Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study. (6th January 2020)

• Record Type: Journal Article
• Authors: Drogan, Dagmar; Dunn, Warwick B; Lin, Wanchang; Buijsse, Brian; Schulze, Matthias B; Langenberg, Claudia; Brown, Marie; Floegel, Anna; Dietrich, Stefan; Rolandsson, Olov; Wedge, David C; Goodacre, Royston; Forouhi, Nita G; Sharp, Stephen J; Spranger, Joachim; Wareham, Nick J; Boeing, Heiner
• Is Part Of: Clinical chemistry. Volume 61:Number 3(2015)
• Page Start: 487
• Page End: 497
• Publication Date: 2020-01-06
• Subjects: Clinical chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; Biochimie -- Périodiques; Diagnostics biologiques -- Périodiques; Biochemistry; Clinical chemistry; Pharmaceutical chemistry; Biochemistry; Laboratory Techniques and Procedures; Klinische chemie; Periodicals; 616.075605
• DOI: 10.1373/clinchem.2014.228965
• Languages: English
• ISSNs: 0009-9147

Abstract

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.