Screening and identification of potential protein biomarkers for the early diagnosis of acute myocardial infarction

The roles of Th cells in myocardial infarction

Myocardial infarction, commonly known as a heart attack, is a serious condition caused by the abrupt stoppage of blood flow to a part of the heart, leading to tissue damage. A significant aspect of this condition is reperfusion injury, which occurs when blood flow is restored but exacerbates the damage. This review first addresses the role of the innate immune system, including neutrophils and macrophages, in the cascade of events leading to myocardial infarction and reperfusion injury. It then shifts focus to the critical involvement of CD4+ T helper cells in these processes. These cells, pivotal in regulating the immune response and tissue recovery, include various subpopulations such as Th1, Th2, Th9, Th17, and Th22, each playing a unique role in the pathophysiology of myocardial infarction and reperfusion injury. These subpopulations contribute to the injury process through diverse mechanisms, with cytokines such as IFN-γ and IL-4 influencing the balance between tissue repair and injury exacerbation. Understanding the interplay between the innate immune system and CD4+ T helper cells, along with their cytokines, is crucial for developing targeted therapies to mitigate myocardial infarction and reperfusion injury, ultimately improving outcomes for cardiac patients.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Diagnostic and treatment value of XPNPEP3 in acute myocardial infarction

Background

At present, acute myocardial infarction (AMI) is a serious cardiovascular disease with high morbidity and mortality. Discovering biomarkers of AMI is important for clinical diagnosis and needs. Therefore, this study aimed to elucidate the role of XPNPEP3 as a potential biomarker for AMI.

Methods

Expression profiling data were downloaded for AMI patients and healthy patients in the GSE24548 and GSE24519 datasets, respectively. The limma package in R was conducted to determine differentially expressed microRNA (DEmiRNA)/messenger RNA (mRNA) [differentially expressed genes (DEGs)]. TargetScan and Cytoscape were used to build regulatory network of miRNA-mRNA. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were applied to determine immune cell score. The gene set variation analysis (GSVA) package was used to calculate pathway score. Key drugs were determined by protein-protein interaction (PPI) and molecular docking.

Results

Totals of 36 DEmiRNAs and 63 DEGs were determined in the GSE24584 dataset and GSE24519 dataset, respectively, and then we constructed a miRNA-mRNA network including 31 DEmiRNAs and 47 DEGs. The correlation analysis between immune cells and 47 DEGs identified that XPNPEP3 was most associated with AMI. Furthermore, XPNPEP3 was negatively correlated with inflammatory response score. A diagnosis model based on XPNPEP3 expression showed an area under the curve (AUC) of 93.38%, and 159 genes were highly correlated with XPNPEP3. Molecular docking analysis showed that DB06909 had the lowest docking score with XPNPEP3, revealing it to be a potential XPNPEP3 inhibitor.

Conclusions

This work discovered that XPNPEP3 is correlated with the development of AMI. These findings may provide theoretical basis for the diagnosis and treatment of AMI.

Identification of novel candidate biomarkers for acute myocardial infarction by the Olink proteomics platform

BACKGROUND

Both pathological and normal processes depend on proteins. In this study, plasma protein profiles were analyzed by a novel proximity extension assay (PEA) to identify potential pathogenic mechanisms and diagnostic biomarkers in patients diagnosed with acute myocardial infarction (AMI).

METHODS

In this study, we identified a total of 92 plasma proteins using the Olink Target 96 Cardiovascular III panel in a cohort consisting of 30 healthy controls (HC), 28 patients with unstable angina (UA) and 30 patients with AMI. Subsequently, we conducted a differential expression analysis to identify protein molecules that were specifically expressed in patients with AMI. To gain insights into the potential functional mechanisms of these differentially expressed molecules, we performed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Following that, the utilization of least absolute shrinkage and selection operator (LASSO) regression facilitated the identification of potential protein biomarkers, enabling the differentiation between AMI and UA. A diagnostic model was subsequently developed through logistic regression, and the effectiveness of these markers was assessed using receiver operating characteristic (ROC) analysis. Ultimately, the diagnostic capabilities of these potential biomarkers were validated in an independent validation cohort consisting of 30 UA cases and 30 AMI cases.

