A plasma proteomic and metabolomic map of diet and food preferences in UK Biobank volunteers

UK Biobank has measured the concentration of hundreds of metabolites and thousands of proteins in the same UK Biobank participants’ samples. In many cases there is also data on food preferences as defined in responses to a UK Biobank questionnaire. Furthermore, at the time of taking blood samples from UK Biobank participants key features of their health status were recorded (e.g. inflammatory disease, type 2 diabetes, cardiovascular disease). This extremely rich dataset offers the opportunity for a unique analysis of blood plasma metabolite and proteome relationship to health status, diet and food preferences using cutting edge informatics and artificial intelligence approaches.

Peripheral blood offers major opportunities to identify and quantify biomarkers of wellness and disease. We and others have shown that the plasma proteome and metabolome changes with respect to diet (e.g. in response to bariatric surgery). UK Biobank has measured the concentration of hundreds of metabolites and thousands of proteins in the same UK Biobank participants’ samples. In many cases there is also data on food preferences as defined in responses to a UK Biobank questionnaire. Furthermore, at the time of taking blood samples from UK Biobank participants key features of their health status were recorded (e.g. inflammatory disease, type 2 diabetes, cardiovascular disease). This extremely rich dataset offers the opportunity for a unique analysis of blood plasma metabolite and proteome relationship to health status, diet and food preferences. There are over 140 food preference questionnaire answers, hundreds of metabolites measured and thousands of proteins measured. In this project we will use advanced data analysis and artificial intelligence approaches to identify relationships between concentrations of specific groups of metabolites and/or proteins and food preferences. We will further, using unsupervised machine learning, identify clusters or endophenotypes of food preferences, and correlate these with molecular biomarkers.

These signatures will then be further correlated with health outcomes and disease comorbidities. This will enable future studies on biomarkers for diseases and risk of diseases to take into account diet, nutrition and health on proteins and metabolites of potential value in clinical or wellness decisions.

The student will be taught advanced informatic, machine learning and artificial intelligence techniques in order to undertake the work described above.

This project would be suitable for students with an upper second class honours degree, for example in biological sciences or computer sciences, or the equivalent, which can include relevant work experience.

This studentship is available for UK and international students.

IELTS requirements: The standard requirement is for a score of 6.5 or above (or equivalent) with 6.0 in each individual category.

Section of Systems Biology