Secondary Logo

Journal Logo


DNA methylation in human lipid metabolism and related diseases

Mittelstraß, Kirstina,b; Waldenberger, Melaniea,b

Author Information
Current Opinion in Lipidology: April 2018 - Volume 29 - Issue 2 - p 116-124
doi: 10.1097/MOL.0000000000000491
  • Open



Abnormalities in the levels of circulating blood lipids, such as triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), contribute to the pathophysiology of common complex diseases, among them diabetes and cardiovascular diseases (CVDs) – two of the major causes of morbidity and mortality in industrialized countries [1–3]. Lipid disorders, also known as dyslipidemias, are primarily a result of unhealthy lifestyle choices: poor diet, lack of physical activity, and overweight, among others. Though these environmental factors are key contributors, the clustering of dyslipidemias in families has also been observed [4], which lends evidence for a genetic influence. Genome-wide association studies (GWAS) have identified a total of 157 common genetic loci associated with lipid levels, though combined these explain 12% or less of trait variance [5]. Consequently, evidence for epigenetic mechanisms playing a role in the regulation of lipid levels is being increasingly recognized. Unlike genetic variation, epigenetic modifications, such as DNA methylation, histone modification, and regulation by RNAs, are dynamically remodeled over time and can be affected by environmental changes [6] and vary according to chromosomal location, alleles, type of cell, or phase of development [7,8]. This dynamism includes reversibility, making epigenetic modifications potentially important pathogenic mechanisms in complex metabolic diseases, and conceivably representing therapeutic targets [9▪].

Recent advances in omics technology allows a hypothesis-free search of epigenetic modifications, and, in particular, DNA methylation. These have helped identify new loci and pathways involved in lipid metabolism. Whereas there are more than five different DNA modifications known, the most widely studied is the transfer of a methyl group to the C5 position of a cytosine to form a 5-methylcytosine. In conjunction with human lipid traits, DNA methylation is by far the most studied epigenetic process [9▪,10]. Epigenome-wide association studies (EWAS) have become a powerful instrument to investigate differences in DNA methylation at the population level. Regarding lipid levels, EWAS have highlighted several robustly replicated methylation markers such as cg06500161, annotated to the ABCG1 gene encoding ATP-binding cassette subfamily G member 1 and cg00574958 within CPT1A gene encoding carnitine palmitoyltransferase I.

Petersen et al.[11] conducted an EWAS of metabolic traits in whole blood and identified associations between multiple lipids (including cholesterol, sphingolipids, and glycerophospholipids) and lipoproteins, and the methylation level of CpG sites in or in close proximity to the genes 24-dehydrocholesterol reductase (DHCR24), thioredoxin-interacting protein (TXNIP), solute carrier family 22 member 25 (SLC25A22), CPT1A, myosin VC (MYO5C), and ABCG1[11]. Irvin et al.[12] reported that four CpG sites in intron 1 of CPT1A were strongly associated with very-low to low-density lipoprotein cholesterol (VLDL-C) and triglycerides. They also showed an inverse association between CPT1A methylation (cg00574958) and expression of CPT1A. A further EWAS – Frazier-Wood et al.[13] – in CD4+ T cells revealed associations between LDL-C and VLDL-C levels, and methylation of CpG sites in CPT1A[13]. The results were later replicated in blood by Gagnon et al.[14]. Pfeiffer et al.[15] reported associations in whole blood between DNA methylation and triglycerides for CpG sites mapping to the genes CPT1A, ABCG1, SREBF1 encoding sterol regulatory element-binding transcription factor 1 and the SCD gene encoding stearoyl-CoA desaturase, between DNA methylation and HDL-C for a CpG in ABCG1, and between DNA methylation and LDL-C for a CpG in TXNIP1. Most of the above reported genes have an important function in lipid metabolism, supporting the hypothesis that epigenetic changes play regulatory roles. Furthermore, several EWAS of lipid-related metabolic phenotypes and diseases, for example, those for BMI, waist circumference [16–19], and type 2 diabetes (T2D) [20–22], have uncovered associations with many of the same CpG sites. In this review, we will summarize the latest results from January 2016 to September 2017 concerning EWAS of DNA methylation and lipid traits, and also lipid-related disease. 

