. Secondly, our study had a small sample size and was only conducted in men, which may limit the external validity of the results. However, previous studies have shown sex differences in insulin resistance and distinctions in its effects on CVD risk [7, 8, 44], which highlights the importance of sex-specific analyses. Evidently, there is a need for follow-up studies larger in size that include women-specific analysis. In addition, analysis of sex chromosome DNA methylation patterns in insulin target tissue such as skeletal muscle may provide further insight into T2D pathology given the previously reported chromosome-wide and site-specific differences in DNA methylation on the X chromosome of human pancreatic islets [45]. Thirdly, DNA methylation was measured in blood but preferable tissue for insulin resistance includes adipose tissue and skeletal muscle. Fourth, we did not have data available for replication in an independent sample of African American men and neither were gene expression data available for these participants. However, we used data from the iMethyl database for CpG-expression associations, with the caveat that expression quantitative trait methylation loci (like other quantitative trait loci) may differ between populations. Omics data from diverse populations would provide the ability to identify potential population-specific CpG-expression associations. Lastly, our cross-sectional study design contributes further to precluding the assignment of causality to any CpG site.

Conclusions

Studying DNA methylation changes associated with insulin resistance can help identify early pathophysiological changes related to prediabetes and T2D. In the present study, we successfully identified three significant HOMA-IR-associated CpGs, one of which has been previously reported and two novel candidate loci that have not been previously reported in other populations. Two of the three loci are implicated in insulin resistance or related traits. More work in African Americans is needed to confirm our findings, as well as functional analyses to understand how such DNA methylation alterations contribute to T2D pathology.

Methods

Study design and population

The Howard University Family Study (HUFS) study is a population-based family study that enrolled participants between 2001 and 2008 to investigate the genetic and environmental basis of common complex traits (including hypertension, obesity, diabetes, and other health outcomes among African Americans) [18]. The study did not ascertain families based on any phenotype in order to maximize the utility of the cohort for the study of multiple common traits. The full details of the study have been published elsewhere [18]. In brief, HUFS enrolled a representative sample of African Americans in the Washington, DC metropolitan area. All study enrollees self-identified as African American and were excluded if they were under 14 years of age, pregnant at the time of enrollment or had acute febrile illness or an acute pain episode at the time of clinical examination. For the present study, 144 men who were unrelated and were at least 20 years of age were selected for DNA methylation profiling. After the exclusion of eight individuals with T2D as defined by American Diabetes Association (ADA) criteria, 136 participants remained for the current analysis.

Phenotypic measurements

The following measurements were obtained through a structured questionnaire: age, sex, alcohol consumption, and tobacco smoking. Alcohol consumption was categorized as any or no consumption. Smoking was categorised into current smokers, past smokers, or non-smokers. Body weight was measured in light clothes on an electronic scale to the nearest 0.1 kg, and height was measured with a stadiometer to the nearest 0.1 cm. Body mass index (BMI) was computed as weight (kg) divided by height squared (m2). Fasting plasma glucose concentration was measured using the enzymatic reference method with hexokinase on Roche Cobas Integra 400 Plus or Modular-E analyzers. Fasting insulin was measured by electrochemiluminescence immunoassay (ECLIA) on Roche Modular-E or Elecsys 2010 analyzers (Roche Diagnostics, Indianapolis, IN). Insulin resistance was estimated using the updated Homeostasis Model Assessment (HOMA2), which takes into account variations in hepatic and peripheral glucose resistance, increases in the insulin secretion curve for plasma glucose concentrations above 10 mmol/L (180 mg/dL) and the contribution of circulating proinsulin. Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) calculations were conducted using the University of Oxford HOMA2 calculator (available at: https://www.dtu.ox.ac.uk/homacalculator/).

