Frontiers in endocrinology · 2025
Association between triglyceride-glucose related indices and at-risk NASH in U.S. adults with NAFLD: results from NHANES 2017-2020.
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Ma Hanchen, Zhang Wenjie, Zhao Zheng, Jin Shuaichen, Li Linchuan, Zong Ruizhao, Li Wuyang, Zhang Ziwei, Cui Shichang, Pan Yunmiao, Zhu Jiankang, Zhang Guangyong
Аннотация
INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD), intricately linked to insulin resistance (IR), obesity, and metabolic syndrome (MetS), affects nearly one-third of the global population. The triglyceride-glucose (TyG) index and its derivative metrics, which integrate anthropometric measures, have emerged as cost-effective tools for assessing IR severity and their associations with NAFLD severity. However, evidence on their association with at-risk nonalcoholic steatohepatitis (NASH), a progressive phenotype characterized by hepatic fibrosis and inflammation, remains limited. This study aimed to evaluate the relationship between TyG related indices and at-risk NASH in a nationally representative NAFLD cohort.
METHODS: Using data from the 2017-2020 National Health and Nutrition Examination Survey (NHANES), adults with NAFLD diagnosed via vibration-controlled transient elastography (VCTE) were analyzed. At-risk NASH was defined using the FibroScan-AST (FAST) score. Five TyG related indices (TyG, TyG-WC, TyG-WHR, TyG-WtHR, TyG-BMI) were calculated and analyzed through weighted logistic regression, stratified analyses, restricted cubic spline (RCS), and receiver operating characteristic (ROC) analyses. Adjustments included demographics and clinical confounders.
RESULTS: Among participants, 10% exhibited at-risk NASH. Composite indices (TyG-WC, TyG-WHR, TyG-WtHR, TyG-BMI) each showed higher ORs than TyG alone in Model 3. TyG-WtHR exhibited the strongest association both as a continuous variable and across tertiles in Model 3, demonstrating a near-linear dose-response relationship. RCS analysis revealed no significant nonlinear relationships in composite indices, whereas the TyG index demonstrated a significant nonlinear association. Stratified analyses revealed elevated risks in males, middle-aged adults, and Non-Hispanic Black individuals. ROC analysis showed the discriminative performance of TyG-WC, while TyG-WtHR balanced sensitivity and specificity.
CONCLUSION: This study demonstrated significant associations between TyG-related indices and at-risk NASH in NAFLD patients. These non-invasive, cost-effective biomarkers may facilitate for early risk stratification, guiding precision interventions, and optimizing clinical trial design to improve patient outcomes.
METHODS: Using data from the 2017-2020 National Health and Nutrition Examination Survey (NHANES), adults with NAFLD diagnosed via vibration-controlled transient elastography (VCTE) were analyzed. At-risk NASH was defined using the FibroScan-AST (FAST) score. Five TyG related indices (TyG, TyG-WC, TyG-WHR, TyG-WtHR, TyG-BMI) were calculated and analyzed through weighted logistic regression, stratified analyses, restricted cubic spline (RCS), and receiver operating characteristic (ROC) analyses. Adjustments included demographics and clinical confounders.
RESULTS: Among participants, 10% exhibited at-risk NASH. Composite indices (TyG-WC, TyG-WHR, TyG-WtHR, TyG-BMI) each showed higher ORs than TyG alone in Model 3. TyG-WtHR exhibited the strongest association both as a continuous variable and across tertiles in Model 3, demonstrating a near-linear dose-response relationship. RCS analysis revealed no significant nonlinear relationships in composite indices, whereas the TyG index demonstrated a significant nonlinear association. Stratified analyses revealed elevated risks in males, middle-aged adults, and Non-Hispanic Black individuals. ROC analysis showed the discriminative performance of TyG-WC, while TyG-WtHR balanced sensitivity and specificity.
CONCLUSION: This study demonstrated significant associations between TyG-related indices and at-risk NASH in NAFLD patients. These non-invasive, cost-effective biomarkers may facilitate for early risk stratification, guiding precision interventions, and optimizing clinical trial design to improve patient outcomes.