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

Abstract

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: Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), 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 the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

Results: During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an 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. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

Conclusions: When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Keywords: Biomarkers; Diabetes; Metabolomics; Risk prediction.

Fig. 1

Associations of metabolic biomarkers with risk of incident type 2 diabetes among 50,519 participants in the association analyses population. Hazard ratios (with 95% confidence intervals) are presented per 1−SD higher metabolic biomarker on the natural log scale, stratified by age-at-risk and sex and adjusted for assessment centre, Townsend Deprivation Index, ethnicity, parental history of diabetes, smoking, alcohol drinking, physical activity, dietary factors (whole and refined grains, fruit, vegetables, cheese, unprocessed red meat, processed meat, non-oily and oily fish, type of spread, caffeinated and decaffeinated coffee, tea and dietary supplements), body mass index, waist-to-hip ratio, fasting duration and spectrometer. *False discovery rate controlled p < 0.01. Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; DHA, docosahexaenoic acid; FA, fatty acids; FAw3, omega-3 fatty acids; FAw6, omega-6 fatty acids; HDL, high-density lipoproteins; HDL-D, high-density lipoprotein particle diameter; IDL, intermediate-density lipoproteins; L, large; LA, linoleic acid; LDL, low-density lipoproteins; LDL-D, low-density lipoprotein particle diameter; LP, lipoprotein; M, medium; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; S, small; SFA, saturated fatty acids; T2D, type 2 diabetes; VLDL, very low-density lipoproteins; VLDL-D, very low-density lipoprotein particle diameter; XL, very large; XS, very small; XXL, extremely large

Fig. 2

Calibration of risk prediction models for incident type 2 diabetes from cross-validation among 65,684 participants in the risk prediction population. For each model, the observed and predicted T2D event rates are shown for each of 10 equally sized groups of absolute predicted risk. Vertical lines represent 95% CIs. Calibration slopes are presented from 10-fold cross-validation (pooled using inverse variance weighting) and were derived from a Cox regression of the predicted risk on the observed risk. Concise model: age, sex, parental history of diabetes, body mass index and HbA1c. Full model: concise model plus waist circumference, triglycerides and HDL cholesterol. Metabolic biomarkers comprise the first 11 metabolic biomarker principal components