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U-shaped relationship of estimated glucose disposal rate with cardiovascular disease risk in cardiovascular-kidney-metabolic syndrome stages 0–3: a population-based prospective study

Diabetology & Metabolic Syndrome volume 17, Article number: 85 (2025) Cite this article

Abstract

Background

Cardiovascular-Kidney-Metabolic (CKM) syndrome is characterized by the interrelatedness of chronic kidney disease (CKD), cardiovascular disease (CVD), and metabolic disorders. The relationship between estimated glucose disposal rate (eGDR) and CVD risk in CKM syndrome remains unclear.

Methods

We analyzed data from 7,849 participants aged ≥ 45 years in the China Health and Retirement Longitudinal Study (CHARLS). The eGDR was calculated using waist circumference, hypertension, and HbA1c. Cox regression and restricted cubic spline (RCS) regression analyses examined the association between eGDR and CVD (stroke or cardiac events).

Results

During a mean follow-up of 8.29 ± 1.67 years, among 7,849 participants (mean age 62.4 ± 8.7 years; 52.82% male), 1,946 CVD events occurred, including 1,504 cardiac events and 663 strokes. CKM stages 0–3 comprised 492 (6.27%), 1,404 (17.89%), 5,462 (69.59%), and 491 (6.26%) of participants, respectively. A U-shaped relationship between eGDR and CVD risk was identified (turning point: 11.82 mg/kg/min). Below this turning point, each unit increase in eGDR decreased CVD risk by 12% (HR: 0.88, 95% CI: 0.86–0.90, P < 0.0001); above it, each unit increase raised the risk by 19% (HR: 1.19, 95% CI: 1.04–1.37, P = 0.0135).

Conclusion

Our findings reveal a U-shaped relationship between eGDR and CVD risk in CKM syndrome stages 0–3. A higher or lower eGDR was associated with an increased CVD risk.

Introduction

In October 2023, the American Heart Association (AHA) coined the term Cardiovascular-Kidney-Metabolic (CKM) syndrome to describe an emerging health concern characterized by the interrelatedness of chronic kidney disease (CKD), cardiovascular disease (CVD), and metabolic disorders such as diabetes and obesity [1, 2]. CVD, metabolic disorders, and renal diseases affect 9-11%, 13%, and 15% of the population in the United States, respectively [3, 4]. Furthermore, 33–40% of adults suffer from CKM syndrome [1, 2, 5]. The classification of CKM syndrome ranging from stage 0 (absence of risk factors) to stage 4 (clinical CVD)—provides a framework for understanding the progression of the syndrome and its implications for patient management [1, 2, 6]. CKM syndrome can harm all organs, however, it is most commonly associated with CVD [1, 2]. Diabetes affects 20% of patients with heart failure, and it raises the chance of developing CVD by 2–4 times [7, 8]. CKD affects around 50% of people with heart failure [9]. The AHA prioritizes preclinical testing in the CKM staging paradigm, emphasizing the necessity of preventing CVD events in stages 0 to 3 [1, 2]. To avoid progression from stages 0–3 of CKM syndrome, it is crucial to address renal, cardiovascular, and metabolic components as a unified system [9,10,11], as evidence suggests that CVD is the leading cause of clinical burden [12].

Recent studies have further highlighted the complex interplay between metabolic parameters and cardiovascular outcomes [13, 14]. Based on waist circumference (WC), hypertension, and hemoglobin A1c (HbA1c), the estimated glucose disposal rate (eGDR) was developed and shown to be a reliable indicator of insulin resistance [15, 16]. The eGDR is perfect for evaluating insulin resistance in a broad patient population since it has been demonstrated to be more accurate than the euglycemic hyperinsulinemic clamp gold standard technique [16, 17]. Several previous studies found that eGDR was significantly associated with CVD in the general population [18], CVD under circadian rhythm and different metabolic states [19], CVD among non-diabetic individuals [20], mortality and risk of stroke in diabetic people [21], stroke in the general population [22]. However, the relationship between the eGDR and CVD in CKM syndrome patients is unknown.

