Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

Bragg, F.; Trichia, E.; Aguilar-Ramirez, D.; Besevic, J.; Lewington, S.; Emberson, J.

Background: Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors. Methods: NMR-metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed. Findings: During median 11.9 (IQR 11.1-12.6) years' follow-up, 1719 participants developed T2D. After accounting for multiple testing, 118 metabolic biomarkers showed independent associations with T2D risk (false discovery rate controlled p<0.05), of which 103 persisted after additional adjustment for HbA1c. Overall, 10 metabolic biomarker PCs were independently associated with T2D. Addition of PCs to the established risk prediction model (including age, sex, parental history of diabetes, body mass index and HbA1c) improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]), and relative (15.0% [10.5%-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. Interpretation: When added to conventional risk factors, circulating NMR-based metabolic biomarkers enhanced T2D risk prediction.