RESUMEN
Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10−300) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.
RESUMEN
Habitual physical activity can diminish the risk of premature death. Identifying a pattern of metabolites related to physical activity may advance our understanding of disease etiology. We quantified 245 serum metabolites in 3802 participants from the Atherosclerosis Risk in Communities (ARIC) study using chromatography-mass spectrometry. We regressed self-reported moderate-to-vigorous intensity leisure-time physical activity (LTPA) against each metabolite, adjusting for traditional risk factors. A standardized metabolite risk score (MRS) was constructed to examine its association with all-cause mortality using the Cox proportional hazard model. We identified 10 metabolites associated with LTPA (p < 2.04 × 10-4) and established that an increase of one unit of the metabolic equivalent of task-hours per week (MET·hr·wk-1) in LTPA was associated with a 0.012 SD increase in MRS. During a median of 27.5 years of follow-up, we observed 1928 deaths. One SD increase of MRS was associated with a 10% lower risk of death (HR = 0.90, 95% CI: 0.85-0.95). The highest vs. the lowest MRS quintile rank was associated with a 22% reduced risk of death (HR = 0.78, 95% CI: 0.62-0.94). The effects were consistent across race and sex groups. In summary, we identified a set of metabolites associated with LTPA and an MRS associated with a lower risk of death. Our study provides novel insights into the potential mechanisms underlying the health impacts of physical activity.