Sex Disparities in Cardiovascular Outcome Trials of Populations With Diabetes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND Sex differences have been described in diabetes cardiovascular outcome trials (CVOTs). PURPOSE We systematically reviewed for baseline sex differences in cardiovascular (CV) risk factors and CV protection therapy in diabetes CVOTs. DATA SOURCES Randomized placebo-controlled trials examining the effect of diabetes medications on major adverse cardiovascular events in people ≥18 years of age with type 2 diabetes. STUDY SELECTION Included trials reported baseline sex-specific CV risks and use of CV protection therapy. DATA EXTRACTION Two reviewers independently abstracted study data. DATA SYNTHESIS We included five CVOTs with 46,606 participants. We summarized sex-specific data using mean differences (MDs) and relative risks (RRs) and pooled estimates using random effects meta-analysis. There were fewer women than men in included trials (28.5–35.8% women). Women more often had stroke (RR 1.28; 95% CI 1.09, 1.50), heart failure (RR 1.30; 95% CI 1.21,1.40), and chronic kidney disease (RR 1.33; 95% CI 1.17; 1.51). They less often used statins (RR 0.90; 95% CI 0.86, 0.93), aspirin (RR 0.82; 95% CI 0.71, 0.95), and β-blockers (RR 0.93; 95% CI 0.88, 0.97) and had a higher systolic blood pressure (MD 1.66 mmHg; 95% CI 0.90, 2.41), LDL cholesterol (MD 0.34 mmol/L; 95% CI 0.29, 0.39), and hemoglobin A1c (MD 0.11%; 95% CI 0.09, 0.14 [1.2 mmol/mol; 1.0, 1.5]) than men. LIMITATIONS We could not carry out subgroup analyses due to the small number of studies. Our study is not generalizable to low CV risk groups nor to patients in routine care. CONCLUSIONS There were baseline sex disparities in diabetes CVOTs. We suggest efforts to recruit women into trials and promote CV management across the sexes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.038 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| 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".