Integrating body composition analysis and machine learning for non-invasive identification of metabolic dysfunction-associated fatty liver disease: a large-scale health examination-based study (original) (raw)
References
Zhao, Q. & Deng, Y. Comparison of mortality outcomes in individuals with MASLD and/or MAFLD. J. Hepatol.80(2), e62–e64 (2024). Google Scholar
Gofton, C., Upendran, Y., Zheng, M. H. & George, J. MAFLD: How is it different from NAFLD?. Clin. Mol. Hepatol.29(Suppl), S17-s31 (2023). Google Scholar
Vitale, A. et al. Epidemiological trends and trajectories of MAFLD-associated hepatocellular carcinoma 2002–2033: the ITA.LI.CA database. Gut72(1), 141–152 (2023). Google Scholar
Kang, S. H., Cho, Y., Jeong, S. W., Kim, S. U. & Lee, J. W. From nonalcoholic fatty liver disease to metabolic-associated fatty liver disease: Big wave or ripple?. Clin. Mol. Hepatol.27(2), 257–269 (2021). Google Scholar
Eslam, M. et al. The Asian Pacific association for the study of the liver clinical practice guidelines for the diagnosis and management of metabolic dysfunction-associated fatty liver disease. Hepatol. Int.19(2), 261–301 (2025). Google Scholar
Comprehensive Medical Evaluation and Assessment of Comorbidities. Standards of Care in Diabetes-2025. Diabetes Care48(1 Suppl 1), S59-s85 (2025). Google Scholar
Sun, D. Q. et al. MAFLD and risk of CKD. Metabolism115, 154433 (2021). Google Scholar
Zhou, X. D. et al. Metabolic dysfunction-associated fatty liver disease and implications for cardiovascular risk and disease prevention. Cardiovasc. Diabetol.21(1), 270 (2022). Google Scholar
Zhang, Y. et al. Association of metabolic dysfunction-associated fatty liver disease with systemic atherosclerosis: a community-based cross-sectional study. Cardiovasc. Diabetol.22(1), 342 (2023). Google Scholar
Kumar, A. et al. Impact of diabetes, drug-induced liver injury, and sepsis on outcomes in metabolic dysfunction associated fatty liver disease-related acute-on-chronic liver failure. Am J Gastroenterol120(4), 816–826 (2025). Google Scholar
Fouad, Y., Alboraie, M. & Shiha, G. Epidemiology and diagnosis of metabolic dysfunction-associated fatty liver disease. Hepatol. Int.18(Suppl 2), 827–833 (2024). Google Scholar
Abasi, S., Aggas, J. R., Garayar-Leyva, G. G., Walther, B. K. & Guiseppi-Elie, A. Bioelectrical impedance spectroscopy for monitoring mammalian cells and tissues under different frequency domains: a review. ACS Meas. Sci. Au.2(6), 495–516 (2022). Google Scholar
Ward, L. C. & Brantlov, S. Bioimpedance basics and phase angle fundamentals. Rev. Endocr. Metab. Disord.24(3), 381–391 (2023). Google Scholar
Coëffier, M. et al. Accuracy of bioimpedance equations for measuring body composition in a cohort of 2134 patients with obesity. Clin. Nutr.41(9), 2013–2024 (2022). Google Scholar
Dupertuis, Y. M. et al. Influence of the type of electrodes in the assessment of body composition by bioelectrical impedance analysis in the supine position. Clin. Nutr.41(11), 2455–2463 (2022). Google Scholar
Lai, C. L. et al. Bioimpedance analysis combined with sagittal abdominal diameter for abdominal subcutaneous fat measurement. Front. Nutr.9, 952929 (2022). Google Scholar
El-Serag, H. B. et al. Bioimpedance analysis predicts the etiology of cirrhosis in a prospective cohort study. Hepatol. Commun.7(10), e0253 (2023). Google Scholar
de Luis, R. D. et al. Evaluation of muscle mass and malnutrition in patients with colorectal cancer using the global leadership initiative on malnutrition criteria and comparing bioelectrical impedance analysis and computed tomography measurements. Nutrients16(17), 3035 (2024). Google Scholar
Younossi, Z. M. et al. Are there outcome differences between NAFLD and metabolic-associated fatty liver disease?. Hepatology76(5), 1423–1437 (2022). Google Scholar
Obrien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant.41(5), 673–690 (2007). Google Scholar
Namdeo, S., Srivastava, V. C. & Mohanty, P. Machine learning implemented exploration of the adsorption mechanism of carbon dioxide onto porous carbons. J. Colloid Interface Sci.647, 174–187 (2023). Google Scholar
Liang, D. et al. Perspective: global burden of iodine deficiency: insights and projections to 2050 using XGBoost and SHAP. Adv. Nutr.16(3), 100384 (2025). Google Scholar
Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol.23(1), 40–55 (2022). Google Scholar
Deo, R. C. Machine learning in medicine. Circulation132(20), 1920–1930 (2015). Google Scholar
Handelman, G. S. et al. eDoctor: Machine learning and the future of medicine. J. Intern. Med.284(6), 603–619 (2018). Google Scholar
Mohr, F. & van Rijn, J. N. Fast and informative model selection using learning curve cross-validation. IEEE Trans. Pattern. Anal. Mach. Intell.45(8), 9669–9680 (2023). Google Scholar
Crane, H. et al. Global prevalence of metabolic dysfunction-associated fatty liver disease-related hepatocellular carcinoma: A systematic review and meta-analysis. Clin. Mol. Hepatol.30(3), 436–448 (2024). Google Scholar
