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Estimated glucose disposal rate for predicting cardiovascular events and mortality in patients with non-diabetic chronic kidney disease: a prospective cohort study

BMC Medicine volume 22, Article number: 411 (2024) Cite this article

Abstract

Background

Evidence suggests that insulin resistance (IR) is an autonomous risk factor for cardiovascular disease (CVD). Nevertheless, the association between estimated glucose disposal rate (eGDR), a novel indicator of IR, and incident CVD and mortality in chronic kidney disease (CKD) patients without diabetes remains uncertain.

Methods

The study included 19,906 participants from the UK Biobank who had an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2 or a urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g and no history of CVD and diabetes. Individuals were divided into three categories based on tertiles of eGDR. The outcome was a composite CVD (coronary heart disease (CHD) and stroke) and mortality (all-cause, non-accidental, and cardiovascular mortality). Furthermore, a cohort of 1,600 individuals from the US National Health and Nutrition Examination Survey (NHANES) was applied to validate the association between eGDR and mortality. The Cox proportional hazards regression models were used to examine the association between eGDR and event outcomes.

Results

During a follow-up of around 12 years, 2,860 CVD, 2,249 CHD, 783 stroke, 2,431 all-cause, 2,326 non-accidental and 492 cardiovascular deaths were recorded from UK Biobank. Higher eGDR level was not only associated with lower risk of CVD (hazard ratio [HR] 0.641, 95% confidence interval [CI] 0.559–0.734), CHD (HR 0.607, 95% CI 0.520–0.709), stroke (HR 0.748, 95% CI 0.579–0.966), but also related to reduced risk of all-cause (HR 0.803, 95% CI 0.698–0.923), non-accidental (HR 0.787, 95% CI 0.682–0.908), and cardiovascular mortality (HR 0.592, 95% CI 0.423–0.829). Validation analyses from NHANES yielded consistent relationship on mortality.

Conclusions

In these two large cohorts of CKD patients without DM, a higher eGDR level was associated with a decreased risk of CVD and mortality.

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Background

Chronic kidney disease (CKD) affects approximately 850 million individuals worldwide, and is the fourth fastest growing cause of death [1]. Cardiovascular disease (CVD) significantly contributes to the death among patients with CKD [2]. Recent studies have shown that insulin resistance (IR) is recognized as an independent risk factor for microvascular complications of diabetes mellitus [3] and CVD, and that there is a close association between IR and CVD and death in people with type 1 (T1DM), type 2 diabetes mellitus (T2DM), and in the general population [4,5,6,7,8]. Although it is widely acknowledged that individuals with CKD who do not have diabetes are commonly accompanied by IR [9, 10], there is limited and conflicting information regarding the connection with IR and the risk of CVD and mortality in this population [11,12,13]. Hence, the purpose of this investigation was to examine the importance of addressing IR in order to provide promising benefits for patients with CKD.

The hyperinsulinemic-euglycemic clamp is widely recognized as the definitive method for assessing IR. Nevertheless, the technique’s invasiveness and time-consuming render it impractical for regular and extensive measurement. The estimated glucose disposal rate (eGDR), which is determined by easily accessible clinical parameters including hemoglobin A1c (HbA1c), hypertension, and WC (waist circumference), exhibits a strong connection with IR measured by the hyperinsulinemic-euglycemic clamp [14]. Importantly, the level of eGDR remains unaffected by renal excretory function. Previous studies have indicated that a higher eGDR level, representing better insulin sensitivity, was linked to a reduced risk of incident CVD and mortality in T1DM and T2DM [15,16,17,18]. However, the relationship between eGDR and the occurrence of CVD and mortality in non-diabetic CKD patients has not yet been determined.

To fill this gap in knowledge, we utilized data from the UK Biobank to assess the connection between eGDR and the likelihood of CVD incidence and mortality in non-diabetic CKD patients. In addition, we also employed data from the National Health and Nutrition Examination Survey (NHANES) to validate the connection between eGDR and mortality. Given the existing evidence, we also sought to explore the potential mediating role of inflammation biomarkers through mediation analysis in CKD patients.

Methods

Study population

During the period from 2006 to 2010, more than half million participants aged 37 to 73 were recruited for the UK Biobank study. At the recruitment, participants completed computerized questionnaires regarding their lifestyle, socio-demographics, and medical history. Blood and urine samples were collected for biological marker assessments. Further details about the UK biobank are available elsewhere [19]. Informed written consent for the study was obtained from all participants (Application No. 90060).

