Abstract
Background
Epicardial adipose tissue (EAT) and soluble suppression of tumorigenicity 2 (sST2) are valuable markers of myocardial fibrosis, but the relationship between EAT and sST2 remains controversial. This study aimed to evaluate the role of combined EAT measurements and levels of sST2 and the risk of major adverse cardiovascular events (MACEs) in patients with diagnosis of non-ST-elevation myocardial infarction (NSTEMI).
Material/Methods
This was a single-center retrospective observational study. Patients diagnosed with NSTEMI from December 2019 to December 2022 were included. All patients completed the sST2 tests and computed tomography angiography during hospitalization. During the 12-month follow-up, MACEs were defined as all-cause death, reinfarction, and new congestive heart failure.
Results
A total of 435 patients were enrolled in this study, of whom 59 patients (13.6%) developed MACEs. After adjusting for confounding factors, multivariate COX regression analysis showed that high EAT index (EATi) (HR=4.60; 95% CI 2.499–8.481; P<0.001) and high sST2 (HR=3.35; 95% CI 1.894–5.914; P<0.001) were the independent predictors of MACEs. According to Pearson correlation analysis, there was a positive correlation between EATi and sST2 (r=0.347, P<0.001). Kaplan-Meier analysis showed the patients with high sST2 or EATi had a significantly higher long-term risk of MACEs (both, log-rank P<0.001). After the addition of EATi and/or sST2, the predictive ability of the new model for MACEs was significantly improved (P<0.005).
Conclusions
EAT and sST2 are positively correlated in patients with NSTEMI. The combination of EAT and sST2 has a solid potential for predicting MACEs in patients with NSTEMI.
Keywords: Cardiology, Prognosis, Biomarkers
Introduction
Myocardial infarction (MI) remains one of the leading causes of death worldwide, with an enormous economic burden on families and society [1]. Although significant progress has been made in reperfusion therapy in recent years, the major adverse cardiovascular events (MACEs) after non-ST-elevation myocardial infarction (NSTEMI) are still high [2]. Therefore, it is of great clinical significance to find biomarkers that can predict MACEs.
Epicardial adipose tissue (EAT) is a unique form of visceral adipose tissue in direct contact with the myocardium, with local and systemic effects. It can regulate heart metabolism and is related to poor cardiac remodeling. Because there is no myofascial separation between each other, EAT is in direct contact with the myocardium and shares the same coronary circulation [3]. Basic studies have shown that EAT can act on the myocardium through various mechanisms, such as fat infiltration, fibrosis, and inflammation, leading to myocardial fibrosis [4,5]. EAT is related to coronary atherosclerosis, the type of MI, and adverse cardiovascular events [6–8]. A prospective study found that EAT plays a valuable role in myocardial tissue repair after infarction [9]. However, there are also studies that have found that EAT can have a protective effect on the myocardium [9,10]. This “obesity paradox” makes it interesting to explore the relationship between EAT and MI.
Suppression of tumorigenicity 2 (ST2) is a member of the interleukin (IL)-1 receptor family, which has 2 forms: transmembrane receptor (ST2L) and soluble form (sST2) [11]. sST2 is more like a “bait receptor”. When stimulated by biological stress, the increase of sST2 produced by cardiomyocytes can block the protective signal transduction of IL-33, leading to fibrosis and ventricular remodeling [12,13]. sST2 has been written as a biomarker in heart failure guidelines [14], and in addition to this, many studies have shown that sST2 can be used as a prognostic marker after MI [15–17]. Both EAT and soluble ST2 (sST2) are valuable markers of myocardial fibrosis, as previous studies have shown that EAT can promote maladaptive heart remodeling through the ST2/IL-33 system [18]; however, the correlation between EAT and ST2 remains controversial [19]. There are still some unknowns and controversies about the role of EAT and ST2 in the prognosis of patients with NSTEMI. Therefore, this study aimed to evaluate the role of combined EAT measurements and levels of sST2 and the risk of MACEs in patients with a diagnosis of NSTEMI.
