Blood lipid levels, including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol levels, are considered heritable modifiable risk factors for coronary artery disease. In addition to drug therapy and lifestyle factors, the genetic background also has an influence on lipid levels. Genome-wide association studies have identified a total of 157 genetic loci associated with lipid levels, explaining ≤12% of trait variance. Beyond this, epigenetic mechanisms may also contribute to the interindividual variation in circulating lipid levels and thereby may contribute to cardiovascular risk. In this study, first the association between genome-wide DNA methylation in whole blood and blood lipid levels (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol) was systematically investigated; second, the relationship between observed DNA methylation changes and prior hospitalization for myocardial infarction was explored. We observed associations between blood lipid levels and DNA methylation of genes involved in cholesterol and fatty acid metabolism. Further analyses reveal that changes in ABCG1 DNA methylation might mediate the association of high triglyceride levels with the risk of developing a myocardial infarction. These results lead to the possibility that epigenetic modification of ABCG1 and its regulatory network may play a key role in the path from altered blood lipid levels to the development of lipid-related disorders, including myocardial infarction. Also, showing that epigenetic changes are associated with both dyslipidemia and myocardial infarction may support the development of new classes of pharmacological agents for the treatment of lipid-related disorders.
DNA Methylation of Lipid-Related Genes Affects Blood Lipid Levels
Circulation: Cardiovascular Genetics
Abstract
Background—
Epigenetic mechanisms might be involved in the regulation of interindividual lipid level variability and thus may contribute to the cardiovascular risk profile. The aim of this study was to investigate the association between genome-wide DNA methylation and blood lipid levels high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol. Observed DNA methylation changes were also further analyzed to examine their relationship with previous hospitalized myocardial infarction.
Methods and Results—
Genome-wide DNA methylation patterns were determined in whole blood samples of 1776 subjects of the Cooperative Health Research in the Region of Augsburg F4 cohort using the Infinium HumanMethylation450 BeadChip (Illumina). Ten novel lipid-related CpG sites annotated to various genes including ABCG1, MIR33B/SREBF1, and TNIP1 were identified. CpG cg06500161, located in ABCG1, was associated in opposite directions with both high-density lipoprotein cholesterol (β coefficient=−0.049; P=8.26E-17) and triglyceride levels (β=0.070; P=1.21E-27). Eight associations were confirmed by replication in the Cooperative Health Research in the Region of Augsburg F3 study (n=499) and in the Invecchiare in Chianti, Aging in the Chianti Area study (n=472). Associations between triglyceride levels and SREBF1 and ABCG1 were also found in adipose tissue of the Multiple Tissue Human Expression Resource cohort (n=634). Expression analysis revealed an association between ABCG1 methylation and lipid levels that might be partly mediated by ABCG1 expression. DNA methylation of ABCG1 might also play a role in previous hospitalized myocardial infarction (odds ratio, 1.15; 95% confidence interval=1.06–1.25).
Conclusions—
Epigenetic modifications of the newly identified loci might regulate disturbed blood lipid levels and thus contribute to the development of complex lipid-related diseases.
Introduction
Coronary artery disease (CAD) is a major cause of death in industrialized countries.1 Blood lipid levels, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, and total cholesterol (TC) levels, are considered heritable, modifiable risk factors for this disease.2
Clinical Perspective on p 342
Lipid levels can be influenced by drug therapy or lifestyle factors such as diet, physical activity, alcohol consumption, and smoking.3 Several studies have also revealed a genetic impact on disturbed blood lipid levels. Genome-wide association studies identified a total of 157 genetic loci associated with lipid levels, explaining ≤12% of trait variance.4 Beyond this, there is evidence that epigenetic mechanisms are also involved in interindividual lipid level variability and thus may contribute to the cardiovascular risk profile. One epigenome-wide analysis in patients with familial hypercholesterolemia identified TNNT1 DNA methylation levels to be associated with HDL-C levels.5 Another epigenome-wide analysis in a nonpopulation-based cohort observed an association between CPT1A DNA methylation levels and very-low-density lipoprotein cholesterol as well as triglyceride levels.6
The aim of this study was to systematically investigate the association between main blood lipid levels (HDL-C, LDL-C, triglycerides, and TC) and genome-wide DNA methylation in whole blood of a large population-based cohort as well as in adipose tissue and skin samples. The identified associations were further explored through expression and functional studies and by investigation of genetic confounding. Finally, the relationship between observed DNA methylation changes and previous hospitalized myocardial infarction (MI) was explored.
Methods
The KORA study (Cooperative health research in the Region of Augsburg) consists of independent population-based samples from the general population living in the region of Augsburg, Southern Germany. The study has been conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent has been given by each participant. The study was reviewed and approved by the local ethical committee (Bayerische Landesärztekammer). For the analysis, whole blood samples of the KORA F4 study were used (n=1776). The replication was done in whole blood samples of KORA F3 (n=499) and InCHIANTI (n=472) as well as in human adipose (n=634) and skin (n=395) samples of the Multiple Tissue Human Expression Resource (MuTHER) study. In the discovery and in the replication cohorts, genome-wide DNA methylation patterns were analyzed using the Infinium HumanMethylation450 BeadChip Array (Illumina). In KORA F4 and in the Invecchiare in Chianti, Aging in the Chianti Area (InCHIANTI) study, the analysis was performed using whole blood DNA of fasting participants; in KORA F3, nonfasting participants were also included. In KORA, blood was drawn in the morning (8:00–10:30 am) and stored at −80°C until analysis. β-mixture quantile normalization7 was applied to the DNA methylation data using the R package wateRmelon, version 1.0.3.8 Table I in the Data Supplement provides a summary of normalized β values of the identified lipid-related CpGs in KORA F4. KORA F4/F3 samples were processed on 20/7 96-well plates in 9/4 batches; plate and batch effects were investigated using principle component analysis and eigenR2 analysis.9 The plate variable explained 4.8% (F4), 6.3% (F3), and 8.1% (InCHIANTI) of variance in the DNA methylation data. Consequently, plate was included as a random effect in the analyses.