Given the significance of CKM syndrome in the development of CVD, we aimed to evaluate the association between eGDR and risk of CVD (stroke or cardiac events) in individuals with CKM syndrome stages 0–3 based on the data from the China Health and Retirement Longitudinal Study (CHARLS).

Methods

Data source and study population

The CHARLS provided the data used in this investigation. Chinese individuals 45 years of age and older are the focus of the nationwide cohort research CHARLS, which conducted surveys periodically between 2011 and 2020 [23]. Using a multi-stage stratified probability-proportional-to-size sampling technique, this study recruited individuals from 28 provinces and 150 counties or districts, both urban and rural [23]. Previous publications have thoroughly described the design and cohort characteristics. The CHARLS study was approved by the Peking University Institutional Review Board (IRB00001052-11015) and complied with the Declaration of Helsinki guidelines. Before participating in the CHARLS study, each participant gave written informed consent. All fieldwork personnel in the CHARLS project underwent professional, methodical training and used standardized questionnaires to conduct in-person interviews. Participants questioned in 2011 and 2012 served as the baseline for this study, and they were subsequently contacted in 2013, 2015, 2018, and 2020.

The inclusion and exclusion criteria are outlined in the flowchart (Fig. 1): (1) participants under 45 years of age or with missing information. (2) participants with CVD or missing information at baseline. (3) participants with less than two years of follow-up. (4) participants with missing information on WC, hypertension, and HbA1c. (5) participants with extreme values (more or less than three standard deviations above the mean) of eGDR. As a result, the analysis comprised 7,849 people in total.

Fig. 1
figure 1

Flowchart of the study population

Full size image

Data collection

For this study, the following information was gathered:

  1. (i)

    Demographic data: age, gender, education level, marital status, and living place.

  2. (ii)

    Lifestyle data: drinking status, smoking status, self-report diabetes, self-report hypertension.

  3. (iii)

    Body measurements: WC, weight, height, systolic blood pressure (SBP), and diastolic blood pressure (DBP).

  4. (iv)

    Laboratory test data: White blood cell (WBC), platelets, C-reactive protein (CRP), Hemoglobin A1c (HbA1C), fasting plasma glucose (FBG), blood urea nitrogen (BUN), serum creatinine (Scr), uric acid (UA), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c).

Diabetes was defined as having an FBG ≥ 7.0 mmol/L (126 mg/dL) or HbA1c ≥ 6.5% at baseline, or through self-report [24]. Participants were classified as hypertensive if SBP ≥ 130 mmHg or DBP ≥ 80 mmHg at baseline or through self-report hypertension [1]. MetS is characterized by three or more of the following [1]: (1) HDL-c < 40 mg/dL for men or < 50 mg/dL for women; (2) TG ≥ 150 mg/dL; (3) WC ≥ 80 cm for women or ≥ 90 cm for men; (4) FBG ≥ 100 mg/dL; and (5) hypertension. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was used to determine the estimated glomerular filtration rate (eGFR) for individuals of “Asian origin” [25]. Then, using Kidney Disease Improving Global Outcomes (KDIGO), it was categorized into CKD stages [1]. The Center for Epidemiologic Studies Depression Scale (CESD10) score of ≥ 10 was used to characterize depression [26].

Variables

eGDR

The current investigation used the previously reported formula to compute the eGDR (mg/kg/min):

eGDR = 21.158-(0.09 * WC)–(3.407 * hypertension)–(0.551 * HbA1c)

[WC = waist circumference (cm), hypertension (yes = 1/no = 0), and HbA1c = HbA1c (%)] [15].

CVD diagnosis

The study focused on the incidence of CVD throughout the follow-up phase (Waves 2–5). To obtain past CVD diagnoses (cardiac events or stroke), a standardized question was asked: “Have you been told by a doctor that you have been diagnosed with heart failure, coronary heart disease, angina, heart attack, or other heart problems?” or “Have you been told by a doctor that you have been diagnosed with stroke?” This aligns with prior similar studies [27, 28]. The CHARLS study team ensured data trustworthiness by implementing strict quality control techniques for recording and verification [23].

