Statins and Risk of Rheumatoid Arthritis: A Nested Case–Control Study
Bibliographic record
Abstract
OBJECTIVE: Statins have antiinflammatory/immunomodulatory effects that may be useful in preventing rheumatoid arthritis (RA), but previous observational studies about the risk of RA with statin use yielded conflicting results. The aim of this study was to determine whether high-intensity statin treatment is associated with reduced risk of RA. METHODS: Using data from the UK Clinical Practice Research Datalink, we performed a nested case-control analysis in a population-based cohort of patients who began receiving statins between 1997 and 2009 and were followed up until a first diagnosis of RA, death, end of registration with the physician's practice, or end of January 2011. For each case of RA, 10 age-, sex-, and calendar year-matched controls were randomly selected from risk sets. We estimated the hazard ratio (HR) of incident RA in the highest quintile of duration-weighted average statin intensity compared to the lowest, using conditional logistic regression. Models were adjusted for smoking status, total cholesterol level, obesity, history of cardiovascular disease, coexistent autoimmune disease, hypothyroidism, and persistence with treatment. RESULTS: The cohort included 528,654 new users of statins, with 1,357 new cases of RA occurring during a mean follow-up of 3.3 years, for an incidence rate of 7.9 per 10,000 person-years. Cases were more likely to be smokers, to have other autoimmune diseases, and to have had lower total cholesterol levels at baseline. The incidence of RA was lower in the highest statin intensity quintile (adjusted HR 0.77 [95% confidence interval 0.63-0.95]) in comparison to the lowest quintile. CONCLUSION: In this large population-based study, high-intensity statin treatment was associated with a reduced risk of RA in comparison to low-intensity statin treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".