Skip to main content
Download PDF

Usefulness of estimated glucose disposal rate in detecting heart failure: results from national health and nutrition examination survey 1999–2018

Diabetology & Metabolic Syndrome volume 16, Article number: 189 (2024) Cite this article

Abstract

Background

Estimated glucose disposal rate (eGDR) is a novel, clinically available, and cost-effective surrogate of insulin resistance. The current study aimed to assess the association between eGDR and prevalent heart failure (HF), and further evaluate the value of eGDR in detecting prevalent HF in a general population.

Methods

25,450 subjects from the National Health and Nutrition Examination Survey 1999–2018 were included. HF was recorded according to the subjects’ reports. Logistic regression was employed to analyze the association between eGDR and HF, the results were summarized as Per standard deviation (SD) change. Then, subgroup analysis tested whether the main result from logistic regression was robust in several conventional subpopulations. Finally, receiver-operating characteristic curve (ROC) and reclassification analysis were utilized to evaluate the potential value of eGDR in improving the detection of prevalent HF.

Results

The prevalence of reported HF was 2.96% (753 subjects). After adjusting demographic, laboratory, anthropometric, and medical history data, each SD increment of eGDR could result in a 43.3% (P < 0.001) risk reduction for prevalent HF. In the quartile analysis, the top quartile had a 31.1% (P < 0.001) risk of prevalent HF compared to the bottom quartile in the full model. Smooth curve fitting demonstrated that the association was linear in the whole range of eGDR (P for non-linearity = 0.313). Subgroup analysis revealed that the association was robust in age, sex, race, diabetes, and hypertension subgroups (All P for interaction > 0.05). Additionally, ROC analysis displayed a significant improvement in the detection of prevalent HF (0.869 vs. 0.873, P = 0.008); reclassification analysis also confirmed the improvement from eGDR (All P < 0.001).

Conclusion

Our study indicates that eGDR, a costless surrogate of insulin resistance, may have a linear and robust association with the prevalent HF. Furthermore, our findings implicate the potential value of eGDR in refining the detection of prevalent HF in the general population.

Introduction

Heart failure (HF) is the destination of most cardiovascular diseases [1]. There was an increasing trend of HF prevalence during the past decades; the prevalence of HF is currently 1–3% worldwide, and more than 64 million people are affected by HF [2]. In America, approximately 6.7 million residents over 20 years old have HF, and the prevalence is expected to rise to 8.5 million by 2030 [3]. Additionally, the burden of HF in many developing countries maintains a rapidly increasing trend [4]. Based on this situation, improving the early diagnosis of HF in the general population is imperative, especially in primary care conditions.

HF is associated with systemic insulin resistance (IR). IR alters the systemic and neurohumoral milieu, resulting in changes in metabolism and signaling pathways in the heart, thereby contributing to myocardial dysfunction [5]. The changes contain activation of proximal insulin signaling pathways that may contribute to adverse left ventricular remodeling, and repression of distal insulin signaling pathways which may impair cardiac metabolism, calcium handling, structure, and function [6]. A recent study has demonstrated the association between IR and left ventricular hypertrophy, a precursor of HF [7]. Furthermore, a previous sizeable epidemiological study has identified that higher cumulative IR exposure adversely impacts left ventricular remodeling and function [8]. Moreover, a recent meta-analysis revealed that a higher level of IR is associated with a higher risk of developing HF, even after accounting for traditional risk factors [9]. However, the gold standard of IR, euglycemic insulin clamp, is expensive, complicated, and invasive [10]. Thus, it is currently only used in scientific research. Due to this reason, there is a demand for a simple, cost-effective, and non-invasive approach to monitor the severity of IR. Several non-invasive indices have been established to estimate IR, such as the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Triglycerides-glucose (TyG) index [11, 12], and previous studies have demonstrated their association with heart failure [13, 14].

Estimated glucose disposal rate (eGDR) was recently proposed to evaluate the degree of IR [15]. Later, it has been identified to fit the severity of IR precisely [16]. Previous studies have revealed that eGDR is valuable in predicting cardiovascular outcomes [17, 18]. Nevertheless, whether eGDR is associated with prevalent HF remains unknown. Therefore, the current work aimed to assess the association between eGDR and prevalent HF, and to evaluate the value of eGDR in improving the detection of prevalent HF in the general population.

