Predicting who will develop type 1 diabetes easier and cheaper thanks to new calculator

Predicting who will develop type 1 diabetes is now cheaper and more accessible, thanks to a new tool based on research conducted at the University of Exeter which could one day help determine who to target with recently approved drugs to delay the disease.

In type 1 diabetes, the body’s own immune system attacks insulin-producing beta cells in the pancreas, which means people need to inject insulin daily.

Knowing who is at highest risk of developing type 1 diabetes in future is crucial, as the United States and the UK have recently licensed a new drug that can delay the onset of disease and the need for insulin by up to three years. The drug only works if given in the earliest stages of disease development.

Now, a team at the University of Exeter has published a series of three papers which identifies ways to improve diagnosis based on genetics, making prediction easier, cheaper, more effective, and more accessible. They have created an online calculator, which is free to access for health professionals around the world. The new tool is based on a decade of research from Exeter, analysing all the genetic factors involved in diabetes to create a test to aid diagnosis known as a genetic risk score.

In the latest study, researchers found differences in the progression of type 1 diabetes between adults and children. They analysed data from 135,914 children (aged under 18 years) and 99,795 adult relatives of individuals with type 1 diabetes screened in the TrialNet Pathway to Prevention study. They found risk differences in adults and children who were positive for the same autoantibody markers, highlighting the need for screening that is tailored to age.

In the two earlier studies, funded by the US National Institutes of Health and supported by the Exeter NIHR Biomedical Research Centre, researchers refined the models, assessing the most accurate and cost-effective method to define a person’s risk of developing type 1 diabetes in the future. This is now needed to identify who to monitor for the earliest signs of disease development, so that they can benefit from the new drug.

Professor Richard Oram, who leads the research group at the University of Exeter Medical School, said: “Early diagnosis is crucial to getting the best treatment, and now there’s a drug licensed in the US which can prevent the onset – but only where clinicians can catch it before it fully develops. Our research and our new calculator make it cheaper, more effective, and more accessible to identify who is at high risk of developing type 1 diabetes, so they can be monitored and access treatment as early as possible.”  

In a recent study published in Diabetologia, the Exeter team compared 1,900 predictive models, which each included different measures from a range of possible combinations. These included the count and types of autoantibodies produced by the immune system which destroy the beta cells in the pancreas, BMI, age, self-reported gender, and measures of glucose and C-peptide, which is indicative of insulin levels yet easier to measure.

The team tested the models in more than 3,900 participants in the US TrialNet Pathway to Prevention study. The participants all had a close relative with type 1 diabetes, but no diagnosis when the study began. A third of participants went on to develop the disease.

Accurately predicting who will develop type 1 diabetes has traditionally come with high costs and sometimes significant burden on patients. The Exeter team identified models that are cost-effective and time-efficient, and as accurate as more complex models. By leveraging simple metrics such as BMI, age, and basic blood tests like HbA1c, the researchers have identified a family of predictive models that reduce costs to a third. These models require just 20 minutes of a patient’s time, from the comfort of their home. This innovation stands in stark contrast to the conventional 2.5-hour clinical visits, marking a significant step towards more accessible and scalable healthcare solutions.

Lauric Ferrat, researcher at the University of Exeter and University of Geneva, said: “Our research demonstrates a substantial advancement in making predictive healthcare models more accessible and less burdensome, offering a practical solution that significantly reduces both time and cost for patients.”

Now, a new paper in BMC Medicine has further tested and enhanced the optimal prediction model, using data from more than 4,000 TrialNet participants, plus more than 7,000 participants from the Environmental Determinants of Diabetes in the Young (TEDDY) study, improving the accuracy of previous models.

The new model incorporates age, whether a sibling or parent has type 1 diabetes, the number of confirmed autoantibodies in a blood test, and the genetic risk score, measuring the genetic susceptibility to develop type 1 diabetes.

Erin Templeman, a PhD candidate at the University of Exeter, said “Our aim is to bridge the gap between research and clinical practice. Our new prediction model includes an accompanying accessible calculator designed to translate our findings into improved clinical care. It will allow clinicians to use the tool alongside patients and families.”

This refined model is now available for clinicians to use, via: https://t1dpredictor.diabetesgenes.org

The Diabetes Care paper is titled “Contrasting Adult and Pediatric Populations in a Cohort of At-Risk Relatives in The T1D TrialNet Pathway to Prevention Study”

The Diabetologia paper is titled “Type 1 diabetes prediction in autoantibody-positive individuals: performance, time and money matter”

The BMC Medicine paper is titles “A Type 1 Diabetes Prediction Model for Multiple Screening Settings with Improved Performance Through Recalibration”