Relevance of oxidative stress biomarkers, hemoglobin A1c, troponin-I, and angiotensin-converting enzyme metabolism to blood pressure in acute myocardial infarction: a case-control study

ABSTRACT

The aim was to investigate this relationship by calculating 1) the correlation between peak troponin-C (peak-cTnI), levels of oxidative stress biomarkers, including lipid peroxidation products (malondialdehyde (MDA), conjugated dienes (CD)), and antioxidant enzyme activity (glutathione peroxidase (GPx)), and HbA1c and 2) the correlation between HbA1c and serum angiotensin-converting enzyme (ACE) activity, and its impact on the rate pressure product (RPP) in acute myocardial infarction (AMI). A case-control study was performed in 306 AMI patients having undergone coronary angiography and on 410 controls. GPx activity was reduced in association with increased MDA and CD in patients. Peak-cTnI was positively correlated with HbA1c, MDA, and CD levels. Serum ACE activity was negatively correlated with GPx. HbA1c was positively correlated with ACE activity and RPP. Linear regression analysis showed that peak-cTnI, ACE activity and HbA1c are significant predictors of AMI. Elevated HbA1c and peak-cTnI levels are associated with RPP elevation causing AMI. In conclusions, patients with elevated HbA1c, elevated ACE activity and cTnI are at increased risk of AMI with increasing RPP. Patients at risk of AMI can be identified at an early stage if the biomarkers HbA1c, ACE activity, and cTnI are measured and preventive measures are taken in a targeted manner.

KEYWORDS:

• Acute myocardial infarction
• blood pressure
• heart rate product
• serum ACE activity
• cardiac troponin I
• lipid peroxidation products
• glutathione peroxidase
• risk

Introduction

Endothelial dysfunction resulting from immune or inflammatory reactions within the vessel wall are central initiating factors of early atherosclerosis, which has been associated with an increased risk of acute myocardial infarction (AMI) [1]. The term AMI is used for defining necrosis of the heart muscle due to the lack of oxygen, which cannot be supplied any longer by the coronary arteries. Several risk factors are associated with endothelial dysfunction and atherosclerosis, such as arterial hypertension (HTA), dyslipidaemia, inflammation, oxidative stress, and advanced glycation-end products [2].

Diabetes is associated with a 2- to 4-fold increase of the risk for AMI [3]. Hemoglobin A1c (HbA1c) has been shown the best indicator of glucose control and risk of micro- and macroangiopathy in clinical trials [4,5]. HbA1c levels >7% are associated with a significant increase in the risk of cardiac events and deaths [6]. HbA1c has been shown to be a positive predictive factor of incidence, mortality and morbidity of conditions such as acute coronary syndrome that results in troponin (cTnI) elevation by its release into the circulation. Individuals with AHT may have a six-fold increased risk of AMI [7]. The hemodynamic determinants of myocardial oxygen demand are heart rate (HR), systolic blood pressure (BP), and rate pressure product (RPP) [8]. HR represents the interaction of diastolic, systolic BP, pulse wave reflection, reduced systolic vascular reservoir, and ejection volume [9]. The HR product is a measure of the stress exposed to the cardiac muscle and is related to the degree of atherosclerosis [10].

Myocardial cell protection and prevention of cell ischemia/necrosis have been therapeutic targets for a long time. Moreover, lifestyle changes and therapeutics that may reduce adiposity could prevent AMI-related morbidity and mortality. AMI causes necrosis of cardiac myocytes by activation of the renin-angiotensin-aldosterone system [11]. Cardiac ischemia causes oxidative stress and reactive oxygen species (ROS) are one of the main cell-damaging products of oxidative stress that could cause subsequent modifications of nucleic acids, lipids and proteins [12]. Measurement of ROS is challenging and assays for the measurement of ROS have failed to show a consistent correlation between AMI and oxidative stress. Moreover, oxidative stress has been found to be an efficient mechanism for generation of oxidized low-density lipoprotein (LDL) and subsequently atherosclerosis [13].

