Threshold effect and age interaction of TyG index on diabetes incidence in normolipidemic population: a multicenter cohort study

Background: Although the triglyceride-glucose (TyG) index-diabetes association has been widely studied, its relationship in normolipidemic populations remains poorly understood.

Methods: A retrospective cohort of 60,103 normolipidemic Chinese adults was included from routine health screening programs conducted across 32 healthcare institutions in China. Data collection included demographic characteristics, anthropometric measurements, serum biochemical parameters, smoking and alcohol consumption history, and family history of diabetes. We employed multivariable Cox regression, restricted cubic spline analysis, threshold effect analysis, stratified analysis, and interaction tests to comprehensively assess the association between the TyG index and incident diabetes.

Results: Multivariable-adjusted Cox regression revealed a robust positive association between the TyG index and incident diabetes in normolipidemic subjects (HR: 10.10, 95% CI: 7.94–12.84, P < 0.001). Restricted cubic spline analysis detected a nonlinear relationship, with a critical threshold at TyG ≥ 8.53, beyond which diabetes risk increased exponentially (HR: 51.84, 95% CI: 24.83–108.24, P < 0.001). Despite consistent findings across subgroups, a significant interaction with age was detected (P for interaction < 0.05).

Conclusions: In normolipidemic individuals, the TyG index demonstrated a nonlinear positive association with diabetes risk, particularly above 8.53. It can serve as an early warning signal for diabetes risk in normolipidemic individuals, facilitating personalized prevention strategies for diabetes prevention and control.

Introduction

Diabetes mellitus is a highly prevalent chronic metabolic disorder worldwide. It is projected that by 2040, the global diabetic population will reach 642 million, with 60% of cases occurring in Asia (1, 2). The situation is particularly severe in Southeast Asia, where the mortality rate among its 82 million diabetic patients is as high as 14% (3). Epidemiological data reveal a dual high-prevalence trend in both developed countries and developing countries. China, the country with the largest diabetic population globally, reported an adult prevalence rate of 11.2% in 2020 (4). Beyond dysregulated glucose metabolism, diabetes leads to multisystem complications, including cardiovascular diseases, diabetic nephropathy, and retinopathy, which significantly impair patients’ quality of life, reduce life expectancy, and impose a substantial burden on healthcare systems. In 2021, China ranked second globally in diabetes-related healthcare expenditures, underscoring the urgency of disease prevention and control (5). Notably, diabetes is highly preventable. Studies have demonstrated that lifestyle modifications, such as balanced nutrition, regular physical activity, and early screening interventions, can effectively reduce diabetes risk and slow disease progression (6).

The development of diabetes mellitus results from a complex interplay of multiple pathological factors, among which impaired insulin sensitivity and defective pancreatic β-cell function serve as the central pathogenic mechanisms (7). Additional contributing factors include genetic predisposition, chronic inflammation, oxidative stress, and gut microbiota dysbiosis (8). Notably, insulin resistance typically precedes β-cell dysfunction and persists throughout the disease course, currently recognized as the primary and most critical initiating factor in diabetes pathogenesis. Dyslipidemia represents a well-established risk factor, with hypertriglyceridemia and reduced high-density lipoprotein cholesterol (HDL-C) levels serving as characteristic markers of insulin resistance (9). Mechanistically, elevated triglycerides exacerbate insulin resistance by activating inflammatory signaling pathways and inhibiting insulin receptor substrate phosphorylation (10). While the detrimental effects of dyslipidemia on metabolic and cardiovascular diseases are well-documented, the diabetes risk factors in normolipidemic individuals remain less clearly defined. Even within normal lipid ranges, these individuals may harbor other underlying metabolic disturbances - including insulin resistance, chronic low-grade inflammation, and oxidative stress - that potentially elevate diabetes risk. This highlights the importance of evaluating diabetes susceptibility in normolipidemic populations, particularly regarding insulin resistance assessment. The hyperinsulinemic-euglycemic clamp remains the gold standard for insulin resistance measurement. However, its widespread clinical application is limited by substantial costs, time-consuming procedures, and requirements for specialized equipment and technical expertise, currently restricting its use primarily to research settings (11).

The triglyceride-glucose (TyG) index, calculated from fasting plasma glucose (FPG) and triglyceride (TG) levels, has emerged as a simple, cost-effective, and reliable surrogate marker for insulin resistance (12). Growing evidence has highlighted its research value in metabolic and cardiovascular diseases in recent years (11, 13–16). Notably, multiple studies have demonstrated that the TyG index outperforms established diabetes risk predictors, including the homeostasis model assessment of insulin resistance, oral glucose tolerance test, and triglyceride to high-density lipoprotein cholesterol ratio, in predicting type 2 diabetes mellitus (17–19). Substantial evidence also indicates a strong association between the TyG index and diabetes risk, suggesting its potential as an early warning indicator for diabetes development (20, 21). However, most existing studies have focused on general populations or high-risk individuals with dyslipidemia (22, 23). Whether the TyG index maintains its predictive value for diabetes risk in normolipidemic populations remains unclear and warrants further investigation.

Our study aims to examine the relationship between the TyG index and diabetes incidence in individuals with normal lipid profiles, thereby evaluating its clinical utility in this specific population. The results could support the development of more effective screening and prevention protocols for diabetes in individuals with apparently normal lipid profiles.

Method

Data source

The data for our study were obtained from the Dryad Digital Repository (https://datadryad.org), a specialized platform for medical and health science research data. Dryad database maintains strict adherence to ethical guidelines and privacy protection principles throughout its data storage and sharing processes. The dataset utilized in our study has been fully anonymized, with all personally identifiable information removed to ensure participant confidentiality. As such, this secondary analysis of de-identified data did not require additional informed consent from participants, in accordance with standard research ethics protocols for publicly available, non-identifiable datasets. The open-access nature of these data supports their legitimate use by researchers for secondary analytical purposes.

