Pereira, Naveen L.

Cardiac fibrosis, a hallmark of heart failure and various cardiomyopathies, represents a complex pathological process that has long challenged therapeutic intervention. High-throughput omics technologies have begun revolutionizing our understanding of the molecular mechanisms driving cardiac fibrosis and are providing unprecedented insights into its heterogeneity and progression. This review provides a comprehensive analysis of how techniques—encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics—are providing insight into our understanding of cardiac fibrosis. Genomic studies have identified novel genetic variants and regulatory networks associated with fibrosis susceptibility and progression, and single-cell transcriptomics has unveiled distinct cardiac fibroblast subpopulations with unique molecular signatures. Epigenomic profiling has revealed dynamic chromatin modifications controlling fibroblast activation states, and proteomic analyses have identified novel biomarkers and potential therapeutic targets. Metabolomic studies have uncovered important alterations in cardiac energetics and substrate utilization during fibrotic remodeling. The integration of these multi-omic data sets has led to the identification of previously unrecognized pathogenic mechanisms and potential therapeutic targets, including cell-type-specific interventions and metabolic modulators. We discuss how these advances are driving the development of precision medicine approaches for cardiac fibrosis while highlighting current challenges and future directions in translating multi-omic insights into effective therapeutic strategies. This review provides a systems-level perspective on cardiac fibrosis that may inform the development of more effective, personalized therapeutic approaches for heart failure and related cardiovascular diseases.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Background: Cardiomyopathies (CM) are a leading cause of heart failure. Identification of genetic risk preemptively may allow detection of the earliest phenotypic manifestations through implementation of interval-based cardiac screening. Previous population-based genetic screening studies have limited follow-up and inadequate phenotyping. Hypothesis: Disease penetrance of CM variants is significantly higher than previously reported. Aims: To determine the prevalence, penetrance, and disease expression of incidental CM-related genetic variants in an unselected, richly phenotyped, adult population. Methods: We analyzed a random sample of 10,000 patients from a quaternary medical center-based biobank cohort for reportable variants in 22-cardiomyopathy genes. Prioritization of genetic variants was performed using Semi-Automated Variant Interpretation, a novel software that has been previously developed and validated at our center. Prioritized variants were then manually curated by 2 independent variant interpretation specialists. Clinical phenotypes and outcomes of participants with likely pathogenic/pathogenic (LP/P) variants were determined. Results: LP/P cardiomyopathy variants were present in 1% of our cohort (n=104) within the following genes: TNNT2 (n=1), DES (n=2), LMNA (n=2), MYL2 (n=2), TNNI3 (n=2), DSG (n=4), FLNC (n=4), DSC2 (n=6), SCN5A (n=7), PKP2 (n=10), DSP (n=12), MYH7 (n=14), TTN (n=17), MYBPC3 (n=21). Only 1 participant carried two different LP/P variants. A definitive disease penetrance defined as heart failure or cardiomyopathy was found in 34% of genotype positive (G+) individuals. Possible disease penetrance defined as presence of structural heart disease identified by echocardiography was found in an additional 16% of participants (concentric left ventricular hypertrophy, diastolic dysfunction, biatrial and left atrial enlargement). Combined definitive and possible disease penetrance was found in 50% of G+ individuals. Arrhythmias and/or cardiac conduction disease (atrial fibrillation, atrioventricular block, ventricular arrhythmia) were present in 31% of G+ individuals. Conclusion: Cardiomyopathy associated LP/P variants are present in a small subset of a large quaternary medical center population as previously reported but unlike prior studies, disease penetrance manifesting as cardiac structural abnormalities and heart failure is high in G+ individuals.

