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.

The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes

“…The present study provides, for the first time, detailed characterisation of the relevance of lipoprotein size and subclass particle concentrations to T2D risk in a Chinese population. As shown in previous Western population studies 5 , 25 , we observed higher T2D risk among participants with higher concentrations of large VLDL particles and lower concentrations of large HDL particles, smaller mean HDL particle size and large mean VLDL particle size, as well as higher TG levels and lower HDL-cholesterol levels. This is consistent with an insulin resistant state 26 , which is a well-established component of the causal relationship between adiposity and T2D 27 .…”

“…Recent prospective analyses among ~ 65,000 UK Biobank (UKB) participants examined the associations of 139 of the biomarkers considered herein (measured using the same NMR-metabolomics platform) with incident T2D (n = 1719) recorded during almost 12 years’ follow-up, adjusting for sociodemographic factors, fasting time, smoking, alcohol drinking and general and central adiposity 25 . Overall, the associations of 98 biomarkers were qualitatively consistent in the two study populations, including significant positive associations of 53 biomarkers with T2D risk and inverse associations of 27 biomarkers.…”

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

“…These data greatly extend the blood biomarker coverage in the UK Biobank and provide a wide span of molecular biomarkers not commonly measured in clinical practice, including amino acids, ketones and fatty acids. With over 118,000 plasma samples profiled in the UK Biobank, the addressable research questions extend vastly beyond biomarker discovery and the large sample size benefits, for example, causal analyses and risk prediction 9 , 13 , 14 , 17 . Due to the streamlined data access policy in UK Biobank, the data release opens possibilities for the research community to use the entire epidemiological toolbox to study the NMR biomarkers in relation to public health.…”

Metabolic profiling of MRI‐measured liver fat in the UK Biobank

“…In this study of objectively measured concentrations of liver fat, detailed biomarker profiling showed multiple adverse characteristics with circulating lipids and metabolites. The patterns of these associations were associated with alterations in biomarkers linked to excess general adiposity [3, 4, 14–16], diabetes [17, 18], CVD [5, 6, 19, 20], and other major chronic diseases. Hazardous metabolic profiles linked to higher concentrations of fat in the liver involved higher levels of VLDL triglycerides, extremely large and very large lipoprotein concentrations, small HDL particles, glycolysis and inflammatory markers, MUFAs and SFAs, and amino acids.…”