JOURNAL ARTICLE

Risk Stratification of Metabolic Risk Factors and Statin Use Associated With Liver and Nonliver Outcomes in Chronic Hepatitis B.

  • Published In: Journal of Infectious Diseases, 2025, v. 231, n. 4. P. 1079 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Xinrong; Nguyen, Vy H; Kam, Leslie Yeeman; Barnett, Scott D; Henry, Linda; Cheung, Ramsey; Nguyen, Mindie H 3 of 3

Abstract

This article investigates the association between metabolic risk factors (MRFs)—including diabetes mellitus (DM), hypertension, hyperlipidemia, and obesity—and statin use with liver and nonliver outcomes in a large nationwide cohort of 52,277 adult patients with chronic hepatitis B (CHB) in the United States. The study found that a higher metabolic burden, particularly the presence of DM, was significantly associated with increased risks of adverse liver outcomes (such as hepatocellular carcinoma, cirrhosis, and liver decompensation) and nonliver complications including cardiovascular disease, chronic kidney disease, and extrahepatic cancers. Statin use was linked to a reduced risk of liver outcomes among patients with lower metabolic burden (two or fewer MRFs) but showed no significant benefit in those with higher metabolic burden (three or more MRFs). These findings highlight the importance of monitoring and managing metabolic comorbidities in CHB patients and suggest that statins may offer protective effects against liver complications in selected populations.

Additional Information

  • Source:Journal of Infectious Diseases. 2025/04, Vol. 231, Issue 4, p1079
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0022-1899
  • DOI:10.1093/infdis/jiae522
  • Accession Number:184524678
  • Copyright Statement:Copyright of Journal of Infectious Diseases is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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