Monocyte and microglial activation in patients with mood-stabilized bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder is associated with medical comorbidities that have been linked to systemic inflammatory mechanisms. There is, however, limited evidence supporting a role of neuroinflammation in bipolar disorder. Here we tested whether microglial activation and associated tissue remodelling processes are related to bipolar disorder by analyzing markers in cerebrospinal fluid (CSF) and serum from patients and healthy controls. METHODS: Serum was sampled from euthymic patients with bipolar disorder and healthy controls, and CSF was sampled from a large subset of these individuals. The levels of monocyte chemoattractant protein-1 (MCP-1), YKL-40, soluble cluster of differentiation 14 (sCD14), tissue inhibitor of metalloproteinases-1 (TIMP-1) and tissue inhibitor of metalloproteinases-2 (TIMP-2), were measured, and we adjusted comparisons between patients and controls for confounding factors. RESULTS: We obtained serum samples from 221 patients and 112 controls and CSF samples from 125 patients and 87 controls. We found increased CSF levels of MCP-1 and YKL-40 and increased serum levels of sCD14 and YKL-40 in patients compared with controls; these differences remained after controlling for confounding factors, such as age, sex, smoking, blood-CSF barrier function, acute-phase proteins and body mass index. The CSF levels of MCP-1 and YKL-40 correlated with the serum levels, whereas the differences between patients and controls in CSF levels of MCP-1 and YKL-40 were independent of serum levels. LIMITATIONS: The cross-sectional study design precludes conclusions about causality. CONCLUSION: Our results suggest that both neuroinflammatory and systemic inflammatory processes are involved in the pathophysiology of bipolar disorder. Importantly, markers of immunological processes in the brain were independent of peripheral immunological activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".