Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential ...methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.
Several genomewide screens have been performed to identify novel loci predisposing to unfavorable serum lipid levels and coronary heart disease (CHD). We hypothesized that the accumulating data of ...these screens in different study populations could be combined to verify which of the identified loci truly harbor susceptibility genes. The power of this strategy has recently been demonstrated with other complex diseases, such as inflammatory bowel disease and asthma. We assessed the largely unknown genetic background of CHD by investigating the most common dyslipidemia predisposing to CHD, familial combined hyperlipidemia (FCHL), affecting 1%–2% of Western populations and 10%–20% of families with premature CHD. To be able to perform a combined data analysis, we unified the diagnostic criteria for FCHL and its component traits and combined the data from two genomewide scans performed in two populations, the Finns and the Dutch. As a result of our pooled data analysis, we identified three chromosomal regions, on chromosomes 2p25.1, 9p23, and 16q24.1, exceeding the statistical significance level of a LOD score >2.0. The 2p25.1 region was detected for the FCHL trait, and the 9p23 and 16q24.1 regions were detected for the low high-density lipoprotein cholesterol (HDL-C) trait. In addition, the previously recognized 1q21 region also obtained additional support in the other study sample, when the triglyceride trait was used. Analysis of the 16q24.1 region resulted in a statistically significant LOD score of 3.6 when the data from Finnish families with low HDL-C were included in the analysis. To search for the underlying gene in the 16q24.1 region, we investigated a novel functional and positional candidate gene, helix/forkhead transcription factor (FOXC2), by sequencing and by genotyping of two single-nucleotide polymorphisms in the families.
Abstract only Introduction: Circadian rhythms regulate key physiological processes, including sleep-wake. Alterations in the circadian rhythm have been associated with greater risk of metabolic ...diseases. Genome-wide association studies (GWAS) have revealed genes variants associated with sleep phenotypes. Hypothesis: Polygenic risk scores (PRS) for significant SNPs reported in GWAS of chronotype (C), sleep duration (SD), and short sleep (SS, <7/h) are associated with metabolic parameters. Methods: We carried out a longitudinal analysis in 6,814 adults (aged 45-84 y at baseline) from the Multi-Ethnic Study of Atherosclerosis (MESA). Glucose, insulin, insulin resistance (HOMA-IR), body mass index (BMI) and waist circumference (WC) were assessed at baseline (2000-2002) and 16-18 y follow-up (Exam 6, N=3,303). DNA was extracted from whole blood, and genotyping data were obtained by the Affymetrix 6.0 SNP array. PRS for the following phenotypes were constructed as weight sum of genoma-wide significant alleles: 1) chronotype PRS-C; 2) continuous SD PRS-SD; and 3) SS duration PRS-SS. Multivariable linear regression models adjusted by sociodemographic, obesity, smoking, Mediterranean diet, physical activity and antidiabetic drugs were performed to assess associations between PRS and study outcomes (fasting glucose and insulin, HOMA-IR, hemoglobin A1c, BMI, and WC) cross-sectionally and longitudinally. Results: At baseline, none of the PRS were associated with glucose, insulin or HOMA-IR (p>0.05). A significant positive association was detected for PRS-C with BMI and WC: greater genetically determined morningness was associated with lower BMI (β=-20.0±3.0; p<0.001) and smaller WC (β=-44.1±8.0; p<0.001). Similar results were observed with PRS-SD (BMI: β=-0.1±0.0; p=0.005; WC: β=-0.3±0.1; p=0.008) and PRS-SS (BMI: β=9.3±2.8; p<0.001; WC: β=19.8±7.4; p=0.007). In longitudinal analyses, we observed prospective associations between PRS-C with BMI (β=-43.5±12.2; p<0.001), WC (β=-116.0.1±29.1; p<0.001), and glucose (β=-204.7±90.0; p=0.024). PRS-SS was longitudinally associated with HOMA-IR (β=214.1±88.0; p=0.017) and a trend was observed for hemoglobin A1c (β=-6.6±3.4; p=0.053), but not with anthropometrics parameters. No associations were noted for PRS-SD with obesity and glycemic outcomes in longitudinal analyses. Conclusion: In the MESA population, PRS for chronotype, sleep duration, and short sleep are associated with anthropometric but not with glycemic measures in cross-sectional analyses. However, PRS for chronotype and short sleep was associated with anthropometric and glycemic parameters in longitudinal analyses, suggesting the importance of genetic predisposition for morningness and adequate sleep duration for metabolic health.
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