Association between waist-to-hip ratio and risk of myocardial infarction: a systematic evaluation and meta-analysis
Bibliographic record
Abstract
Background Myocardial infarction(MI) is one of the most serious health threats. Despite the increasing number of clinical methods used to predict the onset of MI, the prediction of MI is still unsatisfactory and necessitates new methods. Objective To systematically review observational studies from the past two decades on the association between waist-to-hip ratio (WHR) and MI risk. Methods Original literature on the correlation between WHR and MI was searched in PubMed, Embase, Web of Science, Cochrane Library, Science Direct, CNKI, and Wanfang up to January 31, 2024. Two researchers independently screened, extracted data, and assessed quality using the Newcastle-Ottawa Scale (NOS) and Revman5.3. Meta-analysis with Stata 16.0 calculated the combined Odd ratio (OR) for WHR and MI risk. Heterogeneity was assessed with the I2 statistic to select the appropriate effects model. Subgroup analysis, meta-regression, sensitivity analysis, and funnel plots tested for heterogeneity and publication bias. Results A total of 22 observational studies were included, involving 709,093 participants. The meta-analysis showed that an elevated WHR was significantly associated with an increased risk of MI, with a pooled odds ratio (OR) of 1.98 [95% Confidence interval (CI): 1.75–2.24] and high heterogeneity (I2 = 91.5%, P < 0.0001). Subgroup analysis revealed a stronger association between WHR and MI in women (OR: 1.99, 95% CI: 1.43–2.77) compared to men (OR: 1.74, 95% CI: 1.36–2.22). Regional analysis indicated that the association between WHR and MI risk was highest in Asian populations (OR: 2.93 95% CI: 1.61–5.33), followed by American (OR: 1.73, 95% CI: 1.45–2.08) and European populations (OR: 2.19, 95% CI: 1.49–3.22). Sensitivity analysis demonstrated that the results remained stable after excluding one study. Conclusion In the general adult population, a higher WHR is a potentially significant association for MI and has predictive value for MI.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".