Box 1
Box 1:
no caption available


Recent EWAS and candidate gene studies have been able to confirm the strong associations reported above between various CpG sites and blood lipid levels across different ethnicities (Tables 1 and 2) [23▪▪,24▪,25–27,28▪▪,29–31]. Furthermore, they have shown that many CpG sites associated with blood lipids are also associated with lipid metabolism-linked phenotypes and diseases (Table 2). Recently, Hedman et al.[24▪] reported 25 novel CpG sites not previously found to be associated with lipid levels. The annotated genes were enriched in pathways involved in lipid and amino acid metabolism [24▪]. Methylation levels at ABCG1 (cg27243685) were additionally reported in relation to occurrence of CVD events [24▪]. The authors further showed that triglyceride levels were associated with DNA methylation in the serine metabolism gene PHGDH encoding D-3-phosphoglycerate dehydrogenase (cg14476101), a result confirmed by Truong et al.[30]. Public database findings support a functional role of cg1476101 in PHGDH expression [30].

Table 1
Table 1:
Epigenome-wide association studies (EWAS) of DNA methylation and lipid traits
Table 2
Table 2:
Epigenome-wide association studies of DNA methylation and lipid phenotypes or lipid related diseases

Wahl et al.[28▪▪] identified methylation loci associated with BMI in genes [e.g. CPT1A, DHCR24, SREBF1, and SOCS3 (suppressor of cytokine signaling 3)] that are involved in lipid metabolism [28▪▪]. These associations between BMI and lipid-related CpG sites were confirmed by additional studies in Arab and European populations [32,33▪▪,34]. It was additionally uncovered that the SOCS3 methylation locus is associated with multiple metabolic syndrome traits, including central obesity, fat depots, insulin responsiveness, and plasma lipids (HDL-C and triglycerides) [27,35]. Furthermore, SOCS3 was found to be associated with lipid levels and insulin resistance in human GWAS and candidate gene studies [36]. Recent EWAS, conducted in Indian, Arab, and Caucasian populations, found that SOCS3 methylation is associated with BMI and T2D, respectively [20,32,34]. Another interesting methylation site (TXNIP, cg19693031) associated with T2D in several studies [20,22,32,37] was also reported to be associated with triglyceride and LDL-C levels [15,24▪,38▪▪].

Differential DNA methylation of five CpG sites annotated to ABCG1, PHOSPHO1 (phosphoethanolamine/phosphocholine phosphatase), SOCS3, SREBF1, and TXNIP from diabetic versus nondiabetic patients were investigated across different tissues from the same individuals [31]. The results suggest that DNA methylation biomarkers in blood might partly be used as surrogate markers for DNA methylation in inaccessible target tissues, and, importantly, the occurrence of altered DNA methylation in more than one human tissue at the same locus could be mediated by so-called ‘metastable epialleles’ [31]. Metastable epialleles are alleles that are variably expressed in genetically identical individuals due to epigenetic modifications that were established during early development [39]. BMI-related methylation markers identified by Wahl et al.[28▪▪] were strongly enriched for CpG sites with intermediate levels of methylation, consistent with the presence of mosaicism, that is, epigenetic heterogeneity, at these loci. The authors performed replication testing in isolated white cell subsets (monocytes, neutrophils, CD4+ T cells, and CD8+ T cells), showing that epigenetic heterogeneity was present at the majority of loci, in each of the cell subsets studied [28▪▪]. Wahl et al.[28▪▪] compared methylation levels between blood, subcutaneous and omental fat, liver, muscle, spleen, and pancreas. Mean methylation levels at the 187 loci correlated moderately to strongly between the tissues, supporting the view that methylation levels in blood are related to methylation patterns in other tissues at the CpG sites examined.

