Nay Aung

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.

Background: Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF). Methods: A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (>90 day) revascularization and death. Results: 1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, P<0.0001) respectively, even after adjusting for age and co-morbidity. For each 1 unit decrease in MPR the adjusted HR for death and MACE were 2.45 (95% CI 1.42-4.24, P=0.001) and 1.74 (95% CI 1.36-2.22, P<0.0001) respectively. In patients without regional perfusion defects on clinical read and no known macrovascular coronary artery disease (n=783), MPR remained independently associated with death and MACE, with stress MBF remaining associated with MACE only. Conclusions: In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

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.

Excessive trabeculation, often referred to as "noncompacted" myocardium, has been described at all ages, from the fetus to the adult. Current evidence for myocardial development, however, does not support the formation of compact myocardium from noncompacted myocardium, nor the arrest of this process to result in so-called noncompaction. Excessive trabeculation is frequently observed by imaging studies in healthy individuals, as well as in association with pregnancy, athletic activity, and with cardiac diseases of inherited, acquired, developmental, or congenital origins. Adults with incidentally noted excessive trabeculation frequently require no further follow-up based on trabecular pattern alone. Patients with cardiomyopathy and excessive trabeculation are managed by cardiovascular symptoms rather than the trabecular pattern. To date, the prognostic role of excessive trabeculation in adults has not been shown to be independent of other myocardial disease. In neonates and children with excessive trabeculation and normal or abnormal function, clinical caution seems warranted because of the reported association with genetic and neuromuscular disorders. This report summarizes the evidence concerning the etiology, pathophysiology, and clinical relevance of excessive trabeculation. Gaps in current knowledge of the clinical relevance of excessive trabeculation are indicated, with priorities suggested for future research and improved diagnosis in adults and children.

<h2>Abstract</h2> The evidence is increasing that left ventricular noncompaction cardiomyopathy as it is currently defined does not represent a failure of compaction of pre-existing trabecular myocardium found during embryonic development to form the compact component of the ventricular walls. Neither is there evidence of which we are aware to favour the notion that the entity is a return to a phenotype seen in cold-blooded animals. It is also known that when seen in adults, the presence of excessive ventricular trabeculations does not portend a poor prognosis when the ejection fraction is normal, with the risks of complications such as arrhythmia and stroke being rare in this setting. It is also the case that images of "noncompaction" as provided from children or autopsy studies are quite different from the features observed clinically in asymptomatic adults with excessive trabeculation. Our review suggests that the presence of an excessively trabeculated left ventricular wall is not in itself a clinical entity. It is equally possible that the excessive trabeculation is no more than a bystander in the presence of additional lesions such as dilated cardiomyopathy, with the additional lesions being responsible for the reduced ejection fraction bringing a given patient to clinical attention. We, therefore, argue that the term "noncompaction cardiomyopathy" is misleading, because there is neither failure of compaction nor a cardiomyopathic process in most individuals that fulfill widely used diagnostic criteria.

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.

Although left ventricular noncompaction (LVNC) has been associated with an increased risk of adverse cardiovascular events, the accurate incidence of cardiovascular morbidity and mortality is unknown. We, therefore, aimed to assess the incidence rate of LVNC-related cardiovascular events.We systematically searched observational studies reporting the adverse outcomes related to LVNC. The primary end point was cardiovascular mortality.We identified 28 eligible studies enrolling 2501 LVNC patients (mean age, 46 years; male/female ratio, 1.7). After a median follow-up of 2.9 years, the pooled event rate for cardiovascular mortality was 1.92 (95% CI, 1.54-2.30) per 100 person-years. LVNC patients had a similar risk of cardiovascular mortality compared with a dilated cardiomyopathy control group (odds ratio, 1.10 [95% CI, 0.18-6.67]). The incidence rates of all-cause mortality, stroke and systemic emboli, heart failure admission, cardiac transplantation, ventricular arrhythmias, and cardiac device implantation were 2.16, 1.54, 3.53, 1.24, 2.17, and 2.66, respectively, per 100 person-years. Meta-regression and subgroup analyses revealed that left ventricular ejection fraction, not the extent of left ventricular trabeculation, had an important influence on the variability of incidence rates. The risks of thromboembolism and ventricular arrhythmias in LVNC patients were similar to dilated cardiomyopathy patients. However, LVNC patients had a higher incidence of heart failure hospitalization than dilated cardiomyopathy patients.Patients with LVNC carry a similar cardiovascular risk when compared with dilated cardiomyopathy patients. Left ventricular ejection fraction-a conventional indicator of heart failure severity, not the extent of trabeculation-appears to be an important determinant of adverse outcomes in LVNC patients. Registration: https://www.crd.york.ac.uk/PROSPERO/ Unique identifier: CRD42018096313.

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.

Cross-sectional observational studies have reported obesity and cardiometabolic co-morbidities as important predictors of coronavirus disease 2019 (COVID-19) hospitalization. The causal impact of these risk factors is unknown at present.We conducted multivariable logistic regression to evaluate the observational associations between obesity traits (body mass index [BMI], waist circumference [WC]), quantitative cardiometabolic parameters (systolic blood pressure [SBP], serum glucose, serum glycated hemoglobin [HbA1c], low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol and triglycerides [TG]) and SARS-CoV-2 positivity in the UK Biobank cohort. One-sample MR was performed by using the genetic risk scores of obesity and cardiometabolic traits constructed from independent datasets and the genotype and phenotype data from the UK Biobank. Two-sample MR was performed using the summary statistics from COVID-19 host genetics initiative. Cox proportional hazard models were fitted to assess the risk conferred by different genetic quintiles of causative exposure traits.The study comprised 1,211 European participants who were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 387,079 participants who were either untested or tested negative between 16 March 2020 to 31 May 2020. Observationally, higher BMI, WC, HbA1c and lower HDL-cholesterol were associated with higher odds of COVID-19 infection. One-sample MR analyses found causal associations between higher genetically determined BMI and LDL cholesterol and increased risk of COVID-19 (odds ratio [OR]: 1.15, confidence interval [CI]: 1.05-1.26 and OR: 1.58, CI: 1.21-2.06, per 1 standard deviation increment in BMI and LDL cholesterol respectively). Two-sample MR produced concordant results. Cox models indicated that individuals in the higher genetic risk score quintiles of BMI and LDL were more predisposed to COVID-19 (hazard ratio [HR]: 1.24, CI: 1.03-1.49 and HR: 1.37, CI: 1.14-1.65, for the top vs the bottom quintile for BMI and LDL cholesterol, respectively).We identified causal associations between BMI, LDL cholesterol and susceptibility to COVID-19. In particular, individuals in higher genetic risk categories were predisposed to SARS-CoV-2 infection. These findings support the integration of BMI into the risk assessment of COVID-19 and allude to a potential role of lipid modification in the prevention and treatment.

There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features.UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative.Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes.In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.

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.

Abstract We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven ( CAMK2D , PRKD1 , PRKD3 , MAPK3 , TNFSF12 , APOC3 and NAE1 ) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Importance Longer leukocyte telomere length (LTL) is associated with a lower risk of adverse cardiovascular outcomes. The extent to which variation in LTL is associated with intermediary cardiovascular phenotypes is unclear. Objective To evaluate the associations between LTL and a diverse set of cardiovascular imaging phenotypes Design, Setting, and Participants This is a population-based cross-sectional study of UK Biobank participants recruited from 2006 to 2010. LTL was measured using a quantitative polymerase chain reaction method. Cardiovascular measurements were derived from cardiovascular magnetic resonance using machine learning. The median (IQR) duration of follow-up was 12.0 (11.3-12.7) years. The associations of LTL with imaging measurements and incident heart failure (HF) were evaluated by multivariable regression models. Genetic associations between LTL and significantly associated traits were investigated by mendelian randomization. Data were analyzed from January to May 2023. Exposure LTL. Main Outcomes and Measures Cardiovascular imaging traits and HF. Results Of 40 459 included participants, 19 529 (48.3%) were men, and the mean (SD) age was 55.1 (7.6) years. Longer LTL was independently associated with a pattern of positive cardiac remodeling (higher left ventricular mass, larger global ventricular size and volume, and higher ventricular and atrial stroke volumes) and a lower risk of incident HF (LTL fourth quartile vs first quartile: hazard ratio, 0.86; 95% CI, 0.81-0.91; P = 1.8 × 10 −6 ). Mendelian randomization analysis suggested a potential causal association between LTL and left ventricular mass, global ventricular volume, and left ventricular stroke volume. Conclusions and Relevance In this cross-sectional study, longer LTL was associated with a larger heart with better cardiac function in middle age, which could potentially explain the observed lower risk of incident HF.

An elevated red cell distribution width (RDW) and iron deficiency (ID) at baseline predict enhanced mortality in chronic heart failure (CHF), but little is known about the prognostic implications of their temporal trends. We sought to determine the survival implications of temporal changes in RDW and evolving ID in patients with CHF.The relation between red cell indices on first consultation and over time with mortality in 274 stable patients with systolic CHF was analysed. The combination of a rising RDW with a falling mean cell volume (MCV) over time defined evolving ID.Over a median 12 month period, 51% and 23% of patients had a rise in RDW and evolving ID, respectively. After a median follow-up of 27 months, 60 (22%) patients died. A rising RDW predicted enhanced all-cause mortality (unadjusted HR for 1% per week rise 9.27, 95% CI 3.58 to 24.00, P<0.0001) independently and incrementally to baseline RDW, with an absolute increase >0.02% per week optimally predictive. Evolving ID also related to higher rates of mortality (HR 2.78, 95% CI 1.64 to 4.73, P<0.001) and was prognostically worse than a rising RDW alone (P<0.005). Patients with evolving ID who maintained their Hb levels over time had a 2-fold greater risk of death than those whose Hb levels declined without evolving ID.An expanding RDW and evolving iron deficiency over time predict an amplified risk of death in CHF and should be utilised for risk stratification and/or therapeutically targeted to potentially improve outcomes.

