Sex-specific associations of social determinants of health and genetic risk factors with atherosclerotic cardiovascular diseases incidence in the general population
Bibliographic record
Abstract
Abstract Background and aims The combined contribution of polysocial risk factors (socioeconomic status, psychosocial factors and living environment) and genetic background on atherosclerotic cardiovascular disease (ASCVD) risk remains unknown. We investigated the contribution of a comprehensive polysocial risk score (PsRS) and polygenic risk scores (PRS) on ASCVD incidence. Methods We developed a PsRS using latent class analysis based on socioeconomic factors, psychosocial factors and living environment in 321,016 UK Biobank participants free of ASCVD. Participants were divided into three PsRS groups. The impact of the PsRS on incident ASCVD was assessed using Cox proportional hazards. The impact of the PsRS and coronary artery disease (CAD)PRS and ischemic stroke (IS)PRS on the incidence of CAD and IS, respectively, were also assessed. Results During a median follow-up of 12.5 years, 17,737 ASCVD events were recorded. Compared to participants with a low PsRS, those with a high PsRS had a higher risk of ASCVD (HR=1.46 [95% CI, 1.40-1.52], p <0.001). Risk associated with an elevated PsRS was higher for females compared to males. Compared to participants with a low PsRS in the bottom tertile of CAD PRS, those with a high PsRS in the top tertile of CAD PRS were at higher CAD risk (HR=4.24 [95% CI, 3.94-4.55], p <0.001). Similar results were obtained for IS. Conclusions A comprehensive PsRS was associated with incident ASCVD, particularly in females, and may exacerbate genetic susceptibility to both CAD and IS, suggesting that addressing polysocial risk factors is key to implementing preventive ASCVD strategies in the general population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".