Inflammation as a treatment target in mood disorders: review
Bibliographic record
Abstract
BACKGROUND: Mood disorders, i.e. major depressive disorder (MDD) and bipolar disorders, are leading sources of disability worldwide. Currently available treatments do not yield remission in approximately a third of patients with a mood disorder. This is in part because these treatments do not target a specific core pathology underlying these heterogeneous disorders. In recent years, abnormal inflammatory processes have been identified as putative pathophysiological mechanisms and treatment targets in mood disorders, particularly among individuals with treatment-resistant conditions. AIMS: In this selective review, we aimed to summarise recent advances in the field of immunopsychiatry, including emerging pathophysiological models and findings from treatment ttrials of immunomodulatory agents for both MDD and bipolar disorders. METHOD: We performed a literature review by searching Medline for clinical trials of immunomodulating agents as monotherapy or adjunctive treatments in MDD and bipolar disorders. Included studies are randomised controlled trials (RCTs), cluster RCTs or cross-over trials of immunomodulating agents that had an active comparator or a placebo-arm. RESULTS: Current evidence shows an association between inflammation and mood symptoms. However, there is conflicting evidence on whether this link is causal. CONCLUSIONS: Future studies should focus on identifying specific neurobiological underpinnings for the putative causal association between an activated inflammatory response and mood disorders. Results of these studies are needed before further treatment trials of immunomodulatory agents can be justified.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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 | 0.001 |
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".