Integrating nutrition with genomics, lifestyle and environmental factors using AI to develop personalised nutrition approaches for hypertension prevention

Summary

Hypertension affects over 1 billion individuals worldwide and is a major risk factor for cardiovascular disease (CVD), the leading cause of premature mortality worldwide.

Hypertension and CVD increase with age, and as the global population aged over 60 years old is projected to rise to 2.1 billion by 2050, both conditions will continue to place considerable burdens on health services worldwide. Genetic factors are reported to account for 30–50% of BP variation.

The research team in NICHE at Ulster has previously demonstrated that supplementation with riboflavin, required as a co-factor for MTHFR, can lower elevated BP in individuals with the variant MTHFR 677TT genotype.

This gene-nutrient interaction offers a personalised management option for subpopulations worldwide affected by this genetic risk factor for hypertension.

Due to the multifactorial nature of hypertension, this PhD project aims to explore the interrelationships between nutrition, genetic, biological and environmental factors in relation to hypertension risk.

This project will apply artificial intelligence (AI) techniques to the Trinity-Ulster-Department of Agriculture (TUDA) study, an Island of Ireland resource for nutrition and health in ageing.

Aim 1. Conduct GWAS to identify genetic variants associated with BP and use Mendelian randomization to determine the causative risk factors for hypertension integrating nutrition, genetic, lifestyle and environmental data;

Aim 2. Utilise AI and advanced machine learning approaches to identify novel gene-nutrient interactions to inform personalised nutrition solutions for preventing and managing hypertension and CVD;

Aim 3. Investigation of hypertension incidence and antihypertensive drug usage across the island of Ireland by socioeconomic deprivation derived from Geographic Information Systems (GIS) methodology.

The project outcomes will contribute to a better understanding of how risk factors interact in the development of hypertension and will identify evidence-based, personalised nutrition solutions aimed at reducing hypertension and promoting better cardiovascular health in ageing.

This studentship is supported by a highly interdisciplinary supervisory team, combining expertise in multimodal data integration (Dr Ross Murphy), nutrition and diet with a particular focus on B-vitamins, one-carbon metabolism and related polymorphisms in hypertension prevention (Dr Leane Hoey & Dr Catherine Hughes), and advanced AI and machine learning methodologies (Dr Faisal Jamil).

Furthermore, the successful candidate will be based within the Centre fror Genomic Medicine and NICHE at Ulster University as a unique interdisciplinary environment including both health data science and nutrition.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Practice-based research experience and/or dissemination
• Experience using research methods or other approaches relevant to the subject domain
• Sound understanding of subject area as evidenced by a comprehensive research proposal
• Work experience relevant to the proposed project
• Publications record appropriate to career stage
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• BBSRC/UKRI Doctoral Landscape Award

This scholarship will cover tuition fees and provide a maintenance allowance of £21,000* (tbc) per annum for four years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of approximately £5000 per annum is also available.

This scholarship is open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.