Metabolomics and coronary heart disease risk in UK Biobank

Background: Metabolomics is the comprehensive study of numerous metabolites from multiple molecular pathways using high-throughput methods such as nuclear magnetic resonance (NMR). Although the importance of some metabolites to coronary heart disease (CHD) risk is well established, such as low-density lipoprotein cholesterol (LDL-C), the relevance of many hundreds of other metabolites to CHD risk remains unclear.
Objective: To assess the associations of NMR-derived plasma metabolites with incident CHD, and to evaluate the additional benefit of such metabolites to the prediction of 10-year cardiovascular risk.
Methods: UK Biobank is a prospective study of 0.5 million adults aged 40-69 recruited in 2006-2010; in 2012-2013, a subset of the participants were resurveyed. NMR metabolites are currently available in a random subset of 118k participants. After excluding participants with prior cardiovascular disease and those taking statins, Cox regression models (adjusted for major potential confounders) were used to assess the association of each metabolite with CHD risk. Hazard ratios were corrected for regression dilution using the correlation of baseline and resurvey metabolic measures. The additional predictive value of metabolic profiling to an established risk score (QRISK) was evaluated with both Cox regression and machine-learning approaches.
Results: After exclusions, 86,499 participants remained. There were 4,196 (4.9%) incident cases of CHD during a mean follow-up time of 11.5 years. CHD risk was positively associated with the concentrations of very-low-density (VLDL), intermediate-density (IDL), and low-density lipoproteins (LDL), and inversely associated with high-density lipoproteins (HDL). Hazard ratios (HRs) per standard deviation (SD) were 1.20 (95% CI 1.16-1.23), 1.15 (1.12-1.19), 1.17 (1.14-1.21) and 0.90 (0.87-0.91), respectively. There was little evidence that particle size of each of these lipoproteins was related to CHD risk. Given lipoprotein particle concentrations, lipid composition (including cholesterol) was not strongly related to CHD risk, except for triglyceride in LDL particles (HR 1.17 [1.11-1.23]). Independently of other fatty acids, CHD risk was positively associated with saturated fatty acids and monounsaturated fatty acids, inversely associated with omega-3 polyunsaturated fatty acids (PUFA), but there was no strong evidence of an association with omega-6 PUFA: HRs per SD higher fatty acid were 1.12 (1.07-1.17), 1.13 (1.08-1.18), 0.93 (0.90-0.97), and 1.02 (0.98-1.07), respectively. Independently of cholesterol and triglycerides, several non-lipid metabolites, including omega-3 fatty acids, glycine, histidine, albumin and glycoprotein acetyls, remained significantly associated with CHD risk. However, compared with QRISK, adding selected metabolites did not significantly improve measures of discrimination, reclassification or calibration.
Conclusion: Of the lipoprotein characterization assessed, CHD risk was most strongly related to particle concentrations, suggesting that the major mechanism by which plasma lipids are related to CHD risk is through the concentration of lipoprotein particles that transport them. Independent of lipids, omega-3 PUFA and several low-molecular-weight metabolites remained significantly associated with CHD risk. However, there was no evidence that metabolic profiling improved cardiovascular risk prediction in UK primary care. Future research should further explore causality and mechanism pathways, and should include the assessment of the associations and predictive value in more diverse populations and wider-range of metabolites.

Advisor: Lacey, B
Lewington, S
Trichia, E
Islam, N