Detection of biomarkers using a novel proximity extension assay in patients with ST-elevation myocardial infarction

Single-Cell Dissection of the Immune Response After Acute Myocardial Infarction

BACKGROUND

The immune system's role in ST-segment-elevated myocardial infarction (STEMI) remains poorly characterized but is an important driver of recurrent cardiovascular events. While anti-inflammatory drugs show promise in reducing recurrence risk, their broad immune system impairment may induce severe side effects. To overcome these challenges, a nuanced understanding of the immune response to STEMI is needed.

METHODS

For this, we compared peripheral blood mononuclear single-cell RNA-sequencing (scRNA-seq) and plasma protein expression over time (hospital admission, 24 hours, and 6-8 weeks post-STEMI) in 38 patients and 38 controls (95 995 diseased and 33 878 control peripheral blood mononuclear cells).

RESULTS

Compared with controls, classical monocytes were increased and CD56dim natural killer cells were decreased in patients with STEMI at admission and persisted until 24 hours post-STEMI. The largest gene expression changes were observed in monocytes, associating with changes in toll-like receptor, interferon, and interleukin signaling activity. Finally, a targeted cardiovascular biomarker panel revealed expression changes in 33/92 plasma proteins post-STEMI. Interestingly, interleukin-6R, MMP9 (matrix metalloproteinase-9), and LDLR (low-density lipoprotein receptor) were affected by coronary artery disease-associated genetic risk variation, disease status, and time post-STEMI, indicating the importance of considering these aspects when defining potential future therapies.

CONCLUSIONS

Our analyses revealed the immunologic pathways disturbed by STEMI, specifying affected cell types and disease stages. Additionally, we provide insights into patients expected to benefit most from anti-inflammatory treatments by identifying the genetic variants and disease stage at which these variants affect the outcome of these (drug-targeted) pathways. These findings advance our knowledge of the immune response post-STEMI and provide guidance for future therapeutic studies.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

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.

Advanced Proteomics and Cluster Analysis for Identifying Novel Obstructive Sleep Apnea Subtypes before and after Continuous Positive Airway Pressure Therapy

Rationale: Studies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. Objectives: We used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. Methods: Adults with newly diagnosed OSA had blood drawn at baseline and three to four months after CPAP. Samples were analyzed using the Olink proteomics platform, which measures 92 prespecified inflammatory proteins using proximity extension assay. Linear mixed-effects models were used to model changes in protein expression during the period of CPAP use, adjusting for batch, age, and sex. Unsupervised hierarchical clustering was performed to identify unique inflammatory OSA clusters on the basis of inflammatory biomarkers. Within-cluster impact of CPAP on inflammatory protein expression was assessed. Results: Among 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m2, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low (n = 21), intermediate (n = 19), and high (n = 6) inflammatory protein expression. After CPAP, there were significant within-cluster differences in protein expression. The low inflammatory cluster had a significant increase in protein expression (16%; P = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (-20%; P = 0.003), more significant among those compliant with CPAP in the low (25%; P = 0.04) and high (-22%; P = 0.01) clusters. Conclusions: We identified three unique inflammatory clusters in patients with OSA using plasma proteomics, with a differential response to CPAP by cluster. Our results are hypothesis generating and require further investigation in larger longitudinal studies for enhanced cardiovascular risk profiling in OSA.

Screening for Biomarkers Associated with Left Ventricular Function During Follow-up After Acute Coronary Syndrome

A proportion of patients with the acute coronary syndrome (ACS) will suffer progressive remodeling of the left ventricular (LV). The aim was to screen for important biomarkers from a large-scale protein profiling in 420 ACS patients and define biomarkers associated with reduced LV function early and 1 year after the ACS. Transferrin receptor protein 1 and NT-proBNP were associated with LV function early and after 1 year, whereas osteopontin and soluble ST2 were associated with LV function in the early phase and, tissue-type plasminogen activator after 1 year. Fatty-acid-binding protein and galectin 3 were related to worse GLS but not to LVEF 1 year after the ACS. Proteins involved in remodeling and iron transport in cardiomyocytes were related to worse LV function after ACS. Biomarkers for energy metabolism and fibrosis were exclusively related to worse LV function by GLS. Studies on the functions of these proteins might add knowledge to the biological processes involved in heart failure in long term after ACS.

