A Novel Circulating MicroRNA for the Detection of Acute Myocarditis.

BACKGROUND: The diagnosis of acute myocarditis typically requires either endomyocardial biopsy (which is invasive) or cardiovascular magnetic resonance imaging (which is not universally available). Additional approaches to diagnosis are desirable. We sought to identify a novel microRNA for the diagnosis of acute myocarditis. METHODS: To identify a microRNA specific for myocarditis, we performed microRNA microarray analyses and quantitative polymerase-chain-reaction (qPCR) assays in sorted CD4+ T cells and type 17 helper T (Th17) cells after inducing experimental autoimmune myocarditis or myocardial infarction in mice. We also performed qPCR in samples from coxsackievirus-induced myocarditis in mice. We then identified the human homologue for this microRNA and compared its expression in plasma obtained from patients with acute myocarditis with the expression in various controls. RESULTS: We confirmed that Th17 cells, which are characterized by the production of interleukin-17, are a characteristic feature of myocardial injury in the acute phase of myocarditis. The microRNA mmu-miR-721 was synthesized by Th17 cells and was present in the plasma of mice with acute autoimmune or viral myocarditis but not in those with acute myocardial infarction. The human homologue, designated hsa-miR-Chr8:96, was identified in four independent cohorts of patients with myocarditis. The area under the receiver-operating-characteristic curve for this novel microRNA for distinguishing patients with acute myocarditis from those with myocardial infarction was 0.927 (95% confidence interval, 0.879 to 0.975). The microRNA retained its diagnostic value in models after adjustment for age, sex, ejection fraction, and serum troponin level. CONCLUSIONS: After identifying a novel microRNA in mice and humans with myocarditis, we found that the human homologue (hsa-miR-Chr8:96) could be used to distinguish patients with myocarditis from those with myocardial infarction. (Funded by the Spanish Ministry of Science and Innovation and others.).

Using Artificial Intelligence to Better Predict and Develop Biomarkers.

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

MicroRNAs are associated with cardiac biomarkers, cardiac structure and function and incident outcomes in heart failure.

AIMS: The association between microRNAs (miRNAs) and established cardiac biomarkers is largely unknown. We aimed to measure the association between plasma miRNAs and N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin I, soluble urokinase-type plasminogen activator receptor (suPAR), and galectin-3 with cardiac structure and function and clinical outcomes. METHODS AND RESULTS: We quantified 32 plasma miRNAs using the FirePlex miRNA assay and measured biomarkers in 139 individuals with symptomatic heart failure (HF). We used principal component (PC) analysis and linear regression to evaluate the association between miRNAs and biomarkers with ventricular size and function by echocardiography and Cox modelling for the incidence of a first composite event of HF hospitalization, heart transplant, left ventricular assist device implant, or death. The mean (standard deviation) age at baseline was 64.3 (12.4) years, 33 (24%) were female, and 122 (88%) were White. A total of 45 events occurred over a median follow-up of 368 (interquartile range 234, 494) days. Baseline NT-proBNP (ß = -2.0; P = 0.001) and miRNA PC2 (ß = 2.6; P = 0.002) were associated with baseline left ventricular ejection fraction. NT-proBNP (ß = 20.6; P = 0.0004), suPAR (ß = -39.6; P = 0.005), and PC4 (ß = 21.1; P = 0.02) were associated with baseline left ventricular end-diastolic volumes. NT-proBNP [hazard ratio (HR) 1.67, 95% confidence interval (CI) 1.28-2.18, P = 0.0002], galectin-3 (HR 2.02, 95% CI 1.05-3.91, P = 0.036), PC3 (HR 1.75, 95% CI 1.23-2.49, P = 0.002), and PC4 (HR 1.67, 95% CI 1.1-2.52, P = 0.016) were independently associated with incident events. CONCLUSIONS: Biomarkers and miRNA PCs are associated with cardiac structure and function and incident cardiovascular outcomes. Combining information from miRNAs provides prognostic information beyond biomarkers in HF.

Using Artificial Intelligence to Better Predict and Develop Biomarkers.

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Association between proteomic biomarkers and myocardial fibrosis measured by MRI: the multi-ethnic study of atherosclerosis.

BACKGROUND: Cardiac magnetic resonance imaging (CMR) determines the extent of interstitial fibrosis, measured by increased extracellular volume (ECV), and replacement fibrosis with late gadolinium myocardial enhancement (LGE). Despite advances in detection, the pathophysiology of subclinical myocardial fibrosis is incompletely understood. Targeted proteomic discovery technologies enable quantification of low abundance circulating proteins to elucidate cardiac fibrosis mechanisms. METHODS: Using a cross-sectional design, we selected 92 LGE+ cases and 92 LGE- demographically matched controls from the Multi-Ethnic Study of Atherosclerosis. Similarly, we selected 156 cases from the highest ECV quartile and matched with 156 cases from the lowest quartile. The plasma serum proteome was analyzed using proximity extension assays to determine differential regulation of 92 proteins previously implicated with cardiovascular disease. Results were analyzed using volcano plots of statistical significance vs. magnitude of change and Bayesian additive regression tree (BART) models to determine importance. FINDINGS: After adjusting for false discovery, higher ECV was significantly associated with 17 proteins. Using BART, Plasminogen activator inhibitor 1, Insulin-like growth factor-binding protein 1, and N-terminal pro-B-type natriuretic peptide were associated with higher ECV after accounting for other proteins and traditional cardiovascular risk factors. In contrast, no circulating proteins were associated with replacement fibrosis. INTERPRETATIONS: Our results suggest unique circulating proteomic signatures associated with interstitial fibrosis emphasizing its systemic influences. With future validation, protein panels may identify patients who may develop interstitial fibrosis with progression to heart failure. FUNDING: This research was supported by contracts and grants from NHLBI, NCATS and the Inova Heart and Vascular Institute.