CKM stage 0–3 definition

The stages of CKM syndrome were defined based on the criteria established by AHA Presidential Advisory [1]. Stage 0 is characterized by the lack of CKM risk factors: normal lipid profile, normal blood pressure, normal WC, normal BMI, and no signs of either CVD or CKD. Stage 1 is distinguished by excess or dysfunctional adiposity: BMI ≥ 23 kg/m2, WC ≥ 80 cm in women or ≥ 90 cm in men, FBG ≥ 100–124 mg/dL or HbA1c (5.7 − 6.4%), without CKD or other metabolic risk factors. Stage 2 includes moderate to high-risk CKD or metabolic risk factors such as diabetes, MetS, hypertension, or hypertriglyceridemia (≥ 135 mg/dL). Stage 3 refers to subclinical CVD in individuals with CKD, metabolic risk factors, or excess/dysfunctional adiposity [1].

Missing data handling

Missing data were expressed as N (%). They were drinking status (6, 0.08%), smoking status (1, 0.01%), BMI (63, 0.80%), WBC (156, 1.99%), platelet (153, 1.95%), BUN (106, 1.35%), FBG (119, 1.52%), Scr (122, 1.55%), TC (112, 1.43%), TG (110, 1.40%), HDL-c (105, 1.34%), LDL-c (120, 1.53%), CRP (105, 1.34%), UA (105, 1.34%). Missing covariate data were handled using multiple imputations [29]. Missing data analysis was carried out under the presumption that the data was missing at random [30].

Statistical analysis

Continuous data were shown as either means ± standard deviations or medians (interquartile ranges), and categorical variables were shown as N(%). Using one-way ANOVA for continuous data and chi-squared testing for categorical data, the differences after the eGDR quartiles were examined.

Confounding variables were chosen based on changes in impact estimates of greater than 10% or their correlation with the outcomes of interest [31]. Due to the collinearity, TC was excluded by confounding factors. We controlled for the following variables after taking into account the clinical importance and prior research: age, gender, education level, marital status, living place, drinking status, smoking status, depression, WBC, platelets, BUN, FBG, Scr, TG, HDL-c, LDL-c, CRP, and UA. The association between eGDR and CVD was examined using multivariate Cox regression models. Stratified analyses were conducted to assess the consistency of associations across different subgroups, including CKM stages, gender, age groups, drinking status and smoking status. To explore potential non-linear relationships, we performed restricted cubic spline (RCS) regression and threshold effect analysis using a two-piecewise Cox regression model. The turning point was determined by selecting the point that yielded the maximum likelihood value [32]. The likelihood ratio test was used to compare the fitness of the threshold model versus the linear model [32]. Additionally, to confirm the robustness of the findings, sensitivity analyses were performed on the Cardiac events and Stroke outcomes. Moreover, by estimating E-value, we evaluated the possibility of undetected confounding between eGDR and CVD [33].

All results comply with the STROBE statement [31]. Results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Statistical significance was set at P < 0.05. All analyses were performed using R software (version 4.2.0) and EmpowerStats (version 4.2).

Results

Characteristics

Table 1 Characteristics of participants classified by quartiles of the eGDR
Full size table
Fig. 2
figure 2

Distribution of eGDR

Full size image

Table 1 presents the baseline characteristics of 7,849 participants (males: 4,146, 52.82%; females: 3,703, 47.18%) with a mean follow-up of 8.29 ± 1.67 years. Figure 2 shows that eGDR follows a normal distribution, with a mean value of 9.12 ± 2.23 mg/kg/min. Across eGDR quartiles (Q1-Q4), we observed significant trends in participant characteristics. Age decreased from Q1 to Q4, while the proportion of females increased. Metabolic parameters showed consistent improvements with increasing eGDR: TC, BMI, TG, and CRP decreased, while HDL-c increased. The prevalence of comorbidities also showed marked differences: hypertension, MetS, diabetes, and dyslipidemia decreased significantly. The distribution of CKM stages varied significantly across eGDR quartiles. Q1 and Q2 were predominantly stage 2 (100% and 96.23%, respectively), while Q3 and Q4 showed more diverse distributions, with Q4 having the highest proportions in stages 0 (20.17%) and 1 (25.47%). CKM staging showed 492 (6.27%), 1,404 (17.89%), 5,462 (69.59%), and 491 (6.26%) participants in stages 0–3, respectively. During follow-up, 1,504 (19.16%) cardiac events, 663 (8.45%) strokes, and 1,946 (24.79%) CVD events occurred (Table 1).

