Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".