Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort
Bibliographic record
Abstract
<ns3:p>Background This study examined biomarker signatures in men with alcohol use disorder (AUD) in Uzbekistan, with and without viral infections. Methods A cross-sectional study included 292 males with stage II AUD (virus-negative: n = 251; virus-positive: n = 41) and 49 alcohol-free controls. Clinical, hematological, and biochemical parameters were measured, and ROC analysis evaluated diagnostic performance. Results Virus-negative patients showed the clearest biomarker profile of alcohol dependence, with reduced glucose, creatinine, and urea, and elevated total protein, α-amylase, De Ritis ratio, and direct bilirubin. ROC analysis confirmed strong diagnostic value for AST (AUC = 0.951), FIB-4 (0.877), MAP (0.817), and creatinine (0.711). Leukocytes (AUC = 0.790) and lymphocytes (0.735) best differentiated viral status. Fibrosis risk in virus-positive patients was 1.5-fold higher, with splenomegaly in 7.3%. Mild thrombocytopenia, absence of granulocytopenia, and rare delirium (<5.5%) distinguished this cohort from European groups, resembling East Asian patterns. Conclusions Liver enzymes, α-amylase, bilirubin, MCV, FIB-4, and MAP provide strong diagnostic value for AUD. Multimarker panels including leukocyte, lymphocyte, and creatinine levels support viral status differentiation. Findings emphasize population-specific biomarker signatures in Central Asians and the utility of multimarker strategies for personalized AUD management.</ns3:p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".