Utilization of Serum Metabolomics and Polygenic Risk Scores in a Novel Risk Stratification Tool for the Prediction of Incident Atrial Fibrillation
Access to this document is restricted. Some items have been embargoed at the request of the author, but will be made publicly available after the "No Access Until" date.
During the embargo period, you may request access to the item by clicking the link to the restricted file(s) and completing the request form. If we have contact information for a Cornell author, we will contact the author and request permission to provide access. If we do not have contact information for a Cornell author, or the author denies or does not respond to our inquiry, we will not be able to provide access. For more information, review our policies for restricted content.
BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity and mortality. We sought to investigate the predictive value of serum metabolomics for 5-year incident AF in the context of clinical and polygenic risk score (PRS) stratification tools. METHODS: We studied a cohort of 240 628 patients UK Biobank participants with proton nuclear magnetic resonance spectroscopy measurements of 170 serum metabolites at enrollment. Five-year incidence of AF was assessed using Cox proportional hazards models. Cohorts for Heart and Aging Research in Genomic Epidemiology-AF and AF polygenic risk score (AF-PRS) scores were used as benchmark risk models for comparison. Models were trained on 80% of the cohort, and performances were validated on the remaining 20% cohort. Performance of clinical, AF-PRS, and combined metabolomics models was evaluated using time-dependent area under the receiver operating characteristic curve, net reclassification improvement, and relative integrated discrimination improvement analysis. RESULTS: During follow-up, 4174 (1.7%) participants developed AF. After training a model on the full metabolomics panel in addition to Cohorts for Heart and Aging Research in Genomic Epidemiology-AF and AF-PRS, the final model retained 8 metabolites. Creatinine level was associated with increased risk (hazard ratio, 1.01 per 1 SD log-transformed value [95% CI, 1.00-1.03]) while linoleic acid level (hazard ratio, 0.985 [0.979-0.994]) was associated with decreased risk of AF. The addition of metabolomics to the Cohorts for Heart and Aging Research in Genomic Epidemiology-AF+AF-PRS model improved risk prediction (5-year time-dependent area under the receiver operating characteristic curve, 0.789 [0.776-0.802] versus 0.755 [0.738-0.772]; P<0.05) and stratification on the validation set (NRIcases: 11.1%, NRIcontrols: 3.1%, IDIrelative: 11.6%). A model using only age, sex, metabolomics, and AF-PRS had fair risk prediction on the validation set (5-year time-dependent area under the receiver operating characteristic curve, 0.787 [0.773-0.801]). CONCLUSIONS: The addition of metabolomics to clinical and genomic risk scores improves the prediction of 5-year incident AF. A risk stratification tool using age, sex, and serum metabolomics and AF-PRS provides excellent AF risk prediction. Mechanisms by which specific metabolites reflect AF risk require further exploration.