Study population

In our study, we analyzed medical examination data provided by the Rich Healthcare Group, which included adults aged 20 years and older who underwent a minimum of two health check-ups between 2010 and 2016 at 32 sites across 11 cities in China. The initial exclusion criteria, as outlined by Chen et al. (24), were as follows (1): absence of data on body weight, height, or sex (n = 103,947) (2); absence of FPG data (n = 31,370) (3); extreme body mass index (BMI) values, defined as < 15 kg/m² or > 55 kg/m² (n = 152) (4); individuals with an inter-visit interval of less than 2 years (n = 324,233) (5); a baseline diagnosis of diabetes (n = 7,112) (6); undetermined diabetes status (n = 6,630). Following these exclusions, the initial cohort consisted of 211,833 participants. From this cohort, we further excluded individuals with (1): missing lipid profile data, specifically HDL-C (n = 94,562), low-density lipoprotein cholesterol (LDL-C) (n = 93,421), TG (n = 4,887), and total cholesterol (TC) (n = 4,854) (2); dyslipidemia (n = 56,945); and (3) baseline TG equal to zero (n = 23) or missing follow-up FPG data (n = 3). After implementing all exclusion criteria, our final analytical sample comprised 60,103 participants (Figure 1).

Figure 1

Figure 1. Flowchart outlining the structure of the study.

Missing value handling

The dataset exhibited variable-specific missing data patterns, with missingness proportions as follows: smoking status and alcohol consumption shared identical missing rates (73.47%, n = 44,157 each), followed by aspartate aminotransferase (AST; 56.85%, n = 34,169). Other variables showed minimal missingness: alanine aminotransferase (ALT; 0.40%, n = 242), blood urea nitrogen (BUN; 2.23%, n = 1,343), serum creatinine (Scr; 1.35%, n = 814), and blood pressure measures (systolic and diastolic both 0.01%, n = 6 each). The pattern and proportion of missing data for all variables are presented in Supplementary Table 1.

For continuous variables with more than 50% missing data, such as AST, we employed tertile categorization, designating missing values as “NA”. Similarly, categorical variables with over 50% missingness, including smoking and alcohol status, were assigned “NA”. Continuous variables with less than 5% missingness were subjected to multiple imputation using chained equations (25). This stratified methodology was designed to optimize analytical validity while maintaining the integrity of the dataset, despite the presence of substantial incomplete observations. The primary outcome analyses in our study were conducted using the imputed dataset.

Data collection

The study protocol involved systematic acquisition of demographic characteristics (age, sex), lifestyle parameters (tobacco use, alcohol consumption patterns), and familial diabetes history through standardized questionnaires. Certified personnel conducted anthropometric assessments encompassing height, body mass, and blood pressure measurements. During each clinical assessment, fasting venous blood samples (minimum 10-hour fasting duration) were obtained for comprehensive biochemical profiling, including quantitative analysis of lipid profile (HDL-C, LDL-C, TC, TG), FPG, renal function markers (BUN, Scr), liver enzymes (ALT, AST). The observation period spanned from baseline evaluation to either study termination or incident diabetes diagnosis.

Definition

The TyG is a marker based on FPG and TG levels, used to assess insulin resistance. Its calculation formula is: TyG = ln [FBG (mg/dL) × TG (mg/dL)]

Diabetes was defined as either FPG ≥ 7.0 mmol/L or self-reported diabetes (26).

Prediabetes was defined as FPG between 5.6 and 6.9 mmol/L and no history of diabetes (26).

Hyperlipidemia is defined as an abnormal elevation of blood lipids, including:

TC > 5.2 mmol/L, TG > 1.7 mmol/L, LDL-C > 3.12 mmol/L or HDL-C < 1.03 mmol/L (27).

Normal blood pressure is defined as systolic blood pressure (SBP) < 120 mmHg and diastolic blood pressure (DBP) < 80 mmHg (28).

Statistical analysis

Continuous variables were expressed as mean ± standard deviation or median (interquartile range) based on distributional characteristics, while categorical variables were presented as frequencies (percentages). Inter-group comparisons across TyG index quartiles were performed using ANOVA or Kruskal-Wallis tests for continuous measures and chi-square tests for categorical variables. The standardized mean difference (SMD) was calculated to quantify covariate balance between groups, with an absolute SMD >0.1 indicating potential imbalance.

Survival analyses were conducted using Kaplan-Meier estimators stratified by the TyG quartiles with log-rank tests. Multivariable Cox proportional hazards models were constructed to evaluate independent associations. Confounders were selected based on clinical judgment and previous scientific literature (29, 30). We constructed a total of 4 models for the analysis: No covariates were adjusted for Model 1. Model 2 adjusted for age and sex. Model 3 further accounted for BMI, SBP, DBP, ALT, AST, BUN, and Scr base on model 2. Model 4 designated as the main model, included all previous adjustments and additionally accounted for family history of diabetes, smoking status, and alcohol consumption. Effect estimates were expressed as hazard ratios (HR) with 95% confidence intervals (95% CI).

We categorized TyG into quartiles to assess the trend in its association with the diabetes incidence. This approach allowed us to verify the results obtained when considering TyG as a continuous variable and to explore potential non-linear relationships. To evaluate the robustness of our findings, we conducted sensitivity analyses excluding participants with any history of tobacco use or alcohol consumption. Additionally, we validated the stability of imputed results using the original dataset. Finally, we performed separate analyses among individuals with normoglycemia and those with prediabetes at baseline to evaluate the association between the TyG index and the risk of incident diabetes across different glycemic statuses. Moreover, Subgroup analyses were conducted, evaluating TyG-diabetes associations across clinically relevant partitions (age [< 50/≥ 50 years old] (31), sex, BMI [< 24/≥ 24 kg/m²] (32), blood pressure status, and family history of diabetes). Interaction effects were quantified using multiplicative terms, with statistical significance determined via Wald tests.

To further investigate the relationship between the TyG index and diabetes risk, we utilized a restricted cubic spline (RCS) model to generate smoothed curves that visualize the potential non-linear dose-response association. In this model, TyG was treated as a continuous variable, utilizing four knots at the 5th, 35th, 65th, and 95th percentiles. Threshold effects were evaluated through two-piecewise binary logistic regression model, with the statistical significance of inflection points determined by likelihood ratio tests.

