Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution

Abstract

Introduction: Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency. Objectives: To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention. Methods: We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry. Results: In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05). Conclusion: We identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.

Keywords

• Lipidomics
• Coronary heart disease
• Risk prediction
• Mediation
• Ambient air pollution