Global epidemiology of lower-extremities artery disease in the 21st century (2000-2021): a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives The epidemiology of cardiovascular diseases and risk factors are evolving. In this meta-analysis our objectives were (1) to determine the prevalence of lower-extremities artery disease (LEAD) in the general population worldwide and by region in the 21st century and (2) to identify the risk factors associated with LEAD in the general population and by region in this period. Methods We performed a systematic literature review through PubMed, Cochrane, Scopus, Science Direct and Google Scholar databases, restricted to studies in general population performed between 01/2000 and 09/2021 on LEAD, defined by a low ankle brachial index (≤ 0.90). The Newcastle-Ottawa Scale was used to evaluate the quality of the articles before data extraction. A meta-analysis on the association of LEAD with cardiovascular risk factors was conducted. Due to high heterogeneity, random effect model was applied. Results Among 1418 references extracted, 42 articles (129,376 participants) were retained. Overall, the global prevalence in adults, mostly ≥ 40 years of age, was higher prevalence in women than in men (9.7%, 95%CI:6.4-13.0, vs. 7.9%, 95%CI:5.2-10.7) with an increase in prevalence with age in both genders (Figure). The highest prevalence was found in Europe (12.7%, 95%CI: 5.3-20.1) and the lowest in North America (5.6%, 95%CI: 2.5-8.6). Significant associations were found between LEAD and current tobacco smoking (odds-ratio (OR)=1.9, 95%CI:1.4-2.5) and past smoking (OR= 1.6, 95%CI:1.3-1.9) versus never smoking. Significant association was found between the prevalence of LEAD and diabetes (OR=2.3, 95%CI:2.0-2.8) with the strongest association in South-Center Asia (OR= 5.8, 95%CI:1.2-28.1). Hypertension was significantly associated with LEAD (OR=2.3, 95%CI: 1.9-2.8) and in particular in South America (OR=4.0, 95%CI:0.9-18.0). Finally, obesity and total cholesterol level (>240 mg/dL) were also significantly associated with LEAD (OR=1.4, 95%CI:1.2-1.8 and OR=2.2, 95%CI:1.0 -5.3, respectively). Conclusion This meta-analysis highlights the high prevalence of LEAD with significant differences between men and women and the increasing importance of the metabolic syndrome in recent decades, with some variation by gender and global region.Prevalence of LEAD by age and gender
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.011 | 0.012 |
| 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".