Development of integrated high-resolution three-dimensional MRI and computational modelling techniques to identify novel genetic and anthropometric determinants of cardiac form and function

Abstract

BackgroundThe genetic and environmental factors that define cardiac structure and function, particularly at the intersection between health and the earliest stages of disease, remain poorly characterised. Conventional cardiovascular imaging provides limited, semi-quantitative, and global metrics of the heart, and these methods are insensitive to regional variation. We aimed to assess whether three-dimensional (3D) cardiac magnetic resonance (CMR) phenotyping could provide methodological, statistical, and scaling advantages for population studies. MethodsVolunteers without self-reported cardiovascular disease were recruited prospectively. A cardiac atlas-based software was developed to quantitatively analyse two-dimensional (2D) and 3D CMRs. Phenotypes (eg, wall thickness and function) were extracted at 46 000 points across the heart and genotyping and whole-exome sequencing performed. Associations between 3D phenotypes, clinical variables, and genotypes were studied with 3D regression models and Bayesian latent factor analysis. Findings1850 volunteers (mean age 41 years, SD 13) took part in the study. Automatically analysed 3D images were more accurate than 2D images at defining cardiac surfaces, enabling a reduction in the sample size required for epidemiological and genetic studies of the heart. Computational, high-dimensional 3D phenotyping revealed that systolic blood pressure, body composition, and genetic variation (both rare and common) were associated with regional rather than global changes in cardiac phenotypes, which were not detected by 2D CMR. 3D CMR revealed latent precursors of the hypertensive heart phenotype in healthy individuals that were previously unappreciated. In a 3D genome-wide association study we detected a larger number of loci significantly (p<5×10−10) associated with regional cardiac variation, showing genetic control of localised cardiac physiological features. InterpretationWe have shown that quantitative 3D CMR combined with computational modelling and advanced statistical analysis provide new insights into the genetic and anthropometric determinants of cardiac morphological and physiological features. Our approach provides new opportunities for understanding the structure and function of the human heart, and reveals hidden features only apparent through big data analytical methods. This approach can be applied at scale to very large datasets for mechanistic or interventional studies. FundingMedical Research Council, British Heart Foundation, Fondation Leducq grants.