Kenneth Fung

Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Cardiovascular magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function. Reference ranges permit differentiation between normal and pathological states. To date, this study is the largest to provide CMR specific reference ranges for left ventricular, right ventricular, left atrial and right atrial structure and function derived from truly healthy Caucasian adults aged 45-74.Five thousand sixty-five UK Biobank participants underwent CMR using steady-state free precession imaging at 1.5 Tesla. Manual analysis was performed for all four cardiac chambers. Participants with non-Caucasian ethnicity, known cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Remaining participants formed the healthy reference cohort; reference ranges were calculated and were stratified by gender and age (45-54, 55-64, 65-74).After applying exclusion criteria, 804 (16.2%) participants were available for analysis. Left ventricular (LV) volumes were larger in males compared to females for absolute and indexed values. With advancing age, LV volumes were mostly smaller in both sexes. LV ejection fraction was significantly greater in females compared to males (mean ± standard deviation [SD] of 61 ± 5% vs 58 ± 5%) and remained static with age for both genders. In older age groups, LV mass was lower in men, but remained virtually unchanged in women. LV mass was significantly higher in males compared to females (mean ± SD of 53 ± 9 g/m2 vs 42 ± 7 g/m2). Right ventricular (RV) volumes were significantly larger in males compared to females for absolute and indexed values and were smaller with advancing age. RV ejection fraction was higher with increasing age in females only. Left atrial (LA) maximal volume and stroke volume were significantly larger in males compared to females for absolute values but not for indexed values. LA ejection fraction was similar for both sexes. Right atrial (RA) maximal volume was significantly larger in males for both absolute and indexed values, while RA ejection fraction was significantly higher in females.We describe age- and sex-specific reference ranges for the left ventricle, right ventricle and atria in the largest validated normal Caucasian population.

Background: The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present. Methods: The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits—LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events. Results: The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P <1×10 −8 . Three loci were replicated at Bonferroni significance and 7 loci at nominal significance ( P <0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes ( TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB , and MMP11 ) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 – 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score). Conclusions: We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.

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. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.

Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). Results: The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium, and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. Conclusions: The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task.

The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions.To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC.We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores.We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Exposure to ambient air pollution is strongly associated with increased cardiovascular morbidity and mortality. Little is known about the influence of air pollutants on cardiac structure and function. We aim to investigate the relationship between chronic past exposure to traffic-related pollutants and the cardiac chamber volume, ejection fraction, and left ventricular remodeling patterns after accounting for potential confounders.Exposure to ambient air pollutants including particulate matter and nitrogen dioxide was estimated from the Land Use Regression models for the years between 2005 and 2010. Cardiac parameters were measured from cardiovascular magnetic resonance imaging studies of 3920 individuals free from pre-existing cardiovascular disease in the UK Biobank population study. The median (interquartile range) duration between the year of exposure estimate and the imaging visit was 5.2 (0.6) years. We fitted multivariable linear regression models to investigate the relationship between cardiac parameters and traffic-related pollutants after adjusting for various confounders.The studied cohort was 62±7 years old, and 46% were men. In fully adjusted models, particulate matter with an aerodynamic diameter <2.5 μm concentration was significantly associated with larger left ventricular end-diastolic volume and end-systolic volume (effect size = 0.82%, 95% CI, 0.09-1.55%, P=0.027; and effect size = 1.28%, 95% CI, 0.15-2.43%, P=0.027, respectively, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 μm) and right ventricular end-diastolic volume (effect size = 0.85%, 95% CI, 0.12-1.58%, P=0.023, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 μm). Likewise, higher nitrogen dioxide concentration was associated with larger biventricular volume. Distance from the major roads was the only metric associated with lower left ventricular mass (effect size = -0.74%, 95% CI, -1.3% to -0.18%, P=0.01, per interquartile range increment). Neither left and right atrial phenotypes nor left ventricular geometric remodeling patterns were influenced by the ambient pollutants.In a large asymptomatic population with no prevalent cardiovascular disease, higher past exposure to particulate matter with an aerodynamic diameter <2.5 μm and nitrogen dioxide was associated with cardiac ventricular dilatation, a marker of adverse remodeling that often precedes heart failure development.

Mitral annular disjunction is the atrial displacement of the mural mitral valve leaflet hinge point within the atrioventricular junction. Said to be associated with malignant ventricular arrhythmias and sudden death, its prevalence in the general population is not known. The purpose of this study was to assess the frequency of occurrence and extent of mitral annular disjunction in a large population cohort. The authors assessed the cardiac magnetic resonance (CMR) images in 2,646 Caucasian subjects enrolled in the UK Biobank imaging study, measuring the length of disjunction at 4 points around the mitral annulus, assessing for presence of prolapse or billowing of the leaflets, and for curling motion of the inferolateral left ventricular wall. From 2,607 included participants, the authors found disjunction in 1,990 (76%) cases, most commonly at the anterior and inferior ventricular wall. The authors found inferolateral disjunction, reported as clinically important, in 134 (5%) cases. Prolapse was more frequent in subjects with disjunction (odds ratio [OR]: 2.5; P = 0.02), with positive associations found between systolic curling and disjunction at any site (OR: 3.6; P < 0.01), and systolic curling and prolapse (OR: 71.9; P < 0.01). This large-scale study shows that disjunction is a common finding when using CMR. Disjunction at the inferolateral ventricular wall, however, was rare. The authors found associations between disjunction and both prolapse and billowing of the mural mitral valve leaflet. These findings support the notion that only extensive inferolateral disjunction, when found, warrants consideration of further investigation, but disjunction elsewhere in the annulus should be considered a normal finding.

UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Although fully-automated left ventricular (LV) analysis was performed in the original acquisition, this was not designed for unsupervised incorporation into epidemiological studies. We sought to evaluate automated LV mass and volume (Siemens syngo InlineVF versions D13A and E11C), against manual analysis in a substantial sub-cohort of UK Biobank participants. Eight readers from two centers, trained to give consistent results, manually analyzed 4874 UK Biobank cases for LV end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF) and LV mass (LVM). Agreement between manual and InlineVF automated analyses were evaluated using Bland-Altman analysis and the intra-class correlation coefficient (ICC). Tenfold cross-validation was used to establish a linear regression calibration between manual and InlineVF results. InlineVF D13A returned results in 4423 cases, whereas InlineVF E11C returned results in 4775 cases and also reported LVM. Rapid visual assessment of the E11C results found 178 cases (3.7%) with grossly misplaced contours or landmarks. In the remaining 4597 cases, LV function showed good agreement: ESV -6.4 ± 9.0 ml, 0.853 (mean ± SD of the differences, ICC) EDV -3.0 ± 11.6 ml, 0.937; SV 3.4 ± 9.8 ml, 0.855; and EF 3.5 ± 5.1%, 0.586. Although LV mass was consistently overestimated (29.9 ± 17.0 g, 0.534) due to larger epicardial contours on all slices, linear regression could be used to correct the bias and improve accuracy. Automated InlineVF results can be used for case-control studies in UK Biobank, provided visual quality control and linear bias correction are performed. Improvements between InlineVF D13A and InlineVF E11C show the field is rapidly advancing, with further improvements expected in the near future.

The UK Biobank is a large-scale population-based study utilising cardiovascular magnetic resonance (CMR) to generate measurements of atrial and ventricular structure and function. This study aimed to quantify the association between modifiable cardiovascular risk factors and cardiac morphology and function in individuals without known cardiovascular disease.Age, sex, ethnicity (non-modifiable) and systolic blood pressure, diastolic blood pressure, smoking status, exercise, body mass index (BMI), high cholesterol, diabetes, alcohol intake (modifiable) were considered important cardiovascular risk factors. Multivariable regression models were built to ascertain the association of risk factors on left ventricular (LV), right ventricular (RV), left atrial (LA) and right atrial (RA) CMR parameters.4,651 participants were included in the analysis. All modifiable risk factors had significant effects on differing atrial and ventricular parameters. BMI was the modifiable risk factor most consistently associated with subclinical changes to CMR parameters, particularly in relation to higher LV mass (+8.3% per SD [4.3 kg/m2], 95% CI: 7.6 to 8.9%), LV (EDV: +4.8% per SD, 95% CI: 4.2 to 5.4%); ESV: +4.4% per SD, 95% CI: 3.5 to 5.3%), RV (EDV: +5.3% per SD, 95% CI: 4.7 to 5.9%; ESV: +5.4% per SD, 95% CI: 4.5 to 6.4%) and LA maximal (+8.6% per SD, 95% CI: 7.4 to 9.7%) volumes. Increases in SBP were associated with higher LV mass (+6.8% per SD, 95% CI: 5.9 to 7.7%), LV (EDV: +4.5% per SD, 95% CI: 3.6 to 5.4%; ESV: +2.0% per SD, 95% CI: 0.8 to 3.3%) volumes. The presence of diabetes or high cholesterol resulted in smaller volumes and lower ejection fractions.Modifiable risk factors are associated with subclinical alterations in structure and function in all four cardiac chambers. BMI and systolic blood pressure are the most important modifiable risk factors affecting CMR parameters known to be linked to adverse outcomes.

