Search

Background: It remains unknown how the patterns of change of social isolation and loneliness are associated with the onset of cardiovascular disease (CVD) and mortality. We aimed to investigate the longitudinal association of changes in social isolation and loneliness with incident CVD, all-cause mortality, CVD mortality and subsequent cardiac function. Methods: This prospective cohort study included 18,258 participants aged 38–73 years who participated in visit 0 (2006–2010) and visit 1 (2012–2013) using UK Biobank (mean age 57.1, standard deviation [SD] 7.4; 48.7% males). Social isolation or loneliness was categorized into four patterns: never, transient, incident, and persistent. Incident CVD, all-cause and CVD mortality were ascertained through linkage data. Cardiac function was assessed by cardiovascular magnetic resonance imaging in a subsample (N = 5188; visit 2, since 2014). Results: Over a median follow-up of 8.3 (interquartile range [IQR] 8.1–8.6) years, compared with never social isolation, persistent social isolation was associated with the higher risk of incident CVD (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.03–1.33), all-cause (1.42, 1.12–1.81) and CVD (1.53, 1.05–2.23) mortality. Likewise, persistent loneliness was strongly associated with the greater risk of incident CVD (1.13, 1.00–1.27), all-cause (1.28, 1.02–1.61) and CVD mortality (1.52, 1.06–2.18). Conclusions: Persistent social isolation and loneliness posed a substantially higher risk for incident CVD, all-cause and CVD mortality, and cardiac dysfunction than other patterns. Persistent social isolation and loneliness, along with an increasing cumulative score, are associated with lower cardiac function. [ABSTRACT FROM AUTHOR]

The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%–33%; pulse wave analysis, 13%–23%; and magnetic resonance imaging and carotid ultrasound, 0%–10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github. com/LivingMatterLab/CardiovascularDiseaseClassification. [ABSTRACT FROM AUTHOR]

The evidence about the associations of leukocyte telomere length (LTL) and intermediary cardiovascular phenotypes with adverse cardiovascular outcomes is inconclusive. This study assessed these relationships with cardiovascular imaging, electrocardiography, and the risks of sudden cardiac death (SCD), coronary events, and heart failure (HF) admission. We conducted a cross-sectional analysis of UK Biobank participants enrolled between 2006 and 2010. LTL was measured using quantitative polymerase chain reactions. Electronic health records were used to determine the incidence of SCD, coronary events, and HF admission. Cardiovascular measurements were made using cardiovascular magnetic resonance imaging and machine learning. The associations of LTL with SCD, coronary events, and HF admission and cardiac magnetic resonance imaging, electrocardiogram parameters of 33,043 and 19,554 participants were evaluated by multivariate regression. The median (interquartile range) follow-up period was 11.9 (11.2–12.6) years. Data was analyzed from January to May 2023. Among the 403,382 white participants without coronary artery disease or HF, 181,637 (45.0%) were male with a mean age of 57.1 years old. LTL was independently negatively associated with a risk of SCD (LTL third quartile vs first quartile: hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.72–0.92), coronary events (LTL third quartile vs first quartile: HR: 0.88, 95% CI: 0.84–0.92), and HF admission (LTL fourth quartile vs first quartile: HR: 0.84, 95% CI: 0.74–0.95). LTL was also independently positively associated with cardiac remodeling, specifically left ventricular mass index, left-ventricular-end systolic and diastolic volumes, mean left ventricular myocardial wall thickness, left ventricular stroke volume, and with electrocardiogram changes along the negative degree of T-axis. Cross-sectional study results showed that LTL was positively associated with heart size and cardiac function in middle age, but electrocardiography results did not show these associations, which could explain the negative association between LTL and risk of SCD, coronary events, and HF admission in UK Biobank participants. [ABSTRACT FROM AUTHOR]

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk. In this study, a deep learning-based model of left atrial size in UK Biobank enabled genome-wide association studies in 35,049 healthy participants. Several lines of evidence, including the PITX2 locus, linked left atrial dysfunction to atrial fibrillation risk. [ABSTRACT FROM AUTHOR]

