Scientists find new way of predicting who is likely to develop type 2 diabetes

by Barbara Hewitt on September 1, 2017

A new way of identifying people at risk of developing type 2 diabetes has been developed by researchers in the UK who believe that it could be prevented in many cases.

Researchers at the University of Glasgow have revealed a technology-based approach that could lead to a more accurate way of helping to tackle the global increase of type 2 diabetes, which currently affects 415 million people worldwide and is predicted to rise to 642 million by 2040.


The have discovered potential new predictors, or biomarkers, of diabetes, in the form of proteins and molecules called micro-RNAs and they believe this could eventually lead to the development of new diabetes drugs.

‘Many cases of type 2 diabetes could be prevented by earlier and more intense intervention to reduce calorie intake, increase physical activity and prevent the weight gain associated with modern lifestyles,’ said lead researcher professor John Petrie of the Institute of Cardiovascular and Medical Sciences at the University of Glasgow.

‘But a more accurate means of predicting those at greatest risk is an important part of that effort,’ he added with his team working in collaboration with other university and industry researchers, bringing cutting edge technology to have an impact on an important public health issue, using carefully collected samples from well characterised individuals.

The researchers point out that in the years prior to a person developing of type 2 diabetes, cells scattered throughout their pancreas, known as beta cells, work overtime to produce extra insulin and keep blood sugar levels as normal as possible.

By the time diabetes develops, these cells have become exhausted and no longer able to make enough insulin to process and store food.

The researchers looked at the proteins present in the blood samples of people studied three year before they developed type 2 diabetes and compared these with samples from people of similar age and weight who maintained normal blood sugar over the same period.

The project measured 1,129 proteins in each blood samples as well as 754 molecules called micro-RNAs known to regulate the expression of genes. The researchers used statistical modelling to work out which were best at predicting diabetes.

Remarkably, both approaches flagged a series of molecules in the Epithelial-Mesenchymal Transition pathway. This is a series of changes in beta cells that may reflect a form of “stress” as they begin to lose their insulin producing properties due to overwork.

‘We are sharing the findings openly with the diabetes research community today in the hope that our findings can help in the global effort to tackle the ongoing pandemic of type 2 diabetes and its complications,’ added Petrie.