Secondary Mania induced by <scp>TNF</scp>‐α inhibitors: A systematic review
Bibliographic record
Abstract
A growing number of studies support a bidirectional relationship between inflammation and bipolar disorders. Tumor necrosis factor-α (TNF-α) inhibitors have recently attracted interest as potential therapeutic compounds for treating depressive symptoms, but the risk for triggering mood switches in patients with or without bipolar disorders remains controversial. Thus, we conducted a systematic review to study the anti-TNF-α medication-induced manic or hypomanic episodes. PubMed, Scopus, Medline, and Embase databases were screened for a comprehensive literature search from inception until November 2020, using The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Out of the initial 75 references, the screening resulted in the inclusion of four case reports (each describing one patient) and a cohort study (in which 40 patients out of 7600-0.53% - experienced elated mood episodes after infliximab administration). Of these 44 patients, 97.7% experienced a manic episode and 2.3% hypomania. 93.2% of patients had no history of psychiatric disorder or psychotropic treatment. Only 6.8% had a history of psychiatric disorders with the affective spectrum (4.6% dysthymia and 2.3% bipolar disorder). The time of onset of manic or hypomanic symptoms varied across TNF-α inhibitors with an early onset for Infliximab and a later onset for Adalimumab and Etanercept. These findings suggest that medications targeting the TNF-α pathway may trigger a manic episode in patients with or without affective disorders. However, prospective studies are needed to evaluate the relative risk of such side effects and identify the population susceptible to secondary mania.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".