Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study.
Clinical chemistry 2014 ; 61: 487-97.
Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, Brown M, Floegel A, Dietrich S, Rolandsson O, Wedge DC, Goodacre R, Forouhi NG, Sharp SJ, Spranger J, Wareham NJ, and Boeing H
PubMed ID : 25524438
Application of metabolite profiling could expand the etiological knowledge of type 2 diabetes mellitus (T2D). However, few prospective studies apply broad untargeted metabolite profiling to reveal the comprehensive metabolic alterations preceding the onset of T2D.
We applied untargeted metabolite profiling in serum samples obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort comprising 300 individuals who developed T2D after a median follow-up time of 6 years and 300 matched controls. For that purpose, we used ultraperformance LC-MS with a protocol specifically designed for large-scale metabolomics studies with regard to robustness and repeatability. After multivariate classification to select metabolites with the strongest contribution to disease classification, we applied multivariable-adjusted conditional logistic regression to assess the association of these metabolites with T2D.
Among several alterations in lipid metabolism, there was an inverse association with T2D for metabolites chemically annotated as lysophosphatidylcholine(dm16:0) and phosphatidylcholine(O-20:0/O-20:0). Hexose sugars were positively associated with T2D, whereas higher concentrations of a sugar alcohol and a deoxyhexose sugar reduced the odds of diabetes by approximately 60% and 70%, respectively. Furthermore, there was suggestive evidence for a positive association of the circulating purine nucleotide isopentenyladenosine-5'-monophosphate with incident T2D.
This study constitutes one of the largest metabolite profiling approaches of T2D biomarkers in a prospective study population. The findings might help generate new hypotheses about diabetes etiology and develop further targeted studies of a smaller number of potentially important metabolites.