Details

Autor(en) / Beteiligte

• Savolainen, Otto
• Fagerberg, Björn
• Vendelbo Lind, Mads
• Sandberg, Ann-Sofie
• Ross, Alastair B
• Bergström, Göran
• Sánchez-Margalet, Víctor

Titel

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

Ist Teil von

• PloS one, 2017-07, Vol.12 (7), p.e0177738-e0177738

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2017

Link zum Volltext

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• 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 case-control 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 follow-up. 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.