Triglyceride Glucose Index Combined with Anthropometric Indices as Predictors for Non-alcoholic Fatty Liver Disease in Overweight and Obese individuals

Abstract

Background: Non-Alcoholic Fatty Liver Disease linked to obesity and metabolic disorders with global health risks, leading to severe complications like cirrhosis and cancer. Presently, diagnosing it lacks specific biomarkers. Identifying at-risk individuals early is vital for prevention and treatment. The Triglyceride Glucose Index, indicating insulin resistance, along with waist circumference (WC) and body mass index (BMI), are potential predictors due to their association with fat metabolism. Evaluating their combined effectiveness can enhance early NAFLD diagnosis, reducing future adverse outcomes. Aim: To evaluate the role of Triglyceride Glucose Index with anthropometric indices as predictors for Non-alcoholic fatty liver disease in overweight and obese individuals. Methods: This cross-sectional study was done at The Department of Biochemistry and Molecular Biology, BSMMU, Dhaka (March 2023 to February 2024) included 253 adults (18-75 years, both genders) with overweight or obese. Ethical approval was obtained, and subjects were selected from the Hepatology outpatient department using purposive sampling. Informed written consent was taken after explaining the study purpose. Detailed history, examinations, and anthropometric measurements were recorded. Upper abdominal Ultrasonography was done at the Radiology department of BSMMU for detecting NAFLD characteristics. Fasting blood samples were taken for estimation of lipid profile, fasting plasma glucose, and ALT. BMI categorization defined overweight (BMI 23-24.9 kg/m2) and obesity (BMI ≥25 kg/m2). Study subjects were categorized into two groups based on USG findings- NAFLD group and the non-NAFLD group (Reference group). NAFLD group further classified as Grade-I, Grade-II and Grade-III depending on usg grading. TyG-index and TyG combined with anthropometric indices (TyG-BMI, TyG-WC) were compared between NAFLD and non-NAFLD groups. Further, TyG combined with anthropometric indices was compared across NAFLD grades. ROC curves were constructed for TyG-index, TyG-BMI, and TyG-WC to predict NAFLD. SPSS version 27.0 was used for data analysis. Results: The NAFLD group had more TG, FPG, and ALT than the non-NAFLD group. However, TG, FPG, and ALT increased with NAFLD grade, peaking in Grade III. NAFLD group had significantly higher TyG-index, TyG-BMI, and TyG-WC than non-NAFLD group, regardless of gender. Receiver operating characteristic (ROC) curve showed each of the indicators were significant NAFLD AUC predictors. AUCs showed that TyG-WC predicted NAFLD most effectively followed by TyG-BMI and TyG-index. TyG-WC has good sensitivity and specificity. TyG-index and TyG-BMI diagnostic performance showed excellent sensitivity but low specificity. In contrast, the TyG-WC has good sensitivity and specificity. Conclusion: Our study found that the TyG-index, TyG-BMI, and TyG-WC as valuable predictors of NAFLD, with TyG-WC being the most effective. It showed satisfactory results in terms of combined sensitivity and specificity, making it the optimal predictor for NAFLD in overweight and obese individuals.