Background: Dilated Cardiomyopathy (DCM) is a major cause of heart failure (HF) and carries a high mortality rate. Drug treatment response for DCM-related HF patients is highly variable, with some patients having no response. A pharmacogenomic approach, genome-wide association study (GWAS) followed by biologically functional validation, can potentially identify genetic determinants of prognosis in DCM and might provide insight into novel biology of DCM pharmacotherapy. Methods: A GWAS was performed using DNA from 686 patients with recent onset DCM who were on standard HF therapy using change in left ventricular ejection fraction (LVEF) at a median of 6 months after initial diagnosis. Cultured human cardiac fibroblasts (HCFs) were used to functionally validate the GWAS findings in vitro . RNA-seq followed by pathway analysis after NAV3 knock-down (KD) using siRNA in HCFs were performed to explore potential biological mechanisms. Results: A genetic variant, rs11105445(G>A), identified from the GWAS, mapping to the neuron navigator 3 (NAV3) gene (rs11105445, p=2.37E-07; beta 2.74 ± 0.53) was associated with improvement in LVEF. Functional genomic experiments demonstrated that, both in LV tissue and in cultured fibroblasts, the minor allele A was associated with decreased transcription of NAV3 (pvalue: 0.0335 and 0.0432) suggesting that ↓NAV3 expression might be associated with improvement in LVEF. TGF-β1 can induce HCF transdifferentiation into myofibroblasts and we demonstrate that it also increases NAV3 expression. NAV3 KD which mimics the GWAS variant significantly suppressed TGF-β1 induced HCF transdifferentiation demonstrated by decreased expression of α-smooth muscle actin (ACTA2) and collagen I (COL1A1). RNA-seq after NAV3 KD followed by pathway analysis suggested that NAV3 exerted its effect by regulating ECM and cell cycle processes, especially G1/S transition. We demonstrate that NAV3 KD suppressed TGF-β1 induced G1/S arrest by regulating CDK4, CDK6 and INK4B (cyclin-dependent kinase inhibitor 2B/P15, CDK4 inhibitor). Conclusion: Decreased expression of NAV3 is associated with myocardial recovery in DCM, most likely due to its role in suppressing HCF differentiation by regulating TGF-β1 induced G1/S arrest. The role of NAV3 as a novel therapeutic target in DCM needs to be explored.

Background: Patients with normal resting left ventricular ejection fraction (LVEF) without either coronary artery disease (CAD) or a hypertensive response can paradoxically develop reduced LVEF during exercise stress echocardiography (ESE). The clinical phenotype and outcomes of these patients is unknown. Hypothesis: Patients who paradoxically develop reduced LVEF during exercise may represent a sub-phenotype of heart failure with preserved LVEF (HFpEF). Aims: To describe the baseline characteristics and clinical outcomes of this unique patient population. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD by angiogram within 90 days of ESE, who had a resting LVEF ≥50% with a ≥5% LVEF decrease during ESE were identified. All-cause mortality, HF hospitalization, and atrial fibrillation (AF) outcomes were assessed. Kaplan-Meier and Cox regression methods were used to analyze time-to-event outcomes. Results: Among 213,643 stress echocardiograms performed, 134 patients met eligibility criteria (Table 1). The mean age of the population was 66±10 years, 76% were women, and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. Stress electrocardiogram met criteria for ischemia in 14%. The 10-year all-cause mortality risk was 12.9% (95% CI 5.5-20.3) with 10 (37%) of 27 deaths due to cancer (Figure 1, Panel A). The 10-year estimated risk of HF hospitalization was 17.6% (95% CI 9.0-26.2) (Figure 1, Panel B). Among 112 patients without AF at baseline, the 10-year risk of developing AF was 23.4% (95% CI 13.4-33.4). Conclusions: Patients with exercise-induced reduced LVEF in the absence of obstructive CAD have a high incidence of HF hospitalizations and AF. Given the preponderance of women, risk for AF, HF hospitalizations, and cancer-related deaths, the possibility that this condition is an early sub-phenotype of HFpEF should be investigated.

Background: CDCP1 has been associated with reverse remodeling in human dilated cardiomyopathy (DCM) mediating its effect by reducing cardiac fibrosis which has been demonstrated in-vitro using human cardiac fibroblasts (CF) and in-vivo histologically in mice. However, the role of and the molecular mechanisms by which CDCP1 attenuates cardiac fibrosis in-vivo is unknown. Methods: To characterize the transcriptomic profiles of CDCP1 in cardiac fibrosis, Cdcp1 KO FVB/NJ mice were generated, and implanted with osmotic minipumps containing angiotensin II and phenylephrine (Ang II/PE) or saline at age of 10 weeks. There were 4 experimental groups (6 mice each), Saline_WT, Saline_KO, AngII/PE_WT, and AngII/PE_KO. After 4 weeks of AngII/PE induction, mice were euthanized, RNA was extracted from their heart tissue followed by RNA-seq to explore transcriptomic profiles. Fibrosis was histologically determined using picosirius (PSR) staining and was quantified by percentage of fibrosis area. Results: Histological analysis demonstrated that Cdcp1 KO attenuated severe cardiac fibrosis by 32.7% determined by quantification of percentage of fibrosis area. When comparing the transcriptome-wide gene expression in WT mice hearts from Saline to AngII/PE induction, a total of 316 differentially expressed genes (DEGs) were identified ( Fig. 1A ). Expression of Nppb (encodes ProBNP), a marker for heart failure, was significantly upregulated. The most significantly upregulated DEG after AngII/PE induction was Crlf1 ( Fig. 1A ), a gene predominantly expressed in CF, consistent with previous findings. We next compared transcriptomic profiles of AngII/PE induction WT and Cdcp1 KO mice hearts, to explore the role of CDCP1 in cardiac fibrosis. A total of 163 DEGs were identified ( Fig. 1B ). Notably, Cdcp1 -mediated DEGs enriched GO Pathways associated with extracellular matrix organization (Biological Process) and collagen-containing extracellular matrix (Cellular Component) ( Figs. 1C, 1D ), pivotal processes in cardiac fibrosis. In addition, Cdcp1 KO resulted in several “top” DEGs (including Rnase1, Ccl7, Ccl12 Il21r , and Il6 ) which have known function in inflammation, suggesting that CDCP1 might attenuate cardiac fibrosis through immune regulation . Conclusion: Our findings underscore the pivotal role of CDCP1 in modulating cardiac fibrosis in-vivo, potentially through immune regulation. Targeting CDCP1 may offer promising therapeutic avenues for mitigating myocardial fibrosis.