For subgroups exhibiting statistically significant interaction effects, comprehensive assessments of effect modification were conducted. We utilized a dual-stratification analytical framework (1): Age stratification with a cutoff at 50 years old (2); Stratification of the TyG index using thresholds determined through RCS analysis. Utilizing this stratification framework, participants were categorized into four mutually exclusive subgroups for interaction analysis (1): Younger age (< 50 years old) with a low TyG index (below the RCS-derived threshold) (2); Younger age (< 50 years old) with a high TyG index (above the RCS-derived threshold) (3); Older age (≥ 50 years old) with a low TyG index (4); Older age (≥ 50 years old) with a high TyG index. We conducted a systematic evaluation of both multiplicative and additive interactions to thoroughly investigate the relationship between age and the TyG index. Multiplicative interaction refers to whether the combined effect of two factors exceeds the product of their individual effects, whereas additive interaction evaluates whether their joint effect surpasses the sum of their separate effects. To assess multiplicative interaction, we incorporated a product term (age group × TyG category) into multivariable logistic regression models. A statistically significant product term (P < 0.05) signifies effect modification on the multiplicative scale. Additive interaction was assessed utilizing two well-established metrics: the relative excess risk due to interaction (RERI) and the attributable proportion (AP), with statistical significance evaluated through 95% CI. The RERI measures the incremental disease risk attributable to the synergistic interaction between TyG index and diabetes incidence, beyond the anticipated additive effects of their individual contributions. The AP denotes the proportion of disease incidence among individuals exposed to both risk factors that is specifically attributable to their interaction, expressed as a percentage of the overall risk.

Statistical software

All statistical analyses were executed using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and the Free Statistics analysis platform (Version 2.1.1, Beijing, China, http://www.clinicalscientists.cn/freestatistics). Free Statistics is a software package that provides intuitive interfaces for common analyses and data visualization, utilizing R as its underlying statistical engine and employing a graphical user interface developed in Python. A two-sided p-value of less than 0.05 was deemed statistically significant.

Results

Baseline demographic and clinical profiles

As shown in Table 1, the longitudinal cohort (N = 60,103) demonstrated 732 incident diabetes cases during a mean follow-up duration of 3.06 years. Significant gradient patterns across TyG quartiles were observed for all covariates (P < 0.001 for all except family history of diabetes). Progressive elevation of age, BMI, blood pressure parameters, and lipid profiles (TG, TC, LDL-C) accompanied ascending TyG quartiles, while HDL-C exhibited an inverse pattern. The sex distribution shifted from a predominance of females (72.6%) in the first TyG quartile to a predominance of males (63.4%) in the fourth quartile. Additionally, hepatic enzyme levels and the proportions of current smokers and individuals consuming alcohol increased proportionally across TyG quartiles. Notably, the prevalence of diabetes rose from 0.2% in the first TyG quartile to 3.2% in the fourth quartile, indicating a 16-fold increase. Most variables demonstrating statistically significant differences in Table 1 (excluding BUN, smoking or drinking status, and family history of diabetes) exhibited SMD values exceeding 0.1, indicating substantial baseline imbalances across TyG index quartile groups.

Table 1

Table 1. Baseline characteristics of the participants stratified by TyG quartiles.

The association between TyG and diabetes risk

The Kaplan-Meier survival analysis demonstrated a pronounced divergence in diabetes incidence across TyG quartiles, with the highest-risk cohort (quartile 4) exhibiting markedly reduced diabetes-free survival (Log-rank test P < 0.001, Figure 2). As shown in Table 2, multivariable Cox proportional hazards modeling revealed a strong positive association between TyG index elevation and diabetes risk. Each unit increment in TyG corresponded to a 10.10-fold escalation in diabetes hazard (HR: 10.10, 95% CI: 7.94–12.84; P < 0.001), maintaining statistical significance following comprehensive adjustment for clinical covariates (Table 2, Model 4). Notably, quartile-stratified analysis demonstrated a striking gradient effect. Participants in the TyG quartile 4 manifested a 19.76-fold heightened diabetes risk relative to the reference quartile 1 (HR: 19.76, 95% CI: 13.88–28.13; P < 0.001, Table 2, Model 1). This association retained both statistical significance and clinical relevance following multivariable adjustment, with quartile 4 subjects maintaining a 7.98-fold excess risk (HR: 7.98, 95% CI: 5.52–11.52; P < 0.001, Table 2, Model 4).

Figure 2

Figure 2. Kaplan-Meier curves for the probability of diabetes base on TyG quartiles among normolipidemic individuals.

Table 2

Table 2. The association between TyG and the risk of diabetes among individuals with normal lipid metabolism in different models.

Subgroup and sensitivity analyses

As shown in Figure 3, subgroup analyses revealed consistently significant TyG-diabetes associations across all strata (P < 0.001), although the strength of association varied by subgroup. Most notably, age significantly modified this relationship (P for interaction = 0.01), with adults < 50 years old exhibiting markedly higher risk (HR: 11.68, 95%CI: 7.23–18.85) compared to their older counterparts (HR: 8.72, 95%CI: 6.59–11.55). While females showed numerically greater risk estimates than males (13.43 vs. 8.66), this difference did not reach statistical significance (P for interaction = 0.119). A similar non-significant trend was observed in non-overweight participants, who demonstrated marginally stronger associations compared to their overweight counterparts (HR: 11.15 vs. 9.34; P for interaction = 0.079). In contrast, hypertension status showed minimal differential effects (normotensive: 10.46 vs hypertensive: 10.19; P for interaction = 0.280), as did family history of diabetes (positive history: 9.04 vs negative: 10.27; P for interaction = 0.276). After excluding smokers or drinkers, the positive relationship between TyG and diabetes events remained stable. As showed in Supplementary Table 2, the results remained consistent in the analysis of the original data. In participants with baseline prediabetes, the TyG index remained significantly associated with the risk of diabetes. However, the effect strength was even greater in the normoglycemia group, highlighting its particular value in identifying high-risk individuals at a very early stage (HR: 2.99 vs. 4.81).

Figure 3

Figure 3. Forest plot of subgroup analysis of the association between TyG and the risk of diabetes among normolipidemic individuals. The model incorporated adjustments for age, sex, BMI, SBP, DBP, TC, LDL, HDL, ALT, AST, BUN, Scr, smoking status, drinking status, and family history of diabetes, except when family history of diabetes was the stratification variable.

Curve fitting and inflection point analysis

Given the statistically significant variations in the association between the TyG index and diabetes incidence across different age subgroups, we conducted further investigations using curve fitting analysis. Threshold effect analysis was employed to identify the TyG index’s turning points in various age groups. The curve fitting results revealed a pronounced J-shaped relationship between the TyG index and diabetes risk (P for nonlinearity <0.001; Figure 4). As shown in Table 3, a significant association between the TyG index and diabetes risk was observed when values were below 8.53. Strikingly, exceeding this threshold was associated with a dramatic 51.84-fold increase in diabetes risk (HR: 51.84, 95% CI: 24.83–108.24). In younger individuals (< 50 years old), no significant diabetes risk was detected when the TyG index remained below 8.20, but surpassing this level led to a sharp increase in risk (HR: 50.08, 95% CI: 20.91–119.96). In older adults (≥ 50 years old), even TyG levels below the inflection point (8.74) carried a measurable risk (HR: 4.89, 95% CI: 3.41–7.01), which escalated substantially when the threshold was exceeded (HR: 143.92, 95% CI: 29.04–713.27). The likelihood ratio test confirmed that the two-phase regression model provided significantly better fit (P < 0.001).