The associations between cardiovascular disease (CVD) risk factors and the biventricular geometry of the right ventricle (RV) and left ventricle (LV) have been difficult to assess, due to subtle and complex shape changes. We sought to quantify reference RV morphology as well as biventricular variations associated with common cardiovascular risk factors. A biventricular shape atlas was automatically constructed using contours and landmarks from 4329 UK Biobank cardiovascular magnetic resonance (CMR) studies. A subdivision surface geometric mesh was customized to the contours using a diffeomorphic registration algorithm, with automatic correction of slice shifts due to differences in breath-hold position. A reference sub-cohort was identified consisting of 630 participants with no CVD risk factors. Morphometric scores were computed using linear regression to quantify shape variations associated with four risk factors (high cholesterol, high blood pressure, obesity and smoking) and three disease factors (diabetes, previous myocardial infarction and angina). The atlas construction led to an accurate representation of 3D shapes at end-diastole and end-systole, with acceptable fitting errors between surfaces and contours (average error less than 1.5 mm). Atlas shape features had stronger associations than traditional mass and volume measures for all factors (p < 0.005 for each). High blood pressure was associated with outward displacement of the LV free walls, but inward displacement of the RV free wall and thickening of the septum. Smoking was associated with a rounder RV with inward displacement of the RV free wall and increased relative wall thickness. Morphometric relationships between biventricular shape and cardiovascular risk factors in a large cohort show complex interactions between RV and LV morphology. These can be quantified by z-scores, which can be used to study the morphological correlates of disease.

Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1 pixel, and 17.50% with mean contour distance between 1 and 2 pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts.

Diabetes mellitus (DM) is associated with increased risk of cardiovascular disease. Detection of early cardiac changes before manifest disease develops is important. We investigated early alterations in cardiac structure and function associated with DM using cardiovascular magnetic resonance imaging.Participants from the UK Biobank Cardiovascular Magnetic Resonance Substudy, a community cohort study, without known cardiovascular disease and left ventricular ejection fraction ≥50% were included. Multivariable linear regression models were performed. The investigators were blinded to DM status.A total of 3984 individuals, 45% men, (mean [SD]) age 61.3 (7.5) years, hereof 143 individuals (3.6%) with DM. There was no difference in left ventricular (LV) ejection fraction (DM versus no DM; coefficient [95% CI]: -0.86% [-1.8 to 0.5]; P=0.065), LV mass (-0.13 g/m2 [-1.6 to 1.3], P=0.86), or right ventricular ejection fraction (-0.23% [-1.2 to 0.8], P=0.65). However, both LV and right ventricular volumes were significantly smaller in DM, (LV end-diastolic volume/m2: -3.46 mL/m2 [-5.8 to -1.2], P=0.003, right ventricular end-diastolic volume/m2: -4.2 mL/m2 [-6.8 to -1.7], P=0.001, LV stroke volume/m2: -3.0 mL/m2 [-4.5 to -1.5], P<0.001; right ventricular stroke volume/m2: -3.8 mL/m2 [-6.5 to -1.1], P=0.005), LV mass/volume: 0.026 (0.01 to 0.04) g/mL, P=0.006. Both left atrial and right atrial emptying fraction were lower in DM (right atrial emptying fraction: -6.2% [-10.2 to -2.1], P=0.003; left atrial emptying fraction:-3.5% [-6.9 to -0.1], P=0.043). LV global circumferential strain was impaired in DM (coefficient [95% CI]: 0.38% [0.01 to 0.7], P=0.045).In a low-risk general population without known cardiovascular disease and with preserved LV ejection fraction, DM is associated with early changes in all 4 cardiac chambers. These findings suggest that diabetic cardiomyopathy is not a regional condition of the LV but affects the heart globally.

To study the association of daily coffee consumption with all-cause and cardiovascular (CV) mortality and major CV outcomes. In a subgroup of participants who underwent cardiovascular magnetic resonance (CMR) imaging, we evaluated the association between regular coffee intake and cardiac structure and function.UK Biobank participants without clinically manifested heart disease at the time of recruitment were included. Regular coffee intake was categorized into three groups: zero, light-to-moderate (0.5-3 cups/day), and high (>3 cups/day). In the multivariate analysis, we adjusted for the main CV risk factors. We included 468 629 individuals (56.2 ± 8.1 years, 44.2% male), of whom 22.1% did not consume coffee regularly, 58.4% had 0.5-3 cups per day, and 19.5% had >3 cups per day. Compared to non-coffee drinkers, light-to-moderate (0.5-3 cups per day) coffee drinking was associated with lower risk of all-cause mortality [multivariate hazard ratio (HR) = 0.88, 95% confidence interval (CI): 0.83-0.92; P < 0.001] and CV mortality (multivariate HR = 0.83, 95% CI: 0.74-0.94; P = 0.006), and incident stroke (multivariate HR = 0.79, 95% CI: 0.63-0.99 P = 0.037) after a median follow-up of 11 years. CMR data were available in 30 650 participants. Both light-to-moderate and high coffee consuming categories were associated with dose-dependent increased left and right ventricular end-diastolic, end-systolic and stroke volumes, and greater left ventricular mass.Coffee consumption of up to three cups per day was associated with favourable CV outcomes. Regular coffee consumption was also associated with a likely healthy pattern of CMR metrics in keeping with the reverse of age-related cardiac alterations.

Right ventricular (RV) structure and function influence the morbidity and mortality from coronary artery disease (CAD), dilated cardiomyopathy (DCM), pulmonary hypertension and heart failure. Little is known about the genetic basis of RV measurements. Here we perform genome-wide association analyses of four clinically relevant RV phenotypes (RV end-diastolic volume, RV end-systolic volume, RV stroke volume, RV ejection fraction) from cardiovascular magnetic resonance images, using a state-of-the-art deep learning algorithm in 29,506 UK Biobank participants. We identify 25 unique loci associated with at least one RV phenotype at P < 2.27 ×10-8, 17 of which are validated in a combined meta-analysis (n = 41,830). Several candidate genes overlap with Mendelian cardiomyopathy genes and are involved in cardiac muscle contraction and cellular adhesion. The RV polygenic risk scores (PRSs) are associated with DCM and CAD. The findings substantially advance our understanding of the genetic underpinning of RV measurements.

Background Handgrip strength, a measure of muscular fitness, is associated with cardiovascular (CV) events and CV mortality but its association with cardiac structure and function is unknown. The goal of this study was to determine if handgrip strength is associated with changes in cardiac structure and function in UK adults. Methods and results Left ventricular (LV) ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), mass (M), and mass-to-volume ratio (MVR) were measured in a sample of 4,654 participants of the UK Biobank Study 6.3 ± 1 years after baseline using cardiovascular magnetic resonance (CMR). Handgrip strength was measured at baseline and at the imaging follow-up examination. We determined the association between handgrip strength at baseline as well as its change over time and each of the cardiac outcome parameters. After adjustment, higher level of handgrip strength at baseline was associated with higher LVEDV (difference per SD increase in handgrip strength: 1.3ml, 95% CI 0.1–2.4; p = 0.034), higher LVSV (1.0ml, 0.3–1.8; p = 0.006), lower LVM (-1.0g, -1.8 –-0.3; p = 0.007), and lower LVMVR (-0.013g/ml, -0.018 –-0.007; p<0.001). The association between handgrip strength and LVEDV and LVSV was strongest among younger individuals, while the association with LVM and LVMVR was strongest among older individuals. Conclusions Better handgrip strength was associated with cardiac structure and function in a pattern indicative of less cardiac hypertrophy and remodeling. These characteristics are known to be associated with a lower risk of cardiovascular events.

Purpose To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI–tagged images. Materials and Methods In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis. Results Within the test set, myocardial end-systolic circumferential Green strain errors were −0.001 ± 0.025, −0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6–8 minutes per slice for the manual analysis. Conclusion The fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack. Keywords: Adults, Cardiac, MR-Imaging, Neural Networks Published under a CC BY 4.0 license. Supplemental material is available for this article.

Mitral valve (MV) and tricuspid valve (TV) apparatus geometry are essential to define mechanisms and etiologies of regurgitation and to inform surgical or transcatheter interventions. Given the increasing use of cardiovascular magnetic resonance (CMR) for the evaluation of valvular heart disease, we aimed to establish CMR-derived age- and sex-specific reference values for mitral annular (MA) and tricuspid annular (TA) dimensions and tethering indices derived from truly healthy Caucasian adults.5065 consecutive UK Biobank participants underwent CMR using cine balanced steady-state free precession imaging at 1.5 T. Participants with non-Caucasian ethnicity, prevalent cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Absolute and indexed reference ranges for MA and TA diameters and tethering indices were stratified by gender and age (45-54, 55-64, 65-74 years).Overall, 721 (14.2%) truly healthy participants aged 45-74 years (54% women) formed the reference cohort. Absolute MA and TA diameters, MV tenting length and MV tenting area, were significantly larger in men. Mean ± standard deviation (SD) end-diastolic and end-systolic MA diameters in the 3-chamber view (anteroposterior diameter) were 2.9 ± 0.4 cm (1.5 ± 0.2 cm/m2) and 3.3 ± 0.4 cm (1.7 ± 0.2 cm/m2) in men, and 2.6 ± 0.4 cm (1.6 ± 0.2 cm/m2) and 3.0 ± 0.4 cm (1.8 ± 0.2 cm/m2) in women, respectively. Mean ± SD end-diastolic and end-systolic TA diameters in the 4-chamber view were 3.2 ± 0.5 cm (1.6 ± 0.3 cm/m2) and 3.2 ± 0.5 cm (1.7 ± 0.3 cm/m2) in men, and 2.9 ± 0.4 cm (1.7 ± 0.2 cm/m2) and 2.8 ± 0.4 cm (1.7 ± 0.3 cm/m2) in women, respectively. With advancing age, end-diastolic TA diameter became larger and posterior MV leaflet angle smaller in both sexes. Reproducibility of measurements was good to excellent with an inter-rater intraclass correlation coefficient (ICC) between 0.92 and 0.98 and an intra-rater ICC between 0.90 and 0.97.We described age- and sex-specific reference ranges of MA and TA dimensions and tethering indices in the largest validated healthy Caucasian population. Reference ranges presented in this study may help to improve the distinction between normal and pathological states, prompting the identification of subjects that may benefit from advanced cardiac imaging for annular sizing and planning of valvular interventions.