Methods

Study population

The datasets used in the current analysis were obtained from the National Health and Nutrition Examination Survey (NHANES) website, covering 1999 to 2018. NHANES is an ongoing program conducted by the National Center for Health Statistics, involving a series of independent, nationally representative surveys. NHANES adopts a cross-sectional design. The survey has been conducted every two years in the United States over the past two decades. The survey employed a multistage, stratified, and clustered probability sampling design to maintain its representativeness. Data from different survey cycles can be combined for integrated analysis. Detailed information about NHANES, including recruitment procedures, population characteristics, and study design, can be found on the Centers for Disease Control and Prevention’s website (https://www.cdc.gov/nchs/nhanes/index.htm).

For this analysis, we included subjects aged 20 to 85 years who participated in NHANES from 1999 to 2018 (https://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes/Default.aspxBeginYear=1999). The exclusion criteria were missing HF data and missing covariates data (including sex, race, current drinking and smoking, height, weight, weight circumference, blood pressure covariates, glycohemoglobin, lipids covariates, and serum creatinine). In total, our study included 25,450 participants (Fig. 1). The NCHS Institutional Ethics Review Board approved the NHANES protocol, and our study contained no personally identifiable information. Therefore, a further ethical review was not required. Our study’s data can be accessed through the official NHANES website.

Fig. 1
figure 1

Flow chart of the subject’s enrollment. * Covariates included sex, race, current drinking and smoking, height, weight, weight circumference, blood pressure covariates, glycohemoglobin, lipids covariates, and serum creatinine

Full size image

Data collection and measurements

NHANES data collection involved participant interviews and subsequent laboratory tests. Interviews were conducted in homes, and demographic information was gathered using a computer-assisted system. If a subject was unable to answer a question, a family member would provide the answer.

Anthropometric measurements followed a standardized procedure. Heights were measured to the nearest 0.1 centimeter, and weight was measured to the nearest 0.1 kg. Waist circumference (WC) was measured at the horizontal level 1 cm above the umbilicus. Blood pressure measurements were conducted after at least five minutes of quiet sitting. In our study, the mean of three blood pressure readings was analyzed. On the NHANES website, the “Physician Examination Methods Manual” provides further information regarding blood pressure measurement.

Laboratory examinations were conducted at laboratories certified by the CDC. Serum creatinine (Scr) was measured using the Jaffe rate method on the DxC800 modular chemistry side; Fasting plasma glucose (FPG) was measured by the oxygen rate method on the Modular Chemistry side of the Beckman DxC800; Blood lipids were quantified by enzymatic assay on the Roche Modular P and Roche Cobas 60,000 chemistry analyzer.

Definition

Current drinking was defined as consuming alcohol at least twelve times in the year before enrollment. Current smoking was classified as reporting smoking cigarettes’ some days’ or ‘every day’ in response to the question ‘Do you currently smoke cigarettes?’ Socioeconomic status was assessed using the poverty-to-income ratio (PIR), which compares family income to the federal poverty threshold. HF was derived from the question: ‘Has a doctor or other professional health worker ever told you that you had congestive heart failure?‘. Body mass index (BMI) was calculated as weight (kg) ratio to height (m) squared. Participants were considered to be undergoing anti-diabetic therapy if they reported taking medication to lower blood sugar levels or were currently using insulin; FPG ≥ 7 mmol/L and/or self-reported use of anti-diabetic therapy was defined as diabetes [19]. Answering “Yes” to the question “Now taking prescribed medicine for hypertension” was determined as anti-hypertensive therapy; A mean systolic blood pressure (SBP) ≥ 140 mmHg, and/or a mean diastolic blood pressure (DBP) ≥ 90 mmHg, and/or anti-hypertensive therapy were indicated as hypertension [20]. Answering “Yes” to the question “Now taking prescribed medicine for cholesterol” during the interview was defined as lipid-lowering therapy. Coronary heart disease (CHD) history was defined as answering “yes” to the question “Someone ever told you had coronary heart disease.” eGDR was formulated using the equation: eGDR = 21.158-(0.09 * WC) –[3.407 * hypertension (no = 0/yes = 1)] -(0.551* glycohemoglobin) [21]. The identification of HF is based on the questionnaire by asking “Someone ever told you had congestive heart failure?” [22].

