Hypertrophic cardiomyopathy (HCM) is an autosomal dominant genetic disorder characterized by left ventricular hypertrophy and variable clinical manifestations, including asymptomatic states and sudden Show more
Hypertrophic cardiomyopathy (HCM) is an autosomal dominant genetic disorder characterized by left ventricular hypertrophy and variable clinical manifestations, including asymptomatic states and sudden cardiac death (SCD). Data on its phenotype and genotype in the Indian population remain limited. We studied 113 patients diagnosed with HCM. All underwent clinical assessment, 24-h Holter monitoring, echocardiography, and cardiac MRI. Genetic testing was performed in 80 patients. Clinical and imaging features were compared between genotype-positive and genotype-negative groups. The mean age was 47 ± 10.8 years, with 82.6 % being males. Dyspnoea and chest pain were the most frequent symptoms. Obstructive HCM was seen in 70 (61.9 %) patients. Cardiac MRI showed late gadolinium enhancement >15 % in 13 (23.2 %) and apical aneurysms in 2 (3.5 %). Genetic mutations were detected in 40 (50 %) patients, with MYBPC3 (33 %) and MYH7 (26.8 %) being most common. Genotype-positive individuals more frequently had chest pain, a family history of SCD, and more severe hypertrophy. In this Indian HCM cohort, the condition predominantly affected males. Genotype-positive patients exhibited more severe hypertrophy and adverse clinical profiles, underscoring the importance of genetic screening in risk stratification. Show less
Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions Show more
Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. In the ECG-AI GWAS, we identified 3 signals ( Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways. Show less
Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for Show more
Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat (correlation coefficients, 0.97-0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the Show less