Abstract 14864: Impact of Lifestyle and Cardiometabolic Risk Factors on Atherosclerotic Cardiovascular Diseases Incidence Across Body Weight Categories: A Prospective Study of 321,259 UK Biobank Participants
Bibliographic record
Abstract
Introduction: Individuals with an elevated body mass index (BMI) are at higher atherosclerotic cardiovascular diseases (ASCVD) risk. While the impact of an elevated BMI on ASCVD risk in individuals who are “metabolically healthy” is still debated, the respective contributions of BMI and actionable lifestyle and cardiometabolic risk factors on ASCVD risk remains largely unknown. We investigated the respective contributions of lifestyle and cardiometabolic risk factors and BMI to ASCVD incidence in apparently healthy individuals. Methods: We developed a cardiovascular health score (CVHS) based on three lifestyle (smoking status, fruits and vegetables consumption and physical activity levels) and six cardiometabolic (blood pressure, HbA1c, LDL-C, HDL-C, C-Reactive Protein and triglyceride levels) parameters. The study sample included 321,259 participants of the UK Biobank, free of ASCVD recruited between 2006 and 2010. As of August 2021, 15,699 of them had incident ASCVD (fatal or nonfatal myocardial infarction, ischemic stroke or cardiac revascularization procedures. The impact of the CVHS on incident ASCVD alone and in BMI categories was assessed using Cox proportional hazards adjusted for age, sex, ethnicity and deprivation. Results: Compared to participants with a very high CVHS (CVHS=9), those with a very low CVHS (CVHS=0) had a higher ASCVD risk (hazard ratio = 12.2 (95% CI, 6.7-22.2, p<0.001). Figure 1 presents the association between the CVHS and hazard ratio for incident ASCVD in four BMI categories. Conclusions: In participants of the UK Biobank, a cardiovascular health score based on lifestyle and cardiometabolic risk factors was strongly associated with incident ASCVD in all BMI categories. The relationship between BMI and ASCVD incidence across CVHS categories was inconsistent. Strategies aimed at improving healthy lifestyle habits and cardiometabolic risk factors may decrease ASCVD risk in everyone, regardless of their body weight.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".