Central markers of neuroinflammation in alcohol use disorder: A meta‐analysis of neuroimaging, cerebral spinal fluid, and postmortem studies
Bibliographic record
Abstract
Abstract Introduction and aims There is emerging evidence that heavy long‐term alcohol consumption may alter the neuroimmune profile. We conducted a meta‐analysis of the association between alcohol use disorder (AUD) and the extent of neuroinflammation using cerebrospinal (CSF), PET (Positron Emission Tomography), and postmortem studies. Design and methods A comprehensive search of electronic databases was conducted using the Preferred Reporting Items for Systematic Review and Meta‐Analysis Protocols (PRISMA‐P) for AUD‐related terms in combination with neuroinflammatory markers and cytokine‐ and chemokine‐related terms for CSF, PET, and postmortem studies. Participants had to meet established criteria for AUD and/or heavy alcohol consumption with dependence features and be compared with healthy controls. Papers retrieved were assessed for inclusion criteria and a critical appraisal was completed using the Newcastle‐Ottawa Scale. A meta‐analysis was conducted on postmortem and PET studies. Results Eleven papers met the inclusion criteria with CSF, PET, and postmortem studies included in the final analysis. Postmortem studies demonstrate significant heterogeneity (𝑄 (14) = 62.02, 𝑝 < 0.001), with the alcohol group showing higher levels of neuroimmune markers than controls (𝑑 = 1.50 [95% CI 0.56, 2.45]). PET studies demonstrated a lower [11C] PBR28 total volume of distribution (V T) for translocator protein in the hippocampus (g = −1.95 [95% CI −2.72, −1.18], p < 0.001) of the alcohol group compared to controls. Conclusion There is emerging evidence across multiple diagnostic modalities that alcohol impacts neuroimmune signaling in the human brain.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".