Figure 4

Figure 4. Analysis of the dose-response relationship between TyG and diabetes risk in people with normal lipid levels. (A) General population (B) Population <50 years old (C) Population ≥ 50 years old. The solid lines represent the multivariate-adjusted hazard ratios, while the dashed lines depict the 95% confidence intervals derived from restricted cubic spline regression. The horizontal dotted line indicates an hazard ratio of 1.0, serving as the reference point. The reference point for the TyG index was set at the median level within each respective population subgroup. The distribution of TyG population levels is depicted in the blue part of the bar chart. Cox regression analyses were adjusted for potential confounders, including age, sex, BMI, SBP, DBP, TC, LDL, HDL, ALT, AST, BUN, Scr, smoking status, and family history of diabetes. Extreme values of TyG (lowest 0.25%) were excluded from the analysis.

Table 3

Table 3. Threshold effect of TyG on the incidence of diabetes among individuals with normal lipid metabolism.

Association between TyG and diabetes events in different age groups

RCS analysis (Figure 4) identified TyG = 8.53 as a statistically significant inflection point in the TyG-diabetes association (P for nonlinearity < 0.001). As showed in Table 4, when implementing this inflection point-derived TyG cutoff (≥ 8.53 vs < 8.53), the multiplicative interaction term reached statistical significance (P for interaction < 0.001). Age-stratified analyses revealed pronounced risk differentials: younger adults (< 50 years old) with TyG ≥ 8.53 had a 5.66-fold increased diabetes risk (HR: 6.66, 95% CI: 4.95–8.96) relative to the reference group, a 24.57-fold risk elevation in older adults (≥ 50 years old) with similarly elevated TyG levels compared to the group of younger adults (< 50 years old) with TyG < 8.53. The additive interaction measures demonstrated substantial effect modification, with a RERI of 11.98 (HR: 11.98, 95% CI: 7.60–16.36) and AP of 0.47 (HR: 0.47, 95% CI:0.38–0.56), suggesting that 47% of the excess diabetes risk resulted from the interaction between TyG and age.

Table 4

Table 4. Multiplicative and additive interactions between different TyG and age groups on the risk of diabetes.

Discussion

Our large-scale retrospective cohort study provides novel evidence regarding the TyG-diabetes association in normolipidemic populations. The analysis revealed a significant nonlinear positive correlation between TyG index and diabetes incidence, with particularly steep risk escalation observed at TyG levels exceeding 8.53 (HR: 51.84, 95%CI: 24.83–108.24). This association remained robust across all examined subgroups stratified by age, sex, BMI, blood pressure status, and family history of diabetes. Notably, we identified a significant positive interaction between advanced age and a high TyG index. The combination of both factors led to a dramatic rise in disease risk (HR = 25.57), an effect that was significantly greater than the additive effect of each individual factor. In assessing diabetes risk, it is essential to integrate both the TyG index and age, particularly regarding individuals aged 50 or above with elevated TyG levels, who should be classified into the highest-risk category and receive intensive intervention strategies.

Previous studies have extensively investigated the TyG-diabetes association but reported inconsistent findings. A systematic review and meta-analysis identified TyG index as a potential predictor for gestational diabetes mellitus in Asian women (33). Zhang et al. demonstrated a linear positive relationship between TyG index and GDM risk among Chinese singleton pregnancies (34). In American populations, studies revealed a nonlinear positive association, with significantly elevated diabetes risk when TyG exceeded 8.00 in men or 9.00 in women (21). Japanese research showed a U-shaped relationship between TyG and diabetes risk in normoglycemic individuals (35).The same conclusion was also found in the non-alcoholic fatty liver population (36). A dose-response meta-analysis of 14 cohort studies found progressively steeper risk escalation when TyG surpassed 8.6 (37), consistent with findings from a 15-year Chinese prospective study (inflection point = 8.51) (38). A study by Shan Yingqi et al. involving the general Chinese population aged 45 and above reported a TyG index inflection point at 8.516. The HR was 1.927 (95% CI: 1.31–2.83) below this threshold and 1.45 (95% CI: 1.21–1.74) above it (39).These findings align with those of Cao et al., who identified a similar inflection point (TyG = 8.73) in their study of the Chinese general population, with an HR of 1.95 (95% CI: 1.86–2.04) before the inflection point and 1.34 (95% CI: 1.27–1.42) thereafter (40).Among overweight/obese individuals (BMI ≥ 24 kg/m²), Sun Yongbing et al. observed a sharp increase in diabetes risk at TyG indices > 4.46, with older women (BMI 24–28 kg/m²) facing disproportionately higher risk at similar TyG levels (41). A study of elderly Chinese adults (aged ≥ 75 years old) also revealed a significant positive association between the TyG index and diabetes risk (42). Most existing studies have primarily examined the association between the TyG index and diabetes risk in general populations or high-risk groups, including individuals with obesity, hypertension, or advanced age. Emerging evidence suggests that even in populations traditionally considered at low risk for diabetes—such as non-obese young adults (aged < 50 years old)—the TyG index demonstrates a significant nonlinear positive association with diabetes incidence, with an inflection point at 7.3 (43). This finding is corroborated by similar research conducted among non-obese elderly populations (aged 40–69 years old) in South Korea, which likewise revealed a positive association (18). Previous studies have also shown that cumulative exposure to TyG index increases the risk of diabetes (44). In conclusion, the TyG index demonstrates significant potential as a predictive biomarker for diabetes risk. However, current evidence reveals considerable heterogeneity across studies regarding the TyG-T2DM association. Future research should focus on validating these findings through standardized methodologies and elucidating the precise mechanisms underlying this relationship.

