Machine learning approaches and genetic determinants that influence the development of type 2 diabetes mellitus: a genetic association study in Brazilian patients

Abstract

This genetic association study including 120 patients with type 2 diabetes mellitus (T2DM) and 166 non-diabetic individuals aimed to investigate the association of polymorphisms in the genes GSTM1 and GSTT1 (gene deletion), GSTP1 (rs1695), ACE (rs4646994), ACE2 (rs2285666), VEGF-A (rs28357093), and MTHFR (rs1801133) with the development of T2DM in the population of Goiás, Brazil. Additionally, the combined effects of these polymorphisms and the possible differences between sexes in susceptibility to the disease were evaluated. Finally, machine learning models were integrated to select the main risk characteristics for the T2DM diagnosis. Risk associations were found for the GSTT1-null genotype in the non-stratified sample and females, and for mutant C allele of the VEGF-A rs28357093 polymorphism in the non-stratified sample. Furthermore, an association of heterozygous (AG) and mutant (GG) GSTP1 genotypes was observed when combined with GSTT1-null. Machine learning approaches corroborated the results found. Therefore, these results suggested that GSTT1 and GSTP1 polymorphisms may contribute to T2DM susceptibility in a Brazilian sample.