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Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

Journal article
Authors O. Savolainen
Björn Fagerberg
M. V. Lind
A. S. Sandberg
A. B. Ross
Göran Bergström
Published in Plos One
Volume 12
Issue 7
ISSN 1932-6203
Publication year 2017
Published at Wallenberg Laboratory
Institute of Medicine, Department of Molecular and Clinical Medicine
Language en
Keywords insulin-resistance, adiponectin levels, mass-spectrometry, risk, samples, models, women, acid, Science & Technology - Other Topics
Subject categories Endocrinology and Diabetes, Nutrition and Dietetics, Cardiovascular medicine


Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Gas chromatography-tandem mass spectrometry metabolomics was used in a nested casecontrol study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years followup. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711]). Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

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