†These authors contributed equally.
Academic Editor: Gabriele Fragasso
Background: Estimated glucose disposal rate (eGDR) is highly associated
with all-cause mortality in type 2 diabetes mellitus (T2DM) cases undergoing
coronary artery bypass grafting (CABG). Nevertheless, eGDR’s prognostic value in
non-ST-segment elevation acute coronary syndrome (NSTE-ACS) following
percutaneous coronary intervention (PCI) remains unknown. Methods: The
population of this retrospective cohort study comprised NSTE-ACS patients
administered PCI in Beijing Anzhen Hospital between January and December 2015.
The primary endpoint was major adverse cardiac and cerebral events (MACCEs). eGDR
was calculated based on waist circumference (WC) (eGDR
Nowadays, cardiovascular disease (CVD) causes about one-third of deaths worldwide, with the morbidity and deaths related to CVD, especially coronary artery disease (CAD), increasing year by year. In addition, aging is exacerbating this trend [1, 2]. Therefore, many clinical researchers are committed to exploring residual risk factors in CVD cases, discovering novel targets for intervention and formulating individualized and precise treatment plans [3, 4, 5]. Considered a critical risk factor for CAD, type 2 diabetes mellitus (T2DM) is also rising in terms of prevalence [1, 6]. Therefore, the application value of diabetes-related risk factors and assessment indicators in the pathogenesis and prognosis of CVD attracts more and more attention [7, 8, 9, 10, 11].
The hyperinsulinemic-euglycemic clamp is the gold standard for assessing insulin resistance (IR), but its extensive clinical application is limited due to high cost, time-consumption and invasiveness. In 2000, estimated glucose disposal rate (eGDR) was developed to evaluate insulin sensitivity in T1DM patients and the results were verified with the hyperinsulinemic-euglycemic clamp [12, 13]. eGDR was originally calculated based on waist-to-hip ratio (WHR), hypertension and glycosylated hemoglobin (HbA1c). However, researchers have found that using waist circumference (WC) and body mass index (BMI) instead of WHR to calculate eGDR yielded the same results [12, 14]. Nonetheless, higher eGDR indicates greater insulin sensitivity, and lower eGDR reflects stronger IR [15].
Recently, a study confirmed that lower eGDR levels have associations with higher risk of stroke and death [16]. Such associations were independent of other stroke and mortality risk factors. More importantly, in T2DM cases administered coronary artery bypass grafting (CABG), low eGDR was linked to enhanced risk of all-cause mortality, suggesting eGDR might constitute a critical risk factor for T2DM with ischemic heart disease [17]. However, the prognostic potential of eGDR for CAD patients undergoing percutaneous coronary intervention (PCI) is undefined. Therefore, the current work aimed to evaluate the prognostic capability of eGDR for non-ST-elevation acute coronary syndrome (NSTE-ACS) upon PCI treatment.
The present single-center, observational study enrolled NSTE-ACS patients
undergoing PCI in Beijing Anzhen Hospital, China, between January 2015 and
December 2015. NSTE-ACS diagnosis included non-ST-segment elevation myocardial
infarction [NSTEMI] and unstable angina [UA] [18]. Exclusion criteria were: (1)
age
Study flowchart. NSTE-ACS, non-ST-segment elevation acute coronary syndrome; PCI, percutaneous coronary intervention; T1DM, Type 1 Diabetes mellitus; CABG, coronary artery bypass grafting; eGFR, estimated glomerular filtration rate; ALT, alanine transaminase; AST, aspartate transaminase; URL, upper reference limit; eGDR, estimated glucose disposal rate; WC, waist circumference; BMI, body mass index.
