Helen Colhoun Research Group

The SDRN-Epidemiology group conducts diabetes research using the National Diabetes Research Platform, which brings together de-identified electronic health care records linked to other datasets. SDRN-Epi operates as a subgroup of NRS Scotland diabetes research (https://www.nhsresearchscotland.org.uk/research-areas/diabetes). The group includes clinicians and researchers across Scotland. The Diabetes Medical Informatics and Epidemiology (DMI&E) team at University of Edinburgh led by Professor Helen Colhoun have developed the research platform on behalf of SDRN-Epi and provide access to authorised users in a secure setting. 

For general enquiries or further information on how to access the National Diabetes Research Platform, please email sdrn-epi@ed.ac.uk. In brief, the procedure for accessing the platform is as follows:

• Prospective users/collaborators need to complete a two-age project outline form, which is then circulated to the SDRN-Epi group for review and comments. Please contact the SDRN-Epi group via the email above for a copy of the form.
• Once the project is approved, the user(s) will need to complete mandatory data governance training and user agreements.
• The DMI&E group will submit an amendment to their existing Public Benefit and Privacy Panel (HSC-PBPP) approval for the additional authorised user(s).
• Once all governance approvals are received, the platform administrator will provide a secure link to access the platform. 

Examples of recent or ongoing work by the Diabetes Informatics and Epidemiology team using the National Diabetes Research Platform are:

• Pharmacoepidemiology of diabetes drugs - analyses of real world efficacy and safety of drugs used in people with diabetes.
• COVID-19 and diabetes:   Includes for example analyses of association of COVID-19 exposure with incident diabetes, impact of past infection on diabetes outcomes, description of extent of epidemic and excess deaths in diabetes.
• Disease prediction from retinal imaging: Analyses of whether predictors learned from retinal imaging are useful in the prediction of   risk of other outcomes in diabetes including CVD, kidney disease. Analyses of   whether deep learning can be used to improve the autograder used for the screening programme and reduce NHS workload.
• EXCEED - A Pan-European Post-Authorisation Safety Study: Risk Of Pancreatic Cancer Among Type 2 Diabetes Patients Who Initiated Exenatide As Compared With Those Who Initiated Other Non-Glucagon-Like Peptide 1 Receptor Agonists Based Glucose Lowering Drugs.

Publications by the Scottish Diabetes Research Network (SDRN) Epidemiology Group: 

Scottish Diabetes Research Network (SDRN) Epidemiology Group publications – ORCID (external website)

A cohort of people with type 1 diabetes in Scotland who kindly donated biosamples and access to their data for research  into the pathogenenesis and prevention of diabetes and its complications. See https://scottish-diabetes-research-network-t1-bioresource.ed.ac.uk/

Current studies include:

• Investigating the role of cardiac biomarkers as predictors for cardiovascular disease risk stratification and adverse cardiovascular outcomes in T1D. This research will assess whether baseline levels of a range of cardiac biomarkers in T1D patients from the SDRNT1BIO cohort are associated with an increased risk of incident CVD events, independent of traditional risk factors, and evaluate the usefulness of these biomarkers as predictors of CVD events/outcomes in risk prediction models. We will also examine whether any other (risk) factors are associated with baseline levels of cardiac biomarkers in T1D patients.
• Association of C-peptide and change in C-peptide with acute and chronic complications in type 1 Diabetes. This study aims to describe the relationship of baseline C-peptide values with incident complications during follow up in the SDRNT1BIO cohort, and to test whether changes in C peptide over time are associated with subsequent complications incidence.
• Harnessing genetic information in the SDRNT1BIO to understand the pathogenesis of Type 1 diabetes and its complications. This study uses genetic association studies and novel analytical methodologies in the SDRNT1BIO to yield insight into the pathways involved in type 1 diabetes and its complications so that new treatments that interfere with these pathways can be designed.

Publications arising from SDRNT1BIO research (ORCID):

SDRNT1BIO Type 1 Bioresource Publications on ORCID (external website)

As part of the Hypo-RESOLVE research consortium, we worked together with researchers from institutions across Europe to address the problem of hypoglycaemia in type-1 diabetes patients. Hypoglycaemia, also called low blood sugar, is a condition strongly affects patients with type-1 diabetes, especially if they take insulin. Hypoglycaemia can cause cognitive dysfunction, coma, and cardiac arrhythmia, cardiovascular complications, and death. The aim of this study was to bring multilevel expertise from academic and industry partners to reduce the burden of hypoglycaemia amongst patients with diabetes.

Specific objectives included:

1. Create a secure, sustainable database
2. Conduct a series of systematic statistical analyses
3. Establish most reliable and accurate approach to reporting hypoglycaemia
4. Determine psychological burden of hypoglycaemia
5. Establish the true economic/quality of life burden of hypoglycaemia
6. Progress our understanding of the mechanisms and consequences of hypoglycaemia
7. Combine our finding and reclassifications of hypoglycaemia.

Read more about the Hypo-RESOLVE research consortium on the project website:

Hypo-RESOLVE (external link)

During the pandemic we worked with Public Health Scotland on the Epidemiology of COVID-19 and created the REACT-SCOT case control study. 

REACT-SCOT: Epidemiology of COVID -19 publications:

https://orcid.org/0000-0003-0505-8634

Professor Colhoun is an internationally known trialist- she has co-designed and been a Steering Committee member on several pivotal trials in Diabetes and in Cardiovascular Disease including:

• CARDS (Atorvastatin – Pfizer);
• REWIND (Dulaglutide – Eli Lilly);
• SELECT (Semaglutide – Novo Nordisk);
• ODYSSEY-3 (Alirocumab – Sanofi/Regeneron)
• FINE-ONE (Finerenone – Bayer);
• REMOVAL (Metformin – JDRF)

Diabetes, Risk Prediction, Epidemiology, Medical Statistics, ‘Omics, Genetics, Disease Stratification, Covid-19, Machine Learning, Artificial Intelligence