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Tiansu Lv,1,2,* Jie Tian,1,3,* Yaohuan Sun,1,3 Yujuan Zhang,1,2 Fang Qi,1,2 Liulan Xiang,1,2 Yutian Cao,1,2 Wenhui Zhang,1,2 Jiaxuan Huai,1,2 Yinfeng Dong,4 Xiqiao Zhou1,2 1Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2The First Clinical Medical College of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 3School of Nursing, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 4Department of Pathology and Pathophysiology, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiqiao Zhou; Yinfeng Dong, Email zhouxiqiao@njucm.edu.cn; dongyf@njucm.edu.cnBackground: Metabolic Associated Fatty Liver Disease (MAFLD) poses a significant threat to human health, as it can result in hepatic fibrosis and potentially progress to cirrhosis, in addition to causing a range of extrahepatic complications. The early detection of MAFLD is crucial, particularly during the initial stages when the condition may be amenable to reversal and the body composition could be vital importance.Methods: Data from participants at the Jiangsu Province Hospital of Traditional Chinese Medicine, covering the period from January 1 to December 31, 2022, were collected and subsequently randomized into training and validation cohorts. Independent risk factors for MAFLD were identified using statistical methodologies in conjunction with clinical relevance, and these factors were ultimately utilized to develop the nomogram.Results: In the training cohort, there were 356 cases of MAFLD out of a total of 513 patients, representing 71.2%, while in the validation cohort, 161 cases of MAFLD were identified out of 220 patients, accounting for 73.2%. In terms of statistical outcomes and clinical relevance, we identified a total of 12 closely related or significant variables. To enhance our understanding of the critical role of body composition parameters in predicting the incidence of MAFLD, we developed two distinct nomograms, one of which included body composition data. Notably, the nomogram that incorporated body composition demonstrated superior predictive performance, as evidenced by a well-fitted calibration curve and a C-index of 0.893 (with a range of 0.8625 to 0.9242). Furthermore, the decision curve analysis indicated that utilizing the nomogram that included body composition would yield greater benefits.Conclusion: The nomogram serves as an effective tool for predicting MAFLD. Its utility in early risk identification of MAFLD is of significant importance, as it facilitates timely intervention and treatment for patients affected by this condition.Keywords: Metabolic Associated Fatty Liver Disease, Body Composition, Nomogram |
