Association Between Metabolic Syndrome and Radiographic Changes in Psoriatic Arthritis: A Cohort Study
Bibliographic record
Abstract
Objective Metabolic syndrome (MetS) is a known comorbidity of psoriatic arthritis (PsA) and is associated with PsA disease activity. We aimed to explore the association between MetS and radiographic features (peripheral and axial) in PsA. Methods We included patients with PsA followed at our prospective observational cohort for the period between 1978 and 2024. We identified patients with MetS on longitudinal follow‐up and used generalized estimating equations (GEE) analysis to define the radiographic features independently associated with MetS, adjusting for age, sex, PsA disease duration, calendar decade, and use of targeted disease‐modifying antirheumatic drugs. Results The study population consisted of 1,422 patients, out of which 400 (28.1%) had MetS at baseline (clinic entry) and 836 (58.79%) had a record of MetS (per the harmonized definition) over a median follow‐up duration of 10.59 (interquartile range 4.52–18.28) years. The mean (SD) age of our cohort at baseline was 44.43 (12.98) years, with 789 patients (55.5%) identifying as men. Mean (SD) body mass index was 28.79 (6.36) kg/m2. In the GEE analysis, MetS was not significantly associated with axial disease or radiographic damage to peripheral joints, assessed as the presence of syndesmophytes or sacroiliitis and the radiographic damaged joint count, respectively. On the other hand, MetS was significantly associated with calcaneal spurs, diffuse idiopathic skeletal hyperostosis, and degenerative disc disease. Conclusion MetS is associated with degenerative and metabolic changes in the spine and entheses but not with radiographic damage in PsA. image
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".