Globally, Type 2 diabetes mellitus (T2DM) presents a significant healthcare burden, with its prevalence escalating due to rising obesity rates, widespread sedentary behaviors, and the demographic shift toward older populations (1). The International Diabetes Federation estimated that nearly 536.6 million adults aged 20-79 (10.5% prevalence) had diabetes in 2021, a figure projected to reach 783.2 million (12.2%) by 2045 (2). Therefore, it is imperative to develop early identification tools for high-risk individuals and adopt preventive strategies. However, conventional biomarkers such as fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c), have poor sensitivity in detecting early metabolic dysregulation and do not capture the multifaceted biological processes driving disease progression. Evidence indicates that initiating lifestyle modifications early can significantly mitigate or delay T2DM onset among high-risk individuals. Furthermore, early-stage screening alongside risk stratification supports these preventive measures (3). Thus, establishing simple and integrated screening tools will improve the identification of at-risk individuals to improve clinical decisions and public health policies, which is essential to reducing diabetes incidence and improving population health outcomes.

Insulin resistance (IR), which is a hallmark of T2DM characterized by impaired insulin signaling in peripheral tissues and dysregulated hepatic glucose production, is considered the key early risk prediction. This metabolic dysfunction not only precedes hyperglycemia but is also accompanied with lipid abnormalities, chronic inflammation, and oxidative stress, creating a complex network of interrelated mechanisms driving disease progression (4). Despite being the gold standard for quantifying IR, the hyperinsulinemic-euglycemic clamp (HEC) is constrained by its invasive procedure, high cost, and operational complexity, making it impractical for large-scale population screening (5). Similarly, indirect methods, such as the homeostasis model assessment of Insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) are limited by factors including lack of standardization and variability in insulin assays, which calls for the development of simpler, cost-effective alternative markers based on routinely measured biomarkers (6).

To address this gap, composite indices leveraging routine metabolic parameters have been formulated. The triglyceride-glucose (TyG) index, combining fasting triglycerides (TG) and glucose, can help to estimate peripheral IR but does not overlook cholesterol metabolism (7). The atherogenic index of plasma (AIP), derived from the TG-to-high density lipoprotein cholesterol (HDL-C) ratio, reflects atherogenic dyslipidemia, however, it does not consider direct glycemic measures (8). The metabolic score for insulin resistance (METS-IR) integrates body mass index (BMI), FPG, TG, and HDL-C which helps to link adiposity with lipid-glucose metabolism. Anthropometric measures like BMI and waist circumference are well-established, readily accessible indicators of adiposity and correlate strongly with insulin resistance and diabetes risk (9). However, its reliance on BMI can potentially mask the individual contributions of visceral fat (10). Although these indices have shown promising clinical utility, they often focus on isolated pathways—glucose-lipid interplay (TyG), atherogenic lipids (AIP), or adiposity-biased metrics (METS-IR), failing to reflect the synergistic interplay among various metabolic disruptions (e.g., cholesterol dysregulation, oxidative stress) which play important roles in the pathogenesis of T2DM. This shortcoming underscores the necessity for more comprehensive metrics that incorporate multiple metabolic parameters, thereby enhancing risk prognostication and early preventive approaches.

A recently developed composite biomarker, the Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index, has been introduced as a promising diagnostic aid for T2DM, integrating key metabolic parameters into a single, potentially more indicative metric (11). A cross-sectional population study in Iran showed that this index performed better compared with the TyG index. However, in this study, the researchers enrolled individuals aged 35–65 years, did not compare emerging indices like the AIP and METS-IR, and lacked longitudinal data to establish causal relationships. To address these gaps, we leveraged longitudinal data from the NAGALA cohort to evaluate the CHG’s performance compared with the TyG, AIP, and METS-IR in predicting the incidence of T2DM (12). Participants from diverse age ranges were enrolled and several confounders such as liver function and obesity-related variables were adjusted for, which allowed us to validate CHG as a robust biomarker for T2DM risk prediction while investigating its ability to capture multifactorial metabolic dysregulation, including lipid-glucose interplay and systemic inflammation.

