Depression as a risk factor for the development of rheumatoid arthritis: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: Major depressive disorder (MDD) is associated with increased levels of systemic proinflammatory cytokines, including tumour necrosis factor alpha. As these cytokines are pathogenic in autoimmune diseases such as rheumatoid arthritis (RA), our aim was to explore on a population-level whether MDD increases the risk of developing RA. METHODS: A retrospective cohort study was conducted using The Health Improvement Network (THIN) database (from 1986 to 2012). Observation time was recorded for both the MDD and referent cohorts until patients developed RA or were censored. Cox proportional hazards models were used to determine the risk of developing RA among patients with MDD, accounting for age, sex, medical comorbidities, smoking, body mass index and antidepressant use. RESULTS: A cohort of 403 932 patients with MDD and a referent cohort of 5 339 399 patients without MDD were identified in THIN. Cox proportional hazards models revealed a 31% increased risk of developing RA among those with MDD in an unadjusted model (HR=1.31, 95% CI 1.25 to 1.36, p<0.0001). When adjusting for all covariates, the risk remained significantly increased among those with MDD (HR=1.38, 95% CI 1.31 to 1.46, p<0.0001). Antidepressant use demonstrated a confounding effect that was protective on the association between MDD and RA. CONCLUSION: MDD increased the risk of developing RA by 38%, and antidepressants may decrease this risk in these patients. Future research is necessary to confirm the underlying mechanism of MDD on the pathogenesis of RA.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".