The escalating global prevalence of Type 2 diabetes mellitus (T2DM) presents a paramount challenge to public health systems worldwide. According to International Diabetes Federation estimates, approximately 537 million adults were living with diabetes in 2021, with projections indicating this figure will rise to 783 million by 2045 (1). A particularly concerning complication is diabetic kidney disease (DKD), which affects approximately 40% of individuals with diabetes (2) and represents the principal cause of chronic kidney disease and end-stage renal disease globally (3). Beyond substantially elevating cardiovascular event risks and all-cause mortality (4, 5), DKD imposes devastating financial burdens on healthcare systems and society at large (6). Consequently, early identification of high-risk individuals and implementation of timely interventions hold critical importance for both clinical practice and public health initiatives.

DKD arises from multifactorial pathogenesis, characterized by a complex interplay among metabolic imbalances, hemodynamic changes, chronic inflammatory states, and oxidative stress (7, 8). Insulin resistance (IR) serves as a fundamental pathophysiological mechanism in both T2DM development and DKD progression, promoting renal injury through processes including glomerular hyperfiltration, endothelial dysfunction, and enhanced fibrogenesis (9). Although gold-standard IR assessment methods like the hyperinsulinemic-euglycemic clamp offer high accuracy (10), their technical complexity and substantial costs limit routine clinical application. This limitation has motivated the development of straightforward surrogate biomarkers derived from commonly measured clinical parameters.

Among emerging biomarkers, the triglyceride-glucose (TyG) index—calculated as Ln[fasting triglycerides (TG, mg/dL)×fasting blood glucose (FBG, mg/dL)/2]—has gained recognition as a reliable and readily accessible IR indicator (11). Numerous studies have documented strong associations between the TyG index and various metabolic disorders, including non-alcoholic fatty liver disease (NAFLD) and cardiovascular diseases (12, 13). Despite these advantages, the TyG index exhibits certain limitations. Its primary focus on glucose and triglyceride metabolism potentially overlooks other crucial pathophysiological pathways contributing to DKD. For instance, high-density lipoprotein cholesterol (HDL-C) demonstrates anti-inflammatory, antioxidant, and endothelial-protective properties, with low HDL-C levels independently correlating with albuminuria development and renal function decline (14, 15). Additionally, obesity—typically quantified by body mass index (BMI)—represents a well-established DKD risk factor that promotes renal damage through mechanisms such as hyperfiltration, leptin resistance, and chronic inflammation (16). The conventional TyG index fails to incorporate these two critical dimensions of HDL-C metabolism and adiposity, thereby constraining its comprehensive risk stratification potential.

To address these limitations, researchers recently proposed a novel composite index: the triglyceride high-density cholesterol-glucose body index (TyHGB). The TyHGB index is defined by the formula: TG/HDL-C + 0.7×FBG (mmol/L) + 0.1×BMI (kg/m²). This formula, including the specific coefficients (0.7 and 0.1) for FBG and BMI, was originally developed and validated by Xu et al. (17) through multivariate linear regression analysis, aiming to optimize the assessment of insulin resistance in the context of gestational diabetes mellitus. This single metric thereby encapsulates the key dimensions of dyslipidemia, dysglycemia, and adiposity. Initially validated for assessing insulin resistance in gestational diabetes mellitus, TyHGB has subsequently demonstrated superior performance over the TyG index in associating with NAFLD across diverse populations (18, 19). Since all required components are routinely measured in standard clinical practice, TyHGB represents a cost-effective and readily implementable tool.

Given the shared metabolic underpinnings of NAFLD and DKD—particularly regarding IR, dyslipidemia, and chronic inflammation—we hypothesize that TyHGB may similarly serve as a powerful marker for DKD in T2DM patients. However, no investigation to date has examined the relationship between TyHGB and DKD. Therefore, this study aimed to comprehensively evaluate TyHGB’s associative value for DKD in a large T2DM patient cohort, while comparing its performance against the established TyG index. To date, no established clinical cut-off points for TyHGB exist, and its association with DKD remains unexplored.

This community-based investigation establishes a robust and independent relationship between TyHGB—a novel composite metabolic index—and diabetic kidney disease prevalence in adults with type 2 diabetes. The association remained statistically significant following extensive adjustment for demographic, clinical, and biochemical variables. We identified a non-linear dose-response pattern, with DKD risk accelerating substantially beyond a TyHGB threshold of approximately 8.7. However, this threshold should be considered exploratory and requires validation in independent, prospective cohorts. As an independent marker, TyHGB demonstrated enhanced discriminative ability for DKD compared to both the TyG index and serum creatinine, achieving an AUC of 0.775. Incorporation of TyHGB into conventional risk association models significantly improved both discrimination and reclassification accuracy. Collectively, these findings position TyHGB as a practical, integrative metabolic indicator that delivers meaningful incremental prognostic value for DKD risk assessment.

The complex pathogenesis of DKD involves multiple interconnected mechanisms, with insulin resistance, dyslipidemia, chronic inflammation, and oxidative stress representing key pathophysiological contributors (22, 23). The triglyceride-glucose index has emerged as a well-validated IR surrogate that correlates with DKD risk across multiple epidemiological studies (24, 25). Complementary evidence from Jiang et al. confirmed that both TyG and TyG-BMI independently associate with DKD in newly diagnosed type 2 diabetes patients, though with limited diagnostic accuracy (AUC = 0.57) (26). Supporting these observations, Li et al. documented positive correlations between TyG index values and albuminuria severity (27). However, the TyG index predominantly reflects glucose and triglyceride metabolism, potentially overlooking other critical pathways involving anti-inflammatory lipid regulation and adiposity-related mechanisms.