Statistical analysis

In our study, we employed a statistical weighting to account for the survey design of NHANES. Categorical variables were summarized using frequencies and 95% confidence intervals (CI), while continuous variables were presented as mean values with corresponding 95% CIs. To compare categorical variables, we used the Chi-square test, and for continuous variables, we employed the t-test for variables with normal distribution and the rank-sum test for variates with skewed distribution (including PIR, FPG, triglycerides, and glycohemoglobin). The statistical analysis contained two main parts. In part one, the association between eGDR and the risk for prevalent HF was assessed using multivariate logistic regression analysis, and the results were reported as odds ratios (ORs) with 95% CIs. eGDR was first regarded as a continuous variable in the regression analysis, the results were summarized as Per standard deviation (SD) change. To display whether the association followed a linear pattern, eGDR was divided into quartiles and analyzed in the regression analysis as a categorical variable. Finally, a P for trend analysis would test whether the ORs for quartile 1 to 4 had a statistically significant decrease trend. Additionally, we applied a generalized additive model with a spline smooth-fitting function to verify the linearity of the association observed in the quartile analysis, and the linearity was tested via a logarithmic likelihood test. Then, we employed subgroup analysis to test whether the main result from logistic regression was robust in several conventional subpopulations. In part two, we performed receiver-operating characteristic curve (ROC) analysis and reclassification analysis to evaluate the potential value of eGDR in improving the detection of prevalent HF. The reclassification analysis included the continuous net reclassification index (NRI) and integrated discrimination index (IDI). All statistical analyses were conducted using Stata Statistical Software (version 15.0; StataCorp. LLC., College Station, TX, USA), R (The R Foundation), and EmpowerStats (X&Y Solutions, Inc., Boston, MA, USA). Statistical significance was defined as a two-tailed P-value less than 0.05.

Results

Participants characteristics

The characteristics of the participants were listed in Table 1. The prevalence of HF was 2.95% (753/24,697). For demographic data, HF patients were older and had larger percentages of male sex (54.80% vs. 48.70%, P = 0.012), white race (78.93% vs. 73.51%, P = 0.013), and current drinking status (32.80% vs. 26.58%, P < 0.001). PIR condition (2.29 vs. 3.03, P < 0.001) was worse in HF patients than non-HF subjects. Regarding the anthropometric parameters, BMI (31.81 kg/m*2 vs. 28.69 kg/m*2, P < 0.001), WC (109.50 cm vs. 98.52 cm, P < 0.001), mSBP (129.08 mmHg vs. 121.80 mmHg, P < 0.001), and mDBP (66.53 mmHg vs. 70.51 mmHg, P < 0.001) were significantly higher in HF patients than in non-HF participants. Laboratory examinations demonstrated that FPG (6.58 mmol/L vs. 5.45 mmol/L, P < 0.001), glycohemoglobin (6.23% vs. 5.56%, P < 0.001), triglycerides (1.93 vs. 1.71, P < 0.001), and Scr (105.33 µmol/L vs. 78.18 µmol/L, P < 0.001) were significantly lower in non-HF subjects, while TC (4.65 mmol/L vs. 5.08 mmol/L < P < 0.001) and HDL-c (1.26 mmol/L vs. 1.40 mmol/L, P < 0.001) was significantly lower in HF patients. Data related to medical history revealed that the rates of anti-hypertensive therapy (72.05% vs. 24.54%, P < 0.001), anti-diabetic history (29.78% vs. 6.40%, P < 0.001), and lipid-lowering history (53.34% vs. 15.20%, P < 0.001) were remarkably higher in the HF group. Correspondingly, the percentages of prevalent hypertension (77.84% vs. 31.69%, P < 0.001), diabetes (36.91% vs. 10.32%, P < 0.001), and CHD history (40.85% vs. 2.44%, P < 0.001) were also significantly higher in the HF group. Finally, the value of eGDR (4.34 vs. 7.36, P < 0.001) was significantly lower in the HF group than in the non-HF group.

