- The risk factors for type 2 diabetes are both environmental and genetic, and researchers have identified many genetic risk factors to date.
- Now, however, the largest ever genome-wide association study of people with type 2 diabetes has discovered even more locations of risk variants than before.
- It has also identified different clusters of variants that contribute to the risk of developing the disease, revealing more about the different mechanisms that underpin the disease.
Genome-wide association studies have been possible since the human genome was first sequenced in 2003. They allow us to understand which regions of the genome and genetic variants are associated with increased risk of certain conditions.
Coupled with the advent of cell maps and genomic libraries, it is now possible for researchers to identify not only the variants that could impact risk, but also understand what they control and the cellular mechanisms that they play a role in.
So what can genetic markers teach us about widespread conditions, such as type 2 diabetes? That is what the researchers conducting a new study — whose results now appear in
Type 2 diabetes is characterized by reduced insulin sensitivity of cells, which means they are less able to take up glucose in the bloodstream.
This leads to the chronically elevated blood glucose (sugar) levels, which can increase the risk of complications, such as cardiovascular disease and nerve damage.
There are many known risk factors for type 2 diabetes, including family members having it, being of African or Asian ancestry, high blood pressure, obesitym and polycystic ovary syndrome (PCOS), among others.
Genome-wide association studies have revealed some other interesting links between type 2 diabetes and other conditions. For example, a study in 2023 showed that a number of genetic risk variants were shared by type 2 diabetes and depressive symptoms.
Prof. Inga Prokopenko, who studies the impact of genetics on diabetes and glycaemic control from the University of Surrey told Medical News Today that the way we look at type 2 diabetes has changed in recent years:
“A final very important point is that many previous large scale GWAS [genome-wide association studies] meta-analyses in type 2 diabetes have been focusing on [type 2 diabetes] as an outcome, but it was clear that as [type 2 diabetes], as a disease, progresses there are many complications of this disease.These major complications are very important, such as diabetic nephropathy, retinopathy, etc.”
The study recently published in Nature is the largest genome-wide association study of type 2 diabetes to date, and it included genomic data from 2,535,601 individuals, of whom 428,452 had type 2 diabetes.
Many genome-wide association studies feature data from a predominantly white European dataset, but this study featured data from six ancestral groups, namely: European, East Asian, African American, South Asian, South African, and Hispanic with American, West African and European ancestry.
However, the majority of participants were still predominantly European in ancestry, with 60% of the cohort made up of this group.
A global consortium of researchers discovered 1,289 genetic variants, at 611 areas of the genome known as loci, of which 145 were new discoveries.
They then mapped these variants to 37 cardiometabolic phenotypes, including waist-height ratio, liver fat percentage, LDL and HDL cholesterol, blood pressure, fasting insulin and others, to discover if certain variants were associated with certain phenotypes, or traits.
They then identified eight nonoverlapping “clusters” characterized by subsets of variants associated with certain cardiometabolic traits.
These clusters included beta-cell dysfunction, obesity, and liver and lipid (fat) metabolism, among others, and also characterized whether people with these clusters exhibited increased or decreased insulin secretion, or increased or decreased insulin sensitivity.
One of the corresponding authors, Dr. Benjamin F. Voight, of the University of Pennsylvania–Perelman School of Medicine, told MNT that “[i]t turns out the genetic variants that contributed to our clusters do not overlap [with] one another — so patients have their disease risk influenced to different extents by these clusters.”
Next, researchers looked at whether the eight clusters they had determined could be used to predict cardiovascular disease outcomes in these participants.
They developed polygenic scores in a further 279,552 individuals for whom they had genomic data, including 30,288 with type 2 diabetes, and investigated whether there was an association with cardiovascular outcomes and genetic variant clusters.
The most significant associations discovered showed risk of hospitalization for heart failure was increased by 15% in people positive for the obesity cluster of genetic variants.
They also found that having the beta cell proinsulin positive cluster of genetic variants decreased a person’s risk of hospitalization for heart failure by 10%. This cluster was also associated with 10% lower risk of cardiovascular death and 6% lower risk of major cardiovascular events and heart attack.
Dr Voight said:
“I think the potential doorway that this type of work opens is where one can start to dissect how different genetic ‘subtypes’ of type 2 diabetes could modulate risk to complications of diabetes — either a little or a lot. Moreover, genetic subtyping might also give us better clues about the underlying genes and biology that contributes most importantly to those complications.”
Prof. Prokopenko, who was not involved in this research, said it represented significant advancement in analyses of large data sets and increased the number of risk variants we are aware of by a quarter.
“For us scientists, this is an important resource, and it will enable a many follow-up studies, experiments, etc., and further drug discovery,” she told MNT.
“The results of this study and generated information will empower us to improve the lives of people with diabetes with new treatments, new ways of care, new ways of [treating] it in the future, through prediction of individual susceptibility or ability to distinguish potential subtypes of diabetes,” Prof. Prokopenko noted.
