Association between alopecia areata and cardiovascular disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Alopecia areata (AA) is a common autoimmune disorder causing patchy hair loss. Epidemiological observations and molecular studies collectively suggest an underrecognized interplay between AA and cardiovascular disease (CVD). However, the relationship between them remains controversial and requires further investigation. Objective To evaluate the association between AA and CVD through a meta-analysis of combinable results. Methods We systematically searched four databases (MEDLINE, Embase, Web of Science, and Cochrane Library) for relevant studies from inception to December 6, 2024. Studies included in the analysis were cohort or case-control studies that focused on the relationship between AA and CVD. Two independent reviewers extracted the data. The study quality was evaluated using the Newcastle-Ottawa Scale. A random-effects model was used for meta-analysis to calculate the odds ratio (OR) and 95% confidence intervals (CIs). Results Our search yielded five studies involving 238,270 AA patients from three countries. The meta-analysis revealed that AA patients had an increased OR (OR = 1.71; 95% CI: 1.0 to 2.92; p < 0.01) for CVD outcomes compared to the control group. Subgroup analysis revealed a stronger risk in patients with alopecia totalis or alopecia universalis (OR = 3.80; 95% CI: 1.65 to 8.73; p < 0.01). Associations were not observed between patch-type AA and CVD, nor between AA and ischemic stroke or myocardial infarction. Conclusions This meta-analysis suggests that AA patients, especially those with alopecia totalis or alopecia universalis, may have an elevated risk of developing CVD. Given the shared immunological mechanisms, systemic inflammation in AA may contribute to the development of atherosclerosis and increased cardiovascular risk. Further studies are needed to validate these findings and clarify the underlying mechanisms.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".