Pro-oxidative processes produce a number of molecules, which may reflect the intensity of oxidative stress. Oxidation of fatty acids leads to generation of conjugated dienes (CD), which reflect peroxidation of plasma lipids. Malondialdehyde (MDA), a final product of lipid peroxidation, is the most commonly measured biomarker of oxidative stress [14].

Increased levels of plasma cTnI provide information about the severity of myocardial ischemia that caused cellular cTnI degradation and release of troponin degradation products into the circulation. cTnI is expressed only in cardiac muscle, which allows this biomarker to achieve extremely high specificity for myocardial damage [15].

There is growing interest in improving cardiovascular risk stratification including the relation between HTA and AMI and a need for novel biomarkers to identify subjects at increased risk of AMI. Therefore, aims of the present study were to assess the cause of increased BP in AMI patients by calculating (1) the correlation between peak troponin-C (peak-cTnI), levels of oxidative stress biomarkers, including lipid peroxidation products (malondialdehyde (MDA), conjugated dienes (CD)), and antioxidant enzyme activity (glutathione peroxidase (GPx)), and HbA1c and (2) the correlation between HbA1c and serum angiotensin-converting enzyme (ACE) activity, and their implication in the rate pressure product (RPP) in AMI patients compared to controls.

Materials and methods

Study design

The study was carried out on 306 unrelated patients with non-ST-elevated myocardial infarction (NSTEMI) who were recruited from the department of cardiology of Fattouma Bourguiba University Hospital (Monastir, Tunisia) between January 2018 and June 2020. AMI was defined according to the World Health Organization criteria. A diagnosis of NSTEMI was approved in the absence of ST-segment elevation, the presence of ischemic ST-segment or T-wave changes for 24 h with positive cardiac enzymes, and/or atypical clinical presentation. Cardiac catheterization and coronary angiography were performed according to standard procedures. All patients were admitted for acute coronary syndrome and underwent coronary angiography. This study was approved by the Ethical Committee (CER-SVS/ISBM 007/2023), and informed consent was obtained from all patients before their enrollment.

Excluded from the study were patients who suffered stable or unstable anginal chest pain without AMI, congenital heart diseases, valvular heart diseases, cardiomyopathy, viral myocarditis, sarcoidosis, or severe arrhythmias. Serum ACE activity was determined in patients not taking ACE inhibitors or angiotensin II-receptor antagonists for the treatement of HTA.

Four hundred and ten healthy persons matched for sex, age, and geographic origin were enrolled as healthy controls in the study. They came from a population of genetically unrelated friends of the patients. Family history, cardiovascular risk factors, and current treatment were obtained from each patient using a standard questionnaire.

The body mass index (BMI) was calculated as weight divided by the square of the height in meters. Obesity was defined as BMI ≥ 28 kg/m2 [14]. BP of patients was measured repeatedly three times at an interval of two minutes in the morning by Omron electric sphygmomanometer and the mean value of these three measurements was calculated. HTA was defined as the presence of elevated systolic ≥140mmHg and/or diastolic ≥90 mmHg BP and/or or taking antihypertensive drugs. Diabetes was defined as HbA1c ≥ 6.5%, fasting blood glucose level ≥ 7.0 mmol/l or receiving antidiabetics. Hyperlipidemia was defined as high-density lipoprotein cholesterol (HDL-C) ≤ 40 mg/dL, low-density lipoprotein cholesterol (LDL-C) ≥ 140 mg/dL, triglycerides ≥ 150 mg/dL or the need for lipid-lowering medications. RPP, which can be used to estimate the increased metabolic demand that exercise places on the heart, was calculated by multiplying heart rate (HR) with systolic BP.

Biochemical measurements

Biochemical measurements were carried out according to validated methods. Plasma glucose concentration was evaluated using an enzymatic kit (glucose oxidase, Randox, Antrim, UK), HbA1c by an exchange microcolumn chromatographic procedure (Biosystems, Barcelona, Spain), total cholesterol and triglycerides by enzymatic methods using Randox reagents and LDL and HDL-cholesterol determined as described by Smaoui et al. [16].

