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A new interesting article has been published in J Clin Endocrinol Metab. 2019 Aug 7. pii: jc.2019-01104. doi: 10.1210/jc.2019-01104. and titled:

Plasma Metabolomics to Identify and Stratify Patients with Impaired Glucose Tolerance.

Authors of this article are:

Wildberg C, Masuch A, Budde K, Kastenmüller G, Artati A, Rathmann W, Adamski J, Kocher T, Völzke H, Nauck M, Friedrich N, Pietzner M.

A summary of the article is shown below:

OBJECTIVE: Impaired glucose tolerance (IGT) is one of the pre-symptomatic states of type 2 diabetes mellitus and requires an oral glucose tolerance test (OGTT) for diagnosis. Our aims were two-fold: 1) characterize signatures of small molecules predicting the OGTT-response and 2) 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 utilizing mass spectrometry and proton-nuclear magnetic resonance spectroscopy. Linear regression analyses were used to screen for metabolites significantly associated with the OGTT-response after two 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 IGT cases (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 keto-acids, acylcarnitines, lysophospholipids or phosphatidylcholines largely confirming previous studies. By the use of these metabolites, IGT-subjects segregated into two distinct groups. Our IGT prediction model combining both clinical and metabolomics traits achieved an AUC 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 IGT subjects revealed distinct metabolic signatures of otherwise similar individuals pointing towards the possibility of metabolomics for patient stratification.Copyright © 2019 Endocrine Society.

Check out the article’s website on Pubmed for more information:

[link-preview url=https://www.ncbi.nlm.nih.gov/pubmed/31390012 forceshot=true]

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