Helen Colhoun Research Group
Diabetes Medical Informatics and Epidemiology
Section: Biomedical Genomics (HGU) / Genome Medicine (CGEM)

Research in a Nutshell
Our research programme uses large scale population based approaches to further our understanding of the pathogenesis and means of prevention of diabetes complications. A main component of our current work harnesses the increasing availability of e-health record data (EHR) and new technologies for acquiring high dimensional molecular ‘omics data. We quantify contemporaneous absolute risks of complications, evaluate risk factors for complications, and build prediction models using e-health record data. These data are used to inform current health care policy and clinical practice in diabetes. We also use large bioresources linked to these data to quantify the marginal improvement gained by genetics and biomarker panels beyond that achieved by EHR data. Our aim is that these prediction algorithms will then be incorporated into prediction tools for clinical and self-management, and will be useful in clinical trial design. These genetics and biomarker studies also yield important information on the pathogenesis of diabetes complications, that, with more detailed wet-lab research with colleagues in IGMM, can inform future development of new therapies. Our research programme also encompasses the design and conduct of clinical trials of new drugs and approaches for preventing diabetes complications.
During the COVID-19 epidemic Helen has Co-Chaired the COVID-19 Modelling & Research cell at Public Health Scotland.
Professor Colhoun also chairs the SDRNT1BIO Steering committee.

People |
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Helen Colhoun | Leading Professor of the Helen Colhoun Research Group |
Svenja Moser | Personal Assistant and Project Manager to Professor Helen Colhoun |
Luke Blackbourn | Software and Database Analyst |
Sara Hatam | Database Support Engineer |
Stuart McGurnaghan | Senior Software Developer and Data Analyst |
Anita Jeyam | Biostatistician/Epidemiologist |
Joe O’Reilly | Biostatistician/ Epidemiologist |
Andreas Höhn | Biostatistician/ Epidemiologist |
Thomas Caparrotta | Diabetes UK Sir George Alberti Clinical Research Fellow, University of Edinburgh |
Jack Templeton | Honours Student, Pharmacology, University of Edinburgh |
Ioanna Thoma | PhD, student, Precision Medicine, University of Edinburgh |
Paul McKeigue (affiliate) | Professor of Statistical Genetics, Centre for Population Health Sciences, University of Edinburgh |
Athina Spiliopoulou | Chancellor’s Fellow in Data-Driven Innovation at the Usher Institute for Population health Sciences and Informatics, University of Edinburgh |
Joe Mellor (affiliate) | Postdoctoral scientist (Machine Learning Specialist), Centre for Population Health Sciences, University of Edinburgh |
Andrii Iakoliev | Research Fellow (Informatics/ Software Development) |
Marco Colombo (affiliate) |
Consultant |
Contact
Collaborations
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Scottish Diabetes Research Network, Epidemiology Group and SDRN Type 1 Bioresource Group: national collaborations with diabetes researchers across Scottish Universities and NHS clinicians and centres across Scotland.
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JDRF, Diabetic Nephropathy Collaborative Research Initiative that includes researchers at the Harvard University, University of Toronto, Folkhälsan Finland and the Broad Institute, working on genetics of nephropathy.
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JDRF, Biomarker Studies with national and international collaborations including Folkhälsan Finland, GENOS, Joslin Diabetes Centre, Steno Diabetes Centre, University of Cambridge, University of California, University of Dundee, University of Glasgow, University of Michigan, University of Pittsburgh, University of Washington, University of Wisconsin, and cross collaboration with the University of Toronto.
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British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh: Professor Colhoun is affiliated with this Centre.
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Hypo-RESOLVE, Hypo-RESOLVE, EU commission, Hypoglycaemia.
Partners and Funders
- AXA Research Fund
- Diabetes UK
- JDRF
- Hypo-RESOLVE
Scientific Themes
Diabetes, Risk Prediction, Epidemiology, ‘Omics, Genetics, Disease Stratification