The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.
BACKGROUND:Carotid artery intima-media thickness (cIMT) is a highly heritable measure of subclinical atherosclerosis and a predictor of cardiovascular diseases. However, knowledge about its shared genetic basis remains limited. The majority of genome-wide association studies have been conducted in individuals of European ancestry, leaving the genetic determinants of cIMT in non-European ancestry populations largely understudied. METHODS:A single-trait genome-wide association study was performed in 22 370 participants from the China Kadoorie Biobank to identify new genetic variants associated with cIMT. We then leveraged cIMT and cardiometabolic disease and trait data from populations of European and East Asian ancestries, to conduct linkage disequilibrium score regression and genome-wide cross-trait analysis, followed by gene-based analysis and Mendelian randomization. RESULTS:We identified 2 genome-wide significant loci for cIMT in the overall China Kadoorie Biobank analysis, including the known APOE locus and a novel locus at GGT5. In addition, a female-specific analysis identified a further novel locus at LINC02732. cIMT showed significant positive genetic correlations with coronary artery disease, type 2 diabetes, body mass index, and systolic blood pressure in both European and East Asian populations. Pleiotropic analysis found 110 and 699 significant pleiotropic variants in 4 trait pairs for East Asian and European populations, respectively, with 27 and 186 colocalized loci detected. Gene-based analysis highlighted important biological pathways involving lipid metabolism and carbohydrate metabolism. Mendelian randomization estimates indicated that higher systolic blood pressure and body mass index, and coronary artery disease and type 2 diabetes, were causal for cIMT, and a reverse effect was observed between cIMT and systolic blood pressure. CONCLUSIONS:Our large-scale genome-wide association study of cIMT identified novel loci in the Chinese population and shared genetic underpinnings and causal relationships with cardiometabolic diseases and traits. These findings potentially provide targets for intervention that may allow the development of new strategies targeting this aspect of atherosclerosis.
BACKGROUND: Dilated cardiomyopathy (DCM) is associated with shifts in cardiac metabolism. However, those shifts vary widely across patients, likely reflecting the diverse underlying causes of the disease. Identifying metabolic subtypes, or metabotypes, in DCM patients could help tailor treatments to patient needs. Hence, having a practical approach to identify these metabotypes would be a significant advance toward precision medicine in DCM. METHODS: We present a systems biology approach to uncover metabotypes directly from widely available transcriptomic data. We use in silico metabolic modeling methods that we have optimized for cardiac research to predict metabolic function activities from enzyme expression, followed by a hierarchical clustering approach. To demonstrate its power, we applied our method to publicly available cardiac data from end-stage DCM patients (N=164) and nonfailing controls (N=160). RESULTS: We identified 2 distinct metabotypes in end-stage DCM patients. These metabotypes are characterized by unique metabolic changes, notably in calcium handling, amino acid oxidation, and the pentose phosphate pathway. Strikingly, 1 DCM metabotype showed greater metabolic divergence from healthy controls, suggesting a greater metabolic contribution to its underlying etiology—even though disease severity was similar between the 2 identified DCM metabotypes. Further transcriptome-wide analysis revealed immune-related differences between metabotypes, suggesting an underlying interplay between inflammation, the immune response, and metabolism. CONCLUSIONS: Our results imply the presence of distinct metabotypes in end-stage DCM. Our systems biology approach offers an exciting opportunity to uncover novel insights into DCM, paving the way for a deeper understanding of its progression and heterogeneity.