Multivariate analyses

Table 2 Association between the eGDR and CVD risk in participants with CKM stages 0–3
Full size table

The association between eGDR and CVD risk was analyzed in participants with CKM stages 0–3 (Table 2). Each unit increase in eGDR was associated with a 12% reduced risk of CVD (HR = 0.88, 95% CI: 0.86–0.89, p < 0.0001), remaining consistent after full adjustment (HR = 0.89, 95% CI: 0.87–0.91, p < 0.0001). In quartile analysis (Q1: 2.32–7.17 mg/kg/min as reference), the fully adjusted HRs were 0.73 (95% CI: 0.65–0.82), 0.60 (95% CI: 0.53–0.68), and 0.50 (95% CI: 0.44–0.58) for Q2-Q4 respectively, demonstrating a significant inverse relationship (P for trend < 0.0001).

Stratified analyses

Table 3 Stratified analyses of the association between the eGDR and CVD risk
Full size table

Stratified analyses by CKM stages showed that higher eGDR was consistently associated with lower CVD risk, particularly in Stage 2 (HR: 0.88, 95% CI: 0.85–0.90, P < 0.0001). Similar effect sizes were observed in other stages, with no significant interaction effect (P for interaction = 0.7528), indicating that the protective association of eGDR against CVD was consistent across CKD stages. The inverse association between eGDR and CVD risk remained consistent across gender, age groups, drinking status and smoking status (Table 3).

Non-linear analyses

Fig. 3
figure 3

Dose-response relationship between eGDR and CVD risk. Adjusted for age, gender, education level, marital status, living place, drinking status, smoking status, depression, WBC, platelets, BUN, FBG, Scr, TG, HDL-c, LDL-c, CRP, and UA

Full size image

Restricted cubic spline analysis revealed a nonlinear U-shaped association between eGDR and CVD risk (Fig. 3). Threshold effect analysis revealed a nonlinear relationship between eGDR and CVD risk (likelihood ratio test, P < 0.001) (Table 4). The nonlinear model identified a turning point at eGDR = 11.82 mg/kg/min. Below this turning point, each unit increase in eGDR was associated with a 12% decrease in CVD risk (HR: 0.88, 95% CI: 0.86–0.90, P < 0.0001). However, above the turning point, the association reversed, with each unit increase corresponding to a 19% increase in CVD risk (HR: 1.19, 95% CI: 1.04–1.37, P = 0.0135).

Table 4 Threshold effect analysis of eGDR on CVD, cardiac events, and stroke outcomes
Full size table

Sensitivity analyses

Fig. 4
figure 4

Dose-response relationship between eGDR and Cardiac events risk. Adjusted for age, gender, education level, marital status, living place, drinking status, smoking status, depression, WBC, platelets, BUN, FBG, Scr, TG, HDL-c, LDL-c, CRP, and UA

Full size image

Similar to CVD, cardiac events showed a non-linear relationship with eGDR, with a turning point at 11.79 mg/kg/min (Table 4; Fig. 4). Below this turning point, eGDR was protective (HR = 0.88, 95% CI: 0.86–0.91, P < 0.001), while above it, the risk increased (HR = 1.24, 95% CI: 1.07–1.44, P = 0.0034). In contrast, eGDR demonstrated a linear protective effect against stroke (HR = 0.84, 95% CI: 0.81–0.88, P < 0.001) (Table 4).

We employed E-values to estimate the robustness of probable unmeasured confounders in the data, and our results remained consistent unless an unmeasured confounder had an HR greater than 1.51.

Discussion

In this large-scale prospective cohort study (CHARLS) involving 7,850 participants with a mean follow-up of 8.29 years, we observed a non-linear U-shaped relationship between eGDR and CVD risk in CKM syndrome stages 0–3. Below the turning point of 11.82 mg/kg/min, each unit increase in eGDR was associated with a 12% decrease in CVD risk (HR: 0.88, 95% CI: 0.86–0.90, P < 0.0001), while above this turning point, each unit increase corresponded to a 19% increase in CVD risk (HR: 1.19, 95% CI: 1.04–1.37, P = 0.0135).