Zhao, J. et al. MAFLD as part of systemic metabolic dysregulation. Hepatol. Int.18(Suppl 2), 834–847 (2024). Google Scholar
Argenziano, M. E. et al. Epidemiology, pathophysiology and clinical aspects of Hepatocellular Carcinoma in MAFLD patients. Hepatol. Int.18(Suppl 2), 922–940 (2024). Google Scholar
Bai, J. et al. Correlation analysis of the abdominal visceral fat area with the structure and function of the heart and liver in obesity: a prospective magnetic resonance imaging study. Cardiovasc. Diabetol.22(1), 206 (2023). Google Scholar
Wewege, M. A. et al. The effect of resistance training in healthy adults on body fat percentage, fat mass and visceral fat: A systematic review and meta-analysis. Sports Med.52(2), 287–300 (2022). Google Scholar
Kolb, H. Obese visceral fat tissue inflammation: From protective to detrimental?. BMC Med.20(1), 494 (2022). Google Scholar
Mitsushio, K. et al. Interrelationships among accumulations of intra- and periorgan fats, visceral fat, and subcutaneous fat. Diabetes73(7), 1122–1126 (2024). Google Scholar
Feng, H. et al. Myopenic obesity determined by visceral fat area strongly predicts long-term mortality in cirrhosis. Clin. Nutr.40(4), 1983–1989 (2021). Google Scholar
Zhang, S. et al. Increased visceral fat area to skeletal muscle mass ratio is positively associated with the risk of cardiometabolic diseases in a Chinese natural population: A cross-sectional study. Diabetes Metab. Res. Rev.39(2), e3597 (2023). Google Scholar
GorditoSoler, M. et al. Usefulness of body fat and visceral fat determined by bioimpedanciometry versus body mass index and waist circumference in predicting elevated values of different risk scales for non-alcoholic fatty liver disease. Nutrients16(13), 2160 (2024). Google Scholar
Rosa, G. B., Lukaski, H. C. & Sardinha, L. B. The science of bioelectrical impedance-derived phase angle: insights from body composition in youth. Rev. Endocr. Metab. Disord10, 1–22 (2025). Google Scholar
Moh, M. C. et al. Association between neutrophil/lymphocyte ratio and kidney impairment in type 2 diabetes mellitus: A role of extracellular water/total body water ratio. Diabetes Res. Clin. Pract.199, 110634 (2023). Google Scholar
Shibata, K. et al. Prognostic impact of segmental extracellular water to total body water ratio in cardiovascular surgery patients. Clin. Nutr.51, 81–89 (2025). Google Scholar
Kajitani, N. et al. Relationship between extracellular water to total body water ratio and severe diabetic retinopathy in Type 2 diabetes. J. Clin. Endocrinol. Metab.110(7), e2248–e2255 (2025). Google Scholar
Dmitrieva, N. I., Boehm, M., Yancey, P. H. & Enhörning, S. Long-term health outcomes associated with hydration status. Nat. Rev. Nephrol.20(5), 275–294 (2024). Google Scholar
Akimoto, T., Tasaki, K., Ishihara, M., Hara, M. & Nakajima, H. Association of body water balance, nutritional risk, and sarcopenia with outcome in patients with acute ischemic stroke: A single-center prospective study. Nutrients16(13), 2165 (2024). Google Scholar
Kim, Y., Chang, Y., Ryu, S., Wild, S. H. & Byrne, C. D. NAFLD improves risk prediction of type 2 diabetes: With effect modification by sex and menopausal status. Hepatology76(6), 1755–1765 (2022). Google Scholar
Yang, J. D. et al. Patient sex, reproductive status, and synthetic hormone use associate with histologic severity of nonalcoholic steatohepatitis. Clin. Gastroenterol. Hepatol.15(1), 127-131.e122 (2017). Google Scholar
Balakrishnan, M. et al. Women have a lower risk of nonalcoholic fatty liver disease but a higher risk of progression vs men: A systematic review and meta-analysis. Clin. Gastroenterol. Hepatol.19(1), 61-71.e15 (2021). Google Scholar
Yang, X., Xue, X. & Zhou, Y. Methodological concerns and potential confounding factors. JAMA Ophthalmol142(6), 587 (2024). Google Scholar
Ergun, Y. Significance of confounding factors in retrospective observational studies. JCO Oncol. Pract.20(1), 154–155 (2024). Google Scholar
Lan, T. & Tacke, F. Diagnostics and omics technologies for the detection and prediction of metabolic dysfunction-associated steatotic liver disease-related malignancies. Metabolism161, 156015 (2024). Google Scholar
Hu, H., Han, Y., Cao, C. & He, Y. The triglyceride glucose-body mass index: a non-invasive index that identifies non-alcoholic fatty liver disease in the general Japanese population. J. Transl. Med.20(1), 398 (2022). Google Scholar
Bozic, D. et al. Detection of sarcopenia in patients with liver cirrhosis using the bioelectrical impedance analysis. Nutrients15(15), 3335 (2023). Google Scholar
Dumitriu, A. M. et al. Advancing nutritional care through bioelectrical impedance analysis in critical patients. Nutrients17(3), 380 (2025). Google Scholar
Romano, D. et al. Predictive and explainable machine learning models for endocrine, nutritional, and metabolic mortality in Italy using geolocalized pollution data. Appl. Syst. Innov.8(2), 48 (2025). Google Scholar
Yu, Y., Yang, Y., Li, Q., Yuan, J. & Zha, Y. Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods. Sci. Rep.15(1), 12382 (2025). Google Scholar