Clinical genetic testing is now the standard of care for cardiomyopathy, guiding risk stratification, clinical management, and earlier diagnosis in family members. Yet, a large proportion of the genetic basis of cardiomyopathy remains incompletely explained. Prior efforts to identify genetic causes of cardiomyopathy have largely focused on coding DNA sequence, which accounts for only 3% of the human genome, leaving the noncoding regulatory sequence space relatively unexplored. A confluence of emerging technologies is now transforming our capability to identify and interpret noncoding variants. This review summarizes the field's current knowledge of how noncoding variants influence the development of cardiomyopathy, both from the standpoint of rare Mendelian disease variants and population-level risk alleles. In addition, we describe how new technologies have enabled systematic identification and prioritization of regulatory regions that govern gene expression. Beyond identification of regulatory regions, we discuss how causal testing of variants is now possible at an unprecedented scale through massively parallel reporter assays, allowing both detailed mapping of these regions and efficient validation of variants discovered through genome-wide association studies. Finally, we review deep learning approaches that hold the potential for genome-wide noncoding variant interpretation. Together, this review highlights strategies for large-scale interpretation of noncoding variants while also demonstrating the clear need for extension of clinical variant adjudication workflows to the noncoding genome to fully take advantage of increasingly available whole-genome sequencing data.
BACKGROUND:Virtual panel analysis (VPA) of exome data is a common approach for the molecular diagnosis of congenital heart disease (CHD). However, differences in gene panel composition and patient inclusion criteria limit the evaluation of its diagnostic utility. This study aims to assess the diagnostic yield of VPA in a cohort of patients with CHD across 3 academic centers. METHODS:We collected clinical data including phenotypic features and family history, from 853 probands with CHD who underwent VPA analysis at the Center for Medical Genetics Ghent (525 probands; 471 genes), the University Medical Center Groningen (195 probands; 345 genes), and the University Medical Center Utrecht (133 probands; 55 genes). We evaluated the diagnostic yield by comparing the 3 centers with respect to panel composition and clinical presentation. RESULTS:The Center for Medical Genetics Ghent reported a higher diagnostic yield (9.9%) compared with the University Medical Center Groningen (7.2%) and the University Medical Center Utrecht (5.3%). In all centers, the diagnostic yield was higher in patients presenting with a syndromic constellation and did not differ significantly between the sporadic and familial cases. In 1.7% of the 536 nonsyndromic probands, a molecular cause was identified that typically is associated with syndromic CHD. Twelve genes showed likely pathogenic or pathogenic variants in multiple patients and contributed to 56.2% of the identified causes. CONCLUSIONS:We report an overall diagnostic yield of VPA for CHD of 8.6%, to which only a few genes contribute significantly, highlighting the complex origin of CHD. Since panel size, gene panel content, and local practices largely affect the diagnostic yield, we propose a (minimum) core gene panel for suspected isolated CHD, as well as a coordinated testing strategy for CHD to improve diagnosis and counseling and to catalyze collaborative efforts.
BACKGROUND:Cardiorespiratory fitness (CRF) is a strong predictor of mortality and noncommunicable disease risk, but its underlying molecular mechanisms are poorly understood. In this study, we identified 2 signatures of CRF (1 metabolomic and 1 proteomic) from UK Biobank participants who completed a risk-stratified submaximal cycle ergometer test, with CRF estimated from the heart rate response to incremental workload. METHODS:These signatures were validated in an independent sample of UK participants with data on metabolomics (n=354 222) and proteomics (n=29 961) to investigate prospective associations with all-cause mortality and noncommunicable diseases. Prospective associations were evaluated using Cox proportional hazards models adjusted for age, sex, ethnicity, socioeconomic status, lifestyle factors (including smoking, alcohol intake, diet, and body mass index), and relevant medical history. RESULTS:Our findings reveal that higher CRF is characterized by downregulation of pathways related to inflammation, triglyceride metabolism, glycolysis, and vascular dysfunction, and upregulation of pathways related to cholesterol transport, apolipoprotein particle size, and cytoskeletal remodeling. Leveraging these insights, we developed 2 novel signatures of CRF (1 metabolomic and 1 proteomic) that robustly reflect CRF levels (R2: 0.50-0.60). Over an average of 9 years of follow-up, we observed 27 659 cases of all-cause mortality. Across the discovery and validation cohorts, we found that the metabolomic signature of CRF was strongly associated with a 39% to 54% lower risk of all-cause mortality and markedly reduced risk of type 2 diabetes (90% in both), cardiovascular disease (42%-47%), and colorectal cancer (33%-39%). Additionally, the proteomic signature of CRF was associated with a 17% lower risk of all-cause mortality, and with a 22% to 39% lower risk of type 2 diabetes and cardiovascular disease. CONCLUSIONS:Together, these findings indicate that circulating metabolites and proteins are associated with CRF and with subsequent risk of mortality and noncommunicable diseases.
