Bioimpedance Analysis to Predict Clinical Outcomes of Liver Cirrhosis in a Prospective Cohort Study.

Authors

Mohammed Kanan, Michelle Luster, Saira Khaderi, Fasiha Kanwal, Aaron P Thrift, Abeer Alsarraj, Emad Sorial, Emad Salem, Hao Duong, Hashem B El-Serag

Published in

JGH open : an open access journal of gastroenterology and hepatology. Volume 10. Issue 8. Pages e70441. Epub Jul 27, 2026.

Abstract

Bioimpedance analysis (BIA) is a non-invasive method for estimating body composition, which plays a role in the development of cirrhosis. We investigated if BIA measures were associated with cirrhosis complications.
We analyzed data from 566 participants in a prospective cohort study of cirrhosis patients from tertiary care clinics who underwent BIA at enrollment and regular follow up (20.3% female; 47.0% non-Hispanic White, 23.1% Hispanic, 27.7% Black; 47.7% metabolic and alcohol-associated liver disease [MetALD], 30.4% metabolic dysfunction-associated steatotic liver disease [MASLD], 1.9% alcohol-associated liver disease [ALD], 9.0% active hepatitis C virus [HCV]). We performed multivariable logistic regression to assess the association of BIA measures with etiology and prevalent decompensation and Cox proportional hazards regression to assess incident decompensation and HCC.
After adjustment for sex and race, body fat mass was associated with 14-fold increased odds of MASLD (adjusted odds ratio [aOR] 14.10, 95% confidence interval [CI] 5.25-37.85) and decreased odds of ALD (aOR 0.73, 95% CI 0.60-0.88). Lower odds of prevalent decompensation were seen in the highest tertile of trunk lean mass (aOR 0.39, 95% CI 0.21-0.73) and left arm lean mass (aOR 0.40, 95% CI 0.21-0.75) compared with their lowest tertiles. The middle tertile of visceral fat surface area showed an inverse association with incident decompensation (adjusted hazard ratio 0.19, 95% CI 0.04-0.88). There were no significant associations between BIA measures and incident HCC.
BIA could be used as a prognostic tool for patients with cirrhosis, supporting its incorporation into clinical workflows.

PMID:
42516780
Bibliographic data and abstract were imported from PubMed on 28 Jul 2026.

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