Our results reinforce the conceptual framework that composite indices capturing broader metabolic dysregulation may offer superior associative capability. TyHGB advances beyond conventional TyG metrics by incorporating the TG/HDL-C ratio—an established marker of atherogenic dyslipidemia and IR—together with fasting glucose and BMI measurements. This integrated approach enables TyHGB to represent the complex interplay among dyslipidemia, glucotoxicity, and adiposity. The inclusion of HDL-C is particularly relevant given its documented anti-inflammatory, antioxidant, and endothelial-protective functions, with low levels independently associated with renal functional decline (28, 29). Similarly, BMI serves as an indicator for obesity-mediated renal injury, which operates through glomerular hyperfiltration, leptin resistance, and chronic inflammatory pathways (30). Thus, TyHGB provides a more comprehensive assessment of the multifaceted metabolic disturbances driving DKD progression.

Our restricted cubic spline analysis identified a significant non-linear relationship between TyHGB values and DKD risk. The observed threshold effect at TyHGB = 8.74 suggests a critical transition point in metabolic homeostasis, beyond which renal injury risk increases substantially. This pattern aligns with established concepts of threshold effects in microvascular complication pathogenesis. Previous investigations have documented similar non-linear associations for other metabolic indices, including the U-shaped relationship between TyG index and diabetic retinopathy reported by Zhou et al. (31). Our findings extend this paradigm to the TyHGB-DKD relationship, emphasizing the importance of evaluating non-linearity in metabolic risk assessment.

Stratified analyses provided clinically important insights regarding effect modification. The strengthened TyHGB-DKD association in hypertensive individuals implies that metabolic dysregulation and hemodynamic stress may synergistically accelerate renal damage through multiple complementary pathways in the context of hypertension, thereby enhancing the TyHGB-DKD relationship. Chronic low-grade inflammation and upregulation of pro-inflammatory cytokines (e.g., IL-6, TNF-α) in the comorbidity of hypertension and insulin resistance amplify the glomerular and interstitial inflammatory injury triggered by dysglycemia, dyslipidemia, and obesity (32). Concurrently, oxidative stress and lipid peroxidation can damage the glomerular basement membrane and podocytes, promoting proteinuria and the deterioration of filtration function (33). Furthermore, sympathetic nervous system overactivation and hemodynamic alterations (glomerular hypertension, hyperfiltration) exacerbate these metabolically induced pathological processes through sustained mechanical stress and microcirculatory disturbances (34). These interactions collectively manifest in shared pathways such as RAAS activation, mitochondrial dysfunction, and endothelial impairment, forming a pathogenic network that drives DKD progression. Conversely, the consistent associations observed across sex and BMI categories reinforce TyHGB’s generalizability as a risk marker, demonstrating its utility beyond specific demographic or adiposity subgroups.

TyHGB’s superior associative performance (AUC = 0.775) relative to TyG index (AUC = 0.644) and serum creatinine (AUC = 0.625) underscores its clinical potential. These observations are consistent with emerging research evaluating TyHGB in other contexts, including its excellent associative accuracy for gestational diabetes mellitus (AUC 0.707-0.859) as demonstrated by Xu et al. (17). The conceptual approach of enhancing TyG through additional parameters follows established methodology, exemplified by Er and Li et al.’s development of TyG-BMI as a superior IR marker (35, 36); our work successfully translates and validates this approach specifically for DKD risk assessment.

The incorporation of TyHGB into baseline clinical models produced significant improvement in discriminant capacity (AUC increase from 0.715 to 0.788) and reclassification accuracy (NRI = 0.647; IDI = 0.067). These findings indicate that TyHGB captures unique risk information not fully represented by conventional factors, supporting its potential integration into clinical risk scores and electronic health record-based screening protocols.

Furthermore, our findings proved robust in a sensitivity analysis that included renal function markers in the adjustment model, mitigating concerns about over-adjustment bias. While our study benefits from real-world community data, comprehensive confounder adjustment, and sophisticated non-linearity testing that identified an actionable clinical threshold, several limitations merit consideration. The retrospective single-center design may affect generalizability, though multi-site data sourcing enhances real-world applicability. Potential unmeasured confounders, including dietary patterns, physical activity levels, and specific medications (e.g., SGLT2 inhibitors, GLP-1 receptor agonists), could influence observed relationships. Additionally, DKD diagnosis relied on UACR and eGFR criteria without biopsy confirmation, though this approach aligns with current clinical practice guidelines (37). Future multi-center, prospective, and multi-ethnic studies, with serial TyHGB measurements, are needed to validate our findings and elucidate the causal mechanisms.

The triglyceride high-density cholesterol-glucose body index (TyHGB)—a readily calculable composite of TG/HDL-C ratio, fasting glucose, and BMI—demonstrates independent non-linear association with diabetic kidney disease in type 2 diabetes patients. It significantly enhances risk discrimination beyond conventional factors and established indices. TyHGB’s practical derivation and robust performance support its potential role in DKD screening and stratification protocols, warranting additional prospective validation and exploration within integrated assessment frameworks.