Lai et al.[40] showed that eight methylation sites encompassing different genes LPP encoding lipoma-preferred partner, APOA5 encoding apolipoprotein A-V, SREBF1, ABCG1, and CPT1A were associated with triglyceride postprandial responses (TG-PPL), an independent CVD risk factor, after consuming a high-fat meal [40]. These genes had been previously found to be associated with triglyceride and/or HDL-C levels [15,23▪▪,24▪,25,38▪▪]. Data from a Mexican-American study showed cg00574958 and cg17058475 (CPT1A) and cg06500161 (ABCG1) to be associated with hypertriglyceridemic waist (HTGW), which is defined as large waist circumference combined with high serum triglyceride concentration [41]. Both CpG sites in CPT1A were additionally associated with the metabolic syndrome in CD4+ T cells [42]. Recently, CPT1A methylation status was also found to be significantly associated with plasma adiponectin, a widely used biomarker for cardiovascular and metabolic risk [43▪].

So far, EWAS on disorders of lipid metabolism are sparse [44,45]. Sitosterolemia is a rare autosomal recessive sterol storage disease caused by mutations in either of the adenosine triphosphate binding cassette transporter genes ABCG5 or ABCG8 encoding ATP-binding cassette subfamily G member 5 or 8, leading to substantially elevated serum plant sterols with moderate to high total cholesterol and LDL-C levels and increased risk of premature atherosclerosis [46]. Interestingly, ABCG5 methylation was associated with lower LDL-C and reduced risk for coronary artery disease (CAD) [47,48]. In the study by Rask-Andersen et al.[47], a total of 6 out of 211 myocardial infarction-associated CpG sites overlapped with previously identified CVD GWAS loci, among them the ABCG5-ABCG8 locus [47]. The investigation into further lipid classes and studies on disorders of lipid metabolism will provide new and important insights.


Different molecular layers often have complementary roles to jointly perform a certain biological function [49]. Population-based studies adopted the multiomics approach by integrating these molecular layers into their studies. Whereas this approach has been successfully used for available transcriptome, metabolome, or genetic data, studies are sparse that systematically investigate the interaction of epigenetic mechanisms such as regulatory RNAs or histone modifications [50].


The variance of lipid levels explained by the currently known genetic variants is modest. All lipid-associated single-nucleotide polymorphisms (SNPs) together explain 12% or less of the variation in plasma lipid traits [5], although the estimated heritable variance of lipids is reported to be at least 50% [51]. This missing heritability may be partly explained by epigenetic processes such as DNA methylation [52]. SNP allele frequencies are known to differ among populations with varying geographic ancestries, suggesting that ethnic differences in DNA methylation could be due to differences in population-specific alleles that shape CpG and global methylation levels. Regulation of gene expression via DNA methylation may explain an additional component of interindividual variation in lipid levels beyond genetic sequence variants. Linking DNA methylation data with gene expression is a promising avenue to see potential downstream effects in lipid metabolism.

Hedman et al.[24▪] found methylation levels of lipid-related CpG sites associated with mRNA expression levels of nearby genes, including cg17901584 (DHCR24), cg14476101, cg16246545 (both PHGDH), and cg08129017 (SREBF1). For the majority (86%) of these associations, levels of methylation and expression were inversely correlated [24▪]. In agreement with previous studies, they found a large proportion of lipid-related CpG sites to associate with common SNPs in cis. For 12 CpG-transcript pairs, a cis-meQTL was identified and the lead meQTL SNP was significantly associated with both methylation and expression [24▪].

Volkov et al.[35] described methylation quantitative trait loci (meQTLs) in adipose tissue. These meQTLs include reported obesity, lipid, and T2D loci, for example, APOA5, cholesteryl ester transfer protein (CETP), and fatty acid desaturase 2 (FADS2). SNPs in significant meQTLs were also associated with BMI, lipid traits, and glucose and insulin levels [35]. The meQTL at the APOA5 loci was confirmed by Oliva et al.[53] using a candidate gene approach.