Our findings on the association between eGDR and CVD align with several recent studies while offering unique insights. Ren et al., using the same CHARLS cohort, found that each unit increase in eGDR reduced CVD risk by 13% (HR: 0.87, 95% CI: 0.85–0.89) [18], consistent with our observed 12% risk reduction below the threshold (HR: 0.88, 95% CI: 0.86–0.90). However, our study goes further by examining this relationship within CKM syndrome stages 0–3 and identifying a U-shaped relationship. Similarly, Zhang et al.‘s nationwide study reported an 11% lower risk of CVD per unit increase in eGDR (HR: 0.89, 95% CI: 0.87–0.91) [20], though without identifying a threshold. Zabala et al. found lower eGDR linked to increased stroke risk in diabetes patients (HR: 0.84, 95% CI: 0.80–0.88) [21], consistent with our stroke-specific analysis (HR: 0.84, 95% CI: 0.81–0.88). Lu et al. similarly reported the protective effect of higher eGDR in stroke patients (HR: 0.86, 95% CI: 0.82–0.90) [22], though in a different population. Our findings complement Karakayalı et al.‘s work [13, 14] on metabolic parameters and CVD, emphasizing the importance of metabolic markers in CVD risk assessment. Our study uniquely examines the eGDR-CVD relationship across CKM stages and identifies a threshold effect, suggesting that while insulin sensitivity generally protects against CVD, extremely high levels (≥ 11.82 mg/kg/min) may increase risk (HR: 1.19, 95% CI: 1.04–1.37).

This study makes several novel contributions to the literature. First, it is the first to examine the relationship between eGDR and CVD risk specifically within the newly defined CKM syndrome framework, providing crucial insights for this emerging clinical paradigm. Second, we identified a previously unreported U-shaped relationship with a specific turning point at 11.82 mg/kg/min, offering clinicians a concrete threshold for risk stratification. Third, our comprehensive analysis across CKM stages 0–3 provides valuable guidance for early intervention strategies, aligning with the AHA’s recent emphasis on preventing progression in early CKM stages.

The observed U-shaped relationship between eGDR and CVD risk can be explained by the interplay between insulin sensitivity, metabolic health, and cardiovascular physiology. Lower eGDR levels, indicating higher insulin resistance, increase CVD risk through mechanisms such as systemic inflammation, endothelial dysfunction, dyslipidemia, and hypertension, which promote atherosclerosis [34, 35]. The reversal of protection may reflect the adverse effects of excessive insulin sensitivity or related metabolic disturbances. Very high eGDR levels could indicate hyperinsulinemia, a condition associated with increased sympathetic activity, sodium retention, and vascular stress [36]. Alternatively, extremely high eGDR might reflect underlying conditions like malnutrition or sarcopenia, both linked to elevated cardiovascular risk [37, 38].

Our findings of a U-shaped relationship between eGDR and CVD risk suggest the need for differentiated treatment approaches. For patients with low eGDR (< 11.82 mg/kg/min), interventions should focus on improving insulin sensitivity through lifestyle modifications, weight management, and appropriate pharmacological interventions. However, for patients with very high eGDR (> 11.82 mg/kg/min), careful monitoring and investigation of underlying conditions such as malnutrition or sarcopenia may be warranted. This personalized approach to treatment based on eGDR levels could help optimize cardiovascular outcomes across the CKM syndrome spectrum.

Our stratified analysis across CKM stages 0–3 reveals important clinical implications for personalized cardiovascular risk management. The consistent protective association between eGDR and CVD risk, particularly significant in Stage 2 (HR: 0.88, 95% CI: 0.85–0.90), suggests that insulin sensitivity monitoring could be valuable across all CKM stages. This finding extends beyond previous studies that typically focused on specific disease states, offering a comprehensive perspective on the CKM syndrome spectrum. The identification of a turning point at 11.82 mg/kg/min for eGDR provides clinicians with a specific target for risk stratification and intervention. This novel finding suggests that while improving insulin sensitivity is generally beneficial, extremely high eGDR levels may paradoxically increase cardiovascular risk, necessitating a more nuanced approach to metabolic management. The study’s comprehensive examination across CKM stages 0–3 offers valuable insights for early intervention strategies, particularly important given the AHA’s recent emphasis on preventing progression in early CKM stages. Our results support the integration of eGDR monitoring into routine clinical practice as a cost-effective tool for cardiovascular risk assessment, especially in resource-limited settings where more sophisticated diagnostic methods may be unavailable. The observed U-shaped relationship also highlights the need for personalized treatment approaches, suggesting that optimal eGDR targets may vary among different patient populations.

