Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort

Natalya Tseomashko; Timur Syunyakov; Inara Khayredinova; Uktam Tadjibaev; Furkat Bahramov; Zarifjon Ashurov

doi:10.12688/f1000research.172858.2

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Research Article

Revised

Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort

[version 2; peer review: 1 approved with reservations]

Natalya Tseomashko ¹, Timur Syunyakov^1-3, Inara Khayredinova^1,4,5, Uktam Tadjibaev¹, Furkat Bahramov^1,5, Zarifjon Ashurov^1,4,6

Natalya Tseomashko ¹, Timur Syunyakov^1-3, [...] Inara Khayredinova^1,4,5, Uktam Tadjibaev¹, Furkat Bahramov^1,5, Zarifjon Ashurov^1,4,6

PUBLISHED 19 May 2026

Author details Author details

¹ Republican Specialized Scientific and Practical Medical Center for Mental Health, Tashkent, Uzbekistan
² International Centre for Education and Research in Neuropsychiatry (ICERN), Samara State Medical University,, Russia, Samara, Russian Federation
³ Mental-health Clinic No.1 named after N.A. Alexeev of the Moscow Healthcare Department, , Russia, Moscow, Russian Federation
⁴ Department of Psychiatry and Narcology of, Tashkent Medical Academy, Tashkent, Uzbekistan
⁵ Department of Narcology and Adolescent Psychopathology, of the Center for the Develop-ment of Professional Qualifications of Medical Workers, Tashkent, Uzbekistan
⁶ Ministry of Health of the Republic of Uzbekistan, Tashkent, Uzbekistan

Natalya Tseomashko
Roles: Conceptualization, Investigation, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Timur Syunyakov
Roles: Formal Analysis, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Inara Khayredinova
Roles: Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

Uktam Tadjibaev
Roles: Data Curation, Investigation, Methodology

Furkat Bahramov
Roles: Data Curation, Investigation, Resources

Zarifjon Ashurov
Roles: Project Administration, Resources, Supervision

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Addiction and Related Behaviors gateway.

Abstract

Background

This study examined biomarker signatures in men with alcohol use disorder (AUD) in Uzbekistan, with and without chronic viral infections (hepatitis B, hepatitis C, HIV).

Methods

A cross-sectional study included 292 males with AUD (ICD-10 F10.2) (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 AUD, with reduced glucose, creatinine, and urea, and elevated total protein, α-amylase, De Ritis ratio, and direct (conjugated) bilirubin. ROC analysis confirmed strong diagnostic value for AST (AUROC = 0.951), FIB-4 (0.877), and MAP (0.817) in distinguishing patients with AUD from alcohol-free controls, whereas creatinine (AUROC = 0.711), WBC (0.790), and lymphocyte counts (0.735) were informative for differentiating chronic viral infection status within AUD cohorts. Fibrosis risk in virus-positive patients was 1.5-fold higher, with splenomegaly in 7.3%. Mild thrombocytopenia and rare delirium (<5.5%) were observed in the cohort.

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.

Keywords

alcohol use disorder, viral infection, biomarkers, ROC analysis, liver enzymes, popula-tion-specific reference values

Corresponding author: Natalya Tseomashko

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2026 Tseomashko N et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Tseomashko N, Syunyakov T, Khayredinova I et al. Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort [version 2; peer review: 1 approved with reservations]. F1000Research 2026, 14:1449 (https://doi.org/10.12688/f1000research.172858.2) First published: 24 Dec 2025, 14:1449 (https://doi.org/10.12688/f1000research.172858.1) Latest published: 19 May 2026, 14:1449 (https://doi.org/10.12688/f1000research.172858.2)

Revised Amendments from Version 1

This revised version was prepared in response to the published peer review report. The manuscript was revised to improve terminology consistency, methodological transparency, and interpretive accuracy. We clarified the definition of alcohol use disorder (AUD) according to ICD-10 F10.2 and replaced stigmatizing or inconsistent terminology with person-first terminology. We also clarified that the viral comorbidities assessed in this study refer to chronic viral infections, including hepatitis B, hepatitis C, and HIV.
The Methods section was expanded to specify the exact recruitment period, inpatient recruitment setting, use of baseline/admission laboratory values before treatment initiation, control group selection, sources of clinical and anthropometric data, withdrawal severity classification, and glucose sampling conditions. Additional clarification was added regarding the De Ritis ratio, fasting versus non-fasting glucose measurements, and documentation of delirium, hallucinations, and delusional symptoms during withdrawal.
The Results and Discussion sections were revised to avoid overinterpretation and to align the conclusions more closely with the available data. In particular, we clarified the interpretation of segmented neutrophils, granulocytopenia/neutropenia, platelet count findings, neuropsychiatric manifestations, creatinine and MELD-related interpretation, bilirubin terminology, and ROC analysis/class coding. The diagnostic interpretation was refined to specify that ROC analyses evaluated discrimination between patients with AUD and alcohol-free controls, and between AUD cohorts with and without chronic viral infection.
The supplementary materials were clarified by adding abbreviation definitions, reference intervals, and glucose sampling details. The Zenodo dataset file was additionally anonymized without changes to study groups, statistical analyses, numerical results, or reported conclusions. References were checked and corrected where needed.

See the authors' detailed response to the review by Julia Sinclair and Julia Morris

1. Introduction

Alcohol use disorder (AUD) is a serious public health problem with significant social, economic, and medical consequences, as it primarily affects individuals of working and reproductive age. Globally, 5.3% of deaths are attributable to alcohol consumption, and more than 2.4 million people suffer from alcohol-related cirrhosis.¹

Alcohol abuse damages multiple organs and systems, including the liver, pancreas, heart, kidneys, nervous system, and hematopoietic cells.² Reliable diagnostic and prognostic biomarkers provide a minimally invasive approach, most commonly blood-based, for clinical assessment and management. The integration of biomarker screening with instrumental and clinical data forms the basis of modern diagnostics.

Biomarker variability is strongly influenced by sex, age, ethnicity, and geography,³ reflecting environmental and cultural factors such as diet, lifestyle, and alcohol consumption habits. Even closely related ethnic groups with distinct dietary patterns and attitudes toward psychoactive substances demonstrate significant biochemical differences.⁴ These phenotypic traits are reinforced at the genetic level through diverse functional combinations of single nucleotide polymorphisms (SNPs), which regulate physiological and pathological processes.⁵ This underscores the importance of population- and ethnicity-specific research.

The present cross-sectional study assessed hematological and biochemical parameters in patients with AUD, with and without chronic viral infections, in Uzbekistan, compared with alcohol-free controls.

2. Materials and Methods

2.1 Study design and population

A cross-sectional study was conducted at the Republican Specialized Scientific and Practical Medical Center for Mental Health (RSSPMCMH, Tashkent, Uzbekistan). Clinical and laboratory data were collected from 19 March to 22 June 2025. Recruitment was limited to this period because repeated admissions of the same patients became increasingly frequent in the clinical flow.

A total of 292 male inpatients with alcohol use disorder (AUD; ICD-10 code F10.2) were included, together with 49 alcohol-free male controls. Female patients (n = 8) were excluded because their small number was insufficient for meaningful sex-stratified analysis. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations (n = 2) were also excluded because of their very small number and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

The final study population consisted of three groups: Cohort 1, patients with AUD without chronic viral infection (n = 251); Cohort 2, patients with AUD and chronic viral infection, including hepatitis B, hepatitis C, or HIV (n = 41); and Cohort 3, alcohol-free controls (n = 49). Patients were recruited from inpatient units of the center. Laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis; follow-up measurements during treatment were excluded.

The control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. One initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

2.2 Inclusion/Exclusion criteria

Inclusion criteria for patients were male sex, age 20–65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT. Exclusion criteria included female sex, mild alcohol-related conditions or mild withdrawal symptoms, acute infections, incomplete clinical or laboratory data, and clinical profiles inconsistent with established long-term AUD.

Alcohol withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Controls were age-matched alcohol-free males without alcohol or substance use disorders who provided informed consent. Individuals with known or self-reported chronic viral infections were not included in the control group.

2.3 Ethical approval

The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Local Ethics Committee of the Republican Specialized Scientific and Practical Medical Center for Mental Health (RSSPMCMH), Tashkent, Uzbekistan (Approval No. 1/2025, 18 March 2025). Written informed consent was obtained from all participants. All data included in the dataset were anonymized prior to analysis and public sharing.

2.4 Data collection

Somatic status was assessed by clinical examination and instrumental diagnostics. Data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²).

Hematological and biochemical parameters were measured using automated analyzers (Mindray, China) with standardized enzymatic and colorimetric methods, as detailed in Supplementary File S1. Blood samples from patients were collected at admission regardless of fasting status, because patients were admitted at different times of the day. Therefore, glucose levels in the patient group represent random/non-fasting measurements. In contrast, blood samples from control participants were obtained under fasting conditions.

The De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury. Composite indices included FIB-4, De Ritis ratio, mean arterial pressure (MAP), and BMI, calculated using standard formulas.

Delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review. Symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to medical records, these patients had no previous registration or documented history of primary psychotic disorders.

2.5 ROC analysis

Diagnostic accuracy of biomarkers was evaluated by ROC analysis. AUROC values with 95% confidence intervals were estimated using bootstrap resampling (n = 1000). Optimal cut-offs were determined by Youden’s Index, with corresponding sensitivity and specificity reported.

2.6 Statistical analysis

Analyses were performed using Python 3.11 (scikit-learn, numpy, pandas, matplotlib). Nonparametric tests (Mann-Whitney U, Kruskal-Wallis H, χ² with Bonferroni correction) were applied; significance was set at p < 0.05. A retrospective power analysis confirmed ≥85% power for detecting medium-to-large effect sizes despite the smaller control group.

3. Results

3.1 Somatic and clinical profile of alcohol-dependent patients

Among the 292 male patients with AUD examined, 14% (n = 41) were identified as carriers of viral infections, including hepatitis C (n = 18), hepatitis B (n = 18), combined hepatitis B and C (n = 1), triple hepatitis B/C/D (n = 1), HIV (n = 2), and HIV with hepatitis B (n = 1). Despite variation within groups, average age did not differ significantly across cohorts (see Table 1). Patients over 50 years old typically had alcohol use histories exceeding 10 years.

Table 1. Clinical and anthropometric characteristics, including age, BMI, FIB-4, and MAP, in patients with AUD without chronic viral infection (Cohort 1, n = 251), patients with AUD and chronic viral infection (Cohort 2, n = 41), and alcohol-free controls (Cohort 3, n = 49).

Indicators	Level	Cohort 1 (n = 251)	Cohort 2 (n = 41)	Cohort 3 (n = 49)	Test results
Age, Median, (Q1, Q3)		45.0 (38.0, 53.0)	44.0 (39.0, 48.0)	42.0 (36.0, 49.0)	H(2) = 4.52, p = 0.104, ε² = 0.013
BMI, kg/m², Median, (Q1, Q3)		24.0 (22.9, 25.0)	24.3 (23.4, 26.1)	27.1 (24.5, 28.6)	H(2) = 27.13, p < 0.001, ε² = 0.080
BMI, Qualitative, n (%)	Above Norm	61 (24.30%)	16 (39.0%)	34 (69.4%)	χ²(4) = 39.14, p < 0.001, V = 0.240
	Below norm	7 (2.79%)	1 (2.44%)	0 (0.00%)
	Norm	183 (72.9%)	24 (58.5%)	15 (30.6%)
FIB-4, Qualitative, n %	Above Norm	67 (26.69%)	17 (41.5%)	0 (0.00%)	χ²(2) = 22.84, p < 0.001, V = 0.259
FIB-4, Qualitative, n %	Norm	184 (73.3%)	24 (58.5%)	49 (100%)	χ²(2) = 22.84, p < 0.001, V = 0.259
MAP, mmHg, Median, (Q1, Q3)		103.0 (97.0, 103.0)	103.0 (97.0, 107.0)	93.0 (93.0, 97.0)	H(2) = 56.17, p < 0.001, ε² = 0.165
MAP, Qualitative, n%	Above Norm	18 (7.17%)	4 (9.76%)	2 (4.1%)	χ²(2) = 1.12, p = 0.570, V = 0.057
MAP, Qualitative, n%	Norm	233 (92.8%)	37 (90.2%)	47 (95.9%)	χ²(2) = 1.12, p = 0.570, V = 0.057

Note. Values are presented as medians (Q1–Q3) or n (%).

Abbreviations: BMI, body mass index; FIB-4, Fibrosis-4 index; MAP, mean arterial pressure.

Reference intervals used to define qualitative categories: BMI, 20-25 kg/m²; FIB-4, <1.45; MAP, 70-110 mmHg. Values below or above these intervals were classified as “Below norm” or “Above norm”, respectively.