This research sifted 30,365 participants who had evidence of CKD at baseline, determined by estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2or a urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g [20]. The eGFR was determined by serum cystatin C and creatinine [21]. Participants were excluded if they had missing data on eGDR, a history of CVD and DM, or if they had undiagnosed DM but met the criteria of fasting glucose level ≥ 7.0 mmol/L or HbA1c level ≥ 48 mmol/mol (≥ 6.5%) [22] (Fig. 1).

Fig. 1
figure 1

Flow chart for participants selection in UK Biobank and US NHANES. Abbreviations: CVD, cardiovascular disease; DM, diabetes mellitus; eGDR, estimated glucose disposal rate; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; UACR, urinary albumin-to-creatinine ratio

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The NHANES study employed a sophisticated, multistage probability design to select representative samples from U.S. citizens. The study design and data collection methods have been previously described [23]. We included participants who had evidence of CKD at baseline determined by eGFR or UACR, as detailed above, and surveyed from 1999 to 2018. Due to the lack of cystatin C, we used creatinine to calculate eGFR [24]. The exclusion criteria align with those mentioned above. In order to align the age distribution with the UK Biobank cohort, we removed individuals aged less than 40 years and greater than 70 years from the study, as the participants from NHANES covered a broad age range (Fig. 1).

Exposure assessment

As previously mentioned, the equation for eGDR (mg/kg/min) was calculated as follows: eGDR = 21.158-(0.09*WC) -(3.407*HT) -(0.551*HbA1c) [WC = waist circumference (cm), HT = hypertension (yes = 1/no = 0), and HbA1c = HbA1c (% DCCT)] [14]. Hypertension was defined according to the International Classification of Diseases, 10th Revision (ICD-10), with codes I10, or systolic blood pressure ≥ 140 mmHg, and diastolic blood pressure ≥ 90 mmHg, or use of antihypertensive medication or based on general practitioner diagnosis, medication reimbursement, or self-reported information. HbA1c was measured by the High-Performance Liquid Chromatography method. Furthermore, the unit in mmol/mol was transformed to a percentage (%) using the equation: (0.09148 × HbA1c mmol/mol) + 2.152 [25]. The entire population was divided into tertiles of eGDR, with the lowest eGDR tertile representing the most IR as the reference group.

Outcomes of interest

The UK Biobank cohort examined the occurrence of several health outcomes, including incident coronary heart disease (CHD), stroke, CVD (a combination of CHD and stroke), all-cause, non-accidental, and cardiovascular mortality. Information on deaths was obtained from death registry data while incident CVD events were derived from hospital admission data and death registry data. Outcomes were coded by ICD-10: CVD (codes I20-I25, I60, I61, I63, I64, I69), CHD (codes I20-I25), stroke (codes I60, I61, I63, I64, I69), cardiovascular mortality (codes I00-I79), and non-accidental mortality (codes A00-R99). Outcomes were assessed from the date of the recruitment to the date of CVD development, death, or last follow-up. The last follow-up date was October, 2020 for participants.

In NHANES, the vital status of participants was determined by a match between NHANES personal identifiers and connections to death certificates from the National Death Index until December 31, 2019. Cardiovascular mortality was ascertained using the ICD-10 codes I00-I78.

Covariates of interest

The factors considered in the study were age, gender, ethnicity, education, Townsend deprivation indices (TDI, UK Biobank only), annual household income (NHANES only), lifestyle factors including alcohol drinking status, smoking status, physical activity (UK Biobank only), body mass index (BMI), the use of aspirin, antihypertensive and lipid-lowering drugs (UK Biobank only), serum levels of low-density lipoprotein cholesterol (LDL-C), haemoglobin (Hb), albumin (ALB), high-density lipoprotein cholesterol (HDL-C), neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP, UK Biobank only).