BACKGROUND: GDF2 (encoding BMP9) variants have been described in pulmonary arterial hypertension (PAH) and hereditary hemorrhagic telangiectasia (HHT), as well as a few BMP10 variants in PAH. The purpose of the present study was to develop a functional assay capable of discriminating benign from pathogenic variants and to characterize the underlying molecular mechanisms responsible for their loss of function. METHODS: We developed a single-step functional assay in which C2C12 cells stably expressing a BMP (bone morphogenetic protein)-responsive element upstream of a firefly luciferase reporter would be stimulated by the autocrine secretion of BMP9 or BMP10 variants produced by transfected expression plasmids. RESULTS: Using this functional assay, we reclassified all GDF2 variants and 2 out of 5 BMP10 variants identified in patients with PAH as likely pathogenic. In contrast, only 2 of the 4 GDF2 variants identified in suspected patients with HHT were found to be likely pathogenic; nevertheless, none of the patients met the diagnostic criteria for hereditary hemorrhagic telangiectasia. We also showed, using ELISAs and Western blots, that the loss of function of GDF2 and BMP10 variants was mostly due to altered processing (folding/stability defects). Moreover, we found that loss-of-function GDF2 variants impaired the secretion of BMP10, suggesting a potential dominant-negative mechanism. CONCLUSIONS: We developed a functional assay for GDF2 and BMP10 variants, enabling the reclassification of variants of unknown significance. Together, this study further supports the involvement of GDF2 and BMP10 as predisposing genes in PAH. This single-step assay will be transferable to clinical genetic laboratories and will improve diagnosis of patients with PAH and HHT.
BACKGROUND:Performance and transferability of contemporary polygenic risk scores (PRS) for atherosclerotic cardiovascular disease phenotypes may vary across PRS methods, training data, and trait ascertainment. METHODS:We aimed to investigate the performance and transferability of contemporary PRS for atherosclerotic cardiovascular disease subtypes: coronary heart disease (CHD), abdominal aortic aneurysm (AAA), ischemic stroke (IS), and peripheral artery disease (PAD), using the All of Us Workbench, which consists of a large, diverse cohort with whole-genome sequence data. We also developed and evaluated a multi-trait PRS for each subtype. Performance of PRS for 4 atherosclerotic cardiovascular disease subtypes was compared across genetic similarity groups in 245 388 All of Us participants. Groups genetically similar to European, African, admixed American, and remaining groups (combined as other) were used to assess PRS for CHD, IS, AAA, PAD, and multi-trait. RESULTS:PRS for CHD and AAA performed better than IS and PAD. For CHD, CHDPGS003725 performed the best (hazard ratio per SD increase [95% CI]), across genetic ancestry groups, European, and African (1.72 [1.67-1.78], 1.23 [1.17-1.29]), with CHDPGS004696 being best for admixed American (1.91 [1.70-2.15]), and CHDPGS003356 for other (1.75 [1.58-1.95]). The best performing PRS for AAA was AAAMulti for European, other, and admixed American (1.71 [1.52-1.92], 1.59 [1.07-2.37], 1.50 [0.90-2.52]) and AAAPGS003972 for African (1.39 [1.19-1.63]). For IS, ISMulti performed best for other and European (1.49 [1.17-1.89], 1.33 [1.25-1.42]), and ISPGS000039 performed best in admixed American and African (1.17 [1.07-1.27], 1.09 [1.04-1.15]). For PAD, PADMulti performed best for all groups (other, 1.51 [1.19-1.92]; European, 1.32 [1.24-1.41]; admixed American, 1.23 [1.05-1.45]; and African, 1.18 [1.04-1.34]). CONCLUSIONS:Multi-trait and multi-ancestry PRS performed better than individual trait and/or single ancestry PRS for each atherosclerotic cardiovascular disease phenotype across ancestrally diverse and admixed individuals, with minimal change including adjustment for conventional risk factors.