Severe withdrawal symptoms were recorded in 26 of 292 patients with AUD (8.9%). Of these, 23 cases occurred in patients without chronic viral infection (Cohort 1; 7.9% of the total AUD sample and 9.2% within Cohort 1), and 3 cases occurred in patients with chronic viral infection (Cohort 2; 1.0% of the total AUD sample and 7.3% within Cohort 2). Most patients (85.5%) exhibited moderate withdrawal symptoms.

The study population was predominantly Turkic-speaking (94.5%, n = 276), mainly of Uzbek ethnicity. Both patients with AUD (n = 292) and alcohol-free controls (n = 49) demonstrated comparable ethnic composition, ensuring cohort homogeneity. Notably, neuropsychiatric symptoms (delirium and/or hallucinations) were observed in 5.5% (n = 16) of patients with AUD. Among these, 43.8% (n = 7) were of Slavic origin and 18.8% (n = 3) were Tatar, despite these groups comprising only 6.1% and 2.7% of the total AUD cohort, respectively. This disproportion may reflect possible population-specific susceptibility to alcohol-related neuropsychiatric complications.

FIB-4 values were more frequently elevated in patients with AUD and chronic viral infection, with markedly high scores (>4) observed in this subgroup, suggesting a greater likelihood of advanced fibrosis. MAP values were also higher among virus-positive patients, indicating possible vascular dysregulation.

The prevalence of somatic comorbidities varied across study groups (see Figure 1).

Figure 1. Prevalence of somatic comorbidities in patients with alcohol use disorder (AUD) without chronic viral infection (Cohort 1, n = 251), patients with AUD and chronic viral infection (Cohort 2, n = 41), and alcohol-free controls (Cohort 3, n = 49). Hypertension, cardiopathy, chronic pancreatitis, chronic cholecystitis, encephalopathy, splenomegaly, and cirrhosis are shown as relative frequencies (%).

Hypertension was more frequent in patients with AUD (11.5% without viral infection; 12.2% with chronic viral infection) compared to alcohol-free controls (8.2%) (p < 0.05 for both patient groups vs. controls). Cardiopathy occurred in 21.9% of virus-positive patients and 16.3% of virus-negative patients versus 0% in controls (C vs. V, **p < 0.001; C vs. N, **p < 0.001). Chronic pancreatitis was observed in 31.7% of virus-positive and 23.0% of virus-negative patients compared to 0% in controls (C vs. V, **p < 0.001; C vs. N, **p < 0.001). Chronic cholecystitis was the most prevalent condition, affecting 75.6% of virus-positive and 67.5% of virus-negative patients versus 4.1% in controls (C vs. both patient groups, **p < 0.001). Encephalopathy occurred in 7.3% of virus-positive and 9.2% of virus-negative patients, while it was absent in controls (C vs. V, *p < 0.05; not significant for C vs. N or V vs. N). Splenomegaly was found only in virus-positive patients (7.3%, p < 0.01). Cirrhosis was also observed exclusively in this group (4.9%), although differences did not reach statistical significance. No significant variation was identified for ischemic heart disease, hepatomegaly, or chronic pyelonephritis (all p > 0.05).

3.2 Hematological profile analysis

A comparative analysis of hematological parameters was conducted among virus-positive (n = 41) and virus-negative (n = 251) patients with AUD, and an alcohol-free control group (n = 49). Only values outside physiological reference ranges were considered. Hematological parameters are summarized in Supplementary Table S1.

Hemoglobin (Hb) levels were significantly reduced in both AUD cohorts, falling below normal in 78% of virus-positive and 45% of virus-negative patients. Macrocytosis, defined as MCV > 100 fL, was observed in 17–29% of patients with AUD versus 6.1% of controls (p < 0.001), supporting its association with chronic alcohol exposure.

Erythrocyte sedimentation rate (ESR) was elevated in 63.4% of virus-positive and 57.4% of virus-negative patients (p < 0.001), compared to 10.2% of controls, indicating systemic inflammation, especially with viral comorbidity.

Leukocytosis was more common in virus-positive patients (36.6%) than in virus-negative patients (15.1%). An increased proportion of segmented neutrophils was observed in 7.6-9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment. Lymphocytosis, likely reflecting antiviral immune activation, was present in 9.2% of virus-positive and 5.0% of virus-negative individuals, while lymphocytopenia occurred in 16.3% of controls. Unexpectedly, monocytosis was more frequent in controls (44.9%) than in patients with AUD (20.0–26.8%), possibly reflecting transient immune responses.

Thrombocytopenia was identified in 13.5–17.1% of patients with AUD versus 4% of controls (p = 0.029). Mean platelet volume (MPV), a marker of platelet activation and turnover, was elevated in 41.5% of virus-positive patients, compared to 16.7–22.4% in the other groups (p < 0.001).

3.3 Biochemical profile analysis

Biochemical parameters were evaluated in virus-positive (n = 41) and virus-negative (n = 251) patients with AUD, as well as in an alcohol-free control group (n = 49). Detailed biochemical characteristics across the three study cohorts are presented in Supplementary Table S2.

As shown in Supplementary Table S2, glucose levels were slightly lower in AUD groups compared to controls. However, within AUD cohorts, virus-positive patients demonstrated 1.2-fold higher glucose levels than virus-negative individuals (p = 0.002), possibly reflecting altered gluconeogenesis and insulin sensitivity. Creatinine concentrations were reduced in both AUD groups by ~1.5-fold vs. controls, likely indicating reduced muscle mass or altered renal tubular function. α-Amylase (AAMY) levels were significantly higher in patients with AUD, with mean values 1.7 times those of controls (p < 0.001; Dunn’s test; r = 0.01). However, elevated enzyme activity was found in only 2.4–8.2% of individuals. This likely reflects total α-amylase activity, not limited to pancreatic isoforms, which is common in chronic alcohol users.

Liver enzymes showed marked alterations. ALT levels were 2.1 times higher in both AUD cohorts versus controls (p < 0.001), and 1.2 times higher in virus-positive vs. virus-negative patients—suggesting additive liver injury from viral and alcohol-related mechanisms. AST levels were approximately threefold higher in AUD groups, with slightly greater elevation in the virus-negative cohort, possibly due to dominant alcohol-related damage.

Bilirubin profiles varied: direct (conjugated) bilirubin was elevated in 72% of virus-negative and 65.9% of virus-positive patients. However, virus-positive individuals also demonstrated elevated indirect (unconjugated) bilirubin, implying compromised hepatic conjugation capacity.

Thus, according to the obtained data, statistically significant differences (p < 0.001) in blood biomarker profiles between patients with AUD and individuals without alcohol abuse (control cohort) were observed among biochemical markers, specifically liver enzymes (ALT, AST), bilirubin fractions, creatinine, glucose, and urea, while among hematological indicators, the most pronounced distinctions were noted for hemoglobin, erythrocyte sedimentation rate, and mean corpuscular volume of erythrocytes. Detailed distributions of biochemical biomarkers across the three cohorts are shown in Supplementary Figure S1.

3.4 Diagnostic accuracy analysis (ROC)

To evaluate the diagnostic potential of clinical and laboratory markers, Receiver Operating Characteristic (ROC) analysis was performed (see Table 2).

Table 2. Diagnostic performance of biochemical, hematological, anthropometric, and physiological biomarkers in differentiating (a) patients with AUD (Cohorts 1+2) from alcohol-free controls (Cohort 3) and (b) patients with AUD without viral infection (Cohort 1) from patients with AUD and chronic viral infection (Cohort 2).

Class coding: (a) positive class = Cohorts 1+2, negative class = Cohort 3; (b) positive class = Cohort 2, negative class = Cohort 1.

Indicator	Comparison	Dir.	AUROC (95% CI)	Threshold	Se (%)	Sp (%)
AAMY	Cohort 3 vs Cohorts 1+2	↑	0.802 (0.736–0.867)	73.00	70.0	80.0
AAMY	Cohort 1 vs Cohort 2	↑	0.556 (0.459–0.653)	57.00	78.0	38.0
ALT	Cohort 3 vs Cohorts 1+2	↑	0.808 (0.743–0.873)	52.00	79.0	71.0
ALT	Cohort 1 vs Cohort 2	↑	0.567 (0.469–0.664)	59.20	49.0	69.0
AST	Cohort 3 vs Cohorts 1+2	↑	0.951 (0.915–0.987)	73.00	94.0	89.0
AST	Cohort 1 vs Cohort 2	↑	0.514 (0.418–0.610)	149.70	22.0	88.0
Bilirubin, direct	Cohort 3 vs Cohorts 1+2	↑	0.799 (0.727–0.870)	3.10	90.1	61.2
Bilirubin, direct	Cohort 1 vs Cohort 2	↓	0.451 (0.359–0.544)	22.30	15.0	89.0
Bilirubin, total	Cohort 3 vs Cohorts 1+2	↑	0.730 (0.664–0.796)	24.20	52.0	92.0
Bilirubin, total	Cohort 1 vs Cohort 2	↑	0.536 (0.441–0.631)	21.90	68.0	45.0
Creatinine	Cohort 3 vs Cohorts 1+2	↓	0.060 (0.000–0.123)	203.40	0.0	100.0
Creatinine	Cohort 1 vs Cohort 2	↑	0.711 (0.633–0.789)	75.00	88.0	46.0
AST/ALT	Cohort 3 vs Cohorts 1+2	↑	0.773 (0.701–0.845)	1.75	81.0	74.0
AST/ALT	Cohort 1 vs Cohort 2	↓	0.461 (0.364–0.559)	2.15	32.0	72.0
Glucose	Cohort 3 vs Cohorts 1+2	↓	0.242 (0.181–0.300)	19.90	0.0	100.0
Glucose	Cohort 1 vs Cohort 2	↑	0.663 (0.567–0.759)	4.40	66.0	67.0
Total protein	Cohort 3 vs Cohorts 1+2	↑	0.620 (0.495–0.745)	68.00	86.3	61.2
Total protein	Cohort 1 vs Cohort 2	↑	0.528 (0.431–0.624)	77.00	44.0	66.0
Urea	Cohort 3 vs Cohorts 1+2	↓	0.291 (0.205–0.377)	2.07	100.0	2.1
	Cohort 1 vs Cohort 2	↓	0.437 (0.345–0.528)	3.89	85.0	25.0
BMI	Cohort 3 vs Cohorts 1+2	↓	0.276 (0.189–0.362)	45.60	0.0	100.0
	Cohort 1 vs Cohort 2	↑	0.555 (0.458–0.653)	25.10	39.0	75.0
FIB-4	Cohort 3 vs Cohorts 1+2	↑	0.877 (0.820–0.935)	1.16	76.0	96.0
	Cohort 1 vs Cohort 2	↑	0.525 (0.399–0.651)	0.90	92.0	19.0
MAP	Cohort 3 vs Cohorts 1+2	↑	0.817 (0.730–0.904)	97.00	96.0	59.0
	Cohort 1 vs Cohort 2	↑	0.550 (0.453–0.647)	107.00	29.0	80.0
Hb	Cohort 3 vs Cohorts 1+2	↓	0.310 (0.240–0.380)	108.00	99.0	6.0
	Cohort 1 vs Cohort 2	↓	0.260 (0.173–0.346)	109.00	100.0	2.0
MCV	Cohort 3 vs Cohorts 1+2	↑	0.710 (0.635–0.785)	89.30	74.0	62.0
	Cohort 1 vs Cohort 2	↑	0.513 (0.430–0.596)	89.30	88.0	28.0
PLT	Cohort 3 vs Cohorts 1+2	↑	0.520 (0.446–0.594)	230.00	51.0	63.3
	Cohort 1 vs Cohort 2	↓	0.377 (0.291–0.463)	347.00	5.0	96.0
MPV	Cohort 3 vs Cohorts 1+2	↑	0.522 (0.432–0.612)	10.10	77.1	34.7
	Cohort 1 vs Cohort 2	↑	0.539 (0.442–0.636)	11.20	41.0	85.0
Monocytes	Cohort 3 vs Cohorts 1+2	↓	0.409 (0.315–0.502)	16.60	1.4	100.0
	Cohort 1 vs Cohort 2	↑	0.682 (0.587–0.777)	9.50	98.0	37.0
Lymphocyte	Cohort 3 vs Cohorts 1+2	↑	0.585 (0.490–0.681)	28.80	75.0	46.9
	Cohort 1 vs Cohort 2	↑	0.735 (0.643–0.826)	33.90	90.0	64.0
NEUT	Cohort 3 vs Cohorts 1+2	↑	0.510 (0.423–0.598)	66.00	26.7	81.6
	Cohort 1 vs Cohort 2	↑	0.577 (0.480–0.675)	66.40	41.0	79.0
WBC	Cohort 3 vs Cohorts 1+2	↑	0.680 (0.601–0.759)	7.21	55.1	77.6
	Cohort 1 vs Cohort 2	↑	0.790 (0.705–0.876)	7.50	95.0	59.0

Two diagnostic comparisons were assessed. The first comparison evaluated the ability of biomarkers to distinguish all patients with AUD, including both patients without chronic viral infection (Cohort 1) and patients with chronic viral infection (Cohort 2), from alcohol-free controls (Cohort 3). The second comparison evaluated the ability of biomarkers to differentiate patients with AUD and chronic viral infection (Cohort 2) from patients with AUD without chronic viral infection (Cohort 1).