This longitudinal cohort study found that the novel CHG index showed better prediction performed on the incidence of T2DM relative to established indices such as the TyG, AIP, and METS-IR. Specifically, following adjustment for confounders such as obesity, liver function, and glycemic markers, CHG exhibited a 6.18-fold increased risk per unit elevation (95% CI: 3.38–11.27), significantly outperforming the TyG (HR = 1.39), AIP (HR = 1.80), and METS-IR (HR = 1.07). Sensitivity analyses indicated that these findings were robust, with CHG displaying a strong discriminative power during a 12-year follow-up period (time-dependent AUC = 0.74–0.80), with a sensitivity of 79.6% and specificity of 66.1% in comparison to other indices. These findings are consistent with prior evidence that integrated metabolic indices more effectively captures the multifactorial T2DM pathophysiology than single-domain biomarkers. Importantly, given that CHG is cost-effective and is based on routinely measured parameters, it provides an ideal tools for early screening and intervention in resource-limited settings, addressing the critical gap in global T2DM management.

Several studies have validated the utility of TyG, AIP, and METS-IR in the evaluating IR and metabolic dysregulation. The TyG index, which integrates lipid and glucose metabolism parameters, can accurately identify IR across diverse populations and is correlated with traditional markers like HOMA-IR in conditions such as gallstone disease (15, 16). The AIP is an indicator of atherogenic dyslipidemia and cardiovascular risk as it combines lipid profile abnormalities. METS-IR, which incorporates adiposity and metabolic parameters, has been shown to efficiently predict all-cause and cardiovascular mortality compared to other indices, particularly in non-elderly populations, and displays robust diagnostic utility in non-diabetic hypothyroidism (17, 18). In metabolic syndrome, these indices—especially TyG variants like TyG-WHtR—are significantly associated with hepatic steatosis and fibrosis risk (19). Together, these markers offer broad clinical utility, providing insights into IR, cardiovascular risk, and metabolic dysfunction in various populations without the need for direct insulin measurement.

Prior investigations have shown that TyG, AIP, and METS-IR are effective predictors of T2DM. For instance, meta-analyses and cohort studies have highlighted that TyG has high predictive value, with Qin et al. reporting a HR of 2.766 per TyG unit increase (20, 21). In addition, nonlinear associations were observed in some populations, where thresholds like TyG > 8.6 marked significant risk escalation (22). Similarly, AIP exhibited dose-dependent T2DM risks, with some meta-analyses indicating elevated AIP in T2DM patients (SMD = 1.78 vs. TG: 0.93) and longitudinal studies revealing J-shaped risk curves (threshold AIP ≥ -0.04, HR = 3.33) (23–25). The performance of METS-IR, that comprises lipids, glucose, and anthropometrics, varies across populations. Zhang et al. reported an 80% higher T2DM risk per standard deviation (SD) increase in METS-IR in rural Chinese adults (26), while Hou et al. obtained a 7% increase in the risk per unit in U.S. cohorts (27). However, Li et al. reported that METS-IR showed poorer performance compared with TyG in normoglycemic individuals (28), and Cheng et al. concluded that it had better value relative to the use of FBG alone, particularly in normoglycemic high-risk subgroups (29). These discrepancies reveal the context-dependent utility which varies depending on the population characteristics (age, ethnicity, metabolic profiles) and fluctuations in methodologies for measuring lipid-glucose interplay.

Compared to TyG, AIP, and METS-IR, the novel CHG index indicated better discrimination accuracy (AUC = 0.783 vs. TyG’s 0.751, AIP’s 0.745) and robust adjusted HR (6.18 vs. TyG’s 2.39, AIP’s 2.50), particularly in cohorts with hepatic comorbidities. Although the TyG primarily targets triglyceride-glucose dynamics and AIP on atherogenic lipids, CHG integrates TC, HDL-C, and FBG, integrating broad metabolic disturbances (e.g., cholesterol-driven IR), which are often overlooked by conventional indices. The CHG cohort, unlike previous cross-sectional studies (23, 27), benefits from longitudinal data with a 6.3-year follow-up and rigorous adjustment for confounders, including blood pressure and lipid profiles. Pan et al., for instance, identified TyG thresholds for risk stratification (22), CHG provides a continuous, holistic biomarker harmonizing lipid-glucose-cholesterol axes, making it widely applicable in multifactorial T2DM risk stratification in primary care settings. This integration improves early identification of high-risk individuals, even in normotensive or normoglycemic subgroups, suggesting that CHG is a clinically actionable tool for precision prevention.