For measuring serum ACE activity on the automated SYNCHRON CX-4 DE (Beckman-Coulter) analyser with N-[3-(2-furylacryloyl]-L-phenylalanyl-L-glycyl-L-glycine (FAPGG) was used [17,18]. Serum ACE activity and cTn-I were measured as described previously [17,18]. cTn-I (ng/ml) was measured upon patient arrival and at 6, 12, 24, 48 and 96 h after reperfusion on the AxSYM analyzer (Abbott Laboratories, Abbott Park, IL, USA) using the three-step MEIA (Microparticle enzyme immunoassay). Peak troponin I was determined.

Lipid peroxidation was determined by measuring the production of malondialdehyde (MDA) in the plasma following the method of Yoshioka et al. [19]. To precipitate the proteins, 250 µL of plasma sample was mixed with 1.25 mL of trichloracetic acid (20%; w/v). We added Thiobarbituric acid (0.67%; w/v). The mixture was incubated for 30 min at 95° C. After cooling to room temperature, we added 4 mL of butanol and the absorbance was measured at 530 nm (Lambda 25 Spectrophotometer, PerkinElmer, Villebon Sur Yvette, France). The results were expressed as nmol/mg of protein.

CD, another marker of lipid peroxidation, were measured as described by Esterbauer et al. [20]. The results were expressed as µmoles hydroperoxide/mg protein. GPx activity was measured in plasma according to Flohe and Günzler [21]. 50 µL of plasma were incubated with 0.1 mM of reduced glutathione (GSH) and phosphate buffer saline (50 mM, pH 7.8). The reaction was initiated by the addition of H₂O₂, and stopped by incubation with trichloroacetic acid for 30 min (TCA 1%) at 4°C. We centrifuged the mixture at 1000 g for 10 min. The absorbance was measured at 412 nm. The enzyme activity was expressed as U/mg protein.

Statistical analysis

All statistics were carried out using Software Package for Social Sciences version 20.0 (SPSS, Chicago, IL, USA). All continuous variables were tested for normality using Kolmogorov–Smirnov test. Data were expressed as percentages for categorical variables and median with range (25-75% interquartile range) for quantitative variables. A value of p < 0.05 was considered statistically significant. Correlation analysis was performed using the Pearson rank order test. Square test was adopted for categorical data. To compare the control and study groups non-parametric Mann Whitney’s U analysis was performed. Correlation between the parameters was evaluated by multiple regression analysis, with AMI serving as the dependent variable.

Results

1. Basic characteristics

The study group consisted of 306 AMI patients (163 [53.3%] males and 143 [46.7%] females) hospitalized for acute myocardial infarction. The average age in the cohort was 64 years (35 −103 years).

The clinical characteristics of the AMI patients are shown in Table 1. BMI, fasting glucose, HbA1c, triglycerides, total cholesterol, LDL, and HDL-cholesterol were significantly higher in AMI patients as compared to healthy controls (P < 0.001).

Table 1. The characteristics of selected parameters of AMI patients and controls.

	AMI Patients (n = 306)	Controls (n = 410)	P
Mean age (years)	64 [35–103]	63[35–87]	0.085
Sex (male/female)	163/143	228/182	0.292
Smoking, n (%)	220(71.9)	75(18.3)	<0.001
BMI (kg/m^2)	28.2[18–46.2]	26.8[15.4–40.8]	<0.001
Fasting glucose (mmol/L)	6.5[3.8–9.83]	5[4–6.70]	<0.001
HbA1c (%)	6[3.2–15.5]	5 [3–6.7]	<0.001
Total cholesterol (mmol/L)	8.4[5–14.5]	4.1[1–5.7	<0.001
LDL-C (mmol/L)	5.97[2.6–9.4]	2.5[2–4.5]	<0.001
HDL-C (mmol/L)	2[0.8–2.8]	1[0.9–2]	<0.001
Triglycerides(mmol/L)	2[1–6.8]	1.2[0.5–2]	<0.001
DBP(mm Hg)	96[62–196]	83[65–99]	<0.001
SBP (mm Hg)	155[65–226]	129[85–155]	<0.001
HR(bpm)	80[40–160]	78[59–89]	0.013
RPP(mm Hg. Bpm)	12220[4680–31840]	9933[6035–13020]	<0.001
Serum ACE activity (U/L)	97.8[25–219.5]	88[20.44–145]	<0.001
Peak-cTnI (mg/l)	45.7[30–49.8]	0[0–0.1]	<0.001
LDH (mg/l)	643[174–9510]	289[117–432]	<0.001
CPK (mg/l)	730[26–9390]	34[4–1888]	<0.001
CRP (mg/l)	7[2–77]	3[1–9]	<0.001

