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Background: Although arterial stiffness has demonstrated moderate heritability, our knowledge of the genes modulating arterial stiffness is still limited. We conducted genome-wide association studies (GWASs) of aortic distensibility (AoD) in both ascending (AA) and proximal descending aorta (PDA) to discover novel genetic loci. Methods: Our study included ~14,500 European-ancestry participants in the UK Biobank study. AoD in AA and PDA were assessed at the level of pulmonary artery bifurcation using transverse cine images obtained from 1.5 Tesla cardiovascular magnetic resonance scanners1. Relative cross-sectional aortic area change was calculated using an automated tool2. GWASs were performed in a discovery cohort (n = 3,841), with replication in 9,630 individuals. We also performed GWASs for each trait in the combined cohort (n = 14,596). All GWASs were performed under a linear mixed model and adjusted for age, sex, height, weight, systolic blood pressure, diabetes, smoking, genotype array type and the first ten principal components. Results: We found three significant loci (p < 5 × 10–8) for AA AoD and six for PDA AoD (Figure 1A). The ELN locus was discovered and replicated for AA AoD, and was significantly associated with PDA AoD in the combined cohort (Figure 1B). ELN encodes elastin a central component of elastic fibres in the heart and blood vessels. The most significant locus for PDA AoD was FBLN5; FBLN5 encodes fibulin 5 which is vital for elastic fibre formation. Conclusions: In the first GWAS of AoD, we discovered seven unique loci. These results enhance our understanding of the biological processes underlying arterial stiffness. Figure 1AVenn diagram of the 7 genome-wide significant loci from our study Figure 1BMiami plot of distensibility in the ascending (AA) and proximal descending aorta (PDA) in the combined cohort. Quartered cross symbol denotes locus was also genome-wide significant in discovery and replication cohorts.

TOMATO (Total Mapping Toolbox) is a C++ library for the calculation of parametric maps in cardiac magnetic resonance imaging (MRI). As an open source project, TOMATO allows transparent and standardised cardiac longitudinal relaxation time (T1) mapping in clinical applications. With C++ implementation, TOMATO can easily interface and translate between research software environments and commercial vendors’ closed-source C++ environments on scanners as well as post-processing software. To complement the core library implementation, a ready-to-use command line tool has been provided.

IntroductionAortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism.MethodsThe automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0-3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs.ResultsThe automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy).ConclusionThe proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.