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

Type 1 diabetes mellitus (T1DM) involves the autoimmune destruction of pancreatic -cells. Until recently it was assumed that this was accompanied by a complete absence of insulin secretion. However, in recent years it has become apparent that this may not be the case. C-peptide is cleaved from proinsulin to produce insulin and is a well-established marker of endogenous insulin secretion. With the help of ultra- sensitive assays, persistent C-peptide has now been detected in many individuals who have long-standing T1DM. This finding provides hope for the reversal of T1DM after long duration through the preservation and regeneration of -cells. Understanding the determinants, underlying mechanisms and consequences of persistent C-peptide can inform the development of approaches to -cell preservation and regeneration. This should result in better glycaemic control, ameliorating complications, and could contribute to a cure for T1DM.

The main aims of the project are as follows;
i) To understand the genetic and environmental determinants of persistent C-
peptide in people with T1DM
ii) To understand whether there are immunologically-defined substrata of T1DM 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

This project will use a large population representative sample of 6127 people with T1DM in whom biosamples have been collected, and in whom electronic health care linkage has been carried out. Data will be generated on random non-fasting samples of C-peptide, genotype, autoantibodies and will combined with data on diabetes history, glycaemic control and status of complications.

The specific objectives are to document the prevalence of C-peptide by statistical analyses of the dataset. Genetic determinants of persistent C-peptide will be examined by genome-wide association studies (GWAS), with any relevant results analysed using bioinformatics. Whether prevalent autoantibody patterns differ by C- peptide persistence will be explored. The association of prevalent cytokines and other immune markers with C-peptide levels will be measured from stored serum.
Additional evidence on the mechanisms and pathways involved in persistent C- peptide secretion will be obtained by combining the genetic association with other large publicly available datasets on relevant traits. The effect of C-peptide persistence on complications shall be assessed using survival analyses. This shall be used to model the relationship of C-peptide to prevalent and incident diabetes complications. This data is key to informing what future levels of -cell recovery, if not complete, would have important clinical impact. Further development of the project may include follow back studies of a subset of participants at extremes for C- peptide. Fresh peripheral blood mononuclear cells (PBMC) would be obtained to allow a wider range of immune markers to be measured. In the long term, information gathered from this project shall help to determine the immune pathways that lead to persistent C-peptide in those with T1DM.

There are various key skills to be learnt from this project. These include, but are not limited to;
i) Data handling
ii) Various types of statistical analyses, including summary statistics, regression
modelling and cross-sectional associations
iii) Being able to understand and explain statistical outputs
iv) GWAS study design and analysis
v) Use of bioinformatics
vi) Carrying out fieldwork to generate further information