Patients’ demographics were derived from the hospital’s medical information
record system. Definitions and diagnostic criteria for hypertension, T2DM,
dyslipidemia, stroke and peripheral arterial disease (PAD) were based on current
relevant guidelines [19, 20, 21, 22, 23, 24]. The calculation formula for BMI was
weight/height
In this study, eGDR (mg/kg/min) was assessed according to previously proposed
formulae [12, 14, 28]: eGDR calculated by WC (eGDR
Follow-up duration was 48 months post-discharge or until death. Major adverse cardio-cerebral events (MACCEs), comprising all-cause death, non-fatal myocardial infarction (MI), non-fatal ischemic stroke and ischemia-associated revascularization, constituted the primary endpoint. MI was reflected by specific cardiac enzyme amounts surpassing the corresponding upper limits of their normal ranges, accompanied by ischemic symptoms or electrocardiographic changes suggestive of ischemia [29]. Stroke was any ischemic cerebral infarction requiring hospitalization accompanied by overt neurological dysfunction, with lesions demonstrated on brain computed tomography (CT) or magnetic resonance (MR) images. Ischemia-related revascularization referred to the revascularization of target and/or non-target vessels resulting from repeated or chronic ischemia.
All 2308 patients were assessed by the parameters eGDR
Normally distributed continuous data are mean
Kaplan-Meier curve analysis was carried out for describing the cumulative rates
of MACCEs (primary study endpoint) at different levels of eGDR, and between-group
comparisons utilized the log-rank test. Univariate Cox regression analysis was
utilized for initially identifying potential risk factors for MACCEs. Variables
identified as potential risk factors for the primary endpoint in univariate
analysis (p
On the basis of Model 3, the eGDR dose-response of the primary endpoint was represented by a restrictive cubic spline curve. The likelihood ratio test was carried out to examine the nonlinearity. Subgroup analyses stratified by sex, age, smoking history, hyperlipidemia, diabetes, OHA at admission and insulin at admission, with Model 3 adjustment, were performed to determine eGDR’s consistency in predicting MACCEs.
The incremental effects of eGDR on the predictive potential of currently recognized CVD risk factors for MACCEs were illustrated by the Harrell’s C-index, net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
Statistical analysis was carried out with SPSS v26.0 (IBM Corp., Chicago, IL,
USA) and R statistical software v3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). Two-tailed p
A total of 2308 patients aged 60.09
Total population (n = 2308) | Tertile I (n = 770) (eGDR |
Tertile II (n = 770) (6.08 |
Tertile III (n = 768) (eGDR |
p value | |||
Age, years | 60.09 |
59.73 |
61.15 |
59.40 |
|||
Sex, male, n (%) | 1658 (71.8) | 582 (75.6) | 502 (65.2) | 574 (74.7) | |||
BMI, kg/m |
26.09 |
28.45 |
25.18 |
24.63 |
|||
WC, cm | 91.42 |
101.46 |
87.07 |
85.71 |
|||
Heart rate, bpm | 69.67 |
70.75 |
69.51 |
68.77 |
0.001 | ||
