Background/Objectives: Hepatic steatosis, a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), is closely associated with systemic metabolic dysfunction. However, the cumulative impact of metabolic risk factors on liver fat content remains underexplored. To evaluate the association between metabolic risk factors and hepatic steatosis severity using magnetic resonance imaging proton density fat fraction (MR-PDFF) measurement, and to assess the cumulative effect of multiple metabolic abnormalities. Methods: In this cross-sectional study, MASLD patients (n = 132, aged ≥ 18 years, age: 61.3 ± 10.3, male: 54, female: 78) underwent metabolic risk assessment and MR-PDFF liver fat content measurement. The association between certain metabolic risk scores (obesity/overweight, hypertension, hypercholesterolemia, hypertriglyceridemia, impaired fasting glucose or type 2 diabetes mellitus) both continuous and categorized, as well as liver fat content was analyzed using linear regression models. The cumulative effect of increasing metabolic risk was further explored with subgroup comparisons. Results: A significant positive association was observed between continuous metabolic risk scores and MR-PDFF values (β = 0.021, p < 0.001). Participants with higher cumulative metabolic risk (4 and 5 risk factors group) showed significantly higher liver fat content compared to the reference group (p < 0.01) (MetfO0 = 5.7 ± 5.9%; MetfO1 = 11.6 ± 9.5%; MetfO2 = 7.9 ± 5.6%; MetfO3 = 10.2 ± 7.9%; MetfO4 = 16.4 ± 11.0%; MetfO5 = 17.8 ± 9.5%). Intermediate metabolic risk categories showed a trend toward increased steatosis but did not reach statistical significance. Conclusions: Cumulative metabolic risk is strongly associated with increased hepatic fat accumulation. These findings underscore the need for early identification and management of metabolic risk factors to prevent the development and progression of hepatic steatosis.

Background: we evaluated regression models based on quantitative ultrasound (QUS) parameters and compared them with a vendor-provided method for calculating the ultrasound fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We measured the attenuation coefficient (AC) and the backscatter-distribution coefficient (BSC-D) and determined the USFF during a liver ultrasound and calculated the magnetic resonance imaging proton-density fat fraction (MRI-PDFF) and steatosis grade (S0–S4) in a combined retrospective–prospective cohort. We trained multiple models using single or various QUS parameters as independent variables to forecast MRI-PDFF. Linear and nonlinear models were trained during five-time repeated three-fold cross-validation in a retrospectively collected dataset of 60 MASLD cases. We calculated the models’ Pearson correlation (r) and the intraclass correlation coefficient (ICC) in a prospectively collected test set of 57 MASLD cases. Results: The linear multivariable model (r = 0.602, ICC = 0.529) and USFF (r = 0.576, ICC = 0.54) were more reliable in S0- and S1-grade steatosis than the nonlinear multivariable model (r = 0.492, ICC = 0.461). In S2 and S3 grades, the nonlinear multivariable (r = 0.377, ICC = 0.32) and AC-only (r = 0.375, ICC = 0.313) models’ approximated correlation and agreement surpassed that of the multivariable linear model (r = 0.394, ICC = 0.265). We searched a QUS parameter grid to find the optimal thresholds (AC ≥ 0.84 dB/cm/MHz, BSC-D ≥ 105), above which switching from a linear (r = 0.752, ICC = 0.715) to a nonlinear multivariable (r = 0.719, ICC = 0.641) model could improve the overall fit (r = 0.775, ICC = 0.718). Conclusions: The USFF and linear multivariable models are robust in diagnosing low-grade steatosis. Switching to a nonlinear model could enhance the fit to MRI-PDFF in advanced steatosis.

Background: Wilson’s disease (WD) is a genetic disorder of copper metabolism with over 600 disease-causing mutations, leading to variable hepatic and neurological symptoms. Early diagnosis and treatment are crucial. To evaluate hepatic involvement, serum scores and non-invasive imaging techniques complement histology. Methods: This pilot study assessed the utility of ultrasound-based tissue attenuation imaging (TAI), tissue scatter distribution imaging (TSI), and shear-wave elastography (SWE) for quantifying steatosis and fibrosis in WD. Results: Among 131 treated patients, 53 (mean age 40.5 ± 13.1 years, M/F = 35/18) underwent measurements. Based on literature-validated thresholds, 41 patients did not have significant liver fibrosis, 5 patients had moderate (F2) and 4 advanced (F3) fibrosis, while 3 patients had cirrhosis. The LS (liver stiffness) was in moderate correlation with FIB-4 (r = 0.306, p < 0.03), NAFLD fibrosis index (r = 0.336, p < 0.02), and APRI (r = 0.31, p = 0.0857). Among the WD patients, 37 had no steatosis (S0), 14 had mild steatosis (S1), and 2 had intermediate steatosis (S2); none of them had severe steatosis (S3) based on UEFF calculation. LS correlated positively with calculated free copper and negatively with serum ceruloplasmin. Normal-BMI patients exhibited no significant steatosis (R2 = 0. 0065, p = 0.966) by ultrasound-estimated fat fraction (UEFF), while those with BMI > 25 kg/m² had increased UEFF correlating with BMI (R2 = 0.288, p < 0.015). Over a five-year follow-up using liver elastography, the fibrosis score did not progress significantly in adequately treated patients. Conclusions: Ultrasound with artificial intelligence-derived parameters supports the non-invasive evaluation of hepatic steatosis and fibrosis in WD, complementing clinical and laboratory data. However, population-specific liver stiffness thresholds are still needed.