Background: Impaired glucose utilization influences myocardial contractile function. However, the prognostic importance of left ventricular global radial strain (LV-GRS), left ventricular global circumferential strain (LV-GCS), and left ventricular global longitudinal strain (LV-GLS) in predicting new-onset heart failure (HF) in a population with diabetes is unclear. Methods: The study design is prospective cohort from the UK Biobank. Totally 37,899 participants had a complete data of cardiac magnetic resonance (CMR), of which 940 patients with diabetes were included, and all the participants completed follow-up. LV-GRS, LV-GCS, and LV-GLS were measured by completely automated CMR with tissue tagging. Cox proportional hazards regression analysis and C-index was performed to evaluate the association between the strain parameters and the new-onset HF in patients suffering from diabetes. Results: The average age of the 940 participants was 57.67 ± 6.97 years, with males comprising 66.4% of the overall population. With an average follow-up period of 166.82 ± 15.26 months, 35 (3.72%) patients reached the endpoint (emergence of new-onset HF). Significant associations were found for the three strain parameters and the new-onset HF (LV-GRS--hazard ratio [HR]: 0.946, 95% CI: 0.916-0.976; LV-GCS--HR: 1.162, 95% CI: 1.086-1.244; LV-GCS--HR: 1.181, 95% CI: 1.082-1.289). LV-GRS, LV-GCS, and LV-GLS were closely related to the related indicators to HF, and showed a high relationship to new-onset HF in individuals with diabetes at 5 and 10 years: LV-GRS: 0.75 (95% CI, 0.41-0.94) and 0.76 (95% CI, 0.44-0.98), respectively; LV-GCS: 0.80 (95% CI, 0.50-0.96) and 0.75 (95% CI, 0.41-0.98), respectively; LV-GLS: 0.72 (95% CI, 0.40-0.93) and 0.76 (95% CI, 0.48-0.97), respectively. In addition, age, sex, body mass index (BMI), and presence of hypertension or coronary artery disease (CAD) made no impacts on the association between the global strain parameters and the incidence of HF. Conclusion: LV-GRS, LV-GCS, and LV-GLS is significantly related to new-onset HF in patients with diabetes at 5 and 10 years. [ABSTRACT FROM AUTHOR]

BACKGROUND: The consequences of exercise-induced premature ventricular contractions (PVCs) in asymptomatic individuals remain unclear. This study aimed to assess the association between PVC burdens during submaximal exercise and major adverse cardiovascular events (MI/HF/LTVA: myocardial infarction [MI], heart failure [HF], and life-threatening ventricular arrhythmia [LTVA]), and all-cause mortality. Additional end points were MI, LTVA, HF, and cardiovascular mortality. METHODS: A neural network was developed to count PVCs from ECGs recorded during exercise (6 minutes) and recovery (1 minute) in 48 315 asymptomatic participants from UK Biobank. Associations were estimated using multivariable Cox proportional hazard models. Explorative studies were conducted in subgroups with cardiovascular magnetic resonance imaging data (n=6290) and NT-proBNP (N-terminal Pro-B-type natriuretic peptide) levels (n=4607) to examine whether PVC burden was associated with subclinical cardiomyopathy. RESULTS: Mean age was 56.8±8.2 years; 51.1% of the participants were female; and median follow-up was 12.6 years. Low PVC counts during exercise and recovery were both associated with MI/HF/LTVA risk, independently of clinical factors: adjusted hazard ratio (HR), 1.2 (1–5 exercise PVCs, P <0.001) and HR, 1.3 (1–5 recovery PVCs, P <0.001). Risks were higher with increasing PVC count: HR, 1.8 (>20 exercise PVCs, P <0.001) and HR, 1.6 (>5 recovery PVCs, P <0.001). A similar trend was observed for all-cause mortality, although associations were only significant for high PVC burdens: HRs, 1.6 (>20 exercise PVCs, P <0.001) and 1.5 (>5 recovery PVCs, P <0.001). Complex PVC rhythms were associated with higher risk compared with PVC count alone. PVCs were also associated with incident HF, LTVA, and cardiovascular mortality, but not MI. In the explorative studies, high PVC burden was associated with larger left ventricular volumes, lower ejection fraction, and higher levels of NT-proBNP compared with participants without PVCs. CONCLUSIONS: In this cohort of middle-aged and older adults, PVC count during submaximal exercise and recovery were both associated with MI/HF/LTVA, all-cause mortality, HF, LTVAs, and cardiovascular mortality, independent of clinical and exercise test factors, indicating an incremental increase in risk as PVC count rises. Complex PVC rhythms were associated with higher risk compared with PVC count alone. Underlying mechanisms may include the presence of subclinical cardiomyopathy. [ABSTRACT FROM AUTHOR]

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10−8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology. Changes of left ventricular structure are used to predict morbidity and mortality in cardiovascular diseases. Here the authors conducted a study using advanced deep learning technology to analyze left ventricular regional wall thickness (LVRWT) in a large population, identifying 72 significant genetic loci linked to LVRWT traits. [ABSTRACT FROM AUTHOR]