Table 1 Subjects’ characteristics
Full size table

Association between eGDR and the prevalent HF

Data related to the association between eGDR and the prevalent HF were summarized in Table 2. In the crude model, each SD increment of eGDR could decrease the risk of prevalent HF by 62% (OR: 0.380, 95% CI: 0.350–0.412, P < 0.001). After adjusting for age, sex, race, current smoking and drinking status, and PIR, the risk decrement for each SD increase of eGDR diminished to 53.2% (OR: 0.468, 95% CI: 0.415–0.528, P < 0.001). With further adjustment of BMI, WC, Scr, FPG, TC, HDL-c, mSBP, anti-hypertensive therapy, anti-diabetic therapy, lipid-lowering therapy, and CHD history, the risk decrement shrank to 43.3% (OR: 0.567, 95% CI: 0.462–0.697, P < 0.001) for each SD increase of eGDR. In the quartile analysis, the top quartile of eGDR only had a 31.1% (OR: 0.311, 95% CI: 0.171–0.565, P < 0.001) risk of prevalent HF when compared to the bottom quartile in the full model, and there was a trend towards lower risk of prevalent HF from quartile 1 to quartile 4 (P for trend < 0.001).

Table 2 Association between eGDR and the risk of prevalent HF
Full size table

Linearity of the association

Table 2 shows a significant trend towards lower risk of prevalent HF from quartile 1 to quartile 4. We conducted a smooth curve fitting to confirm whether the association between eGDR and prevalent HF was linear. The results (Fig. 2) displayed that the risk of prevalent HF decreased linearly along with the increase of eGDR in the whole range of eGDR, and the statistical test confirmed this linearity (P for non-linearity = 0.313).

Fig. 2
figure 2

Smooth curve fitting to assess the linearity of the association between eGDR and the prevalent HF. The model was adjusted for age, sex, race, current smoking, current drinking, PIR, BMI, WC, Scr, FPG, TC, HDL, SBP, anti-hypertensive therapy, anti-diabetic therapy, lipid-lowering therapy, and CHD history (The same as Model 2 in Table 2). The dotted lines depicted the pointwise 95% CI, and the solid line in the plot displayed the estimated risk of prevalent reported HF. The association followed a linear pattern in the whole range of eGDR

Full size image

Subgroup analysis

Subgroup analysis was conducted to assess whether the association between eGDR and prevalent HF was consistent among several conventional subpopulations (Fig. 3). The logistic regressions were adjusted for every covariate in the Model 2 of Table 2, except for those used to define subgroups. The results showed that the association was robust in sex (male: 0.591, 95% CI: 0.454–0.770; Female: 0.548, 95% CI: 0.423–0.709), age (< 60 years group: 0.608, 95% CI: 0.453–0.816; ≥60 years group: 0.570, 95% CI: 0.424–0.767), race (white: 0.587, 95% CI: 0.464–0.741; Other race: 0.532, 95% CI:0.364–0.776), and diabetes (non-diabetes: 0.615, 95% CI: 0.495–0.764; diabetes: 0.525, 95% CI:0.382–0.722) subgroups (all P for interaction > 0.05). In hypertension subgroups, we observed that the OR value in the normotensive subgroup (0.355, 95% CI:0.240–0.529) was lower than that in the hypertensive subgroup (0.608, 95% CI:0.459–0.804). However, this difference did not achieve statistical significance (P for interaction = 0.101).

Fig. 3
figure 3

Subgroup analysis for the association between eGDR and the prevalent HF. The multivariate logistic model adjusted for all variates used in Model 2 of Table 2, except for the variate used to define subgroups. The association was robust to age, sex, race, and diabetes subgroups; However, in hypertension subgroups, the OR value in the normotensive subgroup was slightly lower than that in the hypertensive subgroup

Full size image

ROC and reclassification analyses

ROC and reclassification analyses were conducted to evaluate the efficacy of eGDR in improving the detection of HF (Table 3 and Figure S1). In ROC analysis, the AUC of eGDR alone was 0.767 (95% CI: 0.762–0.772). When adding eGDR into clinical risk factors, eGDR significantly improved the AUC (0.869 vs. 0.873, P for comparison < 0.001). Regarding the reclassification analysis, both continuous NRI (0.369, 95%CI:0.297–0.442) and IDI (0.004, 95%CI:0.002–0.007) confirmed a significant improvement from eGDR to improve the detection of prevalent HF.

Table 3 ROC and reclassification analysis for eGDR to refine the detection of prevalent HF
Full size table

To test whether the findings from ROC and reclassification analysis were robust in common subpopulations (including sex, age, race, diabetes, and hypertension subpopulations), we conducted a series of sensitivity analyses. The results were displayed in Table S1. As demonstrated, the AUC values of eGDR alone in all these subpopulations were significant (all P < 0.05) and similar to the AUC in the whole population. Both ROC comparison analysis and reclassification analysis revealed significant and positive results in all subpopulations (all P < 0.05).