Arterial stiffness index (ASI) is a non-invasive measure of arterial stiffness using infra-red finger sensors (photoplethysmography). It is a well-suited measure for large populations as it is relatively inexpensive to perform, and data can be acquired within seconds. These features raise interest in using ASI as a tool to estimate cardiovascular disease risk as prior work demonstrates increased arterial stiffness is associated with elevated systolic blood pressure, and ASI is predictive of cardiovascular disease and mortality. We conducted genome-wide association studies (GWASs) for ASI in 127,121 UK Biobank participants of European-ancestry. Our primary analyses identified variants at four loci reaching genome-wide significance (P < 5 × 10-8): TEX41 (rs1006923; P = 5.3 × 10-12), FOXO1 (rs7331212; P = 2.2 × 10-11), C1orf21 (rs1930290, P = 1.1 × 10-8) and MRVI1 (rs10840457, P = 3.4 × 10-8). Gene-based testing revealed three significant genes, the most significant gene was COL4A2 (P = 1.41 × 10-8) encoding type IV collagen. Other candidate genes at associated loci were also involved in smooth muscle tone regulation. Our findings provide new information for understanding the development of arterial stiffness.

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an $$\mathrm {MAE} = 0.14$$ and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.

Aortic 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.The 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.The 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).The 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.

Abstract Background Coronavirus disease 2019 (COVID-19) disproportionately affects older people. Observational studies suggest indolent cardiovascular involvement after recovery from acute COVID-19. However, these findings may reflect pre-existing cardiac phenotypes. Aims We tested the association of baseline cardiovascular magnetic resonance (CMR) phenotypes with incident COVID-19. Methods We studied UK Biobank participants with CMR imaging and COVID-19 testing. We considered left and right ventricular (LV, RV) volumes, ejection fractions, and stroke volumes, LV mass, LV strain, native T1, aortic distensibility, and arterial stiffness index. COVID-19 test results were obtained from Public Health England. Co-morbidities were ascertained from self-report and hospital episode statistics (HES). Critical care admission and death were from HES and death register records. We investigated the association of each cardiovascular measure with COVID-19 test result in multivariable logistic regression models adjusting for age, sex, ethnicity, deprivation, body mass index, smoking, diabetes, hypertension, high cholesterol, and prior myocardial infarction. Results We studied 310 participants ( n = 70 positive). Median age was 63.8 [57.5, 72.1] years; 51.0% ( n = 158) were male. 78.7% ( n = 244) were tested in hospital, 3.5% ( n = 11) required critical care admission, and 6.1% ( n = 19) died. In fully adjusted models, smaller LV/RV end-diastolic volumes, smaller LV stroke volume, and poorer global longitudinal strain were associated with significantly higher odds of COVID-19 positivity. Discussion We demonstrate association of pre-existing adverse CMR phenotypes with greater odds of COVID-19 positivity independent of classical cardiovascular risk factors. Conclusions Observational reports of cardiovascular involvement after COVID-19 may, at least partly, reflect pre-existing cardiac status rather than COVID-19 induced alterations.

Objective Vigorous physical activity (PA) in highly trained athletes has been associated with heightened left ventricular (LV) trabeculation extent. It has therefore been hypothesised that LV trabeculation extent may participate in exercise-induced physiological cardiac remodelling. Our cross-sectional observational study aimed to ascertain whether there is a ‘dose–response’ relationship between PA and LV trabeculation extent and whether this could be identified at opposite PA extremes. Methods In a cohort of 1030 individuals from the community-based UK Biobank study (male/female ratio: 0.84, mean age: 61 years), PA was measured via total metabolic equivalent of task (MET) min/week and 7-day average acceleration, and trabeculation extent via maximal non-compaction/compaction ratio (NC/C) in long-axis images of cardiovascular magnetic resonance studies. The relationship between PA and NC/C was assessed by multivariate regression (adjusting for potential confounders) as well as between demographic, anthropometric and LV phenotypic parameters and NC/C. Results There was no significant linear relationship between PA and NC/C (full adjustment, total MET-min/week: ß=−0.0008, 95% CI −0.039 to –0.037, p=0.97; 7-day average acceleration: ß=−0.047, 95% CI −0.110 to –0.115, p=0.13, per IQR increment in PA), or between extreme PA quintiles (full adjustment, total MET-min/week: ß=−0.026, 95% CI −0.146 to –0.094, p=0.67; 7-day average acceleration: ß=−0.129, 95% CI −0.299 to –0.040, p=0.49), across all adjustment levels. A negative relationship was identified between left ventricular ejection fraction and NC/C, significantly modified by PA (ß difference=−0.006, p=0.03). Conclusions In a community-based general population cohort, there was no relationship at, or between, extremes, between PA and NC/C, suggesting that at typical general population PA levels, trabeculation extent is not influenced by PA changes.

<h3>Background</h3> Automated algorithms are being used regularly to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from these methods is necessary to enable widespread adoption. We outline a visual quality control (QC) process for image analysis performed using automated batch processing methods. We aim to report the performance of automated methods and the reliability of replacing visual checks with a statistical outlier removal approach in UK Biobank CMR scans. <h3>Methods</h3> CMR scans included (n=1987) were from the UK Biobank COVID imaging study. Automated batch processing software developed by Circle Cardiovascular Imaging Inc (CVI 42) was used to extract chamber volumetric data, strain, native T1 and aortic flow data. The video outputs of the automated image analysis (~ 62,000 videos and 2000 images) were visually reviewed and rated by six experienced clinicians using a custom-built R Shiny app. The standardised approach (consisting of grading 1,2,3 for good, satisfactory or poor quality respectively) was agreed during two rounds of scoring followed by open discussion. Interobserver variability was assessed using Gwet's second order agreement co-efficient (AC2) analysis. The data output from scans passing visual QC was compared with data from a statistical outlier removal QC method, using t-test analysis, in a subset of healthy individuals from baseline imaging (n = 1069). <h3>Results</h3> The quality of the automated image analysis was very high with &gt;95% of scans passing the visual QC (scored 1 or 2) for all modalities of image analysis. There was good inter-observer agreement with overall AC2 of 0.91(± 0.14, 95% confidence interval (0.84,0.94)). There was no difference in the overall distribution of data and derived average values from visual QC process or statistical outlier removal in a subset of healthy individuals from this study. <h3>Conclusion</h3> The quality of automated image analysis is very high using the prototypes developed by CVI42 for the UK Biobank imaging study CMR scans. Therefore, larger UK Biobank datasets analysed using these automated algorithms do not need in-depth visual QC. Statistical outlier removal is a sufficient QC measure, with operator discretion for visual checks based on their respective population or research aim.

Background The effect of menopausal hormone therapy (MHT)–previously known as hormone replacement therapy–on cardiovascular health remains unclear and controversial. This cross-sectional study examined the impact of MHT on left ventricular (LV) and left atrial (LA) structure and function, alterations in which are markers of subclinical cardiovascular disease, in a population-based cohort. Methods Post-menopausal women who had never used MHT and those who had used MHT ≥3 years participating in the UK Biobank who had undergone cardiovascular magnetic resonance (CMR) imaging and free of known cardiovascular disease were included. Multivariable linear regression was performed to examine the relationship between cardiac parameters and MHT use ≥3 years. To explore whether MHT use on each of the cardiac outcomes differed by age, multivariable regression models were constructed with a cross-product of age and MHT fitted as an interaction term. Results Of 1604 post-menopausal women, 513 (32%) had used MHT ≥3 years. In the MHT cohort, median age at menopause was 50 (IQR: 45–52) and median duration of MHT was 8 years. In the non-MHT cohort, median age at menopause was 51 (IQR: 48–53). MHT use was associated with significantly lower LV end-diastolic volume (122.8 ml vs 119.8 ml, effect size = -2.4%, 95% CI: -4.2% to -0.5%; p = 0.013) and LA maximal volume (60.2 ml vs 57.5 ml, effect size = -4.5%, 95% CI: -7.8% to -1.0%; p = 0.012). There was no significant difference in LV mass. MHT use significantly modified the effect between age and CMR parameters; MHT users had greater decrements in LV end-diastolic volume, LV end-systolic volume and LA maximal volume with advancing age. Conclusions MHT use was not associated with adverse, subclinical changes in cardiac structure and function. Indeed, significantly smaller LV and LA chamber volumes were observed which have been linked to favourable cardiovascular outcomes. These findings represent a novel approach to examining MHT's effect on the cardiovascular system.

Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies.

