Prediabetes means that your blood sugars are higher than usual, but not high enough for you to be diagnosed with type 2 diabetes. It also means that you are at high risk of developing type 2 diabetes.
The research is published today (10 November) in Nature Medicine. Medical and behavioural interventions in individuals with prediabetes are effective in delaying or preventing the onset of type 2 diabetes, but a substantial proportion of people with prediabetes are missed by current clinical screening and diagnostic techniques. Individuals with isolated impaired glucose tolerance (isolated IGT), a common subtype of prediabetes, can only be identified through oral glucose tolerance testing as they have normal results with more commonly undertaken tests. Oral glucose tolerance testing is a time-consuming procedure requiring repeated blood draws, and is therefore not routinely performed as part of type 2 diabetes clinical screening strategies.
The authors used a proteomic assay, a means of using technologies to identify and quantify proteins present in a cell, to measure blood levels of nearly 5,000 proteins in samples from more than 11,000 participants in the Fenland Study, each of whom underwent an oral glucose tolerance test. The authors created a machine learning algorithm that was able to extract a core set of a few proteins out of the thousands measured that were most informative in identifying people most likely to have isolated IGT in advance of undertaking an oral glucose tolerance test.
The authors identified a signature of only three proteins that when combined with standard screening techniques for impaired glucose tolerance improved identification of individuals with isolated IGT in the Fenland study cohort, and subsequently confirmed this finding in the independent Whitehall II study. Their results also indicate that fasting before the blood sample is taken does not significantly change the reliability of the three-protein signature for identifying people with impaired glucose tolerance, which would greatly increase the application of the test in clinical practice.
Senior author Professor Claudia Langenberg, now Director of Queen Mary University of London's Precision Healthcare University Research Institute, 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.
"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.
"We would now like to evaluate the three-protein signature in other populations and ethnic groups, and, ultimately, to test the three-step strategy for identifying prediabetes in randomised screening trials."
The authors suggest that by replacing the two-step screening strategy recommended by current guidelines with a three-step screening strategy that incorporates testing for the three-protein signature, the number of individuals who need to undergo oral glucose tolerance testing to identify an isolated IGT case could be substantially reduced. However, they note that some individuals with isolated IGT would still be missed, an important consideration for clinical implementation.
Professor Langenberg was recently appointed as Director of Queen Mary's new Precision Healthcare University Research Institute (PHURI). The PHURI will explore overlooked areas of medical research, driving a better understanding of how disease impacts different groups of people. These include genomics, medical technology and devices, as well as therapeutic innovation.
She was also named as one of the top 100 female scientists in the UK.
More information
Carrasco-Zanini J. et al. Proteomic signatures for identification of impaired glucose tolerance; Nature Medicine