Lipid levels were determined in fasting fresh blood samples at most 6 hours after collection, except for KORA F3 which also includes nonfasting samples. In KORA F3 and F4, TC was measured using the cholesterol-esterase method (CHOL Flex, Dade-Behring, Germany). HDL-C and triglyceride levels were determined using the TGL Flex and AHDL Flex methods (Dade-Behring), respectively, and LDL-C was measured by a direct method (ALDL, Dade-Behring). In KORA F4/F3, the intra-assay coefficient of variation for repeated measurements was 1.85%/1.61% (TC), 2.75%/2.65% (triglycerides), 3.25%/2.89% (HDL-C), and 2.7%/3.02% (LDL-C). In InCHIANTI, TC was determined by the cholesterol-esterase method, HDL-C was measured with the Liquid Homogeneous HDL-C assay (Alifax S.p.A., Padova, Italy), and triglycerides through an enzymatic colorimetric test using lipoprotein lipase, glycerokinase, glycerol phosphate oxidase, and peroxidase. All 3 lipids were determined using the analyzer Modular P800 Hitachi (Roche Diagnostics, Mannheim, Germany). The intra-assay coefficient of variation was 0.8% (TC), 1.5% (triglycerides), and 0.8% (HDL-C). The level of LDL-C was calculated using the Friedewald formula (LDL-C=TC−[HDL-C+(triglycerides/5)]).
Detailed information about the cohorts, the process of DNA methylation analysis as well as data preprocessing, quality assessment, and further methods such as genotyping and gene expression are provided in the Data Supplement.
Statistical Analysis
Discovery Step
In the KORA F4 cohort, 26 of 1802 individuals were excluded from the analysis due to missing information in covariates or due to nonfasting status at the time point of blood collection, resulting in a final sample size of 1776 F4 subjects. Associations between DNA methylation β values and lipid levels were analyzed using linear mixed effects models implemented with the nlme package in R with lipid levels as response. To normalize lipid levels, square root (TC and LDL-C) and logarithmic (HDL-C and triglycerides) transformations were applied, followed by standardization to a mean of zero and an SD of 1. The following potential confounders were included as covariates: age, sex, body mass index, smoking, alcohol consumption, intake of lipid-lowering drugs, physical activity, history of MI, current hypertension, hemoglobin A1c levels, C-reactive protein levels, and white blood cell count. Experimental plate was included as a random effect. To correct for multiple comparisons, a genome-wide significance level of 1.1E-07 was used, determined according to the Bonferroni procedure. Because whole blood DNA samples were used, cell heterogeneity had to be considered as a confounder. As no measured cell count information was available for any cohort, sample-specific estimates of the proportion of the major white blood cell types were obtained using a statistical method described by Houseman et al.10 The significant associations of the first model were recalculated, additionally adjusting for the estimated white blood cell proportions (CD8 T cells, CD4 T cells, natural killer cells, B-lymphocytes, monocytes, and granulocytes). To get a measure of the variance in the lipid levels explained by methylation levels, R2 statistics were calculated according to Edwards et al,11 using the R package pbkrtest, version 0.3–7.
Replication Step
Identified loci were replicated using the same statistical model in KORA F3 (n=499) as well as in InCHIANTI (n=472). In KORA F3, an adjustment for C-reactive protein was not possible because this variable was not available for this cohort. A fixed-effects meta-analysis of KORA F3 and InCHIANTI results was conducted with the R package metafor, version 1.9–2. Results were corrected according to the Bonferroni procedure (level of significance=4.5E-03).
For the MuTHER cohort, the Infinium HumanMethylation450 BeadChip Array signal intensities were quantile normalized and methylation β values were calculated using R 2.12 as previously described.12 For cg06500161, no DNA methylation data were available as it did not pass the quality control filters. Data for n=634 adipose and n=395 skin samples were available for the final analysis. A linear mixed effects model was fitted for blood lipid values using the lme4 package in R. The model was adjusted for age, body mass index, smoking, statins, technical covariates (fixed effects), and family relationship and zygosity (random effects). A likelihood ratio test was used to assess significance, and the P value was calculated from the χ2 distribution with 1 df using −2 log (likelihood ratio) as the test statistic. Results were corrected according to the Bonferroni procedure (level of significance=7.14E-03).
Single-Nucleotide Polymorphism Analysis
Investigation of genetic confounding was carried out to identify whether the observed associations between lipid and methylation levels in KORA F4 were due to single-nucleotide polymorphisms (SNPs) being associated with both lipid levels and DNA methylation. One hundred fifty-seven lipid-associated SNPs identified by the Global Lipids Genetics Consortium were included in the analysis.4 SNP rs9411489 was excluded because genotype data were not available for the KORA F4 data set. Genotype data of 156 lipid-associated SNPs as well as DNA methylation data were available for 1710 KORA F4 participants. A preselection was done to reveal the lipid-associated SNPs which were at the same time nominally associated (P<0.05) with differentially methylated lipid-related CpG sites (CpGs; Table II in the Data Supplement). Next, models for each significant CpG–lipid pair were recalculated with additional adjustment for the respective preselected SNPs to see if the association was based on genetic confounding. Discovery, replication step, and SNP analysis were analyzed using the statistical package R, version 2.15.3.
Gene Expression Analysis
For the gene expression analysis, 724 KORA F4 subjects were included, as for these participants both DNA methylation data and expression data were available. We tried to disentangle the relationships between methylation at the CpGs, expression of the corresponding annotated gene, and lipid levels in an ad hoc approach based on a sequence of regression models with and without adjusting for the third of the 3 components. For each significant lipid-methylation pair, the association between lipid level and DNA methylation was recalculated for KORA F4 (n=724). Afterward we repeated the analysis, adjusting for the expression levels of the annotated gene (except for cg07504977 which has no annotation to a gene according to the University of California Santa Cruz [UCSC] Genome Browser; Table III in the Data Supplement). A P value for the association was determined through a likelihood ratio test. Similarly, the association between DNA methylation and transcript levels, and between lipid levels and transcript levels, were determined. All models were also adjusted for age, sex, body mass index, smoking, alcohol consumption, intake of lipid lowering drugs, physical activity, history of MI, current hypertension, hemoglobin A1c levels and C-reactive protein levels, as well as for white blood cell count and estimated white blood cell proportions. Models including expression data were additionally adjusted for the technical variables RNA integrity number, sample storage time, and RNA amplification batch.13 The level of significance was set to 8.3E-04.