Statistical analysis

Participant characteristics were summarized using the mean ± standard deviation for continuous variables following a normal distribution and using percentages for categorical variables. For continuous variables, analysis of variance (ANOVA) was performed to assess differences among groups, while the χ2 test was employed for categorical variables. Multiple imputations were carried out using chained equations to replace missing measurements. The cumulative incidence curves for CVD and mortality were derived using the Kaplan–Meier method and compared using the log-rank test. The Cox proportional hazards regression models were used to examine the association between eGDR and event outcome. The proportional hazards assumption was verified by examining Schoenfeld residuals. Model 1 was a crude model. Model 2 was adjusted for the following variables: sex (male or female), age (continuous), education (College or University degree, O levels/GCSEs or equivalent, NVQ or HND or HNC or equivalent, CSEs or equivalent, A levels/AS levels or equivalent, other), ethnicity (White, Black, Asian, mixed, or other), TDI (< -3.018, -3.018 ~ -0.278, > -0.278), alcohol drinker status (never, former, current, don’t know), and smoking (never, former, current, don’t know). Model 3 included all variables from Model 2, and additional factors such as BMI (25.0, 25.0–29.9, or ≥ 30.0 kg/m2), eGFR (< 30 ml/min/1.73 m2, ≥ 30 ml/min/1.73 m2), UACR (< 30 mg/g, 30–299.99 mg/g, ≥ 300 mg/g), physical activity at goal (yes/no), LDL-C (continuous), HDL-C (continuous), ALB (continuous), Hb (continuous), CRP (continuous), NLR (continuous), the use of aspirin, antihypertensive and lipid-lowering drugs (yes/no). There was no critical correlation between covariates (collinearity), as indicated by the variance inflation factors (all VIFs < 1.687, Additional File 1: Table S1). We used the likelihood ratio test and the Wald test to assess the goodness-of-fit of the model. The link between eGDR as a continuous variable and HRs for event outcome was revealed using a restricted cubic spline curve. Additionally, we conducted subgroup analyses stratified by age, sex, smoking, ethnicity, TDI, and physical activity to investigate potential effect of modifications.

To examine the potential mediating effects of inflammatory status on the association between eGDR and outcomes, we conducted a mediation analysis. Based on the model 3, multivariable linear regression between inflammatory biomarkers and eGDR was performed. Those biomarkers having substantial relationships with eGDR were chosen as mediators. The percentage mediated was computed as indirect effect / (indirect + direct impact). We also carried out multiple sensitivity analyses to verify the robustness of our conclusions, including removing the individuals with missing data on covariates, excluding participants with the interested outcomes occurred within the second and fifth years of the follow-up, using a competing risk model for the outcome of CVD and cardiovascular mortality using the Fine and Gray models, and excluding participants with incident diabetes occurred during follow-up. All analyses were conducted using SPSS 25.0 software (IBM SPSS Inc., USA) and R statistical software (version 4.1.1), and a two-sided P < 0.05 was set as the threshold for statistical significance.

Results

Baseline characteristics

From the UK Biobank, a total of 19,906 CKD patients without DM and CVD at baseline were involved in this study (Fig. 1). As displayed in Table 1, patients with a higher level of eGDR were more likely to be women, younger, physically active, and never-smokers and demonstrated elevated levels of HDL-C, and ALB. Additionally, these individuals had a lower level of CRP, and NLR compared to those participants with lower eGDR levels.

Table 1 Baseline characteristics of 19,906 participants by tertile of eGDR in UK biobank study
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Association between eGDR and incident CVD

Over a median follow-up of 12.86 years, we observed a total of 2,860 CVD events, including 2,249 cases of CHD and 783 cases of stroke. Figure 2 illustrates a lower cumulative incidence of CVD, CHD, and stroke among individuals belonging to the high and medium eGDR groups, as compared to those in the low eGDR groups (log-rank P < 0.001 for all). In the fully adjusted model (model 3), it was observed that the highest eGDR tertile was strongly related to a reduced risk of CVD (HR 0.641, 95% CI 0.559–0.734, P < 0.001), CHD (HR 0.607, 95% CI 0.520–0.709, P < 0.001), and stroke (HR 0.748, 95% CI 0.579–0.966, P = 0.026) compared with the lowest tertile (Fig. 3, Additional File 1: Table S2). There was a trend of decreasing risk of CVD, CHD, and stroke as the tertile of eGDR increased (P for trend < 0.001 for all). The likelihood ratio test and the Wald test for those models were significant, indicating a good fit (Additional File 1: Table S3). Additionally, restricted cubic spline analysis indicated a non-linear decrease in the risk of CVD, CHD, as the eGDR increased (P for non-linearity < 0.05) (Fig. 4).