The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov , NCT04510025. Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; p adjusted =0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; p adjusted =0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; p adjusted =0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification. UK Research and Innovation and National Institute for Health Research. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) is a heterogeneous albeit treatable cardiac disease of variable severity, with the potential for heart failure, atrial fibrillation and arrhythmic sudden death, characterized by otherwise unexplained left ventricular (LV) hypertrophy and affecting all ages and races. Over the last 30 years, several studies have estimated the prevalence of HCM in the general population, employing echocardiography and cardiac magnetic resonance imaging (CMR), as well electronic health records and billing databases for clinical diagnosis. The estimated prevalence in the general population based on the disease phenotype of LV hypertrophy by imaging is 1:500 (0.2%). This prevalence was initially proposed in 1995 in the population-based CARDIA study employing echocardiography, and more recently confirmed by automated CMR analysis in the large UK Biobank cohort. The 1:500 prevalence appears most relevant to clinical assessment and management of HCM. These available data suggest that HCM is not a rare condition but likely underdiagnosed clinically and by extrapolation potentially affects about 700,000 Americans and possibly 15 million people worldwide. Central Illustration. Spectrum of reported population prevalence estimates for HCM. The predominant and most clinically relevant (1:500) is based on the disease phenotype and documented by both echocardiography and CMR. EHR = electronic health records; MRI = magnetic resonance imaging. [Display omitted] • The estimated prevalence of HCM based on the disease phenotype by imaging is 1:500. • HCM prevalence is relevant to clinical practice, funding and public awareness. • HCM is not rare but clinically underdiagnosed by a factor of almost 3×. [ABSTRACT FROM AUTHOR]

Background and Aims: Growing evidence supports an association between fatty liver disease (FLD) and cardiac dysfunction and remodelling, leading to cardiovascular disease and heart failure. Herein, we investigated the independent contribution of FLD to cardiac dysfunction and remodelling in participants from the UK Biobank with cardiac magnetic resonance (CMR) data available. Methods: A total of 18 848 Europeans without chronic viral hepatitis and valvular heart diseases, with liver magnetic resonance imaging and CMR data were included in the analyses. Clinical, laboratory and imaging data were collected using standardized procedures. Multivariable regression models were used to test the association between FLD and CMR endpoints, after adjusting for several cardiometabolic risk factors. Linear regression models with regularization (Least Absolute Shrinkage and Selection Operator [LASSO], Ridge and Elastic Net) were used to generate predictive models for heart‐related endpoints. Results: FLD was independently associated with higher average heart rate, higher cardiac remodelling (higher eccentricity ratio and lower remodelling index), lower left and right ventricular volumes (end‐systolic, end‐diastolic and stroke volumes) as well as with lower left and right atrial maximal volumes (p < 0.001). FLD was the strongest positive predictor for average heart rate, followed by age, hypertension and type 2 diabetes. Male sex was the strongest positive predictor for eccentricity ratio followed by FLD, age, hypertension and BMI. For LV volumes, FLD was the strongest negative predictor along with age. Conclusions: FLD is an independent predictor of higher heart rate and early cardiac remodelling associated with reduced ventricular volumes. [ABSTRACT FROM AUTHOR]

As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous peripheral blood delivery. Systolic distention and diastolic recoil conserve energy and are enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and vascular disease. In this study, we sought to discover epidemiologic correlates and genetic determinants of aortic distensibility and strain. We trained a deep learning model to quantify thoracic aortic area throughout the cardiac cycle from cardiac magnetic resonance images and calculated aortic distensibility and strain in 42,342 UK Biobank participants. Descending aortic distensibility was inversely associated with future incidence of cardiovascular diseases, such as stroke (HR: 0.59 per SD; P = 0.00031). The heritabilities of aortic distensibility and strain were 22% to 25% and 30% to 33%, respectively. Common variant analyses identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were not significantly associated with thoracic aortic diameter. Nearby genes were involved in elastogenesis and atherosclerosis. Aortic strain and distensibility polygenic scores had modest effect sizes for predicting cardiovascular outcomes (delaying or accelerating disease onset by 2%-18% per SD change in scores) and remained statistically significant predictors after accounting for aortic diameter polygenic scores. Genetic determinants of aortic function influence risk for stroke and coronary artery disease and may lead to novel targets for medical intervention. [Display omitted] [ABSTRACT FROM AUTHOR]

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy. A genome-wide association study of cardiac magnetic resonance-derived left ventricular mass index including 43,000 UK Biobank participants reveals 12 associations (11 novel), implicating genes involved in cardiac contractility and cardiomyopathy. [ABSTRACT FROM AUTHOR]

