Linking Estimated Glucose Disposal Rate to Major Adverse Cardio-Cerebrovascular Events in Populations With and Without Diabetes: A Systematic Review and Meta-Analysis

Abstract

Background:

Insulin resistance (IR) contributes significantly to major adverse cardio-cerebrovascular events (MACCE), with the estimated glucose disposal rate (eGDR) serving as a novel marker for assessing IR. This systematic review and meta-analysis investigate the association between eGDR and MACCE outcomes, aiming to clarify its predictive value across different diabetes statuses.

Methods:

We searched databases for studies examining the relationship between eGDR and MACCE, including myocardial infarction (MI), stroke, ischemic heart disease (IHD), cardiovascular disease (CVD), and all-cause mortality. We compared groups with the lowest versus highest eGDR. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using random effect models. Subgroup analyses assessed eGDR efficacy by diabetes status.

Results:

Our search identified 16 studies with 198 626 participants. The group with the lowest eGDR had a significantly higher risk of MACCE compared to the group with the highest eGDR (HR = 2.21, 95% CI 1.17-4.18). Additionally, the group with the lowest eGDR had notably worse outcomes for all-cause mortality, MI, stroke, CVD, and IHD with HRs of 2.03 (95% CI 1.05-3.90), 1.82 (95% CI 1.30-2.55), 2.82 (95% CI 1.66-4.69), 2.95 (95% CI 1.99-4.37), and 7.97 (95% CI 2.57-24.73), respectively. Subgroup analyses revealed consistent results for CVD in both populations with diabetes and non-diabetes status, for stroke in the population with non-diabetes status, and for IHD in the population with diabetes.

Conclusions:

Lower eGDR, indicating higher IR, is linked with a significantly increased risk of MACCE. This parameter could enhance risk stratification models for predicting MACCE. Further studies are needed to evaluate the clinical role of eGDR in managing cardio-cerebrovascular risk across subgroups.

Plain Language Summary

How Insulin Resistance Measured by eGDR Relates to Heart and Stroke Risks in People With and Without Diabetes: A Review and Analysis of Studies

Insulin resistance is a condition where the body does not respond well to insulin, a hormone that helps control blood sugar levels. This condition is known to increase the risk of serious heart and brain problems, such as heart attacks and strokes. A new way to measure insulin resistance is called the estimated glucose disposal rate (eGDR). Our study looked at how well eGDR can predict the chance of having major heart and brain events. We reviewed 16 studies involving nearly 200 000 people. We compared those with the lowest eGDR (meaning higher insulin resistance) to those with the highest eGDR (lower insulin resistance). We found that people with lower eGDR had more than twice the risk of serious heart and brain problems compared to those with higher eGDR. This included a higher risk of death from any cause, heart attacks, strokes, and other heart diseases. We also looked at people with and without diabetes separately. The increased risks were seen in both groups, showing that eGDR is a useful measure regardless of diabetes status. In conclusion, a lower eGDR, which shows greater insulin resistance, is linked to a higher chance of having major heart and brain health problems. Measuring eGDR in patients with risk factors could help doctors better identify people at risk and improve prevention strategies. More research is needed to understand how this measure can be used in everyday medical care.

Plain Language Summary

This graphical abstract summarizes the comparison of clinical outcomes between groups with the lowest versus the highest estimated glucose disposal rate (eGDR). Across all measured outcomes the lowest eGDR group demonstrated a significantly higher risk compared to the highest eGDR group. we further categorized the analyzed studies into 3 subgroups based on diabetes status: (1) studies involving populations with diabetes, (2) studies involving individuals without diabetes, and (3) studies including the general population without specific categorization by diabetes status. The 2 pictures of men in the graphical abstract illustrate the relationship between clinical factors and eGDR. A person with higher blood pressure (indicated by positive hypertension in the eGDR formula), increased waist circumference, and elevated HbA1c tends to have a lower eGDR. This lower eGDR is associated with increased risk of adverse outcomes (CI, confidence interval; CVD, cardiovascular disease; eGDR, estimated glucose disposal rate; HbA1c, glycosylated hemoglobin A1C; HR, hazard ratio; HTN, hypertension; IHD, ischemic heart disease; MACCE, major adverse cardio-cerebrovascular events; MI, myocardial infarction; WC, waist circumference).

Keywords

estimated glucose disposal rate major adverse cardio-cerebrovascular events insulin resistance meta-analysis

Highlights

The estimated glucose disposal rate (eGDR) is recognized as a novel marker for assessing insulin resistance (IR), which has a strong correlation with cardio-cerebrovascular outcomes.

Individuals with lower eGDR values have a significantly higher risk of major adverse cardio-cerebrovascular events (MACCE) compared to those with higher eGDR values.

The comparison of outcomes, including all-cause mortality, myocardial infarction, stroke, cardiovascular disease, and ischemic heart disease reveals significant differences between groups with lower versus higher eGDR values.

