Disease-Modifying Antirheumatic Drugs and Dementia Prevention: A Systematic Review of Observational Evidence in Rheumatoid Arthritis
Bibliographic record
Abstract
BACKGROUND: Many observational studies have examined the association of disease-modifying antirheumatic drugs (DMARDs) with dementia risk, but the evidence has been mixed, possibly due to methodological reasons. This systematic review (PROSPERO: CRD42023432122) aims to assess existing observational evidence and to suggest if repurposing DMARDs for dementia prevention merits further investigation. METHODS: Four electronic databases up to October 26, 2023, were searched. Cohort or case-control studies that examined dementia risk associated with DMARDs in people with rheumatoid arthritis were included. Risk of bias was evaluated using the Cochrane Collaboration's Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) criteria. Findings were summarized by individual drug classes and by risk of bias. RESULTS: Of 12,180 unique records, 14 studies (4 case-control studies, 10 cohort studies) were included. According to the ROBINS-I criteria, there were 2 studies with low risk of bias, 1 study with moderate risk, and 11 studies with serious or critical risk. Among studies with low risk of bias, one study suggested that hydroxychloroquine versus methotrexate was associated with lower incident dementia, and the other study showed no associations of tumor necrosis factor (TNF) inhibitors, tocilizumab, and tofacitinib, compared to abatacept, with incident dementia. CONCLUSION: Studies that adequately addressed important biases were limited. Studies with low risk of bias did not support repurposing TNF inhibitors, tocilizumab, abatacept or tofacitinib for dementia prevention, but hydroxychloroquine may be a potential candidate. Further studies that carefully mitigate important sources of biases are warranted, and long-term evidence will be preferred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".