BMI: Body Mass Index; HR: Heart Rate; RPP: Rate pressure product; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; Creatine phosphokinase: CPK; Reactive protein C: CRP; Lactate dehydrogenase: LDH; cTnI: cardiac troponin T.

Serum ACE activity, peak-cTnI, creatine phosphokinase (CPK), reactive protein C (CRP), and lactate dehydrogenase (LDH) were significantly higher in AMI patients than in controls (Table 1).

1.1 Evaluation of HR and RPP in controls and AMI patients

HR and RPP were significantly increased in patients compared to controls (p = 0.013).

1.2 Evaluation of oxidative status in plasma of controls and AMI patients

Results are summarized in Table 2. MDA and DC were significantly increased in AMI patients compared to controls (p < 0.001). GPx activity was significantly lower in plasma of AMI patients compared to controls (p < 0.001).

Table 2. Malondialdehyde (MDA), conjugated dienes (CD) levels and GPx activity in plasma of controls and AMI patients.

	AMI Patients (n = 306)	Controls (n = 410)	P
CD (µmol of hydroperoxyde/mg of protein)	199[49–917]	88[20.4–356]	<0.001
MDA (nmol/mg of protein)	0.49[0.08–0.94]	0.24[0.09–0.94]	<0.001
GPx (mg/mL/mg of protein)	20.94[12.2–59.8]	50.96[14.7–81.6]	<0.001

MDA: Malondialdehyde; CD: Conjugated Diene; GPx: Glutathione Peroxidase.

2. Pearson's correlation

Pearson's correlation rank was then used to evaluate the correlations between cardiac biomarkers, diastolic BP, systolic BP, lipid profile, and other parameters in AMI patients (as defined in Table 1).

2.1 Correlation the cTnI with serum ACE activity, HbA1c and lipid peroxidation biomarkers and anti-oxidative stress parameters

In AMI patients, cTnI was negatively correlated with GPx activity (r = −0.982; p = 0.001), and positively with serum ACE activity (r = 0.903; p = 0.001), HbA1c (r = 0.244; p = 0.001), DC level (r = 0.845; p = 0.001) and MDA (r = 0.569; p = 0.001) (Figure 1). In addition, serum ACE activity was negatively correlated with GPx (r = −0.926; p = 0.001).

Figure 1. Scatterplot illustrating the correlation between cTnI and (a) MDA (r = 0.569; p = 0.001); (b) DC (r = 0.845; p = 0.001); (c) GPx (r = −0. 0.982; p = 0.001); (d) HbA1c (r = 244; p = 0.001) in AMI patients.

2.2 Correlation of HbA1c with cTnI, serum ACE activity, lipid peroxidation biomarkers, RPP and anti-oxidative stress parameters

A positive correlation was found between HbA1c, serum ACE activity (r = 0.320; p = 0.001), RPP (r = 0.258; p = 0.001), diastolic BP (r = 0,290; p = 0.001) and systolic BP (r = 0,150; p = 0.046) in AMI patients. In contrast, the HbA1c was negatively correlated with GPx (r = −0.257; p = 0.001) (Figure 2). In healthy controls, no correlation was found between serum ACE activity and HbA1c, the diastolic or systolic BP, peak-cTnI, MDA, DC level, GPx activity, and lipid parameters.