Introduction

Cardiovascular disease (CVD) is still recognized as the leading cause of death worldwide, accounting for about one-third (32.3%) of global death.¹ CVD mortality and morbidity are on the rise as a result, in part, to increases in the prevalence of obesity, metabolic syndrome, diabetes, and an aging population.²

Insulin resistance (IR), characterized by reduced responsiveness of insulin target tissues, is a key factor in metabolic and inflammatory disorders and it is correlated with metabolic syndrome.^3
-5 It is a crucial pathogenic factor in type 2 diabetes mellitus (T2DM) and prediabetes,⁶ and it can differ considerably among populations with type 1 diabetes mellitus (T1DM).⁷ However, the gold standard technique for assessing IR is hyperinsulinemic-euglycemic clamp,⁸ its invasive nature and high cost limit its use in clinical practice and large-scale studies.⁷ Consequently, non-invasive approaches such as homeostasis model assessment of insulin resistance (HOMA-IR), triglyceride-glucose (TyG) index, and estimated glucose disposal rate (eGDR) have been developed.^9
-11 However, HOMA-IR derived from fasting glucose and insulin levels, is influenced by insulin consumption and has limitations related to cost and testing duration.^12,13 The TyG index, derived from fasting glucose and triglyceride levels, has shown promise in predicting major adverse cardio-cerebrovascular events (MACCE),^12,13 although controversies regarding its efficacy persist.^14,15 Factors affecting the TyG index’s reliability include renal insufficiency,¹⁶ higher risk of cardiovascular outcomes either in individuals with low triglyceride levels,¹⁷ and the influence of diabetes medications.¹⁸ The estimated glucose disposal rate (eGDR) is another non-invasive marker for assessing IR, derived from clinically relevant parameters such as hypertension (HTN), glycosylated hemoglobin A1c (HbA1c), and waist circumference (WC).¹⁹ Its reliance on commonly measured clinical data makes it suitable for both clinical settings and large cohort studies.

IR is significantly linked to atherosclerosis and CVD,²⁰ with evidence suggesting that reducing IR could lower the prevalence of these conditions substantially. Although the precise mechanism of IR is not yet fully understood, improvement in insulin sensitivity indicated reduced risk of myocardial infarction (MI),²¹ transient ischemic attack (TIA),²² and heart failure (HF).²³ Therefore, early diagnosis of high-risk patients with IR and active treatment to improve insulin sensitivity may help prevent CVD and stroke and improve patient prognosis.¹⁷ This study aims to assess the effectiveness of eGDR as a novel predictor factor for MACCE outcomes across subgroups of individuals with diabetes, individuals with non-diabetes status, and the general population; as well as its potential role as a target for therapeutic interventions.

Materials and Methods

Our systematic review and meta-analysis study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement²⁴ and our protocol was registered in the Prospective Register for Systematic Reviews (PROSPERO; registration number: CRD42024589181).

Search Strategy and Study Selection

Databases including Web of Science, PubMed, Scopus, and Embase were systematically searched to identify published observational studies, such as cross-sectional and cohort studies, examining the association between eGDR and MACCE. The search encompassed studies published in English from the inception of each database until September 2024. The search strategy consisted of the following Mesh terms: estimated glucose disposal rate, eGDR, sensitivity insulin, resistance insulin, insulin sensitivity, major adverse, cardiac event, adverse, hospitalization, rehospitalization, re-hospitalization, readmission, revascularization, mi, myocardial infarction, myocardial infarct, heart attack, stroke, Mortality, death, End-Of-Life, cardiac accident, cardiovascular accident. The search terms utilized in each database are demonstrated in Supplementary Table 1. After removing duplicates from the imported searches, 2 researchers (S.S. and A.M.) independently screened the studies by reviewing their titles and abstracts. The full text of eligible studies was then assessed based on inclusion and exclusion criteria. In cases of disagreement, data were checked by the third author (HR). Original studies that evaluated the association between eGDR and MACCE and/or its components, with at least 2 groups categorized by eGDR and reporting at least 1 MACCE outcome were included. The formula for calculating eGDR is: eGDR (mg/kg/min) = 21.158 − (0.09 × WC [cm]) − (3.407 × HTN) − (−0.551 × HbA1c [%]). In this formula HTN is the presence of HTN by medical document or current physician diagnosis (0 = no, 1 = yes), WC is in centimetre, and HbA1c is in percentage.¹⁹ Exclusion criteria included: (1) conference abstracts, case reports, case series, commentaries, and editorials, (2) studies lacking MACCE outcome or its components, (3) studies that did not categorize populations based on eGDR, and (4) studies without available full-text.

Data Extraction

Two reviewers (S.S. and A.M.) independently extracted data from the included studies. Extracted data encompassed: (1) study identification details such as first author, country, design of study, year of publication, and follow-up duration, (2) study population characteristics including diabetes status, total population size, and subgroup sizes based on eGDR, gender percentage, mean age, mean body mass index (BMI), mean eGDR values along with HTN status, WC, and HbA1c levels, and (3) outcomes related to the incidence of MACCE, MI, stroke, IHD, CVD, and all-cause mortality. Definition of outcomes among the included studies are summarized in Supplementary Table 2. Disagreements among reviewers were resolved through consensus; if a consensus could not be achieved, a senior reviewer (H.R.) was involved.

Risk of Bias Assessment

Two reviewers (P.S. and S.S.) independently evaluated the quality of the included studies. The Newcastle-Ottawa scale (NOS) was employed for assessing the quality of cohort and cross-sectional studies. The NOS score is based on population selection, intergroup comparability, and outcome measurement, with a total score of 9. Scores are categorized as follows: 0 to 3 indicates poor quality, 4 to 6 signifies fair quality, and 7 to 9 represents good quality.²⁵ The modified Jadad scale was utilized for evaluating the quality of randomized controlled trials (RCTs), focusing on domains of randomization, blinding, and withdrawals and dropouts. This scale has a maximum score of 5, with 0 indicating poor quality, 1 to 3 representing moderate quality, and 4 and 5 denoting high quality.²⁶ Disagreements were similarly handled by a discussion with a third author (HR).

