Principal Investigator: Dr Steffen Petersen
Department: William Harvey Research Institute
Institution: Queen Mary, University of LondonTags: 2964, cardiac function, cardiac structure, cardiovascular risk, Magnetic resonance imaging, reference, ultrasound
1 University of Auckland, Professor Alistair Young
University of Auckland, Anatomy with Radiology, 85 Park Road, Grafton
Auckland 1023, New Zealand
2 University of Oxford, Professor Stefan Neubauer
University of Oxford, Cardiovascular Medicine Division, Radcliffe Department of Medicine, John Radcliffe Hospital, Headington, Oxford OX3 9DU
3 Barts Health NHS Trust, Dr Kenneth Fung
Barts Health NHS Trust, Barts Heart Centre, Cardiology, St Bartholomew’s Hospital, West Smithfield EC1A 7BE
4 Circle Cardiovascular Imaging Inc., Mr Kelly Cherniwchan
Circle Cardiovascular Imaging Inc. Research Suite 250, 815, 8th Ave SW
Calgary T2P 3P2, Canada
5 Erasmus MC, Professor Wiro Niessen
Erasmus MC, Radiology & Medical Informatics, ‘s-Gravendijkwal 230
Rotterdam 3015 CE, Netherlands
6 Geneva University Hospitals, Dr Georg Ehret
Geneva University Hospitals, Cardiology, Rue Gabrielle-Perret-Gentil 4
Geneva GE 1205, Switzerland
7 University Hospital Heidelberg, Dr Christopher Schlett
University Hospital Heidelberg, Diagnostic and Interventional Radiology
Im Neuenheimer Feld 110, Heidelberg BW 69120, Germany
8 Imperial College London, Professor Daniel Rueckert
Imperial College London, Department of Computing, 180 Queen’s Gate
South Kensington Campus, London SW7 SAZ
9 Inria,Professor Nicholas Ayache
Asclepios, 2004 Route des Lucioles, Sophia Antipolis 06902, France
10 Klinikum der Universität München (Ludwig Maximilian University of Munich),Dr Moritz Sinner
Ludwig Maximilian University of Munich, University Hospital Munich
Department of Medicine I Marchioninistr. 15 Munich 81377, Germany
11 Universitat Pompeu Fabra, Dr Karim Lekadir
Universitat Pompeu Fabra, Department of I.C.T. Tanger 138, Roc Boronat
Barcelona 08018, Spain
12 Semmelweis University, Dr Pal Maurovich-Horvat
Semmelweis University, Heart and Vascular Center, 68 Varosmajor st.
Budapest 1122, Hungary
13 University of Sheffield, Professor Alejandro Frangi
University of Sheffield, Faculty of Engineering, Pam Liversidge Building – Mappin Street, CISTIB – Prof Frangi (Room C04), Sheffield S1 3JD
14 Sunnybrook Research Institute, Dr Graham Wright
Sunnybrook Research Institute, Schulich Heart Program, Rm M7-611 2075 Bayview Ave, Toronto M4N3M5, Canada
15 University Hospital Basel, Dr Claudia Cavelti-Weder
University Hospital Basel, Endocrinology / Diabetes, Petersgraben 4
Basel bs 4031, Switzerland
16 University of Sao Paulo, Dr Marcio Bittencourt
University of Sao Paulo, University Hospital Internal Medicine
Av. Lineu Prestes 2565, Sao Paulo SP 05508-000, Brazil
17 Yale University, Professor James Duncan
Yale University, Biomedical Engineering & Radiology, 300 Cedar Street
TAC – N309D, New Haven CT 06520, United States of America
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.
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).
Last updated Oct 14, 2019