Association of DNA Methylation With Prevalent MI
To assess the association of the observed lipid-related CpGs with previous hospitalized MI in KORA F4, generalized linear mixed effects models were fitted with adaptive Gauss-Hermite quadrature using the R package lme4, version 1.0–4. Three models were analyzed. The first model was adjusted for age, sex, and estimated white blood cell proportions. In the second model, we additionally included body mass index, smoking, alcohol consumption, physical activity, current hypertension, hemoglobin A1c levels, C-reactive protein levels, and white blood cell count as covariates, and in the third model, the lipid variables (HDL-C, LDL-C, triglycerides, and TC) were also included. The Bonferroni correction was used with a significance level of 6.3E-03. The same analyses were done for KORA F3 and InCHIANTI. This statistical analysis and the gene expression analysis were performed using the statistical package R, version 3.0.2.
Results
Associations Between Genome-Wide DNA Methylation and Blood Lipid Levels
Characteristics of the discovery cohort (KORA F4) as well as the replication cohorts (KORA F3, InCHIANTI, and MuTHER cohorts) are shown in Table 1.
| Characteristic | KORA F4 (n=1776) | KORA F3 (n=499) | InCHIANTI (n=472) | MuTHER cohort (n=856) |
|---|---|---|---|---|
| Age, y | 60.8 (8.9) | 52.9 (9.6) | 71.2 (16.0) | 59.4 (9.0) |
| Sex=male | 867 (48.8%) | 259 (51.9%) | 215 (45.6%) | 0 (0.0%) |
| BMI, kg/m2 | 28.2 (4.8) | 27.2 (4.5) | 27.0 (4.3) | 26.6 (4.9) |
| Current smoker | 258 (14.5%) | 249 (49.9%) | 206 (43.7%) | 84 (9.8%) |
| Physically active | 1021 (57.5%) | 249 (49.9%) | 238 (50.5%) | NA |
| Alcohol consumption, g/d | 15.5 (20.4) | 16.1 (19.6) | 12.6 (15.1) | NA |
| HDL-C, mg/dL | 56.5 (14.6) | 58.2 (17.8) | 56.8 (14.8) | 71.5 (18.2) |
| LDL-C, mg/dL | 140.0 (35.1) | 131.0 (33.2) | 124.5 (32.4) | 124.5 (37.9) |
| Triglyceride, mg/dL | 133.1 (94.7) | 164.6 (121.9) | 119.7 (57.9) | 99.2 (49.6) |
| Cholesterol, mg/dL | 221.9 (39.3) | 220.5 (38.2) | 205.2 (37.9) | 218.9 (38.7) |
| C-reactive protein, mg/L | 2.5 (5.1) | NA | 4.0 (8.0) | NA |
| Leukocytes count, per nL | 5.9 (1.6) | 7.3 (2.1) | 6.3 (1.6) | 6.5 (1.8) |
| HbA1c, % | 5.6 (0.6) | 5.3 (0.5) | 4.9 (0.8)* | NA |
| Self-reported history | ||||
| Hypertension† | 811 (45.7%) | 211 (42.3%) | 124 (26.3%) | 172 (20.1%) |
| Hospitalized myocardial infarction | 60 (3.4%) | 8 (1.6%) | 36 (7.6%) | NA |
| Intake of lipid-lowering drugs (excluding herbal substances) | 290 (16.3%) | 31 (6.2%) | 61 (13.0%) | 69 (8.1%) |
| Fasting at the time of blood collection‡ | 1776 (100.0%) | 47 (9.4%) | 472 (100.0%) | 844 (98.5%) |
Continuous and categorical characteristics are given as mean (SD) or absolute numbers and relative proportions, respectively. BMI indicates body mass index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; InCHIANTI, Invecchiare in Chianti, Aging in the Chianti Area study; KORA, Cooperative Health Research in the Region of Augsburg study; LDL-C, low-density lipoprotein cholesterol; MuTHER, Multiple Tissue Human Expression Resource; and NA, variable not available.
*
In InCHIANTI, HbA1c levels were calculated using the formula (46.7+glucose level)/28.7; in KORA F3/F4, they were analyzed using the high-performance liquid chromatography method.
†
>140/90 mm Hg or medically controlled.
‡
Overnight fast of ≥8 hours.
In KORA F4, DNA methylation levels at 1, 68, 17, and 80 CpGs were associated with HDL-C, triglycerides, LDL-C, and TC levels, respectively. When white blood cell proportions were included as covariates, the number of significant associations (P<1.1E-07) decreased, indicating the presence of blood cell confounding. The association of methylation level at 1 CpG with HDL-C and LDL-C remained significant, as well as the association of 10 CpGs with triglyceride levels. There were no longer any associations with TC. P values ranged from 1.21E-27 to 9.66E-08, with percentage of explained lipid level variance ranging from 1.6% to 6.5% (Table 2). CpG cg06500161, located in ABCG1, was associated in opposite directions with HDL-C (β=−0.049; P=8.26E-17) and triglyceride levels (β=0.070; P=1.21E-27). Triglyceride levels were associated with 9 additional CpGs located in genes including ABCG1, MIR33B, SREBF1, and CPT1A. LDL-C showed a positive association with methylation status of 1 CpG located in TNIP1 (β=0.040; P=4.27E-09).
