Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning

Phenotypic Spectrum of Subclinical Sarcomere-Related Hypertrophic Cardiomyopathy and Transition to Overt Disease

Genetic hypertrophic cardiomyopathy (HCM) is classically caused by pathogenic/likely pathogenic variants in sarcomere genes (G+). Currently, HCM is diagnosed if there is unexplained left ventricular (LV) hypertrophy with LV wall thickness ≥15 mm in probands or ≥13 mm in at-risk relatives. Although LV hypertrophy is a key feature, this binary metric does not encompass the full constellation of phenotypic features, particularly in the subclinical stage of the disease. Subtle phenotypic manifestations can be identified in sarcomere variant carriers with normal LV wall thickness, before diagnosis with HCM (G+/LV hypertrophy-; subclinical HCM). We conducted a systematic review to summarize current knowledge about the phenotypic spectrum of subclinical HCM and factors influencing penetrance and expressivity. Although the mechanisms driving the development of LV hypertrophy are yet to be elucidated, activation of profibrotic pathways, impaired relaxation, abnormal Ca2+ signaling, altered myocardial energetics, and microvascular dysfunction have all been identified in subclinical HCM. Progression from subclinical to clinically overt HCM may be more likely if early phenotypic manifestations are present, including ECG abnormalities, longer mitral valve leaflets, lower global E' velocities on Doppler echocardiography, and higher serum N-terminal propeptide of B-type natriuretic peptide. Longitudinal studies of variant carriers are critically needed to improve our understanding of penetrance, characterize the transition to disease, identify risk predictors of phenotypic evolution, and guide the development of novel treatment strategies aimed at influencing disease trajectory.

Novel Multiplexed Plasma Biomarker Panel Has Diagnostic and Prognostic Potential in Children With Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy. We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death risk in adult patients with HCM. The aim of this study was to investigate the utility of adult biomarkers and perform new discoveries in proteomics for childhood-onset HCM.

METHODS

Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel pediatric plasma proteomic biomarker panels.

RESULTS

Four previously identified adult HCM markers (aldolase fructose-bisphosphate A, complement C3a, talin-1, and thrombospondin 1) and 3 new markers (glycogen phosphorylase B, lipoprotein a and profilin 1) were elevated in pediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training data set (sensitivity 100% [95% CI, 95-100]; specificity 100% [95% CI, 96-100]) and 0.82 in the validation data set (sensitivity 75% [95% CI, 59-86]; specificity 88% [95% CI, 75-94]). Reduced circulating levels of 4 other peptides (apolipoprotein L1, complement 5b, immunoglobulin heavy constant epsilon, and serum amyloid A4) found in children with high sudden cardiac death risk provided complete separation from the low and intermediate risk groups and predicted mortality and adverse arrhythmic outcomes (hazard ratio, 2.04 [95% CI, 1.0-4.2]; P=0.044).

CONCLUSIONS

In children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and another 4-biomarker panel identifies those at high risk of adverse arrhythmic outcomes, including sudden cardiac death.

Proteomics analysis of coronary atherosclerotic heart disease with different Traditional Chinese Medicine syndrome types before and after percutaneous coronary intervention

OBJECTIVE

To investigate the underlying protein molecular mechanisms of "Qi stagnation and blood stasis syndrome" (QS) and "Qi deficiency and blood stasis syndrome" (QD), as two subtypes of coronary artery disease (CAD) in Traditional Chinese Medicine (TCM), following percutaneous coronary intervention (PCI).

METHODS

In this study, a total of 227 CAD patients with QS and 211 CAD patients with QD were enrolled; all participants underwent PCI. Label-free quantification proteomics were employed to analyze the changes in serum in two subtypes of CAD patients before and 6 months after PCI, aiming to elucidate the intervention mechanism of PCI in treating CAD characterized by two different TCM syndromes.