A well understanding on the flow forces and energy loss characteristics in a flapper–nozzle pilot valve is necessarily important in the performance improvement of a two-stage electrohydraulic servo-valve. This paper presents the CFD analysis of flow forces and energy loss characteristics in a flapper–nozzle pilot valve with different null clearances. Five different flapper–nozzle structures with three different null clearances of 0.1 mm, 0.05 mm and 0.033 mm are considered in this analysis. For every flapper–nozzle structure, the systematic CFD simulations of flow forces and energy loss characteristics are performed for seven different flow conditions varying nozzle inlet pressures from 1 MPa to 7 MPa. Experimental measurements are also conducted for energy loss characteristics and then compared with simulated results. Meanwhile, the CFD flow force results are verified with the results of exiting simplified flow force models and vice versa. From each nozzle side, the main flow force acting on the flapper is accompanied by four tiny lateral forces resulted from the impact of radial jet reattachment on the flapper curved surface. For each of given null clearances, the main flow force and lateral forces linearly increase with the increment of nozzle inlet pressure. For the same null clearance, applying larger flapper can give 1.5–13.6% larger lateral force in drag direction and 1.5–10.2% larger lateral force in lift direction compared to deploying smaller flapper. Compared to the main flow force, the magnitudes of the lateral forces on each corner of the flapper are found in the range of 0.8–3.2% in drag direction and 1.6–7.5% in lift direction. For main flow forces, the CFD simulated results show a good agreement with that of flow force model based on momentum conservation. Both existing flow force models are undoubtedly applicable in the prediction of flow forces for small null clearances (less than 0.05). Having a good agreement between each other, both experimental and numerical results show that the energy loss increases with the increment of null clearance and nozzle inlet pressure.

In this work, the effect of riser diameter and its inclination angle on system parameters in a two-phase closed loop thermosyphon solar water heater has been numerically investigated. Here, receivable heat flux by the collector, circulating mass flow rate, driving pressure, total pressure drop, heat transfer coefficient in risers and collector efficiency are defined as system parameters. For this aim, a model of two-phase thermosyphon solar water heater that is acceptable for various inclinations is presented and variations of riser diameter and inclination are considered. The riser tube size is varied from 1.25 cm to 2.5 cm with inclination range 2–75°. The system absolute pressure is set as 3567 Pa and water is chosen as working fluid. The results show that higher inclination angle is required for higher latitude location to obtain maximum solar heat flux. At local solar noon of 21.996 north latitude, the optimum inclination angle increases in the range of 24–44° with increasing of riser diameter giving maximum circulating mass flow rate from 0.02288 kg/s to 0.03876 kg/s. The longer two-phase heat transfer characteristics can be obtained at smaller inclination angles and mass flow rate for all riser tube sizes. Therefore, it is observed that the optimum inclination angles and diameters for solar heat flux, circulating mass flow rate and heat transfer coefficient in two-phase thermosyphon systemdo not coincide. From this work, better understanding and useful information are provided for constructing two-phase thermosyphon solar heaters.

The flapper-nozzle pilot stage, whose performance can be deteriorated by the generated flow cavitation phenomenon, is a vital segment in achieving precise control of electrohydraulic servo-valves. Aiming to find out a reasonable flapper shape to reduce cavitation, this paper presents a numerical study of cavitation phenomenon in a flapper-nozzle pilot stage with different flapper shapes. A simple rectangular shape, carefully designed without disturbing the flow control characteristics of the pilot stage, is set as an innovative flapper shape in this work. Cavitation phenomena in the pilot stage are simulated for both of the traditionally used flapper shape and the innovative flapper shape at flow conditions with various nozzle inlet pressures, 1 MPa to 7 MPa. Then, systematic comparison of resulted cavitation phenomena for the two different flapper shapes is carried out. The results confirm that, for both flapper shapes, cavitation commonly occurs along the nozzle tip wall beyond stagnation region. The curved edge in traditionally used flapper shape is a massive contributor of cavitation in the pilot stage and the selected innovative shape shows a significant reduction of cavitation on its surface. From the flow structure, it is also noticeable that undesired transverse lateral force of sheded vortices is eliminated by using the innovative flapper shape. Meanwhile, the innovative flapper shape highlights the same effectiveness on the performance of flow control as the traditionally used flapper shape. Thus, a simple and effective flapper shape is proposed for cavitation reduction in the flapper-nozzle pilot stage of an electrohydraulic servo-valve.

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.

In strawberry production farms, shape and size classification of harvested strawberry fruits is very important phase before packing and sending to the market. However, it is not only very labour-intensive but also time-consuming task for farmers. Computer vision-based automatic strawberry grading systems are capable to overcome this labour-intensive and time-consuming process. In this work, a simple and efficient image processing algorithm for automatic strawberry shape and size estimation and classification is presented. Being different from other existing methods in literature, the current method is based on the geometrical properties of 'right kite' and 'simple kite' which resemble to strawberry fruit shape. The proposed method is used to estimate diameter, length and apex angle from two-dimensional images of strawberry fruits. Then, these parameters are used as input data to a 3-layer neural network for class-A, B, C and D classification. The performance of proposed method is tested for a total of 337 strawberry samples with and without calyx occlusion. The results show that the accuracies for diameter and length estimations are 94% and 93% respectively for strawberries without calyx occlusion and 94% and 89% for that with calyx occlusion. The classification accuracy is between 94 and 97% and the average processing time for one strawberry (one piece) is below 0.45–0.5 s.

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.

Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.

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.

Ischemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging–derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles. The authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function. Brain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237). Prevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia. Prevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies.

Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to (a) assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and (b) establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed. Automated segmentation of trabeculations was performed using a deep learning algorithm. After excluding individuals with known CV diseases, White adults without CV risk factors (reference group) and those with preexisting CV risk factors (hypertension, hyperlipidemia, diabetes mellitus, or smoking) (exposed group) were compared. Multivariable regression models, adjusted for potential confounders (age, sex, and height), were fitted to evaluate the associations between individual characteristics and CV risk factors and trabeculated LVM. Results Of 43 038 participants (mean age, 64 years ± 8 [SD]; 22 360 women), 28 672 individuals (mean age, 66 years ± 7; 14 918 men) were included in the exposed group, and 7384 individuals (mean age, 60 years ± 7; 4729 women) were included in the reference group. Higher body mass index (BMI) (β = 0.66 [95% CI: 0.63, 0.68]; P < .001), hypertension (β = 0.42 [95% CI: 0.36, 0.48]; P < .001), and higher physical activity level (β = 0.15 [95% CI: 0.12, 0.17]; P < .001) were associated with higher trabeculated LVM. In the reference group, the median trabeculated LVM was 6.3 g (IQR, 4.7-8.5 g) for men and 4.6 g (IQR, 3.4-6.0 g) for women. Median trabeculated LVM decreased with age for men from 6.5 g (IQR, 4.8-8.7 g) at age 45-50 years to 5.9 g (IQR, 4.3-7.8 g) at age 71-80 years (P = .03). Conclusion Higher trabeculated LVM was observed with hypertension, higher BMI, and higher physical activity level. Age- and sex-specific reference ranges of trabeculated LVM in a healthy middle-aged White population were established. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kawel-Boehm in this issue.

The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care.This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date.CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals.The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%).This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

Cavitation in the flapper–nozzle pilot stage is an important source for the noise, performance deterioration and even failure of electrohydraulic servo-valves. In this paper, experimental and numerical investigations of cavitation phenomenon appearing in the flow field between the flapper and nozzle of an electrohydraulic servo-valve are carried out. Experimental observations are conducted with variation of Reynolds number ranging from 630 to 2500 based on the nozzle inlet velocity and diameter. Images of cavitation phenomenon in the flow field are recorded and compared with CFD simulation results to confirm the occurrence and locations of cavitation sources. The computed numerical results show a good agreement with experimental observations. From both types of results, the nozzle inner wall tip, nozzle outer wall tip and flapper leading edge are shown as the locations of cavitation sources. At flow conditions with lower Reynolds numbers, onset cavitation and inception are found at the nozzle outer wall and the flapper leading edge. Further increasing of Reynolds numbers creates a separated flow and then jet flow. Attached cavity is found on the flapper curved surface together with the separated flow and cloud-like cavitation comes with the jet flow. Since numerical results can confirm all of the recorded observations, a reliable computational scheme is also provided by this paper.

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.

Abstract Aims We evaluated the associations of left atrial (LA) structure and function with prevalent and incident cardiovascular disease (CVD), independent of left ventricular (LV) metrics, in 25 896 UK Biobank participants. Methods and results We estimated the association of cardiovascular magnetic resonance (CMR) metrics [LA maximum volume (LAV), LA ejection fraction (LAEF), LV mass : LV end-diastolic volume ratio (LVM : LVEDV), global longitudinal strain, and LV global function index (LVGFI)] with vascular risk factors (hypertension, diabetes, high cholesterol, and smoking), prevalent and incident CVDs [atrial fibrillation (AF), stroke, ischaemic heart disease (IHD), myocardial infarction], all-cause mortality, and CVD mortality. We created uncorrelated CMR variables using orthogonal principal component analysis rotation. All five CMR metrics were simultaneously entered into multivariable regression models adjusted for sex, age, ethnicity, deprivation, education, body size, and physical activity. Lower LAEF was associated with diabetes, smoking, and all the prevalent and incident CVDs. Diabetes, smoking, and high cholesterol were associated with smaller LAV. Hypertension, IHD, AF (incident and prevalent), incident stroke, and CVD mortality were associated with larger LAV. LV and LA metrics were both independently informative in associations with prevalent disease, however LAEF showed the most consistent associations with incident CVDs. Lower LVGFI was associated with greater all-cause and CVD mortality. In secondary analyses, compared with LVGFI, LV ejection fraction showed similar but less consistent disease associations. Conclusion LA structure and function measures (LAEF and LAV) demonstrate significant associations with key prevalent and incident cardiovascular outcomes, independent of LV metrics. These measures have potential clinical utility for disease discrimination and outcome prediction.

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.

