Clinical utility of serum S100B in monitoring alcohol withdrawal syndrome among patients with variable clinical backgrounds.

Alcohol withdrawal syndrome (AWS) and its severe complication, delirium tremens (DT), pose significant clinical challenges due to their unpredictable course and potential life-threatening consequences. This study aimed to evaluate serum S100B protein as a candidate biomarker for AWS, with a focus on its relationship to coexisting mental and somatic disorders, as well as the presence or absence of DT. The study included 47 male patients (age range 23-69 years, mean 44) diagnosed with AWS according to ICD-10 criteria (psychiatric evaluation) and confirmed with DSM-V diagnostic standards, alongside a control group (CG) of 40 healthy males (age range 25-62 years, mean 35). Blood samples were collected at hospital admission and after resolution of withdrawal symptoms (7days), to quantify S100B concentrations. Mental and somatic health status, as well as withdrawal severity, were assessed using validated clinical scales, including the Clinical Institute Withdrawal Assessment for Alcohol, Revised (CIWA-Ar). Serum S100B levels were measured via enzyme-linked immunosorbent assay (ELISA). Notably, contrary to our initial hypothesis, higher S100B concentrations were detected in the CG compared to patients with AWS at the conclusion of the study. Additionally, patients without somatic comorbidities exhibited significantly elevated S100B levels compared to those with somatic illnesses. No significant association was observed between S100B levels and CIWA-Ar scores. These findings highlight the limited but potentially informative clinical utility of serum S100B as a biomarker for monitoring AWS progression and severity. Despite the small sample size and exploratory design, S100B measurement could contribute to more precise risk stratification and personalized management strategies in patients undergoing alcohol withdrawal. Further large-scale studies are warranted to validate these preliminary observations and explore their implications for improving clinical outcomes in AWS and DT.