BACKGROUND: GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance. METHODS: We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis. RESULTS: This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare variant-based risk score derived from the framework demonstrated distinct clinical characteristics compared with a conventional common variant-based PRS. The rare variant-based risk score significantly discriminated CAD cases and predicted cardiovascular mortality in an independent cohort. Furthermore, combining the rare variant-based risk score with the traditional PRS improved CAD prediction compared with the PRS alone (area under the curve, 0.66 versus 0.61; P =0.007). CONCLUSIONS: These findings underscore the distinct and complementary value of the rare variant-based risk score compared with the conventional PRS, highlighting the enhanced predictive power achieved through their integration. This comprehensive approach proposes broader genetic profiling, offering substantial potential for improved clinical risk stratification and personalized prevention strategies.
Background: Homozygous or compound heterozygous loss-of-function variants in GNPTAB cause mucolipidosis type II/III, a progressive multisystem disorder characterized by skeletal abnormalities, short stature, coarse facial features and cardiorespiratory disease. ML III is milder, with an older age of onset and a less severe phenotype. We report two siblings with arrhythmogenic cardiomyopathy and ventricular arrhythmias with compound heterozygous variants in GNPTAB. Methods and Results: The family presented due to the sudden cardiac death of a male in his early 30’s with arrhythmogenic cardiomyopathy identified at autopsy. His sister was found to have cardiomyopathy and experienced a ventricular tachycardia storm. Both siblings had history of skeletal dysplasia first investigated during adolescence. Clinical genetic testing did not identify a cause for the cardiomyopathy, and they were enrolled in the Elusive Hearts study. Following whole genome sequencing, we detected a heterozygous frameshift variant, NM_024312.5( GNPTAB ): c.3503_3504del, and a heterozygous missense variant, NM_024312.5( GNPTAB ): c.1400A>G, p.(Asp467Gly), occurring in trans . Enzymatic testing showed elevated plasma lysosomal enzyme activity, confirming the clinical diagnosis. Cardiomyopathy has rarely been reported in patients with ML III. Conclusion: We expand the cardiac phenotypic features of ML III and suggest that GNPTAB could be considered for testing in patients with genetically undiagnosed cardiomyopathy and/or sudden cardiac arrest.
Bias in clinical research affects not only the internal validity of studies but also the equitable distribution of health benefits derived from studies. Among the most impactful forms are selection bias, attrition bias, and algorithmic bias, each of which is capable of distorting participant representation, treatment effect estimates, and model performance across important subgroups. This scientific statement provides a reference for cardiovascular researchers and clinicians, integrating detection, correction, and prevention strategies to address these biases. Selection bias may be mitigated through approaches such as inverse probability weighting and adjustment for sociodemographic imbalances; attrition bias can be addressed using intention-to-treat analyses and multiple imputation for missing data that are missing at random; algorithmic bias requires fairness-aware modeling, diverse training data sets, and explainable artificial intelligence techniques. These 3 forms of bias are not exhaustive, but their careful management is essential to achieving scientific rigor, fairness, and real-world applicability, and requires multidisciplinary collaboration to embed equity and validity throughout the research lifecycle.
