Sex Differences in the Association Between Polygenic Risk Score and Atrial Fibrillation Incidence: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Although sex disparities in atrial fibrillation (AF) epidemiology and outcomes are well documented, the role of sex in modulating genetic susceptibility to incident AF remains poorly characterized. In this study we assessed sex-specific effects of polygenic risk score (PRS) on AF incidence and the sex-specific PRS effects, stratified by the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE-AF) clinical risk score. METHODS: This prospective cohort study included 444,463 AF-free UK Biobank participants (54.67% women; mean age 56.46 ± 8.09 years). Participants were stratified by sex, AF-PRS (cutoff ≥ 0.295), and CHARGE-AF clinical risk score (cutoff ≥ 12.048). Incident AF was ascertained via ICD-10 codes. Cox hazards regression (adjusting for clinical, metabolic, lifestyle, and socioeconomic covariables) was used to evaluate the multiplicative interactions among AF-PRS, sex, and CHARGE-AF. RESULTS: Over 14.67 ± 3.01 years, 31,070 participants experienced incident AF. A significant interaction between male sex and higher AF-PRS emerged (hazard ratio [HR] 0.95, 95% confidence interval [CI] 0.91-1.00; P = 0.031). Women were more genetically susceptible to AF at higher CHARGE-AF (HR 1.99 vs 1.83 in men) and men at lower CHARGE-AF (HR 2.33 vs 2.11 in women). In addition, the tripartite interaction (AF-PRS × sex × CHARGE-AF, HR 0.84; P < 0.001) further validated the sex-specific results stratified by CHARGE-AF. CONCLUSIONS: The association between AF-PRS and incident AF is modified by sex, with clinical risk burden modifying sex-related PRS effects. Women presented with higher genetic susceptibility at higher CHARGE-AF, and men at lower CHARGE-AF. Rethinking AF genetic susceptibility in a sex- and context-dependent manner may enhance precise prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".