Cellular Immunity, Neuroendocrine, and Inflammatory Factors for Clinical Prognosis in Acute Coronary Syndrome

Introduction According to data from the World Health Organization, cardiovascular disease is the leading cause of death worldwide. It is estimated that in 2015, 18 million people died from this cause, representing 31% of all deaths recorded globally. The majority of these deaths (7.4 million) were due to coronary heart disease. Additionally, cardiovascular disease represents the highest burden of disease, defined by disability-adjusted life years (DALY), with 4,800 DALY per 100,000 inhabitants.

From a clinical perspective, the topic that concerns us within cardiovascular disease is acute coronary syndrome (ACS), which refers to a group of signs and symptoms compatible with acute myocardial ischemia. In this context, for decades, to optimize diagnosis and provide timely treatment, ACS has been subclassified into acute myocardial infarction with (STEMI) and without ST-segment elevation (NSTEMI) and unstable angina (UA).

Among the broad spectrum of ACS, the least severe form is UA, where symptoms suggest myocardial ischemia but there is no biochemical evidence of myocardial infarction. At the other extreme is acute myocardial infarction, whose clinical definition is based on the presence of acute myocardial damage detected by the elevation of cardiac biomarkers (troponin) in the context of evidence of acute myocardial ischemia. It is classified into five types, with type 1, caused by atherothrombotic coronary disease, being the focus of this study. This type is often precipitated by the rupture or erosion of an atherosclerotic plaque.

Background and Rationale Years of exhaustive clinical research have resulted in therapies that reduced mortality and complication rates of ACS, ranging from the creation of the Coronary Care Unit, anticoagulation therapies, beta-blockers, renin-angiotensin-aldosterone system inhibitors, antiplatelet therapies, to reperfusion, whether mechanical or pharmacological. However, in recent years, we have not been able to make a qualitative leap in the pathophysiological approach to coronary disease to create new paradigms in therapeutics. Thus, it is imperative to seek new horizons, new pathophysiological hypotheses, addressing topics such as inflammation, immune response, immunothrombosis, and stress response in the context of ACS.

It is undeniable that inflammation and stress are risk factors for developing ACS. Additionally, ACS triggers both an acute inflammatory response and a stress response, with cortisol being one of the main effectors of the latter, possessing anti-inflammatory effects. However, we currently lack a deep understanding of the mechanisms governing this interesting dialogue between inflammation and stress in the context of ACS. Some authors have described that a higher degree of inflammation is associated with worse outcomes in ACS patients. Recently, we described that this poor outcome is related to plasma cortisol levels.

We know that stress has complex effects on the immune system, influencing both innate and adaptive immunity. Glucocorticoids and catecholamines, in the acute phase of the stress response, can influence the trafficking and/or function of leukocytes (neutrophil demargination), induce a systemic shift from a TH1 (cellular immunity) to a TH2 (humoral immunity) response. Additionally, acute stress can increase circulating pro-inflammatory cytokine levels such as IL-6, IL-1b, and C-reactive protein (CRP).

This bidirectional interaction between stress response effectors and the immune system is evident as pro-inflammatory cytokines stimulate the stress system at multiple levels, including the central and peripheral nervous systems, hypothalamus, pituitary, and adrenal glands, increasing glucocorticoid levels.

The literature extensively supports the utility of leukocyte indices in ACS. One of the most widely used is the neutrophil-to-lymphocyte ratio (NLR), which has demonstrated its association with the severity of clinical presentation and coronary lesions, as well as its ability to predict events during hospitalization and after discharge. However, we lack studies explaining the mechanisms underlying this index with certainty.

We know that exogenous glucocorticoids can cause transient neutrophilia by recruiting the marginal pool and lymphopenia through apoptosis. Recently, other mechanisms have been proposed that could explain the lymphopenia associated with severe acute stress events linked to transient immunodepression, although evidence is scarce. Similarly, although limited, there is interesting information regarding eosinopenia as a prognostic marker in severe acute diseases, with studies published on its utility in stroke, sepsis, and one in ACS. We know that exogenous glucocorticoid administration can induce eosinopenia, and apoptosis is proposed as one of the mechanisms causing it, which is why corticosteroids have been part of the first-line treatment for autoimmune and allergic diseases where these cells play a predominant role.

Currently, we lack human studies demonstrating the association of these cellular immunity components' behavior with stress and inflammation response markers in the context of ACS.

Research Objectives and Hypotheses Hypothesis: In ACS, there is an increase in cortisol levels (as an expression of the stress response) and CRP (as an expression of the inflammatory response) associated with changes in cellular immunity components, which are predictors of clinical events.

Objectives:

Describe the leukocyte, neutrophil, lymphocyte, and eosinophil counts and their association with the clinical presentation of ACS patients.

Describe the leukocyte, neutrophil, lymphocyte, and eosinophil counts and their association with the presence of heart failure in ACS patients.

Describe the leukocyte, neutrophil, lymphocyte, and eosinophil counts and their association with clinical events during hospitalization in ACS patients.

Describe the leukocyte, neutrophil, lymphocyte, and eosinophil counts and their association with clinical events post-discharge in ACS patients.

Correlate the leukocyte, neutrophil, lymphocyte, and eosinophil counts with maximum levels of high-sensitivity troponin, CPK, and CPK-MB as expressions of tissue damage in ACS.

Correlate the leukocyte, neutrophil, lymphocyte, and eosinophil counts with total serum cortisol levels as a marker of stress response in ACS.

Correlate the leukocyte, neutrophil, lymphocyte, and eosinophil counts with CRP levels as a marker of inflammation in ACS.

Methodology A prospective, observational, analytical, single-center study will be conducted on successive patients with acute coronary syndrome, with a follow-up of 6 months. For two years, all eligible patients admitted with a diagnosis of ACS to the Coronary Unit of Hospital de Clínicas José de San Martín in Buenos Aires will be successively registered.

All included patients will undergo a baseline electrocardiogram and physical examination at admission, and appropriate therapeutic measures will be implemented according to their condition, as indicated by the treating physician, without influence from study protocol inclusion.

Demographic and clinical data will be obtained from the medical history. Additionally, a blood sample will be taken at admission for the measurement of ultra-sensitive CRP and cortisol.

During hospitalization, the evolution curve of necrosis markers (troponin, CPK, and CPK-MB) will be evaluated from admission until the peak is confirmed and the decline begins. Total leukocyte, lymphocyte, and eosinophil counts will be recorded at patient admission.

Post-discharge, a telephone follow-up will be conducted for 6 months.

Sample Size Calculation: Considering an event incidence of 20% and an alpha error of 0.5 and beta error of 0.5, the number of patients to be included should be equal to or greater than 150.

For statistical analysis, categorical variables will be presented as frequencies, and continuous variables as mean with standard deviation if normally distributed, or median with interquartile ranges if not. Pearson's chi-square or Fisher's exact test and Student's t-test or Wilcoxon rank-sum test will be used for comparisons depending on whether variables are categorical or continuous and normally distributed or not. Correlations will be assessed using Pearson or Spearman coefficients. Intraobserver and interobserver variability will be analyzed using the intraclass correlation coefficient. Statistical significance will be set at a two-tailed p-value less than 0.05. All calculations will be performed using R version 3.5.1.