Background: Acute myocardial injury in hospitalized patients with coronavirus disease 2019 (COVID-19) has a poor prognosis. Its associations and pathogenesis are unclear. Our aim was to assess the presence, nature, and extent of myocardial damage in hospitalized patients with troponin elevation. Methods: Across 25 hospitals in the United Kingdom, 342 patients with COVID-19 and an elevated troponin level (COVID+/troponin+) were enrolled between June 2020 and March 2021 and had a magnetic resonance imaging scan within 28 days of discharge. Two prospective control groups were recruited, comprising 64 patients with COVID-19 and normal troponin levels (COVID+/troponin-) and 113 patients without COVID-19 or elevated troponin level matched by age and cardiovascular comorbidities (COVID-/comorbidity+). Regression modeling was performed to identify predictors of major adverse cardiovascular events at 12 months. Results: Of the 519 included patients, 356 (69%) were men, with a median (interquartile range) age of 61.0 years (53.8, 68.8). The frequency of any heart abnormality, defined as left or right ventricular impairment, scar, or pericardial disease, was 2-fold greater in cases (61% [207/342]) compared with controls (36% [COVID+/troponin-] versus 31% [COVID-/comorbidity+]; P <0.001> P <0.001> P <0.01)> P <0.001),> P =0.10). Using the Lake Louise magnetic resonance imaging criteria, the prevalence of probable recent myocarditis was 6.7% (23/342) in cases compared with 1.7% (2/113) in controls without COVID-19 (P =0.045). During follow-up, 4 patients died and 34 experienced a subsequent major adverse cardiovascular event (10.2%), which was similar to controls (6.1%; P =0.70). Myocardial scar, but not previous COVID-19 infection or troponin, was an independent predictor of major adverse cardiovascular events (odds ratio, 2.25 [95% CI, 1.12-4.57]; P =0.02). Conclusions: Compared with contemporary controls, patients with COVID-19 and elevated cardiac troponin level have more ventricular impairment and myocardial scar in early convalescence. However, the proportion with myocarditis was low and scar pathogenesis was diverse, including a newly described pattern of microinfarction. Registration: URL: https://www.isrctn.com ; Unique identifier: 58667920. What Is New? * This study is the first large, multicenter, prospective, case-control study investigating the nature and extent of myocardial injury in patients hospitalized with COVID-19 and elevated cardiac troponin level imaged within 28 days of discharge with core laboratory analyses. * Patients with COVID-19 had a much lower prevalence of probable recent myocarditis than previously reported. * In patients with COVID-19, we identified a new pattern of microinfarction on cardiac magnetic resonance imaging, highlighting the prothrombotic nature of this disease. * Among hospitalized patients with COVID-19 and elevated cardiac troponin level, the presence of scar was independently associated with cardiovascular outcomes at 12 months. What Are the Clinical Implications? * We identified a lower prevalence of probable recent myocarditis than previously described and higher proportions of myocardial infarction and microinfarction in COVID-19. Myocardial scar was independently associated with cardiovascular outcomes. This is important to facilitate appropriate management decisions. [ABSTRACT FROM AUTHOR]

Human activities have raised the atmosphere's carbon dioxide (CO2) content by 50% in less than 200 years and by 10% in the last 15 years. Climate change is a great threat and presents a unique opportunity to protect cardiovascular health in the next decades. CO2 equivalent emission is the most convenient unit for measuring the greenhouse gas footprint corresponding to ecological cost. Medical imaging contributes significantly to the CO2 emissions responsible for climate change, yet current medical guidelines ignore the carbon cost. Among the common cardiac imaging techniques, CO2 emissions are lowest for transthoracic echocardiography (0.5–2 kg per exam), increase 10-fold for cardiac computed tomography angiography, and 100-fold for cardiac magnetic resonance. A conservative estimate of 10 billion medical examinations per year worldwide implies that medical imaging accounts for approximately 1% of the overall carbon footprint. In 2016, CO2 emissions from magnetic resonance imaging and computed tomography, calculated in 120 countries, accounted for 0.77% of global emissions. A significant portion of global greenhouse gas emissions is attributed to health care, which ranges from 4% in the United Kingdom to 10% in the United States. Assessment of carbon cost should be a part of the cost-benefit balance in medical imaging. [ABSTRACT FROM AUTHOR]

Background: Hypertrophic cardiomyopathy (HCM) is an inherited autosomal dominant heart disease, characterized by increased left ventricular wall thickness and abnormal loading conditions. Imaging modalities are the first choice for diagnosis and risk stratification. Although heart dimensions have been characterized widely in HCM adults from cardiac imaging, there is limited information about children affected by HCM. The aim of this study is to evaluate left ventricular function and left heart dimensions in a small population of children diagnosed with HCM. Methods: A total of 16 (seven male, nine female) pediatric patients with an average age of 14.0 ± 2.5 years diagnosed with HCM at Great Ormond Street Hospital for Children were included in this study. Cardiac magnetic resonance (CMR) images were used to measure left and right ventricular dimensions, and septal and left ventricular free wall thicknesses in Simpleware ScanIP. The gender groups were compared using student t‐test or non‐parametric Mann‐Whitney U‐test depending on the sample distribution. Results: Differences in heart rate, left ventricular end‐diastolic volume and end‐diastolic volume index, left ventricular stroke volume and stroke volume index, left ventricular end‐systolic long axis length, left ventricular end‐systolic long axis length index, left ventricular end‐diastolic mid‐cavity diameter, left ventricular end‐diastolic free wall thickness, left ventricular end‐diastolic free wall thickness index, right ventricular end‐diastolic long axis length were statistically significant in males and females. Conclusion: Left ventricular wall and intraventricular septal thickness increase affecting left ventricle cavity dimensions and there may be differences in anatomical and physiological parameters in males and females affected by HCM. [ABSTRACT FROM AUTHOR]

