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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes
Ming-Ying Lu, Yu-Ju Wei, Chih-Wen Wang, Po-Cheng Liang, Ming-Lun Yeh, Yi-Shan Tsai, Pei-Chien Tsai, Yu-Min Ko, Ching-Chih Lin, Kuan-Yu Chen, Yi-Hung Lin, Tyng-Yuan Jang, Ming-Yen Hsieh, Zu-Yau Lin, Chung-Feng Huang, Jee-Fu Huang, Chia-Yen Dai, Wan-Long Chuang, Ming-Lung Yu
Ming-Ying Lu, Yu-Ju Wei, Chih-Wen Wang, Po-Cheng Liang, Ming-Lun Yeh, Yi-Shan Tsai, Pei-Chien Tsai, Yu-Min Ko, Ching-Chih Lin, Kuan-Yu Chen, Yi-Hung Lin, Tyng-Yuan Jang, Ming-Yen Hsieh, Zu-Yau Lin, Chung-Feng Huang, Jee-Fu Huang, Chia-Yen Dai, Wan-Long Chuang, Ming-Lung Yu, Hepatitis Center and Hepatobiliary Division, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
Ming-Ying Lu, Ming-Lung Yu, School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung 80708, Taiwan
Co-corresponding authors: Wan-Long Chuang and Ming-Lung Yu.
Author contributions: Lu MY analyzed the data and wrote the manuscript; Wei YJ, Wang CW, Liang PC, Yeh ML, Lin YH, Jang TY, Hsieh MY, Lin ZY, Huang CF, Huang JF, Dai CY, and Chuang WL collected the clinical data; Tsai YS, Ko YM, Lin CC, and Chen KY performed the experiments; Tsai PC confirmed the statistical analysis; Yu ML designed the study, interpreted data, and supervised the manuscript; All authors read and approved the final manuscript.
Supported by the “Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung”, No. NSTC 112-2321-B-001-006; and The Featured Areas Research Center Program within the Framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan, No. MOHW112-TDU-B-221-124007.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Kaohsiung Medical University Hospital [No. KMUHIRB-E(II)-20230129].
Informed consent statement: This study was approved to waive the informed consent of the subjects.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at
fish6069@gmail.com.
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: Ming-Lung Yu, Professor, Hepatitis Center and Hepatobiliary Division, Department of Internal Medicine, Kaohsiung Medical University Hospital, No. 100 Shih-Chuan 1
st Road, Sanmin District, Kaohsiung 80708, Taiwan.
fish6069@gmail.com
Received: November 28, 2024
Revised: January 16, 2025
Accepted: February 12, 2025
Published online: March 14, 2025
Processing time: 90 Days and 10.7 Hours
BACKGROUND
Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases reactive oxygen species production, leading to steatohepatitis and hepatic fibrosis. Artificial intelligence (AI) is a potent tool for disease diagnosis and risk stratification.
AIM
To investigate mitochondrial DNA polymorphisms in susceptibility to MASLD and establish an AI model for MASLD screening.
METHODS
Multiplex polymerase chain reaction was performed to comprehensively genotype 82 mitochondrial DNA variants in the screening dataset (n = 264). The significant mitochondrial single nucleotide polymorphism was validated in an independent cohort (n = 1046) using the Taqman® allelic discrimination assay. Random forest, eXtreme gradient boosting, Naive Bayes, and logistic regression algorithms were employed to construct an AI model for MASLD.
RESULTS
In the screening dataset, only mt12361A>G was significantly associated with MASLD. mt12361A>G showed borderline significance in MASLD patients with 2-3 cardiometabolic traits compared with controls in the validation dataset (P = 0.055). Multivariate regression analysis confirmed that mt12361A>G was an independent risk factor of MASLD [odds ratio (OR) = 2.54, 95% confidence interval (CI): 1.19-5.43, P = 0.016]. The genetic effect of mt12361A>G was significant in the non-diabetic group but not in the diabetic group. mt12361G carriers had a 2.8-fold higher risk than A carriers in the non-diabetic group (OR = 2.80, 95%CI: 1.22-6.41, P = 0.015). By integrating clinical features and mt12361A>G, random forest outperformed other algorithms in detecting MASLD [training area under the receiver operating characteristic curve (AUROC) = 1.000, validation AUROC = 0.876].
CONCLUSION
The mt12361A>G variant increased the severity of MASLD in non-diabetic patients. AI supports the screening and management of MASLD in primary care settings.
Core Tip: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a systemic metabolic disorder affecting the liver-kidney-heart axis. Mitochondrial dysfunction drives the progression of liver steatosis into steatohepatitis and hepatic fibrosis. The mitochondrial mt12361A>G variant increased the severity of MASLD in the non-diabetic group but not in the diabetic group. By integrating genomics and machine learning, we established a random forest model to screen for MASLD with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.876 in the validation dataset. The artificial intelligence model supports the prevention, screening, and management of MASLD in primary care.
