Novel biomarkers for pre-diabetes identified by metabolomics

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Abstract

Three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine C2) were found with significantly altered levels in pre-diabetic individuals compared with normal controls.
Lower levels of glycine and LPC (18:2) were found to predict risks for pre-diabetes and type 2 diabetes (T2D).
Seven T2D-related genes ( PPARG, TCF7L2, HNF1A, GCK, IGF1, IRS1 and IDE) are functionally associated with the three identified metabolites.
The unique combination of methodologies, including prospective population-based and nested case–control, as well as cross-sectional studies, was essential for the identification of the reported biomarkers.

Abstract

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10 ⁻⁴ to 2.1 × 10 ⁻¹³. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

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Most cited references 23

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HMDB: a knowledgebase for the human metabolome

David Wishart, Craig Knox, An Guo … (2009)

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

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Banting lecture 2001: dysregulation of fatty acid metabolism in the etiology of type 2 diabetes.

Lisa McGarry, Peter Kahn (2002)

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Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity

Oded Shaham, Ru Wei, Thomas J Wang … (2008)

Glucose ingestion after an overnight fast triggers an insulin-dependent, homeostatic program that is altered in diabetes. The full spectrum of biochemical changes associated with this transition is currently unknown. We have developed a mass spectrometry-based strategy to simultaneously measure 191 metabolites following glucose ingestion. In two groups of healthy individuals (n=22 and 25), 18 plasma metabolites changed reproducibly, including bile acids, urea cycle intermediates, and purine degradation products, none of which were previously linked to glucose homeostasis. The metabolite dynamics also revealed insulin's known actions along four key axes—proteolysis, lipolysis, ketogenesis, and glycolysis—reflecting a switch from catabolism to anabolism. In pre-diabetics (n=25), we observed a blunted response in all four axes that correlated with insulin resistance. Multivariate analysis revealed that declines in glycerol and leucine/isoleucine (markers of lipolysis and proteolysis, respectively) jointly provide the strongest predictor of insulin sensitivity. This observation indicates that some humans are selectively resistant to insulin's suppression of proteolysis, whereas others, to insulin's suppression of lipolysis. Our findings lay the groundwork for using metabolic profiling to define an individual's 'insulin response profile', which could have value in predicting diabetes, its complications, and in guiding therapy.

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Author and article information

Journal

Journal ID (nlm-ta): Mol Syst Biol

Journal ID (iso-abbrev): Mol. Syst. Biol

Title: Molecular Systems Biology

Publisher: Nature Publishing Group

ISSN (Electronic): 1744-4292

Publication date Collection: 2012

Publication date (Electronic): 25 September 2012

Publication date PMC-release: 25 September 2012

Volume: 8

Page: 615

Affiliations

[1 ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum München , Neuherberg, Germany

[2 ]German Diabetes Center, Institute for Clinical Diabetology, Leibniz Center for Diabetes Research at Heinrich Heine University , Düsseldorf, Germany

[3 ]Institute of Structural Biology, Helmholtz Zentrum München , Neuherberg, Germany

[4 ]Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke , Nuthetal, Germany

[5 ]Shanghai Center for Bioinformation Technology , Shanghai, China

[6 ]Key Lab of Systems Biology, Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai, China

[7 ]Institute of Human Genetics, Helmholtz Zentrum München , Neuherberg, Germany

[8 ]Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München , Neuherberg, Germany

[9 ]Else Kroener-Fresenius-Center for Nutritional Medicine, University Hospital ‘Klinikum rechts der Isar', Technische Universität München , Munich, Germany

[10 ]Institute of Epidemiology II, Helmholtz Zentrum München , Neuherberg, Germany

[11 ]Institute of Epidemiology I, Helmholtz Zentrum München , Neuherberg, Germany

[12 ]Institute of Genetic Epidemiology, Helmholtz Zentrum München , Neuherberg, Germany

[13 ]Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München , Neuherberg, Germany

[14 ]Department of Clinical Nutrition, Institute of Health Biosciences, University of Tokushima Graduate School , Tokushima, Japan

[15 ]Benxi Diabetes Clinic, Benxi Central Hospital , Benxi, China

[16 ]Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig , Leipzig, Germany

[17 ]German Diabetes Center, Institute of Biometrics and Epidemiology, Leibniz Center for Diabetes Research at Heinrich Heine University , Düsseldorf, Germany

[18 ]Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München , Neuherberg, Germany

[19 ]Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai, China

[20 ]Chair of Experimental Genetics, Technische Universität München , Munich, Germany

[21 ]Faculty of Biology, Ludwig-Maximilians-Universität , Planegg-Martinsried, Germany

[22 ]Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q) , Doha, Qatar

[23 ]Department of Metabolic Diseases, University Hospital Düsseldorf , Düsseldorf, Germany

[24 ]Klinikum rechts der Isar, Technische Universität München, Munich, Germany

[25 ]Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität , Munich, Germany

[26 ]Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine (MDC) , Berlin-Buch, Germany

[27 ]Hannover Unified Biobank, Hannover Medical School , Hannover, Germany

Author notes

[a ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum München , 85764 Munich-Neuherberg, Germany. Tel.:+49 89 3187 3978; Fax:+49 89 3187 2428; rui.wang-sattler@ 123456helmholtz-muenchen.de

[*]

These authors contributed equally to this work

Article

Publisher Item ID: msb201243

DOI: 10.1038/msb.2012.43

PMC ID: 3472689

PubMed ID: 23010998

SO-VID: 34a1e5f5-a872-4594-8048-aa99d61caa2a

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This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial Share Alike 3.0 Unported License, which allows readers to alter, transform, or build upon the article and then distribute the resulting work under the same or similar license to this one. The work must be attributed back to the original author and commercial use is not permitted without specific permission.

History

Date received : 13 June 2012

Date accepted : 15 August 2012

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Novel biomarkers for pre-diabetes identified by metabolomics

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Most cited references 23

HMDB: a knowledgebase for the human metabolome

Banting lecture 2001: dysregulation of fatty acid metabolism in the etiology of type 2 diabetes.

Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity

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