RESULTS

Biochemical analysis revealed significant changes in tumor necrosis factor-α, high density lipoprotein cholesterol, blood stasis clinical symptoms observation, and Gensini levels in both patient groups post-PCI; Proteomic analysis identified 79 and 95 differentially expressed proteins in the QS and QD patient groups, respectively, compared to their control groups. complement C8 alpha chain, complement factor H, apolipoprotein H, apolipoprotein B, plasminogen, carbonic anhydrase 2, and complement factor I were altered in both comparison groups. Furthermore, enrichment analysis demonstrated that cell adhesion and connectivity-related processes underwent changes in QS patients post-PCI, whereas lipid metabolism-related pathways, including the peroxisome proliferator-activated receptor signaling pathway and extracellular matrix receptor interaction, underwent changes in the QD group. The protein-protein interaction network analysis further enriched 52 node proteins, including apolipoprotein B, lipoprotein (a), complement C5, apolipoprotein A4, complement C8 alpha chain, complement C8 beta chain, complement C8 gamma chain, apolipoprotein H, apolipoprotein A-Ⅱ, albumin, complement C4-B, apolipoprotein C3, among others. The functional network of these proteins is posited to contribute to the pathophysiology of CAD characterized by TCM syndromes.

CONCLUSION

The current quantitative proteomic study has preliminarily identified biomarkers of CAD in different TCM subtypes treated with PCI, potentially laying the groundwork for understanding the protein profiles associated with the treatment of various TCM subtypes of CAD.

Biomarkers and Proteomics in Sarcomeric Hypertrophic Cardiomyopathy in the Young-FGF-21 Highly Associated with Overt Disease

Background: Any difference in biomarkers between genotype-positive individuals with overt hypertrophic cardiomyopathy (HCM), and genotype-positive but phenotype-negative individuals (G+P-) in HCM-associated pathways might shed light on pathophysiological mechanisms. We studied this in young HCM patients. Methods: 29 HCM patients, 17 G+P--individuals, and age- and sex-matched controls were prospectively included. We analyzed 184 cardiovascular disease-associated proteins by two proximity extension assays, categorized into biological pathways, and analyzed with multivariate logistic regression analysis. Significant proteins were dichotomized into groups above/below median concentration in control group. Results: Dichotomized values of significant proteins showed high odds ratio (OR) in overt HCMphenotype for Fibroblast growth factor-21 (FGF-21) 10 (p = 0.001), P-selectin glycoprotein ligand-1 (PSGL-1) OR 8.6 (p = 0.005), and Galectin-9 (Gal-9) OR 5.91 (p = 0.004). For G+P-, however, angiopoietin-1 receptor (TIE2) was notably raised, OR 65.5 (p = 0.004), whereas metalloproteinase inhibitor 4 (TIMP4) involved in proteolysis, in contrast, had reduced OR 0.06 (p = 0.013). Conclusions: This study is one of the first in young HCM patients and G+P- individuals. We found significantly increased OR for HCM in FGF-21 involved in RAS-MAPK pathway, associated with cardiomyocyte hypertrophy. Upregulation of FGF-21 indicates involvement of the RAS-MAPK pathway in HCM regardless of genetic background, which is a novel finding.

Talin mechanotransduction in disease

Talin protein (Talin 1/2) is a mechanosensitive cytoskeleton protein. The unique structure of the Talin plays a vital role in transmitting mechanical forces. Talin proteins connect the extracellular matrix to the cytoskeleton by linking to integrins and actin, thereby mediating the conversion of mechanical signals into biochemical signals and influencing disease progression as potential diagnostic indicators, therapeutic targets, and prognostic indicators of various diseases. Most studies in recent years have confirmed that mechanical forces also have a crucial role in the development of disease, and Talin has been found to play a role in several diseases. Still, more studies need to be done on how Talin is involved in mechanical signaling in disease. This review focuses on the mechanical signaling of Talin in disease, aiming to summarize the mechanisms by which Talin plays a role in disease and to provide references for further studies.

Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics

OBJECTIVE

Heart failure (HF) is one of the most common and lifestyle-limiting complications of hypertrophic cardiomyopathy (HCM). Prediction of worsening HF using clinical measures alone remains limited. Moreover, the mechanisms by which patients with HCM develop worsening HF have not been elucidated. Therefore, the aim of this study was to develop a plasma proteomics-based model to predict worsening HF among patients with HCM and to identify signalling pathways that are differentially regulated in those who subsequently develop worsening HF.

