Are medical comorbid conditions of bipolar disorder due to immune dysfunction?
Bibliographic record
Abstract
OBJECTIVE: Epidemiological data have shown a clear association between bipolar disorder (BD) and medical comorbidities. The aim of this article was to assess the evidence of immune dysfunction as a key mediator of this observed association. METHOD: For this narrative clinical overview, the MEDLINE/PubMed, EMBASE, Google Scholar, and ClinicalTrials.gov databases were searched for relevant articles. RESULTS: Bipolar disorder has been shown to have an increased prevalence in patients with autoimmune disorders, cardiovascular disease, and metabolic dysfunction. Further, an elevation in proinflammatory cytokines in BD has been repeatedly demonstrated. Several mechanisms have been proposed to explain the effect of immune dysfunction on mood and cognition. Anti-inflammatory agents including TNF-α inhibitors, non-steroidal anti-inflammatory drugs (NSAIDs), minocycline and omega-3 polyunsaturated fatty acids (O3PUFA) are being investigated for their use as novel treatment of BD in patients with immune dysfunction. CONCLUSION: Immune dysfunction appears to be an important mediator of the association observed between BD and medical comorbidities. It therefore serves as a potential novel target for treatment of BD. Further, the observed bidirectional interaction merits screening for psychiatric disorders in patients with immune dysfunction and vice versa to allow for early detection and treatment of this at risk population.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".