Epigenome-wide association study of metabolic syndrome in African-American adults

Clinical epigenetics(2018)

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摘要
Background The high prevalence of obesity among US adults has resulted in significant increases in associated metabolic disorders such as diabetes, dyslipidemia, and high blood pressure. Together, these disorders constitute metabolic syndrome, a clinically defined condition highly prevalent among African-Americans. Identifying epigenetic alterations associated with metabolic syndrome may provide additional information regarding etiology beyond current evidence from genome-wide association studies. Methods Data on metabolic syndrome and DNA methylation was assessed on 614 African-Americans from the Hypertension Genetic Epidemiology Network (HyperGEN) study. Metabolic syndrome was defined using the joint harmonized criteria, and DNA methylation was assessed using the Illumina HumanMethylation450K Bead Chip assay on DNA extracted from buffy coat. Linear mixed effects regression models were used to examine the association between CpG methylation at > 450,000 CpG sites and metabolic syndrome adjusted for study covariates. Replication using DNA from a separate sample of 69 African-Americans, as well as meta-analysis combining both cohorts, was conducted. Results Two differentially methylated CpG sites in the IGF2BP1 gene on chromosome 17 (cg06638433; p value = 3.10 × 10 − 7 ) and the ABCG1 gene on chromosome 21 (cg06500161; p value = 2.60 × 10 − 8 ) were identified. Results for the ABCG1 gene remained statistically significant in the replication dataset and meta-analysis. Conclusion Metabolic syndrome was consistently associated with increased methylation in the ABCG1 gene in the discovery and replication datasets, a gene that encodes a protein in the ATP-binding cassette transporter family and is involved in intra- and extra-cellular signaling and lipid transport.
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关键词
African American Adults,Epigenome-wide Association Studies,Hypertension Genetic Epidemiology Network,ABCG2 Gene,Linear Mixed Effects Regression Models
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