SBP, mmHg | 130.27 |
133.25 |
131.92 |
125.62 |
|||
DBP, mmHg | 76.99 |
78.88 |
76.99 |
75.08 |
|||
Smoking history, n (%) | 1309 (56.7) | 461 (59.9) | 400 (51.9) | 448 (58.3) | 0.004 | ||
Drinking history, n (%) | 536 (23.2) | 190 (24.7) | 164 (21.3) | 182 (23.7) | 0.272 | ||
Family history of CAD, n (%) | 236 (10.2) | 73 (9.5) | 79 (10.3) | 84 (10.9) | 0.641 | ||
Medical history, n (%) | |||||||
Diabetes | 798 (34.6) | 440 (57.1) | 241 (31.3) | 117 (15.2) | |||
Hypertension | 1436 (62.2) | 759 (98.6) | 641 (83.2) | 36 (4.7) | |||
Hyperlipidemia | 1986 (86.0) | 699 (90.8) | 642 (83.4) | 645 (84.0) | |||
Previous MI | 484 (21.0) | 166 (21.6) | 152 (19.7) | 166 (21.6) | 0.590 | ||
Previous PCI | 382 (16.6) | 151 (19.6) | 120 (15.6) | 111 (14.5) | 0.017 | ||
Previous stroke | 264 (11.4) | 112 (14.5) | 101 (13.1) | 51 (6.6) | |||
Previous PAD | 79 (3.4) | 30 (3.9) | 25 (3.2) | 24 (3.1) | 0.670 | ||
Clinical diagnosis, n (%) | 0.103 | ||||||
UA | 1921 (83.2) | 623 (80.9) | 652 (84.7) | 646 (84.1) | |||
NSTEMI | 387 (16.8) | 147 (19.1) | 118 (15.3) | 122 (15.9) | |||
Laboratory examinations | |||||||
TG, mmol/L | 1.48 (1.05, 2.10) | 1.67 (1.21, 2.37) | 1.46 (1.00, 2.02) | 1.33 (0.96, 1.92) | |||
TC, mmol/L | 4.03 (3.40, 4.72) | 4.02 (3.39, 4.75) | 4.01 (3.40, 4.69) | 4.08 (3.44, 4.76) | 0.413 | ||
LDL-C, mmol/L | 2.39 (1.89, 2.98) | 2.39 (1.88, 3.00) | 2.35 (1.86, 2.92) | 2.42 (1.92, 3.02) | 0.147 | ||
HDL-C, mmol/L | 0.99 |
0.93 |
1.02 |
1.01 |
|||
hs-CRP, mg/L | 1.27 (0.58, 3.30) | 1.76 (0.82, 4.23) | 1.17 (0.52, 2.97) | 1.00 (1.45, 2.64) | |||
Creatinine, |
75.83 |
77.40 |
75.27 |
74.83 |
0.006 | ||
eGFR, mL/(min × 1.73m |
93.57 |
92.91 |
92.28 |
95.54 |
0.002 | ||
Uric acid, |
344.67 |
353.63 |
341.91 |
338.46 |
0.001 | ||
FBG, mmol/L | 6.13 |
6.84 |
6.03 |
5.52 |
|||
HbA1c, % | 6.27 |
6.86 |
6.15 |
5.80 |
|||
LVEF, % | 64.01 |
63.69 |
64.44 |
63.90 |
0.075 | ||
Medication at admission, n (%) | |||||||
ACEI/ARB | 511 (22.1) | 258 (33.5) | 215 (27.9) | 38 (4.9) | |||
DAPT | 693 (30.0) | 236 (30.6) | 235 (30.5) | 222 (28.9) | 0.708 | ||
Aspirin | 1220 (52.9) | 417 (54.2) | 410 (53.2) | 393 (51.2) | 0.486 | ||
P2Y12 inhibitors | 738 (32.0) | 245 (31.8) | 251 (32.6) | 242 (31.5) | 0.895 | ||
505 (21.9) | 183 (23.8) | 187 (24.3) | 135 (17.6) | 0.002 | |||
Statins | 707 (30.6) | 229 (29.7) | 233 (30.3) | 245 (31.9) | 0.631 | ||
OHA | 413 (17.9) | 237 (30.8) | 125 (16.2) | 51 (6.6) | |||
Insulin | 225 (9.7) | 136 (17.7) | 58 (7.5) | 31 (4.0) | |||
Medication at discharge, n (%) | |||||||
ACEI/ARB | 1602 (69.4) | 750 (97.4) | 658 (85.5) | 194 (25.3) | |||
DAPT | 2306 (99.9) | 769 (99.9) | 769 (99.9) | 768 (100.0) | 0.607 | ||
Aspirin | 2307 (100.0) | 769 (99.9) | 770 (100.0) | 768 (100.0) | 0.368 | ||
P2Y12 inhibitors | 2308 (100.0) | 770 (100.0) | 770 (100.0) | 768 (100.0) | - | ||
2095 (90.8) | 707 (91.8) | 711 (92.3) | 677 (88.2) | 0.008 | |||
Statins | 2256 (97.7) | 752 (97.7) | 755 (98.1) | 749 (97.5) | 0.771 | ||
OHA | 409 (17.7) | 233 (30.3) | 125 (16.2) | 51 (6.6) | |||
Insulin | 217 (9.4) | 128 (16.6) | 58 (7.5) | 31 (4.0) | |||