Prediction of one-year adverse clinical outcomes by macrophage migration inhibitory factor in stemi patients

Biomarkers have taken one of the first places as diagnostic and prognostic tools in ST-segment elevation myocardial infarction (STEMI) and are consequently widely used as predictors of short-term and long-term prognosis. One of the promising biomarkers for early cardiovascular outcomes prediction is the pro-inflammatory cytokine macrophage migration inhibitory factor (MIF). The aim of the study was to elucidate a plausible predictive value of the MIF levels for one-year clinical outcomes in STEMI patients who underwent primary percutaneous coronary intervention (PCI). Materials and methods. 134 STEMI patients were enrolled in the study after receiving voluntary informed consent. All patients underwent conventional investigations, and additionally, the MIF levels were determined at baseline, directly before and after PCI. During 1-year follow-up, 37 % of patients reached the endpoint, which was composite and included all-cause mortality, non-fatal myocardial infarction, non-fatal stroke, hospitalization for unstable angina, heart failure decompensation, and urgent revascularization. Results. We have found that pre-PCI MIF levels > 3934 pg/mL (AUC=0.7; 95 % CI 0.578 to 0.753; Youden index=0.31; p=0.008) might be an independent predictor of composite endpoints with sensitivity 54 % and specificity 82 %. A positive correlation between MIF and inflammatory biomarkers was revealed (WBC count r=0.33, p=0.0001; CRP r=0.19, p=0.032). Adverse outcomes associated with higher pre- and post-PCI MIF levels (OR 1.0, 95 % CI 1.0001–1.0008; p=0.013 and OR 1.0, 95 % CI 1.0001–1.0009; p=0.019) and CRP that determined during the first week after the event (OR 1.0, 95 % CI 1.005–1.2, p=0.03). Kaplan-Meier analysis has shown a substantially lower long-term survival rate in patients with a MIF level > 3493 pg/ml compared to a MIF level ≤ 3493 pg/ml (Log rank=0.00025). Conclusions. The MIF levels exceeding 3934 ng/ml were associated with a higher risk of one-year adverse clinical outcomes in STEMI patients who underwent primary PCI.

Integrated Multichip Analysis and WGCNA Identify Potential Diagnostic Markers in the Pathogenesis of ST-Elevation Myocardial Infarction

Background

ST-elevation myocardial infarction (STEMI) is a myocardial infarction (MI) with ST-segment exaltation of electrocardiogram (ECG) caused by vascular occlusion of the epicardium. However, the diagnostic markers of STEMI remain little.

Methods

STEMI raw microarray data are acquired from the Gene Expression Omnibus (GEO) database. Based on GSE60993 and GSE61144, differentially expressed genes (DEGs) are verified via R software, and key modules associated with pathological state of STEMI are verified by weighted correlation network analysis (WGCNA). Take the intersection gene of key module and DEGs to perform the pathway enrichment analyses by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Construct the protein-protein interaction (PPI) network by Cytoscape. Then, select and identify the diagnostic biomarkers of STEMI by least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, assess the infiltration of immune cells of STEMI by CIBERSORT and analyze the correlation between diagnostic markers and infiltrating immune cells.

Results

We get 710 DEGs in the STEMI group and 376 genes associated with STEMI in blue module. 92 intersection genes were concentrated in 30 GO terms and 2 KEGG pathways. 28 hub genes involved in the development of STEMI. Moreover, upregulated ALOX5AP (AUC = 1.00) and BST1 (AUC = 1.00) are confirmed as diagnostic markers of STEMI. CD8+T cells, regulatory T (Treg) cells, resting natural killer (NK) cells, M0 macrophages, resting mast cells, and neutrophils are related to the procession of STEMI. Moreover, ALOX5AP and BST1 are positively related to resting NK cells, M0 macrophages, and neutrophils, while ALOX5AP and BST1 are negatively related to CD8+ T cells, Treg cells, and resting mast cells.

