Professor Helen Colhoun
AXA Chair of Medical Informatics and Life Course Epidemiology
- MRC Human Genetics Unit
- Centre for Genomic and Experimental Medicine
- Institute of Genetics and Cancer
Contact details
- Email: helen.colhoun@ed.ac.uk
Background
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 the Institute of Genetics and Cancer, 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 global pandemic she has Co-Chaired the COVID-19 Modelling & Research cell at Public Health Scotland.
Professor Colhoun also chairs the SDRNT1BIO Steering Committee.
Read more about Professor Colhoun's work
Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO)
Follow Diabetes Medical Informatics and Epidemiology Group on Twitter
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Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics
In:
American Journal of Human Genetics, vol. 110
DOI: https://doi.org/10.1016/j.ajhg.2023.04.003
Research output: Contribution to Journal › Article (E-pub ahead of print) -
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes?: A prospective cohort study from the national screening programme in Scotland
In:
International journal of medical informatics, vol. 175, pp. 105072
DOI: https://doi.org/10.1016/j.ijmedinf.2023.105072
Research output: Contribution to Journal › Article (Published) -
Cardiovascular Safety in Type 2 Diabetes With Sulfonylureas as Second-Line Drugs: A Nation-Wide Population-Based Comparative Safety Study
In:
Diabetes Care
DOI: https://doi.org/10.2337/dc22-1238
Research output: Contribution to Journal › Article (E-pub ahead of print) -
Cohort profile: the Scottish Diabetes Research Network national diabetes cohort – a population-based cohort of people with diabetes in Scotland
(27 pages)
In:
BMJ Open, vol. 12
DOI: https://doi.org/10.1136/bmjopen-2022-063046
Research output: Contribution to Journal › Article (Published) -
Large socioeconomic gap in period life expectancy and life years spent with complications of diabetes in the Scottish population with type 1 diabetes, 2013-2018
In:
PLoS ONE, vol. 17, pp. e0271110
DOI: https://doi.org/10.1371/journal.pone.0271110
Research output: Contribution to Journal › Article (Published) -
Relation of Incident Type 1 Diabetes to Recent COVID-19 Infection: Cohort Study Using e-Health Record Linkage in Scotland
In:
Diabetes Care
DOI: https://doi.org/10.2337/dc22-0385
Research output: Contribution to Journal › Article (E-pub ahead of print)