Abstract
Background: Triglyceride glucose (TyG) and its related index (TyG-body mass index, TyG-BMI) are recognized as markers for nonalcoholic fatty liver disease (NAFLD), but their associations with metabolic dysfunction-associated steatotic liver disease (MASLD) and significant liver fibrosis (SLF) risk are less studied. Therefore, this study explores the effectiveness of these indices in assessing MASLD and SLF risk in the U.S. population. Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional study involving 5520 participants from the general population was performed. This research measured demographic, anthropometric, biochemical, comorbid, and lifestyle characteristics, all of which are considered risk factors for MASLD/SLF. Results: Upon controlling for confounding variables, only the TyG-BMI was found to have a consistent positive association with the risk of MASLD and SLF. Specifically, for each standard deviation increase, the odds ratio (OR) and 95% confidence interval (CI) were 4.44 (3.64-9.26, p for trend < 0.001) for MASLD and 2.48 (2.15-2.87, p for trend < 0.001) for SLF. Significant interactions were identified among age, sex, and the risk of MASLD associated with the TyG-BMI. The TyG-BMI also had a significant threshold effect on the risk of MASLD at a cutoff point of 180.71. Furthermore, the area under the receiver operating characteristic curve (AUC) revealed that the TyG-BMI better predicted the risk of MASLD and SLF (AUC 0.820, 95% CI 0.810-0.831; AUC 0.729, 95% CI 0.703-0.756, respectively). In addition, the integrated discrimination improvement (IDI), decision curve analysis (DCA), and net reclassification index (NRI) also demonstrated the satisfactory predictive ability of the TyG-BMI. Conclusions: Within this large dataset, the TyG-BMI was independently associated with both the MASLD score and the SLF in the MASLD cohort. Its predictive efficacy consistently surpassed that of TyG and other noninvasive models, indicating that TyG-BMI has potential for the early identification of MASLD and SLF risk.