ABSTRACT
Background and Aims
Elevated blood glucose levels in diabetes and prediabetes contribute to vascular inflammation and may increase the risk of major adverse cardiac events (MACE). We sought to evaluate the association between different glycemic statuses and 12‐month MACE in patients undergoing elective percutaneous coronary intervention (PCI) at Tehran Heart Center.
Methods
In this cohort study, patients who underwent elective PCI between 2008 and 2017 were stratified by preprocedural fasting blood glucose into normoglycemic, prediabetic, and diabetic groups. The primary endpoint was the 1‐year incidence of MACE, assessed using unadjusted and adjusted regression models.
Results
The data of 10,797 patients (mean age = 64 ± 11 y; 64.6% men) were reviewed. The diabetic patients were not only older (p < 0.001) and more frequently female (p < 0.001) but also had higher frequencies of hypertension (p < 0.001), using antiplatelet drugs (p < 0.001), statin (p < 0.001), and presence of dyslipidemia (p < 0.001), as well as more stenotic vessels (p = 0.007) and B2/C lesions (p = 0.033) than the other two groups. In addition, regression model demonstrated that neither prediabetes nor diabetes was significantly associated with the risk of 12‐month MACE in both unadjusted (hazard ratio [HR]: 1.15, 95% confidence interval [95% CI]: 0.84–1.58; and HR: 1.27, 95% CI: 0.96–1.70, respectively) and adjusted models (HR: 1.19, 95% CI: 0.86–1.66; and HR: 1.11, 95% CI: 0.81–1.52, respectively). Consistently, Kaplan–Meier survival analysis revealed a gradual increase in cumulative MACE incidences across all glycemic categories over 12 months, with the highest event rate observed among diabetic patients; however, these differences were not statistically significant.
Conclusion
Prediabetes and diabetes were not significant predictors of 12‐month MACE in our study population. These findings suggest that glycemic status alone may not be sufficient to stratify cardiovascular risk in patients undergoing elective PCI. Further research is warranted to validate these results and explore additional factors influencing MACE incidence in this context.
Keywords: diabetes, major adverse cardiac event, percutaneous coronary intervention, prediabetes
1. Introduction
The global prevalence of diabetes mellitus has dramatically risen in the past three decades [1, 2]. According to the International Federation of Diabetes, the prevalence of diabetes will be 643 million by 2030, and by 2045, 783 million adults are projected to be living with diabetes [3]. Type II diabetes affects 90%–95% of all adult patients with diabetes. Based on the global Epidemiology of Type 2 diabetes, the prevalence of diabetes is much lower in Asia than in Europe and North America; however, Middle Eastern countries such as Iran and Saudi Arabia are still hot spots for the global epidemic of diabetes [2]. Recent results from the Tehran Cohort Study (TeCS) illustrated that the prevalence of type 2 diabetes and prediabetes among adult residents of Tehran was approximately 16.7% and 25.1%, respectively [4].
In 2021, 6.7 million adults worldwide were estimated to have died directly due to diabetes or its complications, such as higher‐than‐optimal blood glucose concentrations [1, 3]. Almost 43% of these deaths occurred before age 70, the principal cause being cardiovascular diseases, particularly coronary artery disease (CAD). Robust evidence shows the same level of risk for acute coronary syndromes among patients with diabetes without a history of myocardial infarction (MI) and nondiabetic patients with previous MI [5, 6]. Indeed, diabetic patients have a two‐ or threefold higher rate of CAD than their nondiabetic peers. Moreover, diabetes is an independent predictor of a poor prognosis in CAD patients [7].
Furthermore, prior studies demonstrated that prediabetic cases are more susceptible to atherosclerotic cardiovascular diseases [8, 9], and recent meta‐analyzes have shown an association between MI and CAD in individuals with prediabetes [10, 11]. As a result, the prevalence of sudden MI among prediabetic subjects is three times that of normoglycemic individuals [10, 12, 13]. In this regard, glycated hemoglobin (HbA1c), clinically used to evaluate long‐term glycemic control, has increasingly been recognized for its potential role in predicting cardiovascular risk among individuals without diabetes [14].
Considerably, diabetes is a predictor of short and long‐term major adverse cardiac events (MACE) following percutaneous coronary intervention (PCI) [15, 16]. Nonetheless, data regarding the association between prediabetes and MACE are conflicting. Concerning an investigation on coronary artery bypass graft surgery patients, prediabetes could not predict overall and MACE‐free survival [17]. A report from the ARTEMIS study showed that the adjusted risks for cardiac death, MACE, and all‐cause mortality among patients with prediabetes who underwent coronary revascularization were not significantly different from those among normoglycemic participants [18]. In comparison, two other studies recognized prediabetes as a potential predictor of MACE after PCI [19, 20]. Interestingly, a single study showed that prediabetes was associated with higher 6‐month MACE but not in‐hospital MACE [21]. In light of these conflicting results and lack of robust evidence on the relationship between prediabetes and 1‐year MACE, we aimed to determine whether prediabetes could predict the occurrence of 1‐year MACE in diabetic, prediabetic, and normoglycemic patients undergoing elective PCI in our center.
2. Methods
2.1. Study Design and Sample
The present retrospective cohort study included the data of all patients who underwent elective PCI at Tehran Heart Center between March 2008 and March 2017. The inclusion criteria were age ≥ 18 years, elective PCI, stable hemodynamics at admission and during the procedure, and available follow‐up data. The exclusion criteria comprised acute coronary syndromes (i.e., unstable angina, non–ST‐segment elevation MI, and ST‐segment elevation MI) at admission, a history of PCI or coronary artery bypass graft surgery, a history of congenital heart diseases, unsuccessful stenting with residual stenosis > 30%, unprotected involvement of the left main coronary artery, hemodynamic instability during PCI, and plain old balloon angioplasty without stenting.
