Novel determinants of cardiovascular disease: a population approach.

PhD Theses.Background: Cardiovascular disease remains the biggest cause of mortality and morbidity worldwide. New aetiological models for chronic non-communicable conditions, such as cardiovascular disease, propose a more extensive and complex network of interconnected disease determinants than is suggested by traditional risk factor models. Understanding novel disease determinants and their interactions across different organ systems may be key in alleviating the global burden of cardiovascular disease. The UK Biobank comprises comprehensive characterisation of demographics, lifestyle, and clinical status for over half a million participants along with prospective tracking of health outcomes and, for a large subset of participants, detailed cardiovascular magnetic (CMR) imaging. Thus, the UK Biobank provides an ideal platform for investigation of novel cardiovascular disease determinants. Methods: We present a series of observational studies investigating the association of several novel exposures with cardiovascular health in the UK Biobank, with consideration given to exposures acting across key organ systems (heart, brain, gut, bone) and integrated use of CMR data. Additionally, we describe and illustrate the utility of CMR radiomics, a novel image analysis technique for deeper cardiovascular phenotyping. Finally, in light of the coronavirus disease 2019 (COVID-19) pandemic, we investigate the association of pre-existing CMR phenotypes with incident COVID-19. Summary of results: Our findings demonstrate associations between cardiovascular health and novel disease exposures acting across different organ systems. We demonstrate the value of a multi-system approach to understanding cardiovascular health and the importance of cross-system interactions in disease occurrence and progression. We further illustrate the utility of large scale CMR data for epidemiologic research in gaining added insights into such relationships and demonstrate the use of deeper cardiovascular phenotyping with novel CMR radiomics. Conclusions: Our findings suggest that the search for such novel disease determinants is worthwhile and important for improving population burden of cardiovascular diseases

New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics

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

Automated quality-controlled cardiovascular magnetic resonance pericardial fat quantification using a convolutional neural network in the UK Biobank

Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts.Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB).Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set (n = 87), the whole UKB Imaging cohort (n = 45,519), and an external dataset (n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB (n = 42,928).Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium–good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p-value 5.3 ×10−18). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index.Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.<br/

Genome‐Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants

BACKGROUND: Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity‐adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS: The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome‐wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance–derived measures of left ventricular structure and function. We discovered 12 genome‐wide significant variants, with 2 independent sentinel variants (rs6428792, P =4.20×10 −9 and rs11992444, P =1.30×10 −12 ) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T‐box transcription factor 15 ( TBX15 ), tryptophanyl tRNA synthetase 2, mitochondrial ( WARS2 ) and early B‐cell factor‐2 ( EBF2 ) through functional annotation. Bayesian colocalization additionally suggested a role of RP4‐712E4.1. Genetically predicted differences in adiposity‐adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS: This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution

Cardiac magnetic resonance radiomics: basic principles and clinical perspectives

Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work

Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank

Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level

Estimation of biological heart age using cardiovascular magnetic resonance radiomics

We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-diferential patterns. We subtracted actual age from model estimated heart age to calculate a “heart age delta”, which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches

Noninvasive techniques for tracking biological aging of the cardiovascular system

Population aging is one of the most important demographic transformations of our time. Increasing the “health span”—the proportion of life spent in good health—is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging

Differential Patterns and Outcomes of 20.6 Million Cardiovascular Emergency Department Encounters for Men and Women in the United States.

Background We describe sex-differential disease patterns and outcomes of \u3e20.6 million cardiovascular emergency department encounters in the United States. Methods and Results We analyzed primary cardiovascular encounters from the Nationwide Emergency Department Sample between 2016 and 2018. We grouped cardiovascular diagnoses into 15 disease categories. The sample included 48.7% women; median age was 67 (interquartile range, 54-78) years. Men had greater overall baseline comorbidity burden; however, women had higher rates of obesity, hypertension, and cerebrovascular disease. For women, the most common emergency department encounters were essential hypertension (16.0%), hypertensive heart or kidney disease (14.1%), and atrial fibrillation/flutter (10.2%). For men, the most common encounters were hypertensive heart or kidney disease (14.7%), essential hypertension (10.8%), and acute myocardial infarction (10.7%). Women were more likely to present with essential hypertension, hypertensive crisis, atrial fibrillation/flutter, supraventricular tachycardia, pulmonary embolism, or ischemic stroke. Men were more likely to present with acute myocardial infarction or cardiac arrest. In logistic regression models adjusted for baseline covariates, compared with men, women with intracranial hemorrhage had higher risk of hospitalization and death. Women presenting with pulmonary embolism or deep vein thrombosis were less likely to be hospitalized. Women with aortic aneurysm/dissection had higher odds of hospitalization and death. Men were more likely to die following presentations with hypertensive heart or kidney disease, atrial fibrillation/flutter, acute myocardial infarction, or cardiac arrest. Conclusions In this large nationally representative sample of cardiovascular emergency department presentations, we demonstrate significant sex differences in disease distribution, hospitalization, and death

Shorter leukocyte telomere length is associated with adverse COVID-19 outcomes: A cohort study in UK Biobank.

Background Older age is the most powerful risk factor for adverse coronavirus disease-19 (COVID-19) outcomes. It is uncertain whether leucocyte telomere length (LTL), previously proposed as a marker of biological age, is also associated with COVID-19 outcomes. Methods We associated LTL values obtained from participants recruited into UK Biobank (UKB) during 2006-2010 with adverse COVID-19 outcomes recorded by 30 November 2020, defined as a composite of any of the following: hospital admission, need for critical care, respiratory support, or mortality. Using information on 130 LTL-associated genetic variants, we conducted exploratory Mendelian randomisation (MR) analyses in UKB to evaluate whether observational associations might reflect cause-and-effect relationships. Findings Of 6775 participants in UKB who tested positive for infection with SARS-CoV-2 in the community, there were 914 (13.5%) with adverse COVID-19 outcomes. The odds ratio (OR) for adverse COVID-19 outcomes was 1·17 (95% CI 1·05-1·30; P = 0·004) per 1-SD shorter usual LTL, after adjustment for age, sex and ethnicity. Similar ORs were observed in analyses that: adjusted for additional risk factors; disaggregated the composite outcome and reduced the scope for selection or collider bias. In MR analyses, the OR for adverse COVID-19 outcomes was directionally concordant but non-significant. Interpretation Shorter LTL is associated with higher risk of adverse COVID-19 outcomes, independent of several major risk factors for COVID-19 including age. Further data are needed to determine whether this association reflects causality. Funding UK Medical Research Council, Biotechnology and Biological Sciences Research Council and British Heart Foundation.UK Medical Research Council, Biotechnology and Biological Sciences Research Council and British Heart Foundation