Patterns of Immune Dysregulation in Bipolar Disorder
Bibliographic record
Abstract
Background: Bipolar disorder is a debilitating mood disorder associated with a high risk of suicide and characterized by immune dysregulation. In this study, we used a multi-faceted approach to better distinguish the pattern of dysregulation of immune profiles in individuals with BD. Methods: We analyzed peripheral blood mononuclear cells (bipolar disorder N=39, control N=30), serum cytokines (bipolar disorder N=86, control N=58), whole blood RNA (bipolar disorder N=25, control N=25), and whole blood DNA (bipolar disorder N=104, control N=66) to identify immune-related differences in participants diagnosed with bipolar disorder compared to controls. Results: Flow cytometry revealed a higher proportion of monocytes in participants with bipolar disorder together with a lower proportion of T helper cells. Additionally, the levels of 18 cytokines were significantly elevated, while two were reduced in participants with bipolar disorder. Most of the cytokines altered in individuals with bipolar disorder were proinflammatory. Forty-nine genes were differentially expressed in our bipolar disorder cohort and further analyses uncovered several immune-related pathways altered in these individuals. Genetic analysis indicated variants associated with inflammatory bowel disease also influences bipolar disorder risk. Discussion: Our findings indicate a significant immune component to bipolar disorder pathophysiology and genetic overlap with inflammatory bowel disease. This comprehensive study supports existing literature, whilst also highlighting novel immune targets altered in individuals with bipolar disorder. Specifically, multiple lines of evidence indicate differences in the peripheral representation of monocytes and T cells are hallmarks of bipolar disorder.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".