Data Analysis

The study was focused on outcomes of interest that reported hazard ratios (HRs) which are statistically and clinically comparable among the various studies. The combined HRs and 95% confidence interval (CI) were calculated using the variance inverse weighted random effects model. A statistically significant P < .05. Subgroup analyses were conducted based on diabetes status, categorizing studies into those involving populations with diabetes, populations with non-diabetes status, and general populations that included both groups. Meta-regression was conducted to examine the sources of heterogeneity across studies. To assess the effect of each study on overall results, “leave-one-out” analyses were performed. This method repeatedly recalculated the pooled effect estimate, excluding 1 study at a time. The study that most significantly influenced the effect estimates and the one that had the greatest effect on heterogeneity was subsequently removed.

Results

Baseline Characteristics

The comprehensive search process identified a total of 229 studies. After eliminating duplicates and conducting a screening of titles and abstracts, 47 studies were selected for further evaluation. Following a thorough assessment against the exclusion criteria during the full-text screening, 16 studies were included in the meta-analysis. Study selection process is summarized in the PRISMA figure (Figure 1).

Figure 1.

PRISMA chart.

Among the included studies, the majority were cohort studies,^11,27
-36 with 4 classified as cross-sectional,^21,37
-39 and 1 derived from pooled analysis of 2 RCTs.⁴⁰ In terms of geographic distribution, 7 of the 16 studies originated from China,^{11,21,27,28,32,35,36} 3 from Sweden,^29,30,34 2 of them were multinational,^33,40 and the remaining 4 studies were from USA,³⁸ Catanzaro,³⁷ Italy,³¹ and Lithuania.³⁹ The total sample size across these studies was 198 626 participants, with an average age of 60.31 years and a mean BMI of 28.85 kg/m² and males constituted 56.62% of the total population. Follow-up duration of included studies varied between 1 and 8.9 years. Detailed information can be found in Table 1.

Table 1.

Baseline Characteristics of Included Studies.

First author (y)	Study design	Country	Total population	Mean age	Gender (male, %)	Mean BMI	Mean WC	Diabetic status	HTN (%)	Mean HbA1c	Mean eGDR	Follow-up duration
Cutruzzolà (2024)³⁷	Cross-sectional	Catanzaro	158	38.9 ± 11.3	46.9	25.6 ± 4	87.5 ± 8.4	T1DM	27	7.5 ± 1.1	NA	NA
Ebert (2024)⁴⁰	Pooled analysis of 2 RCTs^a	Multinational	13 026	64.7 ± 9.5	69.8	31.3 ± 5	107 ± 11.3	T2DM	NA	7.7 ± 1.2	NA	3 y
Kong (2024)³⁶	Cohort	China	4752	48.4 ± 0.3	60.1	29.5 ± 0.1	NA	Pre-diabetic	NA	NA	NA	8.91 y
Song (2024)³³	Cohort	Multinational	4861	54.7 ± 0.9	57	33.9 ± 0.4	113 ± 1	67.4% Non-DM and 32.6% DM	NA	6.05 ± 0.1	NA	Long term follow-up
Zhang (2024)³⁵	Cohort	China	5512	58.2 ± 8.8	45.9	23.2 ± 3.5	84.5 ± 9.7	Non-DM	NA	5.1 ± 0.4	9.5 ± 2	6.6 y
Epstein (2023)³⁸	Cross-sectional	USA	207	43.7 ± 14.9	58	26.7 ± 5	92.4 ± 14.4	T1DM	44	8.2 ± 1.6	6.51 ± 2.31	NA
Liu (2023)²⁸	Cohort	China	2308	60.1 ± 9	71.8	26.1 ± 3.2	91.4 ± 12.4	34.6% DM	62.2	6.3 ± 1.2	NA	4 y
Lu (2023)¹¹	Cohort	China	6271	61.5 ± 11.4	68.1	24.7 ± 3.4	87.2 ± 11.6	36% Abnormal glycemic	72.2	6.5 ± 1.7	7.1 ± 2.1	1 y
Liu (2022)²⁷	Cohort	China	1510	59.7 ± 9.3	73.7	25.8 ± 3.1	89.6 ± 12	Non-DM	57.2	5.6 ± 0.4	NA	4 y
Ren (2022)³²	Cohort	China	8267	58.9 ± 9.4	47.4	23.2 ± 2.8	NA	Not-defined	NA	NA	NA	6 y
Xuan (2022)²¹	Cross-sectional	China	10 895	59.9 ± 10.1	40.2	24.8 ± 3.8	83.4 ± 10.2	16.22% DM	60.6	5.4 ± 0.5	8.5 ± 2.8	NA
Penno (2021)³¹	Cohort	Italy	15 656	66.6 ± 10.2	56.9	29 ± 3.7	102.5 ± 8	T2DM	83.7	7.5 ± 1.3	NA	7.4 y
Zabala (2021)³⁴	Cohort	Sweden	10 4697	62.9 ± 11.5	56	30.3 ± 5.4	104.5 ± 13.5	T2DM	67	7 ± 3.5	5.6 ± 2.2	5.6 y
Šimonienė (2020)³⁹	Cross-sectional	Lithuania	200	39.9 ± 12.1	42	24 ± 4.1	NA	T1DM	36.5	8.9 ± 2	NA	NA
Nyström (2017)³⁰	Cohort	Sweden	17 050	40.4 ± 15.1	56	25.8 ± 4.3	90.2 ± 13	T1DM	35	8.1 ± 1.3	NA	7.1 y
Nyström (2017)²⁹	Cohort	Sweden	3256	69.6 ± 7.8	77	28.8 ± 4.3	105 ± 12	T2DM	60	7.3 ± 1.1	5.7 ± 2.5	3.1 y

