BackgroundDNA methylation is a key epigenetic mechanism that is suggested to be associated with blood lipid levels. We aimed to identify CpG sites at which DNA methylation levels are associated with blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol in 725 participants of the Rotterdam Study, a population-based cohort study. Subsequently, we sought replication in a non-overlapping set of 760 participants.ResultsGenome-wide methylation levels were measured in whole blood using the Illumina Methylation 450 array. Associations between lipid levels and DNA methylation beta values were examined using linear mixed-effect models. All models were adjusted for sex, age, smoking, white blood cell proportions, array number, and position on array. A Bonferroni-corrected p value lower than 1.08 × 10−7 was considered statistically significant. Five CpG sites annotated to genes including DHCR24, CPT1A, ABCG1, and SREBF1 were identified and replicated. Four CpG sites were associated with triglycerides, including CpG sites annotated to CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), and SREBF1 (cg11024682). Two CpG sites were associated with HDL-C, including ABCG1 (cg06500161) and DHCR24 (cg17901584). No significant associations were observed with LDL-C or total cholesterol.ConclusionsWe report an association of HDL-C levels with methylation of a CpG site near DHCR24, a protein-coding gene involved in cholesterol biosynthesis, which has previously been reported to be associated with other metabolic traits. Furthermore, we confirmed previously reported associations of methylation of CpG sites within CPT1A, ABCG1, and SREBF1 and lipids. These results provide insight in the mechanisms that are involved in lipid metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0304-4) contains supplementary material, which is available to authorized users.
We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10 −8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10 −5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r g ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r g | ≈ 0.1-0.3) and positive genetic correlations with physical activity (r g ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r g ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.
We conducted an epigenome-wide association study on obesity-related traits. We used data from two prospective, population-based cohort studies: the Rotterdam Study (RS) (2006-2013) and the Atherosclerosis Risk in Communities (ARIC) Study (1990-1992). We used RS (n = 1,454) as the discovery panel and ARIC (n = 2,097) as replication panel. Linear mixed-effect models were used to assess the cross-sectional association between genome-wide DNA methylation in leukocytes with body mass index (BMI) and waist circumference (WC) adjusting for sex, age, smoking, leukocyte proportions, array number and position on array. The two latter were modelled as random effects. Fourteen CpGs were associated with BMI and 26 CpGs with WC in RS after Bonferroni-correction (P < 1.07 × 10-7), of which 12 and 13 CpGs replicated in ARIC Study, respectively. The most significant novel CpGs were located at MSI2 (cg21139312) and LARS2 (cg18030453) and were associated both with BMI and WC. CpGs at BRDT, PSMD1, IFI44L, MAP1A, and MAP3K5 were associated with BMI. CpGs at LGALS3BP, MAP2K3, DHCR24, CPSF4L, and TMEM49 were associated with WC. We report novel associations of methylation at MSI2 and LARS2 with obesity-related traits. These results provide further insight in mechanisms underlying obesity-related traits, which can enable identification of new biomarkers in obesity-related chronic diseases.
Current evidence supports an association between epigenetic marks and T2D. However, overall evidence is limited, highlighting the need for further larger-scale and prospective investigations to establish whether epigenetic marks may influence the risk of developing T2D.
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