Anti‐inflammatory agents in the treatment of bipolar depression: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Inflammation has been implicated in the risk, pathophysiology, and progression of mood disorders and, as such, has become a target of interest in the treatment of bipolar disorder (BD). Therefore, the objective of the current qualitative and quantitative review was to determine the overall antidepressant effect of adjunctive anti-inflammatory agents in the treatment of bipolar depression. METHODS: Completed and ongoing clinical trials of anti-inflammatory agents for BD published prior to 15 May 15 2015 were identified through searching the PubMed, Embase, PsychINFO, and Clinicaltrials.gov databases. Data from randomized controlled trials (RCTs) assessing the antidepressant effect of adjunctive mechanistically diverse anti-inflammatory agents were pooled to determine standard mean differences (SMDs) compared with standard therapy alone. RESULTS: Ten RCTs were identified for qualitative review. Eight RCTs (n = 312) assessing adjunctive nonsteroidal anti-inflammatory drugs (n = 53), omega-3 polyunsaturated fatty acids (n = 140), N-acetylcysteine (n = 76), and pioglitazone (n = 44) in the treatment of BD met the inclusion criteria for quantitative analysis. The overall effect size of adjunctive anti-inflammatory agents on depressive symptoms was -0.40 (95% confidence interval -0.14 to -0.65, p = 0.002), indicative of a moderate and statistically significant antidepressant effect. The heterogeneity of the pooled sample was low (I² = 14%, p = 0.32). No manic/hypomanic induction or significant treatment-emergent adverse events were reported. CONCLUSIONS: Overall, a moderate antidepressant effect was observed for adjunctive anti-inflammatory agents compared with conventional therapy alone in the treatment of bipolar depression. The small number of studies, diversity of agents, and small sample sizes limited interpretation of the current analysis.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".