Discussion

Our current work has two parts of findings: (1) There is a significant and negative association between eGDR, a surrogate of IR, and the risk of prevalent HF in the general population. Furthermore, the association is linear in the full range of eGDR. Moreover, the association is robust among conventional cardiovascular sub-populations. (2) eGDR could help to improve the detection of prevalent HF in the general population and several conventional subpopulations. Our findings provide a clue for the potential value of eGDR in improving the detection of prevalent HF in the general population, especially in primary care conditions.

Interpretation of the results

In our analysis, the logistic regression and smooth curve fitting analysis demonstrated a significant and negative association between eGDR and prevalent HF after full adjustment. Therefore, eGDR could have an independent and linear association with the risk of prevalent HF in the general population. Subsequently, subgroup analysis revealed that our main findings were robust to sex (male or female), age (≥ 60yrs or < 60yrs), race (white or others), diabetes, and hypertension subgroups. Therefore, applying the main results to these subpopulations should be reasonable. In the second part of the analysis, both ROC and reclassification analysis achieved significance for eGDR, implicating its potential value to optimize the detection of prevalent HF in the general population and conventional subpopulations.

Comparison with other non-invasive indices for IR

Apart from eGDR, there are several non-invasive indices have been established for estimating insulin sensitivity. Among them, HOMA-IR and TyG index are most commonly studied. HOMA-IR is the earliest non-invasive index for estimating IR. Previous studies have identified its association with HF in multiple populations [23,24,25]. Furthermore, a study demonstrated that HOMA-IR has an inverse correlation with plasma B-type natriuretic peptide levels in patients with HF [26]. Additionally, its value in predicting incident HF has also been revealed [13]. However, the formula of HOMA-IR requires fasting insulin, which is always unavailable in the routine biochemistry test, especially in the primary care conditions. Thus, HOMA-IR has not been widely applied to the primary care conditions to help to screen or predict IR-related diseases. Accordingly, TyG index and eGDR were proposed. TyG index is calculated based on triglycerides and fasting glucose, while eGDR takes central obesity, hypertension, and long-term blood glucose level (glycohemoglobin) into account. Although formulated from different angles, both indices have displayed excellent performance in estimating IR [11, 15]. As for their difference, the most important one is that TyG index is positively associated with IR while eGDR is positively correlated with insulin sensitivity. Regarding their association with HF, a series of studies have demonstrated that the TyG index is associated with incident HF in varied types of populations [14]. However, two vital points should be noted. First, most studies were carried out based on the Chinese population. Studies focusing on the association between the TyG index and heart failure in the western population are still limited. Second, all the published articles pay attention to the value of TyG in predicting HF. Until now, there has been no report about the value of TyG in detecting prevalent HF. Contrary to the wide exploration of the correlation between TyG and HF, limited data have shown the association between eGDR and HF. To address this question, our study assessed the association between eGDR and prevalent HF, and identified the value of eGDR in improving the detection of HF in a general American population. Nevertheless, our results still need more studies to confirm.

Established value of eGDR in the cardiovascular system

Since IR is one of the common pathophysiological mechanisms of cardiovascular diseases [27], eGDR could have a promising value in diagnosing and predicting cardiovascular diseases. Previous studies have demonstrated the value of eGDR in several cardiovascular diseases. In 2021, a UK research group showed that eGDR is correlated with thrombosis risk in 32 patients with type 1 diabetes [28]. Later, Xuan et al. demonstrated that eGDR is associated with prevalent ischemic heart disease, and eGDR could also improve the diagnosis of prevalent ischemic heart disease in the general population [29]. In the same year, Liu et al. revealed that eGDR is associated with in-stent restenosis following percutaneous coronary intervention in 1218 patients with non-ST-segment elevation acute coronary syndrome [30]. Recently, a study published by Li et al. elicited that eGDR could predict atrial fibrillation recurrence after radiofrequency ablation [31]. Since ischemic heart disease and atrial fibrillation are two major causes of HF, our current study expanded their findings. Sun et al. revealed that eGDR is associated with arterial stiffness and could predict long-term all-cause mortality; mediation analysis further demonstrated that arterial stiffness partially mediated the association of eGDR and mortality [32]. Moreover, A recent study identified that eGDR could improve the predictive ability for incident cardiovascular events. In total, published articles have demonstrated the promising diagnostic and predictive value of eGDR in multiple cardiovascular diseases; our study showed consistency with previous studies, and expanded the value of eGDR in detecting prevalent HF.

