Heart Failure (HF) is a complex medical condition with a five-year mortality rate approaching 50% and a cost of ~£2 billion to the NHS each year. HF with preserved ejection fraction (HFpEF) is a subtype of HF, which is characterised by impairment of diastolic function due to increased stiffness and/or impaired relaxation of the heart. HFpEF constitutes almost half of all HF cases and it is more commonly seen in individuals with cardiometabolic diseases as well as in the older age groups. HFpEF is currently the dominant form of HF in the community and, with an ageing population, obesity and diabetes on the rise, the burden of this disease will become of even greater significance.
To date, there is limited knowledge on the pathogenesis of HFpEF and its relationship with contributing cardiometabolic diseases. It is also unclear whether there is a genetic susceptibility to developing HFpEF and to what extent the lifestyle factors may impact this. Currently, there is no consensus for the management of HFpEF and it is not yet known whether personalised treatment could be offered on an individual basis, taking into account the genetics and comorbidities of the patient.
Given that diastolic dysfunction precedes overt HF by many years, it is imperative to study diastology to understand the pathophysiology of HFpEF. We aim to automatically extract diastolic function phenotypes from cardiac magnetic resonance (CMR) studies using artificial intelligence (AI) techniques. We will validate the diastolic measures from CMR against the measures made by echocardiography which is the gold standard. Additionally, we aim to evaluate the genetic determinants of image-derived diastolic phenotypes using genome-wide association studies. Finally, our research will unravel the relationships between cardiometabolic diseases and diastolic function using observational regression models and Mendelian Randomisation.
By providing novel insights into genetic and non-genetic determinants of diastolic function, this exciting research will fundamentally provide the scientific community with a better understanding of the pathophysiology of diastolic function. The knowledge of how cardiometabolic risk factors such as obesity or diabetes influence diastolic dysfunction, a precursor of HFpEF, may lead to targeted public health measures for the vulnerable subsets of the population. Additionally, our research will provide a step closer to developing personalised risk stratification for HFpEF and will ultimately open up the prospect of developing specific therapies based on the individual’s genetics and comorbidities.
This project strongly aligns with Barts Life Sciences’ vision to lead the prevention, prediction and precision health care by leveraging the key strengths of William Harvey Research Institute (genomics), Digital Environment Research Institute (AI) and Barts Heart Centre (clinical cardiology and cardiac imaging). It is ideally suited for a clinically qualified PhD student to learn an excellent breadth of transferable skills ranging from clinical and research applications of CMR, development of AI algorithms, biostatistics and genomics.
Funding Notes:
The Trustees of The Medical College of Saint Bartholomew’s Hospital Trust (MCSBHT) have offered funding for a research studentship, for a clinically qualified candidate to commence in October 2022, leading to a PhD degree from The QMUL Faculty of Medicine and Dentistry.
This studentship will fund a student with a clinical qualification and GMC / GDC registration at any career stage below consultant. The Studentship will cover the successful candidate’s current clinical salary and will include PhD fees (at home fee rate) with up to £6000 pa for consumables. Further consumables / funding for travel may be available on application.
Notice on Equality, Diversity and Inclusion: Barts and The London School of Medicine and Dentistry aims to promote an organisational culture that is respectful and inclusive irrespective of age, disability, gender reassignment, ethnicity, marriage or civil partnership, pregnancy and maternity, race, sex and religion or belief. Moreover, it seeks to ensure that intersectionality is recognised, with explicit acknowledgement of the interconnected nature of social identities including race, class and sex, where these facets can create overlapping levels of discrimination or disadvantage.
Application Web Page: https://mysis.qmul.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RFQM-W6ZF-09&code2=0013
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