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Association of stress hyperglycemia ratio and poor long-term prognosis in patients with myocardial infarction with non-obstructive coronary arteries

Association of stress hyperglycemia ratio and poor long-term prognosis in patients with... Background Stress hyperglycemia ratio (SHR) is a novel biomarker of true acute hyperglycemia condition and is associated with a worse prognosis in patients with myocardial infarction (MI). However, the effects of SHR in the setting of MI with non‑ obstructive coronary arteries (MINOCA) have not been investigated. This study aimed to explore the association between SHR and long‑term clinical outcomes among MINOCA patients. Methods A total of 410 MINOCA patients were included in the final analysis of this study. The patients were divided into three groups based on the SHR tertiles: [SHR1 group (SHR ≤ 0.73), (n = 143); SHR2 group (SHR 0.73–0.84), n = 131; and SHR3 group (SHR ≥ 0.84), n = 136]. Follow‑up for major adverse cardiovascular events (MACE) was conducted on all patients. Cox regression and Kaplan–Meier curve analysis were used to evaluate the relationship between SHR and MACE. The receiver operating curve (ROC) analysis was applied to obtain the optimal cut‑ off value of SHR for predicting clinical MACE. Results A total of 92 patients developed MACE during the mean 34 months of follow‑up. A significant increase in MACE was observed in the SHR3 group compared to the SHR1 and SHR2 groups (35.3% vs. 15.4% and 16.8%, respectively; P < 0.001). The Kaplan–Meier curves demonstrate that SHR3 patients had the highest MACE risk compared to SHR1 and SHR2 patients (log‑rank P < 0.001). In addition, when both SHR tertiles and diabetes status were considered, those with SHR3 and diabetes had the highest hazard of MACE (log‑rank P < 0.001). Multivariate Cox regression analysis showed that the SHR3 is associated with a 2.465‑fold increase in the risk of MACE (adjusted HR, 2.465; 95% CI 1.461–4.159, P = 0.001). The ROC curve analysis showed that the optimal SHR cut‑ off value for predicting clinical MACE among MINOCA was 0.86. Conclusion Our data indicates, for the first time, that SHR is independently associated with poor long‑term prognosis in patients suffering from MINOCA. The optimal SHR cut ‑ off value for predicting clinical MACE among MINOCA patients was 0.86. These findings suggest that SHR may play a potential role in the cardiovascular risk stratification of the MINOCA population. *Correspondence: Yawei Xu xuyawei@tongji.edu.cn Wenliang Che chewenliang@tongji.edu.cn Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 2 of 11 Keywords Myocardial infarction with non‑ obstructive coronary arteries, Stress hyperglycemia ratio, Diabetes mellitus, Clinical outcome Background AMI [16, 18–22]. As of yet, there is no data regarding Cardiovascular diseases (CVD), particularly acute the impact of SHR on the clinical outcomes among myocardial infarction (AMI), remain a growing threat MINOCA patients. to public health and a leading cause of morbidity and As such, this study sought to explore for the first time mortality worldwide [1]. Myocardial infarction with the predictive value of SHR and obtain its optimal cut-off non-obstructive coronary arteries (MINOCA) is a value in predicting long-term clinical outcomes among common clinical condition observed in around 5–10% patients suffering from MINOCA and further deter - of all patients with AMI admitted for coronary angiog- mine whether it may have any clinical relevance in this raphy (CAG) [2, 3]. MINOCA represents a heteroge- population. neous and largely unexplored clinical syndrome with various underlying pathophysiological mechanisms Materials and methods that warrant further investigations [4]. It often remains Study design and population a misdiagnosed and mismanaged illness linked to a During the period 2014 through 2022, we conducted an high incidence of major adverse cardiovascular events observational retrospective study of patients with AMI (MACE), mortality, and a lower quality of life [5, 6]. who underwent CAG and had new-onset chest pain Previous studies reported that MINOCA is associ- with ST-segment elevation MI (STEMI) and non-ST seg- ated with an approximately 23.9% rate of MACE after ment elevation MI (NSTEMI) on ECG presenting at the 4 years of follow-up, and the all-cause mortality rate cardiology department of Shanghai Tenth People’s Hos- of MINOCA patients at 1 year and 3 years were 10.9% pital (Tongji University, Shanghai, China). In this study, and 16.1%, respectively [7, 8]. Therefore, it is essen - MINOCA was defined as patients with evidence of tial to identify easily obtainable determinant factors AMI with non-obstructive coronary arteries (defined as of adverse events to provide optimal management and stenosis less than 50% in any epicardial coronary arter- improve the quality of life in this patient population. ies), as recommended by the 4th universal definition Stress hyperglycemia refers to the acute transient of AMI [23], which excluded myocarditis and Takot- increase in blood glucose levels in response to numer- subo syndrome from the final diagnosis of MINOCA. ous critical conditions and is independently associated The exclusion criteria included the following items: (1) with poor short and long-term clinical outcomes in patients < 18 years; (2) patients with a history of MI or acute coronary syndrome (ACS) patients, particularly obstructive CAD; (3) patients receiving thrombolytic those with AMI [9–11]. It has recently been revealed prior to or during hospitalization, (4) patients with type that admission stress hyperglycemia is also common 3–5 MI; (5) those with severe liver and kidney conditions; among MINOCA patients [12], and it is a strong pre- (6) patients with major valve pathologies, a history of dictor of short- and long-term adverse outcomes in this stroke, and malignant arrhythmias; and (7) patients lost patient group, regardless of diabetes status [13]. Addi- to follow-up or had no complete SHR data. tionally, we and others have recently shown that fasting Our study was approved by the Shanghai Tenth Peo- blood glucose [14], and triglyceride-glucose index [15] ple’s Hospital ethics committee and complied with the were associated with poor clinical outcomes among Declaration of Helsinki. Informed consent has been patients suffering from MINOCA. However, elevated obtained from all patients. glucose levels at the time of hospital admission may be the result of chronic hyperglycemia or acute stress Data collection and definitions response [16]. In this regard, the stress hyperglyce- We retrospectively gathered the baseline demograph- mia ratio (SHR) has been developed as a new marker ics (age, gender, height, weight, body mass index (BMI), to reflect true acute hyperglycemia condition, which heart rate, and blood pressure), past medical history is estimated based on the acute admission glucose (history of hypertension, diabetes, hyperlipidemia, atrial level and the chronic glycemic value [calculated by fibrillation, and smoking history), electrocardiogram, glycosylated hemoglobin (HbA1c)] [17]. Several clini- and echocardiography information for all patients. Blood cal studies have reported that SHR is associated with samples for testing HbA1c, blood glucose, cardiac tro- significantly higher in-hospital mortality and long- ponin-T (cTnT), N-terminal pro-brain natriuretic pep- term MACE than admission glucose in patients with tide (NT-proBNP), creatine kinase-MB (CK-MB), total A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 3 of 11 cholesterol (TC), low-density lipoprotein cholesterol of clinical outcomes, and a log-rank test was used to (LDL-C), high-density lipoprotein cholesterol (HDL-C), determine differences between groups. A Pearson cor - triglyceride (TG), C reactive protein (CRP), and com- relation analysis was performed to determine the corre- plete blood count (white blood cell counts, red blood cell lation between SHR and myocardial injury parameters. counts, and hemoglobin) was obtained from the cubi- Univariate Cox regression was used to evaluate the tal vein after at least eight hours of fasting. An Abbott association between SHR and clinical outcomes. Car- Laboratories (Chicago, IL, USA) was used to analyze diovascular risk factors listed in Table 1 (age, sex, BMI, blood