METHODS

In this multi-centre, prospective cohort study of 389 patients with HCM, plasma proteomics profiling of 4986 proteins was performed at enrolment. A proteomics-based random forest model was developed to predict worsening HF using data from one institution (training set, n=268). This model was externally validated in patients from a different institution (test set, n=121). Pathway analysis of proteins significantly dysregulated in patients who subsequently developed worsening HF compared with those who did not was executed, using a false discovery rate (FDR) threshold of <0.001.

RESULTS

Using the 11-protein proteomics-based model derived from the training set, the area under the receiver-operating characteristic curve to predict worsening HF was 0.87 (95% CI: 0.76 to 0.98) in the test set. Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and related pathways were dysregulated in patients who subsequently developed worsening HF.

CONCLUSIONS

The present study with comprehensive plasma proteomics profiling demonstrated a high accuracy to predict worsening HF in patients with HCM and identified the Ras-MAPK and related signalling pathways as potential underlying mechanisms.

The role of glycolytic metabolic pathways in cardiovascular disease and potential therapeutic approaches

Cardiovascular disease (CVD) is a major threat to human health, accounting for 46% of non-communicable disease deaths. Glycolysis is a conserved and rigorous biological process that breaks down glucose into pyruvate, and its primary function is to provide the body with the energy and intermediate products needed for life activities. The non-glycolytic actions of enzymes associated with the glycolytic pathway have long been found to be associated with the development of CVD, typically exemplified by metabolic remodeling in heart failure, which is a condition in which the heart exhibits a rapid adaptive response to hypoxic and hypoxic conditions, occurring early in the course of heart failure. It is mainly characterized by a decrease in oxidative phosphorylation and a rise in the glycolytic pathway, and the rise in glycolysis is considered a hallmark of metabolic remodeling. In addition to this, the glycolytic metabolic pathway is the main source of energy for cardiomyocytes during ischemia-reperfusion. Not only that, the auxiliary pathways of glycolysis, such as the polyol pathway, hexosamine pathway, and pentose phosphate pathway, are also closely related to CVD. Therefore, targeting glycolysis is very attractive for therapeutic intervention in CVD. However, the relationship between glycolytic pathway and CVD is very complex, and some preclinical studies have confirmed that targeting glycolysis does have a certain degree of efficacy, but its specific role in the development of CVD has yet to be explored. This article aims to summarize the current knowledge regarding the glycolytic pathway and its key enzymes (including hexokinase (HK), phosphoglucose isomerase (PGI), phosphofructokinase-1 (PFK1), aldolase (Aldolase), phosphoglycerate metatase (PGAM), enolase (ENO) pyruvate kinase (PKM) lactate dehydrogenase (LDH)) for their role in cardiovascular diseases (e.g., heart failure, myocardial infarction, atherosclerosis) and possible emerging therapeutic targets.

A Multiplexed Urinary Biomarker Panel Has Potential for Alzheimer's Disease Diagnosis Using Targeted Proteomics and Machine Learning

As disease-modifying therapies are now available for Alzheimer's disease (AD), accessible, accurate and affordable biomarkers to support diagnosis are urgently needed. We sought to develop a mass spectrometry-based urine test as a high-throughput screening tool for diagnosing AD. We collected urine from a discovery cohort (n = 11) of well-characterised individuals with AD (n = 6) and their asymptomatic, CSF biomarker-negative study partners (n = 5) and used untargeted proteomics for biomarker discovery. Protein biomarkers identified were taken forward to develop a high-throughput, multiplexed and targeted proteomic assay which was tested on an independent cohort (n = 21). The panel of proteins identified are known to be involved in AD pathogenesis. In comparing AD and controls, a panel of proteins including MIEN1, TNFB, VCAM1, REG1B and ABCA7 had a classification accuracy of 86%. These proteins have been previously implicated in AD pathogenesis. This suggests that urine-targeted mass spectrometry has potential utility as a diagnostic screening tool in AD.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling

BACKGROUND

Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.

METHODS

In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.

RESULTS

The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy

OBJECTIVE

Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE.

METHODS

We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death.

RESULTS

We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-β pathway).

