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The Epidemiological and Molecular Basis of Cardiometabolic Phenotypes: Insights from the Qatar Biobank

   Faculty of Science

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  Dr I Davies, Dr Abdulmannan Fadel, Dr Mohsen Mazidi, Dr R Webb, Dr Maha Abdulla H I Al-Asmakh  No more applications being accepted  Self-Funded PhD Students Only

About the Project

We aim to investigate the determinants of cardiometabolic phenotypes in an adult Qatari population using an epidemiological approach. This is particularly pertinent given the current overweight/obesity epidemic in Qatar, with over 70% of the population overweight, over 40% obese and 30% at risk of cardiovascular disease (WHO, 2012).

Our proposed self-funded PhD project will require the successful applicant to initially examine the existing literature concerning cardiometabolic phenotypes in Qatari nationals. Then an analysis of data from the Qatar Biobank will be performed by applying a range of traditional statistical methods to a plethora of markers relating to health and lifestyle, together with metabolomic and proteomic data. The findings from this will allow epidemiological and molecular insights to be elucidated. Finally, the PhD project will culminate in a feasibility study informed by the previous findings. We envisage this feasibility study acting as preliminary evidence which would in turn guide the direction and viability of our future research in this area. 

Large-scale studies such as ours are urgently needed to improve our understanding of cardiometabolic phenotypes in Qatar, enable early detection of metabolic disruption which is paramount for preventative treatment and offer improvements in personalised medicine.

The structure of the project is summarised below but the details will be finalised with the student upon commencement of the studentship.


The aim of this PhD project is to determine the epidemiological and molecular basis of cardiometabolic phenotypes in Qatari nationals. 


1. To perform a systematic review of the existing literature to determine the current state-of-the-art regarding cardiometabolic phenotypes in Qatari nationals.

2. To analyse large-scale databases using traditional epidemiological techniques to reveal associations between a large range of clinical, lifestyle and omics markers and cardiometabolic phenotypes.

3. Incorporate findings from Objectives 1 & 2 into a feasibility study to determine the practicality of running future intervention trials investigating cardiometabolic phenotypes in Qatari nationals. PhD Training:

All Postgraduate Research Students at Liverpool John Moores University will automatically become a member of the doctoral academy upon registration of a PhD. As part of this membership students will be required to record their professional development and participate in regular meetings with their supervisory team. Furthermore, students will also be offered the opportunity to attend regular departmental and university-wide research events which provide an excellent forum for networking with peers and staff members and to present their own research findings. The successful student will also be encouraged to engage with academic conferences, external professional development courses and learned societies. In addition to this, the supervisory team will also provide support and training regarding systematic reviews, methodology selection, data analysis and ethical procedures, as well as academic writing and publishing.

This project is a collaboration between Liverpool John Moores University, Qatar University, King’s College London, University of Oxford and Liverpool Hope University. The supervisory team will consist of a small group of staff members from these institutes.

The successful applicant should have at least a 2.1 honours or equivalent undergraduate degree in a quantitative subject (for example, Epidemiology or Statistics), or a life science with a strong quantitative element. A postgraduate qualification in a similar field would be preferred. It is also crucial to have an understanding and interest in nutritional science and be able to demonstrate excellent communication and writing skills.

A comprehensive knowledge of statistical software, such as SPSS, and the programming environment R, are essential and experience of machine learning techniques and advanced epidemiological procedures such as Mendelian randomisation would be desirable.

For an informal discussion about this opportunity please email Dr Ian G. Davies, [Email Address Removed] for more information.

Applicants should email a CV, covering letter detailing their suitability for the project and contact details of two referees to [Email Address Removed].

Applicants must be available for interview the week commencing 29th November 2021.

Funding Notes

This position open self-funding applicants, (UK, EU and International). For international applicants, please see the University English language requirements, Due to the nature of the project relocation by the successful applicant may not be required.