Our study possesses several notable strengths. First, it pioneers the examination of eGDR and CVD within the context of CKM syndrome stages 0–3, offering valuable insights into this newly defined clinical framework. Second, our large cohort of 7,849 participants with an 8.29-year follow-up period ensures robust statistical power. The use of the CHARLS database, with its standardized procedures and rigorous quality control, ensures high data reliability. Third, our sophisticated analytical approach, incorporating restricted cubic spline regression and threshold effect analysis, revealed a novel U-shaped relationship between eGDR and CVD risk. Furthermore, comprehensive sensitivity analyses and extensive control for potential confounders strengthen our findings’ validity.

Several limitations of this study should be acknowledged. First, our study excluded participants under 45 years of age and those with baseline CVD, limiting the generalizability of our findings to younger populations and individuals with established CVD. Second, as this study utilized data exclusively from the CHARLS cohort in China, caution should be exercised when extrapolating these findings to other ethnic populations, as genetic and environmental factors may influence the relationship between eGDR and cardiovascular outcomes. Third, the observational nature of our study precludes establishing causal relationships between eGDR and CVD risk, despite the robust associations observed. Fourth, while we adjusted for numerous potential confounders, unmeasured confounding factors such as dietary habits, physical activity levels, and medication adherence could not be fully accounted for, although our E-value analysis suggests that such unmeasured confounding would need to be substantial to nullify our findings.

Conclusion

In this large-scale prospective cohort study (CHARLS) involving 7,850 participants with a mean follow-up of 8.29 years, we observed a non-linear U-shaped relationship between eGDR and CVD risk in CKM syndrome stages 0–3. Further multicenter studies with different ethnicities are needed to validate the results of this study.

Data availability

Data are available in the China Health and Retirement Longitudinal Study repository [http://charls.pku.edu.cn].

Abbreviations

CKM:

Cardiovascular-kidney-metabolic

CKD:

Chronic kidney disease

CVD:

Cardiovascular diseases

AHA:

American heart association

eGDR:

Estimate glucose disposal rate

WC:

Waist circumference

HbA1c:

Hemoglobin A1c

BMI:

Body mass index

WBC:

White blood cell

FBG:

Fasting plasma glucose

BMI:

Body mass index

SD:

Standard deviations

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

TC:

Total cholesterol

TG:

Triglyceride

HDL-c:

High-density lipoprotein cholesterol

LDL-c:

Low-density lipoprotein cholesterol

BUN:

Blood urea nitrogen

Scr:

Serum creatinine

CRP:

C-reactive protein

UA:

Uric acid

MetS:

Metabolic syndrome

ANOVA:

Analysis of variance

RCS:

Restricted cubic spline

HR:

Hazard ratio

CI:

Confidence interval

References

  1. Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic health: A presidential advisory from the American heart association. Circulation. 2023;148:1606–35.

    Article  PubMed  Google Scholar 

  2. Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A synopsis of the evidence for the science and clinical management of Cardiovascular-Kidney-Metabolic (CKM) syndrome: A scientific statement from the American heart association. Circulation. 2023;148:1636–64.

    Article  PubMed  Google Scholar 

  3. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke Statistics-2020 update: A report from the American heart association. Circulation. 2020;141:e139–596.

    Article  PubMed  Google Scholar 

  4. Johansen KL, Chertow GM, Foley RN, Gilbertson DT, Herzog CA, Ishani A, et al. Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2021;77:A7–8. US Renal Data System 2020 Annual Data Report.