RESULTS

In this study, a comprehensive analysis of plasma proteins identified a total of 92 proteins. Further analysis using analysis of variance revealed that 25 proteins exhibited specific expression in the AMI group compared to the HC and UA groups. Additionally, KEGG enrichment analysis indicated that these differentially expressed proteins were primarily associated with the activation of cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and GnRH signaling pathway. AGRP, TGM2, IL6, GH1, and CA5A were identified through LASSO regression as prospective protein biomarkers for distinguishing between UA and AMI. The diagnostic model comprising these five proteins exhibited exceptional performance in both the discovery and validation datasets, surpassing AUC values of 0.9.

CONCLUSION

The findings of our study provide additional insights into the involvement of the inflammatory response and AKT cascade response in the development of AMI. Moreover, we have identified potential protein markers that could be utilized for the accurate diagnosis of AMI. These results offer a fresh perspective for clinical decision-making in the context of AMI.

Ethnicity and sex-specific 99th percentile upper reference limits of high-sensitivity cardiac troponin I among adults in Xinjiang, China

OBJECTIVES

To determine the 99th percentile upper reference limit (URL) of high-sensitivity cardiac troponin I (hs-cTnI) in a healthy population in Xinjiang, China, and investigate the impact of ethnicity, sex, and age on this limit.

DESIGN AND METHODS

From September 2018 to March 2022, 5,090 Han and Uyghur adults aged 20-79 years were recruited. After questionnaire screening, 2,970 participants with physical and/or laboratory normality were enrolled. Participants recruited between September 2018 and October 2021 (2,109/2,970) were evaluated by ARCHITECTi2000 to determine the 99th percentile URL of hs-cTnI. The results were then validated in 861/2,970 participants recruited from November 2021 to March 2022. A criterion of ≤10% of test results falling outside the original determined value was used to determine whether the newly established reference intervals were valid.

RESULTS

The hs-cTnI concentration was higher among Uyghurs than among Han participants (p<0.001). The 99th percentile URLs were 17.52 ng/L for all participants, 18.96 ng/L for Uyghur, and 16.93 ng/L for Han. Hs-cTnI concentration was also correlated with sex and age. In the Han and Uyghur groups, male participants had a higher hs-cTnI concentration than female participants (p<0.001); the 99th percentile URLs of hs-cTnI among male and female participants were 17.80 vs. 13.67 ng/L and 19.47 vs. 16.52 ng/L, respectively. Stratified by age, hs-cTnI concentrations were higher in participants aged >60 years than in those of other age categories (p<0.001), in both the Han and Uyghur groups. Finally, <2% of these test results exceeded the newly established reference, validating the results.

CONCLUSIONS

This study established the 99th percentile URLs of hs-cTnI in the Xinjiang. Ethnicity and sex influence the value and should be considered.

Gut Microbiota Combined with Metabolites Reveals Unique Features of Acute Myocardial Infarction Patients Different from Stable Coronary Artery Disease

INTRODUCTION

Acute myocardial infarction (AMI) accounts for the majority of deaths caused by coronary artery disease (CAD). Early warning of AMI, especially for patients with stable coronary artery disease (sCAD), is urgently needed. Our previous study showed that alterations in the gut microbiota were correlated with CAD severity.

OBJECTIVES

Herein, we tried to discover accurate and convenient biomarkers for AMI by combination of gut microbiota and fecal/blood/urinary metabolomics.

METHODS

We recruited 190 volunteers including 93 sCAD patients, 49 AMI patients, and 48 subjects with normal coronary artery (NCA), and measured their blood biochemical parameters, 16S rRNA-based gut microbiota and NMR-based fecal/blood/urinary metabolites. We further selected 20 subjects from each group and analyzed their gut microbiota by whole-metagenome shotgun sequencing.

RESULTS

Multi-omic analyses revealed that AMI patients exhibited specific changes in gut microbiota and serum/urinary/fecal metabolites as compared to subjects with sCAD or NCA. Fourteen bacterial genera and 30 metabolites (11 in feces, 10 in blood, 9 in urine) were closely related to AMI phenotypes and could accurately distinguish AMI patients from sCAD patients. Some species belonging to Alistipes, Streptococcus, Ruminococcus, Lactobacillus and Faecalibacterium were effective to distinguish AMI from sCAD and their predictive ability was confirmed in an independent cohort of CAD patients. We further selected nine indicators including 4 bacterial genera, 3 fecal and 2 urinary metabolites as a noninvasive biomarker set which can distinguish AMI from sCAD with an AUC of 0.932.

CONCLUSION

Combination of gut microbiota and fecal/urinary metabolites provided a set of potential useful and noninvasive predictive biomarker for AMI from sCAD.