<![CDATA[Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is highly prevalent worldwide and is strongly associated with metabolic syndrome and its related conditions such as diabetes mellitus and hypertension. Without early detection and intervention, hepatic steatosis can progress to hepatic inflammation, fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC). This study aims to evaluate the relationship between ultrasound-derived fat fraction (UDFF) values and laboratory parameters of metabolic syndrome in MASLD, particularly liver enzymes, lipid profile, and glycemic profile, as well as to determine the optimal UDFF cut-off value for detecting metabolic syndrome risk in Indonesian patients. Methods: A cross-sectional study was conducted on 96 patients who underwent UDFF and laboratory assessments including liver enzymes (SGOT/AST, SGPT/ALT), lipid profile (total cholesterol, HDL, LDL, triglycerides), and glycemic profile (HbA1c, fasting blood glucose). Data analysis included bivariate-multivariate correlation and ROC analysis. Result: The distribution of UDFF (%) was as follows: normal ≤6% (27.1%; n=26), mild >6–15% (37.5%; n=36), moderate >15–25% (21.9%; n=21), and severe >25% (13.5%; n=13). UDFF showed a moderate positive correlation with SGPT (ρ=0.370; p<0.01) and triglycerides (ρ=0.380; p<0.01), and a weak negative correlation with HDL (ρ=−0.221; p<0.05). A UDFF threshold of 14% was able to predict abnormal SGPT levels and elevated triglycerides. Conclusions: UDFF shows a significant correlation with laboratory parameters of metabolic syndrome in MASLD, confirming its potential as an accessible, effective, efficient, non-radiative, and non-invasive imaging modality. These findings support the central role of radiology in the early detection and therapeutic monitoring of MASLD and metabolic syndrome, as well as in preventing disease progression from hepatic steatosis to inflammation, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Large-scale multicenter validation is required to optimize these findings.]]>