  5. Tain Y-L, Hsu C-N. The impact of the Aryl hydrocarbon receptor on antenatal chemical Exposure-Induced Cardiovascular–Kidney–Metabolic programming. Int J Mol Sci. 2024;25:4599.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, et al. Novel prediction equations for absolute risk assessment of total cardiovascular disease incorporating cardiovascular-Kidney-Metabolic health: A scientific statement from the American heart association. Circulation. 2023;148:1982–2004.

    Article  PubMed  Google Scholar 

  7. Seferović PM, Petrie MC, Filippatos GS, Anker SD, Rosano G, Bauersachs J, et al. Type 2 diabetes mellitus and heart failure: a position statement from the heart failure association of the European society of cardiology. Eur J Heart Fail. 2018;20:853–72.

    Article  PubMed  Google Scholar 

  8. Usman MS, Khan MS, Butler J. The interplay between diabetes, cardiovascular disease, and kidney disease. Chronic kidney disease and type 2 diabetes. Arlington (VA): American Diabetes Association; 2021.

    Google Scholar 

  9. Marassi M, Fadini GP. The cardio-renal-metabolic connection: a review of the evidence. Cardiovasc Diabetol. 2023;22:195.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kadowaki T, Maegawa H, Watada H, Yabe D, Node K, Murohara T, et al. Interconnection between cardiovascular, renal and metabolic disorders: A narrative review with a focus on Japan. Diabetes Obes Metab. 2022;24:2283–96.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Suh S, Lee M-K. Metabolic syndrome and cardiovascular diseases in Korea. J Atheroscler Thromb. 2014;21(Suppl 1):S31–35.

    Article  PubMed  Google Scholar 

  12. Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, et al. Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in united States adults. Circulation. 2004;110:1245–50.

    Article  PubMed  Google Scholar 

  13. Karakayalı M, Altunova M, Yakisan T, Aslan S, Artac I, Omar T, et al. Relationship between nonobstructive coronary arteries and metabolic parameters. Kafkas J Med Sci. 2024;14:138–43.

    Article  Google Scholar 

  14. Karakayalı M, Kılıç O, Şahin M, Kelesoglu S, Yilmaz İ, Duz R, et al. The relationship between mortality and Leuko-Glycemic index in coronary care unit patients (MORCOR-TURK LGI). Dicle Tıp Dergisi. 2024;51:315–24.

    Article  Google Scholar 

  15. Williams KV, Erbey JR, Becker D, Arslanian S, Orchard TJ. Can clinical factors estimate insulin resistance in type 1. diabetes? Diabetes. 2000;49:626–32.

    Article  PubMed  CAS  Google Scholar 

  16. Nyström T, Holzmann MJ, Eliasson B, Svensson A-M, Sartipy U. Estimated glucose disposal rate predicts mortality in adults with type 1 diabetes. Diabetes Obes Metab. 2018;20:556–63.

    Article  PubMed  Google Scholar 

  17. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979;237:E214–223.

    PubMed  CAS  Google Scholar 

  18. Ren X, Jiang M, Han L, Zheng X. Estimated glucose disposal rate and risk of cardiovascular disease: evidence from the China health and retirement longitudinal study. BMC Geriatr. 2022;22:968.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Le C, Qin Y, Wang Z, Wang D, Zhong F, Yang S, et al. Association of estimated glucose disposal rate with incident cardiovascular disease under different metabolic and circadian rhythm States: findings from a National population-based prospective cohort study. Diabetol Metab Syndr. 2024;16:257.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Zhang Z, Zhao L, Lu Y, Xiao Y, Zhou X. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol. 2024;23:1–12.

    Article  CAS  Google Scholar 

  21. Zabala A, Darsalia V, Lind M, Svensson A-M, Franzén S, Eliasson B, et al. Estimated glucose disposal rate and risk of stroke and mortality in type 2 diabetes: a nationwide cohort study. Cardiovasc Diabetol. 2021;20:202.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Lu Z, Xiong Y, Feng X, Yang K, Gu H, Zhao X, et al. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol. 2023;22:225.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43:61–8.

    Article  PubMed  Google Scholar 

  24. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S13–27.

    Article  Google Scholar 

  25. Betzler BK, Sultana R, He F, Tham YC, Lim CC, Wang YX, et al. Impact of chronic kidney disease epidemiology collaboration (CKD-EPI) GFR estimating equations on CKD prevalence and classification among Asians. Front Med (Lausanne). 2022;9:957437.

