A distinct metabolic signature predicts development of fasting plasma glucose

Journal of Clinical Bioinformatics

Joachim Spranger⁵  Joachim Selbig²  Lothar Willmitzer⁴  Andreas FH Pfeiffer³  Gareth S Catchpole²  Anke Assmann⁶  Thomas Bobbert⁶  Antje Fischer-Rosinský⁶  Franziska Schwarz⁶  Abdelhalim Larhlimi¹  Manuela Hische¹ 

[1] Department of Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany;Max-Planck-Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Germany;Department of Clinical Nutrition, German Institute of Human Nutrition, Arthur-Scheunert-Allee 144-116, 14558 Nuthetal, Germany;King Abdulaziz University, P.O. Box 80203 Jeddah 21589, KSA;Center for Cardiovascular Research (CCR), Charité-University Medicine Berlin, Berlin, Germany;Clinic of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany

关键词: biomarker;    regression;    metabolite;    random forest;    plasma;    metabolomics;    type 2 diabetes;    fasting glucose;    prediction;   

Others  :  806092
DOI  :  10.1186/2043-9113-2-3

 received in 2011-09-21, accepted in 2012-02-02,  发布年份 2012

【 摘 要 】

Background

High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.

Methods

We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.

Results

We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.

Conclusions

We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.

【 授权许可】

   
2012 Hische et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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