2.2. Ethical Approval
The Research Board of the Cardiology Department of Tehran University of Medical Sciences approved the study protocol, and the Ethics Committee of Tehran Heart Center granted ethical approval (Registration number: 1399‐956). In addition, current research was performed in accordance with the Declaration of Helsinki and its updates. Informed consent was also obtained from all participants upon reassurance that their clinical data would be used anonymously for research purposes.
2.3. Data Collection
The demographic and clinical data of the patients were retrieved from the database of Tehran Heart Center. These data included age, sex, height, weight, cardiovascular risk factors at admission (i.e., diabetes mellitus, hypertension, dyslipidemia, smoking, opium use, and family history of CAD), and coronary angiography. Information regarding fasting blood glucose (FBG) and serum creatinine, measured on the procedure day, was obtained from our laboratory database.
The patients were categorized based on their preprocedural FBG level into three categories: normoglycemic (FBS < 100 mg/dL), prediabetic (FBS = 100–126 mg/dL), and diabetic (FBG > 126 mg/dL). The number of involved vessels was grouped as single‐vessel, double‐vessel, and triple‐vessel CAD. Other data included vessel size (small or large), lesion length, the presence of long lesions, the presence of proximal lesions, lesion type B2 or C based on the American Heart Association classification of coronary lesions [22], stent type, drug type in the drug‐eluting stent (DES), and stent length.
2.4. Follow‐Up and Study Endpoints
Based on our center's routine, clinical follow‐up data were collected prospectively to record all the events from the time of intervention at one, three, six, and twelve months. The follow‐ups were done via outpatient evaluations. We used telephone interviews for those who could not come for the visits. After discharge, patients were also prescribed medications, including aspirin, clopidogrel, and statins.
The primary study endpoint was the incidence of MACE, defined as in‐hospital mortality, cardiac death, nonfatal MI, coronary artery bypass graft, target lesion revascularization, or target vessel revascularization during 12‐month follow‐up following PCI. All the variables and the incidence of MACE were compared between the study groups. Further, the association between MACE incidence and prediabetes and diabetes was tested.
2.5. Statistical Analysis
Continuous variables were tested for normality using the Kolmogorov–Smirnov test and were presented as the mean ± the standard deviation or the median (the interquartile range) where necessary. In addition to the Kolmogorov–Smirnov test, visual assessments, including histograms and Q–Q plots, were employed to evaluate data distribution. The variables were compared between the 3 study groups using the 1‐way ANOVA or Kruskal–Wallis H test as appropriate. Categorical variables were presented as frequencies (percentages) and were compared using the χ2 or Fisher's exact test when appropriate. Survival curves were generated using the Kaplan–Meier method, and the log‐rank test was employed to evaluate differences between the groups. The Cox proportional‐hazards model was applied to assess the effects of prediabetes and diabetes on 12‐month MACE in univariate and adjusted models. Variables with a p value < 0.10 in the univariate analysis were included in the multivariable model. The confounders for this analysis were age, sex, body mass index, hypertension, dyslipidemia, family history, smoking, opium use, serum creatinine, coronary angiography results, left main artery involvement, B2/C lesions, the type of stent, drug, and involvement of the small vessels. A p‐value less than 0.05 was deemed statistically significant in all tests. All statistical analyzes were performed utilizing SPSS version 25.
3. Results
3.1. Demographics and Clinical Features
We reviewed the data of 10,797 patients (mean age 64 ± 11 years; 64.6% men). Prediabetes was present in 2718 patients (mean age = 64 ± 10 years; 66.9% men), and diabetes in 3375 (mean age = 66 ± 10 years; 50.7% men). The mean age and the number of female patients in the diabetic group were significantly higher than those in the other two groups (p < 0.001 for both). The frequencies of hypertension and dyslipidemia were significantly higher in the prediabetic and diabetic groups than in the normoglycemic group (p < 0.001 for both). Family history of CAD was much more frequent in the diabetic group, but the frequencies of current smokers and opium users were notably higher in the normoglycemic group (p < 0.001). Table 1 illustrates the baseline characteristics of all study groups.
Table 1.
General characteristics of the study population and their comparisons between the normoglycemic, prediabetic, and diabetic groups.
