MAO-B status in alcohol use disorder: a [11C]SL25.1188 PET imaging study of putative astrogliosis
Bibliographic record
Abstract
Chronic alcohol exposure may trigger astrogliosis—a process involving hypertrophy and upregulation of astrocyte-specific markers following neuronal stress or injury—as suggested by trends in preclinical studies. However, in vivo evidence of astrogliosis in alcohol use disorder (AUD) is lacking. Here, we investigated the status of the astrocyte marker MAO-B -an enzyme predominantly expressed in astrocytes and upregulated during astrogliosis - using [ 11 C]SL25.1188 positron emission tomography imaging in healthy controls (n = 28) and people with AUD after 3.5 ± 3.7 (n = 24) and 24 ± 7 days (in a subset: n = 8) of abstinence. Clinical symptoms of AUD were assessed, alongside peripheral markers of astrocyte activation and neuronal injury: glial fibrillary acidic protein (GFAP) and neurofilament light chain (NF-L), respectively. While mean [ 11 C]SL25.1188 binding did not differ significantly between people with and without AUD at either abstinence time-point, binding was notably more variable in AUD and inversely correlated with AUD severity, withdrawal and anxiety (p < 0.05). Plasma GFAP and NF-L were elevated in people with AUD. Daily cigarette use was associated with lower [ 11 C]SL25.1188 binding in people with AUD (−40%) and control participants (−33%). These findings reflect variability in MAO-B binding in AUD and support a potential link between lower MAO-B and greater clinical severity. The observed association between cigarette use and lower MAO-B binding replicates prior reports and extends this observation to AUD. The relationship between higher MAO-B binding and lower AUD severity may reflect compensatory reactive astrogliosis. Understanding whether MAO-B status reflects a beneficial or detrimental astrocytic response in AUD may be important for glial-targeted treatments.
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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 | 0.000 | 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) | 0.001 | 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".