The non-invasive assessment of left ventricular (LV) diastolic function and filling pressure in hypertrophic cardiomyopathy (HCM) is still an open issue. Pulmonary blood volume index (PBVI) by cardiovascular magnetic resonance (CMR) has been proposed as a quantitative biomarker of haemodynamic congestion. We aimed to assess the diagnostic accuracy of PBVI for left atrial pressure (LAP) estimation in patients with HCM.We retrospectively identified 69 consecutive HCM outpatients (age 58 ± 11 years; 83% men) who underwent both transthoracic echocardiography (TTE) and CMR. Guideline-based detection of LV diastolic dysfunction was assessed by TTE, blinded to CMR results. PBVI was calculated as the product of right ventricular stroke volume index and the number of cardiac cycles for a bolus of gadolinium to pass through the pulmonary circulation as assessed by first-pass perfusion imaging. Compared to patients with normal LAP, patients with increased LAP showed significantly larger PBVI (463 ± 127 vs. 310 ± 86 mL/m2, P < 0.001). PBVI increased progressively with worsening New York Heart Association functional class and echocardiographic stages of diastolic dysfunction (P < 0.001 for both). At the best cut-off point of 413 mL/m2, PBVI yielded good diagnostic accuracy for the diagnosis of LV diastolic dysfunction with increased LAP [C-statistic = 0.83; 95% confidence interval (CI): 0.73-0.94]. At multivariable logistic regression analysis, PBVI was an independent predictor of increased LAP (odds ratio per 10% increase: 1.97, 95% CI: 1.06-3.68; P = 0.03).PBVI is a promising CMR application for assessment of diastolic function and LAP in patients with HCM and may serve as a quantitative marker for detection, grading, and monitoring of haemodynamic congestion.

Cannabis is one of the most widely produced and consumed recreational drugs in the world, with over 192 million global users ([1][1]). The World Health Organization has warned against the potential harmful health effects of nonmedicinal cannabis use and highlighted the need for more research

Image quality assessment is fundamental as it affects the level of confidence in any output obtained from image analysis. Clinical research imaging scans do not often come with an explicit evaluation of their quality, however reports are written associated to the patient/volunteer scans. This rich free-text documentation has the potential to provide automatic image quality assessment if efficiently processed and structured. This paper aims at showing how the use of Semantic Web technology for structuring free-text documentation can provide means for automatic image quality assessment. We aim to design and implement a semantic layer for a special dataset, the annotations made in the context of the UK Biobank Cardiac Cine MRI pilot study. This semantic layer will be a powerful tool to automatically infer or validate quality scores for clinical images and efficiently query image databases based on quality information extracted from the annotations. In this paper we motivate the need for this semantic layer, present an initial version of our ontology as well as preliminary results. The presented approach has the potential to be extended to broader projects and ultimately employed in the clinical setting.

ABSTRACT Osteoporosis and ischemic heart disease (IHD) represent important public health problems. Existing research suggests an association between the two conditions beyond that attributable to shared risk factors, with a potentially causal relationship. In this study, we tested the association of bone speed of sound (SOS) from quantitative heel ultrasound with (i) measures of arterial compliance from cardiovascular magnetic resonance (aortic distensibility [AD]); (ii) finger photoplethysmography (arterial stiffness index [ASI]); and (iii) incident myocardial infarction and IHD mortality in the UK Biobank cohort. We considered the potential mediating effect of a range of blood biomarkers and cardiometabolic morbidities and evaluated differential relationships by sex, menopause status, smoking, diabetes, and obesity. Furthermore, we considered whether associations with arterial compliance explained association of SOS with ischemic cardiovascular outcomes. Higher SOS was associated with lower arterial compliance by both ASI and AD for both men and women. The relationship was most consistent with ASI, likely relating to larger sample size available for this variable (n = 159,542 versus n = 18,229). There was no clear evidence of differential relationship by menopause, smoking, diabetes, or body mass index (BMI). Blood biomarkers appeared important in mediating the association for both men and women, but with different directions of effect and did not fully explain the observed effects. In fully adjusted models, higher SOS was associated with significantly lower IHD mortality in men, but less robustly in women. The association of SOS with ASI did not explain this observation. In conclusion, our findings support a positive association between bone and vascular health with consistent patterns of association in men and women. The underlying mechanisms are complex and appear to vary by sex. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this discovery. Moreover, we examine the differences between the other large clusters and compare our measures with the ground-truth.

Background: Current guidance from International Society for Heart and Lung Transplantation recommends using body weight for donor-recipient size matching for heart transplantation. However, recent studies have shown that predicted heart mass, using body weight, height, age, and sex, may represent a better method of size matching. We aim to validate a cardiovascular magnetic resonance (CMR)–derived equation for predicted left ventricular mass (LVM) in a cohort of normal individuals in the United Kingdom. Methods: This observational study was conducted in 5065 middle-aged (44–77 years old) UK Biobank participants who underwent CMR imaging in 2014 to 2015. Individuals with cancer diagnosis in the previous 12 months or history of cardiovascular disease were excluded. Predicted LVM was calculated based on participants’ sex, height, and weight recorded at the time of imaging. Correlation analyses were performed between the predicted LVM and the LVM obtained from manual contouring of CMR cine images. The analysis included 3398 participants (age 61.5±7.5 years, 47.8% males). RESULTS: Predicted LVM was considerably higher than CMR-derived LVM (mean±SD of 138.8±28.9 g versus 86.3±20.9 g). However, there was a strong correlation between the 2 measurements (Spearman correlation coefficient 0.802, P &lt;0.0001). Conclusions: Predicted LVM calculated using a CMR-derived equation that incorporates height, weight, and sex has a strong correlation with CMR LVM in large cohort of normal individuals in the United Kingdom. Our findings suggest that predicted heart mass equations may be a valid tool for donor-recipient size matching for heart transplantation in the United Kingdom.

Reduced lung function is common and associated with increased cardiovascular morbidity and mortality, even in asymptomatic individuals without diagnosed respiratory disease. Previous studies have identified relationships between lung function and cardiovascular structure in individuals with pulmonary disease, but the relationships in those free from diagnosed cardiorespiratory disease have not been fully explored.UK Biobank is a prospective cohort study of community participants in the United Kingdom. Individuals self-reported demographics and co-morbidities, and a subset underwent cardiovascular magnetic resonance (CMR) imaging and spirometry. CMR images were analysed to derive ventricular volumes and mass. The relationships between CMR-derived measures and spirometry and age were modelled with multivariable linear regression, taking account of the effects of possible confounders.Data were available for 4,975 individuals, and after exclusion of those with pre-existing cardiorespiratory disease and unacceptable spirometry, 1,406 were included in the analyses. In fully-adjusted multivariable linear models lower FEV1 and FVC were associated with smaller left ventricular end-diastolic (-5.21ml per standard deviation (SD) change in FEV1, -5.69ml per SD change in FVC), end-systolic (-2.34ml, -2.56ml) and stroke volumes (-2.85ml, -3.11ml); right ventricular end-diastolic (-5.62ml, -5.84ml), end-systolic (-2.47ml, -2.46ml) and stroke volumes (-3.13ml, -3.36ml); and with lower left ventricular mass (-2.29g, -2.46g). Changes of comparable magnitude and direction were observed per decade increase in age.This study shows that reduced FEV1 and FVC are associated with smaller ventricular volumes and reduced ventricular mass. The changes seen per standard deviation change in FEV1 and FVC are comparable to one decade of ageing.

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.

Abstract Aims Data regarding the effects of regular alcohol consumption on cardiac anatomy and function are scarce. Therefore, we sought to determine the relationship between regular alcohol intake and cardiac structure and function as evaluated with cardiac magnetic resonance imaging. Methods and results Participants of the UK Biobank who underwent cardiac magnetic resonance were enrolled in our analysis. Data regarding regular alcohol consumption were obtained from questionnaires filled in by the study participants. Exclusion criteria were poor image quality, missing, or incongruent data regarding alcohol drinking habits, prior drinking, presence of heart failure or angina, and prior myocardial infarction or stroke. Overall, 4335 participants (61.5 ± 7.5 years, 47.6% male) were analysed. We used multivariate linear regression models adjusted for age, ethnicity, body mass index, smoking, hypertension, diabetes mellitus, physical activity, cholesterol level, and Townsend deprivation index to examine the relationship between regular alcohol intake and cardiac structure and function. In men, alcohol intake was independently associated with marginally increased left ventricular end-diastolic volume [β = 0.14; 95% confidence interval (CI) = 0.05–0.24; P = 0.004], left ventricular stroke volume (β = 0.08; 95% CI = 0.03–0.14; P = 0.005), and right ventricular stroke volume (β = 0.08; 95% CI = 0.02–0.13; P = 0.006). In women, alcohol consumption was associated with increased left atrium volume (β = 0.14; 95% CI = 0.04–0.23; P = 0.006). Conclusion Alcohol consumption is independently associated with a marginal increase in left and right ventricular volumes in men, but not in women, whereas alcohol intake showed an association with increased left atrium volume in women. Our results suggest that there is only minimal relationship between regular alcohol consumption and cardiac morphology and function in an asymptomatic middle-aged population.