Fig. 2
figure 2

Cumulative incidence of (A) CVD, (B) CHD, (C)stroke, (D) all-cause mortality, (E) non-accidental mortality, and (F) cardiovascular mortality according to baseline eGDR tertiles in the UK Biobank. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease

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Fig. 3
figure 3

Association between eGDR level and the risk of incident CVD and mortality events in the UK Biobank and NHANES. Model 1 is crude. Model 2 is adjusted for age, sex, TDI (UK Biobank only), annual household income (NHANES only), education, smoking, alcohol drinker status, ethnicity. Model 3 is adjusted for model 2 covariates plus eGFR, UACR, physical activity at goal (UK Biobank only), BMI, LDL-C, HDL-C, ALB, Hb, CRP (UK Biobank only), NLR, aspirin (UK Biobank only), antihypertensive drug (UK Biobank only), lipid-lowering drugs (UK Biobank only). Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease

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Fig. 4
figure 4

HR and 95% CI for risk of (A) CVD, (B) CHD, (C)stroke, (D) all-cause mortality, (E) non-accidental mortality, and (F) cardiovascular mortality associated with eGDR in the UK Biobank. Red solid line represents the HR modeled using a restricted cubic spline with 4 knots of eGDR. Dashed lines represent 95% CIs for HR. Based on the fully adjusted model (Cox model)

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Association between eGDR and mortality

During a median follow-up of 11.38 years, a total of 2,431 deaths occurred, consisting of 2,326 non-accidental and 492 cardiovascular deaths. As shown in Fig. 2, individuals with high and medium eGDR exhibited a reduced cumulative incidence of all-cause, non-accidental, and CVD mortality compared to those with low eGDR (log-rank P < 0.001 for all). In the fully adjusted model, compared with the lowest tertile, the highest eGDR tertile was related to a decreased risk of all-cause (HR 0.803, 95% CI 0.698–0.923, P < 0.001), non-accidental (HR 0.787, 95% CI 0.682–0.908, P = 0.001), and CVD mortality (HR 0.592, 95% CI 0.423–0.829, P = 0.002) (Fig. 3, Additional File 1: Table S2). A trend test for decreasing risk of mortality with increasing tertiles of eGDR was also significant (P for trend < 0.05 for all). The likelihood ratio test and the Wald test for those models are significant, indicating a good fit (Additional File 1: Table S3). Furthermore, restricted cubic spline analysis demonstrated a consistent linear decline in the risk of all-cause mortality as the eGDR increased (P for non-linearity < 0.05) (Fig. 4).

Stratified analysis

In terms of modification effects, the P-values for interactions were > 0.05 for most subgroups (Additional File 1: TableS4-S5). However, it was surprising to observe a significant gender discrepancy that a one-unit increment in eGDR was linked to a 5.8% reduction in the risk of all-cause, a 6.8% decrease in non-accidental, and a 19.7% decrease in CVD mortality among women but not among men. This finding suggests a potential interaction between sex and eGDR in terms of the risk of mortality (P for interaction < 0.05, Additional File 1: Table S5). Furthermore, there was a pronounced correlation between eGDR and mortality, particularly among those engaging in ideal physical activity, suggesting a substantial interaction between physical activity and eGDR (P for interaction < 0.05, Additional File 1: Table S5).

Mediation analysis

Results from multivariable linear regression (Additional File 1: Table S6) indicated a significant relationship between eGDR and several inflammatory variables, including CRP, white blood cell (WBC), neutrophil count. Mediation analysis (Fig. 5) demonstrated that 7.8% and 4.8% of the relationship between eGDR and all-cause mortality among individuals with CKD were mediated by CRP and WC, respectively. Similarly, the associations between eGDR and cardiovascular mortality and non-accidental mortality were also mediated by CRP, respectively.

Fig. 5
figure 5

Mediation analysis on associations between eGDR with CVD and mortality in the UK Biobank. A Indirect associations of eGDR with all-cause mortality via CRP and WBC. B Indirect associations of eGDR with non-accidental mortality via CRP and WBC. C Indirect associations of eGDR with cardiovascular mortality via CRP and NC. βIE indicates the indirect effects. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; CRP, C-reactive protein; NC, neutrophil count; WBC, white blood cell

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Sensitivity analyses

The consequences of our study were robust in several sensitivity analyses: (1) excluded the participants with critical missing covariates (Additional File 1: Table S7); (2) excluded participants with incident outcomes that occurred during the second and the fifth years of the recruitment (Additional File 1: Table S8); (3) adjusted for the competing risk of death using the Fine and Gray model (Additional File 1: Table S9); (4) excluding participants with incident diabetes that occurred during follow-up (Additional File 1: Table S10). All these sensitivity analyses generated similar results to the primary findings.

