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. 2017 Jul 10;12(7):e0177738.
doi: 10.1371/journal.pone.0177738. eCollection 2017.

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

Otto Savolainen  1 , Björn Fagerberg  2 , Mads Vendelbo Lind  1   3 , Ann-Sofie Sandberg  1 , Alastair B Ross  1 , Göran Bergström  4
Affiliations

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

Otto Savolainen et al. PLoS One. .

Abstract

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.

Methods: 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.

Results: 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]).

Conclusions: 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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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

References

    1. Bailes BK. Diabetes Mellitus and its Chronic Complications. AORN Journal. 2002;76: 265–282. doi: 10.1016/S0001-2092(06)61065-X - DOI - PubMed
    1. Fagerberg B, Kellis D, Bergstrom G, Behre CJ. Adiponectin in relation to insulin sensitivity and insulin secretion in the development of type 2 diabetes: a prospective study in 64-year-old women. Journal of internal medicine. Blackwell Publishing Ltd; 2011;269: 636–643. doi: 10.1111/j.1365-2796.2010.02336.x - DOI - PubMed
    1. Li S, Shin HJ, Ding EL, van Dam RM. Adiponectin Levels and Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis. JAMA. American Medical Association; 2009;302: 179–188. doi: 10.1001/jama.2009.976 - DOI - PubMed
    1. Lind MV, Savolainen OI, Ross AB. The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples. Eur J Epidemiol. Springer Netherlands; 2016;: 1–17. doi: 10.1007/s10654-016-0166-2 - DOI - PubMed
    1. Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia. 2014;57: 16–29. doi: 10.1007/s00125-013-3061-3 - DOI - PubMed