The existence of undesired flow-induced phenomenon, cavitation, in the flapper–nozzle pilot valve of two-stage servo-valves is a critical issue in practical applications. Here, taking innovation on the flapper shape is one of possible approaches to reduce cavitation in the pilot valve. By means of CFD (Computational Fluid Dynamics) simulations, it has been proved by setting a simple rectangle shape as an innovative flapper shape in our previous attempt. Therefore, in this work, the effectiveness of rectangle-shaped flapper in reducing cavitation is experimentally confirmed by comparing with traditional shape and square shape. The experimental observations of cavitation phenomena in three different flapper shapes are conducted for two different flapper–nozzle null clearances (0.2 mm and 0.1 mm) under four different flow conditions with the variation of inlet pressure in the range of 3–6 MPa. To provide verification on experimental results and a comprehensive understanding, CFD simulations of cavitation phenomenon in each flapper shape are also performed. The results are qualitatively analyzed and compared. The results explain that the cavitation intensity in the flapper–nozzle pilot valve associates with the strength of the turbulent jets and it increases with the increment of flapper–nozzle null clearance and inlet pressure. According to the experimental observations and CFD simulated results, the curved surface of traditional flapper shape is attributed to the spread of turbulent jets and consequent massive cavitation. Compared to traditional shape, the square shape relatively reduces cavitation due to lack of curved boundary on the flapper. However, on the other hand, its shorter flat land and larger annulus are not much effective to control the spread of turbulent jet which is responsible for cavitation in annulus region. Compared to two other flapper shapes, the rectangle shape significantly suppresses the cavitation by attenuating the turbulent jets on its straight and relatively longer flat lands. Therefore, the effectiveness of rectangle shape in reducing cavitation in the flapper–nozzle pilot valve is confirmed in this work.

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.

Cholesterol and triglycerides are among the most well-known risk factors for cardiovascular disease.This study investigated whether higher low-density lipoprotein (LDL) cholesterol and triglyceride levels and lower high-density lipoprotein cholesterol level are causal risk factors for changes in prognostically important left ventricular (LV) parameters.One-sample Mendelian randomization (MR) of 17,311 European individuals from the UK Biobank with paired lipid and cardiovascular magnetic resonance data was performed. Two-sample MR was performed by using summary-level data from the Global Lipid Genetics Consortium (n = 188,577) and UK Biobank Cardiovascular Magnetic Resonance substudy (n = 16,923) for sensitivity analyses.In 1-sample MR analysis, higher LDL cholesterol was causally associated with higher LV end-diastolic volume (β = 1.85 ml; 95% confidence interval [CI]: 0.59 to 3.14 ml; p = 0.004) and higher LV mass (β = 0.81 g; 95% CI: 0.11 to 1.51 g; p = 0.023) and triglycerides with higher LV mass (β = 1.37 g; 95% CI: 0.45 to 2.3 g; p = 0.004). High-density lipoprotein cholesterol had no significant association with any LV parameter. Similar results were obtained by using 2-sample MR. Observational analyses were frequently discordant with those derived from MR.MR analysis demonstrates that LDL cholesterol and triglycerides are associated with adverse changes in cardiac structure and function, in particular in relation to LV mass. These findings suggest that LDL cholesterol and triglycerides may have a causal effect in influencing cardiac morphology in addition to their established role in atherosclerosis.

<h3>Objective</h3> To investigate the prognostic value of baseline and temporal changes in red cell distribution width (RDW) in patients undergoing transcatheter aortic valve implantation (TAVI). <h3>Design</h3> Single-centre retrospective observational study. <h3>Setting</h3> Tertiary cardiac centre. <h3>Patients</h3> 175 patients undergoing TAVI were included in this study. <h3>Main outcome measure</h3> Survival. <h3>Results</h3> We analysed data from 175 TAVI patients (mean (±SD) age 83±7 years, 49% men, mean Logistic EuroSCORE 23±1, 66% preserved left ventricular ejection fraction (LVEF)). Immediately pre-TAVI, mean RDW was 14.6±1.6% with an RDW&gt;15% in 29% of patients. Over median follow-up of 12 months, the median rate of change in RDW was 0.2% per month, and 51 (29%) patients died. On multivariate survival analyses, baseline RDW≥15.5% predicted death (adjusted HR 2.70, 95% CI 1.40 to 5.22, p=0.003) independently of LVEF, transfemoral approach, baseline pulmonary artery systolic pressure, moderate/severe mitral regurgitation and body mass index. A greater rate of increase in RDW over time was associated with increased mortality (adjusted HR 1.11, 95% CI 1.04 to 1.18, p=0.001) independently of baseline RDW and other significant temporal variables including a change in creatinine, bilirubin, mean cell haemoglobin concentration or urea. An increase in RDW&gt;0.1%/month was associated with a twofold increased risk of mortality. <h3>Conclusions</h3> Baseline RDW≥15.5% and a rising RDW over time strongly correlate to an increased risk of death post-TAVI, and could be used to refine risk stratification. Investigating and ameliorating the causes of RDW expansion may improve survival.

The stability and dynamic performance of a flapper–nozzle pilot valve significantly depend on the flow forces acting on the flapper. Due to the shape of the flapper and flow structure in the flapper–nozzle pilot valve there are undesired lateral forces acting on the flapper, which are very potential to interfere with the stability of the flapper. Aiming to reduce these undesired lateral forces, an innovative flapper shape is proposed and a comparative study of flow forces acting on the two different flapper shapes is conducted. A simple rectangle shape is selected as the innovative flapper shape. The flow forces acting on the traditional flapper shape and innovative flapper shape are evaluated by means of CFD (Computational Fluid Dynamics) simulations and verified with the results from the semi-experimental approach. The evaluation of the flow forces is performed for each flapper shape with two different flapper–nozzle clearances of 0.10 mm and 0.05 mm under seven different flow conditions with the variation of inlet pressures from 1 MPa to 7 MPa. A good agreement between CFD results and semi-experimental results shows that the proposed innovative flapper shape has no effect on flow control characteristics since it is giving approximately the same flow rate and main flow force as the traditional flapper shape at every flow condition. Meanwhile the innovative flapper shape effectively reduces the undesired lateral forces acting on the flapper by altering the flow structure and reducing the strength of the jet flow and cavitation occurred in the flow field of flapper–nozzle pilot valve. At the lower part of the flapper with clearance 0.05 mm, the ratio between the X-direction lateral force and main flow force of traditional flapper is around 1.24–11.14%, while it is reduced to 0.18–0.42% by the innovative flapper. Also, the ratio is reduced from 7.93–18.44% to 0.69–0.93% with clearance 0.10 mm. For the Z-direction forces at the lower part, the ratio decreases from 0.20–11.77% and 7.84–17.94% (traditional flapper) to 0.92–2.65% and 1.63–4.08% (innovative flapper) with clearances 0.05 mm and 0.10 mm respectively.

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.

Aims The CHA 2 DS 2 VASc score is used to evaluate the risk of thromboembolic events in patients with non-valvular atrial fibrillation. We assessed the prognostic yield of CHA 2 DS 2 VASc for new-onset atrial fibrillation, cardiovascular morbidity and mortality in a non-atrial fibrillation population. Methods We analysed a population-based cohort of 22,179 middle-aged individuals with ( n = 3542) and without ( n = 18,367) a history of atrial fibrillation; we grouped the population into five CHA 2 DS 2 VASc strata (0–1–2–3–≥4), and compared the risk of major adverse cerebro-cardiovascular events and mortality. Furthermore, we analysed the annual incidence of atrial fibrillation across different CHA 2 DS 2 VASc strata. Results Over a median follow-up of 15 years, 1572 patients (6.9%) had ischaemic strokes, 2162 (9.5%) coronary events and 5899 (26%) died. The cumulative incidence of ischaemic stroke in CHA 2 DS 2 VASc ≥ 4 subjects without atrial fibrillation was similar to patients with atrial fibrillation and CHA 2 DS 2 VASc 2, with a 10-year crude incidence rate of 0.91 (95% confidence interval (CI) 0.68–1.19) and 1.13 (95% CI 0.93–1.36) ischaemic strokes per 100 patient-years, respectively. CHA 2 DS 2 VASc in a non-atrial fibrillation population showed higher predictive accuracy for ischaemic stroke compared with an atrial fibrillation population (area under the curve 0.60 vs. 0.56; P = 0.001). In multivariable Cox regression analysis, CHA 2 DS 2 VASc ≥ 2 was an independent predictor of all-cause death (adjusted hazard ratio (aHR) 2.58; 95% CI 2.42–2.76), cardiovascular death (aHR 3.40; 95% CI 2.98–3.89), ischaemic stroke (aHR 2.20; 95% CI 1.92–2.53) and coronary events (aHR 1.83; 95% CI 1.63–2.04). The cumulative incidence of atrial fibrillation was greater with increasing CHA 2 DS 2 VASc strata, with an absolute annual incidence of more than 2% per year if CHA 2 DS 2 VASc ≥ 4. Conclusion The CHA 2 DS 2 VASc score is a sensitive tool for predicting new-onset atrial fibrillation and adverse outcomes in subjects both with and without atrial fibrillation.

Background: Non-invasive Cardiovascular imaging (NICI), including cardiovascular magnetic resonance (CMR) imaging provides important information to guide the management of patients with cardiovascular conditions. Current rates of NICI use and potential policy determinants in the United States of America (US) and England remain unexplored. Methods: We compared NICI activity in the US (Medicare fee-for-service, 2011–2015) and England (National Health Service, 2012–2016). We reviewed recommendations related to CMR from Clinical Practice Guidelines, Appropriate Use Criteria (AUC), and Choosing Wisely. We then categorized recommendations according to whether CMR was the only recommended NICI technique (substitutable indications). Reimbursement policies in both settings were systematically collated and reviewed using publicly available information. Results: The 2015 rate of NICI activity in the US was 3.1 times higher than in England (31,055 vs. 9,916 per 100,000 beneficiaries). The proportion of CMR of all NICI was small in both jurisdictions, but nuclear cardiac imaging was more frequent in the US in absolute and relative terms. American and European CPGs were similar, both in terms of number of recommendations and proportions of indications where CMR was not the only recommended NICI technique (substitutable indications). Reimbursement schemes for NICI activity differed for physicians and hospitals between the two settings. Conclusions: Fee-for-service physician compensation in the US for NICI may contribute to higher NICI activity compared to England where physicians are salaried. Reimbursement arrangements for the performance of the test may contribute to the higher proportion of nuclear cardiac imaging out of the total NICI activity. Differences in CPG recommendations appear not to explain the variation in NICI activity between the US and England.