Ali et al.[27] assessed the relationship between DNA methylation, obesity, and obesity-related phenotypes in peripheral blood mononuclear cells. They found that the methylation status of cg18181703 (SOCS3) significantly alters SOCS3 gene expression [27,35]. Using RNA-seq data, DNA methylation of six CpG sites was associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL-C), and ABCG1 (for HDL-C) [23▪▪]. The results could be confirmed by Braun et al.[25]. For CPT1A, expression was negatively associated with the methylation of CPT1A at both identified CpG sites (cg00574958 and cg17058475). A study by Bekkering et al.[54] showed that the expression of lipid metabolism genes were altered after oxidized LDL exposure of monocytes. Methylation of CpG sites within exon 3 of APOA5 was positively correlated with triglyceride concentration and with a lipoprotein profile associated with atherogenic dyslipidemia [53]. Another candidate gene study reported decreased methylation levels of the actin-related protein 2/3 complex subunit 3 (ARPC3) promoter-associated CpG site cg10738648 in both visceral adipose tissue and blood for carriers of the rs3759384 T allele in obese patients with hypertriglyceridemia, and showed ARPC3 expression to be correlated with plasma triglyceride levels [55]. Finally, lower TNNT1 DNA methylation levels were found to be independently associated with lower HDL-C levels and a TNNT1 polymorphism in patients with and without familial hypercholesterolemia [29]. Genetic variations of the TNNT1 locus have previously been associated with HDL-C levels in several GWAS [36].


To determine whether lipids influence DNA methylation or DNA methylation causes differences in lipid levels, Mendelian randomization was put forward as a tool for causal inference in DNA methylation studies [56,57]. Although Mendelian randomization can provide strong evidence for causal relationships, the quality of evidence provided by a Mendelian randomization study heavily relies on the underlying assumptions [58]. Applications and limitations of Mendelian randomization in EWAS have been recently reviewed [59].

Dekkers et al.[23▪▪] showed that differential methylation is the consequence of interindividual variation in blood lipid levels and not vice versa. Using multivariate Mendelian randomization, they reported an effect of blood lipids on DNA methylation at six CpG sites. A large-scale EWAS in peripheral blood reported by Mendelson et al.[33▪▪] identified associations between BMI and methylation at 83 replicated CpG sites, with an over-representation of lipid metabolism pathways among those CpG sites associated with gene expression changes. Eleven CpG sites revealed three-way associations, whereby DNA methylation was associated with BMI and expression, and also with BMI-associated expression changes, including the known lipid-related CpG sites within ABCG1, CPT1A, DHCR24, SLC1A5, and SREBF1. Using Mendelian randomization, 16 CpG sites were found to be differentially methylated as a consequence of BMI [33▪▪]. These 16 CpG sites were annotated to 12 genes, including ABCG1. Among the 83 BMI-related CpG sites, only cg11024682 (SREBF1) showed evidence for a causal effect on BMI. Genetically predicted exposure to differential methylation and SREBF1 gene expression was associated with dyslipidemia, adiposity-related traits, and CAD [33▪▪]. Wahl et al.[28▪▪] subsequently showed in whole blood and adipose tissue that DNA methylation at lipid-related CpG sites is predominantly the consequence of adiposity and not the cause. Whereas Dekkers et al.[23▪▪] suggest that methylation of cg11024682 (SREBF1) is induced by triglyceride levels, the analysis of Mendelson et al.'s [33▪▪] study reports a causal effect of the same CpG site on BMI, a result not confirmed by Wahl et al.[23▪▪,28▪▪,33▪▪]. All recently conducted Mendelian randomization studies, however, highlight the causal effect of methylation at the ABCG1 loci on both BMI and lipid levels [23▪▪,28▪▪,33▪▪].


Epigenetics continues to be a promising area of research in lipid-related diseases. Current scientific knowledge does not completely explain the molecular mechanisms behind lipid metabolism and lipid-related diseases. Epigenetic modifications, such as DNA methylation, might form an additional path to understanding the mechanisms of lipid-related diseases. However, many challenges regarding the design, conduct, and interpretation of EWAS persist. The main challenges include accounting for variation in cellular heterogeneity, potential confounding effects, and resolving whether blood samples do indeed mirror relevant targeted tissues. Therefore, longitudinal cohort studies and larger sample sizes are key points for further investigations. Moreover, in addition to the development of cost-effective sequencing applications, a new array has been developed covering more than 850 000 methylation sites across the genome.

