Genomics Promises to Bring us Closer to Precision Medicine for Type 2 Diabetes

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By : Suvarna Sheth

Do you feel like your Type 2 diabetes treatment plan is a “one-size-fits-all” protocol that is not custom-tailored to your individual needs and physiology?

A new study from MIT and Harvard finds that inherited genetic changes may be responsible for differences among patients in the clinic, which potentially lead to outcomes such as diabetes and its resulting consequences.

By analyzing genomic data with a computational tool, the researchers identified five distinct groups of DNA sites that appear to drive distinct forms of the illness in unique ways.

“Genomic studies are unraveling the genetic architecture of complex diseases, and evidence is emerging that in some scenarios genetic information might be clinically useful,” Dr. Jose Florez, chief of the diabetes unit at Massachusetts General Hospital and professor at Harvard Medical School told dLife.

“While the studies that prove clinical utility and cost-effectiveness need to be completed, the situations where this might be the case are mounting,” he says.

Delineating “Subtypes” of Type 2 Diabetes

Florez and his group’s work is the 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,” says Florez. “We need a more granular approach that addresses the many different molecular processes leading to high blood sugar,” he explains.

Researchers have attempted to identify more subtypes of Type 2 diabetes based on other indicators such as beta-cell function, insulin resistance, or body-mass index, but those traits can vary greatly.

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.

Researchers are hoping to group these variations in 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 and would be grouped together.

As part of the research, Miriam Udler, an endocrinologist at Massachusetts General Hospital and a postdoctoral researcher in the Florez lab, took another approach:

She worked with Gaddy Getz and Jaegil Kim of the Broad’s Cancer Genomics team to apply a “soft-clustering” approach, which allows each variant to fall into more than one cluster.

“The soft-clustering method is better for studying complex diseases, in which disease-related genetic sites may regulate not just one gene or process, but several,” Udler reports.

The researcher’s work found five clusters of genetic variants.

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.

The other three clusters contain DNA variants related to insulin resistance, including one cluster mediated by obesity, one defined by disrupted metabolism of fats in the liver, and one driven by defects in the distribution of fat within the body, known as lipodystrophy.

Confirming Their Observations

To confirm these observations, the team analyzed data from the National Institutes of Health’s Roadmap Epigenomics Project, a public resource of epigenomic data for biology and disease research.

They found that the genes contained in the clusters were more active in the tissue types one would expect.

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’ 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.

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 — for example, patients who fell into the obesity-mediated cluster were indeed found to have increased body-mass index and body fat percentage.

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.

“The clusters from our study seem to recapitulate what we observe in clinical practice,” indicates Florez. “Now we need to determine whether these clusters translate to differences in disease progression, complications, and response to treatment.”

Florez says the use of genetic risk scores informed by physiology will help identify the pathways and mechanisms that are predominantly at work in a given patient.

“We then need to show that tailoring prevention, surveillance or treatment according to those pathways defined by genetics, but informed by physiology makes a difference clinically,” he says.

Shedding Useful Light

In addition to paving the way to clinically useful subtypes, the work sheds light on the diverse causes underlying Type 2 diabetes and offers a model for understanding the mechanisms behind 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,” says Udler.

“We’re excited to see how our approach can help researchers make steps towards precision medicine for other illnesses as well.”

Next Steps

According to Florez, at this point, the cost of needs to be taken out of the equation – “this will only be feasible and scalable if the genomic information is readily available as part of the patient’s medical record,” he indicates.

Then, he says there needs to be an interpretation of the genomic profile to extract the clinically actionable information.

Finally, this needs to be conveyed in an intelligible and easily accessible manner to the practitioner at the point of care, with decision support tools to help him/her make the correct decision seamlessly.

Source:

  1. Broad Institute of MIT and Harvard. “Genomic study brings us closer to precision medicine for type 2 diabetes: Analysis reveals disease’s complexity, suggests potential clinical subtypes defined by genetics and physiology.” ScienceDaily, 21 September 2018.

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