Sex‐Specific Impact of Body Weight on Atherosclerotic Cardiovascular Disease Incidence in Individuals With and Without Ideal Cardiovascular Health
Bibliographic record
Abstract
Background The impact of an elevated body mass index (BMI) on atherosclerotic cardiovascular disease (ASCVD) risk in individuals who are metabolically healthy is debated. We investigated the respective contributions of BMI as well as lifestyle and cardiometabolic risk factors combined to ASCVD incidence in 319 866 UK Biobank participants. Methods and Results We developed a cardiovascular health score (CVHS) based on 4 lifestyle and 6 cardiometabolic parameters. The impact of the CVHS on incident ASCVD (15 699 events) alone and in BMI and waist‐to‐hip ratio categories was assessed using Cox proportional hazards in women and men separately. In participants with a high CVHS (8–10), those with a BMI ≥35.0 kg/m 2 had a nonsignificantly higher ASCVD risk (hazard ratio [HR], 1.20 [95% CI, 0.84–1.70]; P =0.32) compared with those with a BMI of 18.5 to 24.9 kg/m 2 . In participants with a BMI of 18.5 to 24.9 kg/m 2 , those with a lower CVHS (0–2) had a higher ASCVD risk (HR, 4.06 [95% CI, 3.23–5.10]; P <0.001) compared with those with a higher CVHS (8–10). When we used the waist‐to‐hip ratio instead of the BMI, a dose–response relationship between the waist‐to‐hip ratio and ASCVD risk was obtained in healthier participants. Results were similar in women compared with men. Conclusions In women and men in the UK Biobank, the relationship between the BMI and ASCVD incidence in healthy individuals was inconsistent, whereas cardiovascular risk factors strongly predicted ASCVD incidence in all BMI categories. Assessing lifestyle and cardiometabolic risk factors as well as body fat distribution indices may help identify individuals at high ASCVD risk, regardless of body weight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".