Cerebrospinal Fluid Biomarkers in Patients With Alcohol Use Disorder and Persistent Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: The prevalence of cognitive impairment is high among alcohol-dependent patients. Although the clinical presentation of alcohol-related cognitive disorder (ARCD) may resemble that of Alzheimer's disease (AD), the prognosis and treatment of the 2 diseases are different. Cerebrospinal fluid (CSF) biomarkers (tau, phosphorylated tau, and amyloid β) have high diagnostic accuracy in AD and are currently being used to discriminate between psychiatric disorders and AD, but are not used to diagnose ARCD. The aim of this study was to characterize CSF biomarkers in a homogeneous, cognitively impaired alcohol-dependent population. METHODS: This single-center study was conducted in an addiction medicine department of a Parisian Hospital. We selected patients with documented persistent cognitive impairment whose MoCA (Montreal Cognitive Assessment) score was below 24/30 after at least 1 month of documented inpatient abstinence from alcohol. We measured the CSF biomarkers (tau, phosphorylated tau, and amyloid β 1-42 and 1-40) in 73 highly impaired alcohol-dependent patients (Alcohol Use Disorders Identification Test score over 11 for women and 12 for men) with. RESULTS: Patients' average age was 60 ± 9.1 years and 45 (61.6%) had a normal CSF profile, 8 (11.0%) had a typical CSF AD profile, and 20 (27.4%) had an intermediate CSF profile. CONCLUSIONS: This study revealed a high prevalence of AD in alcohol-dependent patients with persistent cognitive deficits and several anomalies in their CSF profiles. Thus, it is important to consider AD in the differential diagnosis of persistent cognitive deficits in patients with alcohol dependence and to use CSF biomarkers in addition to imaging and neuropsychological testing to evaluate alcohol-related cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".