Aims: To investigate the effect of diabetes on mortality and incident heart failure (HF) according to sex, in the low risk population of UK Biobank. To evaluate potential contributing factors for any differences seen in HF end-point. Methods: The entire UK Biobank study population were included. Participants that withdrew consent or were diagnosed with diabetes after enrolment were excluded from the study. Univariate and multivariate cox regression models were used to assess endpoints of mortality and incident HF, with median follow-up periods of 9 years and 8 years respectively. Results: A total of 493,167 participants were included, hereof 22,685 with diabetes (4.6%). Two thousand four hundred fifty four died and 1,223 were diagnosed or admitted with HF during the follow up periods of 9 and 8 years respectively. Overall, the mortality and HF risk were almost doubled in those with diabetes compared to those without diabetes (hazard ratio (HR) of 1.9 for both mortality and heart failure) in the UK Biobank population. Women with diabetes (both types) experience a 22% increased risk of HF compared to men (HR of 2.2 (95% CI: 1.9-2.5) vs. 1.8 (1.7-2.0) respectively). Women with type 1 diabetes (T1DM) were associated with 88% increased risk of HF compared to men (HR 4.7 (3.6-6.2) vs. 2.5 (2.0-3.0) respectively), while the risk of HF for type 2 diabetes (T2DM) was 17% higher in women compared to men (2.0 (1.7-2.3) vs. 1.7 (1.6-1.9) respectively). The increased risk of HF in women was independent of confounding factors. The findings were similar in a model with all-cause mortality as a competing risk. This interaction between sex, diabetes and outcome of HF is much more prominent for T1DM (p = 0.0001) than T2DM (p = 0.1). Conclusion: Women with diabetes, particularly those with T1DM, experience a greater increase in risk of heart failure compared to men with diabetes, which cannot be explained by the increased prevalence of cardiac risk factors in this cohort.

Left ventricular maximum wall thickness (LVMWT) is an important biomarker of left ventricular hypertrophy and provides diagnostic and prognostic information in hypertrophic cardiomyopathy (HCM). Limited information is available on the genetic determinants of LVMWT.We performed a genome-wide association study of LVMWT measured from the cardiovascular magnetic resonance examinations of 42 176 European individuals. We evaluated the genetic relationship between LVMWT and HCM by performing pairwise analysis using the data from the Hypertrophic Cardiomyopathy Registry in which the controls were randomly selected from UK Biobank individuals not included in the cardiovascular magnetic resonance sub-study.Twenty-one genetic loci were discovered at P<5×10-8. Several novel candidate genes were identified including PROX1, PXN, and PTK2, with known functional roles in myocardial growth and sarcomere organization. The LVMWT genetic risk score is predictive of HCM in the Hypertrophic Cardiomyopathy Registry (odds ratio per SD: 1.18 [95% CI, 1.13-1.23]) with pairwise analyses demonstrating a moderate genetic correlation (rg=0.53) and substantial loci overlap (19/21).Our findings provide novel insights into the genetic underpinning of LVMWT and highlight its shared genetic background with HCM, supporting future endeavours to elucidate the genetic etiology of HCM.

Inherited cardiomyopathies present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the general population.The authors aimed to determine the risk of mortality and composite cardiomyopathy-related outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the UK Biobank.Using whole exome sequencing data, variants in dilated, hypertrophic, and arrhythmogenic right ventricular cardiomyopathy-associated genes with at least moderate evidence of disease causality according to ClinGen Expert Panel curations were annotated using REVEL (≥0.65) and ANNOVAR (predicted loss-of-function) considering gene-disease mechanisms. Genotype-positive and genotype-negative groups were compared using time-to-event analyses for the primary (all-cause mortality) and secondary outcomes (diagnosis of cardiomyopathy; composite outcome of diagnosis of cardiomyopathy, heart failure, arrhythmia, stroke, and death).Among 200,619 participants (age at recruitment 56.46 ± 8.1 years), 5,292 (2.64%) were found to host ≥1 predicted deleterious variants in cardiomyopathy-associated genes (CMP-G+). After adjusting for age and sex, CMP-G+ individuals had higher risk for all-cause mortality (HR: 1.13 [95% CI: 1.01-1.25]; P = 0.027), increased risk for being diagnosed with cardiomyopathy later in life (HR: 5.75 [95% CI: 4.58-7.23]; P < 0.0001), and elevated risk for composite outcome (HR: 1.29 [95% CI: 1.20-1.39]; P < 0.0001) than CMP-G- individuals. The higher risk for being diagnosed with cardiomyopathy and composite outcomes in the genotype-positive subjects remained consistent across all cardiomyopathy subgroups.Adults with predicted deleterious variants in cardiomyopathy-associated genes exhibited a slightly higher risk of mortality and a significantly increased risk of developing cardiomyopathy, and cardiomyopathy-related composite outcomes, in comparison with genotype-negative controls.

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.

Hydraulic control system is one of the fundamental subsystems of the various aircraft systems, e.g., flight control system, brake system and fuel regulation system. As a pivotal actuator of the hydraulic control system, the nozzle-flapper servo valve converts the control signals to the hydraulic output. The flow cavitation in the valves could lead to some intractable problems, e.g., vibration, noise and erosion, which could produce detrimental effects on the performance and reliability of the hydraulic system, even damage the aircraft. This work provides a numerical investigation on the cavitation attenuation in the nozzle-flapper valve using triangular nozzle exit. The flow imaging and mass flow rate measurement are conducted to qualitatively and quantitatively verify the numerical model, respectively. It is observed that the presence of the vapour phase is remarkably suppressed under the effect of the triangular nozzle exit. For both circular and triangular nozzle exits, the occurrence of the vapour phase is highly affected by the nozzle-to-flapper distance, inlet pressure and chamber diameter while the flapper diameter exerts an insignificant impact on the formation of the vapour phase. Compared with the circular nozzle exit, the triangular nozzle exit could effectively reduce the flow cavitation at the same geometry and inlet pressure. The physical mechanism behind the cavitation suppression may be ascribed to the generation of the inclined impinging jet upon the chamber wall and the wall jet without impingement.

Background: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions. Objective: To define the CMR radiomics signatures for IHD and cerebrovascular disease and study their incremental value for disease discrimination over conventional CMR indices. Methods: We analysed CMR images of UK Biobank's subjects with pre-existing IHD, ischaemic cerebrovascular disease, myocardial infarction (MI), and ischaemic stroke (IS) ( n = 779, 267, 525, and 107, respectively). Each disease group was compared with an equal number of healthy controls. We extracted 446 shape, first-order, and texture radiomics features from three regions of interest (right ventricle, left ventricle, and left ventricular myocardium) in end-diastole and end-systole defined from segmentation of short-axis cine images. Systematic feature selection combined with machine learning (ML) algorithms (support vector machine and random forest) and 10-fold cross-validation tests were used to build the radiomics signature for each condition. We compared the discriminatory power achieved by the radiomics signature with conventional indices for each disease group, using the area under the curve (AUC), receiver operating characteristic (ROC) analysis, and paired t -test for statistical significance. A third model combining both radiomics and conventional indices was also evaluated. Results: In all the study groups, radiomics signatures provided a significantly better disease discrimination than conventional indices, as suggested by AUC (IHD:0.82 vs. 0.75; cerebrovascular disease: 0.79 vs. 0.77; MI: 0.87 vs. 0.79, and IS: 0.81 vs. 0.72). Similar results were observed with the combined models. In IHD and MI, LV shape radiomics were dominant. However, in IS and cerebrovascular disease, the combination of shape and intensity-based features improved the disease discrimination. A notable overlap of the radiomics signatures of IHD and cerebrovascular disease was also found. Conclusions: This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia.

<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.

We examined the prognostic utility of rate of change in serum albumin over time in chronic heart failure (CHF), as well as the utility of multivariate dynamic risk modelling.The survival implication of ∆albumin was analysed in 232 systolic CHF patients and validated in 212 patients. A multivariate dynamic risk score predicated on the rate of change in 6 simple indices including albumin was calculated and related to mortality. In derivation patients, 50 (22%) deaths occurred over 13 months. Greater rates of decline in albumin related to higher mortality (HR 0.55, 95% CI 0.41-0.73, P<0.0001) independently, incrementally and more accurately than other covariates including baseline albumin. A rate of attenuation >0.4 g/dL/month optimally forecasted death and was associated with a 5-fold escalated risk of mortality (HR 5.13, 95% CI 2.92-9.00, P<0.0001). Similar results were seen in the validation cohort. On multivariate dynamic risk modelling, survival at 1-year worsened with higher scores-a score ≥ 3 was associated with a 12-fold greater risk of death than a score of 0, a 6-fold higher risk of death than a score of 1, and a 4-fold enhanced risk of mortality than a score of 2.Attenuations in serum albumin over time relate to increased mortality in CHF, and a risk model predicated on the rate of change in 6 simple indices can identify patients at a 12-fold enhanced risk of death over the coming year.

Orthostatic hypotension (OH) has been linked with higher incidence of cardiovascular disease, but little is known about the mechanisms behind this association. We aimed to identify cardiovascular disease biomarkers associated with OH through a proteomic profiling approach. Seven hundred seventy-eight patients with unexplained syncope or orthostatic intolerance underwent head-up tilt test and supine blood samples. Of these, 220 met diagnostic criteria of OH, and 179 demonstrated normal hemodynamic response during head-up tilt test. Blood samples were analyzed by antibody-based Proximity Extension Assay technique simultaneously measuring 92 cardiovascular disease-related human protein biomarkers. The discovery algorithm was a sequential 2-step process of biomarker signature identification by supervised, multivariate, principal component analysis and verification by univariate ANOVA with Bonferroni correction. Patients with OH were older (67 versus 60 years; P &lt;0.001) and more likely to be women (48% versus 41%; P &gt;0.001) but did not differ from OH-negative patients in medical history. Principal component analysis identified MMP-7 (matrix metalloproteinase-7), TM (thrombomodulin), MB (myoglobin), TIM-1 (T-cell immunoglobulin and mucin domain-1), CASP-8 (caspase-8), CXCL-1 (C-X-C motif chemokine-1), Dkk-1 (dickkopf-related protein-1), lectin-like LOX-1 (oxidized low-density lipoprotein receptor-1), PlGF (placenta growth factor), PAR-1 (proteinase-activated receptor-1), and MCP-1 (monocyte chemotactic protein-1) as the most robust proteomic signature for OH. From this proteomic feature selection, MMP-7 and TIM-1 met Bonferroni-adjusted significance criteria in univariate and multivariate regression analyses. Proteomic profiling in OH reveals a biomarker signature of atherothrombosis and inflammation. Circulating levels of MMP-7 and TIM-1 are independently associated with OH and may be involved in cardiovascular disease promotion.