Validation using NHANES cohort

Furthermore, a total of 1,600 participants from the NHANES were enrolled for external validation (Fig. 1). In line with the demographic characteristics observed in the UK Biobank population, participants with higher eGDR level were more likely to be women, younger and never-smokers, had a higher level of HDL-C, and displayed a lower level of BMI, and NLR compared to those participants with lower eGDR (Additional File 1: Table S11).

During a median follow-up period of 8.84 years, a total of 112 deaths occurred, including 50 cases of cardiovascular deaths. In the full adjusted model 3, it was observed that higher eGDR was associated with a 48.2% lower risk of all-cause (HR 0.518, 95% CI 0.344–0.780, P = 0.001) and a 64.6% lower risk of CVD mortality (HR 0.354, 95% CI 0.169–0.741, P = 0.006), comparing tertile 3 versus tertile 1 (Fig. 3, Additional File 1: Table S12). A notable pattern of decreasing association with all-cause and CVD mortality was identified across low, medium, and high tertiles of eGDR (P for trend < 0.05).

Discussion

In these two large prospective cohorts with approximately ten years of follow-up, we identified that a high eGDR level was associated with a significantly reduced risk of CVD events and mortality in non-diabetic CKD patients.

The results of our study revealed strong connections between elevated eGDR levels and reduced risks of CVD events and mortality, which corroborate prior work conducted in patients with T1DM, T2DM, and non-ST-segment elevation acute coronary syndrome (NSTE-ACS), and further expanded these findings to non-diabetic CKD patients. The SMART cohort with 195 T1DM patients during a median follow-up of 12.9 years, showed that a high eGDR level was linked to a lower risk of cardiovascular events and all-cause mortality [18]. Another cohort study, involving 104,697 T2DM patients during a median follow-up of 5.6 years, also indicated that the individuals with a higher eGDR (> 8 mg/kg/min) was related to a 40% reduced risk of stroke and a 32% decreased risk of all-cause mortality, compared to those with eGDR < 4 mg/kg/min [26], which are consistent with our findings that a lower risk of CVD and mortality was observed in CKD patients with eGDR > 7.998 mg/kg/min. In addition, there was a correlation between eGDR with in-stent restenosis in a cross study involving 1,218 patients who underwent percutaneous coronary intervention (PCI) in NSTE-ACS [27]. Furthermore, an excess risk of all-cause mortality, non-fatal myocardial infarction, and non-fatal ischemic stroke with lower eGDR was also observed among non-diabetic patients with NSTE-ACS [28]. The association between eGDR with CVD and death remained similar across populations, thus suggesting that eGDR could be a dependable and reliable indicator for the occurrence of cardiovascular and mortality in CKD patients.

Nevertheless, the associations between various indicators of IR, such as TG/HDL-C ratio, homeostatic model assessment for IR (HOMA-IR), and triglyceride-glucose index (TyG), and cardiovascular events and mortality in CKD patients were described with conflicting findings. A cohort study with 197 participants and a mean follow-up of 30 months found that CKD patients with higher TG/HDL-C ratios had a greater likelihood of developing CVD [29]. In a cohort study comprising 1,102 individuals with T2DM and CKD, an elevated HOMA-IR was found to be associated with a greater risk of all-cause mortality [13]. Nevertheless, other two investigations arrived at inconsistent findings. A study conducted over around 10 years, involving 1,883 non-diabetic CKD patients, observed that there was no significant association between HOMA-IR and CHD or all-cause mortality [12]. The reasons for the inconsistent conclusions might be due to the predominant presence of skeletal muscle insulin resistance in CKD patients, whereas HOMA-IR primarily reflects hepatic insulin resistance. Additionally, HOMA-IR is calculated using fasting blood glucose and fasting insulin levels, both of which may be influenced by renal function. Therefore, HOMA-IR may not accurately reflect insulin resistance in CKD patients. Another study with 446 nondiabetic men aged 70 to 71 years old, who had CKD stages 3–4, indicated that glucose disposal rate (GDR), as measured by the hyperinsulinemic-euglycemic clamp, did not show any connection to mortality from all-cause or cardiovascular causes [11]. The absence of association may be attributed to a small sample size, the study's exclusive focus on elderly males in this age group, and a high mortality rate of 33.4% within this population, leading to low statistical power and or concealed relationship. Reversely, in a cohort study involving 2,457 patients with stage 1–4 CKD, lower TyG index levels were surprisingly identified to be linked to higher odds of all-cause death [30].