| Lipid | CpG | Chromosome No. | Gene | KORA F4 | KORA F3 | InCHIANTI | Meta-Analysis* | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| β Coefficient | SE | P Value | Exp Var, % | β Coefficient | SE | P Value | β Coefficient | SE | P Value | P Value | ||||
| HDL-C | cg06500161 | 21 | ABCG1 | −0.049 | 0.006 | 8.26E-17 | 3.9 | −0.065 | 0.014 | 2.97E-06 | −0.071 | 0.016 | 1.13E-05 | 9.00E-11† |
| TG | cg06500161 | 21 | ABCG1 | 0.070 | 0.006 | 1.21E-27 | 6.5 | 0.072 | 0.015 | 1.89E-06 | 0.063 | 0.016 | 1.03E-04 | 5.56E-10† |
| cg19693031 | 1 | TXNIP | −0.030 | 0.003 | 1.89E-17 | 4.1 | −0.014 | 0.007 | 5.65E-02 | −0.023 | 0.011 | 3.54E-02 | 5.67E-03 | |
| cg11024682 | 17 | SREBF1 | 0.059 | 0.008 | 5.54E-14 | 3.2 | 0.031 | 0.014 | 2.89E-02 | 0.030 | 0.013 | 2.36E-02 | 1.60E-03† | |
| cg00574958 | 11 | CPT1A | −0.118 | 0.016 | 3.15E-13 | 3.2 | −0.103 | 0.028 | 2.42E-04 | −0.058 | 0.020 | 4.86E-03 | 7.88E-06† | |
| cg27243685 | 21 | ABCG1 | 0.064 | 0.009 | 3.24E-13 | 3.0 | 0.050 | 0.014 | 5.87E-04 | 0.054 | 0.022 | 1.63E-02 | 2.49E-05† | |
| cg07504977 | 10 | NA‡ | 0.026 | 0.004 | 3.93E-12 | 2.7 | 0.026 | 0.008 | 1.87E-03 | 0.027 | 0.009 | 3.74E-03 | 1.91E-05† | |
| cg20544516 | 17 | MIR33B/SREBF1 | 0.043 | 0.007 | 2.84E-09 | 2.7 | 0.032 | 0.013 | 1.39E-02 | 0.032 | 0.018 | 7.16E-02 | 2.22E-03† | |
| cg12556569 | 11 | APOA5 | 0.005 | 0.001 | 6.43E-09 | 1.9 | 0.002 | 0.002 | 2.56E-01 | 0.004 | 0.002 | 1.25E-02 | 1.20E-02 | |
| cg07397296 | 21 | ABCG1 | 0.027 | 0.005 | 9.48E-08 | 2.1 | 0.034 | 0.010 | 1.03E-03 | 0.008 | 0.011 | 4.63E-01 | 3.78E-03† | |
| cg07815238 | 15 | NA‡ | 0.048 | 0.009 | 9.66E-08 | 1.6 | 0.015 | 0.017 | 3.69E-01 | 0.003 | 0.014 | 8.41E-01 | 4.61E-01 | |
| LDL-C | cg22178392 | 5 | TNIP1 | 0.040 | 0.007 | 4.27E-09 | 2.1 | 0.049 | 0.015 | 1.11E-03 | 0.020 | 0.014 | 1.45E-01 | 1.04E-03† |
Exp Var indicates explained variance; HDL-C, high-density lipoprotein cholesterol; InCHIANTI, Invecchiare in Chianti, Aging in the Chianti Area study; KORA, Cooperative Health Research in the Region of Augsburg study; LDL-C, low-density lipoprotein cholesterol; and TG, triglyceride.
*
Meta-analysis of results of replication in KORA F3 and InCHIANTI.
†
CpG with association confirmed by replication meta-analysis; level of significance: 1.1E-07 (discovery cohort) and 4.5E-03 (replication meta-analysis).
‡
No gene annotation for this CpG according to the University of California Santa Cruz Genome Browser.
The lipid-related CpGs were carried forward to replication in a meta-analysis of the KORA F3 and InCHIANTI cohorts. Nine of the 12 associations were confirmed (P values from 9.00E-11 to 3.78E-03; Table 2).
Tissue Expression of Candidate Genes and Replication in an Adipose Tissue Cohort
To address cell specificity and tissue specificity of ABCG1, CPT1A, and SREBF1 expression, we quantified their expression in human blood cell types (peripheral blood mononuclear cells; CD14-, CD19-, CD3-, CD4-, and CD8-positive cells; and regulatory T cells) and human tissues (brain, heart, lung, kidney, small intestine, adipose tissue, and skeletal muscle; Figure I in the Data Supplement). All genes were expressed not only in blood cells but also in adipose tissue. Five of the replicated associations were also significant in adipose tissue of the MuTHER cohort (Table 3). Here, the CpG cg20544516 (MIR33B/SREBF1) showed the strongest association with triglyceride levels (β=0.012; P=1.20E-10), followed by CpGs located in ABCG1 (cg27243685, cg07397296; β=0.013, P=5.86E-08 and β=0.008, 6.59E-07, respectively) and SREBF1 (cg11024682, β=0.007, P=6.72E-04). The association between LDL-C and cg22178392 (TNIP1) was also found to be significant in adipose tissue (β=0.002; P=6.02E-03). In skin tissue, no associations could be determined except for triglyceride levels and cg11024682 (SREBF1) and cg00574958 (CPT1A; β=0.006, P=4.07E-04 and β=-0.005, P=2.81E-03, respectively). These results are in line with the strong expression of MIR33B/SREBF1 in adipose tissue observed in our tissue panel (Figure I in the Data Supplement).
| Lipid | CpG | Gene | Adipose (n=634) | Skin (n=395) | ||||
|---|---|---|---|---|---|---|---|---|
| β Coefficient | SE | P Value | β Coefficient | SE | P Value | |||
| HDL-C | cg06500161 | ABCG1 | NA* | NA* | NA* | NA* | NA* | NA* |
| TG | cg06500161 | ABCG1 | NA* | NA* | NA* | NA* | NA* | NA* |
| cg11024682 | SREBF1 | 0.007 | 0.002 | 6.72E-04 | 0.006 | 0.002 | 4.07E-04 | |
| cg00574958 | CPT1A | 0.0001 | 0.001 | 8.16E-01 | −0.005 | 0.002 | 2.81E-03 | |
| cg27243685 | ABCG1 | 0.013 | 0.002 | 5.86E-08 | 0.003 | 0.003 | 3.91E-01 | |
| cg07504977 | NA† | 0.006 | 0.003 | 4.60E-02 | 0.004 | 0.002 | 1.56E-01 | |
| cg20544516 | MIR33B/SREBF1 | 0.012 | 0.002 | 1.20E-10 | −0.001 | 0.002 | 5.04E-01 | |
| cg07397296 | ABCG1 | 0.008 | 0.002 | 6.59E-07 | 0.001 | 0.002 | 6.64E-01 | |
| LDL-C | cg22178392 | TNIP1 | 0.002 | 0.001 | 6.02E-03 | 0.0003 | 0.001 | 7.49E-01 |
HDL-C indicates high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; and TG, triglyceride.
*
No DNA methylation data were available for this CpG site.
†
No gene annotation for this CpG according to the UCSC Genome Browser. Level of significance: 7.14E-03.
Investigation of Genetic Confounding
Known lipid-related SNPs, which were nominally associated with DNA methylation at the identified lipid-related CpGs and thus acting as potential confounders, are shown in Table II in the Data Supplement. P values ranged between 4.99E-02 and 3.42E-05, except for 1 SNP (rs964184), which was highly significantly associated with DNA methylation of cg12556569 (APOA5; P=3.75E-289). After recalculation of the models for each CpG–lipid pair with additional adjustment for the respective preselected SNP, only the association between methylation of APOA5 and triglycerides was considerably genetically confounded (Table IV in the Data Supplement). Further analyses showed that rs964184, from the preselected SNPs for APOA5, caused the genetic confounding (data not shown).