CONCLUSIONS

Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Blood-based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta-analysis

AIMS

Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease. HCM is an important cause of sudden cardiac death and may also lead to outflow tract obstruction and heart failure. Disease severity is highly variable and risk stratification remains limited. Therefore, we aimed to review current knowledge of prognostic blood-based biomarkers in HCM.

METHODS AND RESULTS

A systematic literature search was performed on PubMed, Embase, and the Cochrane library to identify studies assessing plasma or serum biomarkers for outcomes involving malignant ventricular arrhythmia, outflow tract obstruction, and heart failure. Risk of bias was assessed using the QUIPS tool. Meta-analyses were performed using the random effects method. A total of 26 unique cohort studies assessing 42 biomarkers were identified. Overall risk of bias was moderate. Thirty-two biomarkers were significantly associated to an HCM outcome in at least one study (nine biomarkers in at least two studies). In pooled analyses, cardiovascular mortality was predicted by N-terminal prohormone of brain natriuretic peptide (hazard ratio [HR] 5.38 per log[pg/mL], 95% confidence interval [CI] 2.07-14.03, P < 0.001, I2  = 0%) and high-sensitivity C-reactive protein (HR 1.30 per μg/mL, 95% CI 1.00-1.68, P = 0.05, I2  = 78%), all-cause mortality by low-density lipoprotein cholesterol (HR 0.63 per μmol/mL, 95% CI 0.49-0.80, P < 0.001, I2  = 0%), and a combined congestive heart failure, malignant ventricular arrhythmia, and stroke outcome by high-sensitivity cardiac troponin T (pooled HR 4.19 for ≥0.014 ng/mL, 95% CI 2.22-7.88, P < 0.001, I2  = 0%). Quality of evidence was low-moderate.

CONCLUSIONS

Several blood-based biomarkers were identified as predictors of HCM outcomes. Additional studies are required to validate their prognostic utility within current risk stratification models.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population

Introduction

In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population.

Materials and Methods

We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity.

Results

Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75–0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54–0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61–0.83) and the specificity was 0.78 (95% CI, 0.66–0.90). The sensitivity was 0.71 (95% CI, 0.49–0.92) and the specificity was 0.74 (95% CI, 0.54–0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6–948.5; p = 0.03).

Conclusions

In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.

Metabolomic Analysis Reveals Potential Biomarkers and the Underlying Pathogenesis Involved in Mycoplasma Pneumoniae Pneumonia

Although previous studies have reported the use of metabolomics for infectious diseases, little is known about the potential function of plasma metabolites in children infected with Mycoplasma pneumoniae (MP). Here, a combination of liquid chromatography-quadrupole time-of-flight mass spectrometry and random forest-based classification model was used to provide a broader range of applications in MP diagnosis. In the training cohort, plasma from 63 MP pneumonia children (MPPs), 37 healthy controls (HC) and 29 infectious disease controls (IDC) was collected. After multivariate analyses, 357 metabolites were identified to be differentially expressed among MPP, HC and IDC groups, and 3 metabolites (568.5661, 459.3493 and 411.3208) had high diagnostic values. In an independent cohort with 57 blinded subjects, samples were successfully classified into different groups, demonstrating the reliability of these biomarkers for distinguishing MPPs from controls. A metabolomic signature analysis identified major classes of glycerophospholipids, sphingolipids and fatty acyls were increased in MPPs. These markedly altered metabolites are mainly involved in glycerophospholipid and sphingolipid metabolism. As the ubiquitous building blocks of eukaryotic cell membranes, dysregulated lipid metabolism indicates damage of the cellular membrane and the activation of immunity in MPPs. Moreover, lipid metabolites, differentially expressed between severe and mild MPPs, were correlated with the markers of extrapulmonary complications, suggesting that they may be involved in MPP disease severity. These findings may offer new insights into biomarker selection and the pathogenesis of MPP in children.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

Ischemic Heart Disease Selectively Modifies the Right Atrial Appendage Transcriptome