Furthermore, it is noteworthy that HOMA-IR, a metric based on a patient's fasting plasma insulin and glucose concentrations, primarily indicates hepatic IR [31] and may be affected by insulin and insulin sensitizers. Additionally, it seems that TyG and TG/HDL index mainly indicate muscle IR, as increased levels of TG in both blood and skeletal muscle interfere with muscle glucose metabolism [32]. Nevertheless, both indicators are subject to substantial dietary influence. Moreover, eGDR is an easily accessible clinical indicator that incorporates hypertension, a condition strongly associated with IR in skeletal muscle [33]. Multiple investigations have shown that IR manifests in the skeletal muscle of patients with CKD [34, 35]. Combined with the findings and validation of this study, we deliberately concluded eGDR would be a promising tool as IR for predicting cardiovascular comorbidities and mortality in patients with CKD.

Additional subgroup analyses indicated that factors such as age, race, and TDI did not modify the effect of eGDR on mortality. However, our investigation revealed that increased eGDR levels were linked to a reduced risk of mortality, including CVD mortality, in women with CKD but not in men with CKD, suggesting that gender modified the effect of eGDR on mortality. The gender discrepancy in the risk of mortality may be partially attributed to several variations between males and females. Males exhibit a greater propensity to engage in unhealthy life behaviors, such as excessive alcohol consumption, and smoking [36], which may compromise the beneficial effects of insulin sensitivity. Notably, our investigation revealed that higher eGDR levels were linked to a greater effect against cardiovascular mortality in CKD patients who have never smoked, thus providing further evidence for the potential modification effect of smoking behaviors on the association. In addition, sex-specific hormonal disparities may possibly contribute to this phenomenon. Specifically, estrogen has been demonstrated to exert a safeguarding action on the cardiovascular system [37], but androgens like testosterone have been associated with an increased risk of CVD [38].

Previous studies have demonstrated that IR has the potential to initiate a state of abnormal glucose metabolism and inflammation. These conditions can impair endothelial function, induce oxidative stress, facilitate the formation of plaque, and increase the risk of developing atherosclerosis [39]. Persistent IR may lead to the development of chronic diseases, such as DM and CVD [40, 41]. Thus, better insulin sensitivity (show by a high level of eGDR) might reduce the risk of CVD events. Our mediation analysis identified that inflammation had a role in partially mediating the associations between eGDR and all-cause and cardiovascular mortality. A cohort study of 5,339 diabetic patients with chronic coronary syndrome indicated that hsCRP played a role in mediating association between TyG and all-cause and cardiovascular death, which was consistent with our finding [42]. Therefore, better insulin sensitivity might decrease the risk of mortality partly through anti-inflammatory. Nevertheless, our study revealed that the impact of eGDR on outcomes was mediated in small part by inflammation biomarkers, thus implying the existence of additional pathways that may potentially mediate these consequences. Furthermore, depending on a single baseline measurement of inflammatory biomarkers may not offer a comprehensive depiction of the long-term inflammatory status, which makes it difficult to determine the causal relationships between eGDR and inflammation. Therefore, additional larger-scale prospective cohorts would be warranted to validate whether inflammation act as mediating factors between eGDR and the incidence of CVD and mortality, as well as to explore other exact mechanisms.

To our best knowledge, this is the first study to assess the predictive role of eGDR on CVD events and mortality among non-diabetic patients with CKD from two large prospective cohort studies with around ten years of follow-up. Additionally, the study aimed to determine the mediative effect of inflammation on these associations. Nevertheless, several limitations should be acknowledged. First, the evaluation of eGDR was at baseline and not longitudinally assessed throughout the study process. Second, the participants included were between 40 and 70 years of age and were more likely to be European ancestry, thus limiting the interpretation of these findings to young adults or other ethnic backgrounds. Third, the relationship between the eGDR and the gold standard measure of insulin resistance has not yet been validated in the CKD patients. Finally, there may be other unmeasured residual confounding factors, such as genetic predisposition.