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.

Abstract Aims To determine population-related and technical sources of variation in cardiac magnetic resonance (CMR) reference ranges for left ventricular (LV) quantification through a formal systematic review and meta-analysis. Methods and results This study is registered with the International Prospective Register of Systematic Reviews (CRD42019147161). Relevant studies were identified through electronic searches and assessed by two independent reviewers based on predefined criteria. Fifteen studies comprising 2132 women and 1890 men aged 20–91 years are included in the analysis. Pooled LV reference ranges calculated using random effects meta-analysis with inverse variance weighting revealed significant differences by age, sex, and ethnicity. Men had larger LV volumes and higher LV mass than women [LV end-diastolic volume (mean difference = 6.1 mL/m2, P-value = 0.014), LV end-systolic volume (MD = 4 mL/m2, P-value = 0.033), LV mass (mean difference = 12 g/m2, P-value = 7.8 × 10−9)]. Younger individuals had larger LV end-diastolic volumes than older ages (20–40 years vs. ≥65 years: women MD = 14.0 mL/m2, men MD = 14.7 mL/m2). East Asians (Chinese, Korean, Singaporean-Chinese, n = 514) had lower LV mass than Caucasians (women: MD = 6.4 g/m2, P-value = 0.016; men: MD = 9.8 g/m2, P-value = 6.7 × 10−5). Between-study heterogeneity was high for all LV parameters despite stratification by population-related factors. Sensitivity analyses identified differences in contouring methodology, magnet strength, and post-processing software as potential sources of heterogeneity. Conclusion There is significant variation between CMR normal reference ranges due to multiple population-related and technical factors. Whilst there is need for population-stratified reference ranges, limited sample sizes and technical heterogeneity precludes derivation of meaningful unified ranges from existing reports. Wider representation of different populations and standardization of image analysis is urgently needed to establish such reference distributions.

Circulating cardiac troponin proteins are associated with structural heart disease and predict incident cardiovascular disease in the general population. However, the genetic contribution to cardiac troponin I (cTnI) concentrations and its causal effect on cardiovascular phenotypes are unclear. We combine data from two large population-based studies, the Trøndelag Health Study and the Generation Scotland Scottish Family Health Study, and perform a genome-wide association study of high-sensitivity cTnI concentrations with 48 115 individuals. We further use two-sample Mendelian randomization to investigate the causal effects of circulating cTnI on acute myocardial infarction (AMI) and heart failure (HF). We identified 12 genetic loci (8 novel) associated with cTnI concentrations. Associated protein-altering variants highlighted putative functional genes: CAND2, HABP2, ANO5, APOH, FHOD3, TNFAIP2, KLKB1 and LMAN1. Phenome-wide association tests in 1688 phecodes and 83 continuous traits in UK Biobank showed associations between a genetic risk score for cTnI and cardiac arrhythmias, metabolic and anthropometric measures. Using two-sample Mendelian randomization, we confirmed the non-causal role of cTnI in AMI (5948 cases, 355 246 controls). We found indications for a causal role of cTnI in HF (47 309 cases and 930 014 controls), but this was not supported by secondary analyses using left ventricular mass as outcome (18 257 individuals). Our findings clarify the biology underlying the heritable contribution to circulating cTnI and support cTnI as a non-causal biomarker for AMI in the general population. Using genetically informed methods for causal inference helps inform the role and value of measuring cTnI in the general population.

Abstract Aims Existing evidence suggests links between brain and cardiovascular health. We investigated associations between cognitive performance and cardiovascular magnetic resonance (CMR) phenotypes in the UK Biobank, considering a range of potential confounders. Methods and results We studied 29 763 participants with CMR and cognitive testing, specifically, fluid intelligence (FI, 13 verbal-numeric reasoning questions), and reaction time (RT, a timed pairs matching exercise); both were considered continuous variables for modelling. We included the following CMR metrics: left and right ventricular (LV and RV) volumes in end-diastole and end-systole, LV/RV ejection fractions, LV/RV stroke volumes, LV mass, and aortic distensibility. Multivariable linear regression models were used to estimate the association of each CMR measure with FI and RT, adjusting for age, sex, smoking, education, deprivation, diabetes, hypertension, high cholesterol, prior myocardial infarction, alcohol intake, and exercise level. We report standardized beta-coefficients, 95% confidence intervals, and P-values adjusted for multiple testing. In this predominantly healthy cohort (average age 63.0 ± 7.5 years), better cognitive performance (higher FI, lower RT) was associated with larger LV/RV volumes, higher LV/RV stroke volumes, greater LV mass, and greater aortic distensibility in fully adjusted models. There was some evidence of non-linearity in the relationship between FI and LV end-systolic volume, with reversal of the direction of association at very high volumes. Associations were consistent for men and women and in different ages. Conclusion Better cognitive performance is associated with CMR measures likely representing a healthier cardiovascular phenotype. These relationships remained significant after adjustment for a range of cardiometabolic, lifestyle, and demographic factors, suggesting possible involvement of alternative disease mechanisms.

Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.

Pro-protein convertase subtilisin-kexin type 9 (PCSK9), which is expressed mainly in the liver and at low levels in the heart, regulates cholesterol levels by directing low-density lipoprotein receptors to degradation. Studies to determine the role of PCSK9 in the heart are complicated by the close link between cardiac function and systemic lipid metabolism. Here, we sought to elucidate the function of PCSK9 specifically in the heart by generating and analysing mice with cardiomyocyte-specific Pcsk9 deficiency (CM-Pcsk9-/- mice) and by silencing Pcsk9 acutely in a cell culture model of adult cardiomyocyte-like cells.Mice with cardiomyocyte-specific deletion of Pcsk9 had reduced contractile capacity, impaired cardiac function, and left ventricular dilatation at 28 weeks of age and died prematurely. Transcriptomic analyses revealed alterations of signalling pathways linked to cardiomyopathy and energy metabolism in hearts from CM-Pcsk9-/- mice vs. wild-type littermates. In agreement, levels of genes and proteins involved in mitochondrial metabolism were reduced in CM-Pcsk9-/- hearts. By using a Seahorse flux analyser, we showed that mitochondrial but not glycolytic function was impaired in cardiomyocytes from CM-Pcsk9-/- mice. We further showed that assembly and activity of electron transport chain (ETC) complexes were altered in isolated mitochondria from CM-Pcsk9-/- mice. Circulating lipid levels were unchanged in CM-Pcsk9-/- mice, but the lipid composition of mitochondrial membranes was altered. In addition, cardiomyocytes from CM-Pcsk9-/- mice had an increased number of mitochondria-endoplasmic reticulum contacts and alterations in the morphology of cristae, the physical location of the ETC complexes. We also showed that acute Pcsk9 silencing in adult cardiomyocyte-like cells reduced the activity of ETC complexes and impaired mitochondrial metabolism.PCSK9, despite its low expression in cardiomyocytes, contributes to cardiac metabolic function, and PCSK9 deficiency in cardiomyocytes is linked to cardiomyopathy, impaired heart function, and compromised energy production.

Sleep duration and chronotype have been associated with increased morbidity and mortality. We assessed for associations between sleep duration and chronotype on cardiac structure and function. UK Biobank participants with CMR data and without known cardiovascular disease were included. Self-reported sleep duration was categorized as short (<7 h/d), normal (7-9 h/d) and long (>9 h/d). Self-reported chronotype was categories as "definitely morning" or "definitely evening." Analysis included 3903 middle-aged adults: 929 short, 2924 normal and 50 long sleepers; with 966 definitely-morning and 355 definitely-evening chronotypes. Long sleep was independently associated with lower left ventricular (LV) mass (-4.8%, P = 0.035), left atrial maximum volume (-8.1%, P = 0.041) and right ventricular (RV) end-diastolic volume (-4.8%, P = 0.038) compared to those with normal sleep duration. Evening chronotype was independently associated with lower LV end-diastolic volume (-2.4%, P = 0.021), RV end-diastolic volume (-3.6%, P = 0.0006), RV end systolic volume (-5.1%, P = 0.0009), RV stroke volume (RVSV -2.7%, P = 0.033), right atrial maximal volume (-4.3%, P = 0.011) and emptying fraction (+1.3%, P = 0.047) compared to morning chronotype. Sex interactions existed for sleep duration and chronotype and age interaction for chronotype even after considering potential confounders. In conclusion, longer sleep duration was independently associated with smaller LV mass, left atrial volume and RV volume. Evening chronotype was independently associated with smaller LV and RV and reduced RV function compared to morning chronotype. Sex interactions exist with cardiac remodeling most evident in males with long sleep duration and evening chronotype. Recommendations for sleep chronotype and duration may need to be individualized based on sex.

Rotavirus vaccines have substantially decreased rotavirus hospitalizations in countries where they have been implemented. In some high- and middle-income countries, a low-level of increased risk of intussusception, a type of acute bowel obstruction, has been detected following rotavirus vaccination. However, no increased risk of intussusception was found in India, South Africa, or a network of 7 other African countries. We assessed the association between a 2-dose monovalent rotavirus vaccine (Rotarix) and intussusception in 3 early-adopter low-income Asian countries -- Afghanistan, Myanmar, and Pakistan. Children <12 months of age admitted to a sentinel surveillance hospital with Brighton level 1 intussusception were eligible for enrollment. We collected information about each child's vaccination status and used the self-controlled case series method to calculate the relative incidence of intussusception 1–7 days, 8–21 days, and 1–21 days following each dose of vaccine and derived confidence intervals with bootstrapping. Of the 585 children meeting the analytic criteria, the median age at intussusception symptom onset was 24 weeks (IQR: 19–29). Overall, 494 (84 %) children received the first Rotarix dose and 398 (68 %) received the second dose. There was no increased intussusception risk during any of the risk periods following the first (1–7 days: 1.01 (95 %CI: 0.39, 2.60); 8–21 days: 1.37 (95 %CI: 0.81, 2.32); 1–21 days: 1.28 (95 %CI: 0.78, 2.11)) or second (1–7 days: 0.81 (95 %CI: 0.42, 1.54); 8–21 days: 0.77 (95 %CI: 0.53, 1.16); 1–21 days: 0.78 (95 %CI: 0.53, 1.16)) rotavirus vaccine dose. Our findings are consistent with other data showing no increased intussusception risk with rotavirus vaccination in low-income countries and add to the growing body of evidence demonstrating safety of rotavirus vaccines.