Our study demonstrates a positive association between the TyG index and diabetes risk in individuals with normal lipid metabolism, as identified in a health examination cohort. The robustness of this association was consistently observed across various subgroups stratified by age, sex, BMI, blood pressure status, and family history of diabetes through comprehensive sensitivity analyses. These findings not only corroborate previous reports of the positive relationship between TyG and diabetes risk in normolipidemic populations, but more importantly, reveal a significant modifying effect of age on this association. Notably, the association between TyG index and diabetes risk demonstrated significant age-dependent heterogeneity, with a markedly stronger effect size in participants younger than 50 years old old (HR: 11.68, 95% CI: 7.23–18.85) compared to older individuals (HR: 8.72, 95% CI: 6.59–11.55). Notably, our threshold analysis identified age-specific optimal cutoff values for TyG index in diabetes risk stratification: 8.20 for individuals under 50 years old and 8.74 for those above 50 years old. In the younger population (< 50 years old old), the TyG risk threshold was lower (8.20), with diabetes risk increasing significantly only when TyG exceeded this level. In contrast, the older group (≥ 50 years old old) exhibited a higher TyG inflection point (8.74). Notably, this older cohort demonstrated measurable diabetes risk even at sub-threshold TyG levels. Interestingly, within the TyG range of 8.20–8.74, younger individuals experienced a more pronounced diabetes risk elevation per unit increase in TyG compared to their older counterparts. The observed age-dependent pattern demonstrates a critical divergence: although aging persists as an independent diabetes risk factor, the relative contribution of insulin resistance (quantified by TyG index) becomes more pronounced in younger individuals (< 50 years old old), indicating potential differences in disease pathophysiology between age cohorts. Traditionally, diabetes was considered a disease predominantly affecting elderly populations. However, accumulating epidemiological evidence indicates that the incidence of type 2 diabetes among young adults has increased several-fold in recent decades (45). The modern lifestyle characterized by excessive consumption of high-calorie diets, physical inactivity, and chronic psychosocial stress from work and social pressures may contribute to this concerning trend, even in individuals with apparently normal lipid profiles. In older adults (≥ 50 years old old), diabetes risk escalates progressively with rising TyG levels, showing measurable risk even below the threshold. However, when TyG exceeds 8.74, the hazard ratio surges dramatically to 143.92 - nearly threefold higher than in younger populations (HR: 50.08). These findings carry important clinical implications. For younger adults, TyG monitoring should focus on the 8.20 threshold for preventive intervention. Older populations require vigilant metabolic surveillance even at sub-threshold TyG levels (8.74), with aggressive management recommended when exceeding this cutoff. The striking magnitude of risk differential (143.92 vs 50.08) underscores the necessity of age-tailored approaches when implementing TyG-based diabetes risk stratification in clinical practice. Further prospective cohort studies with serial measurements are warranted to validate these cutoff values and to elucidate the dynamic relationship between TyG index and diabetes risk progression. Mechanistic studies are also needed to clarify the pathophysiological basis underlying the observed age-dependent differences in the TyG-diabetes association, particularly in younger populations with normal lipid metabolism who are increasingly recognized as an important target group for diabetes prevention.

Although our retrospective cohort study design cannot establish causal relationships between the TyG index and diabetes incidence, several well-established pathophysiological mechanisms may help interpret our findings. Primarily, insulin resistance represents one of the fundamental pathological mechanisms underlying type 2 diabetes development (46). The TyG index serves as a reliable surrogate marker of insulin resistance, reflecting its severity. Chronic insulin resistance induces progressive β-cell dysfunction, ultimately leading to impaired insulin secretion, elevated blood glucose levels, and consequently increased diabetes risk. From a pathophysiological perspective, insulin resistance exerts multifaceted metabolic effects (1): it enhances lipolysis in adipose tissue, resulting in elevated circulating lipid levels (2); it impairs lipoprotein metabolism, delaying triglyceride clearance; and (3) it reduces peripheral glucose uptake and utilization efficiency, collectively contributing to systemic metabolic dysregulation. Notably, the interplay between insulin resistance and metabolic disturbances may exacerbate diabetes risk through activation of inflammatory pathways. This mechanistic link is supported by emerging evidence demonstrating significant association between the TyG index and various inflammatory biomarkers. Notably, a key outcome of compensatory insulin resistance is hyperinsulinemia. Accumulating evidence indicates that hyperinsulinemia is associated with a range of long-term pathological changes, independent of blood glucose levels. These include promoting atherosclerosis and increasing cardiovascular risk, accelerating cellular senescence, enhancing susceptibility to certain cancers, and contributing to neurodegenerative disorders such as Alzheimer’s disease (47–50). Early identification of insulin resistance is vital not only to prevent the onset of type 2 diabetes but also to address the broader spectrum of metabolic and cardiovascular diseases associated with this condition. By recognizing and managing insulin resistance early, healthcare providers can implement targeted interventions to reduce the risk of progression to more severe health issues, thereby improving patient outcomes and reducing the burden of chronic diseases.

Several limitations of this study warrant careful consideration. First, as a retrospective observational cohort study, the identified associations do not imply causality and may be influenced by unmeasured confounding variables. Although we rigorously adjusted for all available potential confounders in our multivariate models, residual confounding remains possible. To assess the robustness of our findings, comprehensive sensitivity analyses were performed; these evaluations consistently confirmed the stability of the primary outcomes. Second, the absence of key glycemic parameters, including postprandial glucose levels and hemoglobin A1c measurements, represents an important limitation. Our diabetes definition relied solely on fasting glucose levels and self-reported diagnoses, which may have introduced ascertainment bias and potentially led to underestimation of both diabetes incidence and effect sizes. Third, the mean follow-up duration of 3.06 years may have limited our study’s statistical power to detect diabetes events. This relatively short observation period could have constrained our ability to fully characterize the TyG-diabetes association. Fourth, the data for our study were derived from a previous investigation, which provided only the processed dataset. Consequently, we were unable to assess the selection bias that may have been introduced during the exclusion of participants from the original cohort (n = 685,277) to arrive at the shared dataset (n = 211,833). Despite this limitation, our study retains significant value, as it offers an in-depth longitudinal analysis of a well-defined, large-scale healthy population—delivering unique insights into the research question. Although restricted by the lack of access to the primary data, we employed internal comparisons to evaluate potential biases within the available data to the greatest extent possible. Sensitivity analyses further demonstrated the robustness of our findings. Additionally, while our study benefited from a large sample size, the generalizability of our findings may be limited to Chinese populations with normal lipid metabolism. Caution should be exercised when extrapolating these results to other demographic groups.