Investigation into further lipid classes, beyond the traditional blood lipids, and studies on disorders of lipid metabolism will provide new and important insights. Furthermore, other epigenetic layers need to gain importance, for example, the interplay between microRNAs and other epigenetic regulators such as histone modifications and DNA methylation. For example, it is becoming increasingly evident that post-transcriptional repression by microRNAs, a class of small noncoding RNAs, is a key layer of regulation in several biological processes, including lipid phenotypes [60]. The NIH Roadmap Epigenomics Consortium has generated a large collection of human epigenomes for primary cells and tissues, describing the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression, providing a unique resource for such investigations [61].

Another important task is to assess, and functionally validate, causality of the reported associations, and, if we propose that a change in DNA methylation status is causal for a lipid phenotype, to assess when these changes occur [62]. For example, it has been indicated that for a growing fetus, malnutrition can have harmful effects on prenatal programming and contribute to the development of diseases later in life [63,64]. Perhaps, the greatest challenge is to understand the functional consequences of the confirmed loci. Biological insights can then be translated to clinical benefits, including reliable biomarkers and effective strategies for disease prevention. Functional follow-up studies of confirmed loci will help unravel the precise molecular mechanisms at specific CpG sites, including the identification of methylation-specific binding proteins and characterization of their mode of action.

Although knowledge of epigenetic changes, such as DNA methylation, has the potential to shed light on the differences in lipid concentrations and the underlying pathways’ mechanisms, the ultimate goal remains the translation of this knowledge into the effective prediction and treatment of lipid-related diseases.


We would like to thank Rory Wilson and Sacha E. Horn for revision of the English text.

Financial support and sponsorship

This work was supported by funding from the European Union Seventh Framework Programme under grant agreement (No. 313010) (large-scale prospective cohort studies – BBMRI-LPC;, (No. 602736) (multidimensional omics approach to stratification of patients with low back pain – PAIN-OMICS;, and under grant agreement (No. 603288) (Systems Biology to Identify Molecular Targets for Vascular Disease Treatment – SysVasc;

Conflicts of interest

There are no conflicts of interest.


Papers of particular interest, published within the annual period of review, have been highlighted as:


