Androgenetic Alopecia and its Association with Metabolic Syndrome: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objectives. The study aimed to confirm the association between androgenetic alopecia (AGA) and Metabolic Syndrome (MetS). It also aimed to determine if early-onset AGA among males and AGA among females increases the risk of developing MetS, and if severity of AGA increases the odds of developing MetS. Methods. Observational studies from electronic databases were selected by the consensus of three independent review authors. The Newcastle-Ottawa Scale for assessing the quality of non-randomized studies in meta-analysis was used. Statistical analyses were accomplished using Review Manager software. Results. A total of 11 case-control studies, one prospective cohort study, and five cross-sectional studies were selected. In the meta-analysis of ten case-control studies and three cross-sectional studies (3840 participants), AGA was significantly correlated with MetS (OR 2.59, 95% CI 1.51 to 4.44; p<0.0005). Early-onset AGA among males (<35 years old) showed significant association (OR 3.69, 95% CI 2.15 to 6.33; p<0.00001). AGA among females also increased the odds of developing MetS (OR 5.59, 95% CI 2.06 to 15.12; p<0.0007). Moderate to severe AGA in males, Norwood-Hamilton IV or higher, was also significant (OR 1.65, 95% CI 1.12 to 2.42; p=0.01). The same trend was noted for females with Ludwig II and III (OR 5.82, 95% CI 2.54 to 13.34; p<0.00001). Conclusion. Although the pathophysiology still remains under investigation, the present study points to an association between AGA and MetS. It can be used as a marker to identify patients who should be screened for MetS and managed accordingly.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".