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World J Gastroenterol. Aug 21, 2026; 32(31): 117869
Published online Aug 21, 2026. doi: 10.3748/wjg.117869
Published online Aug 21, 2026. doi: 10.3748/wjg.117869
Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging: Methodological innovations and future perspectives
Yu-Tong Wu, Yan-Qi Dang, Institute of Digestive Diseases, China-Canada Center of Research for Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
Yi-Lin Shi, Jing Ma, Yan-Qi Dang, Dan Hu, Seventh People’s Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200137, China
Xiao-Ying Xie, Department of Hepatobiliary Oncology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Yan-Qi Dang, State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
Co-first authors: Yu-Tong Wu and Yi-Lin Shi.
Co-corresponding authors: Yan-Qi Dang and Dan Hu.
Author contributions: Dang YQ, Hu D, and Wu YT designed research; Wu YT and Shi YL performed research; Wu YT wrote the paper; Dang YQ, Shi YL, and Ma J edited the paper; Ma J, Dang YQ, Hu D, and Xie XY contributed analytic tools; Ma J, Dang YQ, Hu D, and Xie XY analyzed data; Dang YQ and Hu D acquired funding. Rationale for designating two co-corresponding authors: Hu D and Dang YQ are designated as co-corresponding authors because both made substantial, equal, and indispensable contributions to this study. Hu D was mainly responsible for establishing the project, developing the initial research concept, and proposing the overall structure and outline of the manuscript. Dang YQ provided important guidance throughout the manuscript preparation process, including critical review, revision, supervision, and detailed academic suggestions. Both authors also contributed to funding acquisition and provided continuous support during the development and completion of the study. Their contributions were complementary and equally important to the successful completion of the work. Therefore, we believe that designating Hu D and Dang YQ as co-corresponding authors accurately reflects their shared responsibility, academic contribution, and supervisory roles in this manuscript.
AI contribution statement: Portions of this manuscript were edited using AI tools for language refinement. All authors were responsible and agree to accountability for all scientific content.
Supported by Science and Technology Development Fund of Shanghai Pudong New Area, No. PKJ2024-Y19; Investigator-Initiated Trial Program of Shanghai Pudong New Area Health Commission, No. 2025-PWDL-03; National Pilot Zone for Inheritance, Innovation and Development of Traditional Chinese Medicine in Pudong New Area, No. PDZY-2026-0318; and Science and Technology Development Project of Shanghai University of Traditional Chinese Medicine, No. 25KFL82.
Conflict-of-interest statement: No conflict of interest.
Corresponding author: Yan-Qi Dang, Institute of Digestive Diseases, China-Canada Center of Research for Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Fenglin Road Sub-district, Shanghai 200032, China.
Received: December 17, 2025
Revised: February 7, 2026
Accepted: May 25, 2026
Published online: August 21, 2026
Processing time: 229 Days and 12.2 Hours
Revised: February 7, 2026
Accepted: May 25, 2026
Published online: August 21, 2026
Processing time: 229 Days and 12.2 Hours
Core Tip
Core Tip: Recent study have pioneered the integration of nanoparticle tracking analysis with the CatBoost algorithm and SHapley Additive exPlanations interpretability analysis, thereby uncovering non-linear relationships between extracellular vesicles (EV) characteristics and steatosis severity. The study establishes that EV mean size and concentration can function as independent predictors to distinguish S0 stage, while combination with clinical features significantly improved S3 stage identification accuracy. This provides novel insights and methodologies for non-invasive diagnosis and risk stratification of metabolic dysfunction-associated steatotic liver disease. However, limitations in sample size and unclear mechanistic understanding may hinder further clinical translation, representing urgent issues requiring resolution.