TNF inhibitors for the prevention of Alzheimer’s disease: Preliminary findings from the rheumatoid arthritis medication and memory study (RESIST)
Bibliographic record
Abstract
Abstract Background A raised level of serum TNFα observed in patients with Alzheimer’s disease has been associated with an increased rate of neurodegeneration and risk of conversion from mild cognitive impairment (MCI) to AD dementia. This led to the hypothesis that reduction in peripheral TNFα via TNF inhibitors (TNFi) may reduce the rate of neurodegeneration and may protect against development of AD. Method The rheumatoid arthritis medication and memory study (RESIST) is an 18‐month observational study which aims to compare the rate of cognitive decline between TNFi and conventional synthetic disease modifying anti‐rheumatic drugs (csDMARDs) in patients with both rheumatoid arthritis (RA) and MCI. Participants ≥ 55 years of age were recruited from rheumatology clinics in both Northern Ireland and Southampton and were cognitively assessed using the Free and Cued Selective Reminding Test (FCSRT) and Montreal Cognitive Assessment (MoCA). At the time of analysis n=130 participants had completed a 6‐month assessment and n=69 had completed a 12‐month assessment. ANCOVA analysis was conducted to calculate the difference in mean (and 95% CI) FCSRT and MoCA scores at both 6‐ and 12‐month timepoints between TNFi and csDMARD treatment groups, adjusting for baseline cognitive scores. Further adjustments were made for age, gender, RA disease activity and other confounding variables. Result There was no evidence of a difference between TNFi and csDMARD treatment in cognitive outcomes measured by FCSRT (6‐month cohort (mean difference 0.58, 95% CI ‐ 1.40, 2.56, p = 0.565); 12‐month cohort (mean difference ‐1.09, 95% CI ‐3.57, 1.38, p = 0.381)) or MoCA (6‐month cohort (mean difference ‐1.09, 95% CI ‐3.57, 1.38, p = 0.381); 12‐month cohort (mean difference ‐0.04, 95% CI ‐1.06, 0.98, p = 0.940)) after adjustment for baseline cognitive scores nor was there a difference in cognitive outcomes between treatment groups after adjustment for additional confounders. Conclusion This preliminary analysis found little evidence of a relationship between TNFi and better cognitive performance however analysis was limited by lack of power and short follow‐up periods. This analysis will be repeated once RESIST has ended to determine if there is any statistically significant cognitive benefit of TNFi treatment after 18‐months.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".