Material and Methods
Study Population
The Institutional Review Board (IRB) of the Affiliated Hospital of Xuzhou Medical University approved this study protocol (XYFY2024-KL277-01). The requirement for signed written consent was waived, owing to no risk to the patient in accordance with the relevant IRB regulatory guidelines.
This was a single-center retrospective clinical observation study. We continuously included patients diagnosed with NSTEMI in the affiliated Hospital of Xuzhou Medical University from December 2019 to December 2022. Diagnosis of NSTEMI in patients was made according to “the fourth universal definition of myocardial infarction” [20]: (1) presence of evidence of myocardial ischemia; (2) troponins at least 1 occasion above the 99th percentile of the upper limit of the reference value; and (3) absence of ST-segment elevation on electrocardiography. All patients completed computed tomography angiography (CTA) and sST2 examination. The inclusion criterion was percutaneous coronary intervention during hospitalization. The exclusion criteria were history of MI, malignant tumor, or inflammatory disease, severe renal insufficiency (estimated glomerular filtration rate <30 mL·min−1·1.73 m−2), and severe heart failure. A total of 435 patients met the eligibility criteria and were selected (Figure 1).
Figure 1.
Flow diagram of the study cohort. NSTEMI – non-ST-elevation myocardial infarction; PCI – percutaneous coronary intervention; CTA – completed computed tomography angiography; MACEs – major adverse cardiac events; sST2 – soluble suppression of tumorigenicity 2. This figure was generated using Microsoft PowerPoint, Microsoft, Redmond, WA, USA.
Clinical Data Assessment
The clinical baseline data of all patients were collected, including sex, age, body mass index (BMI), current smoking, hypertension, diabetes, and left ventricular ejection fraction (LVEF). Venous blood samples were collected in a fasting state after hospitalization for laboratory testing. High-sensitivity C-reactive protein (hsCRP), high-sensitivity troponin T (hsTnT), and N-terminal B-type natriuretic peptide protein (NT-proBNP) were taken as peaks during hospitalization. Medication use during hospitalization was recorded for all patients.
Measurement of sST2
sST2 was detected by chemiluminescence immunoassay using a kit (Guangzhou Chunkang Biotechnology Co, Ltd, Guangzhou, China). Specifically, standard wells and sample wells were set up first, with 50 μL of standards with different concentrations added to each standard well, and 50 μL of the sample to be tested added to the sample wells. Except for the blank wells, 100 μL of labeled detection antibody was added to each standard well and sample well. The reaction wells were sealed with a sealing film and incubated in a 37°C water bath or incubator for 60 min. After washing, 50 μL of substrate was added to each well and incubated at 37°C in the dark for 15 min for color development. After terminating the reaction, the optical density (OD) value of each well was measured at a wavelength of 450 nm with a microplate reader within 15 min. A standard curve was drawn according to the OD value and concentration of the standards, and the corresponding sST2 concentration was found on the standard curve according to the OD value of the sample.
Measurement of Epicardial Adipose Tissue
The spiral CTA machine (SOMATOM Definition, SIEMENS, Germany) was used for CTA imaging. Enhanced scanning commenced at the ascending aorta root, with a threshold of 90 to 100 HU, starting 6 s after initiation and lasting 5 to 12 s. The scanning range extended from 1 cm below the tracheal carina to 1.5 cm below the heart’s lower edge, using a tube current of 280–350 mA and a voltage of 120 kV. EAT was identified from contrast-enhanced images using Hounsfield units ranging from −50 to −200. The total epicardial adipose tissue, located within the pericardial sac from the pulmonary artery bifurcation to the diaphragm, was manually outlined every 10 mm of axial slices. Then, the sum of all slices was semi-automatically reconstructed, with manual adjustments if necessary. EAT volume was automatically calculated by the software (Figure 2). Image analysis was performed by 2 experienced physicians who were unaware of this study at the time. To mitigate the impact of individual body types, the EAT index (EATi) was calculated and used for statistical analysis.
Figure 2.
The volume and attenuation of epicardial adipose tissue (EAT) were calculated by post-processing software. (A) Cardiac axial map, and yellow regions represent epicardial adipose tissue. (B) Cardiac sagittal map, and yellow regions represent epicardial adipose tissue. (C) Brown area represents EAT volume. (D) At −50 and −200 Hounsfield units (HU), epicardial adipose tissue volume. This figure exported from the computer was generated using Microsoft PowerPoint, Microsoft, Redmond, WA, USA.
