Most patients with type 2 diabetes are treated with a 'one-size-fits-all' protocol, but this approach can leave many cases inadequately managed. New work indicates that inherited genetic changes may underlie the variability seen among diabetes patients, with different physiological processes potentially leading to high blood sugar.
This work represents a first step toward using genetics to identify subtypes of type 2 diabetes. The study was published in PLOS Medicine. Most patients diagnosed with type 2 diabetes are treated with a "one-size-fits-all" protocol that is not tailored to each person's physiology and may leave many cases inadequately managed.
A new study by scientists at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) indicates that inherited genetic changes may underlie the variability observed among patients in the clinic, with several pathophysiological processes potentially leading to high blood sugar and its resulting consequences.
By analyzing genomic data with a computational tool that incorporates genetic complexity, the researchers identified five distinct groups of DNA sites that appear to drive distinct forms of the illness in unique ways.
The work represents a first step toward using genetics to identify subtypes of type 2 diabetes, which could help physicians prescribe interventions aimed at the cause of the disease, rather than just the symptoms.
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. They need a more granular approach that addresses the many different molecular processes leading to high blood sugar.
It's 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.
The 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 the disease.
The new work revealed five clusters of genetic variants distinguished by distinct underlying cellular processes, within the existing major divisions of insulin-resistant and insulin-deficient disease. Two of these clusters contain variants that suggest beta cells aren't working properly, but that differ in their impacts on levels of the insulin precursor, proinsulin.
To further test whether each cluster had been assigned the correct biological mechanism, the researchers gathered data from four independent cohorts of patients with type 2 diabetes and first calculated the patients' 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. When they next analyzed measurements of diabetes-related traits from high-scoring subjects, they saw patterns that strongly reflected the suspected biological mechanism and distinguished them from all other patients with type 2 diabetes.
The results appear to reflect some of the diversity observed by endocrinologists in the clinic. For example, people who scored high on the lipodystrophy-like cluster were likely to be thinner than average but have insulin-resistant diabetes, similar to a rare type of diabetes in which fat accumulates in the liver, which is a fundamentally different process from insulin resistance that results from obesity.
In addition to paving the way to clinically useful subtypes, the work sheds light on the diverse pathophysiology underlying type 2 diabetes and offers a model for unraveling the heterogeneity of other complex diseases.
This study has given us the most comprehensive view to date of the genetic pathways underlying a common illness, which if not adequately treated can lead to devastating complications. They are excited to see how our approach can help researchers make steps towards precision medicine for other illnesses as well.