Angiographic data, n (%) | |||||||
LM lesion | 103 (4.5) | 39 (5.1) | 31 (4.0) | 33 (4.3) | 0.592 | ||
Multi-vessel lesion | 1536 (66.6) | 585 (76.0) | 511 (66.4) | 440 (57.3) | |||
In-stent restenosis | 125 (5.4) | 56 (7.3) | 33 (4.3) | 36 (4.7) | 0.019 | ||
Chronic total occlusion lesion | 299 (13.0) | 111 (14.4) | 98 (12.7) | 90 (11.7) | 0.282 | ||
SYNTAX score | 10.61 |
11.63 |
10.41 |
9.80 |
|||
Procedural information | |||||||
Target vessel territory, n (%) | |||||||
LM | 60 (2.6) | 22 (2.9) | 17 (2.2) | 21 (2.7) | 0.696 | ||
LAD | 1506 (65.3) | 481 (62.5) | 508 (66.0) | 517 (67.3) | 0.119 | ||
LCX | 804 (34.8) | 301 (39.1) | 272 (35.3) | 231 (30.1) | 0.001 | ||
RCA | 978 (42.2) | 373 (48.4) | 315 (40.9) | 290 (37.8) | |||
Complete revascularization, n (%) | 1363 (59.1) | 404 (52.5) | 465 (60.4) | 494 (64.3) | |||
Number of DES | 2.00 (1.00, 3.00) | 2.00 (1.00, 3.00) | 2.00 (1.00, 3.00) | 1.00 (1.00, 2.00) | 0.004 | ||
eGDR |
The Tertile II eGDR
During follow-up (mean follow-up time, 41.06 months), 547 patients (23.7%) had
MACCEs, comprising 36 (1.6%) all-cause death, 112 (4.9%) non-fatal MI, 45
(1.9%) non-fatal ischemic stroke and 354 (15.3%) ischemia-induced
revascularization. The rates of MACCEs (p
Total population (n = 2308) | Tertile I (n = 770) (eGDR |
Tertile II (n = 770) (6.08 |
Tertile III (n = 768) (eGDR |
p value | |
MACCE, n (%) | 547 (23.7) | 261 (33.9) | 159 (20.6) | 127 (16.5) | |
All-cause death, n (%) | 36 (1.6) | 14 (1.8) | 12 (1.6) | 10 (1.3) | 0.716 |
Non-fatal MI, n (%) | 112 (4.9) | 49 (6.4) | 37 (4.8) | 26 (3.4) | 0.025 |
Non-fatal ischemic stroke, n (%) | 45 (1.9) | 26 (3.4) | 14 (1.8) | 5 (0.7) | 0.001 |
Ischemia-driven revascularization, n (%) | 354 (15.3) | 172 (22.3) | 96 (12.5) | 86 (11.2) | |
eGDR |
Kaplan-Meier curve analysis was utilized for assessing the cumulative incidence of MACCEs in the overall, diabetic and non-diabetic cohorts.
Statistically different cumulative incidence rates of MACCEs were found among
the three eGDR
Kaplan-Meier survival analysis based on the tertiles of eGDR.
(A) Kaplan-Meier survival curve analysis of the primary endpoint in the overall
population for the three groups based on eGDR
We built five multivariate models to examine the predictive potential of eGDR
for the primary endpoint (shown in Methods). Univariable Cox proportional hazards
analysis was performed to firstly determine the predictive factors for MACCEs
(Supplementary Table 3). eGDR
As nominal variate |
As continuous variate | |||||
Tertile I HR (95% CI) | p value | Tertile II HR (95% CI) | p value | HR (95% CI) | p value | |
Unadjusted | 2.247 (1.817–2.778) | 1.265 (1.002–1.597) | 0.048 | 1.195 (1.149–1.242) | ||
Model 1 | 1.998 (1.592–2.509) | 1.137 (0.898–1.440) | 0.286 | 1.181 (1.131–1.234) | ||
Model 2 | 1.794 (1.325–2.429) | 1.111 (0.837–1.474) | 0.467 | 1.179 (1.115–1.246) | ||
Model 3 | 1.603 (1.190–2.159) | 0.002 | 1.004 (0.761–1.326) | 0.975 | 1.152 (1.088–1.219) | |
Model 1: adjusted for age, sex, diabetes, hyperlipidemia, previous MI, previous
PCI, previous stroke.