Follow-Up and Endpoint
The MACEs endpoint was a composite of the component events at 1 year (all-cause death, reinfarction, new congestive heart failure). Reinfarction was defined according to the fourth universal definition of MI: ischemic symptoms and/or new significant ST-segment changes, and at least 1 value of increase and/or decrease in troponins was higher than the 99th percentile limit [20]. New-onset congestive heart failure has been identified as the first attack of cardiac compensation disorder and required intravenous diuretic treatment, whether patients were re-hospitalized or not [21]. The incident follow-up was mainly done by telephone and in the outpatient clinic, and the death and date of death of missing patients were determined through the death registry in the area, which is a detailed and mandatory official database. Patients were divided into 2 groups for statistical analysis according to the presence or absence of MACEs.
Statistical Analysis
SPSS 24.0 software (IBM Corp, Armonk, NY, USA) and R 4.3.1 were used for statistical analysis. The Kolmogorov-Smirnov test was used to assess the normality of data. Continuous variables that conformed to a normal distribution were expressed as mean±standard deviation and analyzed using the independent samples t test. Continuous variables that were not normally distributed were described as median (interquartile range [IQR]) and were analyzed using the Mann-Whitney U test. Categorical data were expressed as frequencies and percentages and analyzed using the chi-square test. Pearson correlation analysis was used to evaluate the correlation between EATi and sST2. All possible relevant variables were analyzed by univariate regression. Multivariate analysis included variables with P<0.1 in the univariate model, using the stepwise forward method. The predictive efficacy of EATi and sST2 for MACEs was evaluated by receiver operating characteristic (ROC) curves. The net reclassification improvement index (NRI) was used to measure the net improvement in reclassification of the new model, assessing the classification accuracy of the improved model. The integrated discriminant improvement index (IDI) was used to reflect the strengths and weaknesses of the model in terms of increased probability, measured by the improvement in overall discriminatory power of the new model relative to the old model. The Youden index was used to calculated the cutoff values for sST2 and EATi. The Kaplan-Meier survival curve and log-rank test were used to observe the cumulative survival rate in patients with NSTEMI. P<0.05 was considered statistically significant.
Results
Baseline Characteristics of the Study Population
A total of 435 patients were enrolled in the study: 59 patients (13.6%) had MACEs, of which 17 patients (3.9%) died of all causes, 12 patients (2.8%) had recurrent MI, and 30 patients (6.9%) had heart failure. Compared with the non-MACEs group, the age and hsTnT were significantly higher, and LVEF was significantly lower in the MACEs group (P<0.005). In addition, the levels of EATi, and sST2 in the MACEs group were significantly higher than those in the non-MACEs group (P<0.005; Table 1).
Table 1.
Patient characteristics.
