Chahal, Choudhary Anwar A

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.

ABSTRACT Drug‐induced QTc prolongation (diQTP) is a major risk factor for torsades de pointes and sudden cardiac death. This study evaluated whether carriers of the G allele of the NOS1AP rs10494366 T > G variant have greater risk for diQTP when prescribed high‐risk QT‐prolonging medications. A retrospective case‐control analysis was conducted using the Michigan Genomics Initiative (MGI) biobank, linking genotype and electronic health records for 5848 patients of European ancestry prescribed ≥ 1 CredibleMeds high‐risk drug for torsades de pointes between 2001 and 2022. QTc was Bazett‐corrected, and diQTP was defined as change of > 60 ms from the baseline QTc and/or QTc ≥ 500 ms. Associations between rs10494366 and diQTP were tested using unadjusted and propensity‐score‐adjusted logistic regression. The minor‐allele frequency (G) was 0.36, and genotype frequencies were in Hardy–Weinberg equilibrium ( p = 0.07). diQTP occurred in 12.2% of participants. The G allele was not significantly associated with risk of diQTP in either the unadjusted analysis (OR 0.94 [95% CI 0.84–1.05] p = 0.27) or the adjusted analysis (OR 0.94 [95% CI 0.83–1.06] p = 0.30). In this large, real‐world cohort of patients with European ancestry, the NOS1AP rs10494366 variant was not significantly associated with the risk of diQTP. These findings suggest that previously observed associations between this variant and baseline QTc may not extend to all drug‐induced settings.

ABSTRACT Background Genetic testing for cardiomyopathy and arrhythmia (CM/ARRH) provides diagnostic information and informs screening for at-risk relatives. Clinical guidelines recommend genetic testing for these conditions; however, data on how genetic test results influence clinical management recommendations are limited. Here, we determined the frequency of cardiologist-recommended management changes for patients following CM/ARRH genetic testing. Methods This was a retrospective cross-sectional study of patients referred for multigene panel testing between April 2016 and April 2024. Genetically-experienced cardiologists at multicenter academic clinical practices were recruited for participation to complete surveys indicating clinical decision making on patients who had genetic testing. Cases for review were randomly selected to have both positive and non-positive results. Results Among 249 patients (138 positive, 111 non-positive), 136 (54.6%) received clinical management recommendations for their own or their at-risk relatives’ care. Of these, 75 (55.1%) received recommendations for the patient’s own care, most frequently additional diagnostic tests/procedures (n=33). Compared to non-positive results, patients with positive results were more likely to receive recommendations for their own management (66/138, 47.8% vs 9/111, 8.1%; P<0.00001). Patients with positive results in arrhythmogenic cardiomyopathy genes had 263% higher odds of recommended management changes compared to those with TTN (OR=3.63, CI:1.40-9.84, P=0.009). Conclusions The results from CM/ARRH genetic testing on affected patients influenced cardiologists’ medical decision making and management recommendations. Additional research is needed to evaluate how genetic testing for CM/ARRH impact health outcomes.

PURPOSE The Lynch syndrome (LS) genes, MLH1 , MSH2 , MSH6 , and PMS2 , are considered to be some of the highest-priority genes to be tested for population genomic screening, with the aim to prevent and detect cancers early in those with a higher risk for such diagnoses. The objective of this study was to determine the risk of cancer associated with pathogenic variants in MLH1 , MSH2 , MSH6 , or PMS2 in the general population to build evidence-based guidelines for population genomic screening METHODS This retrospective case-control study uses Helix Research Network data from 230,159 participants across nine US health systems. Variant interpretation was performed following the American College of Medical Genetics and Association for Molecular Pathology guidelines. Clinical diagnoses were identified from electronic health records and claims data for 11 cancer types associated with LS, including colorectal and endometrial cancers. RESULTS Individuals with pathogenic variants in MLH1 , MSH2 , and MSH6 showed substantial risk for LS-related cancers compared with those without pathogenic variants. Conversely, individuals with pathogenic variants in PMS2 had no increase in risk for all 11 LS-associated cancers with hazard ratio (HR) of 1.0 (95% CI, 0.6 to 1.8) compared with those without a pathogenic variant. There was a small increase in risk when restricting the analysis to colorectal cancer with an HR of 3.5 (1.7-7.4). This increase in colorectal cancer risk was predominantly observed after age 60 years, 10 years after US Preventive Services Task Force guidelines recommend starting colonoscopies for the average population. Up to age 60 years, 2.6% of individuals with PMS2 pathogenic variants were diagnosed with colorectal cancer, closer to the general population (0.7%) than those with MLH1 (37.3%), MSH2 (28.9%), or MSH6 (6.7%) pathogenic variants. For 100,000 individuals screened for PMS2 , 854 additional colonoscopies would be performed, which would prevent or detect early 3.2 colorectal cancers. CONCLUSION This analysis across nine health systems highlights the clinical phenotypes of PMS2 pathogenic germline variants in the entire population.

