Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies
Bibliographic record
Abstract
AIMS: The objectives of this study were to determine the association of waist circumference (WC) and waist-to-hip ratio (WHR) with the risk of incident cardiovascular disease (CVD) events and to determine whether the strength of association of WC and WHR with CVD risk is different. METHODS AND RESULTS: This meta-regression analysis used a search strategy of keywords and MeSH terms to identify prospective cohort studies and randomized clinical trials of CVD risk and abdominal obesity from the Medline, Embase, and Cochrane databases. Fifteen articles (n = 258 114 participants, 4355 CVD events) reporting CVD risk by categorical and continuous measures of WC and WHR were included. For a 1 cm increase in WC, the relative risk (RR) of a CVD event increased by 2% (95% CI: 1-3%) overall after adjusting for age, cohort year, or treatment. For a 0.01 U increase in WHR, the RR increased by 5% (95% CI: 4-7%). These results were consistent in men and women. Overall risk estimates comparing the extreme quantiles of each measure suggested that WHR was more strongly associated with CVD than that for WC (WHR: RR = 1.95, 95% CI: 1.55-2.44; WC: RR = 1.63, 95% CI: 1.31-2.04), although this difference was not significant. The strength of association for each measure was similar in men and women. CONCLUSION: WHR and WC are significantly associated with the risk of incident CVD events. These simple measures of abdominal obesity should be incorporated into CVD risk assessments.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.066 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".