The area under the ROC curve (AUROC) was used to quantify discriminatory performance, with higher values indicating stronger classification ability. Sensitivity and specificity reflected the correct identification of true positive and true negative cases, respectively.

In the comparison of all patients with AUD (Cohorts 1+2) versus alcohol-free controls (Cohort 3), the strongest discriminatory performance was observed for AST (AUROC = 0.951; 95% CI: 0.915–0.987; sensitivity 94%, specificity 89% at 73 U/L), FIB-4 (AUROC = 0.877; 95% CI: 0.820–0.935; sensitivity 76%, specificity 96% at 1.16), and MAP (AUROC = 0.817; sensitivity 96%, specificity 59% at 97 mmHg). ALT (AUROC = 0.808), α-amylase/AAMY (AUROC = 0.802), and direct bilirubin (AUROC = 0.799) also demonstrated good discriminatory performance. In contrast, BMI (AUROC = 0.276) and creatinine (AUROC = 0.060) showed inverse discrimination according to the predefined class coding, reflecting lower values in patients with AUD rather than poor discriminatory performance.

In the comparison between AUD subgroups, markers of immune-inflammatory status were more informative for differentiating patients with AUD and chronic viral infection from those without chronic viral infection. WBC showed the highest discriminatory performance (AUROC = 0.790; 95% CI: 0.705–0.876; sensitivity 95%, specificity 59%), followed by lymphocytes (AUROC = 0.735; sensitivity 90%, specificity 64%), creatinine (AUROC = 0.711; sensitivity 88%, specificity 46%), and monocytes (AUROC = 0.682; sensitivity 98%, specificity 37%). These findings suggest that hepatic and systemic biomarkers are most informative for distinguishing AUD from alcohol-free controls, whereas immune-inflammatory markers better reflect chronic viral comorbidity within AUD cohorts.

4. Discussion

This study investigated biochemical, hematological, and somatic characteristics in two AUD cohorts, with and without chronic viral infections, compared to controls. Statistically significant differences were observed across multiple parameters, underscoring the impact of chronic alcohol use and chronic viral comorbidity on systemic physiology. Although the relatively small control group is a limitation, post hoc power analysis for key markers such as ALT indicated sufficient statistical power (≥85%), supporting the robustness of the findings.

Non-invasive indices including BMI, MAP, and the FIB-4 score were employed to assess metabolic and hepatic status. The FIB-4 index identified fibrotic changes in 42% of patients with chronic viral infections and in 27% of virus-negative patients with AUD, consistent with long-term alcohol exposure and previously reported fibrosis rates.⁶

Our results demonstrate a substantial burden of somatic complications in men with AUD, with notable differences between those with and without chronic viral comorbidity. The high prevalence of chronic cholecystitis and pancreatitis highlights cumulative hepatopancreatic injury, amplified in virus-positive patients. Cardiopathy was disproportionately common in virus-positive patients, supporting the notion that viral infection may aggravate cardiovascular vulnerability. Splenomegaly was observed exclusively in virus-positive individuals (7.3%), suggesting a greater likelihood of portal hypertension and advanced hepatic involvement. Cirrhosis was also detected only in virus-positive patients, though without statistical significance, likely reflecting sample size limitations rather than absence of association. Encephalopathy occurred in both patient cohorts but was absent in controls, suggesting it is primarily linked to alcohol exposure rather than viral status. The lack of difference between patient cohorts suggests that AUD may be the principal driver of neurotoxic complications.⁷ In contrast, ischemic heart disease, hepatomegaly, and chronic pyelonephritis showed no significant differences, implying closer relation to lifestyle or cumulative alcohol exposure than to viral comorbidity.

Significant reductions in hemoglobin (Hb) were detected in both AUD cohorts, with subnormal levels in 75% of virus-positive and 50% of virus-negative individuals. Ethanol and acetaldehyde exert cytotoxic effects on erythroid precursors and promote oxidative stress.⁸ Individuals with reduced aldehyde dehydrogenase (ALDH2) activity, common in Asian populations, accumulate more acetaldehyde, leading to enhanced hemoglobin-acetaldehyde adduct (HbAA) formation.⁹ This mechanism may partly explain higher susceptibility to liver injury and anemia in Asian alcohol users.

Our data are consistent with Russian findings showing reduced hemoglobin in individuals with AUD, although baseline values in the Russian cohort were higher, potentially due to ethnic-genetic or environmental differences.¹⁰ Inflammatory markers, including ESR, leukocyte count, and lymphocytes abnormalities, were more pronounced in patients with AUD, particularly in those with chronic viral infection, suggesting ongoing systemic immune activation. This pattern may reflect not only viral-associated inflammatory burden, but also alcohol-related disruption of gut–liver immune signaling. Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and an increased Candida albicans-specific Th17 response, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. In experimental models, ethanol exposure promoted the migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury. These findings support the concept that alcohol-related immune activation may involve gut-derived microbial and fungal immune pathways, which could amplify systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.¹¹

Unexpectedly, monocytosis was more frequent in controls, possibly reflecting immune responses to non-viral stimuli or transient inflammatory variation unrelated to AUD. Chronic alcohol use can induce both immune suppression and hyperinflammation through epigenetic alterations of monocyte/macrophage function.⁸ In addition, alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection.¹² In the present AUD cohorts, however, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Thrombocytopenia was also observed in patients with AUD. As shown in Supplementary Table S1, median PLT values were lower in both AUD cohorts than in controls, with the lowest values in patients with chronic viral infection (Cohort 1: 240.0 [190.5–295.5] ×10⁹/L; Cohort 2: 198.0 [163.0–261.0] ×10⁹/L; Cohort 3: 224.0 [204.0–275.0] ×10⁹/L; p = 0.029). PLT values below the reference range were recorded in 13.55% of patients without viral infection and 17.07% of patients with chronic viral infection, compared with 4.1% of controls. Although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens,¹³ clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range. Thus, reduced PLT was an important hematological feature of AUD, but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

The mean platelet volume (MPV), an indicator of platelet activation, was elevated in 41.5% of virus-positive patients, reflecting increased turnover or stress-induced megakaryopoiesis, potentially mediated by acetaldehyde toxicity and ALDH2 variants.^14,15

Our findings on macrocytosis are consistent with prior studies. MCV was elevated in up to 29% of patients with AUD, supporting its role as a biomarker of chronic alcohol exposure.^16–18 Mechanistically, macrocytosis may reflect direct erythrocyte toxicity or deficiencies in folate, vitamin B12, and liver function.¹⁹

Serum α-amylase was significantly higher in patients with AUD than in controls, despite overall lower activity than reported in Brazilian studies.²⁰ Elevated amylase may reflect pancreatic/hepatic inflammation²¹ or sympathetic activation, as suggested by King and Nater.^22,23

The AST/ALT ratio (De Ritis index) followed expected patterns, being higher in patients with AUD and slightly more elevated in those without chronic viral infections, consistent with alcoholic liver disease and corroborated by Iluz-Freundlich et al.²⁴ Elevated direct (conjugated) bilirubin further supported cholestasis. In contrast, indirect (unconjugated) hyperbilirubinemia was more frequent in virus-positive individuals, suggesting impaired conjugation and hepatocellular function.

Elevated bilirubin in a subset of controls may reflect dietary, metabolic, or lifestyle-related factors reported in other populations. Similar trends were documented in Indian and Chinese populations.^25,26

Creatinine was significantly lower in patients with AUD, consistent with prior reports,²⁷ possibly reflecting lower skeletal muscle mass, malnutrition, or altered protein metabolism. Creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease.²⁸ In the present cohorts, lower creatinine should therefore be interpreted as part of the broader metabolic and somatic profile of patients with AUD, rather than as an isolated renal marker.

Glucose and total protein showed paradoxical trends: hyperglycemia and hypoproteinemia were more common in controls, possibly due to obesity. In contrast, elevated globulin fractions in patients with AUD may reflect chronic inflammation.^29,30

Hypoglycemia was present in 27.1% of virus-negative patients with AUD, consistent with alcoholic ketoacidosis. This finding warrants expanded biochemical assessment in future studies, including ketone body and osmolality measurements.^31–34

Neuropsychiatric symptoms (e.g., delirium, hallucinations) were present in 5.5% of patients with AUD. A disproportionately high frequency of neuropsychiatric symptoms was observed among participants of Slavic origin; however, these findings should be interpreted cautiously because of the small subgroup size. These observations may reflect population-specific differences in enzymatic systems involved in alcohol metabolism.¹⁰

Finally, ROC analysis highlights the effectiveness of liver-specific biomarkers (AST, ALT, FIB-4) and systemic parameters (MAP, AAMY) in distinguishing patients with AUD from alcohol-free controls. Elevated AUROC values for AST and FIB-4 align with prior research indicating hepatic injury and fibrotic transformation.^35,36 MAP elevation is consistent with alcohol-induced sympathetic activation.³⁷ In subgroup analysis, immune-inflammatory markers (WBC, lymphocytes, monocytes) were more relevant for differentiating patients with AUD and chronic viral infection from those without chronic viral infection, suggesting that viral comorbidity contributes additional immunologic changes.^38,39 These findings indicate that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. This is clinically relevant because chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. However, the limited discriminatory performance of some markers, including platelets, hemoglobin, and BMI, suggests that they should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

This study has several limitations. First, the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. Accordingly, the findings should be interpreted as primarily applicable to male patients with AUD, and future studies should include adequately powered female cohorts. Second, the recruitment period was restricted to three months to minimize patient recirculation and ensure the dataset reflected primary screening; while methodologically justified, this limited the overall sample size. Third, the control group was relatively small compared with the patient cohorts. Despite these limitations, the study provides novel population-specific insights into biomarker profiles in AUD with and without chronic viral comorbidity. Importantly, this work represents the first systematic investigation of such biomarkers in a poorly studied population of patients with AUD in Uzbekistan, highlighting the need for further research in this unique setting.

5. Conclusion

ROC analysis confirmed the strong diagnostic value of liver enzymes (AST, ALT), α-amylase, MCV, direct (conjugated) bilirubin, the De Ritis ratio, FIB-4 index, and mean arterial pressure (MAP) in alcohol use disorder (AUD). Leukocyte, lymphocyte, and creatinine levels were the most informative for distinguishing patients with AUD with and without chronic viral comorbidity. These findings highlight the clinical utility of multimarker panels for differentiation and risk stratification in AUD.

Importantly, deviations between Uzbek control values and international reference ranges underline the need for population- and ethnicity-specific interpretation of biochemical and hematological markers in clinical practice. Establishing regionally validated diagnostic thresholds could improve screening accuracy and support personalized management of patients with AUD in Central Asia.

Declaration of generative AI and AI-assisted technologies

The authors used ChatGPT (OpenAI, 2025) to improve the language. After using this tool, the authors reviewed and edited the content and take full responsibility for the publication’s content.

Data availability

Underlying data

Repository: Population-Specific Biomarker Dataset for Alcohol Use Disorder in Uzbekistan.

DOI: https://doi.org/10.5281/zenodo.17673836⁴⁰

The project contains the following underlying data: Raw Dataset – Hematology_Biochemistry_Somatic.xlsx (raw anonymized patient-level hematological, biochemical, somatic, and clinical data for all three cohorts).

Extended data

Repository: Population-Specific Biomarker Dataset for Alcohol Use Disorder in Uzbekistan.

DOI: https://doi.org/10.5281/zenodo.17673836⁴⁰

This project contains the following extended data:

- Supplementary File S1 — Supplementary Methods (detailed description of laboratory methods, formulas for derived indices, and ROC analysis procedures).
- Supplementary Table S1 – Hematological biomarkers (group-level distributions and statistical comparisons of hematological parameters across cohorts).
- Supplementary Table S2 – Biochemical biomarkers (group-level distributions and statistical comparisons of biochemical parameters across cohorts).
- Supplementary Figure S1 – Box plot distributions of biochemical biomarkers in the study cohorts.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0).

Acknowledgments

The work was carried out in accordance with the research plan for the current year and approved by the Ministry of Health of the Republic of Uzbekistan. The work was carried out on the basis of the Republican Specialized Scientific and Practical Medical Center for Mental Health.