glucose, TC, LDL-C, HDL-C, and TG. A diabetes LVEF, hypertension, diabetes, hyperlipidemia, smoking, diagnosis is based on the following: (1) Random plasma atrial fibrillation, STEMI, degree of coronary stenosis, glucose ≥ 11.1 mmol/l (≥ 200  mg/dl); (2) fasting blood cTnT, CK-MB, NT-proBNP, TC, LDL-C, HDL-C, TG, glucose ≥ 7.0 mmol/l (≥ 126  mg/dl); (3) HbA1c ≥ 6.5%; and CRP) which may contribute to an elevated risk of and (4) OGTT glucose level ≥ 11.1 mmol/l (200 mg/dl). adverse outcomes among MINOCA patients served as the variables in the univariate analysis along with SHR index. Clinical covariates that were significant with a Determination of SHR P < 0.10 in the univariate analysis were used for adjust- The blood glucose obtained during the first 24 h of hos - ment in the multivariate analysis by the forward step- pital admission was considered admission blood glu- wise regression method. The receiver operating curve cose. Abbott Laboratories (Chicago, IL, USA) was used (ROC) was applied to calculate the area under the to calculate the HbA1c. The SHR is calculated using the curve (AUC) and obtained the optimal cut-off value of following equation by dividing admission glucose by the SHR for predicting clinical outcomes among MINOCA average glucose calculated from HbA1c: SHR = [(admis- patients, and the Youden index was calculated at the sion glucose (mmol/L) / [1.59 × HbA1c (%) − 2.59] [18]. point where the sensitivity and specificity sum was highest. All analysis was conducted two-sided, and sta- Endpoints and follow up tistical significance was set at P-value < 0.05. In this study, the mean follow-up duration was 34 months. Clinical outcomes were recorded by two experts via telephone calls, clinic visits, review of medical case Results history, and communication with patients’ families. The Baseline characteristics primary observational clinical endpoints of the pre- MINOCA was diagnosed in 488 consecutive patients, sent investigation were MACE, which includes cardiac 78 of whom were lost to follow-up, did not have blood death, heart failure, nonfatal MI, stroke, and angina glucose data, and were excluded from the study. A rehospitalization. Deaths caused by malignant arrhyth- total of 410 patients were included in the final analy - mias, acute MI, heart failure, or other cardiac conditions sis of the present study [216 (52.7%) were males; the were defined as cardiac deaths. Nonfatal MI was defined mean age was 63.55 ± 13.81 years; and 79 (19.3%) had as positive cardiac biomarkers or dynamic changes on diabetes]. In this study, the patients were divided into electrocardiograms in addition to the typical symptoms three groups based on their SHR tertiles: [SHR1 group of myocardial ischemia. A heart failure diagnosis was (SHR ≤ 0.73), (n = 143); SHR2 group (SHR 0.73–0.84), made based on recent ESC guidelines for the diagnosis n = 131; and SHR3 group (SHR ≥ 0.84), n = 136] and treatment of acute and chronic heart failure [24]. A (Fig. 1). stroke is diagnosed when there is evidence of ischemic Table  1 presents the baseline characteristics of the cerebral infarction because of thrombotic or embolic three groups. Patients in the SHR3 group had a higher obstruction. rate of atrial fibrillation. The LVEF in the SHR3 group was lower than those in the SHR1 and SHR2 groups Statistical analysis (52.73% vs. 55.68% and 55.78%, P = 0.041), whereas the Statistical Package for the Social Sciences (SPSS) ver- left atrium size and left ventricular end-diastolic diam- sion 24 was used to analyze our data. GraphPad soft- eter were larger in the SHR3 group. Compared to the ware version 8.0.1 was used to create the figures. We SHR1 and SHR2 groups, the SHR3 group had signifi - expressed continuous variables as means and standard cantly higher serum cTnT, NT-proBNP, and CRP lev- deviations (mean ± SD), while categorical variables els. However, no differences were observed between the as percentages (%). The comparison of clinical data three groups regarding other baseline characteristics or between groups was made using ANOVAs for continu- laboratory findings (all P > 0.05). The glucose-lowering ous variables and Pearson chi-square tests or Fisher’s medications for diabetic patients are shown in Additional exact tests for categorical variables. The Kaplan–Meier file 1: Table S1. curve was used to determine the cumulative incidence Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 4 of 11 Table 1 Clinical characteristics according to different SHR tertiles Variables SHR1 (≤ 0.73) N = 143 SHR2 (0.73–0.84) N = 131 SHR3 (≥ 0.84) P value N = 136 Age (years) 64.05 ± 13.45 63.32 ± 13.45 63.22 ± 14.58 0.860 Male, n (%) 72 (50.3) 73 (55.7) 71 (52.2) 0.667 BMI (kg/m2) 24.13 ± 4.20 24.26 ± 3.15 24.28 ± 4.23 0.970 Comorbidities Hypertension, n (%) 70 (49.0) 69 (52.7) 76 (55.9) 0.510 Diabetes, n (%) 28 (19.6) 23 (17.6) 28 (20.6) 0.816 Smoking history, n (%) 57 (39.9) 51 (38.9) 64 (47.1) 0.332 Atrial fibrillation, n (%) 20 (14.0) 8 (6.1) 21 (15.4) 0.041 Hyperlipidaemia, n (%) 19 (13.3) 21 (16.0) 13 (9.6) 0.285 STEMI, n (%) 45 (31.5) 44 (33.6) 39 (28.7) 0.685 Angiographic characteristics Normal vessels , n (%) 67 (46.9) 62 (47.3) 61 (44.9) 0.910 Vessel with any stenosis , n (%) 76 (53.1) 69 (52.7) 75 (55.1) 0.910 Echocardiography parameters LVEF (%) 55.68 ± 9.82 55.78 ± 10.22 52.73 ± 12.34 0.041 LAD (mm) 37.00 ± 6.53 36.74 ± 6.46 38.71 ± 6.12 0.030 E/e’ 10.86 ± 3.20 10.43 ± 2.82 10.18 ± 2.88 0.334 LVEDD (mm) 44.72 ± 5.10 45.30 ± 5.45 47.10 ± 5.97 0.001 LVESD (mm) 29.73 ± 6.07 30.65 ± 12.06 32.03 ± 7.36 0.116 T TPG (mmHg) 24.74 ± 12.74 25.24 ± 7.77 23.68 ± 6.34 0.623 Laboratory parameters HbA1c (%) 6.51 ± 1.20 6.35 ± 1.46 6.01 ± 1.23 0.005 FBG (mmol/L) 5.13 ± 1.35 5.90 ± 1.81 7.30 ± 3.20 < 0.001 cTnT (ng/mL) 0.42 ± 1.06 0.42 ± 1.04 1.02 ± 3.15 0.018 Creatine kinase‑MB (ng/mL) 14.89 ± 33.52 20.11 ± 53.70 24.54 ± 42.26 0.182 NT‑proBNP (pg/mL) 1477.69 ± 3573.61 4639.57 ± 405.36 6541.46 ± 560.92 0.007 TC (mmol/L) 4.27 ± 1.09 4.15 ± 0.93 4.20 ± 1.12 0.675 LDL‑ C (mmol/L) 2.50 ± 0.92 2.41 ± 0.84 2.43 ± 0.94 0.677 HDL‑ C (mmol/L) 1.14 ± 0.33 1.14 ± 0.31 1.19 ± 0.37 0.445 TG (mmol/L) 1.52 ± 1.17 1.54 ± 0.93 1.48 ± 0.98 0.873 CRP (mg/dL) 0.68 ± 0.62 0.60 ± 0.87 0.95 ± 1.34 0.012 WBC (10 /L) 7.90 ± 2.96 7.89 ± 3.24 8.52 ± 3.74 0.208 RBC (10 /L) 4.34 ± 0.67 4.42 ± 0.64 4.49 ± 0.60 0.163 Hemoglobin (g/L) 131.69 ± 23.93 133.19 ± 20.32 134.25 ± 18.08 0.599 SHR stress hyperglycemia ratio, BMI body mass index, LVEF left ventricular ejection fraction, LAD left atrium diameter, E/e’ mean septal velocity, LVEDD left ventricular end-diastolic diameter, LVESD left ventricular end-systolic diameter, TTPG trans tricuspid pressure gradient, STEMI ST-segment elevation myocardial infarction, HbA1c hemoglobin A1c, FBG fasting blood glucose, cTnT cardiac troponin, NT-proBNP N-terminal pro-brain natriuretic peptide, TC total cholesterol, HDL-C high density lipoprotein, LDL-C low-density lipoprotein, TG triglyceride, CRP C reactive protein, WBC white blood cell counts, RBC red blood cell Vessels with 0% stenosis Vessels with 0–50% stenosis Clinical outcomes according to SHR tertiles and diabetes risk compared to SHR1 and SHR2 patients (log-rank status P < 0.001). For further analysis, the study population A total of 92 patients (22.4%) developed MACE during was divided into six subgroups based on SHR tertiles the follow-up period. A significant increase in MACE was and diabetes status, including SHR1 with and without observed in the SHR3 group as compared to the SHR1 diabetes, SHR2 with and without diabetes, and SHR3 and SHR2 groups (35.3% vs. 15.4% and 16.8, respectively; with and without diabetes. The results showed that those P < 0.001) (Table 2). In Fig. 2A, the Kaplan–Meier curves with SHR3 and diabetes had the highest hazard of MACE demonstrate that SHR3 patients had the highest MACE compared to other groups (log-rank P < 0.001) (Fig. 2B). A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 5 of 11 Fig. 1 Flowchart of the study selection process. MINOCA myocardial infarction with non‑ obstructive coronary arteries, SHR stress hyperglycemia ratio Table 2 Clinical outcomes according to different SHR tertiles SHR1 (≤ 0.73) SHR2 (0.73–0.84) SHR3 (≥ 0.84) P value N = 143 N = 131 N = 136 MACE, n (%) 22 (15.4) 22 (16.8) 48 (35.3) < 0.001 Cardiac death, n (%) 7 (4.9) 4 (3.1) 12 (8.8) 0.110 Non‑fatal MI, n (%) 1 (0.7) 1 (0.8) 2 (1.5) 