Previously undetectable proteins in the blood can be used for early detection of prediabetes, which puts patients at increased risk of developing type 2 diabetes.
Research, led by a team from Queen Mary University of London and published in Nature Medicine, has detected a three-protein signature that can improve the early detection of prediabetes.
The findings could enable medical and behavioural interventions that could delay or prevent the onset of diabetes and alleviate the burden of type 2 diabetes on the NHS, the researchers said.
Higher than normal blood sugar levels put many people at risk of developing type 2 diabetes.
Patients with impaired glucose tolerance (ICT), a type of prediabetes, are often missed in current clinical screening and diagnostic tests. This group of patients can only be identified through oral glucose tolerance, a time-consuming procedure requiring multiple blood draws and therefore not routinely performed as part of type 2 diabetes clinical screening strategies.
The researchers analysed blood from over 11,000 individuals who were part of the Fenland Study. Each individual underwent an oral glucose tolerance test and using proteomic assay, a method of analysing proteins in a cell, the scientists were able to identify and quantify proteins which were present in the blood.
By measuring nearly 5,000 proteins, the authors created a machine learning algorithm that was able to extract a core set of proteins that enabled them to identify people most likely to have ICT in advance of an oral glucose tolerance test.
Three proteins were identified, which, when combined with standard screening techniques, could improve the identification of individuals with ICT. The results were confirmed in an independent study known as Whitehall II.
The results also show that fasting before a blood sample is taken makes no significant difference to the effectiveness of the test. The three-protein signature can still be detected if no fasting has taken place, which would greatly increase the application of the test in clinical practice.
Professor Claudia Langenberg from Queen Mary University of London said: ‘Our strategy has the potential to address an important unmet clinical need: the identification of a meaningful proportion of people with prediabetes who currently remain undetected.’
She added: ‘Early identification would enable preventive lifestyle and behavioural interventions to improve the health of affected individuals and alleviate the burden to health-care systems caused by their delayed diagnosis.’
The researchers would now like to evaluate the three-protein signature in other populations and ethnic groups and to test the three-step strategy for identifying prediabetes in randomised screening trials.