Turbulent aortic flow makes the cardiovascular system less effective. It remains unknown if patients with heart failure with preserved ejection fraction (HFpEF) have disturbed aortic flow. This study sought to investigate advanced markers of aortic flow disturbances in HFpEF.This case-controlled observational study used four-dimensional flow cardiovascular magnetic resonance derived, two-dimensional phase-contrast reformatted plane data at an orthogonal plane just above the sino-tubular junction. We recruited 10 young healthy controls (HCs), 10 old HCs and 23 patients with HFpEF. We analysed average systolic aortic flow displacement (FDsavg), systolic flow reversal ratio (sFRR) and pulse wave velocity (PWV). In a sub-group analysis, we compared old HCs versus age-gender-matched HFpEF (N=10).Differences were significant in mean age (P<0.001) among young HCs (22.9±3.5 years), old HCs (60.5±10.2 years) and HFpEF patients (73.7±9.7 years). FDsavg, sFRR and PWV varied significantly (P<0.001) in young HCs (8±4%, 2±2%, 4±2m/s), old HCs (16±5%, 7±6%, 11±8m/s), and HFpEF patients (23±10%, 11±10%, 8±3). No significant PWV differences existed between old HCs and HFpEF.HFpEF had significantly higher FDsavg versus old HCs (23±10% vs 16±5%, P<0.001). A FDsavg > 17.7% achieved 74% sensitivity, 70% specificity for differentiating them. sFRR was notably higher in HFpEF (11±10% vs 7±6%, P<0.001). A sFRR > 7.3% yielded 78% sensitivity, 70% specificity in differentiating these groups. In sub-group analysis, FDsavg remained distinctly elevated in HFpEF (22.4±9.7% vs 16±4.9%, P=0.029). FDsavg of >16% showed 100% sensitivity and 70% specificity (P=0.01). Similarly, sFRR remained significantly higher in HFpEF (11.3±9.5% vs 6.6±6.4%, P=0.007). A sFRR of >7.2% showed 100% sensitivity and 60% specificity (P<0.001).Aortic flow haemodynamics namely FDsavg and sFRR are significantly affected in ageing and HFpEF patients.

<h3>Introduction</h3> There is growing interest in identifying cardiovascular magnetic resonance (CMR) signatures in ageing due to their relevance to cardiovascular health.¹ It also remains uncertain whether patients with heart failure with preserved ejection fraction (HFpEF) have disruptions in their aortic flow. This study aimed to explore sophisticated indicators of aortic flow disturbances in ageing and in HFpEF. <h3>Materials and Methods</h3> This study used two-dimensional phase-contrast CMR data at an orthogonal plane just above the sino-tubular junction. We recruited 10 young healthy controls (HCs), 10 old HCs and 23 patients with HFpEF. We analysed average systolic aortic flow displacement (FDsavg), systolic flow reversal ratio (sFRR) and pulse wave velocity (PWV). In a sub-group analysis, we compared old HCs versus age-gender-matched HFpEF (N=10). <h3>Results</h3> Differences were significant in mean age (P&lt;0.001) among young HCs (22.9±3.5 years), old HCs (60.5±10.2 years) and HFpEF patients (73.7±9.7 years). FDs_avg, sFRR and PWV varied significantly (P&lt;0.001) in young HCs (8±4%, 2±2%, 4±2m/s), old HCs (16±5%, 7±6%, 11±8m/s), and HFpEF patients (23±10%, 11±10%, 8±3). No significant PWV differences existed between old HCs and HFpEF. <b>(table 1, figure 1</b><b> and figure 2)</b> HFpEF had significantly higher FDs_avgversus old HCs (23±10% vs 16±5%, P&lt;0.001). A FDsavg &gt; 17.7% achieved 74% sensitivity, 70% specificity for differentiating them. sFRR was notably higher in HFpEF (11±10% vs 7±6%, P&lt;0.001). A sFRR &gt; 7.3% yielded 78% sensitivity, 70% specificity in differentiating these groups.<b> (figure 2)</b> In sub-group analysis, FDs_avg remained distinctly elevated in HFpEF (22.4±9.7% vs 16±4.9%, P=0.029). FDs_avg of &gt;16% showed 100% sensitivity and 70% specificity (P=0.01). Similarly, sFRR remained significantly higher in HFpEF (11.3±9.5% vs 6.6±6.4%, P=0.007). A sFRR of &gt;7.2% showed 100% sensitivity and 60% specificity (P&lt;0.001). <b>(figure 3)</b> <h3>Discussion</h3> This study is one of the first to show a rise in sFRR and FDsavg in both ageing and HFpEF with distinct differences between the two groups even when matched for age and gender. CMR-derived FDsavg and sFRR can assist in early detection and sub phenotyping of HFpEF. Our recent work² demonstrated that these aortic flow abnormalities, particularly, FDsavg, can led to reduced exercise capacity and identify high risk individuals. <h3>Conclusion</h3> Aortic flow haemodynamics (FDs_avg and sFRR) are significantly affected in ageing and HFpEF patients. Studies with larger and diverse cohort are required to draw definitive conclusions. <h3>Acknowledgements</h3> <h3>References</h3> Shah M, <i>et al.</i> Environmental and genetic predictors of human cardiovascular ageing. <i>Nature Communications</i> 2023;<b>14</b>(1). Zhao X, <i>et al.</i> Aortic flow is associated with aging and exercise capacity. <i>European Heart Journal Open</i> 2023;<b>3</b>(4).

The main objective of this study was to develop two-dimensional (2D) phase contrast (PC) methods to quantify the helicity and vorticity of blood flow in the aortic root.This proof-of-concept study used four-dimensional (4D) flow cardiovascular MR (4D flow CMR) data of five healthy controls, five patients with heart failure with preserved ejection fraction and five patients with aortic stenosis (AS). A PC through-plane generated by 4D flow data was treated as a 2D PC plane and compared with the original 4D flow. Visual assessment of flow vectors was used to assess helicity and vorticity. We quantified flow displacement (FD), systolic flow reversal ratio (sFRR) and rotational angle (RA) using 2D PC.For visual vortex flow presence near the inner curvature of the ascending aortic root on 4D flow CMR, sFRR demonstrated an area under the curve (AUC) of 0.955, p<0.001. A threshold of >8% for sFRR had a sensitivity of 82% and specificity of 100% for visual vortex presence. In addition, the average late systolic FD, a marker of flow eccentricity, also demonstrated an AUC of 0.909, p<0.001 for visual vortex flow. Manual systolic rotational flow angle change (ΔsRA) demonstrated excellent association with semiautomated ΔsRA (r=0.99, 95% CI 0.9907 to 0.999, p<0.001). In reproducibility testing, average systolic FD (FDsavg) showed a minimal bias at 1.28% with a high intraclass correlation coefficient (ICC=0.92). Similarly, sFRR had a minimal bias of 1.14% with an ICC of 0.96. ΔsRA demonstrated an acceptable bias of 5.72°-and an ICC of 0.99.2D PC flow imaging can possibly quantify blood flow helicity (ΔRA) and vorticity (FRR). These imaging biomarkers of flow helicity and vorticity demonstrate high reproducibility for clinical adoption.NCT05114785.

We used expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) to conduct genome-wide Mendelian randomization (MR) using 27,799 cases of heart failure (HF) with reduced ejection fraction (HFrEF), 27,579 cases of HF with preserved ejection fraction (HFpEF), and 367,267 control individuals from the Million Veteran Program (MVP). We identified 70 HFrEF and 10 HFpEF gene-hits, of which 58 are novel. In 14 known loci for unclassified HF, we identified HFrEF as the subtype responsible for the signal. HFrEF hits ZBTB17, MTSS1, PDLIM5, and MLIP and novel HFpEF hits NFATC2IP, and PABPC4 showed robustness to MR assumptions, support from orthogonal sources, compelling evidence on mechanism of action needed for therapeutic efficacy, and no evidence of an unacceptable safety profile. We strengthen the value of pathways such as ubiquitin-proteasome system, small ubiquitin-related modifier pathway, inflammation, and mitochondrial metabolism as potential therapeutic targets for HF management. We identified IL6R, ADM, and EDNRA as suggestive hits for HFrEF and LPA for HFrEF and HFpEF, which enhances the odds of success for existing cardiovascular investigational drugs targeting. These findings confirm the unique value of human genetic studies in HFrEF and HFpEF for discovery of novel targets and generation of therapeutic target profiles needed to initiate new validation programs in HFrEF and HFpEF preclinical models.

The progress in deep learning solutions for disease diagnosis and prognosis based on cardiac magnetic resonance imaging is hindered by highly imbalanced and biased training data. To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes such as sex, age, body mass index, and health condition. We adopt ControlNet based on a denoising diffusion probabilistic model to condition on text assembled from patient metadata and cardiac geometry derived from segmentation masks using a large-cohort study, specifically, the UK Biobank. We assess our method by evaluating the realism of the generated images using established quantitative metrics. Furthermore, we conduct a downstream classification task aimed at debiasing a classifier by rectifying imbalances within underrepresented groups through synthetically generated samples. Our experiments demonstrate the effectiveness of the proposed approach in mitigating dataset imbalances, such as the scarcity of younger patients or individuals with normal BMI level suffering from heart failure. This work represents a major step towards the adoption of synthetic data for the development of fair and generalizable models for medical classification tasks. Notably, we conduct all our experiments using a single, consumer-level GPU to highlight the feasibility of our approach within resource-constrained environments. Our code is available at https://github.com/faildeny/debiasing-cardiac-mri.

Background Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodeling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH, however its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning (ML) and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes. Methods ECG biomarkers were extracted from the 12-lead ECG of 20,439 hypertensives in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM) and random forest. The models were trained in 80% of participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensives from Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure. Results Among UKB participants 19,408 had normal LV, 758 LV remodeling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable, with SVM having a slightly superior performance (accuracy 0.79 ,sensitivity 0.59, specificity 0.87, AUC 0.69) and similar results observed in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (HR 3.42, CI 1.06-9.86). Conclusions ECG-based ML classifiers represent a potentially accessible screening strategy for early detection of hypertension-mediated LVH phenotypes.

