Frontiers in endocrinology · 2026
The triglyceride-high-density lipoprotein-glucose-body index: a superior novel biomarker for diabetic kidney disease in type 2 diabetes.
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Yang Jian, Xie Bingsong, Deng Zhiling, Zhang Zhifu, Zhou Hairong
Аннотация
BACKGROUND: The triglyceride-glucose (TyG) index is a recognized surrogate marker of insulin resistance but lacks integration of high-density lipoprotein cholesterol (HDL-C) and adiposity measures, which are pivotal in the pathogenesis of diabetic kidney disease (DKD). The novel triglyceride-high-density lipoprotein-glucose-body (TyHGB) index, combining TG/HDL-C ratio, fasting blood glucose (FBG), and body mass index (BMI), may offer a more comprehensive metabolic profile. This study aimed to evaluate the associative value of TyHGB for DKD in type 2 diabetes mellitus (T2DM) patients.
METHODS: A retrospective cross-sectional analysis of 1,382 adults with T2DM was conducted. We employed multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses to assess the independent and non-linear association of the TyHGB index with DKD. Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate and compare its associative performance against the TyG index.
RESULTS: Among the participants, 286 (20.7%) were diagnosed with DKD. After full adjustment for demographic, clinical, and biochemical confounders, TyHGB was independently associated with DKD (OR = 1.11, 95%CI:1.05-1.17, p<0.001). RCS analysis revealed a significant non-linear relationship, with a sharp increase in DKD risk beyond a TyHGB threshold of 8.74. The TyHGB index demonstrated superior discriminative ability (AUC = 0.775, 95% CI: 0.747-0.803) compared to the TyG index (AUC = 0.644, p<0.001). Incorporating TyHGB into a baseline clinical model significantly improved risk association (AUC increased from 0.715 to 0.788, p<0.001) and provided substantial reclassification improvement (NRI = 0.647, IDI = 0.067).
CONCLUSION: The TyHGB index exhibits a robust, independent, and non-linear association with DKD risk in T2DM patients and outperforms the established TyG index. As a readily accessible composite metric, it holds significant promise as a superior tool for early identification and risk stratification of DKD in clinical practice.
METHODS: A retrospective cross-sectional analysis of 1,382 adults with T2DM was conducted. We employed multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses to assess the independent and non-linear association of the TyHGB index with DKD. Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate and compare its associative performance against the TyG index.
RESULTS: Among the participants, 286 (20.7%) were diagnosed with DKD. After full adjustment for demographic, clinical, and biochemical confounders, TyHGB was independently associated with DKD (OR = 1.11, 95%CI:1.05-1.17, p<0.001). RCS analysis revealed a significant non-linear relationship, with a sharp increase in DKD risk beyond a TyHGB threshold of 8.74. The TyHGB index demonstrated superior discriminative ability (AUC = 0.775, 95% CI: 0.747-0.803) compared to the TyG index (AUC = 0.644, p<0.001). Incorporating TyHGB into a baseline clinical model significantly improved risk association (AUC increased from 0.715 to 0.788, p<0.001) and provided substantial reclassification improvement (NRI = 0.647, IDI = 0.067).
CONCLUSION: The TyHGB index exhibits a robust, independent, and non-linear association with DKD risk in T2DM patients and outperforms the established TyG index. As a readily accessible composite metric, it holds significant promise as a superior tool for early identification and risk stratification of DKD in clinical practice.