Teams of researchers across the US have joined forces to test if a new biological ‘sign’ – or biomarker – of beta cell loss could boost current methods that predict if and when a person will develop type 1 diabetes.
By using blood samples from participants in TrialNet’s Pathway to Prevention study – which is part-funded by JDRF – the researchers reported that the new biomarker indicated that people with high levels of beta cell loss developed type 1 an average of five years earlier than those with lower levels. They also found that loss of beta cells was associated with the detection of an autoantibody which is currently used to predict type 1 development.
In the future, this biomarker could be used in conjunction with antibody screening methods to boost predictions of if and when a person will develop type 1, improving diagnosis and treatment.
Why did they do this research?
Antibodies are used by the immune system to target and attack harmful germs, but in type 1 some antibodies target and attack beta cells or insulin, resulting in the loss of beta cells. These are called autoantibodies.
With current methods, the detection of two or more autoantibodies along with certain genetic factors means that a person has a 70% risk of developing type 1 in the next ten years, but will definitely develop it in their lifetime.
However, the rate at which a person may develop type 1 is highly variable, and children in particular tend to be diagnosed with acute symptoms and are often in diabetic ketoacidosis (DKA), which can be fatal. Researchers are therefore looking for ways to more accurately pinpoint if and when a person is likely to develop type 1 so that people can be diagnosed and treated as quickly and efficiently as possible.
Furthermore, better accuracy in predicting type 1 may allow us to target preventative measures, such as (currently experimental) immunotherapies, to the people that are most likely to need them.
What did they do?
TrialNet’s Pathway to Prevention study screens the relatives of people with type 1 for the presence of autoantibodies and monitors them over a number of years while offering entry into upcoming prevention trials.
In the current study, the researchers accessed two years of historical blood samples from TrialNet participants who had then gone on to develop type 1. They detected the levels of beta cell loss using the new biomarker – a measure of the amount of ‘active’ insulin genes in the blood. Active insulin genes are usually only usually found inside beta cells and so their presence in the blood is a sign that beta cells have been destroyed and the gene has been released.
The researchers then looked at how the level of beta cell loss changed over the course of the two years prior to type 1 diagnosis and compared it with the levels of autoantibodies over the same time period. They also compared the level of beta cell loss to the age the participants were when they were diagnosed with type 1.
What did they find?
In the two years leading up to type 1 diagnosis, levels of beta cell loss reflected the levels of a particular screened autoantibody called mIAA, one of the four autoantibodies scientists currently use to predict whether a person is likely to develop type 1.
This indicates that the autoantibody may play an important role in beta cell loss and type 1 diabetes development.
The researchers also found that those showing an increase in beta cell loss (and therefore an increase in mIAA) were diagnosed with type 1 an average of five years earlier than other people.
What does this mean for type 1?
These findings suggest that by using the new biomarker, we may be able to narrow the time window that a person is predicted to be diagnosed with type 1. This could make diagnostic procedures more efficient and could potentially reduce the number of children who are diagnosed whilst in DKA.
Also, by showing that mIAA levels reflected beta cell loss, this study gives fresh insight into the pre-diagnostic stages of type 1 and indicates that these autoantibodies may play a key role in beta cell loss and decline in insulin production. This has potential impacts on the accuracy of predicting type 1, but also for future therapeutic strategies.