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Version 2

VERSION 2 PUBLISHED 24 Dec 2025

Author details Author details

Natalya Tseomashko
Roles: Conceptualization, Investigation, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Timur Syunyakov
Roles: Formal Analysis, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Inara Khayredinova
Roles: Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

Uktam Tadjibaev
Roles: Data Curation, Investigation, Methodology

Furkat Bahramov
Roles: Data Curation, Investigation, Resources

Zarifjon Ashurov
Roles: Project Administration, Resources, Supervision

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

version 2

Revised

Published: 19 May 2026, 14:1449

https://doi.org/10.12688/f1000research.172858.2

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https://doi.org/10.12688/f1000research.172858.1

© 2026 Tseomashko N et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Tseomashko N, Syunyakov T, Khayredinova I et al. Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort [version 2; peer review: 1 approved with reservations]. F1000Research 2026, 14:1449 (https://doi.org/10.12688/f1000research.172858.2)

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Reviewer Report 04 Mar 2026

Julia Sinclair, University of Southampton, Southampton, England, UK

Julia Morris, University of Southampton, Southampton, England, UK

Approved with Reservations

https://doi.org/

General comments:
This study is a helpful addition to the literature, in describing haematological and biochemical biomarkers, and comorbidities, in individuals with and without AUD and chronic viral illnesses in Uzbekistan.

Terminology needs to be

Terminology needs to be clear and and more consistent when describing cases throughout the paper.

For example, within the methodology, cases are described as those with a ‘F10.2 diagnosis of stage II chronic alcoholism’, and later in the paper, ‘stage 2 alcohol use disorder’. The ICD-10 does not describe AUD as having ‘stages’, so the paper should clarify how cases have been defined (including where the data on diagnostic codes has been obtained from). Relatedly, patients with ‘mild AUD’ are excluded, and so the paper should describe how ‘mild AUD’ has been defined. Additionally, cases are described as ‘addicts’ within Figure 1, which is no longer considered an acceptable term in professional writing to describe individuals with substance use disorders.
Abstract:
The abstract could better orientate the reader to the purpose of the paper, by clarifying the viral infections studied refer to chronic viral infections (e.g. Hepatitis B, Hepatitis C, HIV) and not typically self-limiting viral infections (e.g. influenza).
Methodology:

Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant?
Relatedly, it is not clear if controls were selected from patients at the centre or from within another group – this information should be included.
A brief sentence explaining the De Ritis ratio needs to be included in the methods section (rather than in the discussion).
Please explain how data on participant BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms was obtained, as well as how alcohol withdrawal was categorised as ‘mild’, ‘moderate’ or ‘severe’.
Please also comment on whether all delusions/hallucinations referenced in the study are known to be related to alcohol withdrawal, or if these could be related to a primary mental illness, in addition to how this data on specific symptoms was obtained.
The methods section states patients with ‘mild withdrawal symptoms’ were excluded. It would be helpful to explain why this decision was made, as it appears (from within the discussion) that patients with both no withdrawal symptoms, and those with severe withdrawal symptoms, were included.

Results:
Within the results section, a right shift in the differential count is said to be suggestive of megaloblastic anaemia or hepatic/renal impairment. I am not aware ‘right shift’ is a widely accepted term (as ‘left shift’ is) or that this is accepted as suggestive of megaloblastic anaemia or hepatic/renal impairment. Please provide further evidence supporting this statement.
Discussion:
The authors are encouraged to review their discussion to ensure that they do not make claims beyond the data presented or cited

The term ‘granulocytopenia’ is used, however, data on eosinophils and basophils is not provided. Please clarify if this refers specifically to low neutrophils, or to low granulocytes (neutrophils, eosinophils and basophils).
The reference to previous research on candida-albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
The statement ‘these findings support including creatinine in the MELD score, a validated mortality predictor in end-stage liver disease’ is not adequately supported by the findings of the paper, as the paper does not explore mortality. Please adjust this statement as it is important not to talk beyond the data
Please clarify the comment made regarding the small number of female participants, as females were explicitly excluded.
Please clarify the following statement: ‘while low PLT has been associated with withdrawal-related neuropsychiatric complications, no such manifestations were observed in patients with severe thrombocytopenia here’. The paper cited here specifically comments on associations between thrombocytopenia and delirium tremens, and thrombocytopenia and alcohol-withdrawal seizures, when the current paper does not appear to have collected data on either delirium tremens or alcohol-withdrawal seizures. Relatedly, the current paper has analysed blood results categorically (low/normal/high), and therefore has limited ability to comment on the severity of any thrombocytopenia.

The statement ‘the findings reinforce the utility of combining biochemical, haematological, and physiological markers to enhance diagnostic accuracy’ does not make sense without further context on what is being diagnosed (e.g. viral infections, AUD), reflection on why diagnostic accuracy may currently be difficult, and the clinical importance of accurate diagnosis.

Supplementary tables:
Within Table S1, please provide a legend with the abbreviations (e.g. LYM, MON etc.) written out in full. Please also provide the specific thresholds which have been defined as the normal range in the table.
Within Table S2, please clarify if the glucose is a random level or fasted.
Consistency in bilirubin labelling would be helpful (i.e. direct/indirect or conjugated/unconjugated), as this currently changes throughout the paper and table S2.
Dataset:
A participant ID is provided, and separately an ‘encrypted name, nation’. The encrypted name looks to very possibly include the patient’s actual initials. Please consider if this needs to be anonymised further, to meet data anonymity requirements.
A ‘note’ column contains comorbidity information not required for replication of the study findings, and possibly identifying of individual participants if the recommended changes in the methodology (e.g. including specific dates) are made. Please consider removing this from the table.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

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Author Response 19 May 2026

Natalya Tseomashko, Republican Specialized Scientific and Practical Medical Center for Mental Health, Tashkent, Uzbekistan

19 May 2026

Author Response

Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a ... Continue reading Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a helpful addition to the literature on hematological and biochemical biomarkers and comorbidities in individuals with AUD and chronic viral illnesses in Uzbekistan. We also appreciate your detailed suggestions, which helped us improve the clarity, methodological transparency, terminology, interpretation, supplementary materials, and dataset anonymization.
We have revised the manuscript carefully in response to all comments. In addition, the supplementary materials and dataset deposited in Zenodo were revised for clarity and further anonymization. No changes were made to the study groups, statistical analyses, numerical results, or reported conclusions.
Below we provide a point-by-point response.
General comment: Terminology needs to be clear and more consistent when describing cases throughout the paper. The reviewer noted inconsistent use of “stage II chronic alcoholism,” “stage 2 alcohol use disorder,” “mild AUD,” and the term “addicts” in Figure 1.
Thank you for this important comment. We revised the terminology throughout the manuscript to improve consistency and to avoid stigmatizing language. The term “addicts” was removed and replaced with person-first terminology. We now consistently refer to the patient groups as “patients with AUD” or “AUD cohorts.”
We also clarified that the diagnosis was alcohol use disorder according to ICD-10 code F10.2. The wording suggesting that ICD-10 defines AUD by “stages” was removed or revised. In the Methods section, we now specify that inclusion criteria were male sex, age 20-65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT.
We also clarified the exclusion of individuals with mild alcohol-related conditions or clinically mild withdrawal presentations. The revised Methods state that individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

Abstract: The abstract could better orientate the reader by clarifying that the viral infections studied refer to chronic viral infections, such as hepatitis B, hepatitis C, and HIV.
Thank you. We revised the Abstract to clarify this point. The Background now states that the study examined biomarker signatures in men with AUD in Uzbekistan, with and without chronic viral infections, specifically hepatitis B, hepatitis C, and HIV.
This clarification was also applied throughout the manuscript, including the Methods, Results, Discussion, figure/table legends, and data descriptions.

Methodology: Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant.
Thank you. We expanded the Methods section substantially. We now specify that clinical and laboratory data were collected from 19 March to 22 June 2025. We also clarify that patients were recruited from inpatient units of the Republican Specialized Scientific and Practical Medical Center for Mental Health.
We added that laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis, and follow-up measurements during treatment were excluded. This was added to clarify how biomarker values were selected.

Methodology: It is not clear whether controls were selected from patients at the centre or from another group.
Thank you for noting this. We clarified the control group source. The revised Methods state that the control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. We also clarified that one initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

Methodology: A brief sentence explaining the De Ritis ratio needs to be included in the Methods section rather than in the Discussion.
Thank you. We added a methodological explanation in the Data Collection section. The revised manuscript now states that the De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury.

Methodology: Please explain how data on BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms were obtained, and how withdrawal was categorized as mild, moderate, or severe.
Thank you. We expanded the Data Collection and Inclusion/Exclusion Criteria sections. The revised Methods now state that data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires, and BMI was calculated as weight in kilograms divided by height in meters squared.
We also added a detailed explanation of alcohol withdrawal severity classification. Withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Methodology: Please comment on whether delusions/hallucinations were related to alcohol withdrawal or could be related to primary mental illness, and how these data were obtained.
Thank you. We added a clarification in the Data Collection section. The revised manuscript states that delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review.
We also clarified that symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to the medical records, these patients had no previous registration or documented history of primary psychotic disorders.

Methodology: The Methods section states that patients with mild withdrawal symptoms were excluded. Please explain why this decision was made.
Thank you. We clarified this point in the Study Design and Population section and in the Inclusion/Exclusion Criteria section. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD. This was intended to maintain clinical comparability within the analytical cohort.

Results: The statement regarding “right shift” in the differential count and its interpretation as suggestive of megaloblastic anemia or hepatic/renal impairment requires further support.
Thank you for this very helpful comment. We revised this section to avoid unsupported interpretation. The term “right shift” was removed. We now state that an increased proportion of segmented neutrophils was observed in 7.6–9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment.
This revision ensures that the interpretation remains aligned with the data actually available in the study.

Discussion: Please review the Discussion to ensure that claims do not go beyond the data presented or cited.
Thank you. We carefully revised the Discussion to make the interpretation more cautious and better aligned with the available data. Several statements were softened or clarified. For example, causal or overly strong wording was revised to more cautious language such as “may reflect,” “may aggravate,” “suggesting a greater likelihood,” and “should be interpreted cautiously.”
We also revised the interpretation of neuropsychiatric symptoms, platelet counts, creatinine/MELD, ROC findings, bilirubin findings, and biomarker diagnostic utility to avoid overinterpretation.

Discussion: The term “granulocytopenia” is used, but data on eosinophils and basophils are not provided. Please clarify whether this refers specifically to low neutrophils or to low granulocytes.
Thank you. We revised this terminology. Because eosinophil and basophil data were not included, we removed the broader term “granulocytopenia” where it could be misleading and clarified the finding as neutropenia/neutrophilic granulocyte compartment suppression.
The revised Discussion now states that alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection. However, in the present AUD cohorts, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Discussion: The reference to previous research on Candida albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
Thank you. We expanded this part of the Discussion to better explain the relevance of the cited work. The revised text now explains that inflammatory marker abnormalities may reflect not only viral-associated inflammatory burden but also alcohol-related disruption of gut-liver immune signaling. We added that Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and increased Candida albicans-specific Th17 responses, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. We also noted that experimental models showed ethanol-associated migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury.
This explanation was added to clarify why gut-derived microbial/fungal immune pathways may be relevant to systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.

Discussion: The statement that the findings support including creatinine in the MELD score is not adequately supported, as mortality was not explored.
Thank you. We agree and revised this section substantially. The unsupported statement was removed. The revised Discussion now states that creatinine was significantly lower in patients with AUD, consistent with prior reports. We explain that, in the context of chronic alcohol exposure, reduced creatinine may reflect lower skeletal muscle mass, malnutrition, or altered protein metabolism rather than preserved renal function.
We also clarified that creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease. We no longer imply that our findings validate mortality prediction. Instead, we state that lower creatinine in the present cohorts should be interpreted as part of the broader metabolic and somatic profile of patients with AUD rather than as an isolated renal marker.

Discussion: Please clarify the comment regarding the small number of female participants, as females were explicitly excluded.
Thank you. We revised the Limitations section to clarify this point. The revised text states that the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. We also added that the findings should be interpreted as primarily applicable to male patients with AUD, and that future studies should include adequately powered female cohorts.

Discussion: Please clarify the statement regarding low PLT and withdrawal-related neuropsychiatric complications, as the cited paper refers to delirium tremens and withdrawal seizures, and the current paper did not appear to collect these outcomes. The current paper also analyzed blood results categorically and has limited ability to comment on severity of thrombocytopenia.
Thank you for this important clarification. We revised this section to avoid overinterpretation. We no longer refer to severe thrombocytopenia as an outcome category. Instead, we provide the platelet findings in relation to the reference range and explicitly state the available clinical data.
The revised Discussion now states that thrombocytopenia was observed in patients with AUD. We added median PLT values across cohorts and the proportions of participants with PLT values below the reference range. We also clarified that, although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens in previous research, clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD in our cohort; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range.
We therefore revised the conclusion to state that reduced PLT was an important hematological feature of AUD but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

Discussion: The statement about combining biochemical, hematological, and physiological markers to enhance diagnostic accuracy needs further context on what is being diagnosed, why diagnostic accuracy may be difficult, and the clinical importance of accurate diagnosis.
Thank you. We revised the ROC interpretation in both the Results and Discussion sections. We clarified that two diagnostic comparisons were assessed: first, discrimination between all patients with AUD and alcohol-free controls; second, differentiation between patients with AUD and chronic viral infection and patients with AUD without chronic viral infection.
We also clarified that AUROC was used to quantify discriminatory performance and that AUROC values below 0.5 reflected inverse discrimination according to the predefined class coding, rather than necessarily indicating absence of discriminatory value.
In the Discussion, we revised the statement to clarify that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. We also added clinical context: chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. We further clarified that markers with limited discriminatory performance, including platelets, hemoglobin, and BMI, should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

Supplementary tables: Within Table S1, please provide a legend with abbreviations written out in full and provide the thresholds defined as normal range.
Thank you. Supplementary Table S1 was revised. We added a legend defining hematological abbreviations, including Hb, LYM, MCV, MON, MPV, NEUT, PLT, ESR, and WBC. We also added the specific reference intervals used to define low, normal, and high values.