BACKGROUND:SGLT2 (sodium-glucose cotransporter-2) inhibitors and GLP-1R (glucagon-like peptide-1 receptor) agonists reduce the risk of major adverse cardiovascular and kidney events in individuals with various cardiometabolic conditions. The long-term efficacy and safety of these therapies, especially in low- and moderate-risk populations, remain uncertain. METHODS:We conducted a biobank-scale analysis using genetic instruments derived from naturally occurring genetic variations in the genes encoding the targets of SGLT2 inhibitors (SLC5A2) and GLP-1R agonists (GLP1R) that are associated with glycated hemoglobin levels. This Mendelian randomization study utilized data from the All of Us Research Program, which includes whole genome sequencing and electronic health records of 633 547 participants. RESULTS:Higher SGLT2 inhibitor genetic instrument scores were associated with a lower risk of heart failure (odds ratio [OR], 0.97 [95% CI, 0.96-0.99]) and chronic kidney disease (OR, 0.98 [95% CI, 0.96-0.99]). Higher GLP-1R agonist genetic instrument scores were linked to reduced risks of heart failure (OR, 0.97 [95% CI, 0.96-0.99]), chronic kidney disease (OR, 0.96 [95% CI, 0.95-0.98]), and coronary artery disease (OR, 0.98 [95% CI, 0.96-0.99]). We did not detect associations between the GLP-1R agonist instrument and multiple endocrine neoplasia or medullary thyroid carcinoma. PheWAS (Phenome-Wide Association Study) identified associations between the SGLT2 inhibitor and GLP-1R agonist genetic instruments and a lower risk of diabetes, but no other phenotypes. CONCLUSIONS:This study demonstrates the utility of biobank-scale health data for pharmacology research and suggests that, if feasible to implement in routine practice, long-term, primary prevention with an SGLT2 inhibitor or GLP-1R agonist would safely lower the risk of major adverse cardiovascular and kidney events in low- to moderate-risk adults.
BACKGROUND:Hypertrophic cardiomyopathy (HCM) is characterized by substantial heterogeneity in both clinical phenotype and risk of adverse outcomes, including heart failure and sudden cardiac death. This highlights the need for robust biomarkers for risk stratification, and while previous studies have identified the role of select plasma proteins, comprehensive large-scale proteomic analyses have been limited in HCM.METHODS:We performed a case-control analysis of 2922 plasma proteins in 49 588 UK Biobank participants (100 HCM cases) to identify proteins associated with HCM. External replication analyses were performed in the deCODE Genetics Icelandic study (51 cases/38 904 controls) and All of Us (546 cases/41 049 controls) data sets. Associations with adverse clinical outcomes and cardiac endophenotypes of disease severity were further identified, and causal relationships were evaluated using Mendelian randomization. Relative biomarker importance was also assessed by joint modeling via machine learning.RESULTS:We identified novel associations of ANGPT2 (angiopoietin-2) and LTBP2 (latent transforming growth factor-beta binding protein 2) with HCM, with both also showing prognostic utility for heart failure-related outcomes in HCM cases. We also confirmed the associations of established biomarkers (eg, NT-proBNP [N-terminal pro-B-type natriuretic peptide], troponins I and T) with HCM cases, cardiac imaging markers of disease severity, and adverse outcomes. Mendelian randomization analyses supported a causal effect of HCM on increasing NT-proBNP and troponin T levels.CONCLUSIONS:This biobank-scale plasma proteomic study in HCM identified ANGPT2 and LTBP2 as novel HCM biomarkers with potential diagnostic and prognostic utility. These findings highlight the potential for plasma proteomics to improve risk prediction and provide insight into HCM pathobiology.
BACKGROUND:Lymphedema is a chronic condition characterized by the accumulation of fluid due to impaired lymphatic drainage, frequently occurring secondary to chronic venous insufficiency (CVI), malignancy, or obesity. Despite known environmental and clinical risk factors, the genetic contributors to lymphedema and CVI have been understudied. METHODS:We conducted a multipopulation genome-wide association study in participants of the Million Veteran Program without cancer to identify genetic variants associated with CVI, lymphedema, and their cooccurrence. Individuals were categorized into 3 case groups: CVI only (n=34 664), lymphedema only (n=2452), and lymphedema+CVI (n=3283) and were compared with 367 684 controls. RESULTS:We identified 11 genome-wide significant variants (P<5×10-8) in the multipopulation analysis, including 8 for CVI only, 1 for lymphedema only, and 2 for lymphedema+CVI, and 2 population-specific genetic variants. Three independent variants replicated in the UK Biobank for CVI only near CASZ1, SLC12A2, and NDP. Polygenic risk scores derived from the Million Veteran Program were associated with CVI in the UK Biobank and phenome-wide associations of CVI-associated variants revealed pleiotropic associations with cardiometabolic traits. CONCLUSIONS:These findings enhance our understanding of the genetic architecture of lymphatic dysfunction.