Background: Electrocardiography-based artificial intelligence (AI-ECG) validated models that detect cardiac disease are increasingly being applied in clinical practice. The utility of such tools to detect insidious cardiac disease in patients with normal baseline left ventricular ejection fraction (LVEF) who paradoxically develop reduced LVEF during exercise stress echocardiography (ESE), and do not have coronary artery disease (CAD) or hypertension, is unknown. Hypothesis: AI-ECG is useful to diagnose insidious cardiac disease and predict clinical outcomes in patients with exercise-induced cardiomyopathy. Aims: To assess the utility of AI ECG in patients with exercise-induced cardiomyopathy. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD (confirmed by coronary angiography within 90 days after ESE), with resting LVEF ≥50% and a paradoxical ≥5% LVEF decrease during ESE were identified. A previously validated AI-ECG algorithm that predicts atrial fibrillation (AF), reduced LVEF, and cardiac amyloidosis (CA) was applied to the baseline ECG closest to the time of ESE. The predicted probability of AF, reduced LVEF, and CA if above the published thresholds, was determined. Results: There were 134 patients with exercise-induced cardiomyopathy who were identified. The mean age of this cohort was 66±10 years, 76% were women and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. The median follow-up period was 6.8 years (IQR 3.0-12.2). Among patients without a baseline history of AF (n=112), AI-ECG identified 29% with a significant probability of AF, which was associated with a subsequent AF diagnosis at univariable analysis (HR 2.505, 95%CI 1.016-6.177, p=0.046). There were 10 patients with an AI-ECG prediction of reduced LVEF among whom 3 were subsequently hospitalized with HF. AI-ECG was positive for CA in 8 and 3 amongst these had subsequent HF hospitalizations (Table). Conclusions: Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF.

Background: Although genetic testing (GT) in dilated cardiomyopathy (DCM) enables molecular diagnosis, family screening and provides prognostic information, it is underutilized. Our hypothesis was that systemic intervention (INT) in the heart failure (HF) clinic could increase the frequency of GT for DCM. Methods: Eligible DCM patients in a 17-month period before and after the INT were compared for frequency of genetic counseling and GT uptake. The INT comprised of the following: 1. Pre-emptive cardiovascular genomics e-consult (with a specialist in genetic DCM) for all new HF patients that could be canceled by the treating physician if the patient was considered ineligible or was uninterested. If eligible and patient was willing, 2. They were shown a video in person on genetic testing for DCM prepared by a genetics counselor 3. A trained HF clinic nurse provided answers to questions and completed the test requisition 4. Educational material was provided to the patient 5. Results were reviewed electronically and communicated to the patient via letter or phone call by the physician completing the e-consult. Clinical genetics consultation was available throughout the study period. Standard commercially available cardiomyopathy gene panels were used for GT. Results: There were 162 eligible DCM patients before and 194 after the INT. DCM patients were more likely to undergo genetic counseling post-INT versus (vs) pre-INT (33.5% vs 14.8%, OR 2.9, 95% CI 1.7-4.8, P <.0001). Similarly, DCM patients were more likely to undergo GT post-INT vs pre-INT (27.3% vs 13%, OR 2.5, 95% CI 1.4-4.3, P =.001). Consequently, the total number of patients who were diagnosed to have likely pathogenic or pathogenic DCM-related genetic variants increased from 2 pre-INT to 6 post-INT. These variants were detected in the following genes: FLNC (n=2), TTN (n=2), DES , LMNA , PLN and TNNT2 . Conclusion: An intervention that was pre-emptive, easily accessible and available to the healthcare provider was successful in raising the rates of genetic counseling and testing in DCM patients seen in the HF clinic. Whether such low-cost interventions will enhance cascade screening and provide an opportunity for precision medicine remains to be determined.