1. Stitziel NO. Human genetic insights into lipoproteins and risk of cardiometabolic disease. Curr Opin Lipidol 2017; 28:113–119.
2. Wang YC, McPherson K, Marsh T, et al. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011; 378:815–825.
3. Writing Group M, Mozaffarian D, Benjamin EJ, et al. Heart disease and stroke statistics – 2016 update: a report from the American Heart Association. Circulation 2016; 133:e38–e360.
4. Genest JJ Jr, Martin-Munley SS, McNamara JR, et al. Familial lipoprotein disorders in patients with premature coronary artery disease. Circulation 1992; 85:2025–2033.
5. Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nature Genet 2013; 45:1274–1283.
6. Kader F, Ghai M. DNA methylation-based variation between human populations. Mol Genet Genomics 2017; 292:5–35.
7. Tammen SA, Friso S, Choi SW. Epigenetics: the link between nature and nurture. Mol Aspects Med 2013; 34:753–764.
8. Reinius LE, Acevedo N, Joerink M, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PloS One 2012; 7:e41361.
9▪. van der Harst P, de Windt LJ, Chambers JC. Translational perspective on epigenetics in cardiovascular disease. J Am Coll Cardiol 2017; 70:590–606.
10. Brazel AJ, Vernimmen D. The complexity of epigenetic diseases. J Pathol 2016; 238:333–344.
11. Petersen AK, Zeilinger S, Kastenmuller G, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet 2014; 23:534–545.
12. Irvin MR, Zhi D, Joehanes R, 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.
13. Frazier-Wood AC, Aslibekyan S, Absher DM, et al. Methylation at CPT1A locus is associated with lipoprotein subfraction profiles. J Lipid Res 2014; 55:1324–1330.
14. Gagnon F, Aissi D, Carrie A, et al. Robust validation of methylation levels association at CPT1A locus with lipid plasma levels. J Lipid Res 2014; 55:1189–1191.
15. Pfeiffer L, Wahl S, Pilling LC, et al. DNA methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet 2015; 8:334–342.
16. Demerath EW, Guan W, Grove ML, et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet 2015; 24:4464–4479.
17. Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 2014; 383:1990–1998.
18. Arner P, Sinha I, Thorell A, et al. The epigenetic signature of subcutaneous fat cells is linked to altered expression of genes implicated in lipid metabolism in obese women. Clin Epigenetics 2015; 7:93.
19. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity 2015; 23:1493–1501.
20. Chambers JC, Loh M, Lehne B, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol 2015; 3:526–534.
21. Soriano-Tarraga C, Jimenez-Conde J, Giralt-Steinhauer E, et al. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia. Hum Mol Genet 2016; 25:609–619.
22. Kulkarni H, Kos MZ, Neary J, et al. Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet 2015; 24:5330–5344.
23▪▪. Dekkers KF, van Iterson M, Slieker RC, et al. Blood lipids influence DNA methylation in circulating cells. Genome Biol 2016; 17:138.
24▪. Hedman AK, Mendelson MM, Marioni RE, et al. Epigenetic patterns in blood associated with lipid traits predict incident coronary heart disease events and are enriched for results from genome-wide association studies. Circ Cardiovasc Gene 2017; 10:
25. Braun KVE, Dhana K, de Vries PS, et al. Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study. Clin Epigenetics 2017; 9:15.
26. Tremblay BL, Guenard F, Rudkowska I, et al. Epigenetic changes in blood leukocytes following an omega-3 fatty acid supplementation. Clin Epigenetics 2017; 9:43.
27. Ali O, Cerjak D, Kent JW Jr, et al. Methylation of SOCS3 is inversely associated with metabolic syndrome in an epigenome-wide association study of obesity. Epigenetics 2016; 11:699–707.
28▪▪. Wahl S, Drong A, Lehne B, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 2017; 541:81–86.
29. Guay SP, Legare C, Brisson D, et al. Epigenetic and genetic variations at the TNNT1 gene locus are associated with HDL-C levels and coronary artery disease. Epigenomics 2016; 8:359–371.
30. Truong V, Huang S, Dennis J, et al. Blood triglyceride levels are associated with DNA methylation at the serine metabolism gene PHGDH. Sci Rep 2017; 7:11207.
31. Dayeh T, Tuomi T, Almgren P, et al. DNA methylation of loci within ABCG1 and PHOSPHO1 in blood DNA is associated with future type 2 diabetes risk. Epigenetics 2016; 11:482–488.
32. Al Muftah WA, Al-Shafai M, Zaghlool SB, et al. Epigenetic associations of type 2 diabetes and BMI in an Arab population. Clin Epigenetics 2016; 8:13.
33▪▪. Mendelson MM, Marioni RE, Joehanes R, et al. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian randomization approach. PLoS Med 2017; 14:e1002215.
34. Wilson LE, Harlid S, Xu Z, et al. An epigenome-wide study of body mass index and DNA methylation in blood using participants from the Sister Study cohort. Int J Obesity 2017; 41:194–199.
35. Volkov P, Olsson AH, Gillberg L, et al. A genome-wide mQTL analysis in human adipose tissue identifies genetic variants associated with DNA methylation, gene expression and metabolic traits. PloS One 2016; 11:e0157776.