    Article  PubMed  Google Scholar 

  26. Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for epidemiologic studies depression Scale). Am J Prev Med. 1994;10:77–84.

    Article  PubMed  CAS  Google Scholar 

  27. Zheng X, Han W, Li Y, Jiang M, Ren X, Yang P, et al. Changes in the estimated glucose disposal rate and incident cardiovascular disease: two large prospective cohorts in Europe and Asia. Cardiovasc Diabetol. 2024;23:403.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Shi Z, Nicholls SJ, Taylor AW, Magliano DJ, Appleton S, Zimmet P. Early life exposure to Chinese famine modifies the association between hypertension and cardiovascular disease. J Hypertens. 2018;36:54–60.

    Article  PubMed  CAS  Google Scholar 

  29. Groenwold RHH, White IR, Donders ART, Carpenter JR, Altman DG, Moons KGM. Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. CMAJ. 2012;184:1265–9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–99.

    Article  PubMed  Google Scholar 

  31. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014;12:1495–9.

    Article  Google Scholar 

  32. Lin L, Chen C, Yu X. [The analysis of threshold effect using empower stats software]. Zhonghua Liu Xing Bing Xue Za Zhi. 2013;34:1139–41.

    PubMed  Google Scholar 

  33. Haneuse S, VanderWeele TJ, Arterburn D. Using the E-Value to assess the potential effect of unmeasured confounding in observational studies. JAMA. 2019;321:602–3.

    Article  PubMed  Google Scholar 

  34. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37:1595–607.

    Article  PubMed  CAS  Google Scholar 

  35. Bornfeldt KE, Tabas I. Insulin resistance, hyperglycemia, and atherosclerosis. Cell Metab. 2011;14:575–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Ferrannini E, Cushman WC. Diabetes and hypertension: the bad companions. Lancet. 2012;380:601–10.

    Article  PubMed  Google Scholar 

  37. Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int. 2003;63:793–808.

    Article  PubMed  Google Scholar 

  38. Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani RV. Body composition and survival in stable coronary heart disease: impact of lean mass index and body fat in the obesity paradox. J Am Coll Cardiol. 2012;60:1374–80.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors appreciate all of the CHALRS members for their efforts, as well as the participants who provided data.

Funding

This work was supported by Sanming Project of Medicine in Shenzhen (No. SZSM202211016), Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP006), Shenzhen Second People’s Hospital Clinical Research Fund of Guangdong Province High-level Hospital Construction Project (Grant No.20223357008, No.2023xgyj3357003).

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Author notes

  1. Shuiqing Gui, Zemao Xing and Ying Li contributed equally to this work.

Authors and Affiliations

  1. Department of Critical Care Medicine, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    Xiaomin Liang, Kai Lai, Xiaohong Li, Shuiqing Gui, Zemao Xing & Ying Li

Authors
  1. Xiaomin Liang
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  2. Kai Lai
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  3. Xiaohong Li
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  4. Shuiqing Gui
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  5. Zemao Xing
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Contributions

X.M.L: Research conceptualization, statistical analysis and manuscript drafting. K.L and X.H.L: Data cleansing and statistical analyses. S.Q. G, Z.M.X and Y.L: Manuscript revisions and research designs. All authors collaborated on the article and cleared the presented version.

Corresponding authors

Correspondence to Shuiqing Gui, Zemao Xing or Ying Li.

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Ethics approval and consent to participate

The study involving human participants was approved by the Peking University Institutional Review Board (IRB00001052-11015) and complied with the Declaration of Helsinki guidelines. Each participant gave written informed consent.

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Not applicable.

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The authors declare no competing interests.

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Liang, X., Lai, K., Li, X. et al. U-shaped relationship of estimated glucose disposal rate with cardiovascular disease risk in cardiovascular-kidney-metabolic syndrome stages 0–3: a population-based prospective study. Diabetol Metab Syndr 17, 85 (2025). https://doi.org/10.1186/s13098-025-01659-y

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  • DOI: https://doi.org/10.1186/s13098-025-01659-y

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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