Clinical and molecular hepatology · 2026
The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease.
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Shi Yu, Zhou Ruoqi, Kim Seung Up, Yip Terry Cheuk-Fung, Petta Salvatore, Bugianesi Elisabetta, Yoneda Masato, Romero-Gomez Manuel, Tsochatzis Emmanuel, Newsome Philip, Hagström Hannes, Goh George Boon-Bee, Chan Wah-Kheong, Calleja José-Luis, Boursier Jerome, Sanyal Arun J, Fan Jian-Gao, Castera Laurent, Lédinghen Victor de, Lai Michelle, Zhou Xiao-Dong, Wong Vincent Wai-Sun, Zheng Ming-Hua, VCTE-Prognosis Study Group
Аннотация
BACKGROUND/AIMS: Liver 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).
METHODS: In 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.
RESULTS: Among 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.
CONCLUSIONS: LSM-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.
METHODS: In 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.
RESULTS: Among 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.
CONCLUSIONS: LSM-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.