Recent Research Projects

DiabetesUK funded research grant investigating the genetics of type 1 diabetes and its complications involving Genome-Wide Association Studies (GWAS), as well as the development and implementation of novel methodology (Genome-Wide Aggregated Trans Effects; GATE). We conducted a major analysis of the genetics of Type 1 diabetes in which we have identified a range of core genes and peripheral master regulator genes that offer exciting new insights into pathways causal for, and targets for therapeutic development to prevent type 1 diabetes. This work has been published in the American Journal of Human Genetics (Iakovliev et al, 2023), and more information on our research is available from the DiabetesUK page.

Furthermore, our work on the genetic architecture of type 1 diabetes and the association with biomarkers such as C-peptide and N-glycans, including as part of an international meta-analysis, along with a contribution to an international research collaboration into the genetics of Diabetic Kidney Disease/ Nephropathy, has been published. 

Medical Research Council (UKRI) and Canadian Institutes of Health Research funded research collaboration between the University of Edinburgh, Research Institute of the Toronto Hospital for Sick Children in Canada, and the University of Toronto, Canada. The project involved assessing the genetic mechanisms that explain why some people with Type 1 diabetes (T1D) retain the ability to produce a small amount of insulin from their pancreas, measured via C-peptide levels. Previous research from our group had shown that even minimal residual C-peptide secretion offers some level of protection for people with T1D against hypoglycemia and eye disease. This latest research aimed to perform large meta–genome-wide association studies (GWAS) of C-peptide and age at diagnosis (AAD) in T1D and to identify the variants at specific genetic sites (HLA alleles/haplotypes) associated with C-peptide and AAD. We found that some HLA alleles/haplotypes that were associated with T1D also contributed to variability of C-peptide and AAD. Genetic variation within other genetic sites (CTSH region) can affect age at diagnosis only. Our results were published in Diabetes journal, where it was selected as the Paper of the Month in August 2025 in the 'Genetics/Genomes/Proteomics/Metabolomics' theme.
The research is also featured in a UKRI Case Study on UK/Canada research collaborations, which was published on the UKRI site and presented at a Canadian Institute of Health Research showcase event.

Monogenic diabetes is caused by a single gene mutation and is often misdiagnosed as type 1 diabetes. However, making the correct diagnosis has important implications for clinical care in the majority of cases, such as changes in treatment. Detection of monogenic diabetes is not consistent or well defined, while genotyping every person with apparent type 1 diabetes is not cost effective. Diagnostic algorithms that incorporate various biomarkers (e.g. C-peptide, autoimmune antibodies) and variables (e.g. family history, age at onset) have been developed in England and Scotland to aid the detection of monogenic diabetes by identifying those warranting genetic testing, but may still miss certain cases. 

At the time of recruitment to SDRNT1BIO, prospective participants were informed and consented to being re-contacted by the research team (via their doctor) if future analyses of their data and samples indicated that there was a possibility they should be screened for undiagnosed monogenic diabetes. Participants also consented to genetic studies relating to diabetes and its complications on the blood samples they donated at recruitment. In the period since study recruitment, we carried out measurement of autoimmune antibody and C-peptide levels on all study participants. These data, combined with other SDRNT1BIO study data, allowed us to identify and notify a group of participants who warranted genetic testing for monogenic diabetes.  Although many of these participants are likely to eventually be identified as warranting genetic testing under the recently launched Scottish NHS testing programme, their early identification is warranted since it may be several years before all eligible participants are tested under the NHS programme. Given the envisaged clinical benefits to SDRNT1BIO participants, it is ethically imperative that we conduct this research in line with our existing governance permissions for SDRNT1BIO linked data and sample use. 

Research project investigating the association of C-peptide, and change in C-peptide over a follow-up period, with incident complications of type 1 diabetes. This study is a follow-up to our published research that examined the association of baseline C-peptide with incident outcomes in the SDRNT1BIO cohort at 5 years of follow-up. In addition to the baseline C-peptide measurements previously undertaken in the SDRNT1BIO cohort, we had longitudinal C-peptide levels measured in available samples across the follow-up period and combined with baseline values to determine trajectories. For this latest research, we have over 10 years of follow-up, enabling evaluation of longer-term associations. This research can add to the observational evidence for the relationship of residual C-peptide levels to clinical outcomes that in turn could be a useful marker for clinical trial outcomes for prevention of type 1 diabetes.

In people with T1D, CVD is associated with premature morbidity and mortality, whereby risks for CV outcomes in T1D patients is 3-4 fold greater than age-matched people without diabetes, and relative risk is even higher (up to 10-fold) in those who develop diabetes at younger ages. This ultimately leads to a significantly reduced life expectancy in T1D patients with a history of CVD, compared to the general population. It has been postulated that T1D may carry a unique and specific risk for CVD outcomes, although the underlying cellular and molecular mechanisms remain unclear. The measurement of pertinent biomarkers provides a unique opportunity to identify causal factors associated with an elevated risk of developing CVD or occurrence of incident CVD events in T1D patients. 

Fundamentally, there is a lack of data on the levels of cardiac biomarkers in people living with T1D, despite a number of biomarkers already being measured and subsequently utilized in CVD risk prediction models for the general population and T2D patients. This research will assess the levels of a panel of novel CVD biomarkers in relation to cardiovascular outcomes in SDRNT1BIO. We have the opportunity in this study to validate the predictive value of cardiac biomarkers in our cohort, where incident rates of CVD events have been captured. Validating whether cardiac biomarkers can identify the truly highest risk individuals for CVD outcomes, as is proposed here, could improve the statistical power of subsequent research studies and allow for smaller sample sizes. Furthermore, we can use the results of these analyses to determine whether the increased risk of CVD in this cohort is associated with the presence of these biomarkers in accordance with traditional risk factors, which are already included in published risk scores, including by our group.