| Characteristics | Total (n = 10,797) | Normoglycemic (n = 4704) | Prediabetic (n = 2718) | Diabetic (n = 3375) | p value* |
|---|---|---|---|---|---|
| Age, year | 64 (11) | 63 (11) | 64 (10) | 66 (10) | < 0.001 |
| Male sex, n (%) | 6979 (64.6) | 3450 (73.3) | 1817 (66.9) | 1712 (50.7) | < 0.001 |
| Body mass index, kg/m2 | 28.2 (4.5) | 27.5 (4.3) | 28.4 (4.6) | 28.8 (4.6) | < 0.001 |
| Hypertension, n (%) | 4871 (45.3) | 1567 (33.4) | 914 (33.8) | 2390 (71.0) | < 0.001 |
| Dyslipidemia, n (%) | 5622 (52.4) | 1840 (39.4) | 1090 (40.5) | 2692 (80.2) | < 0.001 |
| Family history of CAD, n (%) | 1398 (13.0) | 550 (11.7) | 327 (12.1) | 51 (15.5) | < 0.001 |
| Smoking, n (%) | 1669 (15.5) | 886 (18.9) | 362 (13.4) | 421 (12.5) | < 0.001 |
| Opium, n (%) | 1365 (12.7) | 722 (15.4) | 364 (13.4) | 279 (8.3) | < 0.001 |
| Serum creatinine, mg/dL | 0.90 (0.80, 1.10) | 0.90 (0.80, 1.10) | 0.91 (0.80, 1.10) | 0.90 (0.72, 1.10) | < 0.001 |
| Coronary angiography, n (%) | < 0.001 | ||||
| Single‐vessel | 5099 (47.2) | 2302 (48.9) | 1284 (47.2) | 1513 (44.8) | |
| Double‐vessel | 3871 (35.9) | 1677 (35.7) | 979 (36.0) | 1215 (36.0) | |
| Triple‐vessel | 1827 (16.9) | 725 (15.4) | 455 (16.7) | 647 (19.2) | |
| Left main coronary artery lesion < 50%, n (%) | 715 (6.6) | 312 (6.6) | 210 (7.7) | 193 (5.7) | 0.007 |
| First territory, n (%) | 0.556 | ||||
| Left anterior descending | 6232 (57.7) | 2719 (57.8) | 1542 (56.7) | 1971 (58.4) | |
| Left circumflex | 1944 (18.0) | 862 (18.3) | 487 (17.9) | 595 (17.6) | |
| Right coronary artery | 2621 (24.3) | 1123 (23.9) | 689 (25.3) | 809 (24.0) | |
| Proximal lesion, n (%) | 4854 (45.1) | 2108 (44.9) | 1201 (44.3) | 1545 (45.9) | 0.447 |
| AHA lesion type B2 or C | 7835 (72.6) | 3419 (72.7) | 1925 (70.9) | 2491 (73.8) | 0.033 |
| Type of stent, n (%) | 0.8 | ||||
| Bare‐metal | 1197 (11.1) | 531 (11.3) | 301 (11.1) | 365 (10.8) | |
| Drug‐eluting | 9600 (88.9) | 4173 (88.7) | 2417 (88.9) | 3010 (89.2) | |
| Type of drug, n (%) | < 0.001 | ||||
| Paclitaxel | 832 (7.7) | 371 (7.9) | 195 (7.2) | 266 (7.9) | |
| Biolimus | 1352 (12.5) | 517 (11.0) | 277 (10.2) | 558 (16.5) | |
| Sirolimus | 1233 (11.4) | 618 (13.1) | 427 (15.7) | 188 (5.6) | |
| Zotarolimus | 1211 (11.2) | 600 (12.8) | 340 (12.5) | 271 (8.0) | |
| Everolimus | 4966 (46.0) | 2082 (44.3) | 1186 (43.6) | 1698 (50.3) | |
| Other | 73 (0.7) | 25 (0.5) | 17 (0.6) | 31 (0.9) | |
| Stent length, mm | 23.0 (18.0, 31.0) | 23.0 (18.0, 30.0) | 23.0 (18.0, 32.0) | 23.0 (18.0, 32.0) | 0.843 |
| Small vessel, n (%) | 1910 (17.7) | 759 (16.1) | 474 (17.4) | 677 (20.1) | < 0.001 |
| Long lesion, n (%) | 2710 (25.1) | 1168 (24.8) | 687 (25.3) | 855 (25.3) | 0.85 |
| Multi‐vessel, n (%) | 1660 (15.4) | 712 (15.1) | 400 (14.7) | 548 (16.2) | 0.219 |
| Aspirin, n (%) | 8055 (74.6) | 3138 (66.7) | 1640 (60.3) | 3277 (97.1) | < 0.001 |
| Clopidogrel, n (%) | 7203 (66.7) | 2841 (60.4) | 1413 (52.0) | 2949 (87.4) | < 0.001 |
| Dual antiplatelet therapy, n (%) | 7122 (66.2) | 2810 (59.9) | 13.98 (51.7) | 2914 (86.6) | < 0.001 |
| Statin, n (%) | 6043 (56.0) | 2319 (49.3) | 1192 (43.9) | 2532 (75.0) | < 0.001 |
p‐value < 0.05 was considered statistically significant.
The results of coronary angiography revealed that triple‐vessel disease was more involved in the diabetic group than in the other groups (p < 0.001). Besides, the prevalence of left main coronary artery lesions was statistically higher among prediabetics than in both groups (p = 0.007). However, there was no difference between the three groups in the territory of the affected vessels and proximal lesions. On the other hand, B2/C lesions were more prevalent in the diabetic group, followed by the normoglycemic group (p = 0.033). There were no differences in the types and lengths of stents among the groups; however, diabetic patients received significantly more Everolimus and Biolimus stents (p < 0.001). Moreover, diabetes patients were significantly more likely to use aspirin, statins, or clopidogrel than peers in other groups (p < 0.001 for all). Besides, Diabetic patients also received dual antiplatelet therapy at significantly higher rates (p < 0.001).
During the follow‐up period, 520 (4.8%) patients were completely lost to follow‐up and were excluded from further analysis, while 1,880 (17.4%) had incomplete follow‐up. The overall 12‐month incidence of MACE was 2.4%. MACE occurred in 2.8% of diabetic, 2.5% of prediabetic, and 2.1% of normoglycemic patients (Table 2). Nonfatal myocardial infarction was the most common event (1.0% overall), with similar rates across groups. Cardiac death occurred in 0.7% of patients, more frequently in diabetics (1.1%) compared to prediabetics (0.5%) and normoglycemic (0.4%). Rates of revascularization procedures, including target lesion revascularization, target vessel revascularization, and coronary artery bypass grafting, were low and comparable across groups (≤ 0.4%). No in‐hospital mortality was reported. While diabetics showed slightly higher adverse event rates, overall differences between groups were modest and not statistically significant.
Table 2.
