Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among
Ming-Ying Lu1,2, Yu-Ju Wei1, Chih-Wen Wang1
1Hepatitis Center and Hepatobiliary Division, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan.
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.
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