A population-based phenome-wide association study of cardiac and aortic structure and function

Abstract

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.Cardiac and aortic structure and function are associated with cardiovascular diseases (CVDs) 1, 2 and a wide range of other types of disease [3][4][5][6] . Quantitative phenotypes derived from cardiovascular magnetic resonance (CMR) images enable us to assess cardiac and aortic structure and function in a non-invasive way and provide important biomarkers for the determination of pathological states in CVDs. For example, the left ventricular ejection fraction (LVEF) is an important clinical biomarker for the diagnosis and treatment of heart failure 1 . The left ventricular myocardial mass (LVM) is widely used for classifying hypertrophy and predicting risks of cardiovascular events 7 . Although CMR imaging phenotypes clearly play an important role in disease research and diagnosis, extracting these phenotypes demand significant involvement of experienced image analysts. This has become a limiting factor for applying CMR in large-scale studies and exploiting imaging phenotypes at a population level.Large-scale imaging studies potentially provide a wealth of information for investigating disease risk factors and discovering early-stage image-based biomarkers. Recent large-scale imaging studies collecting CMR images include the Framingham Heart

Deep Learning to Estimate Cardiac Magnetic Resonance-Derived Left Ventricular Mass

“…A study by Aung et al 19 utilized a combination of deep learning and manual segmentation to extend LV mass estimates to approximately 16,000 UK Biobank images, in order to facilitate genetic analyses. Similarly, Bai et al 20 utilized deep learning to estimate several cardiac structural features to enable broad phenotypic association testing. In contrast to previous work, we explicitly compared several approaches to automated LV mass estimation, observing that a deep learning segmentation model demonstrated favorable performance when compared to deep learning-based regression and a recalibrated inlineVF-based method.…”

The year in cardiovascular medicine 2020: digital health and innovation

“…Действительно, в этом году результаты деятельности Биобанка Великобритании подтвердили его потенциал стимулирования инноваций на долгие годы. В только что опубликованном исследовании [35] в Биобанке Великобритании >26 тыс. МРТ-снимков сердца были использованы в алгоритмах машинного обучения, что позволило выявить >2 тыс.…”

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

“…5 GWASs of individual blood-and urine-based biomarkers, which can be assayed accurately with high throughput, have shed light on disease etiology. 6,7 Advances in deep learning have enabled the extraction of medically relevant features from high-dimensional data, such as using cardiac magnetic resonance imaging to infer cardiac and aortic dimensions, 8 color fundus photographs to detect glaucoma risk, 9 and optical coherence tomography images to predict age-related macular degeneration progression. 10 Using medically relevant features extracted from biobank data by machine learning (ML) models as GWAS phenotypes provides an opportunity to identify genetic signals influencing these traits.…”