Supplementary tables: Within Table S2, please clarify whether glucose is random or fasted.
Thank you. Supplementary Table S2 was revised to clarify glucose sampling. We added that glucose values in the patient group represent random/non-fasting blood glucose measured at admission, whereas blood samples from controls were obtained under fasting conditions. This clarification was also added to the Methods section.

Supplementary tables: Consistency in bilirubin labeling would be helpful.
Thank you. We harmonized bilirubin terminology throughout the manuscript and supplementary materials. We now use direct (conjugated) bilirubin and indirect (unconjugated) bilirubin consistently where relevant.

Dataset: Participant ID and “encrypted name, nation” may contain identifying information. Please consider whether further anonymization is needed.
Thank you. We carefully reviewed and revised the dataset. The dataset file deposited in Zenodo was additionally anonymized. Potentially identifying or non-essential fields, including the encrypted name and nationality-related field, were removed. The dataset now retains anonymized participant IDs and the variables required for reproducibility of the analysis.

Dataset: The “note” column contains comorbidity information not required for replication and may be identifying if specific dates are included. Please consider removing this column.
Thank you. We removed the free-text note column from the dataset to reduce re-identification risk. Comorbidity-related information necessary for the analysis is retained in structured, analyzable variables, while potentially identifying free-text information was removed.

Additional data and references
During the revision, we also checked the reference list and corrected/replaced several references where needed to ensure that all cited sources accurately support the corresponding statements. The Zenodo repository was updated to include the additionally de-identified underlying dataset and clarified extended data files. No changes were made to study groups, statistical analyses, numerical results, or reported conclusions.

We are grateful to the reviewer for the detailed and constructive comments. We believe that the revised version is clearer, more transparent, more cautious in interpretation, and better aligned with the available data and reporting standards.
Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a helpful addition to the literature on hematological and biochemical biomarkers and comorbidities in individuals with AUD and chronic viral illnesses in Uzbekistan. We also appreciate your detailed suggestions, which helped us improve the clarity, methodological transparency, terminology, interpretation, supplementary materials, and dataset anonymization.
We have revised the manuscript carefully in response to all comments. In addition, the supplementary materials and dataset deposited in Zenodo were revised for clarity and further anonymization. No changes were made to the study groups, statistical analyses, numerical results, or reported conclusions.
Below we provide a point-by-point response.
General comment: Terminology needs to be clear and more consistent when describing cases throughout the paper. The reviewer noted inconsistent use of “stage II chronic alcoholism,” “stage 2 alcohol use disorder,” “mild AUD,” and the term “addicts” in Figure 1.
Thank you for this important comment. We revised the terminology throughout the manuscript to improve consistency and to avoid stigmatizing language. The term “addicts” was removed and replaced with person-first terminology. We now consistently refer to the patient groups as “patients with AUD” or “AUD cohorts.”
We also clarified that the diagnosis was alcohol use disorder according to ICD-10 code F10.2. The wording suggesting that ICD-10 defines AUD by “stages” was removed or revised. In the Methods section, we now specify that inclusion criteria were male sex, age 20-65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT.
We also clarified the exclusion of individuals with mild alcohol-related conditions or clinically mild withdrawal presentations. The revised Methods state that individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

Abstract: The abstract could better orientate the reader by clarifying that the viral infections studied refer to chronic viral infections, such as hepatitis B, hepatitis C, and HIV.
Thank you. We revised the Abstract to clarify this point. The Background now states that the study examined biomarker signatures in men with AUD in Uzbekistan, with and without chronic viral infections, specifically hepatitis B, hepatitis C, and HIV.
This clarification was also applied throughout the manuscript, including the Methods, Results, Discussion, figure/table legends, and data descriptions.

Methodology: Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant.
Thank you. We expanded the Methods section substantially. We now specify that clinical and laboratory data were collected from 19 March to 22 June 2025. We also clarify that patients were recruited from inpatient units of the Republican Specialized Scientific and Practical Medical Center for Mental Health.
We added that laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis, and follow-up measurements during treatment were excluded. This was added to clarify how biomarker values were selected.

Methodology: It is not clear whether controls were selected from patients at the centre or from another group.
Thank you for noting this. We clarified the control group source. The revised Methods state that the control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. We also clarified that one initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

Methodology: A brief sentence explaining the De Ritis ratio needs to be included in the Methods section rather than in the Discussion.
Thank you. We added a methodological explanation in the Data Collection section. The revised manuscript now states that the De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury.

Methodology: Please explain how data on BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms were obtained, and how withdrawal was categorized as mild, moderate, or severe.
Thank you. We expanded the Data Collection and Inclusion/Exclusion Criteria sections. The revised Methods now state that data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires, and BMI was calculated as weight in kilograms divided by height in meters squared.
We also added a detailed explanation of alcohol withdrawal severity classification. Withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Methodology: Please comment on whether delusions/hallucinations were related to alcohol withdrawal or could be related to primary mental illness, and how these data were obtained.
Thank you. We added a clarification in the Data Collection section. The revised manuscript states that delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review.
We also clarified that symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to the medical records, these patients had no previous registration or documented history of primary psychotic disorders.

Methodology: The Methods section states that patients with mild withdrawal symptoms were excluded. Please explain why this decision was made.
Thank you. We clarified this point in the Study Design and Population section and in the Inclusion/Exclusion Criteria section. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD. This was intended to maintain clinical comparability within the analytical cohort.

Results: The statement regarding “right shift” in the differential count and its interpretation as suggestive of megaloblastic anemia or hepatic/renal impairment requires further support.
Thank you for this very helpful comment. We revised this section to avoid unsupported interpretation. The term “right shift” was removed. We now state that an increased proportion of segmented neutrophils was observed in 7.6–9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment.
This revision ensures that the interpretation remains aligned with the data actually available in the study.

Discussion: Please review the Discussion to ensure that claims do not go beyond the data presented or cited.
Thank you. We carefully revised the Discussion to make the interpretation more cautious and better aligned with the available data. Several statements were softened or clarified. For example, causal or overly strong wording was revised to more cautious language such as “may reflect,” “may aggravate,” “suggesting a greater likelihood,” and “should be interpreted cautiously.”
We also revised the interpretation of neuropsychiatric symptoms, platelet counts, creatinine/MELD, ROC findings, bilirubin findings, and biomarker diagnostic utility to avoid overinterpretation.

Discussion: The term “granulocytopenia” is used, but data on eosinophils and basophils are not provided. Please clarify whether this refers specifically to low neutrophils or to low granulocytes.
Thank you. We revised this terminology. Because eosinophil and basophil data were not included, we removed the broader term “granulocytopenia” where it could be misleading and clarified the finding as neutropenia/neutrophilic granulocyte compartment suppression.
The revised Discussion now states that alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection. However, in the present AUD cohorts, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Discussion: The reference to previous research on Candida albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
Thank you. We expanded this part of the Discussion to better explain the relevance of the cited work. The revised text now explains that inflammatory marker abnormalities may reflect not only viral-associated inflammatory burden but also alcohol-related disruption of gut-liver immune signaling. We added that Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and increased Candida albicans-specific Th17 responses, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. We also noted that experimental models showed ethanol-associated migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury.
This explanation was added to clarify why gut-derived microbial/fungal immune pathways may be relevant to systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.

Discussion: The statement that the findings support including creatinine in the MELD score is not adequately supported, as mortality was not explored.
Thank you. We agree and revised this section substantially. The unsupported statement was removed. The revised Discussion now states that creatinine was significantly lower in patients with AUD, consistent with prior reports. We explain that, in the context of chronic alcohol exposure, reduced creatinine may reflect lower skeletal muscle mass, malnutrition, or altered protein metabolism rather than preserved renal function.
We also clarified that creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease. We no longer imply that our findings validate mortality prediction. Instead, we state that lower creatinine in the present cohorts should be interpreted as part of the broader metabolic and somatic profile of patients with AUD rather than as an isolated renal marker.

Discussion: Please clarify the comment regarding the small number of female participants, as females were explicitly excluded.
Thank you. We revised the Limitations section to clarify this point. The revised text states that the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. We also added that the findings should be interpreted as primarily applicable to male patients with AUD, and that future studies should include adequately powered female cohorts.

Discussion: Please clarify the statement regarding low PLT and withdrawal-related neuropsychiatric complications, as the cited paper refers to delirium tremens and withdrawal seizures, and the current paper did not appear to collect these outcomes. The current paper also analyzed blood results categorically and has limited ability to comment on severity of thrombocytopenia.
Thank you for this important clarification. We revised this section to avoid overinterpretation. We no longer refer to severe thrombocytopenia as an outcome category. Instead, we provide the platelet findings in relation to the reference range and explicitly state the available clinical data.
The revised Discussion now states that thrombocytopenia was observed in patients with AUD. We added median PLT values across cohorts and the proportions of participants with PLT values below the reference range. We also clarified that, although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens in previous research, clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD in our cohort; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range.
We therefore revised the conclusion to state that reduced PLT was an important hematological feature of AUD but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

Discussion: The statement about combining biochemical, hematological, and physiological markers to enhance diagnostic accuracy needs further context on what is being diagnosed, why diagnostic accuracy may be difficult, and the clinical importance of accurate diagnosis.
Thank you. We revised the ROC interpretation in both the Results and Discussion sections. We clarified that two diagnostic comparisons were assessed: first, discrimination between all patients with AUD and alcohol-free controls; second, differentiation between patients with AUD and chronic viral infection and patients with AUD without chronic viral infection.
We also clarified that AUROC was used to quantify discriminatory performance and that AUROC values below 0.5 reflected inverse discrimination according to the predefined class coding, rather than necessarily indicating absence of discriminatory value.
In the Discussion, we revised the statement to clarify that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. We also added clinical context: chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. We further clarified that markers with limited discriminatory performance, including platelets, hemoglobin, and BMI, should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

Supplementary tables: Within Table S1, please provide a legend with abbreviations written out in full and provide the thresholds defined as normal range.
Thank you. Supplementary Table S1 was revised. We added a legend defining hematological abbreviations, including Hb, LYM, MCV, MON, MPV, NEUT, PLT, ESR, and WBC. We also added the specific reference intervals used to define low, normal, and high values.

Supplementary tables: Within Table S2, please clarify whether glucose is random or fasted.
Thank you. Supplementary Table S2 was revised to clarify glucose sampling. We added that glucose values in the patient group represent random/non-fasting blood glucose measured at admission, whereas blood samples from controls were obtained under fasting conditions. This clarification was also added to the Methods section.

Supplementary tables: Consistency in bilirubin labeling would be helpful.
Thank you. We harmonized bilirubin terminology throughout the manuscript and supplementary materials. We now use direct (conjugated) bilirubin and indirect (unconjugated) bilirubin consistently where relevant.

Dataset: Participant ID and “encrypted name, nation” may contain identifying information. Please consider whether further anonymization is needed.
Thank you. We carefully reviewed and revised the dataset. The dataset file deposited in Zenodo was additionally anonymized. Potentially identifying or non-essential fields, including the encrypted name and nationality-related field, were removed. The dataset now retains anonymized participant IDs and the variables required for reproducibility of the analysis.

Dataset: The “note” column contains comorbidity information not required for replication and may be identifying if specific dates are included. Please consider removing this column.
Thank you. We removed the free-text note column from the dataset to reduce re-identification risk. Comorbidity-related information necessary for the analysis is retained in structured, analyzable variables, while potentially identifying free-text information was removed.

Additional data and references
During the revision, we also checked the reference list and corrected/replaced several references where needed to ensure that all cited sources accurately support the corresponding statements. The Zenodo repository was updated to include the additionally de-identified underlying dataset and clarified extended data files. No changes were made to study groups, statistical analyses, numerical results, or reported conclusions.

