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. 2016 Sep;11(9):699-707.
doi: 10.1080/15592294.2016.1216284. Epub 2016 Aug 26.

Methylation of SOCS3 is inversely associated with metabolic syndrome in an epigenome-wide association study of obesity

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Methylation of SOCS3 is inversely associated with metabolic syndrome in an epigenome-wide association study of obesity

Omar Ali et al. Epigenetics. 2016 Sep.

Abstract

Epigenetic mechanisms, including DNA methylation, mediate the interaction between gene and environment and may play an important role in the obesity epidemic. We assessed the relationship between DNA methylation and obesity in peripheral blood mononuclear cells (PBMCs) at 485,000 CpG sites across the genome in family members (8-90 y of age) using a discovery cohort (192 individuals) and a validation cohort (1,052 individuals) of Northern European ancestry. After Bonferroni-correction (Pα=0.05 = 1.31 × 10-7) for genome-wide significance, we identified 3 loci, cg18181703 (SOCS3), cg04502490 (ZNF771), and cg02988947 (LIMD2), where methylation status was associated with body mass index percentile (BMI%), a clinical index for obesity in children, adolescents, and adults. These sites were also associated with multiple metabolic syndrome (MetS) traits, including central obesity, fat depots, insulin responsiveness, and plasma lipids. The SOCS3 methylation locus was also associated with the clinical definition of MetS. In the validation cohort, SOCS3 methylation status was found to be inversely associated with BMI% (P = 1.75 × 10-6), waist to height ratio (P = 4.18 × 10-7), triglycerides (P = 4.01 × 10-4), and MetS (P = 4.01 × 10-7), and positively correlated with HDL-c (P = 4.57 × 10-8). Functional analysis in a sub cohort (333 individuals) demonstrated SOCS3 methylation and gene expression in PBMCs were inversely correlated (P = 2.93 × 10-4) and expression of SOCS3 was positively correlated with status of MetS (P = 0.012). We conclude that epigenetic modulation of SOCS3, a gene involved in leptin and insulin signaling, may play an important role in obesity and MetS.

Keywords: BMI; CpG methylation; EWAS; childhood obesity; epigenetics; family study; metabolic syndrome; obesity.

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Figures

Figure 1.
Figure 1.
Strength of associations of genome-wide autosomal CpG methylation status with BMI% in our TFSE cohort. Manhattan plot shows the significance level of each CpG locus with BMI-percentile. Each gray dot represents an individual CpG site. The red one depicts the genome-wide significance threshold after Bonferroni correction for multiple testing, Pα=0.05 = 1.31 × 10−7. Probes with associations of nominal significance (P < 0.05) are shown. Genes and associated CpG sites that exceed the significance threshold are labeled.
Figure 2.
Figure 2.
Boxplot of methylation β values at cg18181703 (SOCS3, body) against presence or absence of metabolic syndrome. The middle lines show the medians of the data, while the boxes show the 25th to 75th percentiles. The whiskers extend to include 99% of the data while circles represent outliers. The β values at this probe in individuals with and without MetS were significantly different (P = 4.01 × 10−7) when accounted for age, sex, and interactions of the two.

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References

    1. Alwan A. Global Status Report on Noncommunicable Diseases 2010. Geneva, Switzerland: World Health Organization; 2011.
    1. Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM, Bahalim AN, McIntire RK, Gutierrez HR, Cowan M, et al.. The global burden of metabolic risk factors of chronic diseases collaborating group (Body Mass Index), national, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr 2012. November 20; 10(1):22; PMID:23167948; http://dx.doi.org/10.1186/1478-7954-10-22 - DOI - PMC - PubMed
    1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY, et al.. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380(9859):2095-128; PMID:23245604; http://dx.doi.org/10.1016/S0140-6736(12)61728-0 - DOI - PMC - PubMed
    1. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract 2014; 103(2):137-49; PMID:24630390; http://dx.doi.org/10.1016/j.diabres.2013.11.002 - DOI - PubMed
    1. Stančáková A1, Laakso M. Genetics of metabolic syndrome. Rev Endocr Metab Disord 2014. December; 15(4):243-52; http://dx.doi.org/10.1007/s11154-014-9293-9 - DOI - PubMed

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