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

Background and Objectives: Obstructive sleep apnea (OSA) is a common disorder with an increased risk for left ventricular and right ventricular dysfunction. Most studies to date have examined populations with manifest cardiovascular disease using echocardiography to analyze ventricular dysfunction with little or no reference to ventricular volumes or myocardial mass. Our aim was to explore these parameters with cardiac MRI. We hypothesized that there would be stepwise increase in left ventricular mass and right ventricular volumes from the unaffected, to the snoring and the OSA group. Materials and Methods: We analyzed cardiac MRI data from 4978 UK Biobank participants free from cardiovascular disease. Participants were allocated into three cohorts: with OSA, with self-reported snoring and without OSA or snoring (n = 118, 1886 and 2477). We analyzed cardiac parameters from balanced cine-SSFP sequences and indexed them to body surface area. Results: Patients with OSA were mostly males (47.3% vs. 79.7%; p < 0.001) with higher body mass index (25.7 ± 4.0 vs. 31.3 ± 5.3 kg/m²; p < 0.001) and higher blood pressure (135 ± 18 vs. 140 ± 17 mmHg; p = 0.012) compared to individuals without OSA or snoring. Regression analysis showed a significant effect for OSA in left ventricular end-diastolic index (LVEDVI) (β = -4.9 ± 2.4 mL/m²; p = 0.040) and right ventricular end-diastolic index (RVEDVI) (β = -6.2 ± 2.6 mL/m²; p = 0.016) in females and for right ventricular ejection fraction (RVEF) (β = 1.7 ± 0.8%; p = 0.031) in males. A significant effect was discovered in snoring females for left ventricular mass index (LVMI) (β = 3.5 ± 0.9 g/m²; p < 0.001) and in males for left ventricular ejection fraction (LVEF) (β = 1.0 ± 0.3%; p = 0.001) and RVEF (β = 1.2 ± 0.3%; p < 0.001). Conclusion: Our study suggests that OSA is highly underdiagnosed and that it is an evolving process with gender specific progression. Females with OSA show significantly lower ventricular volumes while males with snoring show increased ejection fractions which may be an early sign of hypertrophy. Separate prospective studies are needed to further explore the direction of causality.

Abstract Background There are conflicting reports on the association of coffee consumption with cardiovascular (CV) health. The UK Biobank is a prospective cohort study including data for half a million middle-aged individuals. Purpose We studied the association of daily coffee consumption with all-cause and CV mortality, and incidence of the major CV diseases in the UK Biobank. In a subgroup of participants who underwent cardiovascular magnetic resonance (CMR), we evaluated the association between regular coffee intake and cardiac structure and function parameters. Methods UK Biobank cohort of participants without clinically manifested heart disease at the time of recruitment were included. Regular coffee intake was categorized into 3 groups: zero, light-to-moderate (0.5–3 cups/day) and high (&amp;gt;3 cups/day) coffee drinkers. We estimated association of daily coffee consumption with incident outcomes using multivariable Cox-regression models (median follow-up of 11 years) and, in the subset with CMR data, with left and right ventricular (LV, RV) end-systolic and end-diastolic volumes, LV mass, and LV/RV stroke volume using multivariable linear regression. Models were adjusted for potential confounders and mediators, including: age, sex, non-European ethnicities, body mass index, smoking, physical activity, Townsend deprivation index, alcohol, meat, fruit and vegetable intake, hypertension, diabetes mellitus, and cholesterol level. Results We included 468,629 individuals (mean age 56.2±8.1 years, 44.2% male). Among them, 22.1% did not consume coffee on a regular basis, 58.4% had 0.5–3 cups per day and 19.5% had &amp;gt;3 cups per day. After adjustment for potential confounders and mediators, compared to non-coffee drinkers, light-to-moderate coffee drinking was associated with lower risk of all-cause mortality (HR=0.88, p&amp;lt;0.001), CV mortality (HR=0.83, p=0.006), and incident stroke (HR=0.79; p=0.037). CMR data were available in 30,650 participants. In multivariable analysis, compared to non-coffee drinkers, both the light-to-moderate and high coffee consuming categories, were associated with significantly increased LV and RV ventricular end-systolic (β=0.91 and 1.64 for LV and 1.10 and 1.72 for RV), end-diastolic (β=2.21 and 3.28 for LV and 2.24 and 3.35 for RV) and stroke volumes (β=1.31 and 1.64 for LV and 1.15 and 1.63 for RV), as well as greater LV mass (β=0.78 and 1.64; all p&amp;lt;0.001). Conclusion In this large study of the UK Biobank population, regular coffee consumption of up to 3 cups per day was associated with favorable cardiovascular outcomes, in particular, decreased all-cause and CV mortality and stroke incidence. Regular coffee consumption was also associated with a pattern of CMR metrics in keeping with the reverse of age-related cardiac alterations. Funding Acknowledgement Type of funding sources: None.

Opportunities to participate in leadership and management with protected time can be limited for clinical trainees. The aim of this fellowship was to gain experience of gold standard healthcare management by becoming part of multidisciplinary teams working to deliver transformational change in the National Health Service (NHS).A 6-month pilot fellowship, structured as an Out of Programme Experience was created for two registrars to be seconded to the healthcare division of Deloitte, a leading professional services firm. Competitive selection was jointly administered by the Director of Medical Education at St Bartholomew's Hospital and Deloitte.The successful candidates worked on service-led and digital transformation projects, interfacing with senior NHS executives and directors. Trainees gained direct experience and understanding of high-level decision making in the NHS, tackling complex service delivery problems and the practical realities of delivering change within a constrained budget. One impact of this pilot has been completion of a business case to scale up the fellowship into an established programme that can allow other trainees to apply.This innovative fellowship has allowed interested trainees an opportunity to broaden the relevant skills and experience in leadership and management required in specialty training curriculum with real-life application in the NHS.

Objectives Previous studies have demonstrated that abnormal physiological observations are often recorded on patients’ observation charts but not acted on, with ensuing negative consequences. To address this issue within our hospital, traditional charts with a graphic depiction of observations were replaced with new charts combining early warning scores (EWS) with numerically depicted observations. However, the replacement did not include a graphic display of observations in the form of trend graphs. The present study compared the speed and accuracy of data interpretation between the 2 charts. Methods Six clinical scenarios (low-grade temperature, spiking temperature, tachypnea, Cushing’s response, hypovolaemic shock and normal observations) were identically depicted on old and new charts, creating 12 charts. One hundred health-care professionals were asked to study each of the charts, and the time taken to give a diagnosis was recorded. Time taken and accuracy of response were compared between the 2 charts. Results The old chart was associated with faster responses in all of the scenarios, reaching statistical significance in 5 of the 6 scenarios (P < 0.0001). Additionally, the response was more accurate in all of the scenarios, reaching statistical significance in 3 of the 6 scenarios (P < 0.0001). Overall, response to the old chart was 1.6 times faster (P < 0.0001) and 15% more accurate (90% versus 75%, P < 0.0001) than the new chart. Conclusions Graphic display of data is associated with faster and more accurate assimilation of information. Hence, charts combining EWS with graphic portrayal of observation trends may contribute to earlier recognition of sick patients.

Heart failure (HF) is a major health problem and early diagnosis is important. Atherosclerosis is the main cause of HF and carotid intima-media thickness (IMT) is a recognized early measure of atherosclerosis. This study aimed to investigate whether increased carotid IMT is associated with changes in cardiac structure and function in middle-aged participants of the UK Biobank Study without overt cardiovascular disease.Participants of the UK Biobank who underwent CMR and carotid ultrasound examinations were included in this study. Patients with heart failure, angina, atrial fibrillation, and history of myocardial infarction or stroke were excluded. We used multivariable linear regression models adjusted for age, sex, physical activity, body mass index, body surface area, hypertension, diabetes, smoking, ethnicity, socioeconomic status, alcohol intake, and laboratory parameters. In total, 4301 individuals (61.6 ± 7.5 years, 45.9% male) were included. Multivariable linear regression analyses showed that increasing quartiles of IMT was associated with increased left and right ventricular (LV and RV) and left atrial volumes and greater LV mass. Moreover, increased IMT was related to lower LV end-systolic circumferential strain, torsion, and both left and right atrial ejection fractions (all P < 0.05).Increased IMT showed an independent association over traditional risk factors with enlargement of all four cardiac chambers, decreased function in both atria, greater LV mass, and subclinical LV dysfunction. There may be additional risk stratification that can be derived from the IMT to identify those most likely to have early cardiac structural/functional changes.

We describe the case of a 48-year-old woman whose atrial myxoma was mistaken for vasculitis. The case report highlights the reasons why these two disorders may become confused, the dangers of initiating the wrong treatment and a simple means of avoiding misdiagnosis.

The quantitative measures used to assess the performance of automated methods often do not reflect the clinical acceptability of contouring. A quality-based assessment of automated cardiac magnetic resonance (CMR) segmentation more relevant to clinical practice is therefore needed.We propose a new method for assessing the quality of machine learning (ML) outputs. We evaluate the clinical utility of the proposed method as it is employed to systematically analyse the quality of an automated contouring algorithm.A dataset of short-axis (SAX) cine CMR images from a clinically heterogeneous population (n = 217) were manually contoured by a team of experienced investigators. On the same images we derived automated contours using a ML algorithm. A contour quality scoring application randomly presented manual and automated contours to four blinded clinicians, who were asked to assign a quality score from a predefined rubric. Firstly, we analyzed the distribution of quality scores between the two contouring methods across all clinicians. Secondly, we analyzed the interobserver reliability between the raters. Finally, we examined whether there was a variation in scores based on the type of contour, SAX slice level, and underlying disease.The overall distribution of scores between the two methods was significantly different, with automated contours scoring better than the manual (OR (95% CI) = 1.17 (1.07-1.28), p = 0.001; n = 9401). There was substantial scoring agreement between raters for each contouring method independently, albeit it was significantly better for automated segmentation (automated: AC2 = 0.940, 95% CI, 0.937-0.943 vs manual: AC2 = 0.934, 95% CI, 0.931-0.937; p = 0.006). Next, the analysis of quality scores based on different factors was performed. Our approach helped identify trends patterns of lower segmentation quality as observed for left ventricle epicardial and basal contours with both methods. Similarly, significant differences in quality between the two methods were also found in dilated cardiomyopathy and hypertension.Our results confirm the ability of our systematic scoring analysis to determine the clinical acceptability of automated contours. This approach focused on the contours' clinical utility could ultimately improve clinicians' confidence in artificial intelligence and its acceptability in the clinical workflow.