Aortic dimensions and distensibility are key risk factors for aortic aneurysms and dissections, as well as for other cardiovascular and cerebrovascular diseases. We present genome-wide associations of ascending and descending aortic distensibility and area derived from cardiac magnetic resonance imaging (MRI) data of up to 32,590 Caucasian individuals in UK Biobank. We identify 102 loci (including 27 novel associations) tagging genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. Functional analyses highlight four signalling pathways associated with aortic distensibility (TGF-β, IGF, VEGF and PDGF). We identify distinct sex-specific associations with aortic traits. We develop co-expression networks associated with aortic traits and apply phenome-wide Mendelian randomization (MR-PheWAS), generating evidence for a causal role for aortic distensibility in development of aortic aneurysms. Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, mechanistically linking aortic traits and brain small vessel disease. Aortic distensibility is a risk factor for multiple cardiovascular events, but the genetic etiology is not well understood. Here, the authors identify genetic variants linked to aortic distensibility, highlighting mechanistic pathways and causal relationships between distensibility and both aortic aneurysms and brain small vessel disease. [ABSTRACT FROM AUTHOR]

Background: The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.Objectives: We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.Methods: Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.Results: A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10-6).Conclusions: We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: 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.Methods: 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.Results: 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.Conclusions: 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. [ABSTRACT FROM AUTHOR]

Background: Previous studies suggest that prevalent heart failure (HF) differs based on left ventricular ejection fraction (LVEF) and left ventricular (LV) chamber size. Furthermore, the prevalence of HF with preserved ejection fraction (HFpEF) is often considered approaching, or exceeding that of HF with reduced ejection fraction in the community., Aim: The aim of this study was to evaluate prevalent and incident HF based on LVEF and CMR-determined LV size within a large community-dwelling cohort., Methods: Individuals from the United Kingdom Biobank (UKB) who underwent CMR and had available health record linkage to allow ascertainment of HF diagnosis were included. The cohort was analysed according to LVEF, LV end-diastolic volume (LVEDV) quartiles and LVEDV indexed to body surface area (LVEDVi)., Results: 38,129 individuals were included, comprising those with reduced LVEF (LVEF<50 %, n = 5096) and preserved LVEF (LVEF 50-60 %, n = 22,907, LVEF≥60 %, n = 10,126). Prevalent HF was highest in males with LVEF<50 %, and participants with reduced LVEF had higher rates of incident HF (p < 0.001) during the follow-up period (median = 2.46 years from CMR). Mean LVEDV and LVEDVi were largest in individuals with EF < 50 % (146.9 ± 36.2 ml and 76.8 ± 16.4 ml/m 2 respectively). Compared to the smallest quartiles, the largest quartiles for LVEDV were associated with increased odds of HF (odds ratio 2.14 [95 % confidence interval 1.47-3.12], p < 0.001)., Conclusions: Over 50 % of HF cases occur in individuals with LVEF ≥50 %, however HF prevalence is highest in those with reduced LVEF, particularly in males. Larger LV size is associated with increased HF across the LVEF spectrum., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: High cognitive reserve (CR) has been related to lower dementia risk, but its association with heart disease (HD) is unknown. We aimed to explore the relation of CR to HD and cardiac structure and function., Methods and Results: Within the UK Biobank, 349 907 HD-free participants were followed up. A composite CR indicator involving education/occupation attainment/television viewing time/confiding frequency/social connection frequency/variety of leisure activities was generated, and further categorized into low/moderate/high levels. Incident HD, including coronary HD, cardiac arrhythmia, and heart failure, was ascertained on the basis of medical records. During the follow-up, a subsample (n=31 182) underwent cardiac magnetic resonance imaging to assess ventricular structure and function. Data were analyzed using Cox regression, Laplace regression, and linear regression. Compared with low CR, the hazard ratio and 95% CI of any HD for high CR was 0.78 (0.75-0.80) (including 0.68 [0.66-0.71] for coronary HD, 0.91 [0.87-0.95] for cardiac arrhythmia, and 0.63 [0.58-0.68] for heart failure). Furthermore, high CR was associated with delayed HD onset by 1.59 (95% CI, 1.37-1.82) years compared with low CR. In cardiac magnetic resonance imaging data analysis, compared with low CR, high CR was associated with larger left ventricular end-diastolic volume (β, 0.13 [95% CI, 0.09-0.17]), left ventricular end-systolic volume (β, 0.05 [95% CI, 0.01-0.10]), left ventricular stroke volume (β, 0.16 [95% CI, 0.12-0.21]), and left ventricular ejection fraction (β, 0.08 [95% CI, 0.03-0.13])., Conclusions: High CR is associated with favorable HD health. Our findings suggest that the beneficial effect of CR is not limited to dementia but also HD.

Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.Objectives: The goal of this study was to compare lifetime outcomes and cardiovascular phenotypes according to the presence of rare variants in sarcomere-encoding genes among middle-aged adults.Methods: This study analyzed whole exome sequencing and cardiac magnetic resonance imaging in UK Biobank participants stratified according to sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n = 5,712; 1 in 35), and the prevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was 0.25% (n = 493; 1 in 407). SARC-HCM-P/LP variants were associated with an increased risk of death or major adverse cardiac events compared with controls (hazard ratio: 1.69; 95% confidence interval [CI]: 1.38-2.07; P < 0.001), mainly due to heart failure endpoints (hazard ratio: 4.23; 95% CI: 3.07-5.83; P < 0.001). In 21,322 participants with both cardiac magnetic resonance imaging and whole exome sequencing, SARC-HCM-P/LP variants were associated with an asymmetric increase in left ventricular maximum wall thickness (10.9 ± 2.7 mm vs 9.4 ± 1.6 mm; P < 0.001), but hypertrophy (≥13 mm) was only present in 18.4% (n = 9 of 49; 95% CI: 9%-32%). SARC-HCM-P/LP variants were still associated with heart failure after adjustment for wall thickness (hazard ratio: 6.74; 95% CI: 2.43-18.7; P < 0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have low aggregate penetrance for overt HCM but are associated with an increased risk of adverse cardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease. [ABSTRACT FROM AUTHOR]

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions. Between 2014 and 2023, 100,000 UK Biobank participants are undergoing brain, heart and abdominal MRI, as well as DXA and carotid ultrasound scans. In this review, authors provide a detailed overview of the rationale for the collection of these imaging data, the procedures of data collection and management, and the future directions of the UK biobank imaging enhancement. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM. Structural changes to the left ventricle are characteristic of dilated cardiomyopathy (DCM), a disease for which many rare genetic variants are known. Here, Pirruccello et al. report GWAS of seven cardiac MRI measurements in the left ventricle and describe shared loci and polygenic association with DCM. [ABSTRACT FROM AUTHOR]

Background: Microvascular obstruction affects one-half of patients with ST-segment elevation myocardial infarction and confers an adverse prognosis.Objectives: This study aimed to determine whether the efficacy and safety of a therapeutic strategy involving low-dose intracoronary alteplase infused early after coronary reperfusion associates with ischemic time.Methods: This study was conducted in a prospective, multicenter, parallel group, 1:1:1 randomized, dose-ranging trial in patients undergoing primary percutaneous coronary intervention. Ischemic time, defined as the time from symptom onset to coronary reperfusion, was a pre-specified subgroup of interest. Between March 17, 2016, and December 21, 2017, 440 patients, presenting with ST-segment elevation myocardial infarction within 6 h of symptom onset (<2 h, n = 107; ≥2 h but <4 h, n = 235; ≥4 h to 6 h, n = 98), were enrolled at 11 U.K. hospitals. Participants were randomly assigned to treatment with placebo (n = 151), alteplase 10 mg (n = 144), or alteplase 20 mg (n = 145). The primary outcome was the amount of microvascular obstruction (MVO) (percentage of left ventricular mass) quantified by cardiac magnetic resonance imaging at 2 to 7 days (available for 396 of 440).Results: Overall, there was no association between alteplase dose and the extent of MVO (p for trend = 0.128). However, in patients with an ischemic time ≥4 to 6 h, alteplase increased the mean extent of MVO compared with placebo: 1.14% (placebo) versus 3.11% (10 mg) versus 5.20% (20 mg); p = 0.009 for the trend. The interaction between ischemic time and alteplase dose was statistically significant (p = 0.018).Conclusion: In patients presenting with ST-segment elevation myocardial infarction and an ischemic time ≥4 to 6 h, adjunctive treatment with low-dose intracoronary alteplase during primary percutaneous coronary intervention was associated with increased MVO. Intracoronary alteplase may be harmful for this subgroup. (A Trial of Low-Dose Adjunctive Alteplase During Primary PCI [T-TIME]; NCT02257294). [ABSTRACT FROM AUTHOR]

