It is estimated that 415 million people worldwide suffer from diabetes which will rise to 642 million by 2040 . Ninety percent of cases are type 2 diabetes (T2D). Treatment of secondary complications/comorbidities accounts for ~75% of the costs associated with T2D in the NHS . T2D is associated with hypertension (76%), arthritis (55%), coronary disease (28%), neuropathy (21%) and renal disease (18%) ; 47% of patients suffer 3 or more comorbidities.
Comorbid patients are routinely excluded from clinical trials, limiting our understanding of how treatments affect comorbidities . Comorbid patients commonly receive separate, fragmented treatment for each morbidity, with implications for cost and quality of care . It is not well understood (i) whether the association between comorbidities is driven by pathophysiological or lifestyle factors and (ii) whether one morbidity is causative of another or whether they share common causes.
There is a vast potential to grow our understanding of comorbidity and develop diagnostics, prognostics and interventions that can improve the quality of care for comorbid patients. Here, we will focus on the pathophysiology of comorbidity and establish the relationship between T2D and its key comorbidities.
Aim 1 – Profile comorbidity in populations representative of the UK.
* Extract clinical and ‘omics data from UK Biobank (which does not cover Northern Ireland) and Ulster Genome Project, a large patient cohort study conducted by the Northern Ireland Centre for Stratified Medicine
* Prepare multidimensional analysis of comorbidity in T2D and identify significant morbidity combinations.
Aim 2 – Model the pathway biology of the key comorbidities.
* Use published literature, online databases and systems biology tools to map out the known molecular pathways that connect comorbidities.
* Develop and evaluate dynamic systems biology models of relevant molecular pathways.
Aim 3 – Identify genes/proteins/pathways associated with comorbidity development and targets contained therein.
* Undertake a gene set analysis of genome/proteome-wide association studies to determine SNPs/proteins/pathways statistically associated with development of significant morbidity combinations.
* Identify how loss-of-function/gain-of-function SNPs in genes or elevated/reduced protein/pathway activity drive comorbidity development.
* Identify SNPs in genes or binding sites in proteins that can be targeted to counter comorbidity development, therapeutically moderating comorbidity development.
The output of the project will be sets of predictive biomarkers of comorbidity and hypotheses of how these may be targeted therapeutically. Biomarkers may be patentable and Ulster University (UU) has experience of securing Biomarker Intellectual Property.
The student will be placed at Ulster University (UU, 50% of time) and at Novo Nordisk (NN, 50%), including time at NN Research Centre Oxford (NNRCO) and NN’s headquarters near Copenhagen. They will experience research in academic and industrial environments, with UU supporting data exploration and systems analysis/modelling (aims 1 and 2) and NNRCO supporting the analysis driving therapeutic target identification (aim 3). Comorbidity is an embryonic biomedical field and all aspects of the project will be novel and can lead to scientific publication.
To apply, go to: https://www.ulster.ac.uk/doctoralcollege/find-a-phd/512618
* Dr Steven Watterson (Ulster University)
* Dr Paula McClean (Ulster University)
* Dr Priyank Shukla (Ulster University)
* Dr Joanna Sharman-Soares (Novo Nordisk Research Centre Oxford)
* Dr Joanna Howson (Novo Nordisk Research Centre Oxford)
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
* Demonstrable programming skills and mathematical ability.
* Familiarity with biomedical science is desirable, but not essential.
* Completion of Masters at a level equivalent to commendation or distinction at Ulster is desirable, but not essential.
* A background in biomedical science, stratified/personalised medicine, bioinformatics, biomedical engineering, computer science, mathematics, physics or another quantitative science.
* To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
* Sound understanding of subject area as evidenced by a comprehensive research proposal