The occurrence of self-excited noise felt as squealing noise is a critical issue for an electrohydraulic servovalve that is an essential part of the hydraulic servocontrol system. Aiming to highlight the root causes of the self-excited noise, the effect of oil viscosity on the noise production inside a two-stage servovalve is investigated in this paper. The pressure pulsations' characteristics and noise characteristics are studied at three different oil viscosities experimentally by focusing on the flapper-nozzle pilot stage of a two-stage servovalve. The cavitation-induced and vortex-induced pressure pulsations' characteristics at upstream and downstream of the turbulent jet flow path are extracted and analyzed numerically by comparing with the experimental measured pressure pulsations and noise characteristics. The numerical simulations of transient cavitation shedding phenomenon are also validated by the experimental cavitation observations at different oil viscosities. Both numerical simulations and experimental cavitation observations explain that cavitation shedding phenomenon is intensified with the decreasing of oil viscosity. The small-scale vortex propagation with the characteristic of generating, growing, moving, and merging is numerically simulated. Thus, this study reveals that the oil viscosity affects the transient distribution of cavitation and small-scale vortex, which, in turn, enhances the pressure pulsation and noise. The noise characteristics achieve a good agreement with pressure pulsation characteristics showing that the squealing noise appears accompanied by the flow field resonance in the flapper-nozzle. The flow-acoustic resonance and resulting squealing noise possibly occurs when the amplitude of the pressure pulsations near the flapper is large enough inside a two-stage servovalve.

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.

Large population studies such as the UK Biobank provide great opportunities for understanding the pathophysiology, health impact and prognostic factors associated with COVID-19, a condition that has had significant impact on almost everyone around the world. We highlight the vast opportunities, challenges and limitations for research and collaboration from the UK Biobank and other large population studies in helping us better understand and manage both current and potential future pandemics.

Background: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Design: Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants (n = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants (n = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all p-values. To aid interpretation, we organised the results into six feature clusters. Results: Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. Conclusions: We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.

Abstract Aims To evaluate the relationship between neuroticism personality traits and cardiovascular magnetic resonance (CMR) measures of cardiac morphology and function, considering potential differential associations in men and women. Methods and results The analysis includes 36 309 UK Biobank participants (average age = 63.9 ± 7.7 years; 47.8% men) with CMR available and neuroticism score assessed by the 12-item Eysenck Personality Questionnaire-Revised Short Form. CMR scans were performed on 1.5 Tesla scanners (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) according to pre-defined protocols and analysed using automated pipelines. We considered measures of left ventricular (LV) and right ventricular (RV) structure and function, and indicators of arterial compliance. Multivariable linear regression was used to estimate association of neuroticism score with individual CMR metrics, with adjustment for age, sex, obesity, deprivation, smoking, diabetes, hypertension, hypercholesterolaemia, alcohol use, exercise, and education. Higher neuroticism scores were associated with smaller LV and RV end-diastolic volumes, lower LV mass, greater concentricity (higher LV mass to volume ratio), and higher native T1. Greater neuroticism was also linked to poorer LV and RV function (lower stroke volumes) and greater arterial stiffness. In sex-stratified analyses, the relationships between neuroticism and LV stroke volume, concentricity, and arterial stiffness were attenuated in women. In men, association (with exception of native T1) remained robust. Conclusion Greater tendency towards neuroticism personality traits is linked to smaller, poorer functioning ventricles with lower LV mass, higher myocardial fibrosis, and higher arterial stiffness. These relationships are independent of traditional vascular risk factors and are more robust in men than women.

Abstract Aims Left ventricular filling pressure (LVFP) can be estimated from cardiovascular magnetic resonance (CMR). We aimed to investigate whether CMR‐derived LVFP is associated with signs, symptoms, and prognosis in patients with recently diagnosed heart failure (HF). Methods and results This study recruited 454 patients diagnosed with HF who underwent same‐day CMR and clinical assessment between February 2018 and January 2020. CMR‐derived LVFP was calculated, as previously, from long‐ and short‐axis cines. CMR‐derived LVFP association with symptoms and signs of HF was investigated. Patients were followed for median 2.9 years (interquartile range 1.5–3.6 years) for major adverse cardiovascular events (MACE), defined as the composite of cardiovascular death, HF hospitalization, non‐fatal stroke, and non‐fatal myocardial infarction. The mean age was 62 ± 13 years, 36% were female ( n = 163), and 30% ( n = 135) had raised LVFP. Forty‐seven per cent of patients had an ejection fraction &lt; 40% during CMR assessment. Patients with raised LVFP were more likely to have pleural effusions [hazard ratio (HR) 3.2, P = 0.003], orthopnoea (HR 2.0, P = 0.008), lower limb oedema (HR 1.7, P = 0.04), and breathlessness (HR 1.7, P = 0.01). Raised CMR‐derived LVFP was associated with a four‐fold risk of HF hospitalization (HR 4.0, P &lt; 0.0001) and a three‐fold risk of MACE (HR 3.1, P &lt; 0.0001). In the multivariable model, raised CMR‐derived LVFP was independently associated with HF hospitalization (adjusted HR 3.8, P = 0.0001) and MACE (adjusted HR 3.0, P = 0.0001). Conclusions Raised CMR‐derived LVFP is strongly associated with symptoms and signs of HF. In addition, raised CMR‐derived LVFP is independently associated with subsequent HF hospitalization and MACE.

To report the development of easy-to-use magnetic resonance imaging (MRI) fractal tools deployed on platforms accessible to all. The trabeculae of the left ventricle vary in health and disease but their measurement is difficult. Fractal analysis of cardiac MR images can measure trabecular complexity as a fractal dimension (FD).This Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the local Institutional Review Board. Participants provided written informed consent. The original MatLab implementation (region-based level set segmentation and box-counting algorithm) was recoded for two platforms (OsiriX and a clinical MR reporting platform [cvi42 , Circle Cardiovascular Imaging, Calgary, Canada]). For validation, 100 subjects were scanned at 1.5T and 20 imaged twice for interstudy reproducibility. Cines were analyzed by the three tools and FD variability determined. Manual trabecular delineation by an expert reader (R1) provided ground truth contours for validation of segmentation accuracy by point-to-curve (P2C) distance estimates. Manual delineation was repeated by R1 and a second reader (R2) on 15 cases for intra/interobserver variability.FD by OsiriX and the clinical MR reporting platform showed high correlation with MatLab values (correlation coefficients: 0.96 [95% CI: 0.95-0.97] and 0.96 [0.95-0.96]) and high interstudy and intraplatform reproducibility. Semiautomated contours in OsiriX and the clinical MR reporting platform were highly correlated with ground truth contours evidenced by low P2C errors: 0.882 ± 0.76 mm and 0.709 ± 0.617 mm. Validity of ground truth contours was inferred from low P2C errors between readers (R1-R1: 0.798 ± 0.718 mm; R1-R2: 0.804 ± 0.649 mm).This set of accessible fractal tools that measure trabeculation in the heart have been validated and released to the cardiac MR community (http://j.mp/29xOw3B) to encourage novel clinical applications of fractals in the cardiac imaging domain.3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1082-1088.

Objective To investigate the inflammatory biomarker signature associated with classical orthostatic hypotension (OH). Methods A cross-sectional study including 778 patients with unexplained syncope and/or orthostatic intolerance undergoing head-up tilt test (HUT) and supine blood sampling. Of these, 98 met diagnostic criteria of classical OH and 181 demonstrated normal haemodynamic response during HUT. Blood plasma samples were analysed by antibody-based Proximity Extension Assay technique simultaneously measuring 57 inflammatory and cancer-related human protein biomarkers. The discovery algorithm was a sequential two-step process of biomarker signature identification by multivariate principal component analysis (PCA), and verification by univariate ANOVA with Bonferroni correction. Results Patients with classical OH were older (68 vs. 60 years; p < 0.001) and more likely to be men (58 vs. 41%; p < 0.001). PCA and Bonferroni-adjusted ANOVA identified midkine (MK), immunoglobulin-like transcript 3 (ILT-3), regenerating islet-derived protein 4 (REG-4), and tartrate-resistant acid phosphatase type 5 (TR-AP) as the most robust targeted biomarker signature for OH. In multivariate regression analysis adjusting for age, sex, cardiovascular disease and risk factors, the results remained significant for ILT-3 (p = 0.036), MK (p = 0.008) and REG-4 (p = 0.024), but not for TR-AP. Conclusions Targeted protein profiling in classical orthostatic hypotension reveals a biomarker signature associated with immunoregulatory functions and vascular inflammation. Circulating levels of midkine, immunoglobulin-like transcript-3, regenerating islet-derived protein-4 are elevated in orthostatic hypotension, suggesting a complex interplay among inflammation, autonomic dysfunction and atherothrombosis.

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

This cohort study examines the prevalence of hypertrophic cardiomyopathy in the UK Biobank population.

Background: Early prediction of cardiovascular risk in the general population remains an important issue. The T-wave morphology restitution (TMR), an ECG marker quantifying ventricular repolarization dynamics, is strongly associated with cardiovascular mortality in patients with heart failure. Our aim was to evaluate the cardiovascular prognostic value of TMR in a UK middle-aged population and identify any genetic contribution. Methods: We analyzed ECG recordings from 55 222 individuals from a UK middle-aged population undergoing an exercise stress test in UK Biobank (UKB). TMR was used to measure ventricular repolarization dynamics, exposed in this cohort by exercise (TMR during exercise, TMR ex ) and recovery from exercise (TMR during recovery, TMR rec ). The primary end point was cardiovascular events; secondary end points were all-cause mortality, ventricular arrhythmias, and atrial fibrillation with median follow-up of 7 years. Genome-wide association studies for TMR ex and TMR rec were performed, and genetic risk scores were derived and tested for association in independent samples from the full UKB cohort (N=360 631). Results: A total of 1743 (3.2%) individuals in UKB who underwent the exercise stress test had a cardiovascular event, and TMR rec was significantly associated with cardiovascular events (hazard ratio, 1.11; P =5×10 -7 ), independent of clinical variables and other ECG markers. TMR rec was also associated with all-cause mortality (hazard ratio, 1.10) and ventricular arrhythmias (hazard ratio, 1.16). We identified 12 genetic loci in total for TMR ex and TMR rec , of which 9 are associated with another ECG marker. Individuals in the top 20% of the TMR rec genetic risk score were significantly more likely to have a cardiovascular event in the full UKB cohort (18 997, 5.3%) than individuals in the bottom 20% (hazard ratio, 1.07; P =6×10 -3 ). Conclusions: TMR and TMR genetic risk scores are significantly associated with cardiovascular risk in a UK middle-aged population, supporting the hypothesis that increased spatio-temporal heterogeneity of ventricular repolarization is a substrate for cardiovascular risk and the validity of TMR as a cardiovascular risk predictor.