0.771 Heart failure, n (%) 3 (2.1) 4 (3.1) 8 (5.9) 0.220 Angina rehospitalization, n (%) 10 (7.0) 13 (9.9) 22 (16.2) 0.044 Stroke, n (%) 1 (0.7) 0 4 (2.9) 0.071 MACE major adverse cardiac events, MI acute myocardial infarction, SHR stress hyperglycemia ratio Predictive factors of MACE HR, 2.465; 95% CI 1.461–4.159, P = 0.001), along with The univariate and multivariate Cox regression analy - CK-MB levels (adjusted HR, 1.004; 95% CI  1.001–1.007; sis of MACE are shown in Tables  3,    4. Univariate Cox P = 0.012). regression models showed that the SHR3 is associated with a 2.659-fold increased risk of MACE (HR 2.659; 95% CI 1.604–4.407, P < 0.001). Age, reduced LVEF, diabetes, Optimal cut‑off value of SHR for predicting outcomes atrial fibrillation, CK-MB, and NT-proBNP levels were among MINOCA also predictive factors of MACE in the univariate regres- The ROC curve of SHR, FBG, and HbA1c were displayed sion analysis. in Fig.  3 for the prediction of MACE among MINOCA After excluding confounding factors with a P < 0.10 patients, which demonstrated that SHR was consistently in the univariate analysis, multivariate Cox regression better predictor of MACE, with an AUC of 0.636 (95% analysis showed that the SHR3 group remained CI  0.569–0.703; P < 0.001), while the AUC of FBG and associated with increased 2.465-fold of MACE (adjusted HbA1c were (0.616 95% CI 0.552–0.679; P < 0.001 and Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 6 of 11 Fig. 2 (A) Cumulative incidence of MACE based on the SHR tertiles; (B) Cumulative incidence of MACE based on the SHR tertiles and diabetes status. MACE major adverse cardiovascular events, SHR stress hyperglycemia ratio Table 3 Univariate Cox regression analysis for endpoint events Table 4 Multivariable cox regression analysis for endpoint events HR 95% CI P‑ value HR 95% CI P‑ value Age 1.024 1.008–1.040 0.004 Age 1.016 0.999–1.034 0.063 Sex 0.960 0.638–1.447 0.847 LVEF 0.983 0.965–1.001 0.060 BMI 1.009 0.936–1.087 0.817 Diabetes 1.465 0.904–2.375 0.121 LVEF 0.973 0.957–0.988 0.001 Atrial fibrillation 1.611 0.924–2.809 0.093 Hypertension 1.417 0.932–2.155 0.103 Creatine kinase‑MB 1.004 1.001–1.007 0.012 Diabetes 1.776 1.128–2.799 0.013 NT‑proBNP 1.000 1.000‑1000 0.740 Hyperlipidaemia 1.065 0.592–1.916 0.834 SHR3 2.465 1.461–4.159 0.001 Smoking 1.015 0.670–1.537 0.944 Atrial fibrillation 1.726 1.006–2.960 0.048 LVEF left ventricular ejection fraction, NT-proBNP N-terminal pro-brain natriuretic peptide, SHR stress hyperglycemia ratio, HR hazard ratio, CI confidence interval STEMI 1.063 0.692–1.634 0.780 Coronary stenosis 1.388 0.913–2.110 0.126 cTnT 1.036 0.974–1.103 0.263 0.511 95% CI 0.446–0.576; P = 0.747, respectively). Creatine kinase‑MB 1.003 1.000–1.006 0.024 Notably, we obtained that the optimal SHR cut-off value NT‑proBNP 1.000 1.000‑1000 0.003 for predicting clinical MACE was 0.86. TC 0.905 0.736–1.112 0.341 LDL‑ C 0.924 0.725–1.179 0.526 Correlation between SHR and myocardial injury HDL‑ C 1.039 0.556–1.938 0.905 parameters TG 1.131 0.956–1.337 0.152 The correlation between SHR and myocardial injury CRP 1.052 0.859–1.288 0.621 parameters, such as cTnT, CK-MB, NT-proBNP, and SHR tertiles LVEF, was further examined. The results demonstrated SHR1 Reference Reference that the SHR correlated well with cTnT, NT-proBNP, and SHR2 1.133 0.628–2.047 0.677 LVEF among MINOCA patients (r = 0.116, r = 0.210, and SHR3 2.659 1.604–4.407 < 0.001 r = − 0.194, respectively) (Fig.  4). However, SHR did not BMI body mass index, LVEF left ventricular ejection fraction, STEMI ST-segment correlate with other myocardial injury parameters, such elevation myocardial infarction, cTnT cardiac troponin, NT-proBNP N-terminal pro-brain natriuretic peptide, TC total cholesterol, HDL-C high density as creatine CK-MB (data not shown). lipoprotein, LDL-C low-density lipoprotein, TG triglyceride, CRP C reactive protein, SHR stress hyperglycemia ratio, HR hazard ratio, CI confidence interval A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 7 of 11 Fig. 3 Receiver operating characteristic analysis of the ability of the SHR, FBG, and HbA1c to predict MACE in MINOCA patients. AUC ar ea under the curve, SHR stress hyperglycemia ratio, CI confidence interval MINOCA patients had an all-cause death, and 23.9% Discussion experienced a cardiovascular event during 4.1 years of The present study, to our knowledge, is the first to follow-up [7]. In our investigation, we discovered that evaluate the association between SHR and clinical out- the MACE rate among MINOCA patients was 22.4% comes among MINOCA patients. The novel findings over a mean of 34 months of follow-up. This finding of this study were: (1) a higher risk of clinical outcomes is comparable to those observed in previous clinical was observed for MINOCA patients with high SHR; (2) studies above showing a high incidence of MACE in the SHR was independently associated with long-term risk MINOCA population. Despite no apparent coronary of MACE in patients suffering from MINOCA; (3) SHR stenosis in MINOCA patients, the risk of adverse events cut-off value of 0.86 was able to identify the high-risk is not negligible, which indicates that MINOCA still MINOCA patients, and (4) SHR correlated well with afflicted potential harm and deserves the same level of markers of myocardial injury, such as cTnT, NT-proBNP, significance as obstructive CAD. Data regarding clinical and LVEF. These findings indicate that SHR may play a risk scores and predictors of adverse clinical outcomes vital role in prioritizing patients and a robust biomarker in MINOCA populations is scarce. We and some other to predict future MACE in the MINOCA population. clinical investigations have documented the correlation MINOCA has gained significant attention and between various factors, including cardiac troponin, has been added as a subtype of MI to the fourth age, sex, thyroid hormones, LVEF, metabolic syndrome, global definition of MI [23]. MINOCA represents a challenging heterogeneous clinical syndrome where hyperglycemia, total bilirubin, creatinine, TC, and multiple aetiologies are causative with no optimal C-reactive protein with worse outcomes in MINOCA management therapy, and the prognosis in this high- [14, 27–33]. u Th s, it is necessary to perform rapid and risk patient group is far from benign [4, 25]. A recent accurate risk stratification using robust predictors Italian genetic study on early-onset MI demonstrated beyond traditional clinical measures to identify potential that MACE rates among MINOCA patients were 27.8% factors associated with patient outcomes. over a median follow-up of 19.9 years, which did not Stress hyperglycemia is frequent in AMI patients differ significantly from MACE rates among patients and negatively affects their prognosis, as well as with obstructive coronary artery disease (CAD) [26]. being independently associated with higher mortality The SWEDEHEART registry reported that 13.4% of rates and greater infarct sizes [9–11]. A recent study Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 8 of 11 Fig. 4 Correlation between SHR and cTnT (A), NT‑proBNP (B), and LVEF (C) . SHR stress hyperglycemia ratio, cTnT cardiac troponin, NT-proBNP N‑terminal pro ‑brain natriuretic peptide, LVEF left ventricular ejection fraction all-cause mortality among 5841 STEMI patients and showed that admission stress hyperglycemia was also 4105 NSTEMI patients after one year of fellow up [19]. A common among MINOCA patients and significantly recent study also found that SHR predicts mortality and predicted both short- and long-term adverse outcomes, adverse events in STEMI patients, both in diabetes and implying that hyperglycemia may contribute directly to non-diabetic patients [20]. The prospective, nationwide, myocardial damage [12]. However, stress hyperglycemia multi-center China AMI registry results demonstrated a reflects the severity of an emergency and poor glucose significant positive correlation between SHR and long- control to some extent. Additionally, it may worsen term death in patients with AMI [34]. Numerous clinical acute cardiac illness in several ways, such as increasing studies have also indicated a link between SHR and endothelial dysfunction, decreasing platelet nitric oxide unfavorable outcomes in AMI patients [21, 22, 35, 36]. responsiveness, aggravating microvascular obstruction, Pasquale et al. also