The triglyceride-glucose (TyG) index is a recognized marker of insulin resistance, whereas the frailty index (FI) reflects cumulative physiological decline. However, the combined effect of metabolic dysfunction and frailty—referred to as the TyG-Frailty Index (TyGFI)—has not been systematically evaluated. This study aimed to examine the association between TyGFI, analyzed as both continuous and categorical variable, and the risk of chronic liver disease (CLD). Baseline and follow-up data were collected from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). Multivariable logistic regression models were utilized to evaluate the association between the TyGFI and the risk of CLD, adjusting for demographic characteristics, lifestyle factors, and clinical variables. Restricted cubic spline (RCS) and subgroup analyses were conducted to assess nonlinear dose - response relationships and interaction effects. Sensitivity analyses were performed to evaluate the robustness of the findings. A total of 7,417 participants were included, of whom 265 developed CLD during follow-up. Elevated TyGFI levels were significantly associated with an increased risk of CLD after multivariable adjustment. Participants in the highest TyGFI quartile exhibited the greatest risk of CLD (OR = 3.19, 95% CI: 2.16–4.80, p < 0.001). Each unit increase in TyGFI was associated with a 38% higher odds of incident CLD (OR = 1.38, 95% CI: 1.23–1.45, p < 0.001). RCS analysis demonstrated a consistent nonlinear dose-response relationship. ROC analysis showed that TyGFI had moderate discriminative ability for CLD (AUC = 0.64). No significant interaction was observed across subgroups, and sensitivity analyses further confirmed the robustness of the findings. TyGFI is independently associated with an increased risk of incident CLD and demonstrates modest discriminative ability. By integrating metabolic and functional dimensions, TyGFI may serve as a complementary indicator for risk stratification in middle-aged and older adults, potentially supporting the early identification of individuals at increased risk of CLD.
Huang et al. (Wed,) studied this question.