12‐month major adverse cardiac events (MACE) in patients with complete follow‐up data.
| MACE components | Total (n = 10,277) | Normoglycemic (n = 4485) | Prediabetic (n = 2562) | Diabetic (n = 3230) |
|---|---|---|---|---|
| No MACE | 97.6% | 97.9% | 97.5% | 97.2% |
| TLR | 0.3% | 0.3% | 0.3% | 0.4% |
| TVR | 0.2% | 0.2% | 0.2% | 0.2% |
| CABG | 0.2% | 0.2% | 0.3% | 0.2% |
| Cardiac death | 0.7% | 0.4% | 0.5% | 1.1% |
| Nonfatal MI | 1.0% | 1.1% | 0.9% | 1.0% |
| In hospital mortality | 0.0% | 0.0% | 0.0% | 0.0% |
Abbreviations: CABG, coronary artery bypass graft; MACE, major adverse cardiac events; MI, myocardial infarction; TLR, target lesion revascularization; TVR, target vessel revascularization.
3.2. Adjusted and Unadjusted Cox Regression Analysis
As shown in Table 3, the unadjusted Cox analysis indicated no significant association between glycemic status and 12‐month MACE, with hazard ratios (HR) of 1.15 (95% CI: 0.84–1.58; p = 0.378) for prediabetes and 1.27 (95% CI: 0.96–1.70; p = 0.097) for diabetes, compared to normoglycemic individuals. In the multivariable model, the adjusted hazard ratios were 1.19 (95% CI: 0.86–1.66; p = 0.426) for prediabetic and 1.11 (95% CI: 0.81–1.52; p = 0.754) for diabetic patients, indicate that neither diabetes nor prediabetes was regarded as a risk factor for the development of 12‐month MACE in our study population in both unadjusted and adjusted regression model.
Table 3.
Prognostic effects of prediabetes and diabetes on the occurrence of major adverse cardiac events in patients undergoing percutaneous coronary intervention: unadjusted and adjusted Cox regression models.
| Group | Hazard ratio | 95% Confidence interval | p value |
|---|---|---|---|
| Unadjusted model | |||
| Normoglycemic | Reference | Reference | 0.249 |
| Prediabetic | 1.15 | 0.84–1.58 | 0.378 |
| Diabetic | 1.27 | 0.96–1.70 | 0.097 |
| Adjusted model* | |||
| Normoglycemic | Reference | Reference | 0.729 |
| Prediabetic | 1.19 | 0.86–1.66 | 0.426 |
| Diabetic | 1.11 | 0.81–1.52 | 0.754 |
Adjusted for age, sex, body mass index, hypertension, dyslipidemia, family history, smoking, opium use, serum creatinine, coronary angiography results, the involvement of the left main artery, B2/C lesion, type of stent drug, involvement of small vessels, dual antiplatelet therapy, and statin use.
3.3. Kaplan‐Meier Model for Rates of MACE
As depicted in Figure 1, the Kaplan–Meier curves illustrate a progressive increase in the cumulative incidence of major MACE across all glycemic groups over the 12‐month follow‐up. Although differences between groups were not statistically significant, diabetic and prediabetic patients exhibited slightly higher event rates compared to normoglycemic individuals throughout the period, with the diabetic group showing the highest cumulative MACE incidence by month 12.
Figure 1.
The rate of major adverse cardiac events (MACEs) among diabetic, prediabetic, and normoglycemic patients during the 12‐month follow‐up period.
4. Discussion
Our study depicted that FBG levels could not predict 12‐month MACE in diabetic and prediabetic patients undergoing elective PCI at our center. Related studies have illustrated that the progression of atherosclerosis in diabetic and prediabetic patients following a well‐controlled glycemic state is significantly reduced [23, 24]. Poor diabetes control was strongly associated with coronary artery calcification progression. What this finding underscores is that diabetes does not predict MACE. However, a higher proportion of antiplatelets and statins used in diabetic patients may be the main reason for an almost similar 12‐month MACE rate compared to the other study groups.
Hyperglycemia is associated with the alteration of inflammatory pathways, leading to endothelial dysfunction, thrombogenesis, monocyte activation, foam‐cell transformation, and altered smooth muscle cell migration [12, 19, 25, 26]. Abnormal glucose metabolism is associated with the poor recovery of microvascular integrity after acute MI and shows the prognostic interaction between glucometabolic states and abnormal coronary flow reserves. Several investigations have proved an increased risk of CAD and vascular dysfunction among prediabetic subjects. These mechanisms raise the volume of atheromatous plaques in coronary arteries over time, and CAD progression ensues [25, 26].
Previous investigations on the association between glycemic levels and clinical outcomes after PCI have revealed conflicting results. Prior evidence has expounded that higher FBG levels on admission were related to worse clinical outcomes regardless of the presence or absence of diabetes [27]. Another investigation showed that prediabetes was the principal factor for arrhythmias and sudden cardiac death [28]. Further, a recent study was conducted by Mando et al., considering the association between prediabetes and MACE [29]. The results demonstrated that all groups (prediabetes and diabetes) had statistically significant increases in the odds of developing MACE compared to normoglycemic patients (p < 0.001). Patients with persistent prediabetes had higher odds of MACE after adjusting for baseline characteristics (p = 0.002) compared to those with normal glycemic levels. Due to a link between prediabetes and MACE, prediabetic patients should strive to achieve normal glycemic values to decrease their adjusted MACE risk.