Conclusions

Our 60,103 longitudinal analysis demonstrates that even in individuals with normal lipid metabolism, elevated levels of the TyG index significantly increase the risk of diabetes. This association exhibits a nonlinear dose-response relationship and is significantly age-dependent, with young adults (< 50 years old old) showing a markedly higher sensitivity to the diabetes risk associated with the TyG index compared to older adults. These findings challenge traditional screening approaches, indicating that a normal lipid profile does not rule out the risk of diabetes, particularly in younger individuals. Assessing the TyG index can enhance the detection rate of early-stage diabetes in those with normal metabolic profiles and underscores the importance of age-specific risk stratification. Moreover, the stronger association observed in young adults suggests different pathophysiological mechanisms or varying exposure to modern lifestyle risk factors, necessitating targeted preventive measures for this population.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics statement

The studies involving humans were approved by Rich Healthcare Group Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. Informed consent was exempted as the analysis relied on de-identified data, posing no risk to participant privacy.

Author contributions

WL: Methodology, Project administration, Validation, Supervision, Writing – review & editing, Software, Data curation, Formal analysis, Conceptualization. JW: Writing – review & editing, Methodology, Supervision, Project administration, Conceptualization. HC: Visualization, Conceptualization, Data curation, Methodology, Software, Writing – original draft. QC: Validation, Methodology, Conceptualization, Writing – original draft, Software. LX: Software, Writing – review & editing, Validation. SL: Supervision, Writing – review & editing, Software, Investigation, Validation. XC: Supervision, Data curation, Writing – review & editing, Investigation, Visualization. XH: Supervision, Investigation, Visualization, Writing – review & editing, Data curation.

Funding

The author(s) declare that no financial support was received for the research, and/or publication of this article.

Acknowledgments

We would like to express our sincere gratitude to all individuals who contributed to this study. We extend our appreciation to the Free Statistics team in Beijing, China, for their invaluable technical assistance and for providing practical data analysis and visualization tools.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declare that no Generative AI was used in the creation of this manuscript.

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References

1. Liew JY and Vanoh D. Predictors affecting diabetes related distress among diabetes patients. Malays J Med Sci : MJMS. (2022) 29:94–101. doi: 10.21315/mjms2022.29.2.9

PubMed Abstract | Crossref Full Text | Google Scholar

2. Dong N, Xu B, Shi H, and Lu Y. miR-124 regulates amadori-glycated albumin-induced retinal microglial activation and inflammation by targeting Rac1. Invest Ophthalmol Visual Sci. (2016) 57:2522–32. doi: 10.1167/iovs.15-18224

PubMed Abstract | Crossref Full Text | Google Scholar

3. Ling W, Huang Y, Huang Y-M, Fan R-R, Sui Y, and Zhao H-L. Global trend of diabetes mortality attributed to vascular complications, 2000–2016. Cardiovasc Diabetol. (2020) 19:182. doi: 10.1186/s12933-020-01159-5

PubMed Abstract | Crossref Full Text | Google Scholar

4. Li T, Hu L, Yin X-L, Zou Y, Fu H-Y, and Li H-L. Prevalence and risk factors of osteoporosis in patients with type 2 diabetes mellitus in nanchang (China): a retrospective cohort study. Diabetes Metab Syndr Obes: Targets Ther. (2022) 15:3039–48. doi: 10.2147/DMSO.S372348

PubMed Abstract | Crossref Full Text | Google Scholar

5. Zhao R, Zhao N, Wang S, Zhang X, Ding B, Li Y, et al. Assessment of type 2 diabetes mellitus patients’ behavioral characteristics associated with integrated treatment and prevention services in community health centers in China. Front Public Health. (2023) 10:1084946. doi: 10.3389/fpubh.2022.1084946

PubMed Abstract | Crossref Full Text | Google Scholar

6. Bergman M and Dorcely B. Remission of prediabetes via lifestyle intervention. Lancet Diabetes Endocrinol. (2023) 11:784–5. doi: 10.1016/S2213-8587(23)00258-9

PubMed Abstract | Crossref Full Text | Google Scholar

7. Li Z, Pan X, and Cai Y-D. Identification of type 2 diabetes biomarkers from mixed single-cell sequencing data with feature selection methods. Front Bioeng Biotechnol. (2022) 10:890901. doi: 10.3389/fbioe.2022.890901

PubMed Abstract | Crossref Full Text | Google Scholar

8. Nelios G, Santarmaki V, Pavlatou C, Dimitrellou D, and Kourkoutas Y. New wild-type lacticaseibacillus rhamnosus strains as candidates to manage type 1 diabetes. Microorganisms. (2022) 10:272. doi: 10.3390/microorganisms10020272

PubMed Abstract | Crossref Full Text | Google Scholar

9. Flores-Guerrero JL, Been RA, Shalaurova I, Connelly MA, van Dijk PR, and Dullaart RPF. Triglyceride/HDL cholesterol ratio and lipoprotein insulin resistance score: associations with subclinical atherosclerosis and incident cardiovascular disease. Clin Chim Acta. (2024) 553:117737. doi: 10.1016/j.cca.2023.117737

PubMed Abstract | Crossref Full Text | Google Scholar

10. Suren Garg S, Kushwaha K, Dubey R, and Gupta J. Association between obesity, inflammation and insulin resistance: insights into signaling pathways and therapeutic interventions. Diabetes Res Clin Pract. (2023) 200:110691. doi: 10.1016/j.diabres.2023.110691

PubMed Abstract | Crossref Full Text | Google Scholar

11. Szosland K and Lewiński A. In quest for method of insulin resistance assessment in everyday clinical practice—insulin resistance indices. Diabetes Metab Syndr: Clin Res Rev. (2016) 10:S120–5. doi: 10.1016/j.dsx.2015.10.007

PubMed Abstract | Crossref Full Text | Google Scholar

12. Mohd Nor NS, Lee S, Bacha F, Tfayli H, and Arslanian S. Triglyceride glucose index as a surrogate measure of insulin sensitivity in obese adolescents with normoglycemia, prediabetes, and type 2 diabetes mellitus: comparison with the hyperinsulinemic-euglycemic clamp. Pediatr Diabetes. (2016) 17:458–65. doi: 10.1111/pedi.12303

PubMed Abstract | Crossref Full Text | Google Scholar

13. Cui C, Qi Y, Song J, Shang X, Han T, Han N, et al. Comparison of triglyceride glucose index and modified triglyceride glucose indices in prediction of cardiovascular diseases in middle aged and older chinese adults. Cardiovasc Diabetol. (2024) 23:185. doi: 10.1186/s12933-024-02278-z