Figure 2. Scatterplot illustrating the Pearson's correlation between (a) HbA1c and Serum ACE activity (r = 0.320; p = 0.001); (b) HbA1c and RPP (r = 0.258; p = 0.001) in AMI patients.

3. Multiple regression

Multiple regression analysis was applied to define factors affecting severity of AMI. After adjustment for confounding factors including age, sex, CD and MDA levels, serum ACE activity, GPx activity, diastolic and systolic BP, HbA1c, cTnI and cholesterol levels, the study indicates that the strongest predictive risk factor for AMI was serum ACE activity [OR = 4.04; 95% CI(4.00-10.25); p = 0.024] followed by cTnI [OR = 1.12; 95%CI (1.05-2.92) p = 0.007] and HbA1c [OR = 1.01; 95%CI (1.00-3.52) p = 0.033].

Discussion

The presented results are relevant for prevention and treatment of patients suffering AMI both in the short and long term. Introducing biomarkers that reflect early-phase injury of the myocardiocytes could support early diagnosis and initiation of medical interventions and treatments. It is therefore important to develop preclinical markers of complications, so that higher risk patients can potentially be identified sooner after diagnosis and thereby targeted for early and perhaps even preventative therapies. The study investigated first the relationship between peak-cTnI, lipid peroxidation products (MDA, CD), and antioxidant enzyme activity (GPx) and HbA1c and second, the correlation between HbA1c and serum ACE activity and its implication on RPP causing AMI.

Myocardial ischemia is usually the result of spontaneous complications of atherosclerosis (plaque rupture, ulceration, fissuring, erosion or dissection) resulting in coronary thrombosis. Control of blood pressure, lipid, and blood glucose levels is a proven strategy to reduce the risk of cardiovascular complications [22]. Both, diabetes and HTA, are independent predictors of mortality in AMI patients [17]. We found that the HbA1c, diastolic BP, and systolic BP were significantly higher in AMI patients compared to controls. Moreover, we found that elevated HR and RPP were significantly higher in AMI patients compared to controls. Increased HR, RPP, diastolic BO and systolic BP are strong indicators of increased oxygen demand. A high HR is a risk factor for AMI in healthy subjects as well as in patients with heart disease [23]. Myocardial tissue damage is related to the extent to which myocardiocytes have been deprived of oxygen. Oxygen (O₂) is widely used in AMI patients. The most common locations of AMI concern the left ventricular myocardium, which is exposed to the greatest workload. When myocardiocytes are damaged or destroyed due to deficient oxygen supply or glucose, the cell membrane becomes permeable or ruptures, which results in the leakage of enzymes. The association between circulating lipids and oxidative damage markers is a strong indicator of vascular disease [24].

The present results show that higher serum ACE activity and elevated peak-cTnI levels might be clinically useful biomarkers to assess the risk of AMI, which is in accordance with previous data [17,18]. On other hand, we found that the GPx activity was higher in healthy controls compared to AMI patients. GPx activity was associated with increased levels of peroxidation products (MDA and CD) in AMI patients. This result is in agreement with those of several other studies, which reported significantly decreased GPX activity [25]. Also peak-cTnI was negatively correlated with GPx activity, and positively with MDA and CD levels. Moreover, our results showed that the elevated peak-cTnI correlated positively with HbA1c. Although troponin concentrations and HbA1c levels are the prognostic factors for mortality in AMI patients, it is demanding to prove a causal relation between these two parameters because of the complexity of the whole mechanism that causes myocardial damage.

