Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach
Cristina Menni,Eric B. Fauman,Idil Erte,John R. B. Perry,Gabi Kastenmüller,So-Youn Shin,So-Youn Shin,Ann-Kristin Petersen,Craig L. Hyde,Maria Psatha,Kirsten J. Ward,Wei Yuan,Mike Milburn,Colin N. A. Palmer,Timothy M. Frayling,Jeff K. Trimmer,Jordana T. Bell,Christian Gieger,Rob P. Mohney,M J Brosnan,Karsten Suhre,Nicole Soranzo,Tim D. Spector +22 more
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TLDR
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.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.read more
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Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis
Marta Guasch-Ferré,Adela Hruby,Estefanía Toledo,Clary B. Clish,Miguel Ángel Martínez-González,Miguel Ángel Martínez-González,Jordi Salas-Salvadó,Frank B. Hu,Frank B. Hu +8 more
TL;DR: In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
Journal ArticleDOI
Metabolomics and Metabolic Diseases: Where Do We Stand?
TL;DR: Progress in metabolomics and challenges for the future are reviewed here.
Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes
Kaixin Zhou,Kaixin Zhou,Céline Bellenguez,Chris C. A. Spencer,Amanda J. Bennett,Ruth L. Coleman,Ruth L. Coleman,Roger Tavendale,Roger Tavendale,Simon A. Hawley,Louise A. Donnelly,Louise A. Donnelly,Christopher Schofield,Christopher Schofield,Christopher J. Groves,Christopher J. Groves,Lindsay Burch,Lindsay Burch,Fiona Carr,Fiona Carr,Amy Strange,Colin Freeman,Jenefer M. Blackwell,Elvira Bramon,Elvira Bramon,Matthew A. Brown,Matthew A. Brown,Juan P. Casas,Juan P. Casas,Aiden Corvin,N. Craddock,Panos Deloukas,Panos Deloukas,Serge Dronov,Serge Dronov,A. Duncanson,Sarah Edkins,Sarah Edkins,Emma Gray,Emma Gray,Sarah E. Hunt,Sarah E. Hunt,Joachim Jankowski,Joachim Jankowski,Joachim Jankowski,Cordelia Langford,Cordelia Langford,Hugh S. Markus,Christopher G. Mathew,Robert Plomin,Anna Rautanen,Stephen Sawcer,Nilesh J. Samani,Nilesh J. Samani,Nilesh J. Samani,Richard C. Trembath,Ananth C. Viswanathan,Nicholas W. Wood,Lorna W. Harries,Lorna W. Harries,Andrew T. Hattersley,Andrew T. Hattersley,Alex S. F. Doney,Alex S. F. Doney,Helen M. Colhoun,Andrew D. Morris,Calum Sutherland,Calum Sutherland,D. G. Hardie,D. G. Hardie,Leena Peltonen,Mark I. McCarthy,Rury R. Holman,Colin N. A. Palmer,Colin N. A. Palmer,Peter Donnelly,Ewan R. Pearson +76 more
TL;DR: A genome-wide association study for glycemic response to metformin in 1,024 Scottish individuals with type 2 diabetes with replication in two cohorts including 1,783 Scottish individuals and 1,113 individuals from the UK Prospective Diabetes Study.
Journal ArticleDOI
Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis
Luca A. Lotta,Robert A. Scott,Stephen J. Sharp,Stephen Burgess,Jian'an Luan,Therese Tillin,Amand F. Schmidt,Fumiaki Imamura,Isobel D. Stewart,John R. B. Perry,Luke Marney,Albert Koulman,Edward D. Karoly,Nita G. Forouhi,Rasmus J. O. Sjögren,Erik Näslund,Juleen R. Zierath,Anna Krook,David B. Savage,Julian L. Griffin,Julian L. Griffin,Nishi Chaturvedi,Aroon D. Hingorani,Kay-Tee Khaw,Inês Barroso,Inês Barroso,Mark I. McCarthy,Stephen O'Rahilly,Nicholas J. Wareham,Claudia Langenberg +29 more
TL;DR: Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes.
Journal ArticleDOI
IPO: a tool for automated optimization of XCMS parameters
Gunnar Libiseller,Michaela Dvorzak,Ulrike Kleb,Edgar Gander,Tobias Eisenberg,Frank Madeo,Steffen Neumann,Gert Trausinger,Frank Sinner,Frank Sinner,Thomas R. Pieber,Thomas R. Pieber,Christoph Magnes +12 more
TL;DR: The software package IPO (‘Isotopologue Parameter Optimization’) was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices and the potential of IPO to increase the reliability of metabolomics data was shown.
References
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TL;DR: This article conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent, and identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association.
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Interplay between lipids and branched-chain amino acids in development of insulin resistance.
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Journal ArticleDOI
Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach
Anna Floegel,Norbert Stefan,Zhonghao Yu,Kristin Mühlenbruch,Dagmar Drogan,Hans-Georg Joost,Andreas Fritsche,Hans-Ulrich Häring,Martin Hrabě de Angelis,Annette Peters,Michael Roden,Cornelia Prehn,Rui Wang-Sattler,Thomas Illig,Matthias B. Schulze,Jerzy Adamski,Heiner Boeing,Tobias Pischon +17 more
TL;DR: The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.
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