Early-Onset Obesity and Risk for Psoriatic ArthritisOBESITY AND RISK FOR PSORIATIC ARTHRITIS
Bibliographic record
Abstract
Razieh Soltani-Arabshahi, MD; Bob Wong, PhD; Bing-Jian Feng, PhD; David E. Goldgar, PhD; Kristina Callis Duffin, MD; Gerald G. Krueger, MDObjective: To study whether obesity increases the risk of psoriatic arthritis (PsA), given that obesity is a risk factor for psoriasis and is associated with more severe disease.Design: Case series. We used Cox regression analysis to study the relationship between obesity and PsA while controlling for age at psoriasis onset, current body mass index (BMI), sex, family history of psoriasis, worst-ever body surface area (BSA) involvement, Koebner phenomenon, and nail involvement.Setting: Dermatology clinics at the University of Utah School of Medicine.Patients: Volunteer sample of patients with dermatologist-diagnosed psoriasis enrolled in the Utah Psoriasis Initiative from November 2002 to October 2008 (943 subjects; 50.2% women, 49.8% men).Main Outcome Measures: Physician diagnosis of PsA from self-report questionnaire.Results: In our subjects, we found that BMI at age 18 years was predictive of PsA (odds ratio [OR], 1.06) (P < .01) over and above control variables. Other variables that were predictors of PsA included younger age at psoriasis onset (odds ratio [OR], 0.98) (P < .01), female sex (OR, 1.45) (P = .01), higher worst-ever BSA involvement with psoriasis (OR, 1.01) (P = .04), Koebner phenomenon (OR, 1.59) (P < .01), and nail involvement (OR, 1.76) (P < .01). Current BMI and family history of psoriasis were not significant predictors of PsA.Conclusions: This study suggests that obesity at age 18 years increases the risk of developing PsA. Adiposity is associated with higher levels of inflammatory cytokines known to be associated with psoriasis. This inflammatory milieu could increase the risk of PsA in predisposed subjects. Prevention and early treatment of obesity may decrease the risk of PsA.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".