We are grateful to the reviewer for the detailed and constructive comments. We believe that the revised version is clearer, more transparent, more cautious in interpretation, and better aligned with the available data and reporting standards.
Competing Interests: The authors declare that they have no competing interests. Close
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Author Response 19 May 2026

Natalya Tseomashko, Republican Specialized Scientific and Practical Medical Center for Mental Health, Tashkent, Uzbekistan

19 May 2026

Author Response

Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a ... Continue reading Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a helpful addition to the literature on hematological and biochemical biomarkers and comorbidities in individuals with AUD and chronic viral illnesses in Uzbekistan. We also appreciate your detailed suggestions, which helped us improve the clarity, methodological transparency, terminology, interpretation, supplementary materials, and dataset anonymization.
We have revised the manuscript carefully in response to all comments. In addition, the supplementary materials and dataset deposited in Zenodo were revised for clarity and further anonymization. No changes were made to the study groups, statistical analyses, numerical results, or reported conclusions.
Below we provide a point-by-point response.
General comment: Terminology needs to be clear and more consistent when describing cases throughout the paper. The reviewer noted inconsistent use of “stage II chronic alcoholism,” “stage 2 alcohol use disorder,” “mild AUD,” and the term “addicts” in Figure 1.
Thank you for this important comment. We revised the terminology throughout the manuscript to improve consistency and to avoid stigmatizing language. The term “addicts” was removed and replaced with person-first terminology. We now consistently refer to the patient groups as “patients with AUD” or “AUD cohorts.”
We also clarified that the diagnosis was alcohol use disorder according to ICD-10 code F10.2. The wording suggesting that ICD-10 defines AUD by “stages” was removed or revised. In the Methods section, we now specify that inclusion criteria were male sex, age 20-65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT.
We also clarified the exclusion of individuals with mild alcohol-related conditions or clinically mild withdrawal presentations. The revised Methods state that individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

Abstract: The abstract could better orientate the reader by clarifying that the viral infections studied refer to chronic viral infections, such as hepatitis B, hepatitis C, and HIV.
Thank you. We revised the Abstract to clarify this point. The Background now states that the study examined biomarker signatures in men with AUD in Uzbekistan, with and without chronic viral infections, specifically hepatitis B, hepatitis C, and HIV.
This clarification was also applied throughout the manuscript, including the Methods, Results, Discussion, figure/table legends, and data descriptions.

Methodology: Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant.
Thank you. We expanded the Methods section substantially. We now specify that clinical and laboratory data were collected from 19 March to 22 June 2025. We also clarify that patients were recruited from inpatient units of the Republican Specialized Scientific and Practical Medical Center for Mental Health.
We added that laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis, and follow-up measurements during treatment were excluded. This was added to clarify how biomarker values were selected.

Methodology: It is not clear whether controls were selected from patients at the centre or from another group.
Thank you for noting this. We clarified the control group source. The revised Methods state that the control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. We also clarified that one initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

Methodology: A brief sentence explaining the De Ritis ratio needs to be included in the Methods section rather than in the Discussion.
Thank you. We added a methodological explanation in the Data Collection section. The revised manuscript now states that the De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury.

Methodology: Please explain how data on BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms were obtained, and how withdrawal was categorized as mild, moderate, or severe.
Thank you. We expanded the Data Collection and Inclusion/Exclusion Criteria sections. The revised Methods now state that data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires, and BMI was calculated as weight in kilograms divided by height in meters squared.
We also added a detailed explanation of alcohol withdrawal severity classification. Withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Methodology: Please comment on whether delusions/hallucinations were related to alcohol withdrawal or could be related to primary mental illness, and how these data were obtained.
Thank you. We added a clarification in the Data Collection section. The revised manuscript states that delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review.
We also clarified that symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to the medical records, these patients had no previous registration or documented history of primary psychotic disorders.

Methodology: The Methods section states that patients with mild withdrawal symptoms were excluded. Please explain why this decision was made.
Thank you. We clarified this point in the Study Design and Population section and in the Inclusion/Exclusion Criteria section. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD. This was intended to maintain clinical comparability within the analytical cohort.

Results: The statement regarding “right shift” in the differential count and its interpretation as suggestive of megaloblastic anemia or hepatic/renal impairment requires further support.
Thank you for this very helpful comment. We revised this section to avoid unsupported interpretation. The term “right shift” was removed. We now state that an increased proportion of segmented neutrophils was observed in 7.6–9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment.
This revision ensures that the interpretation remains aligned with the data actually available in the study.

Discussion: Please review the Discussion to ensure that claims do not go beyond the data presented or cited.
Thank you. We carefully revised the Discussion to make the interpretation more cautious and better aligned with the available data. Several statements were softened or clarified. For example, causal or overly strong wording was revised to more cautious language such as “may reflect,” “may aggravate,” “suggesting a greater likelihood,” and “should be interpreted cautiously.”
We also revised the interpretation of neuropsychiatric symptoms, platelet counts, creatinine/MELD, ROC findings, bilirubin findings, and biomarker diagnostic utility to avoid overinterpretation.

Discussion: The term “granulocytopenia” is used, but data on eosinophils and basophils are not provided. Please clarify whether this refers specifically to low neutrophils or to low granulocytes.
Thank you. We revised this terminology. Because eosinophil and basophil data were not included, we removed the broader term “granulocytopenia” where it could be misleading and clarified the finding as neutropenia/neutrophilic granulocyte compartment suppression.
The revised Discussion now states that alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection. However, in the present AUD cohorts, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Discussion: The reference to previous research on Candida albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
Thank you. We expanded this part of the Discussion to better explain the relevance of the cited work. The revised text now explains that inflammatory marker abnormalities may reflect not only viral-associated inflammatory burden but also alcohol-related disruption of gut-liver immune signaling. We added that Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and increased Candida albicans-specific Th17 responses, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. We also noted that experimental models showed ethanol-associated migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury.
This explanation was added to clarify why gut-derived microbial/fungal immune pathways may be relevant to systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.

Discussion: The statement that the findings support including creatinine in the MELD score is not adequately supported, as mortality was not explored.
Thank you. We agree and revised this section substantially. The unsupported statement was removed. The revised Discussion now states that creatinine was significantly lower in patients with AUD, consistent with prior reports. We explain that, in the context of chronic alcohol exposure, reduced creatinine may reflect lower skeletal muscle mass, malnutrition, or altered protein metabolism rather than preserved renal function.
We also clarified that creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease. We no longer imply that our findings validate mortality prediction. Instead, we state that lower creatinine in the present cohorts should be interpreted as part of the broader metabolic and somatic profile of patients with AUD rather than as an isolated renal marker.

Discussion: Please clarify the comment regarding the small number of female participants, as females were explicitly excluded.
Thank you. We revised the Limitations section to clarify this point. The revised text states that the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. We also added that the findings should be interpreted as primarily applicable to male patients with AUD, and that future studies should include adequately powered female cohorts.

Discussion: Please clarify the statement regarding low PLT and withdrawal-related neuropsychiatric complications, as the cited paper refers to delirium tremens and withdrawal seizures, and the current paper did not appear to collect these outcomes. The current paper also analyzed blood results categorically and has limited ability to comment on severity of thrombocytopenia.
Thank you for this important clarification. We revised this section to avoid overinterpretation. We no longer refer to severe thrombocytopenia as an outcome category. Instead, we provide the platelet findings in relation to the reference range and explicitly state the available clinical data.
The revised Discussion now states that thrombocytopenia was observed in patients with AUD. We added median PLT values across cohorts and the proportions of participants with PLT values below the reference range. We also clarified that, although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens in previous research, clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD in our cohort; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range.
We therefore revised the conclusion to state that reduced PLT was an important hematological feature of AUD but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

Discussion: The statement about combining biochemical, hematological, and physiological markers to enhance diagnostic accuracy needs further context on what is being diagnosed, why diagnostic accuracy may be difficult, and the clinical importance of accurate diagnosis.
Thank you. We revised the ROC interpretation in both the Results and Discussion sections. We clarified that two diagnostic comparisons were assessed: first, discrimination between all patients with AUD and alcohol-free controls; second, differentiation between patients with AUD and chronic viral infection and patients with AUD without chronic viral infection.
We also clarified that AUROC was used to quantify discriminatory performance and that AUROC values below 0.5 reflected inverse discrimination according to the predefined class coding, rather than necessarily indicating absence of discriminatory value.
In the Discussion, we revised the statement to clarify that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. We also added clinical context: chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. We further clarified that markers with limited discriminatory performance, including platelets, hemoglobin, and BMI, should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

Supplementary tables: Within Table S1, please provide a legend with abbreviations written out in full and provide the thresholds defined as normal range.
Thank you. Supplementary Table S1 was revised. We added a legend defining hematological abbreviations, including Hb, LYM, MCV, MON, MPV, NEUT, PLT, ESR, and WBC. We also added the specific reference intervals used to define low, normal, and high values.

Supplementary tables: Within Table S2, please clarify whether glucose is random or fasted.
Thank you. Supplementary Table S2 was revised to clarify glucose sampling. We added that glucose values in the patient group represent random/non-fasting blood glucose measured at admission, whereas blood samples from controls were obtained under fasting conditions. This clarification was also added to the Methods section.

Supplementary tables: Consistency in bilirubin labeling would be helpful.
Thank you. We harmonized bilirubin terminology throughout the manuscript and supplementary materials. We now use direct (conjugated) bilirubin and indirect (unconjugated) bilirubin consistently where relevant.

Dataset: Participant ID and “encrypted name, nation” may contain identifying information. Please consider whether further anonymization is needed.
Thank you. We carefully reviewed and revised the dataset. The dataset file deposited in Zenodo was additionally anonymized. Potentially identifying or non-essential fields, including the encrypted name and nationality-related field, were removed. The dataset now retains anonymized participant IDs and the variables required for reproducibility of the analysis.

Dataset: The “note” column contains comorbidity information not required for replication and may be identifying if specific dates are included. Please consider removing this column.
Thank you. We removed the free-text note column from the dataset to reduce re-identification risk. Comorbidity-related information necessary for the analysis is retained in structured, analyzable variables, while potentially identifying free-text information was removed.

Additional data and references
During the revision, we also checked the reference list and corrected/replaced several references where needed to ensure that all cited sources accurately support the corresponding statements. The Zenodo repository was updated to include the additionally de-identified underlying dataset and clarified extended data files. No changes were made to study groups, statistical analyses, numerical results, or reported conclusions.

We are grateful to the reviewer for the detailed and constructive comments. We believe that the revised version is clearer, more transparent, more cautious in interpretation, and better aligned with the available data and reporting standards.
Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a helpful addition to the literature on hematological and biochemical biomarkers and comorbidities in individuals with AUD and chronic viral illnesses in Uzbekistan. We also appreciate your detailed suggestions, which helped us improve the clarity, methodological transparency, terminology, interpretation, supplementary materials, and dataset anonymization.
We have revised the manuscript carefully in response to all comments. In addition, the supplementary materials and dataset deposited in Zenodo were revised for clarity and further anonymization. No changes were made to the study groups, statistical analyses, numerical results, or reported conclusions.
Below we provide a point-by-point response.
General comment: Terminology needs to be clear and more consistent when describing cases throughout the paper. The reviewer noted inconsistent use of “stage II chronic alcoholism,” “stage 2 alcohol use disorder,” “mild AUD,” and the term “addicts” in Figure 1.
Thank you for this important comment. We revised the terminology throughout the manuscript to improve consistency and to avoid stigmatizing language. The term “addicts” was removed and replaced with person-first terminology. We now consistently refer to the patient groups as “patients with AUD” or “AUD cohorts.”
We also clarified that the diagnosis was alcohol use disorder according to ICD-10 code F10.2. The wording suggesting that ICD-10 defines AUD by “stages” was removed or revised. In the Methods section, we now specify that inclusion criteria were male sex, age 20-65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT.
We also clarified the exclusion of individuals with mild alcohol-related conditions or clinically mild withdrawal presentations. The revised Methods state that individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

Abstract: The abstract could better orientate the reader by clarifying that the viral infections studied refer to chronic viral infections, such as hepatitis B, hepatitis C, and HIV.
Thank you. We revised the Abstract to clarify this point. The Background now states that the study examined biomarker signatures in men with AUD in Uzbekistan, with and without chronic viral infections, specifically hepatitis B, hepatitis C, and HIV.
This clarification was also applied throughout the manuscript, including the Methods, Results, Discussion, figure/table legends, and data descriptions.

Methodology: Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant.
Thank you. We expanded the Methods section substantially. We now specify that clinical and laboratory data were collected from 19 March to 22 June 2025. We also clarify that patients were recruited from inpatient units of the Republican Specialized Scientific and Practical Medical Center for Mental Health.
We added that laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis, and follow-up measurements during treatment were excluded. This was added to clarify how biomarker values were selected.

Methodology: It is not clear whether controls were selected from patients at the centre or from another group.
Thank you for noting this. We clarified the control group source. The revised Methods state that the control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. We also clarified that one initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

Methodology: A brief sentence explaining the De Ritis ratio needs to be included in the Methods section rather than in the Discussion.
Thank you. We added a methodological explanation in the Data Collection section. The revised manuscript now states that the De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury.