| MACEs (n=59) | Non-MACEs (n=376) | P value | |
|---|---|---|---|
| Age, years | 60.54±11.17 | 57.73±7.62 | 0.014 |
| Male, n (%) | 28 (47.46) | 149 (39.63) | 0.255 |
| Body mass index, kg/m2 | 25.00±3.21 | 24.88±3.33 | 0.805 |
| Current smoker, n (%) | 33 (55.93) | 189 (50.27) | 0.418 |
| Diabetes, n (%) | 26 (44.07) | 121 (32.18) | 0.073 |
| Hypertension, n (%) | 29 (49.15) | 173 (46.01) | 0.653 |
| Systolic blood pressure, mmHg | 122.41±18.82 | 124.35±15.15 | 0.378 |
| Diastolic blood pressure, mmHg | 75.08±11.38 | 76.33±13.74 | 0.447 |
| Heart rate, bpm | 79.14±11.64 | 77.94±12.78 | 0.499 |
| Total cholesterol, mmol/L | 4.87±1.68 | 4.64±1.39 | 0.240 |
| Triglycerides, mmol/L | 1.80±1.02 | 1.70±1.13 | 0.524 |
| HDL cholesterol, mmol/L | 1.29±0.39 | 1.34±0.45 | 0.433 |
| LDL cholesterol, mmol/L | 2.71±1.15 | 2.63±1.02 | 0.616 |
| Peak hsCRP, mg/L | 50.65±64.44 | 37.71±53.80 | 0.096 |
| Peak hsTnT, ng/L | 3359.0 (1400.0, 4847.5) | 2435.0 (847.1, 4052.3) | 0.018 |
| Peak NT-proBNP, pg/mL | 1213.0 (271.4, 3321.2) | 1143.0 (453.1, 2580.3) | 0.584 |
| Killip class ≥2, n (%) | 15 (25.42) | 53 (14.10) | 0.391 |
| TIMI ≤1, n (%) | 53 (89.83) | 298 (79.26) | 0.056 |
| IRA | |||
| LAD, n (%) | 30 (50.85) | 186 (49.47) | 0.844 |
| LCX, n (%) | 4 (6.78) | 51 (13.56) | 0.145 |
| RCA, n (%) | 25 (42.37) | 139 (36.97) | 0.426 |
| Medications | |||
| Statin, n (%) | 56 (94.92) | 346 (92.02) | 0.606 |
| SGLT-2, n (%) | 5 (8.47) | 53 (14.10) | 0.238 |
| ACEI/ARB/ARNI, n (%) | 24 (40.68) | 159 (42.29) | 0.816 |
| β-blockers, n (%) | 42 (71.19) | 297 (78.99) | 0.179 |
| Spironolactone, n (%) | 9 (15.25) | 48 (12.77) | 0.598 |
| sST2, ng/mL | 67.46±24.46 | 51.23±27.57 | <0.001 |
| LV ejection fraction, % | 49.54±4.60 | 52.31±4.66 | <0.001 |
| EAT (mL) | 87.79±25.05 | 72.40±19.35 | <0.001 |
| EAT index (mL/m2) | 48.88±9.74 | 41.37±11.70 | <0.001 |
MACEs – major adverse cardiac events; HDL-C – high-density lipoprotein cholesterol; LDL-C – low-density lipoprotein cholesterol; hsCRP – high-sensitivity C reactive protein; hsTnT – high-sensitivity cardiac troponin T; NT-proBNP – N terminal pro B type natriuretic peptide; sST2 – soluble suppression of tumorigenicity 2; LV – left ventricular; EAT – epicardial adipose tissue; SGLT-2 – sodium-dependent glucose transporters 2; ACEI – angiotensin-converting enzyme inhibitors; ARB – angiotensin receptor blocker; ARNI – angiotensin receptor-neprilysin inhibitor.
Relationship Between EATi and sST2
In the correlation analysis, there was a positive linear correlation between EATi and sST2 (r=0.347, P<0.001), suggesting the possibility that EAT may be the source of synthesis of sST2 in MI through elusive biochemical pathways (Figure 3).
Figure 3.
A scatter plot showing the relationship between the epicardial adipose tissue index (EATi) and soluble suppression of tumorigenicity 2 (sST2). This figure was generated using GraphPad Prism 9, GraphPad Software, USA.
Prognostic Value of EATi and sST2 in MACEs
The ROC curve was used to analyze the variables as the critical value for predicting the occurrence of MACEs. The cut-off value of sST2 was 53.35 ng/mL, sensitivity was 71.2%, and specificity was 63.0% (AUC=0.720,95% CI: 0.657–0.784, P<0.001); the cut-off value of EATi was 44.80 mL/m2, sensitivity was 76.3%, and specificity was 64.9% (AUC=0.705, 95% CI 0.638–0.773, P<0.001). The combined use of sST2 and EATi had higher predictive value (AUC=0.752, 95% CI: 0.684–0.821, P<0.001), and its sensitivity and specificity were improved to 74.6% and 75.8%, respectively (Table 2, Figure 4).
Table 2.