Additionally, according to a recent meta‐analysis, normoglycemic patients had lower all‐cause mortality, MI, and cardiac mortality than prediabetic patients at the longest follow‐ups [30]. Prediabetic patients also had lower all‐cause mortality and cardiac mortality risks than diabetic patients. A large trial was carried out by Raz et al. and colleagues to determine how fasting versus prandial glycemia affected cardiovascular outcomes in patients with type 2 diabetes. The study found no difference between the groups at 1 year in terms of MACE [31]. However, a high admission glucose level (HR: 1.58, 95% CI: 1.13–2.22) was associated with an increased risk of MACE. Moreover, Kok et al. developed a sub‐study of the multicenter BIO‐RESORT trial and found that prediabetes was associated with a twofold risk for MACE compared with normoglycemia [19]. Patients with prediabetes had higher death and revascularization rates than normoglycemic patients in 12 months of follow‐up, and the composite clinical endpoint rates were higher in the prediabetic group (11.1% vs. 5.7%). Additionally, PCI patients with prediabetes were prone to experience adverse clinical events. Clinical outcomes were similar between the prediabetic and diabetic groups (11.1% vs. 10.5%). Moreover, approximately 50% of the participants had acute MI, and their follow‐up duration was 1 year, similar to our study.
On the other hand, Giraldez et al. [32] described that patients with prediabetes did not experience worse clinical outcomes than those with normal glucose levels. According to Cueva‐Recalde et al. [33], prediabetes was not associated with long‐term adverse cardiovascular outcomes in patients with CAD undergoing PCI. In addition, Arnold et al. [34] reported that the difference in the mortality rate between their prediabetic and normoglycemic groups was nonsignificant during a 3‐year follow‐up period. Our results also demonstrated no significant difference in the incidence of MACE and its components, such as all‐cause mortality, between diabetic, prediabetic, and normoglycemic patients. Notably, the patients in the aforementioned investigations were not confined to those with acute MI receiving the new‐generation DES. Given that the severity of impaired glucose levels is associated with an incremental increase in long‐term mortality, it is crucial to screen for prediabetes among patients with acute MI.
Conversely, Ertan et al. [35] reported that their prediabetic patients had smaller coronary sizes and diffuse coronary narrowing, rendering PCI more challenging and resulting in a worse prognosis. Yang et al. [36] concluded that prediabetic patients might require longer stents in elective PCI. They also reported that more stents were required in prediabetic patients than in normoglycemic patients, with the difference likely leading to more restenosis and worse prognosis, including higher rates of MACE. A meaningful relationship between prediabetes and long‐term patient‐oriented composite outcomes after PCI using the contemporary new‐generation DES in patients with acute MI has also been reported [37].
With respect to recent investigations, the lack of a significant association between FBG and MACE in our study likely reflects the limitations of FBG as a singular metric of glycemic control [38]. Although FBG and HbA1 are widely used, they each capture only specific aspects of glucose metabolism and may inadequately reflect the broader pathophysiological impact of dysglycemia. Increasing attention has been directed toward glycemic variability, which encompasses both the range and frequency of glucose changes. This dynamic marker has been shown to exert more pronounced effects on endothelial dysfunction, oxidative stress, and vascular inflammation than persistent hyperglycemia alone [39]. In patients with CAD, particularly those undergoing PCI, such fluctuations may play a critical role in modulating cardiovascular risk [40]. Furthermore, recent literature illustrated that the use of novel agents such as SGLT‐2 inhibitors and GLP‐1 receptor agonizts has been shown to reduce cardiovascular events significantly [41] and contrast‐associated acute kidney injury in patients undergoing PCI [42]. In line with these findings, combination therapy with these agents offers additive cardioprotective effects [41]. Consequently, future studies should account for antidiabetic treatment profiles to better interpret risk and therapeutic impact.
Lastly, prediabetes was associated with a worse outcome, including cardiac mortality and repeat revascularization, than the normoglycemic patients. However, diabetic patients had worse outcomes than prediabetics in all‐cause death and MI. Still, the current evidence regarding the clinical outcome after acute MI in patients with prediabetes is inconsistent. The possible causes for such inconsistencies are disparities in the number and inclusion criteria of study populations, diagnostic tests and their cutoff points, definitions of prediabetes, and follow‐up lengths. Further clinical studies with larger sample sizes, extended follow‐up periods, and standardized inclusion criteria are required.
4.1. Study Strengths and Limitations
A key strength of this study is its large sample size of over 10,000 patients, which enhances statistical power and generalizability. Additionally, the long inclusion period from 2008 to 2017 allowed for the capture of real‐world clinical data across a broad timeframe. However, a main limitation of our study is the absence of data on glucose‐lowering therapies among diabetic and prediabetic patients. Without detailed pharmacologic data, we could not assess the potential influence of these therapies on clinical outcomes. Retrospective design is also another main limitation of our study. Although the Tehran Heart Center database is one of Iran's most comprehensive cardiology databases with meticulous data collection methods, there is still a chance of missing data or unforeseen variables. Therefore, we did not have complete data regarding the duration of diabetes and treatment modalities in all the studied patients. We also lack data on insulin levels to calculate the HOMA‐R score as an index for insulin resistance, as well as HbA1c for all patients. That our investigation is a single‐center study is another limitation, as patients who presented to our tertiary center may differ from the general population. Another shortcoming is our limited duration of follow‐up. Based on our center's routine, elective PCI patients are strictly followed up for 1 year, after which patient data are incomplete. As any prediabetic individual is likely to develop diabetes mellitus and its complications, our results may have underestimated the predictive power of prediabetes and diabetes in MACE occurrence.
5. Conclusion
Based on our findings, the rate of 12‐month MACE was 2.4% among our study population and did not significantly differ among study groups. Moreover, prediabetes and diabetes in patients undergoing elective PCI are not predictors of 12‐month MACE. The higher proportion of statin and antiplatelet drug use among diabetic patients may explain the similar 12‐month MACE rates observed across the different study groups. However, further research with extended follow‐up periods on this topic is warranted, given the high prevalence of both conditions and the known short and long‐term roles of high FBG levels, particularly prediabetes in CAD development.
