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Most patients diagnosed with type 2 diabetes are treated with a "single" protocol that is not adapted to the physiology of each person and can leave many cases poorly managed. A new study by scientists at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) indicates that hereditary genetic modifications may explain the variability observed in patients, several pathophysiological processes that can lead to high blood glucose and its consequences. .
By analyzing genomic data with a computer tool that integrates genetic complexity, the researchers identified five distinct groups of DNA sites that appear to drive distinct forms of the disease in a unique way.
The work represents a first step towards using genetics to identify subtypes of type 2 diabetes, which could help doctors prescribe interventions aimed at the cause of the disease rather than the symptoms.
The study appears in PLOS Medicine.
"In treating type 2 diabetes, we can use a dozen drugs, but after starting to use the standard algorithm, it's all about a mistake," said Jose Florez, MDH endocrinologist. Broad's metabolism program and professor at Harvard Medical School. "We need a more granular approach that addresses the many molecular processes leading to high blood sugar."
It is known that type 2 diabetes can be globally clustered in case of inability of pancreatic beta cells to produce enough insulin, called insulin deficiency, or inability of liver, muscle, or muscle tissues. fat to use insulin resistance.
Previous research has attempted to define more subtypes of type 2 diabetes based on indicators such as beta cell function, insulin resistance, or body mass index. but these characteristics can vary considerably over the course of life. Heritable genetic differences are present at birth and a more reliable method would be to create subtypes based on DNA variations associated with the risk of diabetes in large-scale genetic studies. These variations can be grouped into clusters based on their impact on the characteristics related to diabetes. For example, genetic modifications linked to high levels of triglycerides are likely to function according to the same biological processes.
Early efforts to achieve this included a "clustering" approach in which each genetic variation was assigned to a single group. However, this failed to produce models that had biological meaning.
Miriam Udler, an endocrinologist at MGH and a postdoctoral researcher at the Florez lab, took another approach. She teamed up with Gaddy Getz and Jaegil Kim of Broad's cancer genomics team to apply a so-called "soft-clustering" approach known as non-negative Bayesian matrix factorization, which allows each variant to group together in several groups.
"The clustering method is better for studying complex diseases, in which genetic sites linked to the disease can regulate not one gene or process, but several," said Udler.
The new work revealed five groups of genetic variants distinguished by distinct underlying cellular processes, within the main existing divisions of insulin-resistant and insulin-deficient diseases. Two of these clusters contain variants that suggest that beta cells do not work properly, but their effects on insulin precursor levels, proinsulin, differ. The other three clusters contain DNA variants related to insulin resistance, including a cluster-mediated obesity, one characterized by a disruption of fat metabolism in the body. liver and lipodystrophy.
To confirm these observations, the team analyzed data from the Epigenomics Roadmap project of the National Institutes of Health, a public epigenomic data resource for biology and disease research. They found that the genes in the groups were more active in the types of tissue that could be expected.
To further test whether the correct biological mechanism had been assigned to each group, researchers collected data from four independent cohorts of patients with type 2 diabetes and first calculated the individual genetic risk scores of patients with type 2 diabetes. patients for each group. They found that nearly one-third of patients had a high score for only one predominant group, suggesting that their diabetes could be dominated by a single biological mechanism.
When they then analyzed the measures of diabetes-related traits in high-score subjects, they observed patterns that strongly reflected the suspected biological mechanism and distinguished them from all other patients with type 2 diabetes-by For example, patients with obesity It was found that clusters had a higher body mass index and body fat percentage.
The results appear to reflect some of the diversity observed by the clinic's endocrinologists. For example, people with a high score in the lipodystrophy-type group were probably thinner than average but had insulin-resistant diabetes, similar to a rare type of diabetes in which fats accumulate in the liver. resistance that results from obesity.
"The clusters in our study seem to summarize what we observe in clinical practice," said Florez. "Now we need to determine whether these clusters translate into differences in disease progression, complications, and response to treatment."
In addition to paving the way for clinically useful subtypes, the work highlights the various pathophysiologies underlying type 2 diabetes and provides a model to better understand the heterogeneity of other complex diseases.
"This study has given us the most comprehensive vision to date of the genetic pathways underlying a common disease that, if not adequately treated, can lead to devastating complications," Udler said. "We are excited to see how our approach can help researchers make steps towards precision medicine for other diseases."
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