Metabolites · 2025
Integrative Assessment of TyG Index, FIB-4, and eGFR as Composite Predictors of Metabolic Risk Clusters in Adults.
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Popoviciu Mihaela Simona, Moldovan Andrada, Dorobantu Florica Ramona, Domocos Petru Cornel, Mariș Lavinia, Trifan Daniela Florina, Ghitea Timea Claudia, Manole Felicia
Аннотация
BACKGROUND: Metabolic syndrome involves interconnected disturbances in insulin sensitivity, hepatic function, and renal performance. Simple, integrative indices may improve early detection of multisystem metabolic risk.
METHODS: In this cross-sectional study, adults were stratified into metabolic risk categories (scores 2-11) and evaluated using the triglyceride-glucose (TyG) index, the fibrosis-4 (FIB-4) score, and estimated glomerular filtration rate (eGFR). Correlation analyses and multivariate regression models (HC3 robust standard errors) were applied to identify independent predictors of hepatic (FIB-4) and renal (eGFR) function.
RESULTS: TyG and FIB-4 increased significantly with higher metabolic risk (ANOVA p < 10-6), while eGFR showed a mild, non-significant decline. TyG correlated strongly with triglycerides (r = 0.78) and fasting glucose (r = 0.69), whereas FIB-4 correlated inversely with eGFR (ρ = -0.30). In regression models, age was the strongest predictor of both FIB-4 (β_std = 0.33) and eGFR (β_std = -0.47). Additional predictors of lower eGFR included FIB-4, systolic blood pressure, BMI, and UACR, whereas TyG showed no independent effect after adjustment.
CONCLUSIONS: The combined use of TyG, FIB-4, and eGFR provides complementary insight into the metabolic-hepatic-renal continuum. These indices highlight progressive insulin resistance, hepatic stress, and subclinical renal involvement, supporting their utility as accessible tools for early identification of high-risk metabolic phenotypes.
METHODS: In this cross-sectional study, adults were stratified into metabolic risk categories (scores 2-11) and evaluated using the triglyceride-glucose (TyG) index, the fibrosis-4 (FIB-4) score, and estimated glomerular filtration rate (eGFR). Correlation analyses and multivariate regression models (HC3 robust standard errors) were applied to identify independent predictors of hepatic (FIB-4) and renal (eGFR) function.
RESULTS: TyG and FIB-4 increased significantly with higher metabolic risk (ANOVA p < 10-6), while eGFR showed a mild, non-significant decline. TyG correlated strongly with triglycerides (r = 0.78) and fasting glucose (r = 0.69), whereas FIB-4 correlated inversely with eGFR (ρ = -0.30). In regression models, age was the strongest predictor of both FIB-4 (β_std = 0.33) and eGFR (β_std = -0.47). Additional predictors of lower eGFR included FIB-4, systolic blood pressure, BMI, and UACR, whereas TyG showed no independent effect after adjustment.
CONCLUSIONS: The combined use of TyG, FIB-4, and eGFR provides complementary insight into the metabolic-hepatic-renal continuum. These indices highlight progressive insulin resistance, hepatic stress, and subclinical renal involvement, supporting their utility as accessible tools for early identification of high-risk metabolic phenotypes.