Metabolomic Pattern Predicts Incident Coronary Heart Disease

Abstract

Objective— Alterations in the serum metabolome may be detectable in at-risk individuals before the onset of coronary heart disease (CHD). Identifying metabolomic signatures associated with CHD may provide insight into disease pathophysiology and prevention. Approach and Results— Metabolomic profiling (chromatography-mass spectrometry) was performed in 2232 African Americans and 1366 European Americans from the ARIC study (Atherosclerosis Risk in Communities). We applied Cox regression with least absolute shrinkage and selection operator to select metabolites associated with incident CHD. A metabolite risk score was constructed to evaluate whether the metabolite risk score predicted CHD risk beyond traditional risk factors. After 30 years of follow-up, we observed 633 incident CHD cases. Thirty-two metabolites were selected by least absolute shrinkage and selection operator to be associated with CHD, and 19 of the 32 showed significant individual associations with CHD, including a sugar substitute, erythritol. Theophylline (hazard ratio [95% CI] =1.16 [1.09–1.25]) and gamma-linolenic acid (hazard ratio [95% CI] =0.89 [0.81–0.97]) showed the greatest positive and negative associations with CHD, respectively. A 1 SD greater standardized metabolite risk score was associated with a 1.37-fold higher risk of CHD (hazard ratio [95% CI] =1.37 [1.27–1.47]). Adding the metabolite risk score to the traditional risk factors significantly improved model predictive performance (30-year risk prediction: Δ C statistics [95% CI] =0.016 [0.008–0.024], continuous net reclassification index [95% CI] =0.522 [0.480–0.556], integrated discrimination index [95% CI] =0.038 [0.019–0.065]). Conclusions— We identified 19 metabolites from known and novel metabolic pathways that collectively improved CHD risk prediction. Visual Overview— An online visual overview is available for this article.