We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this discovery. Moreover, we examine the differences between the other large clusters and compare our measures with the ground-truth.

Abstract 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 &lt; 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.

<h3>Background</h3> The superiority of stress CMR imaging for the detection of coronary artery disease has been demonstrated in recent studies. The identification of perfusion defects has been correlated with adverse outcomes. The percutaneous coronary intervention (PCI) treatment of proximal LAD lesions has been shown to be associated with improved outcome. Patients with previous proximal LAD stenting presenting with chest pain symptoms have often observed to have basal septal perfusion defects which do not propagate beyond the basal segments. This is likely related to compromised flow in the septal perforators in the presence of the stent scaffold. However, the significance of basal septal perfusion defects identified in patients presenting with chest pain symptoms in the presence of angiographically unobstructed proximal LAD stents is unclear. We studied the outcome of patients with previous proximal LAD stenting presenting with chest pain symptoms with or without a basal septal perfusion defect. <h3>Methods</h3> Retrospective analysis of 326 patients presenting with stable angina, who had undergone PCI to the proximal LAD at our institution between January 2005 and November 2011 was performed. All patients were investigated with a standard stress CMR protocol utilising vasodilator stress with adenosine. Patients were divided into two cohorts—234 patients with no basal septal perfusion defect and 92 patients with a basal septal perfusion defect. The primary outcome was all-cause mortality. The mean follow-up after stent implantation was 3.0±1.7 years for no basal septal perfusion defect group and 2.7±1.7 years for basal septal perfusion defect group. <h3>Results</h3> The two groups of patients were well matched in terms of age and gender. Among patients with a basal septal perfusion defect, there was a higher rate of Asian ethnicity and diabetes but this was not statistically significant. There was no significant difference in the use of drug eluting stents (DES) or the number of stents used. There was no significant difference between the two cohorts with regards to indication of PCI and the time elapsed from stenting to CMR scan. On long term follow-up, there was no significant difference in all-cause mortality between the two groups. <h3>Conclusions</h3> The presence of a basal septal perfusion defect in patients with proximal LAD stent is not associated with adverse outcome. The increasing observation of these defects may be related to superior spatial resolution of CMR imaging. However, if these perfusion defects are causing patients9 symptoms, our data does point to the safety of conservative management of these cases. Although there was higher rate of female gender, Asian ethnicity and diabetes among patients with a basal septal perfusion defect, there was no significant patient or procedural factors associated with the identification of perfusion defects in the presence of proximal LAD stent.

The influence of an observation in multiple-group discriminant analysis is investi gated through the estimates on misclassification probabilities under two different classification rules that are obtained using the full sample and the sample without the observation. A fixed underlying multinomial distribution is taken for evaluating the estimates. Influence on misclassification prob ability estimates for individual groups is also considered. The proposed measures may be applied

Results The 598 patients (age 59 ± 12 years, 20% female underwent stress CMR a median of 93 days (IQR: 41, 224 days) after coronary stenting with follow-up for 1.4 years (IQR: 0.6-2.7). Inducible perfusion defects were identified in 294 (49%) patients of whom 18 (6%) died during follow-up compared with 6 (2.0%) patients with no perfusion defects (p = 0.01). Of the 294 patients with perfusion defects, 70 (24%) were revascularised (PCI 55, CABG 27) of whom 5 (7%) died during follow-up compared with 13 (6%) who were not revascularised(p = 0.68). K-M survival analysis confirmed that revascularisation was unassociated with survival benefit, regardless of the severity of ischaemia (Figure 1).

<h3>Background</h3> Residual myocardial ischaemia early after acute coronary syndromes (ACS) is commonly regarded as an adverse prognostic sign and an indication for revascularisation. However, the benefits of revascularisation for improving prognosis are not known. <h3>Methods</h3> Analysis of 597 consecutive patients with ACS treated with coronary stenting, all of whom underwent adenosine stress cardiac magnetic resonance (CMR)&nbsp;perfusion imaging to guide revascularisation decisions. Follow-up data were obtained from hospital electronic health records. <h3>Results</h3> The 597 patients (age 59 ± 12 years, 20% female) underwent stress CMR scan, at median of 93 days (IQR: 41, 224 days) after coronary stenting with follow-up&nbsp;&nbsp;for 1.4 years (IQR: 0.6-2.7). Inducible&nbsp;perfusion defects were identified in 293 (49%) patients of whom 18 (6%) died&nbsp;during follow-up compared with 6 (2.0%) patients with no perfusion defects (p=0.01). Of the 293 patients with perfusion defects (Table 1), 70 (24%) were revascularised (PCI 54, CABG 26) of whom 5 (7%) died during follow-up compared&nbsp;with 13 (6%) who were not evascularised (p=0.66). K-M survival analysis confirmed that revascularisation was unassociated with survival benefit, regardless of the severity of ischaemia (Figure 1). <h3>Conclusion</h3> In our patients with ACS and coronary stenting, inducible ischaemia was associated with increased risk of death during follow-up. Revascularisation did not appear to reduce the risk and should be reserved for improving symptoms in patients on optimal medical therapy.

Image quality assessment is fundamental as it affects the level of confidence in any output obtained from image analysis. Clinical research imaging scans do not often come with an explicit evaluation of their quality, however reports are written associated to the patient/volunteer scans. This rich free-text documentation has the potential to provide automatic image quality assessment if efficiently processed and structured. This paper aims at showing how the use of Semantic Web technology for structuring free-text documentation can provide means for automatic image quality assessment. We aim to design and implement a semantic layer for a special dataset, the annotations made in the context of the UK Biobank Cardiac Cine MRI pilot study. This semantic layer will be a powerful tool to automatically infer or validate quality scores for clinical images and efficiently query image databases based on quality information extracted from the annotations. In this paper we motivate the need for this semantic layer, present an initial version of our ontology as well as preliminary results. The presented approach has the potential to be extended to broader projects and ultimately employed in the clinical setting.

Purpose: European guidelines state that with the exception of cardiogenic shock, percutaneous coronary intervention (PCI) for ST elevation myocardial infarction (STEMI) should be limited to the culprit artery. Although there is no robust randomized controlled trial data available there is a general consensus that non culprit coronary artery lesions identified at the time of STEMI should be treated in a staged manner. Whilst there is general consensus on the above; currently neither guidelines nor evidence exist regarding the optimal time for staged PCI. Our aim was to compare outcomes in patients who underwent staged PCI either as an inpatient or as an outpatient within six weeks time. Methods: This was an observational cohort study of 983 patients with multi-vessel disease who underwent PPCI from 2007 to 2011. Patients with previous CABG, cardiogenic shock were excluded. 102 patients were managed with staged intervention. The primary outcome was major adverse cardiac events (all cause mortality, myocardial infarction, target vessel revascularisation and stroke). Follow-up was for a median of 3.2 years (IQR range 2.0-4.4 years). Results: 24 (23.5%) patients underwent inpatient staged PCI with 78 (76.5%) patients undergoing their intervention within 6 weeks of hospital discharge. Patients who underwent inpatient staged PCI were older compared to those having 6 week staged procedures. (66.1±10.4 vs 59.2±10.6, P=0.02). Otherwise there were no other differences in baseline characteristics. Unadjusted Kaplan-Meier analysis revealed no significant difference in the 3-year event rates between patients undergoing in-patient and 6 week staged procedures. Un-adjusted Cox analysis demonstrated no difference in 3 year outcomes between inpatient and 6 week procedures (HR 0.84 [95% CI 0.27-2.61)), which persisted after multivariate adjustment ((HR 0.60 [95% CI 0.12-2.96)) (factors corrected for: previous MI, DM, access, age, EF, eGFR, DES use, gender, previous CVA, PVD). Conclusion: This observational data suggests that staged procedures performed as an in-patient or at 6 weeks post discharge are associated with similar outcomes in STEMI patients with MVD undergoing primary PCI.

<h3>Background</h3> European guidelines state that with the exception of cardiogenic shock, percutaneous coronary intervention (PCI) for ST elevation myocardial infarction (STEMI) should be limited to the culprit artery. Although there is no robust randomised controlled trial data available there is a general consensus that non-culprit coronary artery lesions identified at the time of STEMI should be treated in a staged manner. Whilst there is general consensus on the above; currently neither guidelines nor evidence exist regarding the optimal time for staged PCI. Our aim was to compare outcomes in patients who underwent staged PCI either as an in-patient or as an out-patient within 6 weeks time following a STEMI. <h3>Methods</h3> This was an observational cohort study of 983 patients with multi-vessel disease who underwent primary PCI from 2007 to 2011. Patients with previous CABG, cardiogenic shock were excluded. 102 patients were managed with staged intervention. The primary outcome was major adverse cardiac events (all cause mortality, myocardial infarction, target vessel revascularisation and stroke). Follow-up was for a median of 3.2 years (IQR range 2.0–4.4 years). <h3>Results</h3> 24 (23.5%) patients underwent in-patient staged PCI with 78 (76.5%) patients undergoing their intervention within 6 weeks of hospital discharge. Differences in baseline characteristics are outlined in table&nbsp;1. Patients who underwent in-patient staged PCI were older compared to those having 6 week staged procedures. (66.1±10.4 vs 59.2±10.6, p=0.02). Otherwise there were no other differences in baseline characteristics. Unadjusted Kaplan-Meier analysis revealed no significant difference in the 3-year event rates between patients undergoing in-patient and 6 week staged procedures (figure&nbsp;1). Un-adjusted Cox analysis demonstrated no difference in 3 year outcomes between in-patient and 6 week procedures (HR 0.84 (95% CI 0.27 to 2.61)), which persisted after multivariate adjustment ((HR 0.60 (95% CI 0.12 to 2.96)) (factors corrected for: previous MI, DM, access, age, EF, eGFR, DES use, gender, previous CVA, PVD). <h3>Conclusions</h3> This observational data suggests that staged procedures performed as an in-patient or at 6 weeks post discharge are associated with similar outcomes in STEMI patients with multi-vessel disease undergoing primary PCI.

