The risk factors and clinical model for predicting hepatic steatosis-related fibrosis in non-viral chronic liver diseases

Abstract

Abstract: Objective This study aims to evaluate the risk factors for hepatic steatosis-related fibrosis in non-viral chronic liver diseases, and further construct its clinical prediction model. Methods Clinical records and pathological data from non-viral chronic liver diseases were retrospectively collected. Logistic methods were used to identify the risk factors for hepatic steatosis-related fibrosis and construct a predictive model. The predictive values of this model were further calculated using OptimalCutpoints package in the R software. Results A Total of 153 cases were included in this study within which 19 (12.4%) patients developed hepatic steatosis-related fibrosis. Univariate and multivariate logistic regression analysis showed that liver stiffness measurement (LSM) and triglycerides were risk factors for hepatic steatosis-related fibrosis in non-viral chronic liver diseases (OR=1.09, 95% CI=1.03~1.15, P=0.002 and OR=6.17, 95% CI=1.29~29.48, P=0.023; respectively). A predictive Model =1.09 × LSM + 6.17 × triglycerides (abnormal, 1; normal, 0) was constructed. The optimal cut-off value of the Model is 10.79, with a sensitivity of 0.95, specificity of 0.54, positive predictive value of 0.23, and negative predictive value of 0.986. Compared with the LSM (AUC=0.71), this model has a higher predictive value (AUC=0.77) and shows a statistically significant trend (P=0.071). Conclusion LSM and serum triglycerides can be used to predict the occurrence of hepatic steatosis-related fibrosis in patients with non-viral chronic liver disease.

Key words: Hepatis steatosis, Liver fibrosis, Model, Triglyceride, Liver stiffness measurement

JIA Jin-rui, ZHANG Jing, SHI Jia, ZHANG Wen-si, CHEN Shi-shi, YE Chen, CHEN Long, LU Yun-fei, YANG Zong-guo. The risk factors and clinical model for predicting hepatic steatosis-related fibrosis in non-viral chronic liver diseases[J]. Chinese Hepatolgy, 2025, 30(8): 1051-1054.

References

[1] 彭静, 陈志远, 周懂晶, 等. 肝脏脂肪含量对超声瞬时弹性成像肝纤维化诊断效能的影响 [J]. 中国医学影像学杂, 2022, 30(8):797-808.
[2] Scheuer P J. Classification of chronic viral hepatitis: a need for reassessment [J]. J Hepatol, 1991, 13(3):372-374.
[3] Kleiner D E, Brunt E M, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease [J]. Hepatology, 2005, 41(6):1313-1321.
[4] Rubin D B, Schenker N. Multiple imputation in health-care databases: an overview and some applications [J]. Stat Med, 1991, 10(4):585-598.
[5] van Doorn D J, Holleboom A G, Takkenberg R B, et al. Can liver stiffness measurement accurately predict disease progression and clinical outcome in patients with metabolic dysfunction-associated steatotic liver disease and bridging fibrosis or cirrhosis? [J]. Hepatobiliary Surg Nutr, 2023, 12(6):912-915.
[6] Hagstrom H, Nasr P, Ekstedt M, et al. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD [J]. J Hepatol, 2017, 67(6):1265-1273.
[7] Wang J, Cao L, Liu F, et al. A multi-institutional study on ultrasound image analysis for staging HBV-derived liver fibrosis: a potential noninvasive alternative to liver stiffness measurement [J]. Clin Transl Gastroenterol, 2024, 15(12):e00780.
[8] Sandmann L, Degasperi E, Port K, et al. Liver stiffness measurement as a noninvasive method for the diagnosis of liver cirrhosis in patients with chronic hepatitis D virus infection [J]. Aliment Pharmacol Ther, 2024, 59(6):752-761.
[9] Chen Z, Huang Y, Zhang Y, et al. Impact of hepatic steatosis on liver stiffness measurement by vibration-controlled transient elastography and its diagnostic performance for identifying liver fibrosis in patients with chronic hepatitis B [J]. Insights Imaging, 2024, 15(1):283.
[10] Ding Y, Wang G, Deng Q, et al. Liver stiffness measurement is useful in predicting type 2 diabetes mellitus among nonalcohol fatty liver disease patients [J]. Diabetes Metab Syndr Obes, 2024, 17:295-304.
[11] Gawrieh S, Vilar-Gomez E, Wilson L A, et al. Increases and decreases in liver stiffness measurement are independently associated with the risk of liver-related events in NAFLD [J]. J Hepatol, 2024, 81(4):600-608.
[12] Kubotsu Y, Sakamoto Y, Tago M, et al. FIB-4 index and liver stiffness measurement are potential predictors of atherosclerosis in metabolic dysfunction-associated steatotic liver disease [J]. J Atheroscler Thromb, 2025, 32(2):239-252.
[13] Fan R, Yu N, Li G, et al. Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis [J]. Liver Int, 2024, 44(3):749-759.
[14] 廖海林, 肖新华. 甘油三酯在非酒精性脂肪肝沉积的机制研究进展 [J]. 临床与病理杂志, 2014, 34(6):831-836.
[15] 魏小勤. 血清甘油三酯及总胆固醇在非酒精性脂肪肝检测中的应用价值 [J]. 临床研究, 2022, 30(9):111-114.
[16] Ying Y, Ji Y, Ju R, et al. Association between the triglyceride-glucose index and liver fibrosis in adults with metabolism-related fatty liver disease in the United States: a cross-sectional study of NHANES 2017-2020 [J]. BMC Gastroenterol, 2025, 25(1):3.
[17] Yang X, Rao H, Yuan Y, et al. Correlation analysis of the triglyceride-glucose index and related parameters in metabolic dysfunction-associated fatty liver disease [J]. Sci Rep, 2025, 15(1):23.
[18] Li S, Feng L, Ding J, et al. Triglyceride glucose-waist circumference: the optimum index to screen nonalcoholic fatty liver disease in non-obese adults [J]. BMC Gastroenterol, 2023, 23(1):376.