Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population

• DOI: 10.1007/s11306-016-1107-5
• Publication Date: 2016-09-15T01:41:18Z
• Authors: Kallyandra Padilha; Gabriela Venturini; Thiago de Farias ...; Andréa R. V. R. H...; Pamella Araujo Ma...; Tamiris Carneiro ...; Bianca Kiers; Camila Maciel Oli...; Rafael de Oliveir...; Celso Blatt; José Eduardo Krieger; Alexandre Costa P...
• Keywords: Omics

The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish. As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes. In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique. We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development. Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.