Diabetes as a risk factor for incident peripheral arterial disease in women compared to men: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Previous meta-analyses have suggested that diabetes confers a greater excess risk of coronary heart disease, stroke, vascular dementia, and heart failure in women compared to men. While the underlying mechanism that explains such greater excess risk is unknown, in the current meta-analysis we hypothesized that we would find a similar sex difference in the relationship between diabetes and peripheral arterial disease (PAD). METHODS: PubMed MEDLINE, the Cochrane Database of Systematic Reviews, and Embase were systematically searched for prospective population-based cohort studies, with no restriction on publication date, language, or country. We included studies that reported the relative risk (RR), and its variability, for incident PAD associated with diabetes in both sexes. We excluded studies that did not adjust at least for age, and in which participants had pre-existing PAD. In cases where sex-specific results were not reported, study authors were contacted. Random-effects meta-analyses with inverse variance weighting were used to obtain summary sex-specific RRs and the women: men ratio of RRs for PAD. The Newcastle-Ottawa scale was used to assess study quality. RESULTS: = 0%). All studies scored 6-8, on the Newcastle-Ottawa scale of 0-9, indicating good quality. Eleven of the 12 studies that met review inclusion criteria did not report sex-specific relative risk, and these data were collected through direct correspondence with the study authors. CONCLUSION/INTERPRETATION: Consistent with other studies, we found evidence that diabetes is an independent risk factor for PAD. However, in contrast to similar studies of other types of cardiovascular disease, we did not find evidence that diabetes confers a greater excess risk in women compared to men for PAD. More research is needed to explain this sex differential between PAD and other forms of CVD, in the sequelae of diabetes. In addition, we found that very few studies reported the sex-specific relative risk for the association between diabetes and PAD, adding to existing evidence for the need for improved reporting of sex-disaggregated results in cardiovascular disease research.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".