Background: Although many pathological changes have been associated with ischemic heart disease (IHD), molecular-level alterations specific to the ischemic myocardium and their potential to reflect disease severity or therapeutic outcome remain unclear. Currently, diagnosis occurs relatively late and evaluating disease severity is largely based on clinical symptoms, various imaging modalities, or the determination of risk factors. This study aims to identify IHD-associated signature RNAs from the atrial myocardium and evaluate their ability to reflect disease severity or cardiac surgery outcomes. Methods and Results: We collected right atrial appendage (RAA) biopsies from 40 patients with invasive coronary angiography (ICA)-positive IHD undergoing coronary artery bypass surgery and from 8 patients ICA-negative for IHD (non-IHD) undergoing valvular surgery. Following RNA sequencing, RAA transcriptomes were analyzed against 429 donors from the GTEx project without cardiac disease. The IHD transcriptome was characterized by repressed RNA expression in pathways for cell-cell contacts and mitochondrial dysfunction. Increased expressions of the CSRNP3, FUT10, SHD, NAV2-AS4, and hsa-mir-181 genes resulted in significance with the complexity of coronary artery obstructions or correlated with a functional cardiac benefit from bypass surgery. Conclusions: Our results provide an atrial myocardium-focused insight into IHD signature RNAs. The specific gene expression changes characterized here, pave the way for future disease mechanism-based identification of biomarkers for early detection and treatment of IHD.

Distinct Metabolomic Signatures in Preclinical and Obstructive Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype–phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics.

ANALYSIS OF ECHOCARDIOGRAPHIC AND MRI ANATOMICAL CHARACTERISTICS AND HEMODYNAMIC CHARACTERISTICS OF LEFT VENTRICULAR OUTFLOW TRACT STENOSIS

Objective: To analyze the anatomical morphological and hemodynamic characteristics of left ventricular outflow tract stenosis (LVOTS) by echocardiography and MRI. Methods: The Echocardiography data of 112 patients with LVOTS were retrospectively analyzed by measuring the basal interventricular septal thickness (IVST-b), the left ventricle posterior wall thickness (LVPWT), and the peak pressure gradient of LVOTS, as well as by observing the characteristics of spectral pattern and the presence of systolic anterior motion of mitral valve leaflets. A Siemens 3.0T MRI scanner was used to scan the contrast-enhanced left ventricular (LV) volume of all cases. The obtained end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV) and ejection fraction (EF) of LV were compared with the Echocardiography results. Results: The 112 patients were divided into four groups: hypertrophic obstructive cardiomyopathy (Group I, 42 cases), hypertensive left ventricular hypertrophy (Group II, 40 cases), basal septal hypertrophy in the elderly (Group III, 21 cases), and the subaortic membrane (Group IV, 9 cases). The continuous wave (CW) Doppler characteristic of Groups I, II, and III was broadsword-shaped jet, and that of Group IV was equilateral triangle-like spectrum. The IVST-b, IVST-b/LVPWT ratio and peak pressure gradient of LVOTS of Group I was significantly higher than those in Groups II and III ([Formula: see text]). The LVPWT of the first three groups was slightly correlated with the LVOTS peak pressure gradient ([Formula: see text], respectively, [Formula: see text]). There were no statistically significant differences between Echocardiography and MRI results regarding the LV EDV, ESV, SV, and EF ([Formula: see text]), and no statistically significant differences were found between Echocardiography and MRI results regarding the myocardial thicknesses of septal, anterior, lateral, and inferior walls ([Formula: see text]). The Pearson’s correlation analysis demonstrated correlations between MRI and Echocardiography results for LV EDV, ESV, SV, and EF ([Formula: see text], 0.002, 0.009, and [Formula: see text], respectively). The MRI enhancement was shown as delayed enhancement in 52 cases, localized enhancement in 8 cases, diffuse enhancement in 6 cases, and transmural enhancement in 3 cases, with abnormal enhancement lesions distributed in the area of ventricular septum free wall junctions and ventricular septum. Conclusion: Using MRI to evaluate LV function of hypertrophic cardiomyopathy is reliable and accurate. MRI enhancement can be used for the quantitative measurement of myocardial fibrosis. Echocardiography can distinguish the stenosis types of LVOTS. The IVST-b and existence of SAM may be important anatomical characteristics determining the degree of dynamic stenosis, and MRI combined with Echocardiography can provide important detailed information.

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors

Background

Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.

Methods

In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis.

Results

In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively.