Abbreviations: BMI, body mass index; DM, diabetes mellitus; eGDR, estimated glucose disposal rate; HbA1c, hemoglobin A1c; HTN, hypertension; RCTs, randomized controlled trials; T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; WC, waist circumference.

Ebert et al is a pooled analysis of 2 RCTs: (FIDELIO-DKD; NCT02540993)⁴¹ and (FIGARO-DKD; NCT02545049).⁴² Although they did not categorize the populations in each trial based on eGDR, these studies did not meet our inclusion criteria. However, we included the pooled analysis as it aligns with our criteria.

Quality Assessment

Out of the 16 studies included in our meta-analysis, 15 were evaluated using the NOS quality assessment tool, while 1 study was assessed with the Jadad scale. The majority of the 15 studies (86.6%) were rated as having good quality, whereas the other 2 studies were classified as fair quality. This reduction in quality was attributed to short follow-up durations and small sample sizes in the mentioned studies. The overall quality of the study assessed with the Jadad scale was found to be high. Supplementary Table 3 summarized the results of the quality assessments for the included studies.

Comparison of Outcomes Between the Groups With the Highest and the Lowest eGDR

In comparing groups with the highest and lowest eGDR, the analyses revealed significant differences in various cardiovascular outcomes. MACCE outcome, assessed across 5 studies, indicated a markedly higher risk of individuals in the group with the lowest eGDR group versus those in the group with the highest eGDR, with (HR = 2.21, 95% CI 1.17-4.18). In terms of all-cause mortality, the analysis indicated a markedly higher risk of individuals in the group with the lowest eGDR compared to those in the group with the highest eGDR, with (HR = 2.03, 95% CI 1.05-3.90). This finding indicates an association between higher eGDR levels and reduced mortality risk. Additionally, the incidence of CVD and IHD was found to be significantly elevated in the lowest eGDR group versus the highest, with (HR = 2.95, 95% CI 1.99-4.37) and (HR = 7.97, 95% CI 2.57-24.7), respectively. These results demonstrate that lower eGDR levels are correlated with an increased risk of CVD and IHD. Regarding MI and stroke, individuals in the lowest eGDR group also exhibited significantly greater risks compared to those in the highest eGDR group, with (HR = 1.82, 95% CI 1.30-2.55) and (HR = 2.82, 95% CI 1.66-4.79), respectively (Figure 2A–F). These results are summarized in Table 2.

Figure 2.

Comparison of outcomes between the groups with the highest and the lowest eGDR. (A) MACCE forest-plot. (B) All-cause mortality forest-plot. (C) Stroke forest-plot. (D) Myocardial infarction forest-plot. (E) Cardiovascular disease forest-plot. (F) Ischemic heart disease forest-plot.

Table 2.

Comparison of Outcomes Among the Groups With the Lowest and the Highest eGDR.

Outcome	Number of studies	HR (95% CI)	I² (%)	P value total	P value heterogeneity
MACCE	5	2.21 (1.17-4.18)	99%	.01	<.01
All-cause mortality	7	2.03 (1.05-3.90)	99%	.03	<.01
MI	3	1.82 (1.30-2.55)	51%	<.01	.13
Stroke	7	2.82 (1.66-4.69)	94%	<.01	<.01
CVD	6	2.95 (1.99-4.37)	82%	<.01	<.01
IHD	3	7.97 (2.57-24.73)	90%	<.01	<.01

Abbreviations: CI, confidence interval; CVD, cardiovascular disease; eGDR, estimated glucose disposal rate; HR, hazard ratio; IHD, ischemic heart disease; MACCE, major adverse cardio-cerebrovascular events; MI, myocardial infarction.

Subgroup Analysis Based on Diabetes Status

The subgroup analysis categorized studies into 3 groups: populations with diabetes, populations with non-diabetes status, and general populations including both individuals with and without diabetes. This analysis yielded significant findings.