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).

The burden of pregnancy-related heart disease has dramatically increased over the last decades due to the increasing age at first pregnancy and higher prevalence of cardiovascular risk factors such as diabetes, hypertension, and obesity. Pregnancy is associated with physiological changes in the cardiovascular system, including hemodynamic, metabolic, and hormonal adaptations to meet the increased metabolic demands of the mother and fetus. It has been postulated that pregnancy may act as a cardiovascular stress test to identify women at high risk for heart disease, where the inability to adequately adapt to the physiologic stress of pregnancy may reveal the presence of genetic susceptibility to cardiovascular disease or accelerate the phenotypic expression of both inherited and acquired heart diseases, such as peripartum cardiomyopathy (PPCM). PPCM is a rare and incompletely understood clinical condition. Despite recent advances in the understanding of its pathogenesis, PPCM is not attributable to a well-defined pathological mechanism, and therefore, its diagnosis still relies on the exclusion of overlapping dilated phenotypes. Cardiac imaging plays a key role in any peripartum woman with signs and symptoms of heart failure in establishing the diagnosis, ruling out life-threatening complications, guiding therapy and conveying prognostic information. Echocardiography represents the first-line imaging technique, given its robust diagnostic yield and its favorable cost-effectiveness. Cardiovascular magnetic resonance is a biologically safe high-throughput modality that allows accurate morpho-functional assessment of the cardiovascular system in addition to the unique asset of myocardial tissue characterization as a pivotal piece of information in the pathophysiological puzzle of PPCM. In this review, we will highlight current evidence on the role of multimodality imaging in the differential diagnosis, prognostic assessment, and understanding of the pathophysiological basis of PPCM.

Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges.The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy.Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model.We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.

Having a clear understanding on the phase distribution of gas-liquid two-phase flow through elbow bends is vital in mixing and separation system designs. This paper presents the computational fluid dynamics (CFD) simulations and experimental observations of gas-liquid two-phase flow pattern characteristic through a vertical to horizontal right angled (90°) elbow. Experimental observations were conducted in a transparent test section that consisted of a vertical pipe, elbow bend and horizontal pipe with an inside diameter of 0.036 m. The CFD simulations were performed by using a computer software package, FLUENT 6.2. Bubbly flow conditions were created in the vertical test section with the variation of superficial liquid Reynolds number from 13 497 to 49 488 and volumetric gas quality from 0.05 to 0.2. The CFD results showed a good agreement with experimental results in the following observations. The results showed that gas-liquid flow pattern inside and downstream of the elbow bend mainly depended on liquid velocity and it is also influenced by gas quality at high liquid velocities. At lower liquid velocities, gas-liquid separation began early in the elbow bend and gas-phase migrated to outer bend. Then, it smoothly transformed to stratified flow at elbow outlet. When the liquid velocity was further increased, the liquid phase occupied the outer bend rubbing the gas phase to the inner bend and delayed the formation of gas layer in the horizontal pipe. The increase of gas quality in higher liquid velocities promoted gas core formation at the elbow exit and caused wavy gas layers at the downstream of the elbow.

Diabetes and its cardiovascular complications are a growing concern worldwide. Recently, some studies have demonstrated that relative risk of heart failure (HF) is higher in women with type 1 diabetes (T1DM) than in men. This study aims to validate these findings in cohorts representing five countries across Europe.This study includes 88,559 (51.8% women) participants, 3,281 (46.3% women) of whom had diabetes at baseline. Survival analysis was performed with the outcomes of interest being death and HF with a follow-up time of 12 years. Sub-group analysis according to sex and type of diabetes was also performed for the HF outcome.6,460 deaths were recorded, of which 567 were amongst those with diabetes. Additionally, HF was diagnosed in 2,772 individuals (446 with diabetes). A multivariable Cox proportional hazard analysis showed that there was an increased risk of death and HF (hazard ratio (HR) of 1.73 [1.58-1.89] and 2.12 [1.91-2.36], respectively) when comparing those with diabetes and those without. The HR for HF was 6.72 [2.75-16.41] for women with T1DM vs. 5.80 [2.72-12.37] for men with T1DM, but the interaction term for sex differences was insignificant (p for interaction 0.45). There was no significant difference in the relative risk of HF between men and women when both types of diabetes were combined (HR 2.22 [1.93-2.54] vs. 1.99 [1.67-2.38] respectively, p for interaction 0.80).Diabetes is associated with increased risks of death and heart failure, and there was no difference in relative risk according to sex.

Imaging plays a fundamental role in the effective diagnosis, staging, management, and monitoring of various cardiac pathologies. Successful radiological analysis relies on accurate image segmentation, a technically arduous process, prone to human-error. To overcome the laborious and time-consuming nature of cardiac image analysis, deep learning approaches have been developed, enabling the accurate, time-efficient, and highly personalised diagnosis, staging and management of cardiac pathologies. Here, we present a review of over 60 papers, proposing deep learning models for cardiac image segmentation. We summarise the theoretical basis of Convolutional Neural Networks, Fully Convolutional Neural Networks, U-Net, V-Net, No-New-U-Net (nnU-Net), Transformer Networks, DeepLab, Generative Adversarial Networks, Auto Encoders and Recurrent Neural Networks. In addition, we identify pertinent performance-enhancing measures including adaptive convolutional kernels, atrous convolutions, attention gates, and deep supervision modules. Top-performing models in ventricular, myocardial, atrial and aortic segmentation are explored, highlighting U-Net and nnU-Net-based model architectures achieving state-of-the art segmentation accuracies. Additionally, key gaps in the current research and technology are identified, and areas of future research are suggested, aiming to guide the innovation and clinical adoption of automated cardiac segmentation methods.

Left ventricular noncompaction (LVNC) is characterized by the presence of an extensive noncompacted myocardial layer lining the cavity of the left ventricle (LV) and potentially leads to cardiac failure, thromboembolism, and malignant arrhythmias [(1)][1]. LVNC is a heterogeneous clinical condition

Abstract Background Postural orthostatic tachycardia syndrome (POTS) is a variant of cardiovascular (CV) autonomic disorder of unknown etiology characterized by an excessive heart rate increase on standing and orthostatic intolerance. In this study we sought to identify novel CV biomarkers potentially implicated in POTS pathophysiology. Methods We conducted a nested case-control study within the Syncope Study of Unselected Population in Malmö (SYSTEMA) cohort including 396 patients (age range, 15–50 years) with either POTS ( n = 113) or normal hemodynamic response during passive head-up-tilt test ( n = 283). We used a targeted approach to explore changes in cardiovascular proteomics associated with POTS through a sequential two-stage process including supervised principal component analysis and univariate ANOVA with Bonferroni correction. Results POTS patients were younger (26 vs. 31 years; p &lt; 0.001) and had lower BMI than controls. The discovery algorithm identified growth hormone (GH) and myoglobin (MB) as the most specific biomarker fingerprint for POTS. Plasma level of GH was higher (9.37 vs 8.37 of normalised protein expression units (NPX); p = 0.002), whereas MB was lower (4.86 vs 5.14 NPX; p = 0.002) in POTS compared with controls. In multivariate regression analysis, adjusted for age and BMI, and stratified by sex, lower MB level in men and higher GH level in women remained independently associated with POTS. Conclusions Cardiovascular proteomics analysis revealed sex-specific biomarker signature in POTS featured by higher plasma level of GH in women and lower plasma level of MB in men. These findings point to sex-specific immune-neuroendocrine dysregulation and deconditioning as potentially key pathophysiological traits underlying POTS.

Background: Greater red and processed meat consumption has been linked to adverse cardiovascular outcomes. However, the impact of these exposures on cardiovascular magnetic resonance (CMR) phenotypes has not been adequately studied. Objective: We describe novel associations of meat intake with cardiovascular phenotypes and investigate underlying mechanisms through consideration of a range of covariates. Design: We studied 19,408 UK Biobank participants with CMR data available. Average daily red and processed meat consumption was determined through food frequency questionnaires and expressed as a continuous variable. Oily fish was studied as a comparator, previously associated with favourable cardiac outcomes. We considered associations with conventional CMR indices (ventricular volumes, ejection fraction, stroke volume, left ventricular mass), novel CMR radiomics features (shape, first-order, texture), and arterial compliance measures (arterial stiffness index, aortic distensibility). We used multivariable linear regression to investigate relationships between meat intake and cardiovascular phenotypes, adjusting for confounders (age, sex, deprivation, educational level, smoking, alcohol intake, exercise) and potential covariates on the causal pathway (hypertension, hypercholesterolaemia, diabetes, body mass index). Results: Greater red and processed meat consumption was associated with an unhealthy pattern of biventricular remodelling, worse cardiac function, and poorer arterial compliance. In contrast, greater oily fish consumption was associated with a healthier cardiovascular phenotype and better arterial compliance. There was partial attenuation of associations between red meat and conventional CMR indices with addition of covariates potentially on the causal pathway, indicating a possible mechanistic role for these cardiometabolic morbidities. However, other associations were not altered with inclusion of these covariates, suggesting importance of alternative biological mechanisms underlying these relationships. Radiomics analysis provided deeper phenotyping, demonstrating association of the different dietary habits with distinct ventricular geometry and left ventricular myocardial texture patterns. Conclusions: Greater red and processed meat consumption is associated with impaired cardiovascular health, both in terms of markers of arterial disease and of cardiac structure and function. Cardiometabolic morbidities appeared to have a mechanistic role in the associations of red meat with ventricular phenotypes, but less so for other associations suggesting importance of alternative mechanism for these relationships.