Pathophysiological mechanisms

Although incompletely understood, there are several pathways linking IR and HF. First, IR could disturb the metabolism of the heart. IR could result in excessive circulating triglycerides and free fatty acids, producing toxic lipid intermediates. Toxic lipid intermediates could induce cardiac lipotoxicity and decrease cardiac efficiency via enhancing fatty acid oxidation [33, 34]. Second, IR could also induce disturbances of systemic inflammatory and metabolic patterns. During IR, the concentrations of proinflammatory adipokines, cytokines, and catecholamines could increase, further promoting low-grade inflammation and hypercatecholaminemia. Consistent inflammation and hypercatecholaminemia may cast detrimental effects on cardiac function [35]. Last but not least, IR is associated with dysfunctional activation of the renin-angiotensin-aldosterone system. Chronic hyperinsulinemia during IR could induce increased expression of angiotensin II receptor and increased release of angiotensinogen from adipose tissue. The over-activation of the renin-angiotensin-aldosterone system could lead to adverse cardiac dysfunction and remodeling [36].

Clinical implication

The clinical implication of our current work is to improve the detection of prevalent HF, especially in primary care conditions. HF is the terminal status of multiple cardiovascular diseases. However, due to the compensatory mechanism of the cardiovascular system, many HF patients are unaware of their condition until cardiac function substantially decreases. Therefore, early detection of HF conditions is vital for secondary prevention. Specific HF biomarkers are not included in the routine examination list in primary care conditions, especially in developing countries. Hence, the early identification rate of HF status is relatively low. Our results implicate that eGDR could improve the detection of prevalent HF in the general population. Since eGDR is simple and cost-effective, applying it to clinical practice may improve the detection rate of HF status in primary care conditions, thereby improving the secondary prevention of HF.

Limitations

Our study has several limitations that need to be acknowledged. First, the cross-sectional design of NHANES made us unable to determine whether there is a causal relationship between eGDR and HF. Therefore, we could not explore the efficacy of eGDR in predicting the incidence of HF in the current study. Future studies with a longitudinal design are needed to expand our findings. Second, our study excluded participants from NHANES who lacked relevant variables, which may introduce selection bias. Thirdly, relying on self-reported information in NHANES raises concerns regarding recall limitations and subjectivity, potentially leading to inaccurate data. Studies with more reliable definitions of HF are needed to confirm our conclusions. Fourth, since NHANES was conducted exclusively in the United States, generalizing our findings to other populations requires caution; therefore, more studies involving different populations are needed to validate our findings. Fifth, because a major part of the enrolled subjects was missing fasting insulin data, we could not calculate the HOMA-IR and compare it with eGDR. Last, although our analysis adjusted a series of covariates, some unincluded confounders could also introduce bias into our results. Accordingly, more detailed information collection studies are also needed to confirm our results.

Data availability

Publicly available datasets were analyzed in this study. The dataset supporting the conclusions of this article is available from the corresponding authors on appropriate request. All the data could be downloaded from the NHANES offcial website (https://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes/Default.aspxBeginYear=1999).

References

  1. Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13(6):368–78.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118(17):3272–87.

    Article  PubMed  Google Scholar 

  3. Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K, Fonarow GC, Heidenreich P, Ho JE, Hsich E, et al. Heart failure epidemiology and outcomes statistics: a report of the Heart Failure Society of America. J Card Fail. 2023;29(10):1412–51.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Global regional. National age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the global burden of Disease Study 2013. Lancet. 2015;385(9963):117–71.

    Article  Google Scholar 

  5. Riehle C, Abel ED. Insulin signaling and heart failure. Circ Res. 2016;118(7):1151–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ho KL, Karwi QG, Connolly D, Pherwani S, Ketema EB, Ussher JR, Lopaschuk GD. Metabolic, structural and biochemical changes in diabetes and the development of heart failure. Diabetologia. 2022;65(3):411–23.