Gene Expression Analysis
Gene expression analysis revealed a negative association between methylation at ABCG1 and mRNA levels of the 6 ABCG1 transcripts (cg06500161: P=5.42E-14, cg27243685: P=1.86E-07). Here, transcript ILMN_2329927 showed the highest association (cg06500161: β=−0.151, P=5.22E-15; cg27243685: β=−0.185, P=4.34E-06) of the 6 ABCG1 transcripts. Adjusted for lipids, the association became less significant. Detailed results of the analyses are shown in Table V in the Data Supplement.
The association between cg06500161 and HDL-C (P=4.10E-07) weakened (P=1.30E-02) after adjusting for ABCG1 transcripts. Similar results were observed for the association between cg06500161 and triglyceride levels and between cg27243685 and triglyceride levels.
ABCG1 transcript levels showed a strong positive association with HDL-C (P=7.76E-13) and a negative association with triglyceride levels (P=1.25E-33). Significance was reduced when adjusting for ABCG1 DNA methylation (see also Figure 1; Figure II in the Data Supplement).
Functional Analysis of cg06500161 (ABCG1) and cg20544516 (MIR33B/SREBF1)
To assess the biological relevance of the DNA methylation status of CpGs found to be associated with lipid levels, electrophoretic mobility shift assays were carried out for cg06500161 (ABCG1). This CpG was chosen because it showed the strongest association with both HDL-C and triglycerides. cg20544516 was also included in the analysis because of its functionally interesting location in SREBF1 in a region coding for a microRNA (MIR33b).
The electrophoretic mobility shift assay for cg06500161 identified a higher binding affinity of a protein complex for the unmethylated status of cg06500161 compared with the methylated status. For cg20544516, a strong protein binding affinity was detected in the methylated status, which was not detectable in the unmethylated status (Figure III in the Data Supplement).
DNA Methylation and Prevalent MI
The CpGs associated with lipid levels were tested for an association with previous hospitalized MI in the discovery cohort KORA F4 (n=1776 with n=60 cases). Three models were analyzed and CpG cg06500161, located in the ABCG1 gene, showed an association with MI independent of lipid levels in all 3 models (eg, model 3: β=0.141, P=1.30E-03; Table VI in the Data Supplement). The results could not be replicated in KORA F3 and InCHIANTI, possibly due to the low number of MI cases (n=8 in KORA F3; n=36 in InCHIANTI).
Discussion
DNA Methylation of Genes Involved in Lipid Metabolism Is Associated With HDL-C, Triglycerides, and LDL-C Levels
Our results indicated that DNA methylation of cg06500161 in ABCG1 was associated in opposite directions with HDL-C and triglyceride levels. Integrating gene expression data revealed an association between cg06500161 methylation and lipid levels which might be partly mediated by ABCG1 expression. DNA methylation at this CpG was also elevated in cases of MIs compared to healthy individuals.
One challenge of genome-wide DNA methylation analyses in blood samples is the difference in methylation patterns between different blood cell types.10,14 In our blood cell expression panel of ABCG1, CPT1A, and SREBF1, varying expression patterns were also detectable (Figure I in the Data Supplement), which underline the issue of cell heterogeneity. After adjustment for estimated blood cell proportions using the method proposed by Houseman et al,10 the number of significant CpGs decreased from 166 to 12. Therefore, in all further analyses, cell proportions were included as covariates to correct for cell heterogeneity.
We identified 7 new lipid-related CpGs located in ABCG1 (HDL-C, triglycerides), MIR33B/SREBF1, in an intergenic region (triglycerides), and in TNIP1 (LDL-C). In addition, we replicated 1 CpG (cg00574958 in CPT1A), which was found to be associated with triglyceride levels in CD4+ T cells in the GOLDN study (n=991).6 Five of the associations were also found in adipose tissue, of which the strongest associations were observed between triglyceride levels and MIR33B/SREBF1 as well as ABCG1 DNA methylation. Both genes are highly expressed in adipose tissue (>1.0E07 copies/μg RNA; Figure I in the Data Supplement). In skin, triglyceride levels were associated with SREBF1 and CPT1A DNA methylation, but there was no significant association with ABCG1 methylation. These results indicate a tissue-specific association between triglyceride levels and MIR33B/SREBF1 and ABCG1 DNA methylation.
Additionally, we examined whether the observed associations between lipid and methylation levels in KORA F4 were based on confounding by lipid-associated SNPs. Most associations remained significant after additional adjustment for SNPs which were nominally associated with DNA methylation at the respective CpG site. Only one CpG-lipid association was found to be confounded. The association between DNA methylation of cg12556569 (located in the promoter region of APOA5) and triglyceride levels was confounded by rs964184, which is known to primarily affect triglyceride levels.4 One study had previously identified this SNP as an mQTL (cytosine modification quantitative trait loci).15 Our results indicate that the nominal associations between trait-associated SNPs and DNA methylation of lipid-related CpGs were dependent on lipids and that the identified lipid–DNA methylation associations were not due to genetic confounding.