Methodology: Please explain how data on BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms were obtained, and how withdrawal was categorized as mild, moderate, or severe.
Thank you. We expanded the Data Collection and Inclusion/Exclusion Criteria sections. The revised Methods now state that data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires, and BMI was calculated as weight in kilograms divided by height in meters squared.
We also added a detailed explanation of alcohol withdrawal severity classification. Withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Methodology: Please comment on whether delusions/hallucinations were related to alcohol withdrawal or could be related to primary mental illness, and how these data were obtained.
Thank you. We added a clarification in the Data Collection section. The revised manuscript states that delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review.
We also clarified that symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to the medical records, these patients had no previous registration or documented history of primary psychotic disorders.

Methodology: The Methods section states that patients with mild withdrawal symptoms were excluded. Please explain why this decision was made.
Thank you. We clarified this point in the Study Design and Population section and in the Inclusion/Exclusion Criteria section. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD. This was intended to maintain clinical comparability within the analytical cohort.

Results: The statement regarding “right shift” in the differential count and its interpretation as suggestive of megaloblastic anemia or hepatic/renal impairment requires further support.
Thank you for this very helpful comment. We revised this section to avoid unsupported interpretation. The term “right shift” was removed. We now state that an increased proportion of segmented neutrophils was observed in 7.6–9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment.
This revision ensures that the interpretation remains aligned with the data actually available in the study.

Discussion: Please review the Discussion to ensure that claims do not go beyond the data presented or cited.
Thank you. We carefully revised the Discussion to make the interpretation more cautious and better aligned with the available data. Several statements were softened or clarified. For example, causal or overly strong wording was revised to more cautious language such as “may reflect,” “may aggravate,” “suggesting a greater likelihood,” and “should be interpreted cautiously.”
We also revised the interpretation of neuropsychiatric symptoms, platelet counts, creatinine/MELD, ROC findings, bilirubin findings, and biomarker diagnostic utility to avoid overinterpretation.

Discussion: The term “granulocytopenia” is used, but data on eosinophils and basophils are not provided. Please clarify whether this refers specifically to low neutrophils or to low granulocytes.
Thank you. We revised this terminology. Because eosinophil and basophil data were not included, we removed the broader term “granulocytopenia” where it could be misleading and clarified the finding as neutropenia/neutrophilic granulocyte compartment suppression.
The revised Discussion now states that alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection. However, in the present AUD cohorts, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Discussion: The reference to previous research on Candida albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
Thank you. We expanded this part of the Discussion to better explain the relevance of the cited work. The revised text now explains that inflammatory marker abnormalities may reflect not only viral-associated inflammatory burden but also alcohol-related disruption of gut-liver immune signaling. We added that Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and increased Candida albicans-specific Th17 responses, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. We also noted that experimental models showed ethanol-associated migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury.
This explanation was added to clarify why gut-derived microbial/fungal immune pathways may be relevant to systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.

Discussion: The statement that the findings support including creatinine in the MELD score is not adequately supported, as mortality was not explored.
Thank you. We agree and revised this section substantially. The unsupported statement was removed. The revised Discussion now states that creatinine was significantly lower in patients with AUD, consistent with prior reports. We explain that, in the context of chronic alcohol exposure, reduced creatinine may reflect lower skeletal muscle mass, malnutrition, or altered protein metabolism rather than preserved renal function.
We also clarified that creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease. We no longer imply that our findings validate mortality prediction. Instead, we state that lower creatinine in the present cohorts should be interpreted as part of the broader metabolic and somatic profile of patients with AUD rather than as an isolated renal marker.

Discussion: Please clarify the comment regarding the small number of female participants, as females were explicitly excluded.
Thank you. We revised the Limitations section to clarify this point. The revised text states that the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. We also added that the findings should be interpreted as primarily applicable to male patients with AUD, and that future studies should include adequately powered female cohorts.

Discussion: Please clarify the statement regarding low PLT and withdrawal-related neuropsychiatric complications, as the cited paper refers to delirium tremens and withdrawal seizures, and the current paper did not appear to collect these outcomes. The current paper also analyzed blood results categorically and has limited ability to comment on severity of thrombocytopenia.
Thank you for this important clarification. We revised this section to avoid overinterpretation. We no longer refer to severe thrombocytopenia as an outcome category. Instead, we provide the platelet findings in relation to the reference range and explicitly state the available clinical data.
The revised Discussion now states that thrombocytopenia was observed in patients with AUD. We added median PLT values across cohorts and the proportions of participants with PLT values below the reference range. We also clarified that, although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens in previous research, clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD in our cohort; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range.
We therefore revised the conclusion to state that reduced PLT was an important hematological feature of AUD but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

Discussion: The statement about combining biochemical, hematological, and physiological markers to enhance diagnostic accuracy needs further context on what is being diagnosed, why diagnostic accuracy may be difficult, and the clinical importance of accurate diagnosis.
Thank you. We revised the ROC interpretation in both the Results and Discussion sections. We clarified that two diagnostic comparisons were assessed: first, discrimination between all patients with AUD and alcohol-free controls; second, differentiation between patients with AUD and chronic viral infection and patients with AUD without chronic viral infection.
We also clarified that AUROC was used to quantify discriminatory performance and that AUROC values below 0.5 reflected inverse discrimination according to the predefined class coding, rather than necessarily indicating absence of discriminatory value.
In the Discussion, we revised the statement to clarify that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. We also added clinical context: chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. We further clarified that markers with limited discriminatory performance, including platelets, hemoglobin, and BMI, should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

Supplementary tables: Within Table S1, please provide a legend with abbreviations written out in full and provide the thresholds defined as normal range.
Thank you. Supplementary Table S1 was revised. We added a legend defining hematological abbreviations, including Hb, LYM, MCV, MON, MPV, NEUT, PLT, ESR, and WBC. We also added the specific reference intervals used to define low, normal, and high values.

Supplementary tables: Within Table S2, please clarify whether glucose is random or fasted.
Thank you. Supplementary Table S2 was revised to clarify glucose sampling. We added that glucose values in the patient group represent random/non-fasting blood glucose measured at admission, whereas blood samples from controls were obtained under fasting conditions. This clarification was also added to the Methods section.

Supplementary tables: Consistency in bilirubin labeling would be helpful.
Thank you. We harmonized bilirubin terminology throughout the manuscript and supplementary materials. We now use direct (conjugated) bilirubin and indirect (unconjugated) bilirubin consistently where relevant.

Dataset: Participant ID and “encrypted name, nation” may contain identifying information. Please consider whether further anonymization is needed.
Thank you. We carefully reviewed and revised the dataset. The dataset file deposited in Zenodo was additionally anonymized. Potentially identifying or non-essential fields, including the encrypted name and nationality-related field, were removed. The dataset now retains anonymized participant IDs and the variables required for reproducibility of the analysis.

Dataset: The “note” column contains comorbidity information not required for replication and may be identifying if specific dates are included. Please consider removing this column.
Thank you. We removed the free-text note column from the dataset to reduce re-identification risk. Comorbidity-related information necessary for the analysis is retained in structured, analyzable variables, while potentially identifying free-text information was removed.

Additional data and references
During the revision, we also checked the reference list and corrected/replaced several references where needed to ensure that all cited sources accurately support the corresponding statements. The Zenodo repository was updated to include the additionally de-identified underlying dataset and clarified extended data files. No changes were made to study groups, statistical analyses, numerical results, or reported conclusions.

We are grateful to the reviewer for the detailed and constructive comments. We believe that the revised version is clearer, more transparent, more cautious in interpretation, and better aligned with the available data and reporting standards.
Competing Interests: The authors declare that they have no competing interests. Close
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Julia Sinclair, University of Southampton, Southampton, UK

Julia Morris, University of Southampton, Southampton, UK

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04 Mar 2026 | for Version 1

Julia Sinclair, University of Southampton, Southampton, England, UK

Julia Morris, University of Southampton, Southampton, England, UK

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Terminology needs to be clear and and more consistent when describing cases throughout the paper.

Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant?
Relatedly, it is not clear if controls were selected from patients at the centre or from within another group – this information should be included.
A brief sentence explaining the De Ritis ratio needs to be included in the methods section (rather than in the discussion).
Please explain how data on participant BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms was obtained, as well as how alcohol withdrawal was categorised as ‘mild’, ‘moderate’ or ‘severe’.
Please also comment on whether all delusions/hallucinations referenced in the study are known to be related to alcohol withdrawal, or if these could be related to a primary mental illness, in addition to how this data on specific symptoms was obtained.
The methods section states patients with ‘mild withdrawal symptoms’ were excluded. It would be helpful to explain why this decision was made, as it appears (from within the discussion) that patients with both no withdrawal symptoms, and those with severe withdrawal symptoms, were included.

The term ‘granulocytopenia’ is used, however, data on eosinophils and basophils is not provided. Please clarify if this refers specifically to low neutrophils, or to low granulocytes (neutrophils, eosinophils and basophils).
The reference to previous research on candida-albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
The statement ‘these findings support including creatinine in the MELD score, a validated mortality predictor in end-stage liver disease’ is not adequately supported by the findings of the paper, as the paper does not explore mortality. Please adjust this statement as it is important not to talk beyond the data
Please clarify the comment made regarding the small number of female participants, as females were explicitly excluded.
Please clarify the following statement: ‘while low PLT has been associated with withdrawal-related neuropsychiatric complications, no such manifestations were observed in patients with severe thrombocytopenia here’. The paper cited here specifically comments on associations between thrombocytopenia and delirium tremens, and thrombocytopenia and alcohol-withdrawal seizures, when the current paper does not appear to have collected data on either delirium tremens or alcohol-withdrawal seizures. Relatedly, the current paper has analysed blood results categorically (low/normal/high), and therefore has limited ability to comment on the severity of any thrombocytopenia.

The statement ‘the findings reinforce the utility of combining biochemical, haematological, and physiological markers to enhance diagnostic accuracy’ does not make sense without further context on what is being diagnosed (e.g. viral infections, AUD), reflection on why diagnostic accuracy may currently be difficult, and the clinical importance of accurate diagnosis.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

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Author Response

19 May 2026

Natalya Tseomashko, Republican Specialized Scientific and Practical Medical Center for Mental Health, Tashkent, Uzbekistan

Dear Reviewer,
We would like to sincerely thank you for your careful and constructive review of our manuscript. We are grateful for your positive assessment that the study is a helpful addition to the literature on hematological and biochemical biomarkers and comorbidities in individuals with AUD and chronic viral illnesses in Uzbekistan. We also appreciate your detailed suggestions, which helped us improve the clarity, methodological transparency, terminology, interpretation, supplementary materials, and dataset anonymization.
We have revised the manuscript carefully in response to all comments. In addition, the supplementary materials and dataset deposited in Zenodo were revised for clarity and further anonymization. No changes were made to the study groups, statistical analyses, numerical results, or reported conclusions.
Below we provide a point-by-point response.
General comment: Terminology needs to be clear and more consistent when describing cases throughout the paper. The reviewer noted inconsistent use of “stage II chronic alcoholism,” “stage 2 alcohol use disorder,” “mild AUD,” and the term “addicts” in Figure 1.
Thank you for this important comment. We revised the terminology throughout the manuscript to improve consistency and to avoid stigmatizing language. The term “addicts” was removed and replaced with person-first terminology. We now consistently refer to the patient groups as “patients with AUD” or “AUD cohorts.”
We also clarified that the diagnosis was alcohol use disorder according to ICD-10 code F10.2. The wording suggesting that ICD-10 defines AUD by “stages” was removed or revised. In the Methods section, we now specify that inclusion criteria were male sex, age 20-65 years, inpatient status, and a clinical diagnosis of AUD (ICD-10 F10.2), established by an addiction psychiatrist and supported by AUDIT.
We also clarified the exclusion of individuals with mild alcohol-related conditions or clinically mild withdrawal presentations. The revised Methods state that individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD.

Abstract: The abstract could better orientate the reader by clarifying that the viral infections studied refer to chronic viral infections, such as hepatitis B, hepatitis C, and HIV.
Thank you. We revised the Abstract to clarify this point. The Background now states that the study examined biomarker signatures in men with AUD in Uzbekistan, with and without chronic viral infections, specifically hepatitis B, hepatitis C, and HIV.
This clarification was also applied throughout the manuscript, including the Methods, Results, Discussion, figure/table legends, and data descriptions.

Methodology: Please include the specific dates of the 3-month study period, whether participants were inpatients or outpatients, and how biomarker values were selected if more than one value was available per participant.
Thank you. We expanded the Methods section substantially. We now specify that clinical and laboratory data were collected from 19 March to 22 June 2025. We also clarify that patients were recruited from inpatient units of the Republican Specialized Scientific and Practical Medical Center for Mental Health.
We added that laboratory measurements were obtained at admission, before treatment initiation, and were considered baseline values. Only admission values were included in the analysis, and follow-up measurements during treatment were excluded. This was added to clarify how biomarker values were selected.

