Genetically predicted waist-to-hip circumference ratio and coronary artery disease: A sex-specific Mendelian randomization study
Bibliographic record
Abstract
Coronary artery disease (CAD) affects millions of individuals worldwide and results in a substantial burden to healthcare systems. Although it is established that CAD affects females differently than males, differences between the sexes are not routinely accounted for. Body mass index is a known risk factor for CAD. However, more accurate metrics of body fat, including waist-to-hip circumference ratio (WHR), could be more meaningful clinically. WHR exhibits sex differences due to sex hormones, differing effects at genetic risk loci, and other factors. It is unclear if WHR is a causal factor for CAD in one or both sexes, but this information will be crucial for improving heart health. Causal inference, however, can be challenging. Large-scale cohorts with genetic data allow for Mendelian randomization, which, given certain assumptions, tests whether there is a causal relationship between an exposure and the outcome using genetic variants. We conducted sex-specific, one-sample MR analyses using two-stage least-squares regression in the UK Biobank with genetic variants robustly associated with WHR. We found evidence of a causal relationship between WHR and CAD risk in females (OR [95% CI] = 1.16 [1.06-1.26]; p value = 7.5E-4), whereas in males, we did not find evidence of a causal relationship (OR [95% CI] = 1.40 [0.98-2.01]; p value = 0.063). Results were supported by two additional MR approaches (using a genetic risk score and two-sample MR using the inverse variance weighted approach). We encourage future work assessing sex-specific effects using causal inference techniques to better understand factors contributing to complex disease risk.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".