Interaction of Genes of Lipid-Associated CpGs
Interestingly, 3 of the genes where the lipid-related CpGs are localized—ABCG1, MIR33B/SREBF1, and CPT1A—and their gene products interact with one another (Figure 2). SREBF1 and SREBF2 (sterol regulatory element-binding transcription factor 1 and 2) code for the membrane-bound transcription factors SREBP1 and SREBP2, which activate the synthesis of fatty acid and the synthesis and uptake of cholesterol.16,17 The intronic microRNAs 33a and 33b (MIR33a/b) are located within SREBF2 and SREBF1, respectively. Coincident with transcription of SREBF2/1, the embedded MIR33a/b is cotranscribed.18 MIR33a/b act as negative regulators, repressing many genes involved in fatty acid oxidation and cholesterol transport,18–23 such as carnitine palmitoyltransferase 1A (CPT1A), which is important for the transport of fatty acids into the mitochondria for their oxidation.24 Studies also identified a role for MIR33a/b in the repression of the ABC transporters ABCA1 and ABCG1.20,25 ABCG1 encodes the ABC-transporter G1, a cholesterol transporter which plays a role in cellular lipid homeostasis. It has been shown that ABCG1 functions cooperatively with ABCA1.26 ABCA1 transports phospholipids and cholesterol to lipid-poor HDL subclasses such as apolipoprotein A–I, whereas ABCG1 has more mature HDL particles as its acceptor.27,28
In the present study, the methylation levels of these genes, MIR33B/SREBF1, ABCG1, and CPT1A, are associated with blood triglyceride levels, suggesting an epigenetic modulation of lipid and fatty acid metabolism. Here, ABCG1 might play a key role because 1 CpG (cg06500161) located in this gene is associated with both HDL-C and triglyceride levels. The function of ABCG1 in HDL-C metabolism has been recorded in several studies and reviews29–31; however, no report yet exists about a direct role of ABCG1 in connection with triglyceride levels. One study showed that genetic variants in the ABCG1 promoter were associated with ABCG1 expression, which showed an influence on the bioavailability of lipoprotein lipase. Accordingly, ABCG1 regulates the bioavailability of macrophage-secreted lipoprotein lipase, thereby promoting lipid accumulation, primarily in the form of triglycerides, in primary human macrophages.32
TNIP1, the methylation of which was associated with LDL-C levels in this study, encodes the tumor necrosis factor-α-induced protein 3–interacting protein 1. This protein seems to be important in regulating multiple receptor–mediated transcriptional activity of peroxisome proliferator–activated receptors33 and retinoic acid receptors.34 Interestingly, ligand-activated retinoic acid receptor increases ABCA1 and ABCG1 expression in human macrophages, modulating ABCG1 promoter activity via LXR responsive elements-dependent mechanisms.35 Additionally, studies revealed that peroxisome proliferator–activated receptor α/γ-activators induce ABCA1 expression in macrophages36 and peroxisome proliferator–activated receptor γ induce ABCG1 expression.37 Therefore, TNIP1 might have an indirect impact on the expression of ABCA1/G1.
Methylation of ABCG1 Is Associated With ABCG1 Transcripts
The identified negative association between ABCG1 methylation (cg06500161, cg27243685) and ABCG1 mRNA levels is possibly mediated by methylation-dependent transcription factor binding, as observed in the electrophoretic mobility shift assay experiments. ABCG1 mRNA levels were additionally associated with HDL-C and triglyceride levels in opposite directions. The negative association between ABCG1 methylation (cg06500161) and HDL-C might be partly mediated by the expression of ABCG1. These results demonstrate the complexity of the relationship between DNA methylation and gene expression.
Our findings could provide the missing link between disturbed blood lipid levels and changed expression patterns of ABCG1. Studies have shown that in patients with type 2 diabetes mellitus, the ABCG1 expression in macrophages is reduced, leading to decreased cholesterol efflux to HDL.38 Interestingly, a recent study shows an association between the methylation status of cg06500161 (ABCG1) and fasting insulin as well as with HOMA-IR (homeostatic model assessment), a surrogate marker of insulin resistance.39 All results indicate a key role of DNA methylation of ABCG1 in the development of complex lipid-related diseases.
ABCG1—An Epigenetic Link Between Blood Lipid Levels and MI?
DNA methylation has been linked to biological processes of cardiovascular disease such as atherosclerosis.40 An association between ABCA1 methylation and HDL-C levels as well as CAD in patients with familial hypercholesterolemia has been reported.41
We identified a positive association between cg06500161 (ABCG1) and MI in the KORA F4 cohort: DNA methylation levels of cg06500161 are higher in subjects with previous hospitalized MI compared with healthy people. Because the number of subjects with self-reported hospitalized MI was low in KORA F3 and InCHIANTI, no replication was achieved. These results need further confirmation by prospective genome-wide DNA methylation studies.
Genetic variants in ABCG1 were shown to be associated with CAD.42,43 However, nothing is yet known about an epigenetic impact of ABCG1 on the development of MI. A human cell culture study showed a reduction of macrophage ABCG1 expression when higher triglyceride levels were present in the culture media. The author suggests that hypertriglyceridemia may increase the risk of CAD via direct actions on macrophages favoring foam cell formation, thus leading to the development of atherosclerotic plaque.44 Changes in ABCG1 DNA methylation might mediate the development of atherosclerotic plaques in response to high triglyceride levels. Thus, with this study, we found hints for a new perspective on the molecular background of CAD.
Conclusions
We found associations between DNA methylation and lipid levels for genes contributing to the modulation of cholesterol and fatty acid metabolism. Epigenetic modification of ABCG1 and its regulatory network could play a key role on the path from disturbed blood lipid levels to the development of complex lipid-related diseases. These results indicate an epigenetic impact on metabolic regulation in humans and give new insights into the complex picture of lipid-related complex diseases.
Acknowledgments
We thank Nadine Lindemann, Viola Maag, and Franziska Scharl for technical support and acknowledge the support of the nonprofit foundation Human Tissue and Cell Research, which holds human tissue on trust, making it broadly available for research on an ethical and legal basis.
CLINICAL PERSPECTIVE
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Appendix
From the Research Unit of Molecular Epidemiology (L.P., S.W., E.R., S.K., A.K., H.G., A.P., M.W.), Institute of Epidemiology II (L.P., S.W., E.R., S.K., A.K., H.G., C.M., A.P., M.W.), Institute of Human Genetics (K.S., H.P.), Genome Analysis Center, Institute of Experimental Genetics (J.A.), Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (C.G.), and German Center for Diabetes Research (DZD) (S.W., H.G.), Neuherberg, Germany; Epidemiology and Public Health Group, University of Exeter Medical School, Exeter, Devon, United Kingdom (L.C.P., D.M.); Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom (J.K.S., P.D.); Institute of Laboratory Medicine, University Hospital Munich and Ludwig Maximilians University Munich, Munich, Germany (L.M.H., D.T.); Department of Dermatology, Venereology and Allergy, Christian Albrechts University Kiel, Kiel, Germany (A.K.); Institute of Human Genetics, Technical University Munich, Munich, Germany (K.S., H.P.); Hannover Unified Biobank, Hannover Medical School, Hannover, Germany (N.K., T.I.); Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, United Kingdom (Å.K.H.); German Center for Diabetes Research (DZD), Düsseldorf, Germany (M.R., C.H.); Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany (M.R., C.H.); Department of Endocrinology and Diabetology, University Hospital, Düsseldorf, Germany (M.R.); Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD (D.G.H., A.B.S.); Biobank under Administration of HTCR, Department of General, Visceral, Transplantation, Vascular and Thoracic Surgery, Hospital of the University of Munich, Munich, Germany (W.E.T.); Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom (T.D.S.); Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria (F.K.); German Research Center for Cardiovascular Disease (DZHK), Partner-site Munich, Munich, Germany (A.P.); William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (P.D.); Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia (P.D.); and Clinical Research Branch, National Institute on Aging, Baltimore, MD (L.F.).
