Ultrasound-Derived Fat Fraction and Metabolic Parameters in Predicting Panvascular Disease Risk in Metabolic Dysfunction-Associated Steatotic Liver Disease Patients: A Prospective Study.

Abstract

Backgruound: Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are at increased risk of panvascular disease (PD). Noninvasive markers for early risk stratification are needed. We evaluated whether ultrasound-derived fat fraction (UDFF) improves the prediction of PD occurrence in MASLD patients.

Methods: In this prospective cohort, 525 adults with MASLD were enrolled between October 2022 and December 2023 and followed for 6 months for PD occurrence. Baseline UDFF and clinical/metabolic variables were collected. Multivariable logistic regression analyses reported standardized coefficients (per-standard deviation [SD] β) and odds ratios (ORs) per 1-SD (OR). A decision tree model was constructed to explore differences across metabolic subgroups, including obesity, hypertension, diabetes, and multiple metabolic abnormalities. Discriminative performance was compared using the area under the curve (AUC; DeLong test).

Results: Body mass index (BMI), systolic blood pressure (SBP), homeostasis model assessment of insulin resistance (HOMA-IR), and UDFF were each associated with increased PD risk. In the multivariable model, the strongest standardized effects were observed for UDFF (β=3.28; OR, 2.10; 95% confidence interval [CI], 1.45 to 2.75) and BMI (β=1.87; OR, 1.68; 95% CI, 1.25 to 2.34), followed by SBP (β=1.33; OR, 1.15; 95% CI, 1.03 to 1.26). HOMA-IR (β=0.96; OR, 1.65; 95% CI, 1.30 to 2.15), age (β=0.56; OR, 1.06; 95% CI, 1.01 to 1.12), and liver stiffness measurement (β=0.14; OR, 1.03; 95% CI, 1.01 to 1.07) also showed smaller but significant associations. The decision tree model reflected similar patterns and achieved higher discrimination than logistic regression (AUC 0.85 vs. 0.80, P<0.05).

Conclusion: UDFF is a key predictor of PD risk in MASLD patients, underscoring the importance of risk stratification for early intervention and personalized management strategies.