The albumin-to-creatinine ratio predicts and explores potential mediation of mortality in metabolic dysfunction-associated steatotic liver disease in U.S. adults: evidence from NHANES 1999-2018.

backgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) frequently coexists with chronic kidney disease, which may exacerbate adverse outcomes. The albumin-to-creatinine ratio (ACR), an established marker of renal damage, has been associated with mortality risk in this population, yet its prognostic value and potential role in related pathways remain unclear.

methodsWe used data from the 1999–2018 National Health and Nutrition Examination Survey (NHANES) to construct MASLD cohorts defined by three indices: the Fatty Liver Index (FLI), United States Fatty Liver Index (USFLI), and Hepatic Steatosis Index (HSI). Weighted Cox models, restricted cubic splines, subgroup and sensitivity analyses assessed associations between ACR and all-cause and cardiovascular mortality. Mediation analyses examined the extent to which ACR might partially account for the observed associations between diabetes, hypertension, and mortality. Machine learning models were applied to evaluate predictive performance and identify key mortality predictors.

resultsHigher ACR levels were significantly associated with increased risks of all-cause and cardiovascular mortality (P < 0.001). Mediation analysis suggested that ACR may partially account for 33%–49% of the association between diabetes and all-cause mortality, and 19%–25% of the association between hypertension and all-cause mortality, although sensitivity analyses indicated that unmeasured confounding could influence these estimates. In stratified analyses, mortality risks increased progressively with higher fibrosis-4 index stages. Machine learning analyses demonstrated robust predictive performance across models and cohorts, with age and ACR consistently ranked as top predictors. Results were consistent across MASLD definitions and robust in sensitivity analyses.

conclusionsACR independently predicts mortality in MASLD and may partly account for the associations of diabetes and hypertension with mortality. Machine learning analyses supported its role as a key predictor of all-cause mortality. Its association with outcomes remains robust across fibrosis stages, underscoring its utility as a non-invasive biomarker for clinical risk assessment.