Clínica Universidad de Navarra-Body Adiposity Estimator Index as A Predictor of Metabolic Dysfunction-associated Steatotic Liver Disease: A Large-scale Cross-sectional Analysis.

Abstract

Background and aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 32.4% of the global population and is a major cause of chronic liver disease and cardiometabolic complications. Early detection is challenging due to limitations of conventional obesity indices like body mass index (BMI) and waist circumference (WC), which do not account for age- and sex-specific adiposity variations. The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index, integrating BMI, age, and gender, may offer a better measure of body fat percentage, but its utility in MASLD prediction is unexplored. We aimed to evaluate the predictive value of the CUN-BAE index for MASLD and to compare it with conventional obesity indices.

Methods: 1,003 participants from Guang'anmen Hospital were recruited (2023-2024). MASLD diagnosis was based on established criteria, and participants underwent clinical and laboratory assessments. Logistic regression models were used to determine the association between the CUN-BAE index and MASLD, with predictive performance assessed using area under the receiver operating characteristic curve (AUROC).

Results: Among 949 participants, 555 (58.5%) had MASLD. The CUN-BAE index showed a significant positive association with MASLD risk (Model 3: OR=1.16, 95%CI: 1.12-1.20, p<0.001). Its AUROC (0.677) surpassed BMI (0.612) and WC (0.598), especially in women (AUROC=0.809 vs. BMI=0.721, WC=0.698) compared to men (AUROC=0.664 vs. BMI=0.603, WC=0.591). A dose-response relationship was observed, with increased CUN-BAE levels correlating with higher MASLD risk beyond a threshold of 29.097.

Conclusions: The CUN-BAE index is a robust predictor of MASLD, outperforming BMI and WC, particularly in women. It captures age- and gender-specific adiposity variations, enhancing its utility as a non-invasive screening tool. Future research should focus on longitudinal validation and integrating additional metabolic parameters.