Conclusions

The study identified a strong association between high eGDR levels and reduced risks of CVD events and mortality in non-diabetic CKD patients, suggesting eGDR could serve as a promising predictor and therapeutic target for preventing CVD events and mortality among non-diabetic CKD patients.

Availability of data and materials

Data from the UK Biobank cannot be shared publicly. However, data are available from the UK Biobank Institutional Data Access/Ethics Committee (contact: http://www.ukbiobank.ac.uk/or by email at access@ukbiobank.ac.uk) for researchers who meet the criteria for access to the confidential data. The data from the National Health and Nutrition Examination Survey is openly available at https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

ALB:

Albumin

ANOVA:

Analysis of variance

BMI:

Body mass index

CKD:

Chronic kidney disease

CRP:

C-reactive protein

CVD:

Cardiovascular disease

CHD:

Coronary heart disease

eGDR:

Estimated glucose disposal rate

eGFR:

Estimated glomerular filtration rate

TDI:

Townsend deprivation index

TG:

Triglyceride

TC:

Total cholesterol

TyG:

Triglyceride and glucose index

HbA1c:

Hemoglobin A1c

UACR:

Urinary albumin-to-creatinine ratio

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

Hb:

Haemoglobin

HOMA-IR:

Homeostatic model assessment for insulin resistance

NLR:

Neutrophil-to-lymphocyte ratio

NSTE-ACS:

Non-ST-segment elevation acute coronary syndrome

NHANES:

National Health and Nutrition Examination Survey

T1DM:

Type 1 diabetes mellitus

T2DM:

Type 2 diabetes mellitus

WC:

Waist circumference

WBC:

White blood cell

IR:

Insulin resistance

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Acknowledgements

This research has been conducted using the UK Biobank Resource, approved project number 90060, and US National Health and Nutrition Examination Survey. We are grateful to the participants and those managing data.

Funding

This study is funded by the National Natural Science Foundation of China (82173905 and 82070759), The Scientific Research Program of FuRong Laboratory (2022RC3023), Science and Technology Innovation Young Talents of Hunan Province (2024RC3050), and Hunan Provincial Natural Science Fund for Outstanding Young Scholars (No.2024JJ4090). 

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Author notes

  1. Juan Peng and Yan Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Nephrology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China

    Juan Peng, Weilin Chen, Li Chen, Bin Yi & Zhijun Huang

  2. Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Yan Zhang & Yiqun Zhu

  3. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, China

    Yan Zhang

  4. Clinical Research Center for Critical Kidney Disease in Hunan Province, Changsha, Hunan, China

    Weilin Chen, Li Chen, Bin Yi & Zhijun Huang

  5. Health Management Center, Xiangya Hospital Central South University, Changsha, Hunan, China

    Fangyu Ma

  6. Furong Laboratory, Changsha, Hunan, China

    Zhijun Huang

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Contributions

FYM, BY, and ZJH conceptualized and designed the study. JP, YQZ, and YZ managed, analysed and verified the data. JP and YZ prepared the first draft. JP, YQZ, LC, and WLC interpreted the data, and YQZ, JP, and YZ were responsible for editing and proofreading the manuscript. FYM, BY, and ZJH supervised the study. All authors contributed to the critical revision of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fangyu Ma, Bin Yi or Zhijun Huang.

Ethics declarations

Ethics approval and consent to participate

The UK Biobank was approved by the North West Multi-Centre Research Ethics Committee (16/NW/0274). All participants provided written and informed consent for data collection, analysis, and record linkage. This study was performed under UK Biobank application number 90060. The NHANES study protocol was approved by the NCHS Research Ethics Review Board, and written informed consent was provided by all participants.

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The authors declare that they have no competing interests.