Notably, the overall discriminative accuracy of CHG (AUC = 0.783) was closely matched by METS-IR (AUC = 0.780), underscoring that both are highly accurate predictive tools. It is important to note that while CHG and METS-IR showed comparable overall discriminative capacity, their clinical implications may differ. The significantly higher HR associated with CHG after full adjustment, coupled with its robust performance in sensitivity analyses excluding individuals with obesity (BMI ≥ 25 kg/m²), suggests that CHG may capture diabetes risk pathways that are less dependent on overall adiposity as reflected by BMI. This could make CHG particularly useful for identifying high-risk individuals within normal BMI ranges who might be overlooked by BMI-inclusive indices like METS-IR. Nevertheless, the high AUC of METS-IR reaffirms its value as a powerful composite metric, especially in settings where BMI is a routine and central component of metabolic assessment. It should be emphasized that the AUC values among the four parameters (CHG, TyG, AIP, and METS-IR) were notably similar, further supporting the utility of composite indices in T2DM risk stratification.

The clinical relevance of CHG extends beyond predicting T2DM onset, as evidenced by its association with diabetes-related complications. In a cross-sectional study of patients with long-standing T2DM, Çatak et al. (30) reported that the CHG index was significantly elevated in both diabetic retinopathy and nephropathy, and was the only index independently predictive of nephropathy—outperforming TyG, VAI, and LAP. This suggests that CHG may better capture the integrated metabolic stress underlying microvascular injury. Similarly, Mo et al. (31), in a large longitudinal cohort, found that the CHG index exhibited a linear dose-response relationship with cardiovascular disease risk, demonstrating predictive capability comparable to that of the TyG index. These findings collectively highlight the utility of CHG not only in predicting T2DM onset but also in stratifying risk for its microvascular and macrovascular sequelae. Given that a key goal of diabetes screening is the prevention of complications, the ability of CHG to reflect multiple pathophysiological pathways—glucose dysregulation, cholesterol metabolism, and HDL-related anti-inflammatory and antioxidant functions—supports its clinical relevance as a holistic biomarker for comprehensive risk assessment.

The high predictive power of CHG is based on its integration of multifaceted pathways that link IR, chronic inflammation, and oxidative stress. Dyslipidemia, a condition characterized by elevated TC and reduced HDL-C, exacerbates IR via inducing lipid metabolic dysfunction, macrophage-driven adipose inflammation, and proinflammatory cytokine release (e.g., TNF-α, IL-6), disrupting insulin signaling pathways (32, 33). Low HDL-C simultaneously exacerbates oxidative and endoplasmic reticulum (ER) stress, further impairing insulin sensitivity (34, 35). Research suggests that the reduced antioxidant capacity of HDL-C in T2DM contributes to increased oxidative stress, while lipid imbalances intensify ER stress, a key factor linked to IR (33, 36). Hyperglycemia tends to perpetuate this cycle by inducing oxidative stress, damaging pancreatic β-cell function, and worsening insulin secretion and action (37). Therefore, the CHG’s potential to capture these interlinked mechanisms, lipid dysregulation, inflammatory cascades, and oxidative damage, suggests that it is a robust predictor of T2DM progression.