Receiver operating characteristic curve for the prediction of major adverse cardiac events.
| Parameter | AUC | 95% CI | Cutoff value | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|
| sST2, ng/mL | 0.720 | 0.657–0.784 | 53.35 | 0.712 | 0.630 |
| EAT index (mL/m2) | 0.705 | 0.638–0.773 | 44.80 | 0.763 | 0.649 |
| sST2+EAT index | 0.752 | 0.684–0.821 | – | 0.746 | 0.758 |
sST2 – soluble suppression of tumorigenicity 2; EAT – epicardial adipose tissue; AUC – area under the curve; CI – confidence interval.
Figure 4.
The receiver-operating characteristic (ROC) curve for epicardial adipose tissue index (EATi), soluble suppression of tumorigenicity 2 (sST2), and the combined value for predicting major adverse cardiac events (MACEs). This figure was generated using GraphPad Prism 9, GraphPad Software, USA.
Cox Regression Analysis for MACEs
In univariate Cox regression analysis, age, LVEF, hs-TnT, high sST2 (>53.35 ng/mL), and high EATi (>44.80 mL/m2) were associated with MACEs. In multivariate Cox regression analysis, the variables with a P value ≤0.1 in the univariate were included. After adjusting for the above variables, the result showed age, LVEF, high sST2, and high EATi were still significantly associated with MACEs (Table 3).
Table 3.
Association of patient characteristics with major adverse cardiac events at 1 year: Univariate and Cox multivariate regression analysis.
| Variable | Univariate | Multivariate | ||
|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | |
| Age, years | 1.05 (1.012–1.085) | 0.009 | 1.05 (1.010–1.082) | 0.011 |
| Male, n (%) | 1.39 (0.832–2.313) | 0.209 | ||
| Body mass index, kg/m2 | 1.01 (0.934–1.089) | 0.826 | ||
| Current smoker, n (%) | 1.23 (0.734–2.053) | 0.434 | ||
| Diabetes mellitus, n (%) | 1.60 (0.956–2.672) | 0.074 | ||
| Hypertension, n (%) | 1.15 (0.689–1.912) | 0.597 | ||
| Heart rate, bpm | 1.01 (0.987–1.028) | 0.478 | ||
| Total cholesterol, mg/dL | 1.12 (0.943–1.338) | 0.194 | ||
| Triglycerides, mg/dL | 1.07 (0.877–1.302) | 0.511 | ||
| HDL cholesterol, mg/dL | 0.79 (0.433–1.421) | 0.785 | ||
| LDL cholesterol, mg/dL | 1.07 (0.836–1.356) | 0.612 | ||
| Peak hsCRP, mg/L | 1.10 (0.945–1.290) | 0.213 | ||
| Peak hsTnT, ng/L | 1.29 (1.023–1.632) | 0.032 | ||
| Peak NT-pro BNP, pg/mL | 1.05 (0.853–1.285) | 0.658 | ||
| Killip class ≥2, n (%) | 1.79 (0.998–3.223) | 0.051 | ||
| TIMI ≤1, n (%) | 2.17 (0.934–5.053) | 0.072 | ||
| LV ejection fraction, % | 0.92 (0.878–0.954) | <0.001 | 0.95 (0.903–0.990) | 0.016 |
| High sST2 >53.35 (ng/mL) | 3.77 (2.146–6.625) | <0.001 | 3.35 (1.894–5.914) | <0.001 |
| High EATi >44.80 (mL/m2) | 5.21 (2.857–9.489) | <0.001 | 4.60 (2.499–8.481) | <0.001 |
MACEs – major adverse cardiac events; HDL-C – high-density lipoprotein cholesterol; LDL-C – low-density lipoprotein cholesterol; hs-CRP – high-sensitivity C reactive protein; hs-TnT – high-sensitivity cardiac troponin T; NT-proBNP – N terminal pro B type natriuretic peptide; sST2 – soluble suppression of tumorigenicity 2; LV – left ventricular; EAT – epicardial adipose tissue; HR – hazard ratio; CI – confidence interval.
Incremental Value of High sST2 and High EATi in the Prediction of MACEs
Next, the NRI and IDI were calculated. The results showed that when high sST2 or high EATi was integrated into the model a (including age and LVEF), the discrimination and reclassification accuracy for MACEs were significantly improved (P<0.05). When both the high sST2 and high EATi were integrated into the model a, the NRI>0 (NRI 0.4071, 95% CI 0.231–0.583, P<0.001) and the IDI value was improved by 13.56% (IDI 0.1356, 95% CI 0.091–0.180, P<0.001), suggesting that the integration of high sST2 and/or high EATi could significantly improve the ability of the model for MACEs (Table 4).