PubMed Abstract | Crossref Full Text | Google Scholar

14. Lee JH, Heo S-J, and Kwon Y-J. Sex-specific comparison between triglyceride glucose index and modified triglyceride glucose indices to predict new-onset hypertension in middle-aged and older adults. J Am Heart Assoc. (2023) 12:e030022. doi: 10.1161/JAHA.123.030022

PubMed Abstract | Crossref Full Text | Google Scholar

15. García-Poblet M, Nso-Roca AP, Martínez-Sanz JM, and Sospedra I. Triglyceride-glucose index in adolescents with type 1 diabetes mellitus. Diabetes Res Clin Pract. (2025) 221:112048. doi: 10.1016/j.diabres.2025.112048

PubMed Abstract | Crossref Full Text | Google Scholar

16. Jiang Y and Lai X. Association between the triglyceride glucose index, triglyceride-glucose body mass index and diabetic kidney disease in adults with newly diagnosed type 2 diabetes. Front Med. (2024) 11:1328601. doi: 10.3389/fmed.2024.1328601

PubMed Abstract | Crossref Full Text | Google Scholar

17. Park HM, Lee HS, Lee Y-J, and Lee J-H. The triglyceride-glucose index is a more powerful surrogate marker for predicting the prevalence and incidence of type 2 diabetes mellitus than the homeostatic model assessment of insulin resistance. Diabetes Res Clin Pract. (2021) 180:109042. doi: 10.1016/j.diabres.2021.109042

PubMed Abstract | Crossref Full Text | Google Scholar

18. Park B, Lee HS, and Lee Y-J. Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the korean genome and epidemiology study cohort. Transl Res. (2021) 228:42–51. doi: 10.1016/j.trsl.2020.08.003

PubMed Abstract | Crossref Full Text | Google Scholar

19. Lee MJ, Bae JH, Khang AR, Yi D, Yun MS, and Kang YH. Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers. Diabetes Res Clin Pract. (2024) 210:111640. doi: 10.1016/j.diabres.2024.111640

PubMed Abstract | Crossref Full Text | Google Scholar

20. Qiao Q, Liang K, Wang C, Wang L, Yan F, Chen L, and Hou X. J-shaped association of the triglyceride glucose-body mass index with new-onset diabetes. Sci Rep. (2024) 14:13882. doi: 10.1038/s41598-024-64784-0

PubMed Abstract | Crossref Full Text | Google Scholar

21. Zhang L and Zeng L. Non-linear association of triglyceride-glucose index with prevalence of prediabetes and diabetes: a cross-sectional study. Front Endocrinol. (2023) 14:1295641. doi: 10.3389/fendo.2023.1295641

PubMed Abstract | Crossref Full Text | Google Scholar

22. He L, Zheng W, Li Z, Chen L, Kong W, and Zeng T. J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies. J Transl Med. (2023) 21:175. doi: 10.1186/s12967-023-04006-9

PubMed Abstract | Crossref Full Text | Google Scholar

23. Chen G-Y, Li L, Dai F, Li X-J, Xu X-X, and Fan J-G. Prevalence of and risk factors for type 2 diabetes mellitus in hyperlipidemia in China. Med Sci Monitor: Int Med J Exp Clin Res. (2015) 21:2476–84. doi: 10.12659/MSM.894246

PubMed Abstract | Crossref Full Text | Google Scholar

24. Chen Y, Zhang X-P, Yuan J, Cai B, Wang X-L, Wu X-L, et al. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open. (2018) 8:e021768. doi: 10.1136/bmjopen-2018-021768

PubMed Abstract | Crossref Full Text | Google Scholar

25. Harel O, Mitchell EM, Perkins NJ, Cole SR, Tchetgen Tchetgen EJ, Sun B, et al. Multiple imputation for incomplete data in epidemiologic studies. Am J Epidemiol. (2018) 187:576–84. doi: 10.1093/aje/kwx349

PubMed Abstract | Crossref Full Text | Google Scholar

26. American Diabetes Association Professional Practice Committee. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care. (2022) 45:S17–38. doi: 10.2337/dc22-S002

PubMed Abstract | Crossref Full Text | Google Scholar

27. Zhu W, Shi P, Fu J, Liang A, Zheng T, Wu X, et al. Development and application of a novel model to predict the risk of non-alcoholic fatty liver disease among lean pre-diabetics with normal blood lipid levels. Lipids Health Dis. (2022) 21:149. doi: 10.1186/s12944-022-01752-5

PubMed Abstract | Crossref Full Text | Google Scholar

28. Liu Y, Zhang J-H, Gao X-B, Wu X-J, Yu J, Chen J-F, et al. Correlation between blood pressure changes and AMS, sleeping quality and exercise upon high-altitude exposure in young chinese men. Mil Med Res. (2014) 1:19. doi: 10.1186/2054-9369-1-19

PubMed Abstract | Crossref Full Text | Google Scholar

29. Lu S, Huang X, Chen Y, Lin Y, Zou J, Chen Y, et al. The de ritis ratio mediates the association between creatinine-to-body weight ratio and normoglycemic conversion in chinese health examinees. Sci Rep. (2025) 15:5994. doi: 10.1038/s41598-025-90501-6

PubMed Abstract | Crossref Full Text | Google Scholar

30. Chen X, Liu D, He W, Hu H, and Wang W. Predictive performance of triglyceride glucose index (TyG index) to identify glucose status conversion: a 5-year longitudinal cohort study in Chinese pre-diabetes people. J Transl Med. (2023) 21:624. doi: 10.1186/s12967-023-04402-1

PubMed Abstract | Crossref Full Text | Google Scholar

31. Tura A, Pacini G, Moro E, Vrbíková J, Bendlová B, and Kautzky-Willer A. Sex- and age-related differences of metabolic parameters in impaired glucose metabolism and type 2 diabetes compared to normal glucose tolerance. Diabetes Res Clin Pract. (2018) 146:67–75. doi: 10.1016/j.diabres.2018.09.019

PubMed Abstract | Crossref Full Text | Google Scholar

32. He W, Li Q, Yang M, Jiao J, Ma X, Zhou Y, et al. Lower BMI cutoffs to define overweight and obesity in China. Obes (silver Spring Md). (2015) 23:684–91. doi: 10.1002/oby.20995

