Psoriasis and metabolic syndrome: implications for the management and treatment of psoriasis
Bibliographic record
Abstract
Psoriasis is a chronic systemic inflammatory disorder associated with several comorbidities in addition to the characteristic skin lesions. Metabolic syndrome (MetS) is the most frequent comorbidity in psoriasis and a risk factor for cardiovascular disease, a major cause of death among patients with psoriasis. Although the exact causal relationship between these two disorders is not fully established, the underlying pathophysiology linking psoriasis and MetS seems to involve overlapping genetic predispositions and inflammatory pathways. Dysregulation of the IL-23/Th-17 immune signalling pathway is central to both pathologies and may be key to promoting susceptibility to metabolic and cardiovascular diseases in individuals with and without psoriasis. Thus, biological treatments for psoriasis that interrupt these signals could both reduce the psoriatic inflammatory burden and also lessen the risk of developing atherosclerosis and cardiometabolic diseases. In support of this hypothesis, improvement of skin lesions was associated with improvement in vascular inflammation in recent imaging studies, demonstrating that the beneficial effect of biological agents goes beyond the skin and could help to prevent cardiovascular disease. This review will summarize current knowledge on underlying inflammatory mechanisms shared between psoriasis and MetS and discuss the most recent clinical evidence for the potential for psoriasis treatment to reduce cardiovascular risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".