For MACCE, while there was a notable difference between the group with the lowest eGDR and the one with the highest eGDR, the analysis within the subgroup with individuals with diabetes did not indicate meaningful differences between these 2 groups (HR = 2.36, 95% CI 0.88-6.34). In the overall analysis of all-cause mortality, a significant difference between the groups with the lowest and highest eGDR was observed only in the general population subgroup, which exhibited lower heterogeneity (HR = 2.27, 95% CI 1.09-4.73, I² = 69%, P < .01). When examining stroke outcomes, the subgroup with population with non-diabetes status displayed a significantly elevated risk in the group with the lowest eGDR compared to the group with the highest eGDR (HR = 2.93 95% CI 2.18-3.94). For CVD outcome, both subgroups with populations with and without diabetes revealed a significantly increased risk associated with the lowest eGDR group compared to their highest eGDR counterparts (HR = 4.45, 95% CI 2.90-6.84) and (HR = 2.64, 95% CI 1.36-5.13), respectively. However, high heterogeneity persisted in both subgroups. Furthermore, within the subgroup with population with diabetes, there was a significantly higher risk of IHD in those with the lowest eGDR compared to those with the highest eGDR (HR = 14.69, 95% CI 11.99-18.00) However, subgroup analyses revealed high heterogeneity, and the limited number of studies included in each subgroup, along with small sample sizes, could introduce bias into the results. Therefore, further studies are needed to explore the effectiveness of eGDR in different diabetes status subgroups with larger populations to obtain a more reliable conclusion (Supplementary Figures 1–6). Table 3 summarizes the subgroup analyses of outcomes.

Table 3.

Subgroup Analyses of Outcomes Based on Diabetes Status.

Outcome	Number of studies	HR (95% CI)	I² (%)	P value total	P value heterogeneity
MACCE	5	2.21 (1.17-4.18)	99%	.01	<.01
Subgroups
Non-DM	1	1.67 (1.33-2.09)	—	—	—
DM	3	2.36 (0.88-6.34)	99%	.09	<.01
General population	1	2.25 (1.82-2.78)	—	—	—
All-cause mortality	7	2.03 (1.05-3.90)	99%	.03	<.01
Subgroups
Non-DM	1	1.11 (0.45-2.74)	—	—	—
DM	4	2.23 (0.75-6.59)	99%	.15	<.01
General population	2	2.27 (1.09-4.73)	69%	.03	.07
MI	3	1.82 (1.30-2.55)	51%	.13	<.01
Subgroups
Non-DM	1	1.25 (0.76-2.03)	—	—	—
DM	1	2.24 (1.68-2.97)	—	—	—
General population	1	1.89 (1.17-3.05)	—	—	—
Stroke	7	2.82 (1.66-4.69)	94%	<.01	<.01
Subgroups
Non-DM	2	2.93 (2.18-3.94)	0%	<.01	.95
DM	2	4.22 (0.89-20.08)	98%	.07	<.01
General population	3	2.03 (0.99-4.18)	86%	.05	<.01
CVD	6	2.95 (1.99-4.37)	82%	<.01	<.01
Subgroups
Non-DM	2	2.64 (1.36-5.13)	91%	<.01	<.01
DM	3	4.45 (2.90-6.84)	0%	<.01	.68
General population	1	1.78 (1.54-2.08)	—	—	—
IHD	3	7.97 (2.57-24.73)	90%	<.01	<.01
Subgroups
DM	2	14.69 (11.99-18.00)	0%	<.01	.94
General population	1	3.10 (1.64-5.88)	—	—	—

Abbreviations: CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; IHD, ischemic heart disease; MACCE, major adverse cardio-cerebrovascular events; MI, myocardial infarction.

Meta-Regression

Based on the meta-regression analysis of MACCE, all-cause mortality, stroke, and CVD, we found that studies with higher mean age in the population were associated with a reduction in the HR of outcomes when comparing the group with the lowest eGDR to the group with the highest eGDR. Specifically, we observed estimates of −0.122, −0.191, −0.230, and −0.092 with P < .005 for the group with the lowest eGDR mean age meta-regression, and −0.068, −0.090, −0.087, and −0.064 with P < .005 for the group with the highest eGDR mean age meta-regression across MACCE, all-cause mortality, stroke, and CVD outcomes. Additionally, the higher prevalence of male participants and mean BMI in the group with the highest eGDR were correlated with a lower HR for all-cause mortality (estimates of −0.071 and −0.207, respectively, with P < .005). A summary of these meta-regression findings can be found in Supplementary Table 4.

Sensitivity Analysis

Although we observed a high level of heterogeneity in the analysis, we performed a comprehensive evaluation of potential sources contributing to this variability. Studies were categorized based on diabetes status into 3 groups. Subgroup analyses based on diabetes status helped to reduce heterogeneity within the all-cause mortality subgroups; however, other outcomes did not show a decrease in heterogeneity among the included studies (see Supplementary Figures 7–12). The presence of other comorbidities and the demographic characteristics of participants may have contributed to the high heterogeneity. However, the significant variation in these characteristics limited our ability to conduct additional subgroup analyses and provide necessary insights for further original research in this area. A sensitivity analysis was performed using a leave-one-study-out approach. This method indicated that removing each of the 2 studies from the all-cause mortality analysis^29,33 and 1 study from the myocardial infarction (MI) analysis²⁸ resulted in non-significant differences between the lowest and highest eGDR groups. The potential bias introduced by these studies was further examined and discussed.