    Article  PubMed  Google Scholar 

  7. Shi W, Qin M, Wu S, Xu K, Zheng Q, Liu X. Value of estimated glucose disposal rate to detect prevalent left ventricular hypertrophy: implications from a general population. Postgrad Med. 2023;135(1):58–66.

    Article  PubMed  Google Scholar 

  8. Kishi S, Gidding SS, Reis JP, Colangelo LA, Venkatesh BA, Armstrong AC, Isogawa A, Lewis CE, Wu C, Jacobs DR Jr, et al. Association of Insulin Resistance and glycemic metabolic abnormalities with LV structure and function in Middle Age: the CARDIA Study. JACC Cardiovasc Imaging. 2017;10(2):105–14.

    Article  PubMed  Google Scholar 

  9. Erqou S, Adler AI, Challa AA, Fonarow GC, Echouffo-Tcheugui JB. Insulin resistance and incident heart failure: a meta-analysis. Eur J Heart Fail. 2022;24(6):1139–41.

    Article  PubMed  Google Scholar 

  10. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979;237(3):E214–223.

    CAS  PubMed  Google Scholar 

  11. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.

    Article  PubMed  Google Scholar 

  12. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.

    Article  CAS  PubMed  Google Scholar 

  13. Zeng X, Han D, Zhou H, Xue Y, Wang X, Zhan Q, Bai Y, Huang X, Zeng Q, Zhang H, et al. Triglyceride-glucose index and Homeostasis Model Assessment-Insulin Resistance in Young Adulthood and Risk of Incident Congestive Heart failure in midlife: the coronary artery risk development in young adults study. Front Cardiovasc Med. 2022;9:944258.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Khalaji A, Behnoush AH, Khanmohammadi S, Ghanbari Mardasi K, Sharifkashani S, Sahebkar A, Vinciguerra C, Cannavo A. Triglyceride-glucose index and heart failure: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):244.

    Article  PubMed  PubMed Central  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(4):626–32.

    Article  CAS  PubMed  Google Scholar 

  16. Nyström T, Holzmann MJ, Eliasson B, Svensson AM, Sartipy U. Estimated glucose disposal rate predicts mortality in adults with type 1 diabetes. Diabetes Obes Metab. 2018;20(3):556–63.

    Article  PubMed  Google Scholar 

  17. Zabala A, Darsalia V, Lind M, Svensson AM, Franzén S, Eliasson B, Patrone C, Jonsson M, Nyström T. Estimated glucose disposal rate and risk of stroke and mortality in type 2 diabetes: a nationwide cohort study. Cardiovasc Diabetol. 2021;20(1):202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Helliwell R, Warnes H, Kietsiriroje N, Campbell M, Birch R, Pearson SM, Ajjan RA. Body mass index, estimated glucose disposal rate and vascular complications in type 1 diabetes: beyond glycated haemoglobin. Diabet Med. 2021;38(5):e14529.

    Article  CAS  PubMed  Google Scholar 

  19. Report of the expert committee on the. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2003;26(Suppl 1):S5–20.

    Google Scholar 

  20. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr., Jones DW, Materson BJ, Oparil S, Wright JT Jr et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension : 2003, 42(6):1206–1252.

  21. 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(1):968.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu Z, Liu H, Deng Q, Sun C, He W, Zheng W, Tang R, Li W, Xie Q. Association between Dietary Inflammatory Index and Heart failure: results from NHANES (1999–2018). Front Cardiovasc Med. 2021;8:702489.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Son TK, Toan NH, Thang N, Le Trong Tuong H, Tien HA, Thuy NH, Van Minh H, Valensi P. Prediabetes and insulin resistance in a population of patients with heart failure and reduced or preserved ejection fraction but without diabetes, overweight or hypertension. Cardiovasc Diabetol. 2022;21(1):75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ahsan M, Azmi MB, Ahmed SDH, Sheikh SI, Mudassir HA, Iqbal T, Imtiaz F. Homeostasis model assessment of insulin resistance in non-diabetic heart failure patients: a case-control investigation at tertiary care hospitals of Karachi. Pak J Pharm Sci. 2018;31(2Suppl):719–25.

    CAS  PubMed  Google Scholar 

  25. Li X, Wang J, Niu L, Tan Z, Ma J, He L, Yu P, Liu X, Li J. Prevalence estimates of the insulin resistance and associated prevalence of heart failure among United Status adults. BMC Cardiovasc Disord. 2023;23(1):294.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Inoue Y, Kawai M, Minai K, Ogawa K, Nagoshi T, Ogawa T, Yoshimura M. The impact of an inverse correlation between plasma B-type natriuretic peptide levels and insulin resistance on the diabetic condition in patients with heart failure. Metabolism. 2016;65(3):38–47.

