The effect of disease-modifying anti-rheumatic drugs on skeletal muscle mass in rheumatoid arthritis patients: a systematic review with meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Rheumatoid arthritis (RA) is an autoimmune disease, characterized by chronic and systemic inflammation. Besides, it is known that RA patients may present several comorbidities, such as sarcopenia, a condition where patients present both muscle mass and muscle quality impairment. RA treatment is mostly pharmacological and consists in controlling systemic inflammation and disease activity. Despite that, the effect of pharmacological treatment on sarcopenia is not well characterized. OBJECTIVE: To summarize the effects of disease-modifying anti-rheumatic drugs (DMARDs) on skeletal muscle tissue in rheumatoid arthritis (RA) patients. METHODS: A systematic review of randomized clinical trials and observational studies was conducted using MEDLINE, Embase, Cochrane Library, and Web of Science. We selected studies with rheumatoid arthritis patients treated with disease-modifying anti-rheumatic drugs (DMARDs) that analyzed muscle mass parameters such as lean mass and appendicular lean mass. Methodological quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Standardized mean difference (SMD) and 95% confidence intervals (CI) were set. A meta-analysis of observational studies was performed using the R software, and we considered significant statistics when p < 0.05. RESULTS: Nine studies were included in this systematic review. In the meta-analysis, DMARD treatment had no positive difference (p = 0.60) in lean mass. In the same way, in the appendicular lean mass parameter, our results showed that DMARDs did not have changes between baseline and post-treatment analysis (p = 0.93). CONCLUSION: There is no evidence of a significant effect of DMARD therapy, either synthetic or biological, on muscle mass. However, this association should be investigated with more studies.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".