Conclusion

The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

Combinatorial peptides: A library that continuously probes low-abundance proteins

After a decade of experimental applications, it is the objective of this review to make a point on combinatorial peptide ligand libraries dedicated to low-abundance proteins from animals to plants and to microorganism proteomics. It is, thus, at the light of the recent technical developments and applications that we will examine the state of the art, its usage within the scientific community, and its openness to unexplored fields. The improvements of the methodology and its implementation in connection with analytical determinations of combinatorial peptide ligand library (CPLL)-treated samples are extensively reviewed and commented upon. Relevant examples covering few critical aspects describe the performance of the technology. Finally, a reflection on the technological future is attempted in particular by involving new concepts adapted to the limited availability of certain biological samples.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive.

RESULTS

The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.

Development and Validation of a Random Forest Diagnostic Model of Acute Myocardial Infarction Based on Ferroptosis-Related Genes in Circulating Endothelial Cells

The high incidence and mortality of acute myocardial infarction (MI) drastically threaten human life and health. In the past few decades, the rise of reperfusion therapy has significantly reduced the mortality rate, but the MI diagnosis is still by means of the identification of myocardial injury markers without highly specific biomarkers of microcirculation disorders. Ferroptosis is a novel reported type of programmed cell death, which plays an important role in cancer development. Maintaining iron homeostasis in cells is essential for heart function, and its role in the pathological process of ischemic organ damages remains unclear. Being quickly detected through blood tests, circulating endothelial cells (CECs) have the potential for early judgment of early microcirculation disorders. In order to explore the role of ferroptosis-related genes in the early diagnosis of acute MI, we relied on two data sets from the GEO database to first detect eight ferroptosis-related genes differentially expressed in CECs between the MI and healthy groups in this study. After comparing different supervised learning algorithms, we constructed a random forest diagnosis model for acute MI based on these ferroptosis-related genes with a compelling diagnostic performance in both the validation (AUC = 0.8550) and test set (AUC = 0.7308), respectively. These results suggest that the ferroptosis-related genes might play an important role in the early stage of MI and have the potential as specific diagnostic biomarkers for MI.

AntimiR-132 Attenuates Myocardial Hypertrophy in an Animal Model of Percutaneous Aortic Constriction

BACKGROUND

Pathological cardiac hypertrophy is a result of afterload-increasing pathologies including untreated hypertension and aortic stenosis. It features progressive adverse cardiac remodeling, myocardial dysfunction, capillary rarefaction, and interstitial fibrosis often leading to heart failure.

OBJECTIVES

This study aimed to establish a novel porcine model of pressure-overload-induced heart failure and to determine the effect of inhibition of microribonucleic acid 132 (miR-132) on heart failure development in this model.

METHODS

This study developed a novel porcine model of percutaneous aortic constriction by implantation of a percutaneous reduction stent in the thoracic aorta, inducing progressive remodeling at day 56 (d56) after pressure-overload induction. In this study, an antisense oligonucleotide specifically inhibiting miR-132 (antimiR-132), was regionally applied via intracoronary injection at d0 (percutaneous transverse aortic constriction induction) and d28.

RESULTS

At d56, antimiR-132 treatment diminished cardiomyocyte cross-sectional area (188.9 ± 2.8 vs. 258.4 ± 9.0 μm² in untreated hypertrophic hearts) and improved global cardiac function (ejection fraction 48.9 ± 1.0% vs. 36.1 ± 1.7% in control hearts). Moreover, at d56 antimiR-132-treated hearts displayed less increase of interstitial fibrosis compared with sham-operated hearts (Δsham 1.8 ± 0.5%) than control hearts (Δsham 10.8 ± 0.6%). Of note, cardiac platelet and endothelial cell adhesion molecule 1⁺ capillary density was higher in the antimiR-132-treated hearts (647 ± 20 cells/mm²) compared with in the control group (485 ± 23 cells/mm²).

CONCLUSIONS

The inhibition of miR-132 is a valid strategy in prevention of heart failure progression in hypertrophic heart disease and may be developed as a treatment for heart failure of nonischemic origin.