Stress increases the levels of circulating glucose, and chronic stress may lead to the development of physiological as well as cognitive impairment, especially in individuals that lack proper adaptive mechanisms [26]. Hyperglycemia is associated with massive production of ROS. ROS cause insulin resistance in peripheral tissues by reducing glucose uptake, downregulating insulin receptor substrate 1 tyrosine phosphorylation and decreasing glucose transporter 4 translocation (GLUT4). In addition, ROS can be generated through the interaction of increased AGEs with RAGE (the receptor for AGEs) in diabetic patients [27]. The interaction between AGEs and RAGE activates various signal transduction cascades, including generation of cytosolic and mitochondrial superoxide and activation of transcription factors such as nuclear factor-κB [28]. In cultured human umbilical vein endothelial cells, AGEs/RAGE interaction was reported to induce intracellular generation of ROS through the activation of both NAD(P)H-oxidase and the mitochondrial electron transport system [29]. The exact mechanism for the role of oxidative stress in vascular damage and development of diabetes-associated complications is complex.

Proinflammatory cytokines and chemokines increase the monocyte-phagocytic system activity, which theoretically could increase troponin clearance but also increase the progression of atherosclerotic changes and modify the intensity of cardiac damage, consequently elevating troponin concentrations [5]. Although HbA1c levels correlate with thrombin and monocytic-phagocyte system activity, which participate in degradation and clearance of troponin, their effects are ambiguous because thrombin activity perpetuates thrombus formation while monocytic-phagocyte activity perpetuates atherosclerosis and, in both cases, increase incidence and severity of heart injury increasing troponin concentrations [5].

Furthermore, our results showed that the HbA1c correlated positively with serum ACE activity and RPP. Considering the relationship between the RAAS and systemic vascular function in patients, we found that higher levels of RAAS mediators correlate with increased BP. HTA is associated with the serum concentrations of various vasoconstrictors, such as angiotensin II (Ang II). Ang II effects are mediated via Ang II type 1 and Ang II type 2 receptors, which couple to various signaling molecules. The increase in ACE activity can increase the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase [30,31], which in turn increases the release of ROS [32–34] such as superoxide radical (O2-) and H₂O₂. ROS are involved in diverse signaling functions that may impair ventricular microvascular blood flow causing myocardial ischemia, cTnI-release, and ventricular dysfunction [35,36].

ROS can rapidly react with NO, leading to peroxynitrite formation and reduced NO bioavailability increasing vascular reactivity [37]. NO prevents platelet aggregation and activation of particularly monocytes, which are transformed into macrophages containing lipids, and inhibits proliferation of smooth muscle cells, which are integral components of atherosclerotic vascular lesions and stimulants of ROS and oxidative stress. Although the effect of Ang II on ROS production is becoming clearer, there is still little knowledge of its mechanism and how these redox-sensitive processes lead to vascular inflammation and fibrosis, and which factors act as damaging stress signals to induce vascular injury. Atherosclerotic plaques narrow the vessel lumen and restrict blood flow progressively leading to ischemic events and oxygen shortage. Complete blockage of blood flow, frequently via a thrombotic event, induces a myocardial infarction [38]. Inflammatory response at the infarct region and associated neuro-hormonal activation determine a serious modification in myocardial metabolism [39].

In AMI patients, the underlying mechanism in the increase of mortality associated with blood pressure is poorly understood. Since the number of patients is small in the present study, further studies are necessary to confirm our results.

In conclusion, plasma biomarkers of AMI patients as measured in the present study reflect strong modifications of the redox status, which favor oxidative damage in AMI patients. These findings might explain that elevated HbA1c causes oxidative stress and reduced GPX in AMI patients. It is challenging to unambiguously assess the effect of HbA1c on overall mortality and morbidity and to evaluate its relationship with troponin and RPP concentrations as the prognostic markers of AMI. Further work is required to determine whether RAAS markers can help to identify patients at higher risk of future cardiovascular complications.

Acknowledgements

This study was supported by The Ministry of Higher Education, Scientific Research and Information and Communication Technologies, Tunisia.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

R. Chaaba: Conception and design, S. Hammami: acquisition and analysis ofdata, S. Mehri and J. Finsterer: writing, review, and/or revision of the manuscript, M. Hammami: study supervision, All authors have read and agreed to the published version of the manuscript.

Data availability statement

The authors confirm that data presented in this study are available in article.