Abstract Aims Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP). Methods and results We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants’ median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition, and CMR labelling as important factors. Conclusion Our findings support the feasibility of scalable DL-based screening tools for the prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

Abstract Aims Left ventricular hypertrophy (LVH) regionality carries diagnostic and prognostic importance. Mean absolute deviation of maximum segmental wall thickness (MadWT) is a novel left ventricular wall thickness (LVWT) heterogeneity biomarker from cardiovascular magnetic resonance imaging (CMR). Objectives To compare MadWT to indexed LV mass (LVMi), maximum (MaxWT) and mean (MeanWT) wall thickness to predict incident cardiovascular disease (CVD) and differentiate physiological from pathological LVH in highly physically active individuals. Methods and results Deep learning-assisted analysis of 44 930 UK Biobank CMR scans produced WT indices. Cox regression modelled major adverse cardiovascular events (MACE), heart failure (HF), arrhythmia, and all-cause death against LVWT indices. In the top 1% most physically active biomarker differences between propensity score matched hypertensive and non-hypertensive groups were compared. Over median (Q1, Q3) follow-up of 5.7 (4.9, 7.1) years, MadWT, MaxWT, MeanWT, and LVMi were associated with greater risk of MACE, HF, arrhythmia (P &lt; 0.05), but not all-cause death (P &gt; 0.05). After adjusting for CMR biomarkers, including LVMi, MadWT remained independently prognostic of the greatest number of endpoints, including MACE, HF, and arrhythmia [HR 1.13 (1.04–1.23); HR 1.15 (1.01–1.32); and HR 1.26 (1.18–1.35) respectively]. In the top 1% most physically active by three metrics, MadWT was the only significantly different biomarker between hypertensive and non-hypertensive participants (P &lt; 0.05). Conclusion MadWT is important prognostically beyond LV mass and may be useful when differentiating physiological from hypertensive LVH. Although findings require confirmation in athletic and diseased cohorts, MadWT is readily translatable to deep learning-assisted clinical CMR reporting, especially in early unexplained LVH.

ABSTRACTBackgroundLate gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR) imaging is a well‐established indicator of myocardial fibrosis in hypertrophic cardiomyopathy (HCM). However, its association with electrocardiographic (ECG) abnormalities and the risk of atrial fibrillation (AF) remains uncertain.ObjectivesTo investigate the association between the presence and burden of LGE with ECG characteristics, including precordial voltage, depolarization and repolarization abnormalities, and the incidence of AF in adults with HCM.MethodsWe conducted a retrospective cohort study of 144 adults with HCM with CMR and 12‐lead ECG within 30 days of each other. LGE was quantified as a percentage of LV mass and categorized as absent, < 5%, or ≥ 5%. ECG parameters, including QRS voltage, repolarization abnormalities, and LVH criteria, were analyzed. Incident AF was assessed during a median follow‐up of 6.6 years.ResultsLGE was present in 96 (67%) patients, with 21 (22%) having ≥ 5% LGE. There were no significant differences in precordial voltage between patients with and without LGE across Sokolow‐Lyon, Cornell, and Romhilt‐Estes criteria. However, T‐wave inversion was more common in leads I (41% vs. 19%, p = 0.009), aVL (50% vs. 31%, p = 0.033), and V4 (41% vs. 23%, p = 0.035) in patients with LGE. Patients with ≥ 5% LGE had a significantly lower median LVEF (64% vs. 74%, p = 0.003). Additionally, LGE presence was not associated with an increased risk of incident AF (HR 1.8, 95% CI 0.6–5.3, p = 0.308).ConclusionIn contrast to pediatric HCM, LGE is associated with specific ECG repolarization abnormalities, particularly T‐wave inversion in lateral leads, but does not significantly affect precordial voltage in adults.

BACKGROUND: This study aimed to evaluate the prevalence of pathogenic/likely pathogenic cardiomyopathy variant carriers in a multiancestry US population and examine the risk of adverse clinical outcomes. METHODS: This retrospective cohort study included multiancestry US adults aged ≥18 years with sequencing data from the All of Us Research Program. Pathogenic/likely pathogenic variants in cardiomyopathy genes were identified using the ClinVar database. The primary outcome was heart failure. Secondary outcomes included cardiomyopathy and arrhythmia. Outcomes were identified from electronic health records. Interval-censored Cox models, taking age on the timescale, were used to assess the risk of outcomes in pathogenic/likely pathogenic variant carriers with noncarriers as the reference group. RESULTS: Among 167 435 individuals (median age, 55.2 [39.5–66.3] years; 61.7% female; 40.7% non-European ancestry) included, the prevalence of pathogenic/likely pathogenic cardiomyopathy variant carriers was 0.7% in the overall population and 0.8%, 0.8%, 0.5%, and 1.2% among European, African, East Asian, and South Asian ancestry individuals, respectively. Over their lifetime, there were 12 867 heart failure events (205 in carriers and 12 662 in noncarriers), with an incidence rate of 3.05 (95% CI, 2.66–3.49) per 1000 person-years in carriers and 1.37 (95% CI, 1.35–1.40) in noncarriers (adjusted hazard ratio, 2.30 [95% CI, 2.04–2.60]). Cardiomyopathy occurred in 5164 (161 in carriers and 5003 in noncarriers), with an incidence rate of 2.38 (95% CI, 2.04–2.78) per 1000 person-years among carriers and 0.54 (95% CI, 0.53–0.56) in noncarriers (adjusted hazard ratio, 4.31 [95% CI, 3.73–4.97]). There were 19 405 arrhythmia events (263 in carriers and 19 142 in noncarriers), with an incidence rate of 3.93 (95% CI, 3.48–4.44) per 1000 person-years among carriers and 2.09 (95% CI, 2.06–2.12) in noncarriers (adjusted hazard ratio, 2.12 [95% CI, 1.78–2.53]). CONCLUSIONS: Pathogenic/likely pathogenic cardiomyopathy variant carriers have an increased risk of heart failure, cardiomyopathy, and arrhythmias. Despite the modest overall prevalence, the associated risks suggest potential benefits of targeted genetic screening for early detection and management.