Metabolic Syndrome Is Associated With an Increased Risk of Rheumatoid Arthritis: A Prospective Cohort Study Including 369,065 Participants
Bibliographic record
Abstract
OBJECTIVE: To explore the associations between metabolic syndrome (MetS) and its individual components and the risk of rheumatoid arthritis (RA). METHODS: A total of 369,065 individuals were included in the present study based on the UK Biobank. Multivariable Cox proportional hazards regression models were applied to estimate the associations between MetS and its individual components and the risk of RA. Mediation analysis was performed to further assess the potential mediating role of C-reactive protein (CRP) in the relationship between MetS and RA. RESULTS: During a median follow-up period of 12.04 years, a total of 4901 incident RA cases were documented. MetS (hazard ratio [HR] 1.22, 95% CI 1.14-1.30) and 4 of its 5 components (elevated waist circumference [WC; HR 1.21, 95% CI 1.12-1.32], elevated triglyceride [TG] level [HR 1.12, 95% CI 1.05-1.19], reduced high-density lipoprotein cholesterol [HDL-C] level [HR 1.31, 95% CI 1.23-1.39], and hyperglycemia [HR 1.15, 95% CI 1.05-1.25]) were associated with an increased risk of RA. In addition, the risk of RA increased as the number of diagnosed MetS components increased, with the highest risk in participants with all 5 components. Mediation analysis showed that CRP might mediate the association between MetS and RA, accounting for 9.27% of the total effect. CONCLUSION: These findings indicated positive associations between MetS and 4 of its components (WC, TG, HDL-C, and hyperglycemia) and the risk of RA, highlighting the importance of MetS management in the prevention 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".