Table 4.
Incremental Value of sST2 and EATi in the Prediction of MACEs.
| NRI | IDI | |||
|---|---|---|---|---|
| Estimate (95% CI) | P | Estimate (95% CI) | P | |
| Model a: age+LVEF | Reference | – | Reference | – |
| Model b: age+LVEF+High sST2 | 0.2083 (0.040–0.377) | 0.015 | 0.0490 (0.025–0.073) | <0.001 |
| Model c: age+LVEF+High EATi | 0.2638 (0.090–0.438) | 0.003 | 0.0683 (0.040–0.097) | <0.001 |
| Model d: age+LVEF+High sST2+High EATi | 0.4071 (0.231–0.583) | <0.001 | 0.1356 (0.091–0.180) | <0.001 |
MACEs – major adverse cardiac events; LVEF – left ventricular ejection fraction; sST2 – soluble suppression of tumorigenicity 2; EATi – epicardial adipose tissue index; CI – confidence interval; IDI – integrated discrimination index; NRI – net reclassification improvement.
Kaplan-Meier Survival Analysis for MACEs
Kaplan-Meier survival analysis showed that, compared with the patients with sST2 ≤53.35 ng/mL or EATi ≤44.80 mL/m2, the patients with sST2 >53.35 ng/mL or EATi >44.80 mL/m2 had a significantly higher long-term risk of MACEs (both, log-rank P<0.001; Figure 5).
Figure 5.
Kaplan-Meier survival curves for major adverse cardiac events (MACEs) during a year following non-ST-elevation myocardial infarction (NSTEMI) under and over cut-off values for (A) soluble suppression of tumorigenicity 2 (sST2), and (B) epicardial adipose tissue index (EATi). This figure was generated using GraphPad Prism 9, GraphPad Software, USA.
Discussion
The main findings of this study were as follows: (1) EATi and sST2 were positively correlated in patients with NSTEMI; (2) EATi and sST2 were independently associated with MACEs in patients with NSTEMI; (3) patients with high sST2 or EATi had a significantly higher long-term risk of MACEs; and (4) integration of EATi or/and sST2 significantly improved the prediction of MACEs.
There are few studies on the relationship between EAT and sST2. The previous study showed that dysfunctional EAT might be one of the sources of ST2 in adjacent cardiomyocytes of patients with cardiovascular diseases, resulting in a significant increase in local and circulating sST2 [18]. Another study showed that an increase in EAT thickness was associated with cardiac fibrosis after MI, in which the metabolic activity of EAT and the level of ST2 were related but showed different correlations in different populations [19]. In the present study, we found that there was a positive correlation between EATi and sST2. However, the weak correlation suggests that EATi and sST2 may contribute to myocardial fibrosis and remodeling through other pathways.