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide. Preventing ASCVD is of utmost importance; however, a large proportion of preventable cases is not discovered early enough to initiate relevant treatment. Risk stratification for ASCVD includes classical risk factors, such as sex, age, smoking habits, blood pressure, cholesterol levels, and diabetes. Current risk prediction models, including the Systematic Coronary Risk Evaluation 2 algorithms, are designed for individuals aged 40 to 69 years and relate to 10-year risk and not to lifetime risk, thereby being inaccurate for the young. Another problem is the underdiagnosis of events in women, thereby underestimating risk. Multiomics, encompassing genomics, epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, offers new opportunities. Polygenic risk scores derived from genomic data may improve ASCVD risk classification. While genomic risk is established at inception, epigenomics captures the influence of environmental exposures over the lifespan through dynamic DNA modifications that regulate gene expression. Proteomics-based prediction reflects interactions between genetic inheritance, and modifiable and nonmodifiable influences. Transcriptomic analyses of carotid plaques have clustered human atherosclerotic lesions into distinct molecular subgroups, and changes in RNA methylation of circulating blood cells have been linked to clinical outcomes after ASCVD. Metabolomics identifies metabolic signatures, including lipid subclass alterations, amino acid imbalances, and inflammatory markers, all associated with cardiovascular disease incidence. In this review, we highlight current challenges, explore potential solutions, and discuss how integrating multiple omic layers through computational modeling (multiomics) could enhance patient stratification, optimize clinical management, and reduce the global burden of ASCVD.
BACKGROUND:Arrhythmogenic cardiomyopathy is an inherited disorder characterized by fibro-fatty myocardial replacement and ventricular arrhythmias. Although desmosomal mutations such as desmoglein-2 (DSG2) are well-established causes, the pathogenic mechanisms of specific missense variants remain incompletely defined. METHODS:We generated a physiologically relevant Dsg2F536C/F536C knock-in mouse model using CRISPR/Cas9 to mimic the human DSG2 p.Phe531Cys mutation. Comprehensive phenotyping included histopathology, immunostaining, transcriptomic profiling, in vitro cardiomyocyte and fibroblast assays, in vivo imaging and ECG analysis, and ex vivo optical mapping. Therapeutic potential was assessed using the PPAR-γ (peroxisome proliferator-activated receptor gamma) antagonist GW9662. RESULTS:Dsg2F536C/F536C mice developed progressive cardiac hypertrophy, interstitial fibrosis, lipid accumulation, and inducible ventricular arrhythmias following isoproterenol infusion and programmed electrical stimulation. These changes led to severe cardiac dysfunction and reduced survival. Mechanistically, the mutation caused reduced DSG2 and nuclear accumulation of β-catenin and PPAR-γ, promoting triacylglycerol biosynthesis, oxidative stress, cardiomyocyte death, and calcium-handling abnormalities. We also identified activation of epicardial epithelial-to-mesenchymal transition and paracrine fibroblast activation via IL-6 (interleukin-6) and PDGF-BB (platelet-derived growth factor-BB) as key contributors to fibrotic remodeling. Optical mapping revealed prolonged and heterogeneous action potential duration, with both reentrant and focal ectopic mechanisms of ventricular tachycardia. Treatment with GW9662 attenuated lipid accumulation, fibrosis, reactive oxygen species production, and arrhythmogenic susceptibility. CONCLUSIONS:The Dsg2F536C/F536C knock-in mouse is a genotype-specific arrhythmogenic cardiomyopathy model that links desmosomal dysfunction to metabolic remodeling, epicardial epithelial-to-mesenchymal transition, and electrophysiological instability. PPAR-γ inhibition ameliorated structural and arrhythmogenic remodeling, supporting PPAR-γ as a potential therapeutic target and advancing precision strategies for desmosome-related cardiomyopathies.
