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Construction and validation of the diagnostic model for metabolic dysfunction-associated steatotic liver disease among the non-obese population

Published on Feb. 25, 2025Total Views: 33 timesTotal Downloads: 12 timesDownloadMobile

Author: WANG Yufeng 1 HU Minxuan 1 LI Yuntao 1 ZHU Kexin 2 JI Guozhong 1 YUAN Yuan 2

Affiliation: 1. Department of General Practice, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China 2. Health Management Centre, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China

Keywords: Metabolic dysfunction-associated steatotic liver disease Non-obese Diagnostic model Logistic regression Predictive model

DOI: 10.12173/j.issn.1004-5511.202410125

Reference: Wang YF, Hu MX, Li YT, Zhu KX, Ji GZ, Yuan Y. Construction and validation of the diagnostic model for metabolic dysfunction-associated steatotic liver disease among the non-obese population[J]. Yixue Xinzhi Zazhi, 2025, 35(2): 141-150. DOI: 10.12173/j.issn.1004-5511.202410125.[Article in Chinese]

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Abstract

Objective  To analyze the predictive indicators of non-obese metabolic dysfunction-associated steatotic liver disease (MASLD) and construct a diagnostic model.

Methods A retrospective analysis was conducted on data from individuals who underwent health examinations at the Health Management Center of the Second Affiliated Hospital of Nanjing Medical University between August 2022 and July 2024. The study population was divided into a modeling group (those who completed examinations between August 2022 and May 2024) and a validation group (those who completed examinations between June 2024 and July 2024). Lasso regression was used to screen potential predictive indicators, and binary Logistic regression was employed to identify key indicators and construct a nomogram. Model performance was evaluated using a confusion matrix, receiver operating characteristic (ROC) curve analysis with area under the curve (AUC), calibration curve analysis (CCA), and decision curve analysis (DCA).

Results  A total of 791 physical examination subjects were included, with 607 cases in the modeling group and 184 cases in the validation group. Among them, 292 cases were non-obese MASLD, with a prevalence of 36.92%. Multivariate Logistic regression analysis identified body mass index (BMI) [OR=1.860, 95%CI(1.559, 2.219)], fasting blood glucose [OR=1.415, 95%CI(1.174, 1.707)], triglyceride [OR=1.308, 95%CI(1.021, 1.675)], gamma-glutamyl transferase [OR=1.012, 95%CI(1.005, 1.020)], and the uric acid to high-density lipoprotein cholesterol ratio [OR=1.004, 95%CI(1.002, 1.007)] as predictive indicators for non-obese MASLD. The accuracy rates for the modeling and validation groups were 74.0% and 72.8%, respectively, while the precision rates were 67.7% and 72.7%, respectively. The AUC values were 0.814[95%CI(0.780, 0.848)] for the modeling group and 0.819[95%CI(0.755, 0.883)] for the validation group. The Hosmer-Lemeshow test showed no statistical significance (P>0.05) for both groups, indicating good model fit. CCA demonstrated strong agreement between predicted and actual probabilities, and DCA indicated favorable net benefits of the model.

Conclusion  The diagnostic model developed in this study exhibits good diagnostic performance for non-obese MASLD and can be used for early screening in individuals with a normal BMI.

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