The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease
Abstract
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.
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BibTeX
@article{shi2026natural,
title = {{The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease}},
author = {Yu Shi and Ruoqi Zhou and Seung Up Kim and Terry Cheuk-Fung Yip and Salvatore Petta and Elisabetta Bugianesi and Masato Yoneda and Manuel Romero-Gomez and Emmanuel Tsochatzis and Philip Newsome and Hannes Hagström and George Boon-Bee Goh and Wah-Kheong Chan and José-Luis Calleja and Jerome Boursier and Arun Sanyal and Jian-Gao Fan and Laurent Castera and Victor De Lédinghen and Michelle Lai and Xiao-Dong Zhou and Vincent Wai-Sun Wong and Ming-Hua Zheng},
journal = {Clinical and molecular hepatology},
year = {2026},
month = jul,
doi = {10.3350/cmh.2026.0279},
url = {https://hal.science/hal-05841019v1},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease
AU - Yu Shi
AU - Ruoqi Zhou
AU - Seung Up Kim
AU - Terry Cheuk-Fung Yip
AU - Salvatore Petta
AU - Elisabetta Bugianesi
AU - Masato Yoneda
AU - Manuel Romero-Gomez
AU - Emmanuel Tsochatzis
AU - Philip Newsome
AU - Hannes Hagström
AU - George Boon-Bee Goh
AU - Wah-Kheong Chan
AU - José-Luis Calleja
AU - Jerome Boursier
AU - Arun Sanyal
AU - Jian-Gao Fan
AU - Laurent Castera
AU - Victor De Lédinghen
AU - Michelle Lai
AU - Xiao-Dong Zhou
AU - Vincent Wai-Sun Wong
AU - Ming-Hua Zheng
PY - 2026
DA - 2026/07/01
JO - Clinical and molecular hepatology
DO - 10.3350/cmh.2026.0279
UR - https://hal.science/hal-05841019v1
AB - 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.
SN - 2287-2728
SN - 2287-285X
ER -