The Time Has Come to Explore Plasma Biomarkers in Genetic Cardiomyopathies

For patients with hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) or arrhythmogenic cardiomyopathy (ACM), screening for pathogenic variants has become standard clinical practice. Genetic cascade screening also allows the identification of relatives that carry the same mutation as the proband, but disease onset and severity in mutation carriers often remains uncertain. Early detection of disease onset may allow timely treatment before irreversible changes are present. Although plasma biomarkers may aid in the prediction of disease onset, monitoring relies predominantly on identifying early clinical symptoms, on imaging techniques like echocardiography (Echo) and cardiac magnetic resonance imaging (CMR), and on (ambulatory) electrocardiography (electrocardiograms (ECGs)). In contrast to most other cardiac diseases, which are explained by a combination of risk factors and comorbidities, genetic cardiomyopathies have a clear primary genetically defined cardiac background. Cardiomyopathy cohorts could therefore have excellent value in biomarker studies and in distinguishing biomarkers related to the primary cardiac disease from those related to extracardiac, secondary organ dysfunction. Despite this advantage, biomarker investigations in cardiomyopathies are still limited, most likely due to the limited number of carriers in the past. Here, we discuss not only the potential use of established plasma biomarkers, including natriuretic peptides and troponins, but also the use of novel biomarkers, such as cardiac autoantibodies in genetic cardiomyopathy, and discuss how we can gauge biomarker studies in cardiomyopathy cohorts for heart failure at large.

Proteomic and Structural Manifestations of Cardiomyopathy in Rat Models of Obesity and Weight Loss

Obesity cardiomyopathy increases the risk of heart failure and death. Obesity is curable, leading to the restoration of the heart phenotype, but it is not clear if there are any after-effects of obesity present after weight loss. We characterize the proteomic landscape of obesity cardiomyopathy with an evaluation of whether the cardiac phenotype is still shaped after weight loss. Cardiomyopathy was validated by cardiac hypertrophy, fibrosis, oversized myocytes, and mTOR upregulation in a rat model of cafeteria diet-induced developmental obesity. By global proteomic techniques (LC-MS/MS) a plethora of molecular changes was observed in the heart and circulation of obese animals, suggesting abnormal utilization of metabolic substrates. This was confirmed by increased levels of cardiac ACSL-1, a key enzyme for fatty acid degradation and decreased GLUT-1, a glucose transporter in obese rats. Calorie restriction and weight loss led to the normalization of the heart's size, but fibrosis was still excessive. The proteomic compositions of cardiac tissue and plasma were different after weight loss as compared to control. In addition to morphological consequences, obesity cardiomyopathy involves many proteomic changes. Weight loss provides for a partial repair of the heart's architecture, but the trace of fibrotic deposition and proteomic alterations may occur.

Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages.

Genetic variant affecting the myosin light chain 2 related to familial hypertrophic cardiomyopathy

Familial hypertrophic cardiomyopathy (FHCM) is a genetic disease characterized by left ventricle (LV) or interventricular septum hypertrophy. FHCM is a common heart disease (affecting 1 out of 500 individuals) associated with genetic variants in genes related to the sarcomere, including the MYL2 (myosin light chain 2) gene that is affected in 1 to 3% of the cases. As described in this report, the genetic mutation p.Gly87Ala, rs 397516399 in the MYL2 gene is likely pathogenic. Reported here is the case of a 37-year-old Colombian man with asymmetric septal hypertrophic cardiomyopathy and ventricular tachycardia. The man had progressive symptomatology, a family history of FHCM with a dominant inheritance pattern, a mother and 2 brothers with FHCM, and 2 brothers who died suddenly before the age of 35. A molecular panel of 17 genes for hypertrophic cardiomyopathy identified a heterozygous variant, p.Gly87Ala, of the MYL2 gene. This variant can be found in Ensembl, dbSNP, and ClinVar, where it has conflicting interpretations: it either has an uncertain significance or it is likely pathogenic. This is the first report of a Colombian case of FHCM secondary to a mutation in the MYL2 gene, highlighting the importance of molecular diagnosis, genetic counseling, and bioinformatic analysis in these patients.

Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine

Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.

Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements

Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.

Analytical techniques for multiplex analysis of protein biomarkers

INTRODUCTION

The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research.

AREAS COVERED

We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses.

EXPERT COMMENTARY

The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.