Model 2: adjusted for variates in Model 1 and TG, TC, HDL-C, eGFR, FBG, LVEF, ACEI/ARB at discharge, OHA at discharge, insulin at discharge. Model 3: adjusted for variates in Model 2 and LM lesion, multi-vessel lesion, in-stent restenosis, chronic total occlusion lesion, SYNTAX score, LM treatment, LCX treatment, RCA treatment, complete revascularization, number of DES. eGDR |
The eGDR
Restricted cubic smoothing for the risk of the primary endpoint
based on eGDR
The predictive power of eGDR
Subgroup analysis evaluating the robustness of eGDR
Addition of eGDR
Harrell’s C-index | Continuous-NRI | IDI | |||||||
Estimation | 95% CI | p for comparison | Estimation | 95% CI | p value | Estimation | 95% CI | p value | |
Baseline model | 0.768 | 0.750–0.786 | - | - | - | - | - | - | - |
eGDR |
0.778 | 0.760–0.796 | 0.003 | 0.125 | 0.067–0.176 | 0.016 | 0.008–0.027 | ||
eGDR |
0.769 | 0.751–0.788 | 0.198 | 0.061 | –0.009–0.109 | 0.066 | 0.002 | 0.000–0.006 | 0.126 |
NRI, net reclassification improvement; IDI, integrated discrimination
improvement; CI, confidence interval; eGDR |
The current work mainly assessed eGDR’s predictive value for negative outcome in NSTE-ACS patients after PCI. The data revealed low eGDR was tightly correlated with high incidence of MACCEs. Reduction in eGDR represented a significant and independent predictive factor of adverse outcomes in the examined population. Furthermore, compared with eGDR calculated by BMI, eGDR determined by WC was more potent in predicting poor prognosis in NSTE-ACS individuals following PCI. Moreover, addition of eGDR improved the ability of the model incorporating currently recognized cardiovascular risk factors for predicting a negative prognosis.
eGDR was proposed for IR assessment in T1DM patients and validated by the
hyperinsulinemic-euglycemic clamp, which ensures its accuracy to a certain
extent. eGDR is a continuous variable and thus can be used as a dynamic measure
to assess the effectiveness of a particular treatment. In T1DM patients, lower
eGDR reflects greater risk, which promotes the occurrence of renal disease [30],
peripheral vascular disease [31], CAD [32, 33] and death [34]. IR assessed by
eGDR is considered the only factor consistently associated with all chronic
complications of T1DM [35]. A cross-sectional study of T1DM patients found that
individuals showing low eGDR had remarkably enhanced risk of CVD [36].
Additionally, eGDR effectively predicted survival outcomes tightly linked to
all-cause mortality and cardiovascular mortality in T1DM cases [37]. Furthermore,
similar to HbA1c, eGDR is also considered a reliable, clinically applicable
marker for the assessment of T2DM and could be used to monitor the responses to
specific treatments [14]. These results suggest eGDR can be used as an effective
predictor of the occurrence and development of CVD. According to a nationwide
observational study of 3256 individuals with T2DM who underwent CABG with a
3.1-year median follow-up, low eGDR was strongly correlated with an enhanced risk
of all-cause mortality, independently of other cardiac vascular and metabolic
risk factors [17]. The current results indicate eGDR better predicts long-term
prognosis in patients undergoing revascularization. These patients often have
severe coronary artery disease and poor control of risk factors, which requires
more frequent prognostic evaluation. The characteristics of eGDR are only
suitable for this requirement. Based on the above studies, this work also
revealed consistent findings, further clarifying the predictive potential of eGDR
reduction for adverse outcomes in NSTE-ACS individuals undergoing PCI.