found that SHR significantly increases and inducing further hyperglycemic-mediated vascular the risk of rehospitalization among 2,874 patients with damage mechanisms [16]. The SHR is a novel marker of ischemia with non-obstructive coronary arteries [37]. true acute hyperglycemia conditions and is associated Given that SHR was used as an indicator for predicting with adverse outcomes in patients with AMI. SHR future clinical events in patients with cardiovascular was first reported by Roberts et  al. as an independent disease, it is still unclear whether it can effectively biological biomarker for clinical outcomes among predict the risk of clinical outcomes in the MINOCA patients with various clinical disorders [17]. A large population. Recently, we found that fasting blood glucose cohort study in Asia found a correlation between SHR and early and late cardiac outcomes among ACS was associated with poor clinical outcomes among patients [16]. An analysis by Xu et al. found a significant patients with MINOCA [14]. In this study, the sample association between SHR and in-hospital mortality in size and follow-up period of MINOCA were expanded, patients with CAD [18]. The SHR significantly predicted and SHR was examined for the first time as a predictive A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 9 of 11 factor for clinical outcomes among the MINOCA cut-off value of the SHR for predicting in-hospital out - cohort. We demonstrated that the risk of adverse events comes was 1.20, while 1.32 for predicting in-hospital was significantly higher in MINOCA patients with a mortality [42]. However, a definite SHR cut-off value high SHR group. Interestingly, after adjusting for age, for predicting adverse events among MINOCA has not sex, traditional cardiovascular risk factors, and other yet been studied. The present study found that the best relevant biochemical parameters, multivariate cox optimal SHR threshold from the AUC for predicting regression analysis showed that high SHR remained clinical MACE in MINOCA was 0.86. It is necessary to significantly correlated with worse clinical outcomes. conduct more large-scale prospective clinical studies in Our findings suggest that SHR may play a potential role the MINOCA population to determine whether a cut-off in the cardiovascular risk stratification of the MINOCA value for SHR can accurately predict future poor clinical population. outcomes. The underlying mechanisms that relate SHR to unfa - vorable outcomes in the MINOCA are not recognized. Strengths and limitations In our study, patients with high SHR levels were associ- The strength of this study is that it is the first to evaluate ated with worse baseline features, as demonstrated by a the role of SHR and obtain its cut-off value in predicting higher degree of myocardial injury (elevated cTnT and poor clinical outcomes among MINOCA patients. The NT-proBNP) and reduced LVEF compared to patients information provided by our study can be used by phy- with low SHR. We also found a significant correlation sicians to follow up on selected patients more closely, between SHR and NT-proBNP, cTnT, and LVEF, which increase the intensity of their goal-directed medical treat- may contribute to an increased risk of MACE to some ment, control their risk factors, and improve the quality extent. Several clinical studies have confirmed such of life among patients with MINOCA. However, there results among MI patients, which reported that SHR are several limitations of our investigation that must be had a significant correlation with myocardial injury as noted. First, the present study has a retrospective design shown by high peak cTnT and peak CK-MB values and with a small sample size; the validity of these findings their association with the severity of CAD (assessed by requires further prospective multi-center studies. Sec- the Gensini score and Syntax score) [10, 35, 38]. On the ond, the results demonstrated here were conducted on other hand, we found that inflammatory marker such as the Chinese MINOCA population; consequently, they CRP was higher among the high SHR group, which may may not generalize to other populations. Third, although also be associated with some unfavorable outcomes. It SHR is linked to adverse outcomes even after adjust- has been demonstrated in previous MI studies that stress ing for a few potential variables, the impact of unmeas- hyperglycemia leads to increase inflammation burden, ured confounding variables cannot be removed entirely. ischemia-reperfusion damage, and endothelial dysfunc- Fourth, we did not consider other adverse outcomes, tion, which are all strongly associated with large infarct such as a reduction in left ventricular ejection fraction sizes and poor clinical outcomes [9, 39, 40]. Further stud- or infarction size, in addition to the association between ies should confirm this finding in larger MINOCA cohort SHR and MACE. In addition, retrospective observational and determine the underlying mechanisms of SHR in nature of our study and lack of other inflammatory mark - MINOCA. ers such as procalcitonin or systemic immune-inflamma - The best cut-off value of SHR to predict clinical out - tory index limit us to speculate their effects on clinical comes in CAD patients differs among studies. Among outcomes. Lastly, our study lacks data regarding hypo- 19,929 patients with CAD, 0.741 was the best cut-off glycemic therapy during the follow-up period; therefore, value for SHR to predict clinical outcomes [18]. An opti- we are not able to assess their effect on the prognosis of mal SHR cut-off value of 0.78 predicted poor prognosis patients. in ACS patients [16]. The optimal SHR cut-off value for predicting all-cause mortality at one year among diabetic Conclusion STEMI patients was 1.68 and 1.51 among non-diabetic Our data indicate, for the first time, that SHR is inde - STEMI patients; however, the SHR for NSTEMI was 1.53 pendently associated with poor long-term prognosis in in diabetics and 1.27 in non-diabetics [19]. Among dia- patients suffering from MINOCA. The optimal SHR cut- betic and non-diabetic AMI populations, Cui et al. found off value for predicting clinical MACE among MINOCA that 1.20 and 1.08 were optimal cut-off values for SHR patients was 0.86. These findings suggest that SHR may in predicting 2-year mortality [34]. Luo et  al. found that play a potential role in the cardiovascular risk stratifica - 1.24 and 1.14 were the optimal cut-off values for SHR in tion of the MINOCA population. diabetics and non-diabetics with AMI, respectively [41]. Additionally, among elderly AMI patients, the optimal Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 10 of 11 Abbreviations Received: 27 December 2022 Accepted: 10 January 2023 CVD Cardiovascular diseases AMI Acute myocardial infarction MINOCA Myocardial infarction with non‑ obstructive coronary arteries MACE Major adverse cardiac events ACS Acute coronary syndrome SHR Stress hyperglycemia ratio References LVEF Left ventricular ejection fraction 1. Najjar SS, Rao SV, Melloni C, Raman SV, Povsic TJ, Melton L, et al. HbA1c Hemoglobin A1c Intravenous erythropoietin in patients with ST‑segment elevation FBG Fasting blood glucose myocardial infarction: REVEAL: a randomized controlled trial. Jama. cTnT Cardiac troponin 2011;305(18):1863–72. NT‑proBNP N‑terminal pro ‑brain natriuretic peptide 2. Reynolds HR, Maehara A, Kwong RY, Sedlak T, Saw J, Smilowitz NR, TC Total cholesterol et al. 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Association between initial serum total bilirubin and clinical outcome in myocardial infarction Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : with non‑ obstructive coronary arteries. Int J Med Sci. 2022;19(6):986–92. 33. Nordenskjöld AM, Baron T, Eggers KM, Jernberg T, Lindahl B. Predictors fast, convenient online submission of adverse outcome in patients with myocardial infarction with thorough peer review by experienced researchers in your field non‑ obstructive coronary artery (MINOCA) disease. Int J Cardiol. 2018;261:18–23. rapid publication on acceptance 34. Cui K, Fu R, Yang J, Xu H, Yin D, Song W, et al. Stress hyperglycemia ratio support for research data, including large and complex data types and long‑term mortality after acute myocardial infarction in patients • gold Open Access which fosters wider collaboration and increased citations with and without diabetes: a prospective, nationwide, and multicentre registry. Diabetes Metab Res Rev. 2022;38(7):e3562. maximum visibility for your research: over 100M website views per year 35. Meng S, Zhu Y, Liu K, Jia R, Nan J, Chen M, et al. The stress hyperglycaemia ratio is associated with left ventricular remodelling after first acute At BMC, research is always in progress. ST‑segment elevation myocardial infarction. BMC Cardiovasc Disord. Learn more biomedcentral.com/submissions 2021;21(1):72. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cardiovascular Diabetology Springer Journals