Author Contributions
Ali Hosseinsabet: writing – review and editing, writing – original draft, supervision, data curation, validation, investigation. Nasrin Etesamifard: investigation, validation, writing – review and editing, writing – original draft, data curation. Akbar Shafiee: validation, investigation, data curation, methodology, writing – review and editing. Arash Jalali: methodology, validation, formal analysis, data curation, investigation, writing – review and editing. Amirhossein Heidari: writing – original draft, writing – review and editing, validation, data curation. Nazila Heidari: writing – original draft, writing – review and editing, validation, data curation. Hamid Ariannejad: writing – review and editing, data curation, validation. Hassan Aghajani: conceptualization, methodology, supervision, data curation, writing – review and editing, investigation, project administration.
Ethics Statement
The Research Board of the Cardiology Department of Tehran University of Medical Sciences approved the study protocol, and the Ethics Committee of Tehran Heart Center granted ethical approval (Registration number: 1399‐956). In addition, current research was performed in accordance with the Declaration of Helsinki.
Consent
Informed consent was also obtained from all participants upon reassurance that their clinical data would be used anonymously for research purposes.
Conflicts of Interest
The authors declare that they have no competing interests.
Transparency Statement
The lead author Hassan Aghajani affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Acknowledgments
The internal fund of Tehran Heart Center supported this study.
Hosseinsabet A., Etesamifard N., Shafiee A., et al., “Predictors of 12‐Month MACE Among Diabetic, Prediabetic, and Normoglycemic Patients Undergoing Elective Percutaneous Coronary Intervention: 10 Years' Experience From Tehran Heart Center,” Health Science Reports 9 (2025): 1‐9, 10.1002/hsr2.71369.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request. All the data generated or analyzed during the current study are available from the corresponding author (Hassan Aghajani) upon reasonable request. All authors have read and approved the final version of the manuscript (Hassan Aghajani), have full access to all of the data in this study, and take complete responsibility for the integrity of the data and the accuracy of the data analysis.
References
- 1. Organization WH . Global Report on Diabetes: World Health Organization. Report No: 9789241565257 WHO. 2016.
- 2. Khan M. A. B., Hashim M. J., King J. K., Govender R. D., Mustafa H., and Al Kaabi J., “Epidemiology of Type 2 Diabetes ‐ Global Burden of Disease and Forecasted Trends,” Journal of Epidemiology and Global Health 10, no. 1 (2020): 107–111, 10.2991/jegh.k.191028.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Federation. ID. IDF Diabetes Atlas. 10th edn. ed. Brussels, Belgium: 2021. https://www.diabetesatlas.org; 2021.
- 4. Oraii A., Shafiee A., Jalali A., et al., “Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes Mellitus Among the Adult Residents of Tehran: Tehran Cohort Study,” BMC Endocrine Disorders 22, no. 1 (2022): 248, 10.1186/s12902-022-01161-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Singh G. M., Danaei G., Farzadfar F., et al., “The Age‐Specific Quantitative Effects of Metabolic Risk Factors on Cardiovascular Diseases and Diabetes: A Pooled Analysis,” PLoS One 8, no. 7 (2013): e65174, 10.1371/journal.pone.0065174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Danaei G., Lawes C. M., Vander Hoorn S., Murray C. J., and Ezzati M., “Global and Regional Mortality From Ischaemic Heart Disease and Stroke Attributable to Higher‐Than‐Optimum Blood Glucose Concentration: Comparative Risk Assessment,” Lancet 368, no. 9548 (2006): 1651–1659, 10.1016/S0140-6736(06)69700-6. [DOI] [PubMed] [Google Scholar]
- 7. The Emerging Risk Factors Collaboration , “Diabetes Mellitus, Fasting Blood Glucose Concentration, and Risk of Vascular Disease: A Collaborative Meta‐Analysis of 102 Prospective Studies,” Lancet 375, no. 9733 (2010): 2215–2222, 10.1016/S0140-6736(10)60484-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Kleinherenbrink W., Osei E., den Hertog H. M., and Zandbergen A. A. M., “Prediabetes and Macrovascular Disease: Review of the Association, Influence on Outcome and Effect of Treatment,” European Journal of Internal Medicine 55 (2018): 6–11, 10.1016/j.ejim.2018.07.001. [DOI] [PubMed] [Google Scholar]
- 9. Brannick B. and Dagogo‐Jack S., “Prediabetes and Cardiovascular Disease,” Endocrinology and Metabolism Clinics of North America 47, no. 1 (2018): 33–50, 10.1016/j.ecl.2017.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Laichuthai N., Abdul‐Ghani M., Kosiborod M., Parksook W. W., Kerr S. J., and DeFronzo R. A., “Newly Discovered Abnormal Glucose Tolerance in Patients With Acute Myocardial Infarction and Cardiovascular Outcomes: A Meta‐Analysis,” Diabetes Care 43, no. 8 (2020): 1958–1966, 10.2337/dc20-0059. [DOI] [PubMed] [Google Scholar]
- 11. Huang Y., Cai X., Mai W., Li M., and Hu Y., “Association Between Prediabetes and Risk of Cardiovascular Disease and All Cause Mortality: Systematic Review and Meta‐Analysis,” BMJ 355 (2016): i5953, 10.1136/bmj.i5953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Brandon Stacey R., Leaverton P. E., Schocken D. D., Peregoy J. A., and Bertoni A. G., “Prediabetes and the Association With Unrecognized Myocardial Infarction in the Multi‐Ethnic Study of Atherosclerosis,” American Heart Journal 170, no. 5 (2015): 923–928, 10.1016/j.ahj.2015.08.003. [DOI] [PubMed] [Google Scholar]
- 13. Lipscomb E. R., Finch E. A., Brizendine E., Saha C. K., Hays L. M., and Ackermann R. T., “Reduced 10‐Year Risk of Coronary Heart Disease in Patients Who Participated in a Community‐Based Diabetes Prevention Program,” Diabetes Care 32, no. 3 (2009): 394–396, 10.2337/dc08-1622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Jakubiak G. K., Chwalba A., Basek A., Cieślar G., and Pawlas N., “Glycated Hemoglobin and Cardiovascular Disease in Patients Without Diabetes,” Journal of Clinical Medicine 14, no. 1 (2024): 53, 10.3390/jcm14010053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Zhuo X., Zhang C., Feng J., Ouyang S., Niu P., and Dai Z., “In‐Hospital, Short‐Term and Long‐Term Adverse Clinical Outcomes Observed in Patients With Type 2 Diabetes Mellitus vs Non‐Diabetes Mellitus Following Percutaneous Coronary Intervention: A Meta‐Analysis Including 139,774 Patients,” Medicine 98, no. 8 (2019): e14669, 10.1097/MD.0000000000014669. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Xiong S., Chen Q., Zhang Z., et al., “A Synergistic Effect of the Triglyceride‐Glucose Index and the Residual SYNTAX Score on the Prediction of Intermediate‐Term Major Adverse Cardiac Events in Patients With Type 2 Diabetes Mellitus Undergoing Percutaneous Coronary Intervention,” Cardiovascular Diabetology 21, no. 1 (2022): 115, 10.1186/s12933-022-01553-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Fallahzadeh A., Sheikhy A., Hosseini K., et al., “Prognostic Impact of Prediabetes on Patient Outcomes After Coronary Artery Bypass Grafting: A Single‐Center Cohort Study,” Critical Pathways in Cardiology 20, no. 4 (2021): 220–225, 10.1097/HPC.0000000000000268. [DOI] [PubMed] [Google Scholar]
- 18. Kiviniemi A. M., Lepojärvi E. S., Tulppo M. P., et al., “Prediabetes and Risk for Cardiac Death Among Patients With Coronary Artery Disease: The ARTEMIS Study,” Diabetes Care 42, no. 7 (2019): 1319–1325, 10.2337/dc18-2549. [DOI] [PubMed] [Google Scholar]
- 19. Kok M. M., von Birgelen C., Sattar N., et al., “Prediabetes and its Impact on Clinical Outcome After Coronary Intervention in a Broad Patient Population,” EuroIntervention 14, no. 9 (2018): e1049–e1056, 10.4244/EIJ-D-17-01067. [DOI] [PubMed] [Google Scholar]
- 20. Fefer P., Hod H., Ilany J., et al., “Comparison of Myocardial Reperfusion in Patients With Fasting Blood Glucose ≤ 100, 101 to 125, and > 125 mg/dl and ST‐Elevation Myocardial Infarction With Percutaneous Coronary Intervention,” American Journal of Cardiology 102, no. 11 (2008): 1457–1462, 10.1016/j.amjcard.2008.07.031. [DOI] [PubMed] [Google Scholar]
- 21. Sinnaeve P. R., “Association of Elevated Fasting Glucose With Increased Short‐Term and 6‐month Mortality in ST‐Segment Elevation and Non‐ST‐Segment Elevation Acute Coronary Syndromes: The Global Registry of Acute Coronary Events,” Archives of Internal Medicine 169, no. 4 (2009): 402–409, 10.1001/archinternmed.2008.572. [DOI] [PubMed] [Google Scholar]
- 22. Ryan T. J., Faxon D. P., Gunnar R. M., et al., “Guidelines for Percutaneous Transluminal Coronary Angioplasty. A Report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Subcommittee on Percutaneous Transluminal Coronary Angioplasty),” Circulation 78, no. 2 (1988): 486–502, 10.1161/01.cir.78.2.486. [DOI] [PubMed] [Google Scholar]
- 23. Nicholls S. J., Tuzcu E. M., Kalidindi S., et al., “Effect of Diabetes on Progression of Coronary Atherosclerosis and Arterial Remodeling,” Journal of the American College of Cardiology 52, no. 4 (2008): 255–262, 10.1016/j.jacc.2008.03.051. [DOI] [PubMed] [Google Scholar]
- 24. Kowall B., Lehmann N., Mahabadi A.‐A., et al., “Progression of Coronary Artery Calcification is Stronger in Poorly Than in Well Controlled Diabetes: Results From the Heinz Nixdorf Recall Study,” Journal of Diabetes and its Complications 31, no. 1 (2017): 234–240, 10.1016/j.jdiacomp.2016.08.011. [DOI] [PubMed] [Google Scholar]
- 25. Wang J., Liu L., Zhou Y., et al., “Increased Fasting Glucose and the Prevalence of Arterial Stiffness: A Cross‐Sectional Study in Chinese Adults,” Neurological Research 36, no. 5 (2014): 427–433, 10.1179/1743132814Y.0000000345. [DOI] [PubMed] [Google Scholar]
- 26. Blake D. R., Meigs J. B., Muller D. C., Najjar S. S., Andres R., and Nathan D. M., “Impaired Glucose Tolerance, but Not Impaired Fasting Glucose, Is Associated With Increased Levels of Coronary Heart Disease Risk Factors,” Diabetes 53, no. 8 (2004): 2095–2100, 10.2337/diabetes.53.8.2095. [DOI] [PubMed] [Google Scholar]
- 27. Kolman L., Hu Y. C., Montgomery D. G., Gordon K., Eagle K. A., and Jackson E. A., “Prognostic Value of Admission Fasting Glucose Levels in Patients With Acute Coronary Syndrome,” American Journal of Cardiology 104, no. 4 (2009): 470–474, 10.1016/j.amjcard.2009.04.006. [DOI] [PubMed] [Google Scholar]
- 28. Jouven X., Lemaître R. N., Rea T. D., Sotoodehnia N., Empana J. P., and Siscovick D. S., “Diabetes, Glucose Level, and Risk of Sudden Cardiac Death,” European Heart Journal 26, no. 20 (2005): 2142–2147, 10.1093/eurheartj/ehi376. [DOI] [PubMed] [Google Scholar]
- 29. Mando R., Waheed M., Michel A., Karabon P., and Halalau A., “Prediabetes as a Risk Factor for Major Adverse Cardiovascular Events,” Annals of Medicine 53, no. 1 (2021): 2090–2098, 10.1080/07853890.2021.2000633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Ahsan M. J., Latif A., Ahmad S., et al., “Outcomes of Prediabetes Compared With Normoglycaemia and Diabetes Mellitus in Patients Undergoing Percutaneous Coronary Intervention: A Systematic Review and Meta‐Analysis,” Heart International 17, no. 1 (2023): 45–53, 10.17925/hi.2023.17.1.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Raz I., Wilson P. W. F., Strojek K., et al., “Effects of Prandial Versus Fasting Glycemia on Cardiovascular Outcomes in Type 2 Diabetes: The HEART2D Trial,” Diabetes Care 32, no. 3 (2009): 381–386, 10.2337/dc08-1671. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Giraldez R. R., Clare R. M., Lopes R. D., et al., “Prevalence and Clinical Outcomes of Undiagnosed Diabetes Mellitus and Prediabetes Among Patients With High‐Risk Non‐ST‐Segment Elevation Acute Coronary Syndrome,” American Heart Journal 165, no. 6 (2013): 918–925.e2, 10.1016/j.ahj.2013.01.005. [DOI] [PubMed] [Google Scholar]
- 33. Cueva‐Recalde J. F., Ruiz‐Arroyo J. R., and Roncalés garcía‐Blanco F., “Prediabetes Y Pronóstico Clínico De Los Pacientes Con Cardiopatía Isquémica Y Revascularización Coronaria Percutánea,” Endocrinología y Nutrición 63, no. 3 (2016): 106–112, 10.1016/j.endonu.2015.11.007. [DOI] [PubMed] [Google Scholar]
- 34. Arnold S. V., Lipska K. J., Li Y., et al., “Prevalence of Glucose Abnormalities Among Patients Presenting With an Acute Myocardial Infarction,” American Heart Journal 168, no. 4 (2014): 466–470.e1, 10.1016/j.ahj.2014.06.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Ertan C., Ozeke O., Gul M., et al., “Association of Prediabetes With Diffuse Coronary Narrowing and Small‐Vessel Disease,” Journal of Cardiology 63, no. 1 (2014): 29–34, 10.1016/j.jjcc.2013.06.015. [DOI] [PubMed] [Google Scholar]
- 36. Yang J., Zhou Y., Zhang T., et al., “Fasting Blood Glucose and HbA(1c) Correlate With Severity of Coronary Artery Disease in Elective PCI Patients With HbA(1c) 5.7% to 6.4,” Angiology 71, no. 2 (2020): 167–174, 10.1177/0003319719887655. [DOI] [PubMed] [Google Scholar]
- 37. Kim Y. H., Her A. Y., Jeong M. H., et al., “Effects of Prediabetes on Long‐Term Clinical Outcomes of Patients With Acute Myocardial Infarction Who Underwent Pci Using New‐Generation Drug‐Eluting Stents,” Diabetes Research and Clinical Practice 160 (2020): 107994, 10.1016/j.diabres.2019.107994. [DOI] [PubMed] [Google Scholar]
- 38. Nusca A., Tuccinardi D., Albano M., et al., “Glycemic Variability in the Development of Cardiovascular Complications in Diabetes,” Diabetes/Metabolism Research and Reviews 34, no. 8 (2018): e3047, 10.1002/dmrr.3047. [DOI] [PubMed] [Google Scholar]
- 39. Alfieri V., Myasoedova V. A., Vinci M. C., et al., “The Role of Glycemic Variability in Cardiovascular Disorders,” International Journal of Molecular Sciences 22, no. 16 (2021): 8393, 10.3390/ijms22168393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Nusca A., Tuccinardi D., Proscia C., et al., “Incremental Role of Glycaemic Variability over HbA1c in Identifying Type 2 Diabetic Patients With High Platelet Reactivity Undergoing Percutaneous Coronary Intervention,” Cardiovascular Diabetology 18, no. 1 (2019): 147, 10.1186/s12933-019-0952-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Marfella R., Prattichizzo F., Sardu C., et al., “GLP‐1 Receptor agonists‐SGLT‐2 Inhibitors Combination Therapy and Cardiovascular Events After Acute Myocardial Infarction: An Observational Study in Patients With Type 2 Diabetes,” Cardiovascular Diabetology 23, no. 1 (2024): 10, 10.1186/s12933-023-02118-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Nusca A., Di Bitonto M. P., Spanò A., et al., “Effects of Novel Antidiabetic Agents on Contrast‐Associated Acute Kidney Injury in Diabetic Patients Undergoing Percutaneous Coronary Intervention,” American Journal of Cardiology 240 (2025): 50–56, 10.1016/j.amjcard.2025.01.007. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request. All the data generated or analyzed during the current study are available from the corresponding author (Hassan Aghajani) upon reasonable request. All authors have read and approved the final version of the manuscript (Hassan Aghajani), have full access to all of the data in this study, and take complete responsibility for the integrity of the data and the accuracy of the data analysis.