Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
Postprint
DOI
PMC
 |
Open Access Green |
as soon as is submitted to ZB.
Plasma metabolomics to identify and stratify patients with impaired glucose tolerance.
J. Clin. Endocrinol. Metab. 104, 6357-6370 (2019)
Objective: Impaired glucose tolerance (IGT) is one of the presymptomatic states of type 2 diabetes mellitus and requires an oral glucose tolerance test (OGTT) for diagnosis. Our aims were twofold: (i) characterize signatures of small molecules predicting the OGTT response and (ii) identify metabolic subgroups of participants with IGT.Methods: Plasma samples from 827 participants of the Study of Health in Pomerania free of diabetes were measured using mass spectrometry and proton-nuclear magnetic resonance spectroscopy. Linear regression analyses were used to screen for metabolites significantly associated with the OGTT response after 2 hours, adjusting for baseline glucose and insulin levels as well as important confounders. A signature predictive for IGT was established using regularized logistic regression. All cases with IGT (N = 159) were selected and subjected to unsupervised clustering using a k-means approach.Results and Conclusion: In total, 99 metabolites and 22 lipoprotein measures were significantly associated with either 2-hour glucose or 2-hour insulin levels. Those comprised variations in baseline concentrations of branched-chain amino ketoacids, acylcarnitines, lysophospholipids, or phosphatidylcholines, largely confirming previous studies. By the use of these metabolites, subjects with IGT segregated into two distinct groups. Our IGT prediction model combining both clinical and metabolomics traits achieved an area under the curve of 0.84, slightly improving the prediction based on established clinical measures. The present metabolomics approach revealed molecular signatures associated directly to the response of the OGTT and to IGT in line with previous studies. However, clustering of subjects with IGT revealed distinct metabolic signatures of otherwise similar individuals, pointing toward the possibility of metabolomics for patient stratification.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Cholesteryl Ester Transfer; Chain Amino-acids; Insulin-resistance; Diabetes-mellitus; Risk; Association; Biomarkers; Markers; Protein; Impact
ISSN (print) / ISBN
0021-972X
e-ISSN
1945-7197
Quellenangaben
Volume: 104,
Issue: 12,
Pages: 6357-6370
Publisher
Endocrine Society
Publishing Place
Bethesda, Md.
Non-patent literature
Publications
Reviewing status
Peer reviewed