PubMed Abstract | Crossref Full Text | Google Scholar

33. Song T, Su G, Chi Y, Wu T, Xu Y, and Chen C. Triglyceride–glucose index predicts the risk of gestational diabetes mellitus: a systematic review and meta-analysis. Gynecol Endocrinol. (2022) 38:10–5. doi: 10.1080/09513590.2021.1940932

PubMed Abstract | Crossref Full Text | Google Scholar

34. Zhang J, Fang X, Song Z, Guo X, Lin D, Jiang F, et al. Positive association of triglyceride glucose index and gestational diabetes mellitus: a retrospective cohort study. Front Endocrinol (Lausanne). (2025) 15:1475212. doi: 10.3389/fendo.2024.1475212

PubMed Abstract | Crossref Full Text | Google Scholar

35. Xuan X, Hamaguchi M, Cao Q, Okamura T, Hashimoto Y, Obora A, et al. U-shaped association between the triglyceride-glucose index and the risk of incident diabetes in people with normal glycemic level: a population-base longitudinal cohort study. Clin Nutr (edinb Scotl). (2021) 40:1555–61. doi: 10.1016/j.clnu.2021.02.037

PubMed Abstract | Crossref Full Text | Google Scholar

36. Liang X, Lai K, Li X, Li Y, Xing Z, and Gui S. Non-linear relationship between triglyceride glucose index and new-onset diabetes among individuals with non-alcoholic fatty liver disease: a cohort study. Lipids Health Dis. (2025) 24:94. doi: 10.1186/s12944-025-02518-5

PubMed Abstract | Crossref Full Text | Google Scholar

37. Pranata R, Huang I, Irvan N, Lim MA, and Vania R. The association between triglyceride-glucose index and the incidence of type 2 diabetes mellitus-a systematic review and dose-response meta-analysis of cohort studies. Endocrine. (2021) 74:254–62. doi: 10.1007/s12020-021-02780-4

PubMed Abstract | Crossref Full Text | Google Scholar

38. Wang Z, Zhao L, and He S. Triglyceride-glucose index as predictor for future type 2 diabetes mellitus in a chinese population in southwest China: a 15-year prospective study. Endocrine. (2021) 72:124–31. doi: 10.1007/s12020-020-02589-7

PubMed Abstract | Crossref Full Text | Google Scholar

39. Shan Y, Liu Q, and Gao T. Triglyceride-glucose index in predicting the risk of new-onset diabetes in the general population aged 45 years and older: a national prospective cohort study. BMC Endocr Disord. (2025) 25:25. doi: 10.1186/s12902-025-01848-w

PubMed Abstract | Crossref Full Text | Google Scholar

40. Cao C, Hu H, Xiao P, Zan Y, Chang X, Han Y, et al. Nonlinear relationship between triglyceride-glucose index and the risk of prediabetes and diabetes: a secondary retrospective cohort study. Front Endocrinol. (2024) 15:1416634. doi: 10.3389/fendo.2024.1416634

PubMed Abstract | Crossref Full Text | Google Scholar

41. Gong R, Liu Y, Luo G, Liu W, Jin Z, Xu Z, et al. Associations of TG/HDL ratio with the risk of prediabetes and diabetes in chinese adults: A chinese population cohort study based on open data. Int J Endocrinol. (2021) 2021:9949579. doi: 10.1155/2021/9949579

PubMed Abstract | Crossref Full Text | Google Scholar

42. Fu X, Liu H, Liu J, Li N, Li L, Ke D, et al. Association between triglyceride–glucose index and the risk of type 2 diabetes mellitus in an older chinese population aged over 75 years. Front Public Health. (2022) 9:796663. doi: 10.3389/fpubh.2021.796663

PubMed Abstract | Crossref Full Text | Google Scholar

43. Han J, Dai W, Chen L, Huang Z, Li C, and Wang K. Elevated triglyceride-glucose index associated with increased risk of diabetes in non-obese young adults: a longitudinal retrospective cohort study from multiple asian countries. Front Endocrinol. (2024) 15:1427207. doi: 10.3389/fendo.2024.1427207

PubMed Abstract | Crossref Full Text | Google Scholar

44. Wang Y, Liu L, Yang P, Li Y, Zhou Y, Yang S, et al. Associations of triglyceride-glucose index cumulative exposure and variability with the transitions from normoglycaemia to prediabetes and prediabetes to diabetes: insights from a cohort study. Diabetes Res Clin Pract. (2024) 217:111867. doi: 10.1016/j.diabres.2024.111867

PubMed Abstract | Crossref Full Text | Google Scholar

45. The Lancet N. Type 2 diabetes: the urgent need to protect young people. Lancet (lond Engl). (2018) 392:2325. doi: 10.1016/S0140-6736(18)33015-0

PubMed Abstract | Crossref Full Text | Google Scholar

46. Wang T, Lu J, Shi L, Chen G, Xu M, Xu Y, et al. Association of insulin resistance and β-cell dysfunction with incident diabetes among adults in China: a nationwide, population-based, prospective cohort study. Lancet Diabetes Endocrinol. (2020) 8:115–24. doi: 10.1016/S2213-8587(19)30425-5

PubMed Abstract | Crossref Full Text | Google Scholar

47. Yang W, Cai X, Hu J, Wen W, Mulalibieke H, Yao X, et al. The metabolic score for insulin resistance (METS-IR) predicts cardiovascular disease and its subtypes in patients with hypertension and obstructive sleep apnea. Clin Epidemiol. (2023) 15:177–89. doi: 10.2147/CLEP.S395938

PubMed Abstract | Crossref Full Text | Google Scholar

48. Bareja A, Lee DE, and White JP. Maximizing longevity and healthspan: multiple approaches all converging on autophagy. Front Cell Dev Biol. (2019) 7:183. doi: 10.3389/fcell.2019.00183

PubMed Abstract | Crossref Full Text | Google Scholar

49. Zwezdaryk K, Sullivan D, and Saifudeen Z. The p53/adipose-tissue/cancer nexus. Front Endocrinol (Lausanne). (2018) 9:457. doi: 10.3389/fendo.2018.00457

PubMed Abstract | Crossref Full Text | Google Scholar

50. Barbagallo M and Dominguez LJ. Type 2 diabetes mellitus and alzheimer’s disease. World J Diabetes. (2014) 5:889–93. doi: 10.4239/wjd.v5.i6.889

PubMed Abstract | Crossref Full Text | Google Scholar