alexa SNPs in PPARG associate with type 2 diabetes and interact with physical activity.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Kilpelinen TO, Lakka TA, Laaksonen DE, Lindstrm J, Eriksson JG,

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Abstract PURPOSE: To study the associations of seven single-nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptor gamma (PPARG) gene with the conversion from impaired glucose tolerance (IGT) to type 2 diabetes (T2D), and the interactions of the SNPs with physical activity (PA). METHODS: Overweight individuals with IGT who participated in the Finnish Diabetes Prevention Study (DPS) (N = 479) were followed, on average, 4.2 yr. PA was assessed yearly with a 12-month validated questionnaire. RESULTS: In Cox regression analyses, the rare alleles of rs17036314 and rs1801282 (Pro12Ala) predicted conversion to T2D (P = 0.038 and 0.037, respectively), but only rs17036314 predicted T2D after adjustment for baseline fasting glucose (P = 0.030). The change in the total amount of PA, stratified by median, modified the association of rs17036314 and rs1801282 with the risk of T2D during the intervention (P = 0.002 and 0.031, respectively, for interaction between PA change and genotype); an increase in PA seemed to remove the effect of the risk alleles. The distinct rs1152003 polymorphism interacted with the study group on the conversion to T2D (P = 0.027) and tended to increase the risk of T2D in the intervention group (P = 0.050). No interaction between rs1152003 and the change in PA was found. CONCLUSIONS: The rs17036314, rs1801282 (Pro12Ala), and rs1152003 were associated with the risk of T2D in the DPS. Increased PA seemed to decrease the effect of the risk alleles of rs17036314 and rs1801282 on the conversion to T2D. The effect of rs1152003 was modified by other lifestyle changes or the lifestyle intervention as a whole. This article was published in Med Sci Sports Exerc and referenced in Journal of Data Mining in Genomics & Proteomics

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