Approved Research

Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound

Lay summary

1a: Imaging of the heart and blood vessels is performed in a large subset of the UK Biobank cohort. Many measures defining the state of the heart and blood vessels can be derived from the images acquired. These measures are influenced by various health conditions and modifiable and non-modifiable factors, such as age, gender and ethnicity. The aim of this proposal is to describe the measures of the heart and blood vessel in the UK Biobank population and investigate how much modifiable and non-modifiable factors influence them. All new data will be made available for future research.

1b: Knowing the reference ranges for common imaging measures of the heart and circulation and how they are influenced by factors, such as age, gender, ethnicity, risk factors for heart attacks and strokes, is key for improving making diagnoses and predicting health outcomes.

1c: Descriptive statistics will be performed for all image derived phenotypes (IDPs) from the cardiovascular magnetic resonance (CMR) and carotid ultrasound images. We will perform subgroup analysis for important clinical factors, such as age, gender, cardiovascular risk, chronic conditions (e.g. Diabetes). We will apply descriptive statistics to a subpopulation considered “healthy without cardiovascular disease or presence of modifiable risk factors”. Univariate and multivariate regression analysis will be used to assess relationships between IDPs and relevant co-variates. We will also assess intra- and inter-observer variability for IDP measurement when repeat analysis is available.

1d: Initial 5000 subjects from the imaging enhancement study.

Project extension: 10/09/2019

More than 500 million individuals globally have diabetes and the prevalence (6-7% in the UK currently) is increasing. Heart failure (HF) is an increasingly common complication of diabetes. Patients with diabetes have an increased risk of heart failure, which cannot be mitigated by strict risk factor management. Women are at increased risk of developing HF related to diabetes compared to men. The mechanisms are essentially unexplored.

We hypothesise, that sex differences in HF relates to differences in how diabetes affects cardiac morphology and function (independent of coronary disease), and that these changes can be detected before manifest disease develops.

We will study sex differences in outcomes related to diabetes in UK Biobank. We will determine sex differences in cardiac morphology and function using clinical CMR techniques (cine, native T1 etc.) as well as advanced image analysis (radiomics, atlas). Findings will move our understanding significantly forward thereby increasing the understanding of the sex disparity in diabetic HF, identify early changes relating to diabetes, and transfer advanced technology into the clinical domain.

Project extension:

1) Genome-wide association study identifies loci for arterial stiffness index in 127,121 UK Biobank participants: This scope extension uses a measure of arterial stiffness using Arterial stiffness index (ASI)  a non-invasive measure of arterial stiffness using infra-red finger sensors (photoplethysmography). This measure is available in a larger sample than the CMR aortic distensibility originally part of the scope of #2964. We wish to include this now as part of a series of publications we hope to publish to better understand heritability and genetic basis of vascular stiffness measures. We will return the CMR derived aortic distensibility measures we generate and hope to submit this by September the latest once we have 35,000 aortas analysed.

2) Linking resting heart rate with cardiovascular outcomes – gaining mechanistic insights: We wish to analyse the relationship of resting heart rate and cardiovascular outcomes and then to tease out some potential mechanisms that could explain such a link of lower heart rates being protective from cardiovascular outcomes. It may be that we will first report the outcome paper (without CMR) followed by a paper that investigates the changes seen in our CMR markers related to resting heart rate.

Project extension – October 2019: 

Using modern AI techniques, we will generate synthetic cardiovascular magnetic resonance images of the heart and blood vessels and, potentially, synthetic clinical records from non-imaging data. We expect a novel range of exciting possibilities will be opened up by this research. In particular using this data to improve the reliability and applicability of our current imaging analysis research, through improved testing and development mechanisms, will have a clear benefit to public health and we believe broadly remains in keeping with the original scope.

In broad terms, the generation of synthetic data uses an initial sample dataset to extract features, patterns and characteristics to create generative algorithms. These algorithms can then be used on an ongoing basis to generate further unique synthetic datasets and/or images. Synthetic numerical and categorical datasets can be produced using modern data science and statistical techniques which reproduce certain statistical characteristics of the underlying population. Synthetic medical images can be produced using techniques such as generative adversarial networks (GANs) which reproduce key image features and the relationships between them.

Meaningful synthetic data requires underlying datasets of thousands or tens of thousands of items and does not result from the manipulation of individual participant records. Synthetic data is therefore anonymised as it cannot be mapped back to identifiable persons and may therefore be shared free from the privacy constraints related to the original data (we intend to share with UK Biobank, the MyHealthMyData and the euCanSHare consortia (www.myhealthmydata.eu and www.eucanshare.eu).