Soluble Suppression of Tumorigenicity-2 Associates With Ventilator Dependence in Coronavirus Disease 2019 Respiratory Failure.

OBJECTIVES: We hypothesize that elevated soluble suppression of tumorigenicity-2 concentrations, a marker of pulmonary epithelial injury, reflect ongoing lung injury in acute hypoxemic respiratory failure due to coronavirus disease 2019 and associate with continued ventilator dependence. DESIGN: We associated serial plasma soluble suppression of tumorigenicity-2 levels and markers of systemic inflammation including d-dimer, C-reactive protein, and erythrocyte sedimentation rate with 30-day mortality and ventilator dependence. SETTING: Adult medical ICUs and general medicine wards at an academic teaching hospital in Boston, MA. PATIENTS: Adult patients with severe acute respiratory syndrome coronavirus 2 infection and acute hypoxemic respiratory failure admitted to the ICU (n = 72) and non-ICU patients managed with supplemental oxygen (n = 77). INTERVENTIONS: Observational study from April 25 to June 25, 2020. MEASUREMENTS AND MAIN RESULTS: ICU patients had a higher baseline body mass index and median soluble suppression of tumorigenicity-2, d-dimer, and C-reactive protein concentrations compared with non-ICU patients. Among ICU patients, elevated baseline modified Sequential Organ Failure Assessment score and log (soluble suppression of tumorigenicity-2) were associated with 30-day mortality, whereas initial Pao2/Fio2 and markers of systemic inflammation were similar between groups. Only log (soluble suppression of tumorigenicity-2) associated with ventilator dependence over time, with the last measured log (soluble suppression of tumorigenicity-2) concentration obtained on ICU day 11.5 (interquartile range [7-17]) higher in patients who required reintubation or tracheostomy placement compared with patients who were successfully extubated (2.10 [1.89-2.26] vs 1.87 ng/mL [1.72-2.13 ng/mL]; p = 0.03). Last measured systemic inflammatory markers, modified Sequential Organ Failure Assessment score, and Pao2/Fio2 were not different between patients who were successfully extubated compared with those with continued ventilator dependence. CONCLUSIONS: Plasma soluble suppression of tumorigenicity-2 is a biomarker readily measured in blood that can provide dynamic information about the degree of a patient's lung injury and real-time assessment of the likelihood of extubation success. Measures of systemic inflammation, illness severity, and oxygenation did not associate with ventilator outcomes.

Finding a Needle in a Haystack: Proteomics in Heart Failure.

Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay-based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.

Proteomic Signatures During Treatment in Different Stages of Heart Failure.

BACKGROUND: Proteomics have already provided novel insights into the pathophysiology of heart failure (HF) with reduced ejection fraction. Previous studies have evaluated cross-sectional protein signatures of HF, but few have characterized proteomic changes following HF with reduced ejection fraction treatment with ARNI (angiotensin receptor/neprilysin inhibitor) therapy or left ventricular assist devices. METHODS: In this retrospective omics study, we performed targeted proteomics (N=625) of whole blood sera from patients with American College of Cardiology/American Heart Association stage D (N=29) and stage C (N=12) HF using proximity extension assays. Samples were obtained before and after (median=82 days) left ventricular assist device implantation (stage D; primary analysis) and ARNI therapy initiation (stage C; matched reference). Oblique principal component analysis and point biserial correlations were used for feature extraction and selection; standardized mean differences were used to assess within and between-group differences; and enrichment analysis was used to generate and cluster Gene Ontology terms. RESULTS: Core sets of proteins were identified for stage C (N=9 proteins) and stage D (N=18) HF; additionally, a core set of 5 shared HF proteins (NT-proBNP [N-terminal pro-B type natriuretic peptide], ESM [endothelial cell-specific molecule]-1, cathepsin L1, osteopontin, and MCSF-1) was also identified. For patients with stage D HF, moderate (δ, 0.40-0.60) and moderate-to-large (δ, 0.60-0.80) sized differences were observed in 8 of their 18 core proteins after left ventricular assist devices implantation. Additionally, specific protein groups reached concentration levels equivalent (g<0.10) to stage C HF after initiation on ARNI therapy. CONCLUSIONS: HF with reduced ejection fraction severity associates with distinct proteomic signatures that reflect underlying disease attributes; these core signatures may be useful for monitoring changes in cardiac function following initiation on ARNI or left ventricular assist device implantation.

Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease.

Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs-cTn (high-sensitivity cardiac troponin)-based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs-cTnI [high-sensitivity cardiac troponin I], adiponectin, and kidney injury molecule-1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (P<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs-cTnI testing ("indeterminate zone," n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001). Conclusions A model including hs-cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs-cTnI concentrations.