Objective To investigate the associations of visceral fat metabolism indices the Metabolic Score for Visceral Fat (METS-VF), the Metabolic Score for Insulin Resistance (METS-IR), the lipid accumulation product (LAP), the visceral adiposity index (VAI), and the cardiometabolic index (CMI) with liver fibrosis in individuals with steatotic liver disease (SLD).

Methods Based on data of year 2017 to 2020 from the National Health and Nutrition Examination Survey (NHANES), weighted multivariate Logistic regression and trend analysis were used to explore the relationships between visceralfat metabolism indices and liver fibrosis in SLD. Restricted cubic spline models were constructed to examine potential nonlinear relationships. Receiver operating characteristic (ROC) curves were plotted to briefly evaluate the diagnostic performance of visceral fat metabolism indices for liver fibrosis.

Results In the entire SLD population and the metabolic dysfunction-associated steatotic liver disease (MASLD) subgroup (92.6%), the Logistic regression analysis revealed that METS-VF, METS-IR, and LAP were significantly positively associated with the risk of liver fibrosis, with risks increasing progressively across quartiles of these indices (P_trend < 0.01). In fully adjusted models, the highest quartiles of these indices were associated with a significantly elevated risk of liver fibrosis (OR for the entire population: 25.622, 11.426 and 5.651, respectively; OR for the MASLD subgroup: 29.538, 18.346 and 5.526, respectively; all P < 0.01). Restricted cubic spline analysis indicated that METS-VF exhibited a linear association with liver fibrosis in both the entire population and the MASLD subgroup (P_overall < 0.05, P_non-linear>0.05); METS-IR showed a nonlinear association with liver fibrosis in the MASLD subgroup (P_overall < 0.05, P_non-linear < 0.05); METS-IR demonstrated a linear association with liver fibrosis in the entire SLD population (P_overall < 0.05, P_non-linear=0.251); LAP and CMI exhibited nonlinear associations with liver fibrosis in both the entire SLD population and the MASLD subgroup (P_overall < 0.05, P_non-linear < 0.05). ROC curve analysis further confirmed that in both the entire SLD population and the MASLD subgroup, the predictive performance (area under the curve values) of METS-VF and METS-IR for liver fibrosis were significantly superior to those of traditional indices (P < 0.001).

Conclusion The risk of liver fibrosis in SLD increases significantly with rising METS-VF, suggesting its potential as a non-invasive screening tool for liver fibrosis in SLD.