ArticleClinical and molecular hepatology2026
The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease.
Article in Clinical and molecular hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
aimsLiver stiffness measurement (LSM) is a key tool for risk stratification in metabolic dysfunction-associated steatotic liver disease (MASLD), yet static thresholds fail to capture dynamic transition across risk strata. We aimed to characterize LSM-risk transitions and develop a time-updated, individualized model for predicting state transitions, liver-related events and death (LREs/death).
methodsIn a real-world MASLD cohort, we applied a multi-state, time-homogeneous Markov model to quantify annual transition probabilities and mean state occupancy times across LSM-defined low-, intermediate-, and high-risk strata. A Markov model incorporating age, sex, type 2 diabetes (T2D), hypertension was used to generate individualized, time-updated risk trajectories and probabilities of LREs/death.
resultsAmong 11,514 MASLD individuals with ≥2 vibration-controlled transient elastography assessments, the low-risk category demonstrated notable stability, with 92% remaining unchanged at 1 year and a mean occupancy time of 8.43 years (95% confidence interval [CI] 7.94-8.95). Contrarily, the intermediate-risk category was highly dynamic, with only 39% remaining unchanged after 1 year and a mean occupancy time of 0.92 years (95% CI 0.88-0.96). T2D, hypertension, and obesity substantially shorten low-risk occupancy time, whereas antidiabetic medication was associated with more favorable transitions. Finally, we developed a dynamic, multi-state Markov model integrating longitudinal LSM-defined risk states with relevant covariates to generate individualized predictions of state transitions and risks of LREs/death.
conclusionsLSM-based strata in MASLD represent distinct and meaningful dynamic trajectories. In particular, the marked instability of the intermediate-risk state supports more frequent reassessment. By quantifying transition pathways and time-updated risks of LREs/death, this model may inform the personalized surveillance intervals and risk-adapted management.
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