Experts make breakthrough in the fight against Type 2 diabetes

Jul 23 2018

Experts from the University of Stirling have made a breakthrough in understanding how people respond to lifestyle treatment for preventing Type 2 diabetes.

The team, including academics from the Faculty of Health Sciences and Sport, discovered a new genomic signature in people whose Type 2 diabetes status improves following a treatment intervention. Significantly, it is the first reliable signature for insulin sensitivity in human muscle.

Scientists believe that the findings - published in leading journal Nucleic Acids Research - will inform future research by helping understand why not all people are able to eliminate the risk of the condition by changing their lifestyle.

Dr Iain J Gallagher, of the University of Stirling, one of the research team, said: "Our hypothesis was that, with sufficient numbers of well-characterized subjects and our new analysis methods, we could reveal a robust signature for what is known as 'insulin resistance' - an important precursor for developing Type 2 diabetes.

"Importantly, because we could also examine how the activation status of each 'insulin resistance' gene responded to treatment, we have also discovered a potential explanation for why not all people eliminate their Type 2 diabetes risk by following a lifestyle and exercise training program."

The team - which included a number of international partners - analyzed more than 1,000 human muscle samples and five distinct treatment regimes. In doing so, they demonstrated that 16 genes are consistently "switched" on or off in muscle tissue - but only in those people whose Type 2 diabetes risk factors improved. In such cases, the gene changes increased the individuals' insulin sensitivity - a measure of how effectively the hormone insulin is working.

Activation of the signature is impaired in people with poor insulin sensitivity, and is dysregulated to a greater extent following various types of standard lifestyle treatment.

The signature includes more than 300 measures of gene activity, representing both protein coding and long non-coding genes. It was extensively modeled to take into account body weight and age, as well as exercise capacity.