Association of stress hyperglycemia ratio and poor long-term prognosis in patients with myocardial infarction with non-obstructive coronary arteries

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10.1186/s12933-023-01742-6
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Abstract

Background Stress hyperglycemia ratio (SHR) is a novel biomarker of true acute hyperglycemia condition and is associated with a worse prognosis in patients with myocardial infarction (MI). However, the effects of SHR in the setting of MI with non‑ obstructive coronary arteries (MINOCA) have not been investigated. This study aimed to explore the association between SHR and long‑term clinical outcomes among MINOCA patients. Methods A total of 410 MINOCA patients were included in the final analysis of this study. The patients were divided into three groups based on the SHR tertiles: [SHR1 group (SHR ≤ 0.73), (n = 143); SHR2 group (SHR 0.73–0.84), n = 131; and SHR3 group (SHR ≥ 0.84), n = 136]. Follow‑up for major adverse cardiovascular events (MACE) was conducted on all patients. Cox regression and Kaplan–Meier curve analysis were used to evaluate the relationship between SHR and MACE. The receiver operating curve (ROC) analysis was applied to obtain the optimal cut‑ off value of SHR for predicting clinical MACE. Results A total of 92 patients developed MACE during the mean 34 months of follow‑up. A significant increase in MACE was observed in the SHR3 group compared to the SHR1 and SHR2 groups (35.3% vs. 15.4% and 16.8%, respectively; P < 0.001). The Kaplan–Meier curves demonstrate that SHR3 patients had the highest MACE risk compared to SHR1 and SHR2 patients (log‑rank P < 0.001). In addition, when both SHR tertiles and diabetes status were considered, those with SHR3 and diabetes had the highest hazard of MACE (log‑rank P < 0.001). Multivariate Cox regression analysis showed that the SHR3 is associated with a 2.465‑fold increase in the risk of MACE (adjusted HR, 2.465; 95% CI 1.461–4.159, P = 0.001). The ROC curve analysis showed that the optimal SHR cut‑ off value for predicting clinical MACE among MINOCA was 0.86. Conclusion Our data indicates, for the first time, that SHR is independently associated with poor long‑term prognosis in patients suffering from MINOCA. The optimal SHR cut ‑ off value for predicting clinical MACE among MINOCA patients was 0.86. These findings suggest that SHR may play a potential role in the cardiovascular risk stratification of the MINOCA population. *Correspondence: Yawei Xu xuyawei@tongji.edu.cn Wenliang Che chewenliang@tongji.edu.cn Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 2 of 11 Keywords Myocardial infarction with non‑ obstructive coronary arteries, Stress hyperglycemia ratio, Diabetes mellitus, Clinical outcome Background AMI [16, 18–22]. As of yet, there is no data regarding Cardiovascular diseases (CVD), particularly acute the impact of SHR on the clinical outcomes among myocardial infarction (AMI), remain a growing threat MINOCA patients. to public health and a leading cause of morbidity and As such, this study sought to explore for the first time mortality worldwide [1]. Myocardial infarction with the predictive value of SHR and obtain its optimal cut-off non-obstructive coronary arteries (MINOCA) is a value in predicting long-term clinical outcomes among common clinical condition observed in around 5–10% patients suffering from MINOCA and further deter - of all patients with AMI admitted for coronary angiog- mine whether it may have any clinical relevance in this raphy (CAG) [2, 3]. MINOCA represents a heteroge- population. neous and largely unexplored clinical syndrome with various underlying pathophysiological mechanisms Materials and methods that warrant further investigations [4]. It often remains Study design and population a misdiagnosed and mismanaged illness linked to a During the period 2014 through 2022, we conducted an high incidence of major adverse cardiovascular events observational retrospective study of patients with AMI (MACE), mortality, and a lower quality of life [5, 6]. who underwent CAG and had new-onset chest pain Previous studies reported that MINOCA is associ- with ST-segment elevation MI (STEMI) and non-ST seg- ated with an approximately 23.9% rate of MACE after ment elevation MI (NSTEMI) on ECG presenting at the 4 years of follow-up, and the all-cause mortality rate cardiology department of Shanghai Tenth People’s Hos- of MINOCA patients at 1 year and 3 years were 10.9% pital (Tongji University, Shanghai, China). In this study, and 16.1%, respectively [7, 8]. Therefore, it is essen - MINOCA was defined as patients with evidence of tial to identify easily obtainable determinant factors AMI with non-obstructive coronary arteries (defined as of adverse events to provide optimal management and stenosis less than 50% in any epicardial coronary arter- improve the quality of life in this patient population. ies), as recommended by the 4th universal definition Stress hyperglycemia refers to the acute transient of AMI [23], which excluded myocarditis and Takot- increase in blood glucose levels in response to numer- subo syndrome from the final diagnosis of MINOCA. ous critical conditions and is independently associated The exclusion criteria included the following items: (1) with poor short and long-term clinical outcomes in patients < 18 years; (2) patients with a history of MI or acute coronary syndrome (ACS) patients, particularly obstructive CAD; (3) patients receiving thrombolytic those with AMI [9–11]. It has recently been revealed prior to or during hospitalization, (4) patients with type that admission stress hyperglycemia is also common 3–5 MI; (5) those with severe liver and kidney conditions; among MINOCA patients [12], and it is a strong pre- (6) patients with major valve pathologies, a history of dictor of short- and long-term adverse outcomes in this stroke, and malignant arrhythmias; and (7) patients lost patient group, regardless of diabetes status [13]. Addi- to follow-up or had no complete SHR data. tionally, we and others have recently shown that fasting Our study was approved by the Shanghai Tenth Peo- blood glucose [14], and triglyceride-glucose index [15] ple’s Hospital ethics committee and complied with the were associated with poor clinical outcomes among Declaration of Helsinki. Informed consent has been patients suffering from MINOCA. However, elevated obtained from all patients. glucose levels at the time of hospital admission may be the result of chronic hyperglycemia or acute stress Data collection and definitions response [16]. In this regard, the stress hyperglyce- We retrospectively gathered the baseline demograph- mia ratio (SHR) has been developed as a new marker ics (age, gender, height, weight, body mass index (BMI), to reflect true acute hyperglycemia condition, which heart rate, and blood pressure), past medical history is estimated based on the acute admission glucose (history of hypertension, diabetes, hyperlipidemia, atrial level and the chronic glycemic value [calculated by fibrillation, and smoking history), electrocardiogram, glycosylated hemoglobin (HbA1c)] [17]. Several clini- and echocardiography information for all patients. Blood cal studies have reported that SHR is associated with samples for testing HbA1c, blood glucose, cardiac tro- significantly higher in-hospital mortality and long- ponin-T (cTnT), N-terminal pro-brain natriuretic pep- term MACE than admission glucose in patients with tide (NT-proBNP), creatine kinase-MB (CK-MB), total A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 3 of 11 cholesterol (TC), low-density lipoprotein cholesterol of clinical outcomes, and a log-rank test was used to (LDL-C), high-density lipoprotein cholesterol (HDL-C), determine