DNA methylation processing, profiling, and quality control

Bisulfite treatment of DNA (Zymo EZ DNA MethylationTM kit) was used to deaminate unmethylated cytosine to produce uracil in DNA to conform to the manufacturer’s protocol. The converted DNA was amplified and hybridized on the Infinium® HumanMethylation450 BeadChip which quantifies DNA methylation levels of approximately 485,000 CpG sites. The samples were randomized over two bisulfite conversion and hybridization batches. Raw 450 K data were processed for primary quality control using the statistical software platform “R” (version 3.6.1) and MethylAid package (version 1.20). An overview of all “R” packages used in the analyses can be found in Additional file 2: Supplementary Table S10. MethylAid detects poor-quality samples using sample-dependent and sample-independent control CpG sites present on the 450 K array itself. MethylAid threshold values included methylated and unmethylated intensities of 10.5, overall quality control of 11.75, bisulfite control of 13.25, hybridization control of 12.50 and a detection p value of 0.94 [46]. The cluster plots showed all samples were of good quality and none were excluded from further analyses (Additional file 2: Supplementary Fig. S2). We used plots from Minfi package to predict sex by clustering samples based on their mean DNA methylation intensities on the X and Y chromosomes. We identified a single sex-discordant sample, which was removed. Quantile normalization was used to normalize the raw 450 K data on the assumption that we would only detect very small DNA methylation changes in association with continuous HOMA-IR values. The minfi package was used to remove 17,351 cross-reactive probes and probes containing single nucleotide polymorphisms (SNPs) according to the Illumina 450 k manifest. Removal of these probes resulted in a final set of 456,513 CpG sites that were used in subsequent analysis. Finally, as cell mixture is a source of variability in DNA methylation, white blood cell types were estimated using the method developed by Houseman et al. [19] and included as covariates in the analysis.

Statistical analysis

Differentially methylated positions and regions

Statistical analysis was carried out using packages in “R” statistical computing environment [47]. Study population characteristics were presented as proportions for categorical variables and as means (with standard deviations) for normally distributed continuous variables. Linear regressions were conducted to determine associations between DNA methylation and HOMA-IR using the minfi package (with DNA methylation are the dependent variable). Age, alcohol consumption, tobacco smoking, BMI, estimated cell types, and technical effects (hybridization batch and array position) were included as covariates. Model fitting was evaluated using a QQ-plot (Additional file 1: Supplementary Fig. S3). For all differentially methylated position (DMP) analyses, M values were calculated as the log2 ratio of the intensities of methylated CpG site versus unmethylated CpG site. Identification of significant DMPs was determined based on P-values corresponding with M values, while beta values were used for visualization [48]. The GapHunter function [49] was applied to the top 100 DMPs with the lowest M-value P-values to detect potential multimodal distributions. False discovery rate (FDR) was used to correct for multiple testing. A 5% FDR was considered genome-wide significant. Regulatory regions around these DMPs were visualized using the MEAL package [50, 51]. To detect differentially methylated regions (DMRs), we tested two different methods. First, we fitted models similar to DMP analyses using the Bumphunter function [52] with a cutoff of 0.0151 (which corresponds to 1.5% difference in the Beta-values) and 1000 permutations. Second, the DMRcate package was used to detect additional DMRs [53]. Three or more CpG sites within the direct vicinity of each other was considered a DMR. For Bumphunter, a family-wise error rate (FWER) < 0.05 was considered statistically significant and for DMRcate, an FDR of < 0.05.

In silico replication

We conducted in silico replication of previously reported DMPs from five previous EWAS for insulin resistance among Europeans, European ancestry Americans, Indian Asians, and Mexican Americans to test the transferability of these candidate loci in our study. First, we assessed whether our top DMPs were on the list of previously reported DMPs. Second, we assessed whether any of the candidate CpG sites had statistical significance in our study at a nominal p-value of 0.05. Lastly, we performed a separate statistical analysis on the candidate loci, employing linear regression methods similar to our main DMP analyses. For this analysis, we assumed statistical significance at a Bonferroni-corrected p-value (0.05/number of candidate loci).

Post-omic analyses

We performed several post-omic analyses in order to ascertain biological relevance of our findings. First, we performed pathway enrichment analyses using the clusterProfiler package on the top 100 DMPs. We filtered GO and KEGG pathways with a 5% FDR. Second, we performed molecular enrichment analysis for CpG Islands, gene position, and chromatin state separately at an alpha < 0.05. Third, we used the same set of 100 DMPs to perform enrichment analysis with the EWAS Toolkit [21]. Next, we assessed for correlations between top DMPs and gene expression in white blood cells using the publicly available IMETHYL database [23] and the EWAS Toolkit for other tissues [21]. IMETHYL provides whole-DNA, whole-genome, and whole-transcriptome data for normal CD4 + T-lymphocytes, monocytes, and neutrophils collected from approximately 100 healthy subjects [23]. Finally, we searched in the GWAS catalog (https://www.ebi.ac.uk/gwas/), GeneCards (https://www.genecards.org/),the EWAS catalog (http://www.ewascatalog.org/), and EWAS Atlas (https://ngdc.cncb.ac.cn/ewas/atlas) to determine whether genes annotated to top DMPs were linked to insulin resistance.