| Title: | Utilization of Serum Metabolomics and Polygenic Risk Scores in a Novel Risk Stratification Tool for the Prediction of Incident Atrial Fibrillation |
| Journal: | Circulation Arrhythmia and Electrophysiology |
| Published: | 19 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41711031/ |
| DOI: | https://doi.org/10.1161/circep.125.013858 |
Publication 17703
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Abstract
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.</p>
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 (CHARGE-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.</p>
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 CHARGE-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]).</p>
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.</p>
17 Keywords
- Aged
- Atrial Fibrillation
- Biomarkers
- Female
- Genetic Risk Score
- Humans
- Incidence
- Male
- Metabolomics
- Middle Aged
- Multifactorial Inheritance
- Predictive Value of Tests
- Prognosis
- Risk Assessment
- Risk Factors
- Time Factors
- United Kingdom
10 Authors
- Subhanik Purkayastha
- Joseph Park
- Sebastian E. Beyer
- Arun Chandra
- Steven M. Markowitz
- Bruce B. Lerman
- Olivier Elemento
- Jan Krumsiek
- James C. Lo
- Jim W. Cheung
1 Application
| Application ID | Title |
|---|---|
| 137851 | Prognostic value of deep learning-based retinal image analysis, metabolomics, genomics and environmental and behavioral exposures in patients with cardiac arrhythmias |
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