References
1.
Andrawes WF, Bussy C, Belmin J. Prevention of cardiovascular events in elderly people. Drugs Aging. 2005;22:859–876.
2.
Elder SJ, Lichtenstein AH, Pittas AG, Roberts SB, Fuss PJ, Greenberg AS, et al. Genetic and environmental influences on factors associated with cardiovascular disease and the metabolic syndrome. J Lipid Res. 2009;50:1917–1926. doi: 10.1194/jlr.P900033-JLR200.
3.
Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, et al.; American Heart Association Nutrition Committee. Diet and lifestyle recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee. Circulation. 2006;114:82–96. doi: 10.1161/CIRCULATIONAHA.106.176158.
4.
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al.; Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–1283. doi: 10.1038/ng.2797.
5.
Guay SP, Voisin G, Brisson D, Munger J, Lamarche B, Gaudet D, et al. Epigenome-wide analysis in familial hypercholesterolemia identified new loci associated with high-density lipoprotein cholesterol concentration. Epigenomics. 2012;4:623–639. doi: 10.2217/epi.12.62.
6.
Irvin MR, Zhi D, Joehanes R, Mendelson M, Aslibekyan S, Claas SA, et al. Epigenome-wide association study of fasting blood lipids in the Genetics of Lipid-lowering Drugs and Diet Network study. Circulation. 2014;130:565–572. doi: 10.1161/CIRCULATIONAHA.114.009158.
7.
Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29:189–196. doi: 10.1093/bioinformatics/bts680.
8.
Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;14:293. doi: 10.1186/1471-2164-14-293.
9.
Chen LS, Storey JD. Eigen-R2 for dissecting variation in high-dimensional studies. Bioinformatics. 2008;24:2260–2262. doi: 10.1093/bioinformatics/btn411.
10.
Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86. doi: 10.1186/1471-2105-13-86.
11.
Edwards LJ, Muller KE, Wolfinger RD, Qaqish BF, Schabenberger O. An R2 statistic for fixed effects in the linear mixed model. Stat Med. 2008;27:6137–6157. doi: 10.1002/sim.3429.
12.
Grundberg E, Meduri E, Sandling JK, Hedman AK, Keildson S, Buil A, et al.; Multiple Tissue Human Expression Resource Consortium. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet. 2013;93:876–890. doi: 10.1016/j.ajhg.2013.10.004.
13.
Schurmann C, Heim K, Schillert A, Blankenberg S, Carstensen M, Dörr M, et al. Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium. PLoS One. 2012;7:e50938. doi: 10.1371/journal.pone.0050938.
14.
Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlén SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012;7:e41361. doi: 10.1371/journal.pone.0041361.
15.
Moen EL, Zhang X, Mu W, Delaney SM, Wing C, McQuade J, et al. Genome-wide variation of cytosine modifications between European and African populations and the implications for complex traits. Genetics. 2013;194:987–996. doi: 10.1534/genetics.113.151381.
16.
Edwards PA, Tabor D, Kast HR, Venkateswaran A. Regulation of gene expression by SREBP and SCAP. Biochim Biophys Acta. 2000;1529:103–113.
17.
Horton JD. Sterol regulatory element-binding proteins: transcriptional activators of lipid synthesis. Biochem Soc Trans. 2002;30(Part 6):1091–1095.
18.
Marquart TJ, Allen RM, Ory DS, Baldán A. miR-33 links SREBP-2 induction to repression of sterol transporters. Proc Natl Acad Sci U S A. 2010;107:12228–12232. doi: 10.1073/pnas.1005191107.
19.
Najafi-Shoushtari SH, Kristo F, Li Y, Shioda T, Cohen DE, Gerszten RE, et al. MicroRNA-33 and the SREBP host genes cooperate to control cholesterol homeostasis. Science. 2010;328:1566–1569. doi: 10.1126/science.1189123.
20.
Rayner KJ, Suárez Y, Dávalos A, Parathath S, Fitzgerald ML, Tamehiro N, et al. MiR-33 contributes to the regulation of cholesterol homeostasis. Science. 2010;328:1570–1573. doi: 10.1126/science.1189862.
21.
Dávalos A, Goedeke L, Smibert P, Ramírez CM, Warrier NP, Andreo U, et al. miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling. Proc Natl Acad Sci U S A. 2011;108:9232–9237. doi: 10.1073/pnas.1102281108.
22.
Gerin I, Clerbaux LA, Haumont O, Lanthier N, Das AK, Burant CF, et al. Expression of miR-33 from an SREBP2 intron inhibits cholesterol export and fatty acid oxidation. J Biol Chem. 2010;285:33652–33661. doi: 10.1074/jbc.M110.152090.
23.
Horie T, Ono K, Horiguchi M, Nishi H, Nakamura T, Nagao K, et al. MicroRNA-33 encoded by an intron of sterol regulatory element-binding protein 2 (Srebp2) regulates HDL in vivo. Proc Natl Acad Sci U S A. 2010;107:17321–17326. doi: 10.1073/pnas.1008499107.
24.
McGarry JD, Brown NF. The mitochondrial carnitine palmitoyltransferase system. From concept to molecular analysis. Eur J Biochem. 1997;244:1–14.
25.
Rotllan N, Fernández-Hernando C. MicroRNA regulation of cholesterol metabolism. Cholesterol. 2012;2012:847849. doi: 10.1155/2012/847849.
26.
Gelissen IC, Harris M, Rye KA, Quinn C, Brown AJ, Kockx M, et al. ABCA1 and ABCG1 synergize to mediate cholesterol export to apoA-I. Arterioscler Thromb Vasc Biol. 2006;26:534–540. doi: 10.1161/01.ATV.0000200082.58536.e1.
27.
Wang N, Lan D, Chen W, Matsuura F, Tall AR. ATP-binding cassette transporters G1 and G4 mediate cellular cholesterol efflux to high-density lipoproteins. Proc Natl Acad Sci U S A. 2004;101:9774–9779. doi: 10.1073/pnas.0403506101.