    Article  CAS  PubMed  Google Scholar 

  27. Hill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, Sowers JR. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766.

    Article  CAS  PubMed  Google Scholar 

  28. O’Mahoney LL, Kietsiriroje N, Pearson S, West DJ, Holmes M, Ajjan RA, Campbell MD. Estimated glucose disposal rate as a candidate biomarker for thrombotic biomarkers in T1D: a pooled analysis. J Endocrinol Invest. 2021;44(11):2417–26.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Xuan J, Juan D, Yuyu N, Anjing J. Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study. BMC Cardiovasc Disord. 2022;22(1):378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liu C, Zhao Q, Zhao Z, Ma X, Xia Y, Sun Y, Zhang D, Liu X, Zhou Y. Correlation between estimated glucose disposal rate and in-stent restenosis following percutaneous coronary intervention in individuals with non-ST-segment elevation acute coronary syndrome. Front Endocrinol (Lausanne). 2022;13:1033354.

    Article  PubMed  Google Scholar 

  31. Li X, Zhou Z, Xia Z, Dong Y, Chen S, Zhan F, Wang Z, Chen Y, Yu J, Xia Z, et al. Association between estimated glucose disposal rate and atrial fibrillation recurrence in patients undergoing radiofrequency catheter ablation: a retrospective study. Eur J Med Res. 2024;29(1):325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sun J, Wang N, Li S, Li M, Zhang A, Qin B, Bao Q, Cheng B, Cai S, Wang S et al. Estimated glucose disposal rate and risk of arterial stiffness and long-term all-acuse mortality: a 10-year prospective study. J Epidemiol Community Health 2023, 78(3).

  33. Yazıcı D, Sezer H. Insulin resistance, obesity and lipotoxicity. Adv Exp Med Biol. 2017;960:277–304.

    Article  PubMed  Google Scholar 

  34. Kolwicz SC Jr., Purohit S, Tian R. Cardiac metabolism and its interactions with contraction, growth, and survival of cardiomyocytes. Circ Res. 2013;113(5):603–16.

    Article  CAS  PubMed  Google Scholar 

  35. Nishida K, Otsu K. Inflammation and metabolic cardiomyopathy. Cardiovasc Res. 2017;113(4):389–98.

    Article  CAS  PubMed  Google Scholar 

  36. Samuelsson AM, Bollano E, Mobini R, Larsson BM, Omerovic E, Fu M, Waagstein F, Holmäng A. Hyperinsulinemia: effect on cardiac mass/function, angiotensin II receptor expression, and insulin signaling pathways. Am J Physiol Heart Circ Physiol. 2006;291(2):H787–796.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors appreciate the NHANES participants, staff, and investigators.

Funding

This study was supported by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08Y481).

Author information

Author notes

  1. Daoliang Zhang and Wenrui Shi contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Fuwai Shenzhen Hospital, Chinese Academy of Medical Sciences, No. 12, Langshan Road, Xili Street, Nanshan District, Shenzhen, China

    Daoliang Zhang, Chao Li, Zhaohui Ding & Jian Zhang

  2. Department of Cardiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, China

    Wenrui Shi

  3. Heart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Tao An & Jian Zhang

Authors
  1. Daoliang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenrui Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao An
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhaohui Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DZ and WS designed the current study. WS, TA, and CL integrated and analyzed the data. DZ, WS, and TA drafted the manuscript. JZ revised the manuscript and proofread it for publication. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Daoliang Zhang or Jian Zhang.

Ethics declarations

Ethics approval and consent to participate

The NCHS institutional Ethics Review Board approved the study design of NHANES; an additional Ethics Review for the current study is unnecessary. All methods were carried out in accordance with the NHANES analysis guideline. Informed consent was obtained from all subjects and/or their legal guardian(s).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Shi, W., An, T. et al. Usefulness of estimated glucose disposal rate in detecting heart failure: results from national health and nutrition examination survey 1999–2018. Diabetol Metab Syndr 16, 189 (2024). https://doi.org/10.1186/s13098-024-01402-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13098-024-01402-z

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us