Paradoxically lower prevalence of peripheral arterial disease in South Asians: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: While people of South Asian (SA) descent have higher rates of cardiovascular disease compared with people of White European (WE) descent, a lower prevalence of lower extremity peripheral arterial disease (PAD) has been suggested in SA. Our intent was to systematically review the literature on PAD prevalence in people of SA descent and to conduct a meta-analysis to identify differences in PAD prevalence between SA and WE. METHODS: Standard Cochrane systematic review methodology was used for conducting a literature review of published research. Population prevalence studies of PAD in SA with a WE comparison group were included. Full text studies were selected and reviewed by two authors with independent data extraction. Prevalence differences between SA and WE were analysed using ORs. FINDINGS: 129 studies were initially identified and ultimately 15 (n=240 003 patients) studies were included. Only one study reported direct comparative general PAD prevalence between SA and WE (OR=0.26, 95%CI 0.17 to 0.38, p<0.001, n=77 855). Fourteen studies with comparative prevalence data between SA and WE in high-risk populations confirm significantly lower odds of PAD in SA with coronary artery disease (CAD) (OR=0.47, 95%CI 0.39 to 0.56, p<0.001, n=139 313) and diabetes (OR=0.44; 95% CI 0.30 to 0.63, p<0.001, n=22 835). INTERPRETATION: Reported PAD prevalence is significantly lower in SA than WE for both the CAD and diabetes populations. Explanations for these findings, if true, are unclear. These results underscore the need for further study to clarify mechanisms of ethnic divergence in PAD prevalence.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".