Abstract 4342101: Development of a Metabolomics-Based Model for Predicting Atrial Fibrillation: A Prospective Case-Cohort Study

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a major cause of stroke and heart failure. Early identification of individuals at risk of AF is critical for preventive interventions. We aimed to develop and validate a novel metabolomics-based model to predict incident AF in a general population cohort. Methods: Our original cohort comprised 37,627 participants who underwent annual health checkups at St. Luke’s International Hospital, Tokyo, between 2015 and 2016. A case-cohort study was subsequently conducted with a selected sub-cohort. The participants were followed-up for three years. At health check-ups, anthropometric measurements and electrocardiography were performed and laboratory tests were ordered. Serum metabolomic profiling targeting 40 metabolites was performed using a GCMS-TQ8040 (Shimadzu Corporation) in multiple reaction monitoring mode. Least absolute shrinkage and selection operator (LASSO)-Cox regression with cross-validation was used for model development. Risk categories were defined based on hazard ratios (HRs) relative to the median hazard. Metabolite Set Enrichment Analysis (MSEA) was also performed. Results: The sub-cohort was 6,463 participants and 109 had incident AF. Those with AF were significantly older (66.8 vs. 52.8 years), had a higher body mass index (BMI) (24.8 vs. 22.5 kg/m2), and had elevated levels of other indicators of metabolic syndrome. The three-year incidence of AF ranged from 0.03% (lowest-risk, HR<0.5) to 1.86% (highest-risk, HR>4). Time-dependent receiver operating characteristic curve analysis of the model showed a high area under the curve (AUC) of 0.803 for incident AF within three years. The predictive performance of the model improved by incorporating BMI and age (AUC, 0.877). Additionally, the LASSO-based score provided good predictive performance in any age-group (HR for highest- vs. lowest-risk: 4.2 for <50 years; 6.3 for 50–59 years; 7.3 for 60–69 years; and 5.1 for ≥70 years). MSEA revealed alterations in amino acid metabolism, suggesting metabolic remodeling in AF. Conclusion: We developed a metabolomics-based model that stratified individuals according to the risk of incident AF with good predictive performance. This model may assist in the early identification of high-risk individuals and support targeted preventive strategies against AF, even after age stratification. Further studies are required to validate the predictive ability in different populations.