Signature Genes for Type 2 Diabetes in an African American Population

Abstract

BACKGROUND AND AIM: Diabetes disproportionately affects racial/ethnic minority populations. Type 2 Diabetes Mellitus (T2DM) is viewed as an outcome of the interaction of environmental and biological risk factors. Our previous studies established the biomarkers (genes) of T2DM-related biological pathways in Caucasian and Asian populations. This present work aims to investigate on T2DM-related metabolic pathways in a diabetic African American population. METHODS: RNAs were extracted from blood samples of an African American (AA) population (n=48 Diabetic-cases) compared to Calibrator (matched non-diabetic-Controls; Male/Female) recruited in Washington DC, between the ages of 45-65 years; High-throughput Taqman Low Density Array (TLDA) provided analysis of differential gene expression. We used a custom array containing genes (24 transcripts) related to diabetes and metabolic dysfunction. RESULTS:Differential gene expression analysis revealed distinct differences in T2DM compared to controls in the AA population. 90% of the 24 genes were downregulated in the participants with T2DM, including INSR, ARNT, CYP2D6, RRAD, IL6, HNF1B, INS, and SLC2A2, which are associated with dysregulated homeostasis and xenobiotic metabolism. CONCLUSIONS:The importance of gene-environment (GxE) interactions lies not only in a better understanding of the complex interplay of genetic and environmental risk factors relevant to human diseases; the identification of GxE can also influence risk prediction and identify subgroups of individuals that are most genetically susceptible developing diabetes. The results obtained here contrast with our previous results and therefore tend to confirm the GxE model. Further large-scale population validation with the addition of gender- age, smoking, and other epigenomic conditions may further delineate the sensitivity of relative gene expressions in fine tuning towards clinical application of these markers. KEYWORDS: Exposures, Obesity & Metabolic Disorders, Omics Technologies