The Association Between Obesity and Clinical Features of Psoriatic Arthritis: A Case-control Study
Bibliographic record
Abstract
OBJECTIVE: To assess whether obesity is associated with distinct psoriatic arthritis (PsA) features and whether it interacts with PsA HLA susceptibility alleles. METHODS: Patients with early PsA were compared with patients with psoriasis without arthritis (PsC). The primary predictor was the body mass index (BMI) at the first visit to the clinic. The clinical features across 3 BMI groups were compared by linear trend test and Cochrane-Armitage trend test. The interaction between BMI and HLA risk alleles for psoriatic disease (HLA-B*27, B*3901, B*3801, B*0801, B*4402, B*4403, and C*0602) were assessed using logistic regression analysis. RESULTS: There were 314 patients with early PsA, and 498 patients with PsC were analyzed. Obesity was more frequent in patients with PsA compared with PsC (OR 1.77; p = 0.002). Higher BMI was associated with older age at onset of PsA (p < 0.0001) and psoriasis (p = 0.009). The frequency of HLA-B*27 was higher in patients with normal weight compared with those with higher BMI (p = 0.002). A significant interaction was found for the combined effect of HLA-B*27 and obesity in logistic regression analysis (p = 0.036). In patients who were HLA-B*27-negative, the association between obesity and PsA was statistically significant (OR 2.39; p < 0.001), but obesity was less frequent in patients with PsA who were HLA-B*27-positive. CONCLUSION: Obesity is linked with late-onset psoriasis and PsA, while normal weight is associated with the presence of the HLA-B*27 allele and an earlier onset of the disease. These results highlight the differential risk factors that may drive the inflammatory process in psoriatic disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".