English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  A distinct metabolic signature predicts development of fasting plasma glucose

Hische, M., Larhlimi, A., Schwarz, F., Fischer-Rosinsky, A., Bobbert, T., Assmann, A., et al. (2012). A distinct metabolic signature predicts development of fasting plasma glucose. Journal of Clinical Bioinformatics, 2(1), 3. doi:10.1186/2043-9113-2-3.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-1F8E-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-1F8F-3
Genre: Journal Article

Files

show Files
hide Files
:
Hische-2012-A distinct metabolic.pdf (Any fulltext), 574KB
Name:
Hische-2012-A distinct metabolic.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Hische, M.1, Author              
Larhlimi, A.1, Author              
Schwarz, F.2, Author
Fischer-Rosinsky, A.2, Author
Bobbert, T.2, Author
Assmann, A.2, Author
Catchpole, G. S.2, Author
Pfeiffer, A. F.2, Author
Willmitzer, L.3, Author              
Selbig, J.1, Author              
Spranger, J.2, Author
Affiliations:
1BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              
2External Organizations, ou_persistent22              
3Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753340              

Content

show
hide
Free keywords: -
 Abstract: ABSTRACT: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2012-02-042012
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Clinical Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 2 (1) Sequence Number: - Start / End Page: 3 Identifier: -