Discussion

The aim of this study was to compare the cardiovascular outcomes between individuals with the highest and lowest eGDR and to evaluate the impact of DM status on these outcomes. This study demonstrated that lower eGDR, a marker of IR, is associated with significantly worse cardiovascular outcomes, including higher risks of MACCE, CVD, and all-cause mortality particularly in patients with DM. The risk of MACCE was more than double in the group with the lowest eGDR compared to the group with the highest eGDR (HR = 2.21), and all-cause mortality showed a similar trend (HR = 2.03). Notably, the risk of CVD was nearly 3 times higher (HR = 2.95), while IHD risk increased dramatically, with a 7-fold higher risk in the lowest eGDR group (HR = 7.97). Additionally, significantly higher risk of MI and stroke were demonstrated in the groups with the lowest eGDR vs. the highest eGDR (HR = 1.82 and HR = 2.82, respectively), perhaps could be attributed to increased atherosclerosis and endothelial dysfunction, both of which are more prevalent in individuals with IR, contributing to a pro-inflammatory and pro-thrombotic state that accelerates vascular complications.^43,44 Subgroup analyses based on diabetes status demonstrated that normal glucose status individuals with low eGDR had a notably higher risk of stroke and CVD, whereas individuals with DM had a dramatically elevated risk of IHD (HR = 14.69). To the best of our knowledge, this systematic review and meta-analysis is the first thorough investigation of the relationship between eGDR and the risk of MACCE and its components in patients with various glycemic statuses.

The eGDR is a metabolic marker reflecting insulin sensitivity and, therefore, aligns more closely with T2DM compared to T1DM, where IR and metabolic dysregulation are prevalent¹¹ and eGDR is an important predictor of cardiovascular risk in patients. Typically, T1DM has been associated with a long-standing hyperglycemia-related increase in atherosclerosis and CAD, while T2DM has been related to comorbid states where IR is common such as HTN, predisposing to higher rates of HF and MI.^45,46 Sun et al⁴⁷ conducted a systematic review with a keen focus on the relation of eGDR with CVD and all-cause mortality in T1DM. Both our study and Sun’s demonstrate a meaningful association between lower eGDR and elevated CVD and all-cause mortality risk. Sun et al reported that for every 1-unit increase in eGDR, the risk of all-cause mortality decreased by 16% (HR = 0.84, 95% CI 0.81-0.87). Likewise, Sun et al indicated that by each unit of elevation in eGDR 17% of reduction in composite CVD outcomes shown (HR = 0.83, 95% CI 0.78-0.90). Differences in effect size could be attributed to population differences, as Sun et al focused solely on T1DM, missing the critical insights related to the metabolic and cardiovascular differences in T2DM.

Different IR markers have been extensively investigated in previous literature⁴⁸; each of them carrying advantages and disadvantages in individual characteristics and clinical settings. Specifically, the eGDR is a widely available non-invasive IR marker as it is estimated by generally accessible variables like HbA1c, WC, and HTN.^32,34 Yet, this tends to underestimate IR in those with a normal-weight category or without traditional cardiovascular risk factors. On the other hand, HOMA-IR, based on insulin and glucose levels during fasting, may be influenced by inflammation.^49,50 High levels of inflammatory markers, such as C-reactive protein, may falsely elevate HOMA-IR values and thereby render it less useful for patients with major active conditions of inflammation.⁵¹ Another common marker, the triglyceride-glucose index (TyG), combines fasting glucose and triglyceride levels, making it a strong indicator of IR, particularly in normal glucose status populations or individuals with metabolic syndrome.^52,53 Unlike eGDR, TyG tends to be more reflective of lipid metabolism and can better capture IR have associated with visceral adiposity. Whereas in individuals with T1DM, eGDR may be a better proxy for cardiovascular risk and also for metabolic health in general,⁵⁴ in those situations where HOMA-IR or other markers are assessed, they are best suited for populations in which inflammation and obesity are the main drivers of IR. Understanding these nuances is critical to tailoring appropriate screening protocols aimed at improving the detection of insulin sensitivity states and reducing cardiovascular and metabolic risks.

To delve deeper into the subgroup analysis and meta-regression results of our study, one important reason that possibly resulted in the DM subgroup not showing significant differences with respect to MACCE is that DM per se continues to be one of the major risk factors for cardiovascular events, and any further reductions in eGDR are not likely to increase this risk.⁵⁵ This suggests that IR may not add further predictive value in patients with DM, as their elevated cardiovascular risk is already driven by existing metabolic and inflammatory pathways, leaving little room for eGDR-related changes to alter outcomes significantly. This is because patients with DM often have multiple co-existing risk factors, such as chronic inflammation, endothelial dysfunction, and dyslipidemia that have independent drives on cardiovascular outcomes.⁵⁶ From a public health perspective, this finding suggests that strategies solely focused on improving insulin sensitivity may not achieve adequate risk reduction for MACCE in populations with DM. Thus, comprehensive cardiovascular risk management becomes essential, as addressing blood pressure, lipid profiles, and lifestyle factors such as smoking cessation may more effectively mitigate the broad array of cardiovascular risks in these patients, beyond merely targeting insulin sensitivity. Instead, comprehensive cardiovascular risk management, including blood pressure control, lipid management, and smoking cessation, should be prioritized with glycemic control. To further elucidate the multifactorial influences underlying the observed outcomes in individuals with non-diabetes status and with low eGDR, a recent study uniquely assessed the predictive performance of eGDR for incident CVD in this population by incorporating eGDR into basic risk models. This study demonstrated that adding eGDR significantly enhanced the predictive accuracy for incident CVD, heart disease, and stroke among non-diabetics, suggesting its potential to refine future CVD risk scoring systems.³⁵ Importantly, the study also performed mediation analysis to explore the role of obesity in the relationship between eGDR and incident CVD, revealing that obesity partially mediates this association. This finding highlights the complex interplay between IR, hyperglycemia, and adiposity in driving cardiovascular risk, where hyperglycemia induced by IR may contribute to obesity, which in turn exacerbates vascular damage. Consequently, controlling body mass emerges as a critical intervention point to mitigate the detrimental cardiovascular effects associated with low eGDR in population with non-diabetes status. These insights complement our subgroup analyses results, which showed that normal glucose status populations likely reflect the heightened sensitivity of these individuals to reductions in eGDR, as they are less likely to have other risk factors that overshadow the impact of IR. In contrast, individuals with DM may show a higher IHD risk due to the compounded effects of multiple metabolic disturbances that persist despite variations in eGDR, making them less responsive to changes in insulin sensitivity alone. Public health policy may consider targeted early screening for IR among individuals with normal glucose levels who also present additional risk factors for CVD. Such an approach requires careful evaluation of the potential benefits and limitations, including cost-effectiveness, feasibility, and the impact of identifying IR in asymptomatic populations. This approach could help prevent early vascular changes in those who may not yet exhibit overt metabolic abnormalities but are at risk of progressing to more severe insulin resistance. The stronger effects of IR in all-cause mortality within the general population indicate a wider ramification beyond CVD, affecting mortality risk through chronic low-grade inflammation and metabolic dysfunction. Therefore, the focus should not only be placed on targeting high-risk populations such as DM but rather should focus on broader preventive measures through public education to maintain healthy insulin sensitivity.

