Back

Association of Antihypertensive Medication and Steatotic Liver Disease with Liver Fibrosis and Mortality among US Adults

Wu, Y.; Fang, F.

2025-05-21 gastroenterology
10.1101/2025.05.20.25328037 medRxiv
Show abstract

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) and hypertension frequently coexist in adults across the Americas, yet evidence to guide pharmacologic management in individuals with both conditions remains limited. Antihypertensive medications may influence liver outcomes, but comparative data across drug classes are sparse. MethodsWe performed a cross-sectional analysis using pooled data from the US National Health and Nutrition Examination Survey (1999 to 2018), linked to mortality records through 2019. Hepatic steatosis was assessed using the US Fatty Liver Index (US-FLI), Fatty Liver Index (FLI), and Hepatic Steatosis Index (HSI). Fibrosis was assessed using noninvasive scores. Antihypertensive exposure, identified through prescription records, included ACE inhibitors, ARBs, beta blockers, calcium channel blockers (CCBs), and diuretics. Associations with liver fibrosis were estimated using logistic regression. All cause and cardiovascular mortality were assessed using Cox proportional hazards models with inverse probability of treatment weighting. FindingsAmong 2,909 adults with MASLD receiving monotherapy antihypertensive treatment, use of ACEIs and ARBs was associated with lower odds of liver fibrosis compared with CCBs. In adjusted models, ACEIs were associated with reduced all-cause mortality (adjusted hazard ratio 0.30; 95% CI 0.11-0.82), as were ARBs (0.25; 95% CI 0.07-0.94). No significant differences were observed for cardiovascular mortality across medication classes. InterpretationUse of angiotensin converting enzyme inhibitors and angiotensin receptor blockers was associated with lower fibrosis burden and improved survival in individuals with metabolic dysfunction associated steatotic liver disease. These findings are hypothesis generating and warrant confirmation in prospective studies. FundingNo Funding.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.