Abstract 381: Ideal Cardiovascular Health and Cardiovascular Disease: Heterogeneity Across Event Phenotype and Contribution of Multiple Biomarkers
Bibliographic record
Abstract
Aims: To investigate whether or not the association between baseline cardiovascular health (CVH) and incident cardiovascular disease (CVD) differs by event phenotypes and to address the mediating effect of inflammatory and haemostatic blood biomarkers. Methods: The association of ideal CVH with outcomes was computed in 9312 middle-aged men from Northern Ireland and France (whole cohort) in multivariable Cox proportional hazards regression analysis. The mediating effect of baseline blood biomarkers was evaluated in a case control study nested within the cohort after 10 years of follow-up. Results: After a median follow-up of 10 years, 614 first CHD events and 117 first stroke events were adjudicated. Compared to those with poor CVH, those with an ideal CVH profile at baseline had a 72% lower risk of CHD (HR=0.28; 95% CI: 0.17; 0.46) and a 76% lower risk of stroke (HR=0.24; 95% CI: 0.06; 0.98). No heterogeneity was detected across main CHD and main stroke phenotypes. While significantly lower mean concentrations of hs-CRP, IL-6 (inflammatory markers), and fibrinogen, von Willbrandt factor (haemostatic factors) were noted in the controls with higher CVH status, the association of CVH with incident CHD was not attenuated upon adjustment for these biomarkers. Conclusion: these results support the universal promotion of ideal CVH for CVD in general and suggest that the lower risk of CHD associated with ideal CVH is independent from inflammatory and haemostatic biomarkers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".