Abstract
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
| Original language | English |
|---|---|
| Pages (from-to) | 487-497 |
| Journal | Clinical Chemistry |
| Volume | 61 |
| Issue number | 3 |
| Early online date | 18 Dec 2014 |
| DOIs | |
| Publication status | Published - Mar 2015 |
