Biomarkers in Acute Myocardial Infarction Diagnosis and Prognosis

Neuron-specific enolase as a prognostic biomarker in acute ischemic stroke patients treated with reperfusion therapies

Introduction

Ischemic stroke is a significant global health concern, with reperfusion therapies playing a vital role in patient management. Neuron-specific enolase (NSE) has been suggested as a potential biomarker for assessing stroke severity and prognosis, however, the role of NSE in predicting long-term outcomes in patients undergoing reperfusion therapies is still scarce.

Aim

To investigate the association between serum NSE levels at admission and 48 h after reperfusion therapies, and functional outcomes at 90 days in ischemic stroke patients.

Methods

This study conducted a prospective cross-sectional analysis on consecutive acute ischemic stroke patients undergoing intravenous fibrinolysis and/or endovascular thrombectomy. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days post-stroke and two groups were defined according to having unfavorable (mRS3-6) or favorable (mRS0-2) outcome. Demographic, clinical, radiological, and laboratory data were collected, including NSE levels at admission and 48 h. Spearman's coefficient evaluated the correlation between analyzed variables. Logistic regression analysis was performed to verify which variables were independently associated with unfavorable outcome. Two ROC curves determined the cut-off points for NSE at admission and 48 h, being compared by Delong test.

Results

Analysis of 79 patients undergoing reperfusion treatment following acute stroke revealed that patients with mRS 3-6 had higher NIHSS at admission (p < 0.0001), higher NIHSS at 24 h (p < 0.0001), and higher NSE levels at 48 h (p = 0.008) when compared to those with mRS 0-2. Optimal cut-off values for NSE0 (>14.2 ng/mL) and NSE48h (>26.3 ng/mL) were identified, showing associations with worse clinical outcomes. Adjusted analyses demonstrated that patients with NSE48h > 26.3 ng/mL had a 13.5 times higher risk of unfavorable outcome, while each unit increase in NIHSS24h score was associated with a 22% increase in unfavorable outcome. Receiver operating characteristic analysis indicated similar predictive abilities of NSE levels at admission and 48 h (p = 0.298). Additionally, a strong positive correlation was observed between NSE48h levels and mRS at 90 days (r = 0.400 and p < 0.0001), suggesting that higher NSE levels indicate worse neurological disability post-stroke.

Conclusion

Serum NSE levels at 48 h post-reperfusion therapies are associated with functional outcomes in ischemic stroke patients, serving as potential tool for patient long-term prognosis.

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

A biomarker discovery of acute myocardial infarction using feature selection and machine learning

Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.

Cardiac biomarkers and detection methods for myocardial infarction

Background

A significant heart attack known as a myocardial infarction (MI) occurs when the blood supply to the heart is suddenly interrupted, harming the heart muscles due to a lack of oxygen. The incidence of myocardial infarction is increasing worldwide. A relationship between COVID-19 and myocardial infarction due to the recent COVID-19 pandemic has also been revealed.

Objective

We propose a biomarker and a method that can be used for the diagnosis of myocardial infarction, and an aptamer-based approach.

Results

For the diagnosis of myocardial infarction, an algorithm-based diagnosis method was developed using electrocardiogram data. A diagnosis method through biomarker detection was then developed.

Conclusion

Myocardial infarction is a disease that is difficult to diagnose based on the aspect of a single factor. For this reason, it is necessary to use a combination of various methods to diagnose myocardial infarction quickly and accurately. In addition, new materials such as aptamers must be grafted and integrated into new ways.

Purpose of Review

The incidence of myocardial infarction is increasing worldwide, and some studies are being conducted on the association between COVID-19 and myocardial infarction. The key to properly treating myocardial infarction is early detection, thus we aim to do this by offering both tools and techniques as well as the most recent diagnostic techniques.

Recent Findings

Myocardial infarction is diagnosed using an electrocardiogram and echocardiogram, which utilize cardiac signals. It is required to identify biomarkers of myocardial infarction and use biomarker-based ELISA, SPR, gold nanoparticle, and aptamer technologies in order to correctly diagnose myocardial infarction.

A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach

Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.

The Utility of New Biomarker-based Predictive Model for Clinical Outcomes Among ST-elevation Myocardial Infarction Patients

Aim:

To determine the discriminative potency of score to prognosticate poor clinical outcomes in ST-Segment Elevation Myocardial Infarction (STEMI) patients.

Methods:

From the entire population of STEMI (n=268), we enrolled 177 individuals with acute STEMI who underwent complete revascularization with primary Percutaneous Coronary Intervention (PCI). Clinical assessment, echocardiography, Doppler, and biomarkers’ measure were performed at baseline.

Results:

Combined endpoint (Major Cardiovascular Events - MACEs [composite of cardiovascular death, recurrent myocardial infarction, newly diagnosed Heart Failure] and hospitalization) was determined in 75 patients with acute STEMI population (40.6%). Newly onset heart failure (HF) was reported in 46 patients (26.0%), Cardiovascular (CV) death occurred in 12 patients (6.8%), MACEs were determined in 58 patients (32.8%), and recurrent hospitalization due to CV reasons was found in 17 (9.6%). The conventional risk predictive models were engineered by a combination of TIMI risk score +acute HF Killip class ≥ II + the levels of NT-pro brain natriuretic peptide > 300 pg / mL and troponin >0.05 ng/mL. We developed a new predictive model based on the presentation of T786С genotype of endothelial NO syntase gene (rs 2070744), А1166С in angiotensin-ІІ receptor-1 gene (rs5186) and serum levels of soluble suppressor tumorigenicity ≥35 pg/mL, vascular endothelial growth factor ≤172 pg/mL and macrophage inhibitory factor ≥2792.7 pg/mL. STEMI patients who had >5 score points demonstrated significantly worse prognosis than those who had ≤5 score points.

Conclusion:

Here we have reported that a new original predictive model is better than a conventional model in discriminative ability to predict combined clinical outcome in STEMI patients.