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  Stratifying Type 1 diabetes by residual insulin secretion: implications for risk management, therapy and disease prevention

   College of Medicine and Veterinary Medicine

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  Prof H Colhoun, Prof P McKeigue  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project


The objective of this project is to investigate how stratifying patients with Type 1 diabetes by residual insulin secretion affects their management and outcome, and how this residual insulin secretion can be preserved, allowing better control, prevention and ultimately even cure of type 1 diabetes (T1DM). T1DM is an auto-immune disease characterised by destruction of the pancreatic beta cell leading to depletion of insulin. Until recently it was believed that all beta cells were destroyed. However the development of ultra-sensitive assays for C-peptide (a marker of insulin production) has led to the realization that some people with long-standing T1DM maintain residual insulin production. This is important since it gives hope for therapies not just to prevent T1DM before clinical onset but also for reversal even after long duration. If we can understand the determinants, underlying mechanisms and consequences of such persistent C-peptide this will inform the development of therapies to preserve beta cells and even regenerate beta cells in people with T1DM leading to an improvement in, and possible reversal of, diabetes.

i) To understand the genetic and environmental determinants of persistent C- peptide in people with type 1 diabetes
ii) To understand whether there are immunologically-defined substrata of type 1 diabetes in which persistent C-peptide is more prevalent
iii) To delineate the relevance of such determinants for intervention pathways
iv) To quantify the impact of persistent C-peptide on diabetes complications

To study these and other questions a large bioresource of biosamples from people with type 1 diabetes has been linked retrospectively and prospectively to their clinical records and C- peptide levels and auto-antibody levels are being measured In this project the student will work with these data to explore the genetic and environmental determinants of persistent C-peptide, the relationship to auto-antibody profile and the relationship to diabetes complications. This will lead to further studies in which specific subset of patients will have detailed immune biomarker profiles measured. This project will involve learning and using standard statistical methods used in gene discovery data analysis. This will extend this though to using more novel approaches such as machine learning to combine information about many genes to extract additional information on relevant genes. The pathways underlying associations will then be further explored using a range of bioinformatics techniques. These further analyses include seeking evidence on the mechanisms and pathways involved by combining the genetic association with other large publicly available datasets on relevant traits such as gene expression in immune cells. The project will also involve using statistical methods including survival analysis for modelling the relationship of C-peptide to prevalent and incident diabetes complications since such data are key to informing what future levels of beta cell recovery, if not complete, would nonetheless have important clinical impact.

Professor Colhoun is an internationally recognised leader in the field of diabetes epidemiology including the application of large scale genetic and other ‘omic discovery techniques for understanding pathogenesis and improving prediction. Professor McKeigue is an internationally recognised leader in the field of epidemiology, and statistical genetics.

This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow:
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit

Funding Notes

Start date:
September/October 2016

Qualifications criteria:
Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or soon will obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria:
The MRC DTP in Precision Medicine grant provides tuition fees and stipend of £14,296 (RCUK rate 2016/17) for UK and *EU nationals that meet all required eligibility criteria.

(*must have been resident in the UK for three years prior to commencing studentship)

Full qualifications and residence eligibility details are available here:

General enquiries regarding programme/application procedure: [Email Address Removed]


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2. Roep BO, Peakman M. Surrogate end points in the design of immunotherapy trials: emerging lessons from type 1 diabetes. Nat Rev Immunol. 2010 Feb;10(2):145-52. doi: 10.1038/nri2705. PubMed PMID: 20098462.
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4. Postmus I, Trompet S, Deshmukh HA, et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat Commun. 2014 Oct 28;5:5068. doi: 10.1038/ncomms6068. PubMed PMID: 25350695; PubMed Central PMCID: PMC4220464.

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