Previous studies showed that the increase of sST2 associated with MI is related to dysfunctional EAT and the result of activation of endothelial cells and cardiomyocytes after acute MI [22]. EAT can induce atherosclerosis by releasing various pro-inflammatory cytokines or adipokines into the coronary artery. Its pathogenesis may be “from the outside to the inside” [23]. Previous studies have shown that EAT is associated with coronary plaque characteristics, calcification scores, degree of coronary stenosis, MI, and adverse outcomes [6,24,25]. However, few studies have assessed whether increased EAT increases the incidence of MACEs in patients with NSTEMI. Consistent with previous studies [26], in this study, we found that high EATi was associated with the risk of long-term MACEs in patients with NSTEMI, with higher baseline EATi in patients with MACEs. sST2 acts as a bait receptor to eliminate the positive role of IL-33 through binding to IL-33 in inflammation and cardiac stress response [12]. As a biomarker of cardiomyocyte stress and fibrosis, the guidelines [27] recommend that sST2 be used in risk stratification in patients with acute or chronic heart failure to predict the prognosis of patients with heart failure. Recent studies have also confirmed the long-term value of sST2 in predicting acute MI or all-cause death in patients with coronary heart disease [28]. Eggers et al [29] found that sST2 is an independent predictor of 1-year MACEs in patients with acute coronary syndrome. Dhillon et al [30] also found that, although the increase of sST2 did not significantly improve the risk stratification of established markers, it could still predict the adverse outcome of acute coronary syndrome. However, the value of serum sST2 in predicting the prognosis of MI is contradictory. Kim et al [17] found that sST2 concentration can predict left ventricular remodeling in STEMI patients receiving percutaneous coronary intervention but has nothing to do with short-term and long-term MACEs. This may be due to the use of different kits in these studies and the generally short follow-up period. The high sensitivity sST2 test approved by the FDA in the United States shows that the critical value of 35 ng/mL is more specific to distinguish the risk of death from all causes of cardiovascular disease [14]. No specific threshold has been proposed for patients with AMI. In the study of Liu et al [31], the critical value of sST2 >58.7 ng/mL had high specificity in predicting MACEs in patients with STEMI. Consistent with previous studies [16], we found that sST2 was significantly correlated with long-term MACEs after NSTEMI. After adjusting other parameters, high sST2 and high EATi were independent predictors of MACEs. According to the optimal critical value of the ROC curve, the enrolled population was divided into 2 groups. The patients with high sST2 or EATi had a significantly higher long-term risk of MACEs. In addition, our study found that EATi and sST2 showed good predictive ability in predicting long-term risk assessment in patients with STEMI.
Consistent with previous studies, our study also found LVEF and age to be independent risk factors for MACEs [32,33]. Indeed, age differences in sST2 levels were demonstrated in myocarditis, cardiomyopathy, and MI, but not in coronary artery disease and congestive heart failure [34]. Thus, the effect of age on sST2 in patients with NSTEMI may require additional studies to further clarify. Next, IDI and NRI were calculated, the results showed that combining EATi and/or sST2 could improve the accuracy of predicting MACEs. Therefore, the combination of EAT and sST2 may be a very useful predictive tool for MACEs. Patients with NSTEMI in clinical practice need to be risk stratified and then guided to the next step of the treatment plan; therefore, our study has an important clinical value. The model included sST2 might be integrated into existing risk assessment frameworks for NSTEMI patients, and consider implications for personalized medicine.
Limitations
Several limitations should be acknowledged in this study. First, this was a single-center retrospective study with a relatively small sample size, and therefore, some results may need to be re-validated. Second, this was a retrospective study of a Chinese population, which may not apply to the rest of the world. Third, although an important premise of this study is myocardial fibrosis, this study lacked useful indicators for assessing myocardial fibrosis, such as cardiac magnetic resonance or myocardial biopsy, because of clinical conditions. Forth, our study on risk stratification may be preliminary. However, the topic of our study was the prognostic value of sST2 and EAT in patients with NSTEMI, and the results are encouraging. Finally, our study followed up only 12 months of changes. As remodeling is a gradual process, more patient groups and longer follow-up times are needed to further study the effects of EAT and sST2 on MACE in patients with NSTEMI.
Conclusions
EAT and sST2 are positively correlated in patients with NSTEMI. The combination of EAT and sST2 has a solid potential for predicting MACEs in patients with NSTEMI, and the integration of EAT or/and sST2 can significantly improve the prediction of MACEs.
Abbreviations
- EAT
epicardial adipose tissue
- sST2
soluble suppression of tumorigenicity 2
- MACEs
major adverse cardiovascular events
- NSTEMI
non-ST-elevation myocardial infarction
Footnotes
Conflict of interest: None declared
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher
Declaration of Figures’ Authenticity: All figures submitted have been created by the authors, who confirm that the images are original with no duplication and have not been previously published in whole or in part.
Financial support: This work was financially supported by the National Natural Science Foundation of China (82170831), Discipline Construction Support Project of the Second Affiliated Hospital of Soochow University (XKTJ-RC202403), and Young Investigator Pre-Research Foundation of the Second Affiliated Hospital of Soochow University (grant number SDFEYQN 2019)
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