Prediction of Independent Risk Factors for Non-Alcoholic Fatty Liver Disease Based on Regression Analysis

Abstract

Background: Nonalcoholic Fatty Liver Disease (NAFLD) is a common liver disease globally, but there are no optimal methods for its prediction or diagnosis. Diagnostic markers for NAFLD are still needed for screening individuals at risk. The present cross-sectional study proposes a non-invasive tool for NAFLD screening. To establish a risk prediction model of NAFLD by Multivariable logistic regression analysis, to identify the independent risk factors for NAFLD and thus provide management strategies for preventing this disease. Materials and methods: This cross-sectional study was conducted inthe Department of Biochemistry in collaboration with Institute of Nuclear Medicine and Allied Sciences (INMAS) Chittagong Medical College Hospital (CMCH) for a period of one year from June-2017 till June- 2018. One hundred and fifty (150) subjects aged between 18 to 60 years were enrolled using a non-probability consecutive sampling method. The variables of interest were Age, Body Mass Index (BMI) Central Obesity, Fasting Blood Glucose/Sugar (FBS) Fasting Plasma Insulin (FPI) Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) AST, ALT Total Cholesterol (TC) Triglycerides (TG) and High-density Lipoprotein Cholesterol (HDL-c). NAFLD subjects were identified through ultrasonography and Insulin Resistance (IR) was evaluated using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Results: It revealed that after multivariate adjustment, only Age, BMI, FBS, FPI and HOMA-IR >2.6 have significant predictive value for NAFLD. Respondent who had age more than 40 years, BMI ≥25kg/mt2, Central obesity, FBS ≥100 mg/dl, FPI >15mIU/L and HOMA-IR >2.6 were significantly more likely to have NAFLD than their counterpart. Conclusion: The predictive indicators can have a certain effect on the early screening and the timely prevention of the progress of related complications. As a result, introducing the predictive indicators is useful for the prediction of NAFLD individuals.

Chatt Maa Shi Hosp Med Coll J; Vol.24 (2); July 2025; Page 66-71