Current SOPs for the analysis of cardiovascular magnetic resonance imaging funded by the BHF PG/14/89/31194

The Guizhou provincial government issued several directives in 2000-2001 to implement the policy in the province, including abolishing rural schools. The implementation of the policy without considerations of the local situation has resulted in a number of adverse effects. The study gives recommendations for Oxfam's position, programme, and advocacy work.

<h3>Introduction</h3> Intra-ocular pressure (IOP) measurement is an integral part a comprehensive eye examination. In addition to IOP, corneal biomechanical characteristics such as corneal hysteresis (CH), a measurement of viscoelastic compliance, and corneal resistance factor (CRF), derived from corneal deformability, have also been identified as useful indicators of incidence and progression of primary open angle glaucoma (POAG) (1,2). Corneal tissue shares compositionally similar properties with arterial tissue (3,4). Our cross-sectional observational study aimed to ascertain whether corneal biomechanical metrics (CH &amp; CRF) are associated with arterial stiffness – a well-established marker of future cardiovascular (CV) events and mortality. <h3>Methods</h3> From an initial pool of 5065 participants from the community-based UK Biobank study, 4018 were rejected for missing data, leaving a cohort of 1047 individuals (male/female ratio: 0.496, mean age: 62 years, white ethnicity: 96.1%) (Table 1). Corneal biomechanical metrics (CH &amp; CRF), were obtained using a Reichert Ocular Response Analyzer (ORA). Arterial compliance was quantified by aortic distensibility (AoD) derived by cardiovascular magnetic resonance (CMR) imaging. The relationship between corneal and vascular compliance parameters was assessed using both Spearman rank correlation coefficient analysis, and univariable and multivariable regression analyses adjusting for potential influential confounding variables – age, sex, ethnicity, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, regular alcohol intake, diabetes status and dyslipidaemia. <h3>Results</h3> A significant weakly positive correlation was observed between CH and AoD at both the ascending aorta (AA) and proximal descending aorta (PDA) (AA: Rho = 0.08, p = 0.01; PDA: Rho = 0.11, p &lt;0.01), however no significant correlation was observed between CRF and AoD. In univariable analysis, only CH produced significant changes in AoD at both the AA and PDA (AA: ß = +3.0% per 10% increase in CH, 95% CI = 0.6 to 5.5, p = 0.02; PDA: ß = +2.6% per 10% increase in CH, 95% CI = 0.8 to 4.4, p = 0.004) (Figure 1). There was no significant linear relationship between CH or CRF and AoD in multivariable regression analysis, at both the AA and PDA (CH at AA: ß = +0.8% per 10% increase in CH, 95% CI = -0.9 to 2.6, p = 0.37; CH at PDA: ß = +0.8% per 10% increase in CH, 95% CI = -0.3 to 2.0, p = 0.16; CRF at AA: ß = +1.3% per 10% increase in CH, 95% CI = -0.4 to 3.0, p = 0.13; CRF at PDA: ß = +0.9% per 10% increase in CH, 95% CI = -0.3 to 2.0, p = 0.13). <h3>Conclusion</h3> In this community-based cohort, we observed a weakly significant general correlation between CH and AoD. After adjustment for potential confounding factors, we then observed no significant relationship between corneal and aortic biomechanical indices, suggesting that in a general population, biomechanical corneal indices are not independently associated with parameters of central arterial compliance. <h3>Conflict of Interest</h3> None

Objective: Hypertension has progressive end-organ effects such as left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been described using cardiac magnetic resonance (CMR) LV mass to volume ratio; normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between LVH phenotypes is unclear. Design and method: As a preliminary analysis, 5,065 participants in the UK Biobank were categorised into the 4 CMR-defined LVH phenotypes. The 12 lead ECG of each participant was analysed using MATLAB to derive ECG biomarkers known to have an association with LVH (QRS duration, QRS ascending and descending slopes, Q wave, R wave, S wave and QRS wave amplitudes). ANOVA compared differences in ECG biomarkers across LVH phenotypes. Univariate logistic regression was used to test association of each ECG biomarker with the LVH phenotypes. ECG biomarkers with a significant association (P < 0.05) were included in the multinomial logistic regression model. Results: In combination, the set of 7 ECG biomarkers detected a difference (P < 2.2e-16) across the LVH phenotypes. Using logistic regression, QRS duration, S wave and QRS waves amplitude were able to differentiate between the 4 LVH phenotypes. With normotensive group as a reference, there was a significant association between S wave amplitude and normal LV (Odds Ratio 1.80, P 1.46e-03), QRS wave amplitude and normal LV (OR 2.23, P 9.01e-05), global QRS duration with eccentric LVH (OR 1.05, P 4.45e-06) and concentric LVH (OR 1.03, P 1.02e-05). Conclusions: We identified a set of ECG markers from the QRS complex that can differentiate between the 4 LVH phenotypes, providing support for the ECG to identify subclinical LVH identified using CMR. We are currently extending our analyses to the full 45,000 UK Biobank imaging cohort for validation.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute For Health Research (NIHR), UK Introduction Aberrations in left ventricular (LV) filling or relaxation – known as diastolic dysfunction – occur in heart failure with preserved ejection fraction. CMR is the reference modality for the assessment of ventricular systolic function, however, its role in evaluation of diastolic function is limited at present. One promising technique to assess diastolic function by CMR is the derivation of LV filling and emptying rates from the volume-time curves of cine images. Purpose To automatically assess the quality of LV filling-rate curves and annotate the peak emptying and filling rates. Methods A previously-described deep-learning network was used to automatically segment the entire cardiac cycle captured by short-axis SSFP cine images from the UK Biobank1. The LV filling-rate curves derived from the volume-time data were smoothed with Savitzky–Golay filter. The peak emptying rate (PER), early peak filling rate (PFR-E) and late peak filling rate (PFR-A) were first annotated by a simple peak finding algorithm from Python Scipy signal module. The preliminary annotated curves were reviewed by five human experts (i) to check for peak-annotation errors and (ii) to provide the curve quality score ranging from 1 to 3 for each peak (score 1 denotes good quality, score 2 represents moderate quality and score 3 indicates poor quality). Higher total score (minimum = 3, maximum = 9), therefore, represents poorer overall curve quality. This expert-annotated dataset was used to train two separate one-dimensional convolutional neural networks (1D-CNN) (Figure 1) for peak annotation and curve quality assessment (QA) using Tensorflow library in Python. Results The data from 6,328 LV filling-rate curves were split into the training and testing sets (80:20). The fine-tuned 1D-CNN comprising six hidden layers with two residual connections annotated the PER, PFR-E and PFR-A with the test-set accuracy of 95%, 95% and 98%, respectively. A second trained 1D-CNN for QA based on similar architecture predicted the overall curve quality score with a small error rate (mean absolute error: 0.46, mean squared error: 0.68). These two networks were used to quality check and label 19,409 UK Biobank CMR studies (See Figure 2 for exemplary results). After removing data from poor-quality curves (quality score ≥ 5), 18,735 studies remained. The mean±standard deviation of PER, PFR-E and PFR-A are 461±110 ml/s, 359±117 ml/s and 336±120 ml/s, respectively. Ageing is associated with lower PFR-E (−58.4 ml/s, 95% confidence interval [CI]: −56.1 to −60.7 ml/s per decade increment) and higher PFR-A (18.3 ml/s, 95% CI: 15.8 to 20.8 ml/s per decade increment). Conclusion The 1D-CNN models can be used to automatically grade the quality of LV filling rate curves and label important diastolic parameters with a high level of accuracy. The derived data recapitulate impaired LV relaxation pattern associated with ageing and can be used as surrogate indices of diastology by CMR. Figure 1Figure 2

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

We read with interest the paper by Jain et al. [(1)][1] assessing right atrial (RA) phasic function in heart failure (HF) with preserved and reduced ejection fraction. They compared their HF cohort to patients identified as not having HF but having a clinically indicated cardiovascular magnetic

