Study of Hepatic Steatosis Algorithms as A Potential Marker of Metabolic Dysfunction Associated Steatotic Liver Disease - Bolakhe, Kalasha; Bista, Pabitra; Thapa, Tika Bahadur; Pokhrel, Sushant; Joshi, Govardhan; Aryal, Suraj; Gurung, Dhan Bahadur; Maharjan, Sanju; Gautam, Sujan; Khanal, Sudip; Lamichhane, Anit (Journal of Manmohan Memorial Institute of Health Sciences, 2024)
Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide, with increased liver-related morbidity and mortality. Various non-invasive algorithms have been developed for predicting the presence of MASLD using anthropometric and biochemical parameters. Hence, this study aims to determine hepatic steatosis algorithms as a potential marker of MASLD. Methods: A total of 200 participants were included in the study, of which 100 were MASLD cases, and 100 were healthy control. Serum ALT, AST, TG, and Glucose were estimated, and Hepatic steatosis algorithms (LAP, FSI, TyG, and HSI) were calculated. The ROC curve was estimated to validate algorithms in patients with MASLD. Results: Hepatic steatosis algorithms like FSI, LAP, TyG, and HSI were significantly higher (p<0.05) in patients with MASLD compared to healthy control. The AUROC of LAP, FSI, TyG, and HSI was 0.789 (95% CI,0.727-0.851), 0.776 (95% CI, 0.711-0.841), 0.765 (95% CI, 0.697-0.833) and 0.693 (95% CI, 0.620-0.766) respectively. The optimal cut-off value of LAP, FSI, TyG, and HSI for the prediction of MASLD were 31 (71% sensitivity and 70% specificity), 23 (74% sensitivity and 72% specificity), 8.9 (73% sensitivity and 70% specificity) and 34.5 (67% sensitivity and 62% specificity) respectively. Conclusion: The non-invasive and cost-effective algorithms like LAP, FSI, TyG, and HSI can be potential screening tools for predicting MASLD.