Conclusion

ALOX5AP and BST1 may be the diagnostic markers of STEMI. Immune cell infiltration plays a key role in the development of STEMI.

Precision medicine in secondary prevention of ischemic stroke: how may blood-based biomarkers help in clinical routine? An expert opinion

PURPOSE OF REVIEW

One in eight patients unfortunately suffers a new stroke within 5 years of their first stroke, even today. Research in precision medicine could lead to a more individualized treatment allocation, possibly achieving lower recurrence rates of ischemic stroke. In this narrative review, we aim to discuss potential clinical implementation of several promising candidate blood biomarkers.

RECENT FINDINGS

We discuss specifically some promising blood-based biomarkers, which may improve the identification of underlying causes as well as risk stratification of patients according to their specific cerebrovascular risk factor pattern.

SUMMARY

Multimodal profiling of ischemic stroke patients by means of blood biomarkers, in addition to established clinical and neuroradiological data, may allow in the future a refinement of decision algorithms for treatment allocation in secondary ischemic stroke prevention.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Screening of biomarkers for prediction of multisite artery disease in patients with recent myocardial infarction

A few studies have examined biomarkers in patients with myocardial infarction (MI) and peripheral artery disease (PAD), i.e. multisite artery disease (MSAD). The aim of the study was firstly, to associate biomarkers with the occurrence of PAD/MSAD and secondly, if those can, in addition to clinical characteristics, identify MI patients with MSAD.In two prospectively observational studies including unselected patients with recent MI, PAD was defined as an abnormal ankle-brachial index (ABI) score (<0.9 or >1.4). The proximity extension assay (PEA) technique was used, simultaneously analyzing 92 biomarkers with association to cardiovascular disease. Biomarkers were tested for univariate associations with PAD. Random forest was used to identify biomarkers with a higher association to PAD. The additional discriminatory accuracy of adding biomarkers to clinical characteristics was analyzed by the c-statistics. Nine biomarkers were identified as significantly associated with MSAD/PAD in the primary patient cohort, analyzed early after the MI. In the prediction analysis, six biomarkers were identified associated with PAD. Three of these; Tumor necrosis factor receptor (TNFR-1), Tumor necrosis factor receptor 2 (TNFR-2) and Growth Differentiation Factor 15 (GDF-15) improved c-statistics when added to clinical characteristics from 0.683 (95% CI 0.610-0.756) to 0.715 (95% CI 0.645-0.784) in the primary patient cohort with a similar result, 0.729 (95% CI 0.687-0.770) to 0.752 (95% CI 0.771-0.792) in the secondary patient cohort. Biomarkers associated with inflammatory pathways are associated with MSAD in MI patients. Three biomarkers of 92; TNFR-1, TNFR-2 and GDF-15, in this exploratory added information in the prediction of MSAD and emphasis the importance of further studies.

Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction

Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.

Exploring biomarkers associated with deteriorating vascular health using a targeted proteomics chip: The SABPA study

In this observational study, by the use of a multiplex proteomic platform, we aimed to explore associations between 92 targeted proteins involved in cardiovascular disease and/or inflammation, and phenotypes of deteriorating vascular health, with regards to ethnicity.Proteomic profiling (92 proteins) was carried out in 362 participants from the Sympathetic activity and Ambulatory Blood Pressure in Africans (SABPA) study of black and white African school teachers (mean age 44.7 ± 9.9 years, 51.9% women, 44.5% Black Africans, 9.9% with known cardiovascular disease). Three proteins with <15% of samples below detectable limits were excluded from analyses. Associations between multiple proteins and prevalence of hypertension as well as vascular health [Carotid intima-media thickness (cIMT) and pulse wave velocity (PWV)] measures were explored using Bonferroni-corrected regression models.Bonferroni-corrected significant associations between 89 proteins and vascular health markers were further adjusted for clinically relevant co-variates. Hypertension was associated with growth differentiation factor 15 (GDF-15) and C-X-C motif chemokine 16 (CXCL16). cIMT was associated with carboxypeptidase A1 (CPA1), C-C motif chemokine 15 (CCL15), chitinase-3-like protein 1 (CHI3L1), scavenger receptor cysteine-rich type 1 protein M130 (CD163) and osteoprotegerin, whereas PWV was associated with GDF15, E-selectin, CPA1, fatty acid-binding protein 4 (FABP4), CXCL16, carboxypeptidase B (CPB1), and tissue-type plasminogen activator. Upon entering ethnicity into the models, the associations between PWV and CPA1, CPB1, GDF-15, FABP4, CXCL16, and between cIMT and CCL-15, remained significant.Using a multiplex proteomic approach, we linked phenotypes of vascular health with several proteins. Novel associations were found between hypertension, PWV or cIMT and proteins linked to inflammatory response, chemotaxis, coagulation or proteolysis. Further, we could reveal whether the associations were ethnicity-dependent or not.

Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction

BACKGROUND

Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long-term outcomes.

METHODS

A total of 175 different biomarkers from MI patients enrolled in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes.

RESULTS

A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min^-1 /1.73 m² were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor-1, adipocyte fatty acid-binding protein-4, TNF-related apoptosis-inducing ligand receptor 2, growth differentiation factor-15 and TNF receptor-2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization.

CONCLUSION

In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long-term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.

Using proximity extension proteomics assay to identify biomarkers associated with infarct size and ejection fraction after ST-elevation myocardial infarction

Plasma concentrations of many cardiovascular and inflammatory proteins are altered after ST-elevation myocardial infarction (STEMI) and may provide prognostic information. We conducted a large-scale proteomic analysis in patients with STEMI, correlating protein levels to infarct size and left ventricular ejection fraction (LVEF) determined with cardiac magnetic resonance imaging. We analysed 131 cardiovascular and inflammatory proteins using a multiplex proximity extension assay and blood samples obtained at baseline, 6, 24, and 96 h from the randomised clinical trial CHILL-MI. Cardiac magnetic resonance imaging data at 4 ± 2 days and 6 months were available as per trial protocol. Using a linear regression model with bootstrap resampling and false discovery rate adjustment we identified five proteins (ST2, interleukin-6, pentraxin-3, interleukin-10, renin, and myoglobin) with elevated values corresponding to larger infarct size or worse LVEF and four proteins (TNF-related apoptosis-inducing ligand, TNF-related activation induced cytokine, interleukin-16, and cystatin B) with values inversely related to LVEF and infarct size, concluding that among 131 circulating inflammatory and cardiovascular proteins in the acute and sub-acute phase of STEMI, nine showed a relationship with infarct size and LVEF post-STEMI, with IL-6 and ST2 exhibiting the strongest association.

STEMI, Cardiogenic Shock, and Mortality in Patients Admitted for Acute Angiography: Associations and Predictions from Plasma Proteome Data

AIM

Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity, and cardiogenic shock (CS) a major cause of hospital mortality after AMI. Especially for ST elevation myocardial infarction (STEMI) patients, fast intervention is essential.Few proteins have proven clinically applicable for AMI. Most proposed biomarkers are based on a priori hypothesis-driven studies of single proteins, not enabling identification of novel candidates. For clinical use, the ability to predict AMI is important; however, studies of proteins in prediction models are surprisingly scarce.Consequently, we applied proteome data for identifying proteins associated with definitive STEMI, CS, and all-cause mortality after admission, and examined the ability of the proteins to predict these outcomes.

METHODS AND RESULTS

Proteome-wide data of 497 patients with suspected STEMI were investigated; 381 patients were diagnosed with STEMI, 35 with CS, and 51 died during the first year. Data analysis was conducted by logistic and Cox regression modeling for association analysis, and by multivariable LASSO regression models for prediction modeling.Association studies identified 4 and 29 proteins associated with definitive STEMI or mortality, respectively. Prediction models for CS and mortality (holding two and five proteins, respectively) improved the prediction ability as compared with protein-free prediction models; AUC of 0.92 and 0.89, respectively.

CONCLUSION

The association analyses propose individual proteins as putative protein biomarkers for definitive STEMI and survival after suspected STEMI, while the prediction models put forward sets of proteins with putative predicting ability of CS and survival. These proteins may be verified as biomarkers of potential clinical relevance.