<h3>Introduction</h3> Coffee is widely reported to be the world’s most popular drink and previous studies revealed acute increases in arterial stiffness with its consumption. But, the reported chronic effects of coffee on arterial stiffness are inconsistent and limited by modest number of studied subjects. This study aims to evaluate the association of coffee consumption on arterial stiffness using two forms of stiffness measures in a large population cohort. Aortic distensibility (AoD) is a local measure of arterial stiffness whilst arterial stiffness index (ASI) is a measure of wave reflection. Both measures have been shown to be predictors of cardiovascular events. <h3>Methods</h3> This cross-sectional cohort analysis comprised of 17,932 participants in the UK Biobank Imaging Study who underwent both cardiovascular magnetic resonance (CMR) imaging and pulse waveform measurements via finger probes. Participants with known cardiovascular disease were excluded. Coffee consumption habits were self-reported at the time of imaging and those who drink &gt;25 cups/day were excluded. Coffee consumption was categorised into 3 groups (≤1, 1–3, &gt;3 cups/day) with the lowest group used as the reference in the analyses. AoD was derived using an automated method to obtain the maximum and minimum luminal areas in the ascending (AA) and descending aorta (DA) from cine CMR images. ASI was calculated by dividing the participant’s height by the time interval between the peaks of the waveform recorded. AoD and ASI outliers (1.5x inter-quartile range rule) were excluded. Log transformation of AoD values was performed prior to regression analyses. Associations between coffee consumption and stiffness measures were assessed separately using univariate linear regression models adjusting for age, sex, ethnicity, Townsend deprivation index, current smoking, higher levels of education, height, weight, regular alcohol consumption (≥3 times/week), systolic blood pressure, resting heart rate, presence of hypertension, hypercholesterolaemia or diabetes, intake of vegetable, meat, water and tea consumption. <h3>Results</h3> Baseline characteristics of the 8,412 participants included in the final analyses are summarised in table 1. Moderate and heavy coffee drinkers were more likely to be male, smoke and consumed alcohol regularly. No significant differences were observed in the systolic blood pressures and heart rates between the groups. The unadjusted distributions of AoD in AA and DA were similar across the 3 groups (figure 1). Our regression models found no statistically significant differences in all three arterial stiffness measures for individuals who drink 1–3 cups or &gt;3 cups of coffee/day compared with the reference group (table 2). <h3>Conclusion</h3> In this large middle-aged cohort without cardiovascular disease, moderate to heavy coffee consumption was not associated with significant changes in arterial stiffness measured by AoD and ASI compared with individuals who drink ≤1 cups of coffee/day. <h3>Conflict of Interest</h3> None

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Innovate UK Background Regional assessment of septal native T1 values with cardiovascular magnetic resonance (CMR) is used to characterise diffuse myocardial diseases. Previous studies suggest its potential role in detecting early pathological alterations, which may help identify high-risk subjects at early disease stages. Automated analysis of myocardial native T1 images may enable faster CMR analysis and reduce inter-observer variability of manual analysis. However, the technical performance of such methodologies has not been previously reported. Purpose We tested, in a subset of UK Biobank participants, the degree of agreement between CMR septal myocardial T1 values obtained from our machine learning (ML) algorithm and septal native T1 values computed from manual segmentations. Methods We analysed the first 292 participants who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and had CMR imaging (1.5 Tesla, Siemens MAGNETOM Aera). T1 mapping was performed in a single mid-ventricular short axis (SAX) slice using ShMOLLI (WIP780B) sequences. Three experienced CMR readers independently measured native T1 values by manually placing a single region of interest (ROI) covering half of the anteroseptal and half of the inferoseptal wall using cvi42 post-processing software (version 5.11). A mean T1 value for each participant was then calculated. A ML algorithm developed by Circle Cardiovascular Imaging Inc. was then applied to the same images to derive the myocardium T1 values automatically. The algorithm was previously trained to segment myocardium from SAX T1 and non-T1 mapping images on two external CMR datasets. We compared the mean septal ROI T1 values to the mean myocardium T1 values predicted by the ML algorithm. Results Two studies were excluded after quality control. The ML-derived and the manually calculated mean T1 values were significantly correlated (r = 0.82, p &amp;lt; 0.001). The Bland-Altman analysis between the two methods showed a mean bias of 3.64 ms, with 95% limits of agreement of −38.88 to 53.46 ms, indicating good agreement (figure 1). Conclusions We demonstrated strong correlation and good agreement between native T1 values obtained from our automated analysis method and manual T1 septal analysis in a subset of UK Biobank participants. This algorithm may represent a valuable tool for clinicians allowing for fast and potentially less operator-dependent myocardial tissue characterisation. However, validation of more extensive datasets and quality control processes are needed.

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): AK has been funded by the Egyptian cultural centre and educational bureau of the Egyptian embassy in London and the Ministry of higher education in Egypt. SEP acknowledges support from the “SmartHeart” EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1) and the London Medical Imaging and AI Centre for Value-Based Healthcare. This new centre is one of the UK Centres supported by a £50m investment from the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). SEP acknowledges support from the CAP-AI programme, London’s first AI enabling programme focused on stimulating growth in the capital’s AI Sector. CAP-AI is led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and is funded by the European Regional Development Fund and Barts Charity. SEP also acts as a paid consultant to Circle Cardiovascular Imaging Inc., Calgary, Canada and Servier onbehalf Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK Background Manual contouring of cardiovascular magnetic resonance (CMR) cine images remains common practice and the reference standard for left ventricular (LV) volumes and mass evaluation. However, it is time-consuming and machine learning (ML) may significantly reduce the time required for contouring. Accurate LV contours are the basis for reliable LV strain analysis using tissue tracking. Purpose To assess the impact of a ML contouring tool alone versus expert adjusted contours on LV strain. Methods We retrospectively selected 402 CMR studies with diagnoses of myocardial infarction (n = 108), myocarditis (n = 130) and healthy controls (n = 164) from the Barts BioResource between January 2015 to June 2018. CMR examinations were obtained using 1.5T and 3T scanners (Siemens Healthineers, Germany). We excluded 32 cases due to phase inconsistency between short (SAX) and long axes (LAX) cine images or suboptimal cine image quality. For the remaining 370 cases, steady state free precession cine images for LAX and SAX were analysed by the ML contouring tool (using CVI42 research prototype software 5.11). Manual expert adjustment for the contours was done for each case if considered suboptimal for strain analysis in the reference end-diastolic phase. Strain results from ML and expert adjusted ML methods were compared for strain agreement. Times taken by these methods were recorded and compared against the time taken for standard manual contouring. Results SAX and LAX derived strains by ML and expert adjusted ML methods showed good agreement by Bland-Altman analysis (Figure 1) with excellent coefficient of concordance using Kendall W which is 0.98 for global SAX, radial and circumferential strains (mean difference(MD) = -1.7% (lower and upper limits of agreement (UL,LL) -6.6,3.2), MD = 0.5% (-1.0,2.1)) and is 0.95 for global LAX derived strain (radial and longitudinal, MD = 0.7% (UL,LL -8.7 ,7.4),MD= 0.2% (-1.9,2.5), respectively). Time taken for adjustment of ML contours was significantly shorter than manual contouring (1.35 minutes vs 8.0 minutes, around 590% time saving in ML adjusted method). Conclusions ML contouring compared to expert manual adjustment has a clinically reasonable agreement when used for measuring LV strain. Also, using the ML tool with expert adjustment shows significant time saving for analysis and reporting time compared to entirely manual analysis, favouring its application in routine clinical practice. Abstract Figure.

<h3>Introduction</h3> Cardiovascular magnetic resonance (CMR)-derived aortic distensibility (AoD) is a validated tool to measure arterial stiffness¹. This local stiffness marker is an independent predictor of cardiovascular events and all-cause mortality². Our knowledge of genes modulating aortic stiffness remains limited. We conducted genome-wide association studies (GWASs) for AoD in the ascending aorta (AA) and proximal descending aorta (PDA). <h3>Methods</h3> 34,039 UK Biobank (UKB) participants of European ancestry with transverse cine images of pulmonary trunk and right pulmonary artery acquired using a 1.5T CMR scanner were included. AoD is defined as the relative change in cross-sectional aorta area for a given change in central pulse pressure, they were either manually derived or processed through a developed automated algorithm³. GWASs were performed using linear mixed models⁴ using ~7 million imputed variants adjusted for age, sex, height, weight, systolic blood pressure, aortic area segmentation method (manual/automated), imaging centre, genotype array, smoking, diabetes mellitus and top 10 principal components. Bioinformatic analyses were performed including investigation of trait pleiotropy. Polygenic risk scores (PRS) for AA and PDA were calculated using PRSice⁵ and tested for association with major adverse cardiovascular events (MACE) in the UKB cohort without imaging data (n= 98,559). <h3>Results and discussion</h3> Variants at 18 independent loci for AA and 16 loci for PDA AoD were genome-wide significant, P &lt; 5 × 10⁻⁸ (table 1 and figure 1) with four shared loci between the two phenotypes. The heritability for both AA and PDA AoD was ~27%, much higher than arterial stiffness index (6%)⁶ but comparable to pulse wave velocity (36–40%)^7,8. We captured a modest percentage of the genetic variance for each trait (2.7% for AA; 2.1% for PDA). Several candidate genes are highlighted including <i>ELN</i> for both traits, with <i>ISL1</i> and <i>PCSK1</i> both involved in insulin regulation that may explain the interplay with diabetes^9,10. Variants at 13 loci were significant with blood pressure and/or coronary artery disease traits. We observed no significant difference in odds ratio for MACE between the top and bottom quintiles for each PRS. <h3>Conclusion</h3> We identified 30 genetic loci providing new candidate genes for exploration of biological mechanism of AoDs. <h3>References</h3> Nelson A, Worthley S, Cameron J, Willoughby S, Piantadosi C, Carbone A, Dundon B, Leung M, Hope S, Meredith I, Worthley M. Cardiovascular magnetic resonance-derived aortic distensibility: validation and observed regional differences in the elderly. <i>Journal of Hypertension</i>. 2009;27:535–542. 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