differences between groups. A Pearson cor - triglyceride (TG), C reactive protein (CRP), and com- relation analysis was performed to determine the corre- plete blood count (white blood cell counts, red blood cell lation between SHR and myocardial injury parameters. counts, and hemoglobin) was obtained from the cubi- Univariate Cox regression was used to evaluate the tal vein after at least eight hours of fasting. An Abbott association between SHR and clinical outcomes. Car- Laboratories (Chicago, IL, USA) was used to analyze diovascular risk factors listed in Table 1 (age, sex, BMI, blood glucose, TC, LDL-C, HDL-C, and TG. A diabetes LVEF, hypertension, diabetes, hyperlipidemia, smoking, diagnosis is based on the following: (1) Random plasma atrial fibrillation, STEMI, degree of coronary stenosis, glucose ≥ 11.1 mmol/l (≥ 200  mg/dl); (2) fasting blood cTnT, CK-MB, NT-proBNP, TC, LDL-C, HDL-C, TG, glucose ≥ 7.0 mmol/l (≥ 126  mg/dl); (3) HbA1c ≥ 6.5%; and CRP) which may contribute to an elevated risk of and (4) OGTT glucose level ≥ 11.1 mmol/l (200 mg/dl). adverse outcomes among MINOCA patients served as the variables in the univariate analysis along with SHR index. Clinical covariates that were significant with a Determination of SHR P < 0.10 in the univariate analysis were used for adjust- The blood glucose obtained during the first 24 h of hos - ment in the multivariate analysis by the forward step- pital admission was considered admission blood glu- wise regression method. The receiver operating curve cose. Abbott Laboratories (Chicago, IL, USA) was used (ROC) was applied to calculate the area under the to calculate the HbA1c. The SHR is calculated using the curve (AUC) and obtained the optimal cut-off value of following equation by dividing admission glucose by the SHR for predicting clinical outcomes among MINOCA average glucose calculated from HbA1c: SHR = [(admis- patients, and the Youden index was calculated at the sion glucose (mmol/L) / [1.59 × HbA1c (%) − 2.59] [18]. point where the sensitivity and specificity sum was highest. All analysis was conducted two-sided, and sta- Endpoints and follow up tistical significance was set at P-value < 0.05. In this study, the mean follow-up duration was 34 months. Clinical outcomes were recorded by two experts via telephone calls, clinic visits, review of medical case Results history, and communication with patients’ families. The Baseline characteristics primary observational clinical endpoints of the pre- MINOCA was diagnosed in 488 consecutive patients, sent investigation were MACE, which includes cardiac 78 of whom were lost to follow-up, did not have blood death, heart failure, nonfatal MI, stroke, and angina glucose data, and were excluded from the study. A rehospitalization. Deaths caused by malignant arrhyth- total of 410 patients were included in the final analy - mias, acute MI, heart failure, or other cardiac conditions sis of the present study [216 (52.7%) were males; the were defined as cardiac deaths. Nonfatal MI was defined mean age was 63.55 ± 13.81 years; and 79 (19.3%) had as positive cardiac biomarkers or dynamic changes on diabetes]. In this study, the patients were divided into electrocardiograms in addition to the typical symptoms three groups based on their SHR tertiles: [SHR1 group of myocardial ischemia. A heart failure diagnosis was (SHR ≤ 0.73), (n = 143); SHR2 group (SHR 0.73–0.84), made based on recent ESC guidelines for the diagnosis n = 131; and SHR3 group (SHR ≥ 0.84), n = 136] and treatment of acute and chronic heart failure [24]. A (Fig. 1). stroke is diagnosed when there is evidence of ischemic Table  1 presents the baseline characteristics of the cerebral infarction because of thrombotic or embolic three groups. Patients in the SHR3 group had a higher obstruction. rate of atrial fibrillation. The LVEF in the SHR3 group was lower than those in the SHR1 and SHR2 groups Statistical analysis (52.73% vs. 55.68% and 55.78%, P = 0.041), whereas the Statistical Package for the Social Sciences (SPSS) ver- left atrium size and left ventricular end-diastolic diam- sion 24 was used to analyze our data. GraphPad soft- eter were larger in the SHR3 group. Compared to the ware version 8.0.1 was used to create the figures. We SHR1 and SHR2 groups, the SHR3 group had signifi - expressed continuous variables as means and standard cantly higher serum cTnT, NT-proBNP, and CRP lev- deviations (mean ± SD), while categorical variables els. However, no differences were observed between the as percentages (%). The comparison of clinical data three groups regarding other baseline characteristics or between groups was made using ANOVAs for continu- laboratory findings (all P > 0.05). The glucose-lowering ous variables and Pearson chi-square tests or Fisher’s medications for diabetic patients are shown in Additional exact tests for categorical variables. The Kaplan–Meier file 1: Table S1. curve was used to determine the cumulative incidence Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 4 of 11 Table 1 Clinical characteristics according to different SHR tertiles Variables SHR1 (≤ 0.73) N = 143 SHR2 (0.73–0.84) N = 131 SHR3 (≥ 0.84) P value N = 136 Age (years) 64.05 ± 13.45 63.32 ± 13.45 63.22 ± 14.58 0.860 Male, n (%) 72 (50.3) 73 (55.7) 71 (52.2) 0.667 BMI (kg/m2) 24.13 ± 4.20 24.26 ± 3.15 24.28 ± 4.23 0.970 Comorbidities Hypertension, n (%) 70 (49.0) 69 (52.7) 76 (55.9) 0.510 Diabetes, n (%) 28 (19.6) 23 (17.6) 28 (20.6) 0.816 Smoking history, n (%) 57 (39.9) 51 (38.9) 64 (47.1) 0.332 Atrial fibrillation, n (%) 20 (14.0) 8 (6.1) 21 (15.4) 0.041 Hyperlipidaemia, n (%) 19 (13.3) 21 (16.0) 13 (9.6) 0.285 STEMI, n (%) 45 (31.5) 44 (33.6) 39 (28.7) 0.685 Angiographic characteristics Normal vessels , n (%) 67 (46.9) 62 (47.3) 61 (44.9) 0.910 Vessel with any stenosis , n (%) 76 (53.1) 69 (52.7) 75 (55.1) 0.910 Echocardiography parameters LVEF (%) 55.68 ± 9.82 55.78 ± 10.22 52.73 ± 12.34 0.041 LAD (mm) 37.00 ± 6.53 36.74 ± 6.46 38.71 ± 6.12 0.030 E/e’ 10.86 ± 3.20 10.43 ± 2.82 10.18 ± 2.88 0.334 LVEDD (mm) 44.72 ± 5.10 45.30 ± 5.45 47.10 ± 5.97 0.001 LVESD (mm) 29.73 ± 6.07 30.65 ± 12.06 32.03 ± 7.36 0.116 T TPG (mmHg) 24.74 ± 12.74 25.24 ± 7.77 23.68 ± 6.34 0.623 Laboratory parameters HbA1c (%) 6.51 ± 1.20 6.35 ± 1.46 6.01 ± 1.23 0.005 FBG (mmol/L) 5.13 ± 1.35 5.90 ± 1.81 7.30 ± 3.20 < 0.001 cTnT (ng/mL) 0.42 ± 1.06 0.42 ± 1.04 1.02 ± 3.15 0.018 Creatine kinase‑MB (ng/mL) 14.89 ± 33.52 20.11 ± 53.70 24.54 ± 42.26 0.182 NT‑proBNP (pg/mL) 1477.69 ± 3573.61 4639.57 ± 405.36 6541.46 ± 560.92 0.007 TC (mmol/L) 4.27 ± 1.09 4.15 ± 0.93 4.20 ± 1.12 0.675 LDL‑ C (mmol/L) 2.50 ± 0.92 2.41 ± 0.84 2.43 ± 0.94 0.677 HDL‑ C (mmol/L) 1.14 ± 0.33 1.14 ± 0.31 1.19 ± 0.37 0.445 TG (mmol/L) 1.52 ± 1.17 1.54 ± 0.93 1.48 ± 0.98 0.873 CRP (mg/dL) 0.68 ± 0.62 0.60 ± 0.87 0.95 ± 1.34 0.012 WBC (10 /L) 7.90 ± 2.96 7.89 ± 3.24 8.52 ± 3.74 0.208 RBC (10 /L) 4.34 ± 0.67 4.42 ± 0.64 4.49 ± 0.60 0.163 Hemoglobin (g/L) 131.69 ± 23.93 133.19 ± 20.32 134.25 ± 18.08 0.599 SHR stress hyperglycemia ratio, BMI body mass index, LVEF left ventricular ejection fraction, LAD left atrium diameter, E/e’ mean septal velocity, LVEDD left ventricular end-diastolic diameter, LVESD left ventricular end-systolic diameter, TTPG trans tricuspid pressure gradient, STEMI ST-segment elevation myocardial infarction, HbA1c hemoglobin A1c, FBG fasting blood glucose, cTnT cardiac troponin, NT-proBNP N-terminal pro-brain natriuretic peptide, TC total cholesterol, HDL-C high density lipoprotein, LDL-C low-density lipoprotein, TG triglyceride, CRP C reactive protein, WBC white blood cell counts, RBC red blood cell Vessels with 0% stenosis Vessels with 0–50% stenosis Clinical outcomes according to SHR tertiles and diabetes risk compared to SHR1 and SHR2 patients (log-rank status P < 0.001). For further analysis, the study population A total of 92 patients (22.4%) developed MACE during was divided into six subgroups based on SHR tertiles the follow-up period. A significant increase in MACE was and diabetes status, including SHR1 with and without observed in the SHR3 group as compared to the SHR1 diabetes, SHR2 with and without diabetes, and SHR3 and SHR2 groups (35.3% vs. 15.4% and 16.8, respectively; with and without diabetes. The results showed that those P < 0.001) (Table 2). In Fig. 2A, the Kaplan–Meier curves with SHR3 and diabetes had the highest hazard of MACE demonstrate