Methodology: It is not clear whether controls were selected from patients at the centre or from another group.
Thank you for noting this. We clarified the control group source. The revised Methods state that the control group consisted of male volunteers of approximately similar age, including employees of the same institution and relatives of employees. Controls had no history of alcohol use disorder or psychoactive substance use, as confirmed by questionnaire-based screening and clinical assessment. We also clarified that one initially screened control participant was excluded because of a self-reported history of chronic hepatitis B, as the control group was defined to include individuals without known chronic viral infections.

Methodology: A brief sentence explaining the De Ritis ratio needs to be included in the Methods section rather than in the Discussion.
Thank you. We added a methodological explanation in the Data Collection section. The revised manuscript now states that the De Ritis ratio was calculated as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) and was used to characterize patterns of liver injury.

Methodology: Please explain how data on BMI, ethnicity, comorbidities, and alcohol withdrawal symptoms were obtained, and how withdrawal was categorized as mild, moderate, or severe.
Thank you. We expanded the Data Collection and Inclusion/Exclusion Criteria sections. The revised Methods now state that data on age, ethnicity, comorbidities, alcohol-use history, alcohol withdrawal symptoms, and clinical status were obtained from medical records, physician assessments, patient interviews, available collateral history, ultrasound reports, and documented diagnoses. Anthropometric data, including height and weight, were collected from medical records and questionnaires, and BMI was calculated as weight in kilograms divided by height in meters squared.
We also added a detailed explanation of alcohol withdrawal severity classification. Withdrawal severity was assessed clinically by the treating addiction psychiatrist and classified as mild, moderate, or severe according to the intensity of autonomic, somatic, and neuropsychiatric symptoms, including tremor, agitation, anxiety, sleep disturbance, nausea/vomiting, disorientation, hallucinations, delusional symptoms, and the need for inpatient monitoring or pharmacological management. Mild withdrawal was defined as limited autonomic or anxiety-related symptoms without clinically significant neuropsychiatric manifestations or somatic instability. Moderate withdrawal included more pronounced symptoms requiring inpatient observation and treatment, whereas severe withdrawal was characterized by delirium, hallucinations, delusional symptoms, marked agitation, or clinically significant somatic instability.

Methodology: Please comment on whether delusions/hallucinations were related to alcohol withdrawal or could be related to primary mental illness, and how these data were obtained.
Thank you. We added a clarification in the Data Collection section. The revised manuscript states that delirium, hallucinations, and delusional symptoms were recorded only when documented by the treating addiction psychiatrist during the withdrawal episode. These data were obtained from clinical examination, patient interview, medical history, available collateral history, and medical record review.
We also clarified that symptoms were considered withdrawal-related when they occurred in the context of alcohol withdrawal and were not documented as part of a primary psychiatric disorder. According to the medical records, these patients had no previous registration or documented history of primary psychotic disorders.

Methodology: The Methods section states that patients with mild withdrawal symptoms were excluded. Please explain why this decision was made.
Thank you. We clarified this point in the Study Design and Population section and in the Inclusion/Exclusion Criteria section. Individuals with mild alcohol-related conditions (ICD-10 F10.1) or clinically mild withdrawal presentations were excluded because their number was very small and because their short alcohol-use history and clinical profile were not comparable with patients with established long-term AUD. This was intended to maintain clinical comparability within the analytical cohort.

Results: The statement regarding “right shift” in the differential count and its interpretation as suggestive of megaloblastic anemia or hepatic/renal impairment requires further support.
Thank you for this very helpful comment. We revised this section to avoid unsupported interpretation. The term “right shift” was removed. We now state that an increased proportion of segmented neutrophils was observed in 7.6–9.8% of patients; however, because direct morphological assessment of neutrophil hypersegmentation was not performed, this finding should be interpreted cautiously and cannot be used to infer megaloblastic anemia or hepatic/renal impairment.
This revision ensures that the interpretation remains aligned with the data actually available in the study.

Discussion: Please review the Discussion to ensure that claims do not go beyond the data presented or cited.
Thank you. We carefully revised the Discussion to make the interpretation more cautious and better aligned with the available data. Several statements were softened or clarified. For example, causal or overly strong wording was revised to more cautious language such as “may reflect,” “may aggravate,” “suggesting a greater likelihood,” and “should be interpreted cautiously.”
We also revised the interpretation of neuropsychiatric symptoms, platelet counts, creatinine/MELD, ROC findings, bilirubin findings, and biomarker diagnostic utility to avoid overinterpretation.

Discussion: The term “granulocytopenia” is used, but data on eosinophils and basophils are not provided. Please clarify whether this refers specifically to low neutrophils or to low granulocytes.
Thank you. We revised this terminology. Because eosinophil and basophil data were not included, we removed the broader term “granulocytopenia” where it could be misleading and clarified the finding as neutropenia/neutrophilic granulocyte compartment suppression.
The revised Discussion now states that alcohol-related disruption of granulopoiesis has been described as a mechanism contributing to impaired innate immune defense and increased susceptibility to infection. However, in the present AUD cohorts, neutropenia was rare and comparable between virus-positive and virus-negative patients, suggesting that suppression of the neutrophilic granulocyte compartment was not a prominent hematological feature in these patients.

Discussion: The reference to previous research on Candida albicans-specific Th17 cells would benefit from further explanation of its relevance to this work.
Thank you. We expanded this part of the Discussion to better explain the relevance of the cited work. The revised text now explains that inflammatory marker abnormalities may reflect not only viral-associated inflammatory burden but also alcohol-related disruption of gut-liver immune signaling. We added that Zeng et al. showed that chronic alcohol exposure is associated with intestinal fungal dysbiosis and increased Candida albicans-specific Th17 responses, with these cells detected in the circulation and liver of patients with alcohol-associated liver disease. We also noted that experimental models showed ethanol-associated migration of Candida albicans-specific Th17 cells from the intestine to the liver, where IL-17-mediated signaling contributed to hepatic inflammation and injury.
This explanation was added to clarify why gut-derived microbial/fungal immune pathways may be relevant to systemic and hepatic inflammation in patients with AUD and chronic viral comorbidity.

Discussion: The statement that the findings support including creatinine in the MELD score is not adequately supported, as mortality was not explored.
Thank you. We agree and revised this section substantially. The unsupported statement was removed. The revised Discussion now states that creatinine was significantly lower in patients with AUD, consistent with prior reports. We explain that, in the context of chronic alcohol exposure, reduced creatinine may reflect lower skeletal muscle mass, malnutrition, or altered protein metabolism rather than preserved renal function.
We also clarified that creatinine remains an important component of liver disease severity assessment, including the MELD score, where it is considered together with bilirubin and INR to support risk stratification in advanced liver disease. We no longer imply that our findings validate mortality prediction. Instead, we state that lower creatinine in the present cohorts should be interpreted as part of the broader metabolic and somatic profile of patients with AUD rather than as an isolated renal marker.

Discussion: Please clarify the comment regarding the small number of female participants, as females were explicitly excluded.
Thank you. We revised the Limitations section to clarify this point. The revised text states that the analysis was restricted to male patients because only eight female patients met the initial screening criteria, which was insufficient for meaningful sex-stratified analysis. Given known sex-related differences in alcohol metabolism, body composition, and biochemical and hematological profiles, women were therefore excluded from the final analytical dataset. We also added that the findings should be interpreted as primarily applicable to male patients with AUD, and that future studies should include adequately powered female cohorts.

Discussion: Please clarify the statement regarding low PLT and withdrawal-related neuropsychiatric complications, as the cited paper refers to delirium tremens and withdrawal seizures, and the current paper did not appear to collect these outcomes. The current paper also analyzed blood results categorically and has limited ability to comment on severity of thrombocytopenia.
Thank you for this important clarification. We revised this section to avoid overinterpretation. We no longer refer to severe thrombocytopenia as an outcome category. Instead, we provide the platelet findings in relation to the reference range and explicitly state the available clinical data.
The revised Discussion now states that thrombocytopenia was observed in patients with AUD. We added median PLT values across cohorts and the proportions of participants with PLT values below the reference range. We also clarified that, although platelet counts below 119,000/μL have been associated with increased risk of withdrawal seizures and delirium tremens in previous research, clinically documented delirium and hallucinations during withdrawal were recorded in only 16 of 292 patients with AUD in our cohort; among them, only one had PLT below the reference range, one had PLT at the lower reference limit, and 14 had values within the reference range.
We therefore revised the conclusion to state that reduced PLT was an important hematological feature of AUD but showed no clear correspondence with recorded withdrawal-related delirium or hallucinations in this cohort.

Discussion: The statement about combining biochemical, hematological, and physiological markers to enhance diagnostic accuracy needs further context on what is being diagnosed, why diagnostic accuracy may be difficult, and the clinical importance of accurate diagnosis.
Thank you. We revised the ROC interpretation in both the Results and Discussion sections. We clarified that two diagnostic comparisons were assessed: first, discrimination between all patients with AUD and alcohol-free controls; second, differentiation between patients with AUD and chronic viral infection and patients with AUD without chronic viral infection.
We also clarified that AUROC was used to quantify discriminatory performance and that AUROC values below 0.5 reflected inverse discrimination according to the predefined class coding, rather than necessarily indicating absence of discriminatory value.
In the Discussion, we revised the statement to clarify that combined biomarker panels may be more informative than isolated markers for distinguishing AUD from alcohol-free controls and for identifying viral comorbidity within AUD cohorts. We also added clinical context: chronic alcohol exposure and viral infections can produce overlapping hepatic, inflammatory, and metabolic abnormalities, making interpretation of single laboratory parameters difficult. We further clarified that markers with limited discriminatory performance, including platelets, hemoglobin, and BMI, should be interpreted as supportive clinical features rather than standalone diagnostic indicators.

Supplementary tables: Within Table S1, please provide a legend with abbreviations written out in full and provide the thresholds defined as normal range.
Thank you. Supplementary Table S1 was revised. We added a legend defining hematological abbreviations, including Hb, LYM, MCV, MON, MPV, NEUT, PLT, ESR, and WBC. We also added the specific reference intervals used to define low, normal, and high values.

Supplementary tables: Within Table S2, please clarify whether glucose is random or fasted.
Thank you. Supplementary Table S2 was revised to clarify glucose sampling. We added that glucose values in the patient group represent random/non-fasting blood glucose measured at admission, whereas blood samples from controls were obtained under fasting conditions. This clarification was also added to the Methods section.

Supplementary tables: Consistency in bilirubin labeling would be helpful.
Thank you. We harmonized bilirubin terminology throughout the manuscript and supplementary materials. We now use direct (conjugated) bilirubin and indirect (unconjugated) bilirubin consistently where relevant.

Dataset: Participant ID and “encrypted name, nation” may contain identifying information. Please consider whether further anonymization is needed.
Thank you. We carefully reviewed and revised the dataset. The dataset file deposited in Zenodo was additionally anonymized. Potentially identifying or non-essential fields, including the encrypted name and nationality-related field, were removed. The dataset now retains anonymized participant IDs and the variables required for reproducibility of the analysis.

Dataset: The “note” column contains comorbidity information not required for replication and may be identifying if specific dates are included. Please consider removing this column.
Thank you. We removed the free-text note column from the dataset to reduce re-identification risk. Comorbidity-related information necessary for the analysis is retained in structured, analyzable variables, while potentially identifying free-text information was removed.

Additional data and references
During the revision, we also checked the reference list and corrected/replaced several references where needed to ensure that all cited sources accurately support the corresponding statements. The Zenodo repository was updated to include the additionally de-identified underlying dataset and clarified extended data files. No changes were made to study groups, statistical analyses, numerical results, or reported conclusions.

We are grateful to the reviewer for the detailed and constructive comments. We believe that the revised version is clearer, more transparent, more cautious in interpretation, and better aligned with the available data and reporting standards.

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Competing Interests

The authors declare that they have no competing interests.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Population-Specific Biomarker Signatures in Alcohol Use Disorder: Ethnic and Viral Influences in a Central Asian Cohort

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 1

1. Introduction

2. Materials and Methods

2.1 Study design and population

2.2 Inclusion/Exclusion criteria

2.3 Ethical approval

2.4 Data collection

2.5 ROC analysis

2.6 Statistical analysis

3. Results

3.1 Somatic and clinical profile of alcohol-dependent patients

Table 1. Clinical and anthropometric characteristics, including age, BMI, FIB-4, and MAP, in patients with AUD without chronic viral infection (Cohort 1, n = 251), patients with AUD and chronic viral infection (Cohort 2, n = 41), and alcohol-free controls (Cohort 3, n = 49).

3.2 Hematological profile analysis

3.3 Biochemical profile analysis

3.4 Diagnostic accuracy analysis (ROC)

4. Discussion

5. Conclusion

Declaration of generative AI and AI-assisted technologies

Data availability

Underlying data

Extended data

Acknowledgments

References

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