Associations between higher mean age and HR reduction for MACCE, all-cause mortality, stroke, and CVD may reflect a complex interaction of age-related physiological changes with IR. While one of the most prevalent underpinning factors for cardiovascular events and mortality in younger subjects is IR,^57,58 aging, frailty, comorbid conditions, and even the very process of cellular aging may become stronger drivers of inferior outcomes, whereby the relative role of eGDR as a determinant of outcomes may be weakened. In older populations, cardiovascular risks are often mediated by factors like arterial stiffness, HF, and chronic inflammation,^59
-61 which may not be as strongly linked to IR as in younger individuals. Moreover, from the healthcare standpoint, elderly individuals might have been prescribed a more thorough medical treatment, including statins, antihypertensives, and DM medications, that can ameliorate the effects of low eGDR. There are a few contributing factors as to why a higher prevalence of males within the highest eGDR group associates with lower HR values for all-cause mortality: generally, men have higher muscle mass and lower overall body fat than women, possibly contributing to better glucose utilization and insulin sensitivity, particularly in the higher levels of eGDR.^62,63 Besides, men also tend to vary in cardiovascular risk patterns; their response to lifestyle and metabolic risk factor interventions is generally stronger.⁶⁴ There are also hormonal influences that may moderate negative effects in males, such as testosterone, which has positive effects on muscle mass and insulin sensitivity.⁶⁵ Counterintuitively, the fact that higher BMI was associated with lower all-cause mortality HRs might be explained by the well-observed “obesity paradox” effect, quite particularly among the elderly or ill^66,67; indeed, this may be a protective metabolic reserve in that population. Individuals with higher BMI often have more fat-free mass, which contributes to better insulin sensitivity and a more favorable metabolic profile, especially in those with higher eGDR.⁶⁸ While long-term obesity is detrimental, in specific contexts, a higher BMI can mitigate some of the risks associated with lower eGDR and offer a survival advantage.

Several limitations must be considered in the interpretation of these findings. First, the high level of heterogeneity observed across studies, particularly in the analyses of MACCE, CVD, and stroke, suggests substantial variability in the methodologies may have influenced the results and limited the generalizability of the findings. Additionally, the subgroup analyses, while informative, were constrained by the relatively small number of studies available for each subgroup, especially for normal glucose status populations and the very small number of studies involving T1DM and T2DM made it impossible for us to perform subgroup analysis on the type of DM. The limited power in these analyses could explain the lack of significant findings in some subgroups. Furthermore, the meta-regression analysis highlights demographic factors such as age, gender, and BMI that may confound the observed associations, but the inability to control for other unmeasured confounders, such as lifestyle factors and medication use, reduces the robustness of the conclusions. The lack of detailed individual-level data also hinders the ability to explore the role of other important covariates, such as the duration of DM or family history of CVD. Future research should focus on addressing these limitations by including more homogenous populations to minimize variability and better control for potential confounders, such as medication use, lifestyle factors, and comorbid conditions, to more accurately delineate the relationship between eGDR and cardiovascular outcomes.

Conclusion

This study emphasizes that high levels of eGDR protect against MACCE outcomes. Improvements in insulin sensitivity could result in substantial reductions in cardiovascular risk and mortality; thus, we propose that prevention protocols should use eGDR for better risk stratification of especially metabolically-dysregulated patients. This is especially pertinent in both DM and normal glucose status populations, where IR is a strong predictor of adverse outcomes. Consequently, clinicians should be considering the routine assessment of eGDR or related markers in CVD prevention risk stratification models. Future studies are needed to elucidate the underlying mechanisms of low eGDR associated with cardiovascular risk and assess the effectiveness of interventions.

