Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study
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- Dagmar Drogan
- Department of Epidemiology and
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- Warwick B Dunn
- Centre for Endocrinology and Diabetes, Institute of Human Development, and
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- Wanchang Lin
- Centre for Endocrinology and Diabetes, Institute of Human Development, and
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- Brian Buijsse
- Department of Epidemiology and
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- Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
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- Claudia Langenberg
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
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- Marie Brown
- Centre for Endocrinology and Diabetes, Institute of Human Development, and
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- Anna Floegel
- Department of Epidemiology and
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- Stefan Dietrich
- Department of Epidemiology and
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- Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
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- David C Wedge
- School of Chemistry and Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK
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- Royston Goodacre
- School of Chemistry and Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK
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- Nita G Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
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- Stephen J Sharp
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
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- Joachim Spranger
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Berlin, Germany
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- Nick J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
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- Heiner Boeing
- Department of Epidemiology and
抄録
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>BACKGROUND</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>METHODS</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSIONS</jats:title> <jats:p>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.</jats:p> </jats:sec>
収録刊行物
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- Clinical Chemistry
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Clinical Chemistry 61 (3), 487-497, 2015-03-01
Oxford University Press (OUP)