28.
Kennedy MA, Barrera GC, Nakamura K, Baldán A, Tarr P, Fishbein MC, et al. ABCG1 has a critical role in mediating cholesterol efflux to HDL and preventing cellular lipid accumulation. Cell Metab. 2005;1:121–131. doi: 10.1016/j.cmet.2005.01.002.
29.
Tarling EJ, Edwards PA. ATP binding cassette transporter G1 (ABCG1) is an intracellular sterol transporter. Proc Natl Acad Sci U S A. 2011;108:19719–19724. doi: 10.1073/pnas.1113021108.
30.
Tarling EJ, Edwards PA. Dancing with the sterols: critical roles for ABCG1, ABCA1, miRNAs, and nuclear and cell surface receptors in controlling cellular sterol homeostasis. Biochim Biophys Acta. 2012;1821:386–395. doi: 10.1016/j.bbalip.2011.07.011.
31.
Ramasamy I. Recent advances in physiological lipoprotein metabolism. Clin Chem Lab Med. 2014;52:1695–1727.
32.
Olivier M, Tanck MW, Out R, Villard EF, Lammers B, Bouchareychas L, et al. Human ATP-binding cassette G1 controls macrophage lipoprotein lipase bioavailability and promotes foam cell formation. Arterioscler Thromb Vasc Biol. 2012;32:2223–2231. doi: 10.1161/ATVBAHA.111.243519.
33.
Flores AM, Gurevich I, Zhang C, Ramirez VP, Devens TR, Aneskievich BJ. TNIP1 is a corepressor of agonist-bound PPARs. Arch Biochem Biophys. 2011;516:58–66. doi: 10.1016/j.abb.2011.08.014.
34.
Gurevich I, Aneskievich BJ. Liganded RARalpha and RARgamma interact with but are repressed by TNIP1. Biochem Biophys Res Commun. 2009;389:409–414. doi: 10.1016/j.bbrc.2009.08.159.
35.
Ayaori M, Yakushiji E, Ogura M, Nakaya K, Hisada T, Uto-Kondo H, et al. Retinoic acid receptor agonists regulate expression of ATP-binding cassette transporter G1 in macrophages. Biochim Biophys Acta. 2012;1821:561–572. doi: 10.1016/j.bbalip.2012.02.004.
36.
Chinetti G, Lestavel S, Bocher V, Remaley AT, Neve B, Torra IP, et al. PPAR-alpha and PPAR-gamma activators induce cholesterol removal from human macrophage foam cells through stimulation of the ABCA1 pathway. Nat Med. 2001;7:53–58. doi: 10.1038/83348.
37.
Li AC, Binder CJ, Gutierrez A, Brown KK, Plotkin CR, Pattison JW, et al. Differential inhibition of macrophage foam-cell formation and atherosclerosis in mice by PPARalpha, beta/delta, and gamma. J Clin Invest. 2004;114:1564–1576. doi: 10.1172/JCI18730.
38.
Mauldin JP, Nagelin MH, Wojcik AJ, Srinivasan S, Skaflen MD, Ayers CR, et al. Reduced expression of ATP-binding cassette transporter G1 increases cholesterol accumulation in macrophages of patients with type 2 diabetes mellitus. Circulation. 2008;117:2785–2792. doi: 10.1161/CIRCULATIONAHA.107.741314.
39.
Hidalgo B, Irvin MR, Sha J, Zhi D, Aslibekyan S, Absher D, et al. Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network study. Diabetes. 2014;63:801–807. doi: 10.2337/db13-1100.
40.
Wierda RJ, Geutskens SB, Jukema JW, Quax PH, van den Elsen PJ. Epigenetics in atherosclerosis and inflammation. J Cell Mol Med. 2010;14:1225–1240. doi: 10.1111/j.1582-4934.2010.01022.x.
41.
Guay SP, Brisson D, Munger J, Lamarche B, Gaudet D, Bouchard L. ABCA1 gene promoter DNA methylation is associated with HDL particle profile and coronary artery disease in familial hypercholesterolemia. Epigenetics. 2012;7:464–472. doi: 10.4161/epi.19633.
42.
Xu Y, Wang W, Zhang L, Qi LP, Li LY, Chen LF, et al. A polymorphism in the ABCG1 promoter is functionally associated with coronary artery disease in a Chinese Han population. Atherosclerosis. 2011;219:648–654. doi: 10.1016/j.atherosclerosis.2011.05.043.
43.
Schou J, Frikke-Schmidt R, Kardassis D, Thymiakou E, Nordestgaard BG, Jensen G, et al. Genetic variation in ABCG1 and risk of myocardial infarction and ischemic heart disease. Arterioscler Thromb Vasc Biol. 2012;32:506–515. doi: 10.1161/ATVBAHA.111.234872.
44.
Wong BX, Kyle RA, Croft KD, Quinn CM, Jessup W, Yeap BB. Modulation of macrophage fatty acid content and composition by exposure to dyslipidemic serum in vitro. Lipids. 2011;46:371–380. doi: 10.1007/s11745-011-3528-2.
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Received: 12 February 2014
Accepted: 16 December 2014
Published online: 12 January 2015
Published in print: April 2015
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Sources of Funding
The Cooperative Health Research in the Region of Augsburg study was initiated and financed by the Helmholtz Zentrum München—German Research Center for Environmental Health, which is funded by the German Bundesministerium für Bildung und Forschung and by the State of Bavaria. This project has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 261433 (Biobank Standardisation and Harmonisation for Research Excellence in the European Union [BioSHaRE-EU]) and under grant agreement: 603288. The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia (Düsseldorf, Germany). This study was supported, in part, by a grant from the German Bundesministerium für Bildung und Forschung to the Deutsches Zentrum für Diabetesforschung (DZD e.V.). The Multiple Tissue Human Expression Resource Study was funded by the Wellcome Trust (081917/Z/07/Z) and core funding for the Wellcome Trust Centre for Human Genetics (090532). TwinsUK was funded by the Wellcome Trust and European Community’s Seventh Framework Programme (FP7/2007–2013). The study also receives support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy’s and St. Thomas’ National Health Service Foundation Trust in partnership with King’s College London. Single-nucleotide polymorphism genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via National Institutes of Health/Center Center for Inherited Disease Research. Dr Deloukas’ work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit, supported and funded by the NIHR.
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