Beyond Joints: Neuropsychiatric Benefits of TNF-α and IL-6 Inhibitors in Rheumatoid Arthritis – a Narrative Review
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease that, beyond joint destruc-tion, contributes to neuropsychiatric symptoms such as depression, anxiety, and cogni-tive impairment. These symptoms are often underrecognized despite their major impact on quality of life. Accumulating evidence suggests that pro-inflammatory cytokines, particularly tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), play a key role in this neuroimmune interface. This narrative review examined 16 clinical studies evaluating the effects of biologic therapies targeting TNF-α and IL-6 on mental health outcomes in RA. The total study population comprised 9,939 patients, including 2,467 treated with TNF-α inhibitors and 7,472 with IL-6 or IL-6 receptor inhibitors. TNF-α inhibitors were associated with improved depressive symptoms and emotional well-being. IL-6 inhibitors demonstrated similar psychiatric benefits, particularly in patients with elevated IL-6 levels. The findings highlight that biological therapies in RA may influence not only physical symptoms but also mental health, likely through modulation of neuroimmune pathways including blood–brain barrier permeability, microglial acti-vation, and HPA axis regulation. Future research is needed to clarify these Effects in populations stratified by psychiatric comorbidity and inflammatory biomarkers. Clini-cal implications: Incorporating psychiatric symptom screening and considering neuroin-flammatory profiles may help guide the selection of biologic therapy in RA, particularly in patients with comorbid depression or fatigue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".