Supplemental Material

sj-docx-1-end-10.1177_11795514251372702 – Supplemental material for Linking Estimated Glucose Disposal Rate to Major Adverse Cardio-Cerebrovascular Events in Populations With and Without Diabetes: A Systematic Review and Meta-Analysis

Supplemental material, sj-docx-1-end-10.1177_11795514251372702 for Linking Estimated Glucose Disposal Rate to Major Adverse Cardio-Cerebrovascular Events in Populations With and Without Diabetes: A Systematic Review and Meta-Analysis by Shayan Shojaei, Hanieh Radkhah, Alireza Azarboo, Pedram Soltani, Sadaf Esteki and Asma Mousavi in Clinical Medicine Insights: Endocrinology and Diabetes

Footnotes

Author Contributions

S.S., A.A., P.S., S.E., and A.M. conducted the methodology. H.R., A.M., and S.S. investigated the full manuscript and checked the validation. A.A. analyzed the data. All authors contributed in writing the manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

List of Abbreviations

MACCE, major adverse cardio-cerebrovascular events; CVD, cardiovascular disease; IR, insulin resistance; T2DM, type 2 diabetes mellitus; T1DM, type 1 diabetes mellitus; TyG, triglyceride-glucose index; eGDR, estimated glucose disposal rate; HOMA-IR, homeostasis model assessment of insulin resistance; HTN, hypertension; WC, waist circumference; HbA1c, glycosylated hemoglobin A1C; MI, myocardial infarction; HF, heart failure; TIA, transient ischemic attack; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROSPERO, Prospective Register for Systematic Reviews; BMI, body mass index; IHD, ischemic heart disease; NOS, Newcastle-Ottawa scale; RCTs, randomized controlled trials; HR, hazard ratio; CI, confidence interval.

Glossary

Atherosclerosis: Atherosclerosis is a disease where plaque, made of fat, cholesterol, and other substances, builds up inside the arteries, causing them to narrow and harden.

eGDR: eGDR or estimated glucose disposal rate, is a practical measure used to assess insulin sensitivity, or how well the body uses insulin to regulate blood sugar.

IHD: Ischemic heart disease (IHD), also known as coronary artery disease or coronary heart disease, refers to heart problems caused by narrowed or blocked arteries supplying blood to the heart muscle.

Insulin Resistance: Insulin resistance is a condition where the body’s cells become less responsive to the effects of insulin, requiring more insulin to be produced to maintain normal blood sugar levels.

Insulin Sensitivity: Insulin sensitivity refers to how effectively your body’s cells respond to insulin, enabling them to take up glucose from the blood and regulate blood sugar levels. It was assessed using the hyperinsulinemic-euglycemic clamp technique in a subset of participants, following standardized protocols.

HbA1c: HbA1c, also known as glycated hemoglobin or glycosylated hemoglobin, is a blood test that reflects average blood sugar levels over the past 2 to 3 months. It is a key tool for diagnosing and monitoring diabetes. The test measures the percentage of red blood cells that have glucose attached to their hemoglobin and measured using high-performance liquid chromatography methods in certified laboratories.

HOMA-IR: HOMA-IR stands for homeostatic model assessment for insulin resistance. It’s a method used to estimate insulin resistance, a condition where the body’s cells become less responsive to insulin, making it harder to regulate blood sugar. It was calculated from fasting blood samples (glucose and insulin) collected after overnight fast.

Hyperinsulinemic-Euglycemic Clamp: The hyperinsulinemic-euglycemic clamp is a technique used to assess how well the body utilizes insulin to process glucose. It involves infusing insulin to raise and maintain elevated insulin levels, while simultaneously infusing glucose at a variable rate to keep blood glucose levels constant (euglycemia).

Obesity Paradox: The obesity paradox refers to the observation that in certain patient populations, particularly those with chronic diseases like heart failure or after certain surgical procedures, individuals classified as obese may have a better survival rate or improved outcomes compared to those with a normal or underweight BMI.

MACCE: Major adverse cardio-cerebrovascular events (MACCE) is a composite endpoint frequently used in cardiovascular and cerebrovascular research, often defined as a combination of outcomes like myocardial infarction, stroke, and cardiovascular death. It’s a way to assess the safety and effectiveness of treatments by combining several serious cardio-cerebrovascular events into one measure.

Metabolic Syndrome: Metabolic syndrome is a cluster of conditions – including abdominal obesity, high blood pressure, high blood sugar, high triglycerides, and low HDL cholesterol – that increase the risk of heart disease, stroke, and type 2 diabetes.

Prediabetes: Prediabetes is a condition where blood sugar levels are higher than normal but not high enough to be diagnosed as type 2 diabetes. It’s a warning sign that without lifestyle changes, type 2 diabetes may develop.

Revascularization: Revascularization refers to medical procedures that restore or improve blood flow to an organ or body part that has experienced ischemia (restricted blood supply).

TIA: A transient ischemic attack (TIA), also known as a “mini-stroke,” is a temporary disruption of blood flow to the brain, causing stroke-like symptoms that resolve within 24 hours.

TyG: The triglyceride-glucose (TyG) index is a composite marker used to assess insulin resistance. It’s calculated using fasting triglyceride and fasting glucose levels, and is considered a reliable and cost-effective alternative to more complex methods of measuring insulin resistance.

ORCID iDs

Shayan Shojaei

Asma Mousavi

Data Availability Statement

The data supporting the findings of this study are available in the supplementary materials.

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