that SHR3 patients had the highest MACE compared to other groups (log-rank P < 0.001) (Fig. 2B). A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 5 of 11 Fig. 1 Flowchart of the study selection process. MINOCA myocardial infarction with non‑ obstructive coronary arteries, SHR stress hyperglycemia ratio Table 2 Clinical outcomes according to different SHR tertiles SHR1 (≤ 0.73) SHR2 (0.73–0.84) SHR3 (≥ 0.84) P value N = 143 N = 131 N = 136 MACE, n (%) 22 (15.4) 22 (16.8) 48 (35.3) < 0.001 Cardiac death, n (%) 7 (4.9) 4 (3.1) 12 (8.8) 0.110 Non‑fatal MI, n (%) 1 (0.7) 1 (0.8) 2 (1.5) 0.771 Heart failure, n (%) 3 (2.1) 4 (3.1) 8 (5.9) 0.220 Angina rehospitalization, n (%) 10 (7.0) 13 (9.9) 22 (16.2) 0.044 Stroke, n (%) 1 (0.7) 0 4 (2.9) 0.071 MACE major adverse cardiac events, MI acute myocardial infarction, SHR stress hyperglycemia ratio Predictive factors of MACE HR, 2.465; 95% CI 1.461–4.159, P = 0.001), along with The univariate and multivariate Cox regression analy - CK-MB levels (adjusted HR, 1.004; 95% CI  1.001–1.007; sis of MACE are shown in Tables  3,    4. Univariate Cox P = 0.012). regression models showed that the SHR3 is associated with a 2.659-fold increased risk of MACE (HR 2.659; 95% CI 1.604–4.407, P < 0.001). Age, reduced LVEF, diabetes, Optimal cut‑off value of SHR for predicting outcomes atrial fibrillation, CK-MB, and NT-proBNP levels were among MINOCA also predictive factors of MACE in the univariate regres- The ROC curve of SHR, FBG, and HbA1c were displayed sion analysis. in Fig.  3 for the prediction of MACE among MINOCA After excluding confounding factors with a P < 0.10 patients, which demonstrated that SHR was consistently in the univariate analysis, multivariate Cox regression better predictor of MACE, with an AUC of 0.636 (95% analysis showed that the SHR3 group remained CI  0.569–0.703; P < 0.001), while the AUC of FBG and associated with increased 2.465-fold of MACE (adjusted HbA1c were (0.616 95% CI 0.552–0.679; P < 0.001 and Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 6 of 11 Fig. 2 (A) Cumulative incidence of MACE based on the SHR tertiles; (B) Cumulative incidence of MACE based on the SHR tertiles and diabetes status. MACE major adverse cardiovascular events, SHR stress hyperglycemia ratio Table 3 Univariate Cox regression analysis for endpoint events Table 4 Multivariable cox regression analysis for endpoint events HR 95% CI P‑ value HR 95% CI P‑ value Age 1.024 1.008–1.040 0.004 Age 1.016 0.999–1.034 0.063 Sex 0.960 0.638–1.447 0.847 LVEF 0.983 0.965–1.001 0.060 BMI 1.009 0.936–1.087 0.817 Diabetes 1.465 0.904–2.375 0.121 LVEF 0.973 0.957–0.988 0.001 Atrial fibrillation 1.611 0.924–2.809 0.093 Hypertension 1.417 0.932–2.155 0.103 Creatine kinase‑MB 1.004 1.001–1.007 0.012 Diabetes 1.776 1.128–2.799 0.013 NT‑proBNP 1.000 1.000‑1000 0.740 Hyperlipidaemia 1.065 0.592–1.916 0.834 SHR3 2.465 1.461–4.159 0.001 Smoking 1.015 0.670–1.537 0.944 Atrial fibrillation 1.726 1.006–2.960 0.048 LVEF left ventricular ejection fraction, NT-proBNP N-terminal pro-brain natriuretic peptide, SHR stress hyperglycemia ratio, HR hazard ratio, CI confidence interval STEMI 1.063 0.692–1.634 0.780 Coronary stenosis 1.388 0.913–2.110 0.126 cTnT 1.036 0.974–1.103 0.263 0.511 95% CI 0.446–0.576; P = 0.747, respectively). Creatine kinase‑MB 1.003 1.000–1.006 0.024 Notably, we obtained that the optimal SHR cut-off value NT‑proBNP 1.000 1.000‑1000 0.003 for predicting clinical MACE was 0.86. TC 0.905 0.736–1.112 0.341 LDL‑ C 0.924 0.725–1.179 0.526 Correlation between SHR and myocardial injury HDL‑ C 1.039 0.556–1.938 0.905 parameters TG 1.131 0.956–1.337 0.152 The correlation between SHR and myocardial injury CRP 1.052 0.859–1.288 0.621 parameters, such as cTnT, CK-MB, NT-proBNP, and SHR tertiles LVEF, was further examined. The results demonstrated SHR1 Reference Reference that the SHR correlated well with cTnT, NT-proBNP, and SHR2 1.133 0.628–2.047 0.677 LVEF among MINOCA patients (r = 0.116, r = 0.210, and SHR3 2.659 1.604–4.407 < 0.001 r = − 0.194, respectively) (Fig.  4). However, SHR did not BMI body mass index, LVEF left ventricular ejection fraction, STEMI ST-segment correlate with other myocardial injury parameters, such elevation myocardial infarction, cTnT cardiac troponin, NT-proBNP N-terminal pro-brain natriuretic peptide, TC total cholesterol, HDL-C high density as creatine CK-MB (data not shown). lipoprotein, LDL-C low-density lipoprotein, TG triglyceride, CRP C reactive protein, SHR stress hyperglycemia ratio, HR hazard ratio, CI confidence interval A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 7 of 11 Fig. 3 Receiver operating characteristic analysis of the ability of the SHR, FBG, and HbA1c to predict MACE in MINOCA patients. AUC ar ea under the curve, SHR stress hyperglycemia ratio, CI confidence interval MINOCA patients had an all-cause death, and 23.9% Discussion experienced a cardiovascular event during 4.1 years of The present study, to our knowledge, is the first to follow-up [7]. In our investigation, we discovered that evaluate the association between SHR and clinical out- the MACE rate among MINOCA patients was 22.4% comes among MINOCA patients. The novel findings over a mean of 34 months of follow-up. This finding of this study were: (1) a higher risk of clinical outcomes is comparable to those observed in previous clinical was observed for MINOCA patients with high SHR; (2) studies above showing a high incidence of MACE in the SHR was independently associated with long-term risk MINOCA population. Despite no apparent coronary of MACE in patients suffering from MINOCA; (3) SHR stenosis in MINOCA patients, the risk of adverse events cut-off value of 0.86 was able to identify the high-risk is not negligible, which indicates that MINOCA still MINOCA patients, and (4) SHR correlated well with afflicted potential harm and deserves the same level of markers of myocardial injury, such as cTnT, NT-proBNP, significance as obstructive CAD. Data regarding clinical and LVEF. These findings indicate that SHR may play a risk scores and predictors of adverse clinical outcomes vital role in prioritizing patients and a robust biomarker in MINOCA populations is scarce. We and some other to predict future MACE in the MINOCA population. clinical investigations have documented the correlation MINOCA has gained significant attention and between various factors, including cardiac troponin, has been added as a subtype of MI to the fourth age, sex, thyroid hormones, LVEF, metabolic syndrome, global definition of MI [23]. MINOCA represents a challenging heterogeneous clinical syndrome where hyperglycemia, total bilirubin, creatinine, TC, and multiple aetiologies are causative with no optimal C-reactive protein with worse outcomes in MINOCA management therapy, and the prognosis in this high- [14, 27–33]. u Th s, it is necessary to perform rapid and risk patient group is far from benign [4, 25]. A recent accurate risk stratification using robust predictors Italian genetic study on early-onset MI demonstrated beyond traditional clinical measures to identify potential that MACE rates among MINOCA patients were 27.8% factors associated with patient outcomes. over a median follow-up of 19.9 years, which did not Stress hyperglycemia is frequent in AMI patients differ significantly from MACE rates among patients and negatively affects their prognosis, as well as with obstructive coronary artery disease (CAD) [26]. being independently associated with higher mortality The SWEDEHEART registry reported that 13.4% of rates and greater infarct sizes [9–11]. A recent study Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 8 of 11 Fig. 4 Correlation between SHR and cTnT (A), NT‑proBNP (B), and LVEF (C) . SHR stress hyperglycemia ratio, cTnT cardiac troponin, NT-proBNP N‑terminal pro ‑brain natriuretic peptide, LVEF left ventricular ejection fraction all-cause mortality among 5841 STEMI patients and showed that admission stress hyperglycemia was also 4105 NSTEMI patients after one year of fellow up [19]. A common among MINOCA patients and significantly recent study also found that SHR predicts mortality and predicted both short- and long-term adverse outcomes, adverse events in STEMI patients, both in diabetes and implying that hyperglycemia may contribute directly to non-diabetic patients [20]. The prospective, nationwide, myocardial damage [12]. However, stress hyperglycemia multi-center China AMI registry results demonstrated a reflects the severity of an emergency and poor glucose significant positive correlation between SHR and long- control to some extent. Additionally, it may worsen term death in patients with AMI [34]. Numerous clinical acute cardiac illness in several ways, such as increasing studies have also indicated a link between SHR and endothelial dysfunction, decreasing platelet nitric oxide unfavorable outcomes in AMI patients [21, 22, 35, 36]. responsiveness, aggravating microvascular obstruction, Pasquale et al. also found that SHR significantly increases and inducing further hyperglycemic-mediated vascular the risk of rehospitalization among 2,874 patients with damage mechanisms [16]. The SHR is a novel marker of ischemia with non-obstructive coronary arteries [37]. true acute hyperglycemia conditions and is associated Given that SHR was used as an indicator for predicting with adverse outcomes in patients with AMI. SHR future clinical events in patients with cardiovascular was first reported by Roberts et  al. as an independent disease, it is still unclear whether it can effectively biological biomarker for clinical outcomes among predict the risk of clinical outcomes in the MINOCA patients with various clinical disorders [17]. A large population. Recently, we found that fasting blood glucose cohort study in Asia found a correlation between SHR and early and late cardiac outcomes among ACS was associated with poor clinical outcomes among patients [16]. An analysis by Xu et al. found a significant patients with MINOCA [14]. In this study, the sample association between SHR and in-hospital mortality in size and follow-up period of MINOCA were expanded, patients with CAD [18]. The SHR significantly predicted and SHR was examined for the first time as a predictive A bdu et al. Cardiovascular Diabetology (2023) 22:11 Page 9 of 11 factor for clinical outcomes among the MINOCA cut-off value of the SHR for predicting in-hospital out - cohort. We demonstrated that the risk of adverse events comes was 1.20, while 1.32 for predicting in-hospital was significantly higher in MINOCA patients with a mortality [42]. However, a definite SHR cut-off value high SHR group. Interestingly, after adjusting for age, for predicting adverse events among MINOCA has not sex, traditional cardiovascular risk factors, and other yet been studied. The present study found that the best relevant biochemical parameters, multivariate cox optimal SHR threshold from the AUC for predicting regression analysis showed that high SHR remained clinical MACE in MINOCA was 0.86. It is necessary to significantly correlated with worse clinical outcomes. conduct more large-scale prospective clinical studies in Our findings suggest that SHR may play a potential role the MINOCA population to determine whether a cut-off in the cardiovascular risk stratification of the MINOCA value for SHR can accurately predict future poor clinical population. outcomes. The underlying mechanisms that relate SHR to unfa - vorable outcomes in the MINOCA are not recognized. Strengths and limitations In our study, patients with high SHR levels were associ- The strength of this study is that it is the first to evaluate ated with worse baseline features, as demonstrated by a the role of SHR and obtain its cut-off value in predicting higher degree of myocardial injury (elevated cTnT and poor clinical outcomes among MINOCA patients. The NT-proBNP) and reduced LVEF compared to patients information provided by our study can be used by phy- with low SHR. We also found a significant correlation sicians to follow up on selected patients more closely, between SHR and NT-proBNP, cTnT, and LVEF, which increase the intensity of their goal-directed medical treat- may contribute to an increased risk of MACE to some ment, control their risk factors, and improve the quality extent. Several clinical studies have confirmed such of life among patients with MINOCA. However, there results among MI patients, which reported that SHR are several limitations of our investigation that must be had a significant correlation with myocardial injury as noted. First, the present study has a retrospective design shown by high peak cTnT and peak CK-MB values and with a small sample size; the validity of these findings their association with the severity of CAD (assessed by requires further prospective multi-center studies. Sec- the Gensini score and Syntax score) [10, 35, 38]. On the ond, the results demonstrated here were conducted on other hand, we found that inflammatory marker such as the Chinese MINOCA population; consequently, they CRP was higher among the high SHR group, which may may not generalize to other populations. Third, although also be associated with some unfavorable outcomes. It SHR is linked to adverse outcomes even after adjust- has been demonstrated in previous MI studies that stress ing for a few potential variables, the impact of unmeas- hyperglycemia leads to increase inflammation burden, ured confounding variables cannot be removed entirely. ischemia-reperfusion damage, and endothelial dysfunc- Fourth, we did not consider other adverse outcomes, tion, which are all strongly associated with large infarct such as a reduction in left ventricular ejection fraction sizes and poor clinical outcomes [9, 39, 40]. Further stud- or infarction size, in addition to the association between ies should confirm this finding in larger MINOCA cohort SHR and MACE. In addition, retrospective observational and determine the underlying mechanisms of SHR in nature of our study and lack of other inflammatory mark - MINOCA. ers such as procalcitonin or systemic immune-inflamma - The best cut-off value of SHR to predict clinical out - tory index limit us to speculate their effects on clinical comes in CAD patients differs among studies. Among outcomes. Lastly, our study lacks data regarding hypo- 19,929 patients with CAD, 0.741 was the best cut-off glycemic therapy during the follow-up period; therefore, value for SHR to predict clinical outcomes [18]. An opti- we are not able to assess their effect on the prognosis of mal SHR cut-off value of 0.78 predicted poor prognosis patients. in ACS patients [16]. The optimal SHR cut-off value for predicting all-cause mortality at one year among diabetic Conclusion STEMI patients was 1.68 and 1.51 among non-diabetic Our data indicate, for the first time, that SHR is inde - STEMI patients; however, the SHR for NSTEMI was 1.53 pendently associated with poor long-term prognosis in in diabetics and 1.27 in non-diabetics [19]. Among dia- patients suffering from MINOCA. The optimal SHR cut- betic and non-diabetic AMI populations, Cui et al. found off value for predicting clinical MACE among MINOCA that 1.20 and 1.08 were optimal cut-off values for SHR patients was 0.86. These findings suggest that SHR may in predicting 2-year mortality [34]. Luo et  al. found that play a potential role in the cardiovascular risk stratifica - 1.24 and 1.14 were the optimal cut-off values for SHR in tion of the MINOCA population. diabetics and non-diabetics with AMI, respectively [41]. Additionally, among elderly AMI patients, the optimal Abdu et al. Cardiovascular Diabetology (2023) 22:11 Page 10 of 11 Abbreviations Received: 27 December 2022 Accepted: 10 January 2023 CVD Cardiovascular diseases AMI Acute myocardial infarction MINOCA Myocardial infarction with non‑ obstructive coronary arteries MACE Major adverse cardiac events ACS Acute coronary syndrome SHR Stress hyperglycemia ratio References LVEF Left ventricular ejection fraction 1. Najjar SS, Rao SV, Melloni C, Raman SV, Povsic TJ, Melton L, et al. HbA1c Hemoglobin A1c Intravenous erythropoietin in patients with ST‑segment elevation FBG Fasting blood glucose myocardial infarction: REVEAL: a randomized controlled trial. Jama. cTnT Cardiac troponin 2011;305(18):1863–72. NT‑proBNP N‑terminal pro ‑brain natriuretic peptide 2. Reynolds HR, Maehara A, Kwong RY, Sedlak T, Saw J, Smilowitz NR, TC Total cholesterol et al. 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Journal

Cardiovascular DiabetologySpringer Journals

Published: Jan 16, 2023

Keywords: Myocardial infarction with non-obstructive coronary arteries; Stress hyperglycemia ratio; Diabetes mellitus; Clinical outcome

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