Global prevalence of metabolic syndrome in patients with psoriasis in the past two decades: current evidence
Bibliographic record
Abstract
Patients with psoriasis are at an increased risk of metabolic syndrome (MetS); however, a systematic analysis of its global prevalence has not been performed to date. Here, we performed a systematic review and meta-analysis to assess the prevalence of MetS among patients with psoriasis. We searched five databases from inception through September 2021 and used the Agency for Healthcare Research and Quality (AHRQ) and Newcastle-Ottawa Scale (NOS) tools to assess observational study quality. Stata SE 15.1 was used to perform the data analysis. Subgroup, meta-regression and sensitivity analyses were used to evaluate interstudy heterogeneity. Publication bias was evaluated using Egger's and Begg's linear tests. The global prevalence of MetS in patients with psoriasis was 32% (95% confidence interval [CI], 0.26-0.38). The prevalence in adults was 32% (95% CI, 0.29-0.36), while that in children and adolescents was 9% (95% CI, 0.00-0.18). Latin America had the highest prevalence of 47% (95% CI, 0.43-0.51), whereas North America had the lowest prevalence of 26% (95% CI, 0.16-0.37). Patients with psoriasis vulgaris (29%; 95% CI, 0.23-0.35) or severe psoriasis (37%; 95% CI, 0.27-0.46) had a higher prevalence of MetS than those with other psoriasis types. These findings suggest that MetS should be appropriately recognized and managed in patients with psoriasis. More population-based prospective observational studies are required to elucidate the mechanisms underlying the coexistence of MetS in patients with psoriasis.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".