Guo YC, Hong Y, Huang L, Xu XW, Sun JQ, Ji KK, Li CN. Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations. World J Gastroenterol 2025; 31(38): 112166 [PMID: 41112009 DOI: 10.3748/wjg.v31.i38.112166]
Corresponding Author of This Article
Chao-Nian Li, MD, PhD, Assistant Professor, Chief Physician, Department of Traditional Chinese Medicine, Binhai County People's Hospital, No. 299 Haibin Avenue, Yancheng 224500, Jiangsu Province, China. lichaonian2022@126.com
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Gastroenterology & Hepatology
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Letter to the Editor
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Oct 14, 2025 (publication date) through Nov 23, 2025
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Publication Name
World Journal of Gastroenterology
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1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Guo YC, Hong Y, Huang L, Xu XW, Sun JQ, Ji KK, Li CN. Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations. World J Gastroenterol 2025; 31(38): 112166 [PMID: 41112009 DOI: 10.3748/wjg.v31.i38.112166]
World J Gastroenterol. Oct 14, 2025; 31(38): 112166 Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.112166
Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations
Yan-Chun Guo, Ye Hong, Li Huang, Xiao-Wei Xu, Jing-Qi Sun, Kang-Kang Ji, Chao-Nian Li
Yan-Chun Guo, Department of Ophthalmology, Binhai County People's Hospital, Yancheng 224500, Jiangsu Province, China
Ye Hong, Li Huang, Xiao-Wei Xu, Department of Clinical Nutrition, Binhai County People's Hospital, Yancheng 224500, Jiangsu Province, China
Jing-Qi Sun, Chao-Nian Li, Department of Traditional Chinese Medicine, Binhai County People's Hospital, Yancheng 224500, Jiangsu Province, China
Kang-Kang Ji, Department of Clinical Medical Research, Binhai County People’s Hospital, Yancheng 224500, Jiangsu Province, China
Co-corresponding authors: Kang-Kang Ji and Chao-Nian Li.
Author contributions: Li CN and Ji KK conceived and designed the letter; Guo YC and Li CN wrote the manuscript; Hong Y, Huang L, Xu XW, and Sun JQ provided critical opinions about this topic; Li CN and Guo YC contributed to the revised version; All authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chao-Nian Li, MD, PhD, Assistant Professor, Chief Physician, Department of Traditional Chinese Medicine, Binhai County People's Hospital, No. 299 Haibin Avenue, Yancheng 224500, Jiangsu Province, China. lichaonian2022@126.com
Received: July 21, 2025 Revised: August 22, 2025 Accepted: September 9, 2025 Published online: October 14, 2025 Processing time: 87 Days and 6.9 Hours
Core Tip
Core Tip: Amid metabolic dysfunction-associated fatty liver disease (MAFLD’s) escalating global burden-a leading cause of chronic liver disease with significant economic strain - Tian et al pioneer an integrated traditional Chinese medicine (TCM) - machine learning model (area under the curve: 0.82) using dual-feature selection (LASSO + RFE) to predict hepatic steatosis in high metabolic risk populations. The inclusion of TCM tongue features (edge redness, greasy coating) addresses MAFLD’s heterogeneity and offers cost-saving potential over imaging. However, single-center validation and unmechanized TCM indicators limit clinical translation. Future work must prioritize multiethnic validation, subtype-specific modeling, and TCM-microbiome mechanistic studies to revolutionize early detection in resource-limited settings.