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Supplementary Information

12916_2024_3582_MOESM1_ESM.docx

Additional file 1: Table S1. The variance inflation factors for all covariates. Abbreviations: BMI, body mass index; TDI, Townsend deprivation index. HbA1c, hemoglobin A1c; UACR, urinary albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ALB, albumin; Hb, haemoglobin; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio; VIF, variance inflation factors. Table S2. Association between eGDR and incident CVD and mortality in UK Biobank Study. Estimates are hazard ratios (95%CI) from Cox proportional hazard models. Model 1: crude. Model 2: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity. Model 3 is adjusted for model 2 covariates plus eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drug, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease. Table S3. Goodness of fit for all models. Model 1: crude. Model 2: age, sex, TDI (UK Biobank only), annual household income (NHANES only), education, smoking, alcohol drinker status, ethnicity. Model 3 is adjusted for model 2 covariates plus eGFR, UACR, physical activity at goal (UK Biobank only), BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin (UK Biobank only), antihypertensive drug (UK Biobank only), lipid-lowering drugs (UK Biobank only). Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease. Table S4. Association Between eGDR and Incidence of CVD by Specific Baseline Characteristics in UK Biobank Study. The full adjusted model: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; TDI, Townsend deprivation index. Table S5. Association Between eGDR And Incidence of Mortality by Specific Baseline Characteristics in UK Biobank Study. Estimates are hazard ratios (95%CI) from Cox proportional hazard models. The full adjusted model: for age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; TDI, Townsend deprivation index. Table S6. Multivariable Linear Regression for the Associations Between eGDR and Inflammation Biomarkers Among Individuals with CKD in UK Biobank Study. Abbreviations: CRP, C-reactive protein; NC, Neutrophil count; NLR, neutrophil-to-lymphocyte, WBC, white blood cell. Table S7. Association Between eGDR and Incident CVD and Mortality After Excluding Participants with Missing Covariates in UK Biobank Study (N = 17,561). Estimates are hazard ratios (95%CI) from Cox proportional hazard models. The full adjusted model: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; a P < 0.001, b P < 0.05. Table S8. Association Between eGDR and Incident CVD and Mortality in CKD After Excluding Incident Events Occurred within the Second Year (N = 19,292) and the Fifth Year (N = 18,275) of Follow up in UK Biobank Study. Estimates are hazard ratios (95%CI) from Cox proportional hazard models. The full adjusted model: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; a P < 0.001, b P < 0.05. Table S9. Association Between eGDR and Incident CVD and Mortality in CKD Using Competing Risk Regression (Fine and Gray) in UK Biobank Study (N = 19,906). Estimates are hazard ratios (95%CI) from Cox proportional hazard models. The full adjusted model: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; a P < 0.001, bP <0.05. Table S10. Association Between eGDR and Incident CVD and Mortality in CKD After Excluding Participants with Diabetes During Follow-up in UK Biobank Study (N = 18,557). Estimates are hazard ratios (95%CI) from Cox proportional hazard models. The full adjusted model: age, sex, TDI, education, smoking, alcohol drinker status, ethnicity, eGFR, UACR, physical activity at goal, BMI, LDL-C, HDL-C, ALB, Hb, CRP, NLR, drugs aspirin, antihypertensive drugs, lipid-lowering drugs. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; a P < 0.001, bP < 0.05. Table S11. Baseline Characteristics of 1,600 Participants by Tertile of eGDR in NHANES Study. Values for categorical variables are presented as count (%), and values for continuous variables are presented as mean ± SD. Abbreviations: BMI body mass index; HbA1c, hemoglobin A1c; UACR, urinary albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ALB, albumin; Hb, haemoglobin; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio. a P values derived from χ2 tests (categorical variables) or ANOVA (continuous variables) comparing values across tertiles. Table S12. Association Between eGDR and Incident CVD and Mortality in CKD in NHANES Study. Estimates are hazard ratios (95%CI) from Cox proportional hazard models. Model 1 :crude. Model 2: age, sex, annual household income, education, smoking, alcohol drinker status, ethnicity. Model 3 is adjusted for model 2 covariates plus eGFR, UACR, BMI, LDL-C, HDL-C, ALB, Hb, NLR. Abbreviations: CVD, cardiovascular disease; CHD, coronary heart disease; aP < 0.001, >bP < 0.05.

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Peng, J., Zhang, Y., Zhu, Y. et al. Estimated glucose disposal rate for predicting cardiovascular events and mortality in patients with non-diabetic chronic kidney disease: a prospective cohort study. BMC Med 22, 411 (2024). https://doi.org/10.1186/s12916-024-03582-x

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