Multivariate and subgroup analyses suggested eGDR was a strong and stable
predictor of prognosis in NSTE-ACS. This study also found that the predictive
ability of eGDR
eGDR was calculated based on three factors, including hypertension, HbA1c and WC. Hypertension, with a well-known impact on ASCVD development and prognosis, is the most important component in the calculation formula [12]. HbA1c is a known predictor of CAD severity and early prognosis of stable angina pectoris [48]. In diabetics with successful DES implantation, HbA1c is highly correlated with enhanced risk of major adverse cardiovascular events [49]. In obesity, HbA1c is associated with both IR and underlying diseases such as hypertension, dyslipidemia, CVD and stroke [39, 50]. WC is the preferred index of the World Health Organization for the evaluation of central obesity. It shows a strong association with visceral fat content measured by CT, and is also linked to the incidence rates of cardiac death and non-fatal MI in patients undergoing PCI [42]. IR assessed by eGDR is independently correlated with carotid plaque burden in T1DM [51]. In addition, a study examining the correlations between eGDR and thrombotic biomarkers in T1DM patients showed eGDR is a suitable indicator of prothrombotic status, superior to BMI and insulin requirements [52].
This study had limitations. Firstly, given its single-center, retrospective, observational features, larger prospective multicenter trials are warranted to validate the present findings and improve the power of this analysis. Secondly, UA patients accounted for the majority of all NSTE-ACS cases in this study, so these results might not reflect the prognostic potential of eGDR in NSTEMI patients. Thirdly, this study did not compare the predictive powers of eGDR and HOMA-IR. Fourthly, the study population did not involve patients with emergent PCI and chronic coronary syndromes, and the findings need to be further validated in these populations. In addition, eGDR is a measure of IR in T1DM, and more evidence in the T2DM population is needed. Finally, only Chinese individuals were included, and the generalizability and stability of the above findings need to be verified in other ethnic groups.
In NSTE-ACS cases undergoing PCI, low eGDR is strongly linked to high MACCE
incidence and constitutes an independent predictor of poor prognosis in NSTE-ACS.
Incorporating eGDR greatly enhanced the predictive ability of currently accepted
prognostic models. Furthermore, eGDR
CVD, cardiovascular disease; CAD, coronary artery disease; T2DM, type 2 diabetes
mellitus; IR, insulin resistance; eGDR, estimated glucose disposal rate; T1DM,
type 1 diabetes mellitus; WHR, waist-to-hip ratio; HbA1c, glycosylated
hemoglobin; WC, waist circumference; BMI, body mass index; PCI, percutaneous
coronary intervention; CABG, coronary artery bypass grafting; NSTE-ACS,
non-ST-segment elevation acute coronary syndrome; NSTEMI, non-ST-segment
elevation myocardial infarction; UA, unstable angina; eGFR, estimated glomerular
filtration rate; PAD, peripheral arterial disease; SYNTAX, the synergy between
PCI with taxus and cardiac surgery; eGDR
The datasets used in the current study are available from the corresponding author upon reasonable request.
CL made substantial contributions to data collection, data analysis and manuscript writing. QZ made substantial contributions to study design and intellectual direction. YJZ, XLL, XTM, YJC, YS, DZ made contributions to data collection and analysis. All authors read and approved the final manuscript.
This research protocol was approved by the Clinical Research Ethics Committee of Beijing Anzhen Hospital, Capital Medical University (Approval ID: 2022189X). Although the study design was retrospective, participants provided written or verbal informed consent.
Not applicable.
The study was funded by National Key Research and Development Program of China (2017YFC0908800) and Beijing Municipal Administration of Hospitals “Mission plan” (SML20180601).
The authors declare no conflict of interest.