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Untangling the role of Nuts in Atherosclerotic Cardiovascular Disease

   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

Despite nuts often being regarded as an unhealthy food due to their high fat and energy content there is a strong body of evidence demonstrating that their consumption as part of a healthy diet is associated with a reduced risks of all-cause mortality and atherosclerotic cardiovascular disease (ASCVD). These benefits have been attributed to a variety of factors including their fatty acid composition and the food matrix effect; however, much work remains to fully elucidate the role of nuts and to stratify these effects by nut type.

We aim to confront aspects such as these through a self-funded PhD project, where the successful applicant will firstly examine the existing literature regarding role of nut food products and dietary patterns in ASCVD. Then an analysis of existing large-scale datasets will be performed by applying a range of advanced epidemiological techniques to data comprised of advanced lipid metrics (such as lipoprotein particle diameter and size) and additional metabolomic and genetic data pertaining to ASCVD risk. The findings from this will enable causal effects of nuts to be determined and allow mechanistic 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. 

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 mechanistic impact of nuts upon ASCVD.


1. To perform a systematic review of the existing literature to determine the current state-of-the-art regarding the relationship between nuts and ASCVD.

2. To analyse large-scale databases using advanced epidemiological techniques to reveal the implications of nuts regarding ASCVD.

3. Incorporate findings from Objectives 1 & 2 into a feasibility study to determine the practicality of running future intervention trials investigating the impact of nuts upon markers of ASCVD.

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, 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, https://www.ljmu.ac.uk/study/courses/international%20entry%20requirements. Due to the nature of the project relocation by the successful applicant may not be required.


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