36. Asselbergs FW, Guo Y, van Iperen EP, et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 2012; 91:823–838.
37. Florath I, Butterbach K, Heiss J, et al. Type 2 diabetes and leucocyte DNA methylation: an epigenome-wide association study in over 1,500 older adults. Diabetologia 2016; 59:130–138.
38▪▪. Sayols-Baixeras S, Subirana I, Lluis-Ganella C, et al. Identification and validation of seven new loci showing differential DNA methylation related to serum lipid profile: an epigenome-wide approach. The REGICOR study. Hum Mol Genet 2016; 25:4556–4565.
39. Rakyan VK, Blewitt ME, Druker R, et al. Metastable epialleles in mammals. Trends Genet 2002; 18:348–351.
40. Lai CQ, Wojczynski MK, Parnell LD, et al. Epigenome-wide association study of triglyceride postprandial responses to a high-fat dietary challenge. J Lipid Res 2016; 57:2200–2207.
41. Mamtani M, Kulkarni H, Dyer TD, et al. Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families. Clin Epigenetics 2016; 8:6.
42. Das M, Sha J, Hidalgo B, et al. Association of DNA methylation at CPT1A locus with metabolic syndrome in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. PloS One 2016; 11:e0145789.
43▪. Aslibekyan S, Do AN, Xu H, et al. CPT1A methylation is associated with plasma adiponectin. Nutr Metab Cardiovasc Dis 2017; 27:225–233.
44. Gidding SS, Champagne MA, de Ferranti SD, et al. The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association. Circulation 2015; 132:2167–2192.
45. Ripatti P, Ramo JT, Soderlund S, et al. The contribution of GWAS loci in familial dyslipidemias. PLoS Genet 2016; 12:e1006078.
46. Plana N, Nicolle C, Ferre R, et al. Plant sterol-enriched fermented milk enhances the attainment of LDL-cholesterol goal in hypercholesterolemic subjects. Eur J Nutr 2008; 47:32–39.
47. Rask-Andersen M, Martinsson D, Ahsan M, et al. Epigenome-wide association study reveals differential DNA methylation in individuals with a history of myocardial infarction. Hum Mol Genet 2016; 25:4739–4748.
48. Ross S, D’Mello M, Anand SS, et al. Effect of bile acid sequestrants on the risk of cardiovascular events: a Mendelian randomization analysis. Circ Cardiovasc Genet 2015; 8:618–627.
49. Sun YV, Hu YJ. Integrative analysis of multiomics data for discovery and functional studies of complex human diseases. Adv Genet 2016; 93:147–190.
50. Liep J, Rabien A, Jung K. Feedback networks between microRNAs and epigenetic modifications in urological tumors. Epigenetics 2012; 7:315–325.
51. Goode EL, Cherny SS, Christian JC, et al. Heritability of longitudinal measures of body mass index and lipid and lipoprotein levels in aging twins. Twin Res Hum Genet 2007; 10:703–711.
52. Johannes F, Colot V, Jansen RC. Epigenome dynamics: a quantitative genetics perspective. Nat Rev Genet 2008; 9:883–890.
53. Oliva I, Guardiola M, Vallve JC, et al. APOA5 genetic and epigenetic variability jointly regulate circulating triacylglycerol levels. Clin Sci 2016; 130:2053–2059.
54. Bekkering S, Quintin J, Joosten LA, et al. Oxidized low-density lipoprotein induces long-term proinflammatory cytokine production and foam cell formation via epigenetic reprogramming of monocytes. Arterioscler Thromb Vasc Biol 2014; 34:1731–1738.
55. de Toro-Martin J, Guenard F, Tchernof A, et al. A CpG-SNP located within the ARPC3 gene promoter is associated with hypertriglyceridemia in severely obese patients. Ann Nutr Metab 2016; 68:203–212.
56. Dekkers KF, Slagboom PE, Jukema JW, et al. The multifaceted interplay between lipids and epigenetics. Curr Opin Lipidol 2016; 27:288–294.
57. Zhong J, Agha G, Baccarelli AA. The role of DNA methylation in cardiovascular risk and disease: methodological aspects, study design, and data analysis for epidemiological studies. Circ Res 2016; 118:119–131.
58. Burgess S, Butterworth AS, Thompson JR. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. J Clin Epidemiol 2016; 69:208–216.
59. Relton CL, Davey Smith G. Mendelian randomization: applications and limitations in epigenetic studies. Epigenomics 2015; 7:1239–1243.
60. Sayols-Baixeras S, Irvin MR, Elosua R, et al. Epigenetics of lipid phenotypes. Curr Cardiovasc Risk Rep 2016; 10:31.
61. Roadmap Epigenomics C, Kundaje A, Meuleman W, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015; 518:317–330.
62. Ek WE, Rask-Andersen M, Johansson A. The role of DNA methylation in the pathogenesis of disease: what can epigenome-wide association studies tell? Epigenomics 2016; 8:5–7.
63. Fulin L, Jin Z, Wei Z, et al. Epigenetic regulation and related diseases during placental development. Yi Chuan 2017; 39:263–275.
64. Navarro E, Funtikova AN, Fito M, et al. Prenatal nutrition and the risk of adult obesity: Long-term effects of nutrition on epigenetic mechanisms regulating gene expression. J Nutr Biochem 2017; 39:1–14.

DNA methylation; lipid metabolism; EWAS; blood lipids

Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc.