Impact of elevated glucose levels on cardiac function in STEMI patients: glucose delta as a prognostic biomarker

Abstract

Background

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Elevated glucose levels have emerged as a significant prognostic factor following acute myocardial infarction (AMI).

Objective

This study aimed to evaluate glycemic parameters associated with infarct size and left ventricular function.

Research design and methods

A total of 244 patients with ST-segment elevation myocardial infarction (STEMI) treated using a pharmacoinvasive strategy were included. Glucose delta was calculated as the difference between mean glucose levels estimated from glycated hemoglobin (HbA1c) and serum glucose levels collected at hospital admission. Infarct size and left ventricular ejection fraction (LVEF) were assessed 30 days post-infarction using cardiac magnetic resonance (CMR) imaging.

Results

Higher glucose delta values were significantly associated with reduced LVEF and larger infarct size, regardless of diabetes diagnosis. Differences in infarcted ventricular mass were noted between diabetic and non-diabetic patients above specific thresholds: (18.62 ± 11.0 g) vs. (16.24 ± 13.17 g), p = 0.019, with an effect size of 0.55. The receiver operating characteristic curve yielded an area under the curve (AUC) of 0.65 (95% CI 0.57–0.72).

Conclusions

In STEMI patients undergoing pharmacoinvasive treatment, a higher glycemic delta was associated with greater infarct size and lower LVEF. This straightforward glycemic parameter provides valuable prognostic insight for both diabetic and non-diabetic populations.

Graphical Abstract

Introduction

Elevated glucose levels are frequently associated with poorer clinical outcomes in various scenarios, such as polytrauma and sepsis. Stress hyperglycemia (SH), a transient rise in blood glucose during acute physiological stress, has been correlated with adverse clinical outcomes, particularly in cardiovascular conditions. Several studies have explored these associations, shedding light on the underlying mechanisms and potential therapeutic approaches to mitigate their effects [1, 2]. Maintaining optimal glycemic levels is critical not only for prevention, but also for managing adverse mechanisms such as microvascular dysfunction and inflammation [3,4,5,6].

The pathophysiology of SH involves counterregulatory hormones like cortisol, glucagon, and catecholamines, which enhance glucose production and reduce insulin sensitivity. Inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6, further exacerbate insulin resistance and promote hepatic gluconeogenesis. Besides, adipose tissue inflammation represents a sophisticated process characterized by the intricate interplay and regulation of multiple signaling pathways. Within cellular environments, inflammatory signaling is orchestrated by cytokines and their respective receptors, alongside intracellular specific kinases [7,8,9,10]. In turn and additionally, SH is common in hospitalized patients, even in those without prior diabetes mellitus (DM), and is compounded by metabolic alterations like increased lipolysis and proteolysis [2, 11, 12].

Acute myocardial infarction (AMI) is closely linked to SH and its adverse outcomes, regardless of preexisting diabetes [13, 14]. In this study, we aimed to investigate the association between SH and cardiac outcomes in ST-segment elevation myocardial infarction (STEMI) patients treated with a pharmacoinvasive strategy. To quantify glycemic dysregulation, we used glucose delta, a metric derived from the difference between estimated average glucose (from HbA1c) and admission blood glucose levels. Cardiac magnetic resonance (CMR) imaging was employed to assess infarct size and left ventricular ejection fraction (LVEF) 30 days post-STEMI.

Methods

Ethics statement

The study protocol was approved by local ethics committee (IRB 0297/2014; CAAE: 71,652,417.3.0000.5505) which follows the last version of Helsinki Declaration. All patients signed the informed consent prior to any study procedure.

Study design

This sub-analysis is part of the BATTLE-AMI study, an open-label, randomized trial evaluating the effects of four therapeutic strategies involving statins and antiplatelet agents on infarct size and LVEF in STEMI patients treated with fibrinolysis prior to coronary angiography (ClinicalTrials.gov, NCT02428374) [15].

Patient selection

Inclusion criteria: Adults ≥ 18 years diagnosed with STEMI, treated with fibrinolysis within 6 h of symptom onset, and admitted to public hospitals in Sao Paulo, as part of the STEMI Treatment Network. Diagnosis was based on chest pain and ECG findings (ST-segment elevation in ≥ 2 contiguous leads or new left bundle branch block) [16].

Patients with diabetes, pre-diabetes, or without diabetes, were classified based on the hospital admission HbA1c. Diabetes was defined by hospital admission HbA1c ≥ 6.5% (or a history of diabetes), prediabetes with HbA1c 5.7–6.4%, and without diabetes if HbA1c < 5.7%.

Exclusion criteria: Patients > 75 years, prior myocardial infarction or revascularization, stroke, contraindications to study drugs, advanced kidney or liver disease, malignancies, active rheumatic or hematologic diseases, or clinical instability.

Electrocardiographic variables

The cohort was classified into four types of STEMI based on Electrocardiogram (ECG) patterns corresponding to different anatomical regions:

1) Anterior STEMI: Indicated by significant ST-segment elevation in two contiguous ECG leads ranging from V1 to V6; 2) Inferior STEMI: Characterized by significant ST-segment elevation in two contiguous ECG leads III, aVF, and II; 3) Posterior STEMI: Noted by the absence of significant ST-segment elevation, but with significant ST-segment depression in ECG leads V1 and V2; 4) Lateral STEMI: Defined by significant ST-segment elevation in ECG leads I and aVL.

Clinical care

Consecutive STEMI patients of both sexes, aged 18–75 years, were treated by tenecteplase (Metalyse®, Boehringer Ingelheim) in the first 6 h of symptom onset and received 300 mg of clopidogrel and 300 mg of aspirin. Coronary angiography was performed within 24 h to guide further interventions (e.g., PCI or CABG). This pharmacoinvasive strategy integrated early pharmacological treatment with advanced diagnostics to optimize outcomes. Primary percutaneous coronary intervention (pPCI) is the “gold standard” treatment for patients experiencing STEMI. Prompt reperfusion of the coronary artery upon arrival at the emergency department is crucial to reducing mortality and morbidity rates. In scenarios where pPCI is not immediately available, fibrinolysis serves as a vital alternative therapeutic strategy. Additional coronary interventions in non-culprit lesions were performed at the same time or electively according to the hemodynamic team decision.

After hospital discharge, all patients were followed weekly to ensure they received optimal medical therapy, 91% received betablockers (BB); 97% angiotensin converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB); calcium-channel blockers (CCB) after discharge according to clinical experience; and 100% statins monotherapy or combined with ezetimibe. Median (IQR) time for pharmacological thrombolysis was 199 (120–270) minutes and the median time from thrombolysis to percutaneous coronary intervention was 18 (9–25) hours.

Data collection

Medical history, concomitant therapies, were collected during the baseline. Demographic and clinical data, including gender, age, body mass index (BMI) and biochemical samples were collected, including lipid profile, high-sensitivity C-reactive protein (hs-CRP), high-sensitivity troponin (hs-TnT), creatinine, and eGFR, by (CKD-EPI formula) [17]. Biochemical variables were collected during the hospital admission period, prior to the angiographic study. Smoking: patients who smoke in the last month, regardless of the number of cigarettes. Hypertension: [systolic blood pressure (SBP) ≥ 140 mm Hg, and/or diastolic blood pressure (DBP) ≥ 90 mm Hg, or anti-hypertensive treatment].

The glucose delta was calculated by the hospital admission glycemia minus the estimated average glycemia evaluated by HbA1c, according to the following equation: (28.7 × HbA1c—46.7) [18].

Laboratory test analyses

Blood samples were collected at hospital admission or in the following morning in cases of overnight hospitalization and analyzed at the Central Laboratory of Hospital Sao Paulo using standard automated techniques.

Cardiac magnetic resonance imaging (CMR)

Patients underwent imaging using 3.0 T clinical scanners (Siemens, Erlangen, Germany; Philips^®) while in the supine position with a phased-array receiver cardiac coil placed on the chest. ECG-gated cine images were acquired during 8 s breath-holds for each slice, focusing on short-axis views (from the mitral valve to the apex) and three long-axis views. Slice thickness was 8 mm, with a 2 mm gap. All imaging studies involved the intravenous injection of gadolinium-based contrast agents (gadopentetate dimeglumine or gadoteridol) at 0.2 mmol/kg body weight. First-pass perfusion imaging captured short-axis slices (8–10 mm thickness) at every heartbeat, with a spatial resolution of 2–3 × 2–3 mm. Stress images were obtained after a 6-min dipyridamole infusion (0.56 mg/kg/min), followed by aminophylline to reverse hyperemia. Rest-phase imaging was performed using the same contrast agent for comparison. After a 5-min delay to allow gadolinium washout from healthy myocardium, late gadolinium enhancement (LGE) images were acquired using magnitude and phase-sensitive inversion recovery sequences to assess myocardial viability. Protocol adjustments were made to optimize image quality, with a typical voxel size of 1.9 × 1.4 × 8 mm. Segmented cine CMR (short- and long-axis views) was used to evaluate cardiac function, followed by segmented LGE imaging for viability. Contrast agents were administered at a precise dosage of 0.15 mmol/kg body weight. Images were analyzed by blinded imaging specialists, who calculated infarcted mass (grams and percentage) and left ventricular ejection fraction (LVEF).

Angiographic findings and PCI details

Coronary angiography and percutaneous coronary intervention were performed during the first 24 h of STEMI. We obtained information on variables that study vascular flow, such as TIMI-flow (Thrombolysis In Myocardial Infarction) [19], as well as the identification of the infarction-related artery (IRA).The procedural times were meticulously recorded by a catheterization laboratory technician and included several key intervals: door time (the time of patient arrival at the hospital), start-case time (the time of patient arrival at the catheterization laboratory), needle time (the time of the first attempt at arterial puncture), balloon time (the time of the first balloon inflation within the culprit artery), and end-case time (the time of the patient departing from the catheterization laboratory). Multivessel disease was defined as having more than 50% stenosis in two or more vessels situated within different epicardial vascular systems, such as the left anterior descending artery (LAD), the left circumflex artery (LCx), and the right coronary artery (RCA). To ensure data accuracy and protocol adherence, all cardiac catheterization films were subsequently reviewed by a board-certified cardiologist.

Statistical analyses

Data analyses were performed using SPSS software (v23.0, SPSS Inc., Chicago, IL^®). The cohort included 244 patients who met the inclusion criteria. Normality of continuous variables was assessed using visual inspection (histograms, probability plots), residual analysis (QQ-plots), and Kolmogorov–Smirnov tests. Levene's test was used to evaluate homogeneity. Continuous variables were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables were presented as frequencies and percentages. Patients were divided into three groups: diabetes, prediabetes, and without diabetes. Differences in categorical variables were analyzed using chi-square or Fisher's exact tests, while continuous variables were assessed using one-way ANOVA with Tukey post-hoc tests or the Kruskal–Wallis test, as appropriate.

Association analyses explored relationships between independent variables (e.g., glycemic measures, lipid profile, demographics) and outcomes (LVEF, infarcted mass in grams and percentage). Spearman's Rho provided correlation coefficients. In conducting the linear regression analysis, we focused on two primary variables of interest: LVEF and the extent of left ventricular involvement, measured as the infarcted mass both in percentage and in grams. To enhance the robustness of our model, we incorporated predictor variables that demonstrated a statistical significance level with an entry threshold of p < 0.10. This approach ensures that the included predictors have a meaningful impact on the regression outcomes, thereby improving the model's explanatory power regarding the relationships between the variables. Statistical significance was set at p < 0.05, two tailed, with an alpha error of 5%. Effect sizes were calculated using the Common Language Effect Size (cumulative probability ÷ 1.41). Receiver operating characteristic (ROC) curves were generated to evaluate the binary classification system's performance.

Results

Participant characteristics

Our cohort included 244 patients with STEMI. The main characteristics of the study population are summarized in Table 1. Most patients were male, with a median age of 56 years. A substantial proportion of both female and male participants reported experiencing chest pain, which was characterized by sensations such as pain, pressure, tightness, or discomfort. Hypertension was more prevalent in the diabetes (DM) and pre-diabetes (pre-DM) groups. As shown in Table 2, there were no significant differences between groups in lipid profiles, renal function parameters, or systolic (SBP) and diastolic blood pressure (DBP).

Table 1 Clinical and epidemiological variables obtained during the baseline period

Table 2 Laboratory variables at baseline

In our study, all patients received high-intensity lipid-lowering therapy, with comparable standard lipid panels between arms at baseline and after 30 days, date on which the imaging tests were performed. Similarly, hs-CRP and hs-TnT levels did not differ among the groups. Expectedly, patients with DM exhibited higher admission glucose levels, HbA1c, and estimated average glucose values.

Variables obtained in the hemodynamics laboratory

The culprit coronary arteries were predominantly left anterior descending artery (LAD) or right coronary artery (RCA) and less frequently left circumflex (LCx). There were no differences between groups according to the type of culprit artery (p = 0.77, Chi square test). There were no differences for the TIMI flow grade (2, 3) pre (p = 0.78) or post PCI (p = 0.58; Chi-square test), consistently observed across all subgroups classified as TIMI-3. This score signifies normal blood flow, and its consistent occurrence across all subgroups suggests uniform outcomes regarding vascular patency and the effectiveness of evaluated treatments or interventions. This observation carries significant implications for therapeutic strategies and prognostic evaluations in cardiovascular research. By understanding these patterns, researchers can better identify strategies to enhance myocardial perfusion and improve patient outcomes in various clinical settings. However, we observed a higher frequency of TIMI-flow 0/1 in the group of diabetic patients. This observation suggests that individuals with diabetes may demonstrate an increased propensity for significantly impaired coronary blood flow post-infarction, as evidenced by TIMI flow grades of 0 and 1.

Additionally, we observed a good correlation between the electrocardiographic findings, (region with acute ischemic changes), and the data obtained in the catheterization lab. Predicting the culprit vessel in STEMI carries clinical relevance for several reasons. It is crucial for interventional cardiologists to identify the infarct-related artery (IRA) prior to the procedure in order to decide whether to adopt a'culprit-first'or'non-culprit-first'approach. Accurate identification of the IRA can profoundly affect the strategy and outcome of the intervention, ultimately shaping patient recovery and prognosis. By determining the IRA early, we can develop a more customized treatment plan, which has the potential to minimize procedural risks and boost overall efficiency.

Cardiac magnetic resonance (CMR) imaging findings

The median (interquartile range) values for fibrosis were 14 (7–22) g and 16 (8–26) %, and for LVEF, 48 (39–57) %. The variables that measure left ventricular function and the degree of ventricular involvement among the groups can be seen in the Table 3. Figure 1 illustrates the relationship between glucose delta and cardiac parameters: a positive correlation between glucose delta and fibrosis percentage (Fig. 1A). A negative correlation between glucose delta and LVEF (Fig. 1B). These findings indicate that higher glucose delta values are associated with increased fibrosis and impaired left ventricular function.

Table 3 The variables that measure LVEF and ventricular involvement between the groups

Fig. 1

(A) Positive linear correlation between fibrosis area and glucose delta; (B) Negative correlation between glucose delta with LVEF

Regression and ROC analyses

Linear regression analysis, incorporating glycemic variables (HbA1c, admission blood glucose, and glucose delta), showed a significant association between glucose delta and reduced LVEF: Unstandardized coefficient (Exp B): −0.05 [95% CI −0.04 to −0.02], p = 0.04, standard error: 0.019. In the context of linear regression analysis, (Model 1) includes potential predictor variables (clinical epidemiological) as well as lipid and glycemic metabolism biomarkers. In the adjusted model (Model 2), we incorporated only the variables that demonstrated statistical significance in the initial model, in the evaluation of the prediction of infarcted mass (grams) as a dependent variable. Glucose delta and fibrosis: Unstandardized coefficient (Exp B): 0.06 [95% CI 0.015 to 0.106], p = 0.009, standard error: 0.023, Table 4.

Table 4 Unadjusted and adjusted regression results (unstandardized B coefficients) and 95% confidence intervals for association between predictors and left ventricular involvement (infarcted mass in grams)

Additionally, glucose delta was positively associated with greater infarct size (as a percentage of LV mass). ROC curve analysis demonstrated an area under the curve (AUC) of 0.65 [95% CI: 0.57–0.72] for glucose delta and LVEF, illustrating the relationship between sensitivity and specificity at different thresholds, Fig. 2. Our study highlights the association between glucose delta in STEMI patients treated with a pharmacoinvasive strategy and worse cardiac outcomes, including larger myocardial areas at risk and greater final infarct size. Thirty days post-myocardial infarction (MI), CMR revealed no statistically significant differences in fibrosis (quantified in grams and as a percentage of left ventricular mass) or LVEF among the subgroups, Table 4. In the supplementary material (Suppl. Mat), we created a graphical abstract illustrating the association between glucose delta values and metrics of left ventricular function, assessed by magnetic resonance imaging, in patients affected by acute myocardial infarction.

Fig. 2

ROC curve and AUC for patients with elevated Glucose Delta values and LVEF

When analyzing stress-induced glycemic parameters, we categorized patients using fasting glucose cutoffs of 110 mg/dL for non-diabetics, and 140 mg/dL for those with a prior DM diagnosis. Significant differences in LVEF were observed between groups (50.17 ± 11.86% vs. 46.39 ± 19.8%, p = 0.02), with an effect size of 0.57. Similarly, the percentage of infarcted LV mass differed, with an effect size of 0.55. These findings shed light on the adverse impact of hyperglycemia on cardiac function and suggest avenues for improved clinical management strategies.

Discussion

Pathophysiological insights

Our study revealed that in STEMI patients receiving pharmaco-invasive treatment, an increased glycemic delta correlated with a larger infarct size and reduced (LVEF), and the association remains significant after adjusting for multiple variables. These findings emphasize the importance of closely monitoring glycemic fluctuations in STEMI patients as they may serve as reliable indicators of cardiac damage. The persistence of this association after considering various confounding factors underscores its potential clinical relevance. However, while the focus on LVEF and infarct size as imaging endpoints is a significant strength of this study, the ROC curve (AUC of 0.65) indeed reflects only a moderate discriminative capacity for glucose delta when analyzed alone. In our view, for clinical application, glucose delta should be interpreted alongside other validated risk markers. Furthermore, these findings open fascinating questions about their pathophysiological mechanisms that may not be completely mitigated by glycemic control. Although the long-term observation of these patients was not a primary objective of our study, we must emphasize the importance of continuous outpatient follow-up for these patients. Comprehensive longitudinal follow-up of these high-risk patients serves multiple critical functions: facilitating timely medication titration and optimization, ensuring adherence to evidence-based pharmacotherapy, implementing personalized treatment strategies aligned with contemporary specialized guidelines, monitoring for adverse effects, addressing modifiable risk factors, and ultimately reducing ischemic recurrence rates while enhancing overall cardiovascular outcomes and quality of life. In this context, a recent multicenter study may be particularly relevant: it investigated the effects of in-hospital bleeding on post-discharge therapy and prognosis in patients with acute coronary syndrome (ACS), providing complementary insights into the vulnerability of post-ACS patients and the need for personalized therapy planning [20].

A prospective cohort study, encompassing acute coronary syndrome patients over a two-year follow-up, identified a J-shaped relationship between SH and in-hospital cardiac death [21]. Following myocardial infarction, the heart undergoes dynamic tissue changes such as edema, inflammation, microvascular obstruction, hemorrhage, and cardiomyocyte necrosis, ultimately resulting in fibrosis. The extent of these changes is critical in determining long-term prognosis [22]. Stress-induced hyperglycemia (SH), a transient elevation in blood glucose during acute conditions such as acute myocardial infarction (AMI), is driven by activation of the hypothalamic–pituitary–adrenal axis and the sympathetic nervous system. This leads to the release of stress hormones (e.g., cortisol, adrenaline), which enhance gluconeogenesis, glycogenolysis, and insulin resistance [23,24,25,26,27]. Several molecular mechanisms involved in glucose detection, which trigger counterregulatory responses—particularly the induction or suppression of glucagon secretion during hyperglycemic episodes—have been identified. These mechanisms are closely related to those present in insulin-secreting beta cells. Specifically, factors such as the glucose transporter GLUT2 and the K-ATP dependent channels play significant roles, alongside regulatory pathways that involve the central nervous system and the gut-brain hormone glucagon-like peptide 1 (GLP-1). The parallel mechanisms in insulin-secreting beta cells underscore the integrated nature of glucose regulation across different cellular environments, highlighting the shared pathways and functionalities within the endocrine system [28, 29]. Stress-induced hyperglycemia can lead to non-enzymatic glycation of platelet glycoproteins, enhancing platelet activation and thrombosis risk. Numerous well-designed epidemiological studies have documented a significant association between diabetes and elevated susceptibility to diverse infectious diseases, notably lower respiratory tract infections including pulmonary tuberculosis and pneumonia. [30, 31]. One study of particular interest showed that indicated that SH is linked to a higher incidence of pulmonary infections during the hospitalization of acute myocardial infarction patients [32].

Hyperglycemia and outcomes

Stress hyperglycemia is an independent predictor of adverse outcomes, with prevalence rates of 25–50% in acute coronary syndrome (ACS) patients. Admission glucose levels above 110 mg/dL in non-diabetes, and 180 mg/dL in diabetes are associated with worse outcomes, including higher mortality [33,34,35]. Maintaining glucose levels below 140 mg/dL has been linked to reduced 30-day mortality rates [36]. We further emphasize that elevated glucose levels upon admission have been associated with increased mortality rates in patients with and without diabetes. In the context of acute care, addressing stress-induced hyperglycemia is crucial, as such hyperglycemic conditions are strongly correlated with heightened morbidity and mortality rates [37, 38]. The recognition of SH highlights its significance as a diagnostic and therapeutic target in the management of acute illnesses.

Hyperglycemia upon hospital admission is a common occurrence in patients presenting with acute coronary syndrome and is significantly associated with adverse clinical outcomes [39]. This condition is indicative of both physiological stress and potential underlying glucose metabolism abnormalities, serving as a prognostic marker for increased risk of complications such as heart failure, arrhythmias, and mortality. In a study involving 210 STEMI patients, individuals were randomized to receive either intravenous exenatide or a placebo, before percutaneous coronary intervention (PCI). The results showed that hyperglycemia was associated with a larger area at risk and greater infarct size. However, when adjusting for the area at risk, there was no significant difference in the salvage index and infarct size between hyperglycemic and normoglycemic groups. Importantly, exenatide treatment improved the salvage index in both groups [40]. Hyperglycemia exacerbates myocardial injury through mechanisms such as increased oxidative stress, inflammation, and endothelial dysfunction, impairing perfusion and recovery [41,42,43]. In our study, the glucose delta was correlated with LVEF and myocardial fibrosis, highlighting its potential as a marker of cardiac impairment.

A comprehensive study of 507 participants (268 type 1 diabetic, 159 type 2 diabetic, and 80 non-diabetic controls) over 16 weeks employed both continuous and self-monitored glucose measurements to assess glycemic variability. Strong intercorrelations were observed among variability metrics, with pre-prandial glucose showing the strongest association with HbA1c—especially in type 2 diabetes—emphasizing the importance of fasting and pre-meal regulation in long-term glycemic control. [44]. The authors also established a robust linear relationship between HbA1c and estimated average glucose (AG [mg/dL] = 28.7 × HbA1c − 46.7; R² = 0.84), independent of age, sex, diabetes type, or ethnicity.

Therapeutic implications

Routine glycemic evaluations (fasting glucose, HbA1c) and glucose delta offer valuable insights into metabolic derangements post-MI. Glucose Delta, calculated as baseline glucose minus estimated average glucose (28.7 × HbA1c—46.7), was particularly useful in identifying impaired cardiac outcomes. The high prevalence of smoking (28% in the DM group) further compounded cardiovascular risk in our cohort. Additionally, hs-CRP levels, although not statistically different between subgroups, have been previously associated with worse outcomes in ACS [45, 46].

The relationship between glucose delta and LVEF highlights the impact of metabolic factors on cardiac performance, potentially guiding therapeutic strategies. However, glucose delta should be interpreted in conjunction with other validated risk markers, rather than used alone to guide therapy. Tight glycemia control in patients with myocardial infarction undergoing percutaneous coronary intervention appears to benefit these patients by additional mechanisms beyond better glucose levels. In fact, in randomized study with STEMI patients with hyperglycemia, lower levels of oxidative stress and inflammatory markers were found in those with stricter blood glucose, and lower rates of restenosis were observed during the six months of follow-up [47].

Another interesting study examined how poor glycemic control impairs endothelial progenitor cells (EPCs) in type 2 diabetes patients. The authors reported lower levels of silent information regulator 1 (SIRT1) in endothelial progenitor cells and increased expression of platelet-activating factor (PAF) among those patients with poor glycemic control [48].

Personalized approaches targeting stress-induced hyperglycemia could help mitigate adverse cardiac events, improve outcomes, and refine management strategies [49, 50]. This insight highlights the importance of integrating metabolic evaluations into the cardiac care pathway. The consistent findings across various subgroups underscore the universal significance of addressing stress-induced hyperglycemia. Effective management can play a pivotal role in reducing cardiovascular risks and improving long-term health outcomes, establishing it as a critical focus in both preventive and therapeutic strategies.

Whilst longitudinal observation was not our primary objective, we emphasize the criticality of continuous outpatient monitoring for these high-risk patients, which enables medication optimization, ensures adherence to evidence-based therapies, facilitate guideline-aligned personalized treatment, monitors adverse effects, addresses modifiable risk factors, and ultimately improves cardiovascular outcomes. A recent multicenter study involving over 23,000 acute coronary syndrome patients provides complementary evidence of post-discharge vulnerability, demonstrating that in-hospital bleeding occurred predominantly in older female patients with multiple risk factors who frequently received suboptimal therapy, resulting in significantly higher one-year mortality, major bleeding, and reinfarction rates. This association persisted after statistical adjustment, identifying in-hospital bleeding as an indicator of patient frailty requiring enhanced follow-up. These findings underscore the importance of comprehensive longitudinal care, particularly for vulnerable subgroups including those with hemorrhagic complications, women, and elderly patients, who commonly receive inadequate continuous optimized therapy [51].

Conclusions

Our findings demonstrate that glycemic variability, particularly glucose delta, is associated with larger myocardial infarct size and reduced LVEF in STEMI patients. These associations were robust across subgroups, underscoring the importance of managing stress-induced hyperglycemia to improve cardiovascular outcomes. Future investigations should elucidate the molecular pathways and cellular mechanisms underlying these associations, while simultaneously developing and evaluating targeted therapeutic interventions that could potentially mitigate these adverse outcomes in high-risk populations. Furthermore, prospective longitudinal studies incorporating diverse patient demographics and comorbidity profiles are essential to validate these findings across different clinical contexts. Additionally, exploring the interplay between genetic predisposition, environmental factors, and treatment response would provide valuable insights for personalized medicine approaches and inform evidence-based clinical practice guidelines.

Limitations

This study is limited to patients undergoing pharmacoinvasive interventions, excluding those referred for primary percutaneous coronary intervention or with contraindications for fibrinolysis. We acknowledge several important limitations to the generalizability of our findings: the unique geographic context of our study, the use of a pharmaco-invasive strategy (which may not be representative of centers where primary PCI is the standard of care), and the absence of external validation for delta glucose thresholds. We emphasize that prospective validation studies conducted in different clinical contexts will be essential to confirm our findings and define the clinical applicability of these thresholds in a broader population. Additionally, patients with prior cardiovascular events were not included, potentially narrowing the generalizability of the findings.