Models that utilize self-attention mechanisms, including but not limited to Vision Transformers (ViTs), have shown promising performance in visual tasks like semantic segmentation. This is attributed to their capacity to capture global features of images, enabling them to learn more comprehensive representations. However, transformer-based models typically demand a considerable amount of training data to achieve satisfactory performance, while being deficient in the ability to efficiently extract local image features. As a result, these models may not be as effective in some computer vision tasks that involve small-scale datasets, like medical image segmentation. To address these issues, this paper proposes a dual-stream encoding-based transformer dubbed as Dual-stream Transformer (DS-Former). The dual-stream module in DS-Former can simultaneously acquire local and global features in the image and construct relation between the two kinds of features via self-attention. Compared with the simple splicing or serial connection, the dual-stream module can extract more comprehensive and hierarchical feature information from the fusion interaction of the two features. Our method is evaluated on the UK Biobank (UKBB) cardiac magnetic resonance imaging (CMR) dataset and The Beyond the Cranial Vault (BTCV) abdominal challenge dataset. The experimental results indicate that our DS-Former outperforms other state-of-the-art approaches on both datasets, indicating its potential for medical images semantic segmentation. [ABSTRACT FROM AUTHOR]

Objectives: To investigate sex differences in left ventricular remodelling and outcome in patients undergoing surgical or transcatheter aortic valve replacement (SAVR/TAVR).Methods: In this multicentre, observational, outcome study with imaging core-lab analysis, patients with severe aortic stenosis (AS) listed for intervention at one of six UK centres were prospectively recruited and underwent cardiovascular magnetic resonance imaging. The primary endpoint was all-cause mortality and secondary endpoint was cardiovascular mortality.Results: 674 patients (425 men, 249 women, age 75±14 years) were included: 399 SAVR, 275 TAVR. Women were older, had higher surgical risk scores and underwent TAVR more frequently (53% vs 33.6%, p<0.001). More men had bicuspid aortic valves (BAVs) (26.7% vs 14.9%, p<0.001) and demonstrated more advanced remodelling than women. During a median follow-up of 3.6 years, 145 (21.5%) patients died, with no significant sex difference in all-cause mortality (23.3% vs 20.5%, p=0.114), but higher cardiovascular mortality in women (13.7% vs 8.5%, p=0.012). There were no significant sex-related differences in outcome in the SAVR or TAVR subgroups, or after excluding those with BAV. Factors independently associated with all-cause mortality were age, left ventricular ejection fraction (LVEF), BAV (better) and myocardial fibrosis detected with late gadolinium enhancement (LGE) in men, and age, LVEF and LGE in women. Age and LGE were independently associated with cardiovascular mortality in both sexes.Conclusions: Men demonstrate more advanced remodelling in response to a similar severity of AS. The higher cardiovascular mortality observed in women following AVR is accounted for by women having less BAV and higher risk scores resulting in more TAVR. LGE is associated with a worse prognosis in both sexes. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery.Methods: We defined all-cause HF among 488010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30201).Results: We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci ( P<1×10-6), the majority linked to upstream HF risk factors, ie, coronary artery disease ( CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation ( PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy ( BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-offunction variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10-5).Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk factors and that are associated with subclinical left ventricular dysfunction. [ABSTRACT FROM AUTHOR]

Introduction: The VisiTag module (CARTO3) provides an objective assessment of radiofrequency (RF) ablation parameters. This study aimed to determine the predictive value and optimal VisiTag threshold settings for prediction of gaps in mature atrial scar, as assessed non‐invasively using cardiac magnetic resonance (CMR) imaging. Methods: Twenty‐four subjects (11 paroxysmal atrial fibrillation) underwent first‐time RF ablation with operators blinded to VisiTag data. Three‐dimensional late gadolinium enhancement (LGE) CMR scans were performed at 3 months (1.3 × 1.3 × 4 mm3). A survey of UK operators defined the standard VisiTag settings ("Force," 8 g; "Time," 10 seconds; "Percentage Time," 50%; "Range," 3 mm; "Impedance" and "Temperature" "off"). Each ablation procedure was exported 27 times, varying single VisiTag parameters from default values. The presence of gaps in VisiTag markers (18 sectors) was assessed for each export and compared with gaps in CMR enhancement. Results: At default settings, VisiTag gaps were specific (97.5%) but less sensitive (50.4%) for CMR gaps. Sensitivity improved at higher thresholds (89.2% at 20 g, 85.6% at 30 seconds, 88.5% impedance 10 Ω, 92.8% temperature 42°C), but with a lower positive predictive value (PPV) (42.3%, 42.7%, 41.1%, and 37.7%, respectively, vs 90.9% at baseline). "Force" thresholds demonstrated stable PPV from 2 to 8 g (P = 0.24), but a rapid fall at forces more than 10 g. The binomial logistic regression model explained 41.7% of gaps; χ2(4), 148; P < 0.0001, correctly classifying 82% of cases (specificity 94.9%, sensitivity 56.8%). Conclusion: Gaps in VisiTags predict gaps in CMR LGE enhancement with high specificity at default settings. Sensitivity may be improved using more stringent thresholds but at the potential cost of unnecessary ablation, particularly when a force more than 10 g is stipulated. [ABSTRACT FROM AUTHOR]

