Published online Mar 14, 2025. doi: 10.3748/wjg.v31.i10.103716
Revised: January 16, 2025
Accepted: February 12, 2025
Published online: March 14, 2025
Processing time: 90 Days and 12.5 Hours
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.
To investigate mitochondrial DNA polymorphisms in susceptibility to MASLD and establish an AI model for MASLD screening.
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.
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].
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.