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Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

Authors :
Jeff K. Trimmer
Tim D. Spector
Rob P. Mohney
Eric B. Fauman
Craig L. Hyde
Kirsten J. Ward
So-Youn Shin
Maria Psatha
Wei Yuan
M J Brosnan
Mike Milburn
Jordana T. Bell
Cristina Menni
Gabi Kastenmüller
Christian Gieger
Timothy M. Frayling
Karsten Suhre
Colin N. A. Palmer
Idil Erte
Nicole Soranzo
John R. B. Perry
Ann-Kristin Petersen
Source :
Diabetes, Diabetes 62, 4270-4276 (2013), Diabetes; Vol 62
Publication Year :
2013
Publisher :
American Diabetes Association, 2013.

Abstract

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

Details

ISSN :
1939327X and 00121797
Volume :
62
Database :
OpenAIRE
Journal :
Diabetes
Accession number :
edsair.doi.dedup.....6c446ed59379ff7e78ab47488cc6ea41
Full Text :
https://doi.org/10.2337/db13-0570