Beyond its predictive superiority, the CHG index holds significant translational potential for real-world T2DM prevention strategies. Its derivation from routinely measured biomarkers (TC, HDL-C, FBG) enables seamless integration into existing clinical workflows without additional costs or specialized equipment—a critical advantage for resource-constrained settings. This accessibility could facilitate widespread implementation in primary care screenings, particularly for identifying high-risk individuals with normal BMI but adverse lipid-glucose profiles who are often overlooked by conventional adiposity-centric tools (e.g., METS-IR). Furthermore, CHG’s linear dose-response relationship supports straightforward risk stratification using clinically actionable thresholds (e.g., CHG > 5.24), empowering clinicians to initiate timely interventions such as lifestyle modification or HDL-C-targeted therapies.

This study has major advantages. First, it provides longitudinal evidence regarding the CHG’s temporal relationship with T2DM onset, which overcomes the inherent limitations of cross-sectional studies in establishing causality. Therefore, we utilized a robust analytical framework—integrating survival analysis, sensitivity testing, and comparative benchmarking—to validate the CHG’s diagnostic superiority over conventional indices (TyG, AIP, METS-IR) in both discriminative accuracy and clinical interpretability. Furthermore, comprehensive adjustment for various metabolic confounders (e.g., hepatic dysfunction, adiposity-related variables) and advanced statistical modeling were adopted to clarify the linear risk gradients, ensuring reproducibility across heterogeneous populations. Finally, the translational potential of CHG is enhanced by its operational simplicity, which leverages routinely measured biomarkers to facilitate scalable risk stratification in resource-limited settings. Moreover, its potential to inform early preventive interventions is consistent with the global need for precise public health interventions.

Nevertheless, interpretation of the results should be tempered by three key study limitations. Notably, as our cohort comprised Japanese adults, the generalizability of our findings to other ethnic populations (particularly those with different average BMI profiles and body composition, such as Latino or African groups) may be limited, and future studies are needed to validate the CHG index in these specific populations. Furthermore, the study’s methodological limitations include the use of HbA1c and FBG without OGTT. This approach likely leads to a systematic underestimation of T2DM incidence by failing to identify a non-negligible subgroup of individuals with isolated postprandial hyperglycemia, who are often characterized by early-phase insulin secretion defects and peripheral insulin resistance (38, 39). Consequently, the reported predictive performance of the CHG index, which incorporates FBG, may be most robust for identifying T2DM cases accompanied by fasting hyperglycemia and might not fully capture its utility in the broader spectrum of dysglycemia. Therefore, further studies are warranted to validate and potentially recalibrate its predictive power across the full range of glucose metabolic disorders. Moreover, the absence of gold-standard IR assessments, such as HEC, limits the ability to mechanistically validate CHG’s association with peripheral/hepatic insulin signaling pathways. Moreover, unmeasured confounders—such as dietary macronutrient patterns, gut microbiota dysbiosis, and epigenetic regulators—may introduce residual bias, while static CHG measurements do not capture dynamic risk trajectories associated with longitudinal metabolic fluctuations. To address these gaps, future research should prioritize: (1) conduct multicenter validation across global populations to establish ethnicity-specific risk thresholds; (2) integrate multi-omics profiling (metabolomics, proteomics) to explore the CHG-associated molecular networks; (3) perform longitudinal monitoring of CHG dynamics to refine time-dependent risk algorithms; and (4) develop AI-driven models combining CHG with radiomic features (hepatic fat quantification) and digital health metrics (continuous glucose monitoring) for precision prediction. Such investigations will solidify the CHG’s role in equitable, evidence-based diabetes prevention.

In this study, we found that the novel CHG index showed better performance in predicting the incidence of T2DM compared to traditional metabolic indices. The index integrates cholesterol homeostasis (TC, HDL-C) and FPG, and CHG effectively captures the multifactorial pathophysiology, including aspects, such as IR and lipid-inflammatory crosstalk. Clinically, CHG offers a cost-effective, practical tool that uses the measured parameters, enabling early identification of high-risk individuals—even those with normal BMI but subclinical metabolic disturbances. Its comprehensive approach facilitates the development of targeted interventions, including HDL-C modulation and cholesterol-lowering strategies, aimed at interrupting disease progression in prediabetic populations. CHG harmonizes the key metabolic pathways, addressing T2DM complexity unlike conventional single-domain biomarkers, thereby enhancing disease prevention.