Metabolic dysfunction-associated fatty liver disease (MAFLD), metabolic dysfunction-associated steatotic liver disease (MASLD), and hepatic fibrosis represent significant global health challenges. This study aimed to evaluate the clinical value of the triglyceride-glucose (TyG), C-reactive protein-triglyceride-glucose (CTI), and triglyceride-glucose-waist-to-height ratio (TyG-WHtR) indices as non-invasive predictors for MAFLD, MASLD, and hepatic fibrosis in American adults. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 database, which included 1,898 adults (≥20 years). Weighted multivariate logistic regression analysis and restricted cubic spline regression analysis were used to assess the association between these indices and disease prevalence. Receiver operating characteristic curves were used to evaluate their diagnostic efficacy, and subgroup analysis was performed to explore the heterogeneity of the associations. After adjusting for potential confounders, the TyG-related indices showed a significant positive association with MAFLD, MASLD, and hepatic fibrosis. Specifically, the CTI demonstrated a significant positive nonlinear, J-shaped correlation with MAFLD prevalence (OR: 3.22, 95% CI: 2.61–3.98). A similar correlation was observed between the TyG-WHtR index and the prevalence of MAFLD (OR: 6.52, 95% CI: 4.69–9.06), MASLD (OR: 2.74, 95% CI: 2.13–3.52), and hepatic fibrosis (OR: 2.02, 95% CI: 1.69–2.41). The TyG-WHtR index was the most robust predictor for MAFLD, MASLD, and hepatic fibrosis, with areas under the curve (AUCs) of 0.875, 0.791, and 0.711, respectively. All three indices showed a more pronounced correlation with hepatic fibrosis in high-risk metabolic subgroups, such as individuals with diabetes mellitus, cardiovascular disease, hyperlipidemia, or MAFLD. In conclusion, The TyG-related indices are important and easily accessible non-invasive tools for predicting MAFLD, MASLD, and hepatic fibrosis. The TyG-WHtR index, in particular, stands out for its superior predictive performance, making it an ideal biomarker for early risk stratification, targeted screening, and continuous monitoring of at-risk individuals in clinical practice.
Yong et al. (Tue,) studied this question.