Personalised therapies may help better manage diabetes

Inherited genetic changes can explain why a "one-size-fits-all" treatment for diabetes is not always successful, say scientists who identified five distinct groups of DNA sites that drive the illness in unique ways.

The study, published in the journal PLOS Medicine, suggests that a personalised therapy tailored to each person's physiology may help better manage diabetes.

Scientists from the Harvard University and Massachusetts Institute of Technology in the US analysed genomic data with a computational tool that incorporates genetic complexity.

"When treating type 2 diabetes, we have a dozen or so medications we can use, but after you start someone on the standard algorithm, it's primarily trial and error," said Jose Florez, an endocrinologist at Massachusetts General Hospital (MGH) in the US.

"We need a more granular approach that addresses the many different molecular processes leading to high blood sugar," said Florez, also a professor at Harvard Medical School.

It is known that type 2 diabetes can be broadly grouped into cases driven either by the inability of pancreatic beta cells to make enough insulin, known as insulin deficiency, or by the inability of liver, muscle or fat tissues to use insulin properly, known as insulin resistance.

Previous research attempted to define more subtypes of type 2 diabetes based on indicators such as beta-cell function, insulin resistance, or body-mass index, but those traits can vary greatly through life and during the course of disease.

Inherited genetic differences are present at birth, and so a more reliable method would be to create subtypes based on DNA variations that have been associated with diabetes risk in large-scale genetic studies.

These variations can be grouped into clusters based on how they impact diabetes-related traits; for example, genetic changes linked to high triglyceride levels are likely to work through the same biological processes.

Researchers identified five clusters of genetic variants distinguished by distinct underlying cellular processes, within the existing major divisions of insulin-resistant and insulin-deficient disease.

To test whether each cluster had been assigned the correct biological mechanism, researchers gathered data from four independent cohorts of patients with type 2 diabetes and first calculated the patients' individual genetic risk scores for each cluster.

They found nearly a third of patients scored highly for only one predominant cluster, suggesting that their diabetes may be driven predominantly by a single biological mechanism.