Five newly identified ‘soft clusters’ of genetic variants could explain the personal variations in type 2 diabetes.
Teams at Massachusetts Institute of Technology and Harvard University — as well as other top-tier research institutions in Cambridge, MA — have been working to find a good way of assessing which ranges of factors are most likely to determine the development of type 2 diabetes in a person.
“When treating type 2 diabetes,” reports senior study author Jose Florez, “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.”
“We need,” he continues, “a more granular approach that addresses the many different molecular processes leading to high blood sugar.”
The researchers’ recent work led to the identification of five clusters of genetic variants that may influence distinct subtypes of type 2 diabetes. These findings now appear in the journal PLOS Medicine.
A more accurate genetic ‘map’
The two most widely recognized subtypes of type 2 diabetes are those driven by insulin resistance (in which the body does not process insulin correctly) and insulin deficiency (in which the pancreas simply does not produce enough insulin).
Research released this spring in The Lancet, however, has argued that there are several subtypes of type 2 diabetes, focusing on the influence of factors such as the body mass index (BMI), insulin resistance, and how well beta cells in the pancreas function.
However, the team behind the new study says that these factors can change throughout a person’s lifetime and as the condition progresses.
They argue that a more reliable way of identifying which relevant factors play a more important role in disease progression for each person is by looking at their genetic makeup.
Thus, they identified five “soft clusters” of genetic variations grouped based on which diabetes-related mechanisms they impact, such as the presence of high triglyceride levels.
Soft clusters are so called because they take into consideration the fact that one genetic variation may, at the same time, impact more than one trait and this, the scientists argue, is a much more workable framework than a “hard cluster” approach, which does not allow for such overlaps.
“The soft-clustering method,” notes study co-author Miriam Udler, “is better for studying complex diseases, in which disease-related genetic sites may regulate not just one gene or process, but several.”
Development driven by one mechanism
Of the five genetic clusters that the team identified, two are linked with the improper functioning of beta cells, though each of them impacts proinsulin — the precursor of insulin — to a different degree.
The other clusters are all linked to insulin resistance. However, one is obesity-mediated, another is mediated by lipodystrophy (misdistribution of fats throughout the body), and the third is mediated by the malfunctioning metabolism of fats in the liver.
Florez and colleagues verified these findings by analyzing relevant data collected via the National Institutes of Health’s (NIH) Roadmap Epigenomics Mapping Consortium, a public database accessible to researchers.
The scientists also looked at information collected from four different groups of people diagnosed with type 2 diabetes, calculating each person’s genetic risk score for each of the five clusters of genetic variations.
Almost a third of all the participants scored highly for just one cluster, which also suggested that, in most people, a single mechanism may facilitate type 2 diabetes.
“The clusters from our study seem to recapitulate what we observe in clinical practice,” says Florez, adding, “Now we need to determine whether these clusters translate to differences in disease progression, complications, and response to treatment.”
The study authors also claim that theirs provides the most detailed overview of the genetic factors that underlie the development of type 2 diabetes in different individuals.
“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.
She also points out that the methods used in the recent study “can help researchers make steps towards precision medicine for other illnesses as well.”