• A framework for LRV detection, segmentation in large CMR studies. • Automatic initialization of bi-ventricles for the first time frame without any user intervention. • Automatic End-diastole and End-systole phase assessments. • Supervised deep neural network employed to train and segment the heart. • 3D-ASM image search procedure improved by combining image intensity models with derived distance maps for LV and RV edge localization. The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo- and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data. This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization. The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentation. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis. Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging. [ABSTRACT FROM AUTHOR]

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

Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available. [Display omitted] • We propose an unsupervised probabilistic motion generative model (DragNet). • We infer probabilistic displacement fields in their original high dimensional spaces. • Generating high temporal resolution image sequences given only one reference frame. • Explicit modelling spatio-temporal uncertainties at the pixel level for motion fields. [ABSTRACT FROM AUTHOR]

Focal But Not Diffuse Fibrosis Burden Predicts CRT Remodeling Response Introduction Many heart failure patients with dyssynchrony do not reverse remodel (RR) in response to cardiac resynchronization therapy (CRT). The presence of focal and diffuse interstitial myocardial fibrosis may explain this high nonresponse rate. T1 mapping is a new cardiac magnetic resonance imaging (CMR) technique that overcomes the limitations of conventional contrast CMR and provides reliable quantitative assessment of diffuse myocardial fibrosis. The study tested the hypothesis that focal and diffuse fibrosis quantification would correlate with a lack of left ventricular (LV) RR to CRT. Methods and Results In a prospective study of 48 consecutive patients (27 ischemic cardiomyopathy, 21 dilated cardiomyopathy) LV scar burdens were quantified (scar core and gray zone using late gadolinium enhancement LGE CMR; interstitial fibrosis using T1 mapping) before CRT implant. LV RR was defined by a ≥ 15% reduction in LV end-systolic volume 6 months postimplant. Twenty-seven (56%) patients were responders with RR. Association between scar quantification and LV RR was assessed using the Poisson regression model. Univariate analysis showed that QRS duration/morphology, scar core, and gray zone volumes expressed as % of LV mass and extracellular volume index (ECV) (a measure of interstitial fibrosis from T1 mapping) to be significant predictors of LV RR. Multivariable-adjusted analyses demonstrated scar core quantification (≥13.7% LV mass) to be the only independent predictor of LV RR (prevalence ratio 0.40, P = 0.038). Conclusions Focal scar burden detected by LGE CMR is associated with a poor response to CRT. Diffuse interstitial fibrosis assessment by T1 mapping, however, is not independently predictive of CRT response. [ABSTRACT FROM AUTHOR]

• In this work, 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 • To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation • 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, in total 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 [Display omitted] 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. [ABSTRACT FROM AUTHOR]

The article presents the findings of a survey related to cardiovascular magnetic resonance (CMR) activity in the Great Britain. The results of the survey show that CMR services are led by cardiologists and radiologists in equal proportion in Great Britain. It also showed that the number of CMR scans performed increased in just two years. The survey also showed that trainees were mainly located in large volume centres and enrolled in cardiology as opposed to radiology training programmes.

Background: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable heart muscle disease that causes sudden cardiac death in the young. Inflammatory myocardial infiltrates have been described at autopsy and on biopsy, but there are few data on the presence of myocarditis in living patients with ARVC using non-invasive imaging techniques. FDG-PET is a validated technique for detecting myocardial inflammation in clinically suspected myocarditis. We aimed to determine the prevalence of myocardial inflammation in patients with ARVC using 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET)., Methods and Results: We performed a retrospective analysis of a single centre cohort of patients with ARVC referred for FDG-PET scans between 2012 and 2017 for investigation of symptoms or suspected device infection. Sixteen patients (12 male; age 42 ± 13 years) with a definite diagnosis of ARVC were identified. Seven had positive FDG-PET scans, two of whom had cardiac sarcoidosis on endomyocardial biopsy. Of the remaining five, two carried pathogenic desmoplakin mutations. FDG uptake was found in the left ventricular myocardium in all cases. One patient also had right ventricular uptake., Conclusion: In this exploratory study, we show that some patients with ARVC have evidence for myocardial inflammation on FDG-PET, suggesting that myocarditis plays a role in disease pathogenesis., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Introduction: Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. Methods: We defined all-cause HF among 488,010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease (CAD) as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy (DCM) genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4,158) and echocardiographic data (n=30,201). Results: We identified 7,382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci (P < 1x10-6), the majority linked to upstream HF risk factors, i.e. CAD (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2,038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in DCM (BAG3 , CLCNKA-ZBTB17). DCM signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. Additionally, loss-of-function analyses implicated BAG3 as a disease-susceptibility gene for NICM (carrier frequency=0.01%, OR=12.03, P=3.62x10-5). Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independent of clinical HF risk factors, and which are associated with subclinical left ventricular dysfunction. [ABSTRACT FROM AUTHOR]