- Risk factors for type 2 diabetes are both environmental and genetic, and researchers have identified many genetic risk factors.
- But now, the largest-ever genome-wide association study in people with type 2 diabetes has discovered the location of even more risk variants than ever before.
- We also identified different clusters of mutations that contribute to the risk of developing the disease, revealing more about the different mechanisms underlying the disease.
Since the human genome was first sequenced in 2003, genome-wide association studies have become possible. This allows us to understand which regions of the genome and genetic variations are associated with increased risk of certain diseases.
Coupled with the advent of cell maps and genomic libraries, researchers are not only identifying variants that may impact risk, but also understanding what they control and the cellular mechanisms in which they play a role. It is now possible to understand.
So what can genetic markers tell us about a wide range of diseases such as type 2 diabetes? That’s what researchers say in a new study, the results of which are now published in the following paper:
Type 2 diabetes is characterized by decreased cellular insulin sensitivity, which means the cells have a reduced ability to take glucose into the bloodstream.
This can lead to chronically elevated blood sugar levels, increasing the risk of complications such as cardiovascular disease and nerve damage.
There are many known risk factors for type 2 diabetes, including having a family history of diabetes, being of African or Asian descent, high blood pressure, obesity, and polycystic ovarian syndrome (PCOS). .
Genome-wide association studies have revealed some interesting associations between type 2 diabetes and other conditions. For example, there are studies such as: 2023 showed that many genetic risk variants are common to type 2 diabetes and depressive symptoms.
Professor Inga ProkopenkoResearchers at the University of Surrey, who study the influence of genetics on diabetes and blood sugar control, said: Today’s medical news The way we look at type 2 diabetes has changed over the years.
“A final, very important point is that many of the large-scale GWAS to date have [genome-wide association studies] Meta-analyses for type 2 diabetes focus on: [type 2 diabetes] As a result, it is clear [type 2 diabetes]”This disease comes with many complications as the disease progresses. Major complications such as diabetic nephropathy and retinopathy are very important.”
In a recently published study, Nature This is the largest genome-wide association study of type 2 diabetes to date, including genomic data from 2,535,601 individuals, of whom 428,452 had type 2 diabetes.
While many genome-wide association studies feature data from predominantly Caucasian European datasets, this study included data from European, East Asian, African American, South Asian, and South African populations. It features data from six ancestry groups: Hispanic and American, West African, and European. ancestor.
However, the majority of participants were still primarily of European ancestry, with 60% of the cohort comprising this group.
A global consortium of researchers discovered 1,289 genetic variations in 611 regions of the genome known as loci, 145 of which were new discoveries.
We then mapped these variants to 37 cardiometabolic phenotypes, including waist-to-height ratio, liver fat percentage, LDL and HDL cholesterol, blood pressure, fasting insulin, etc., and determined that specific variants were associated with specific phenotypes or traits. I discovered whether it is related to
We then identified eight nonoverlapping “clusters” characterized by subsets of variants associated with specific cardiometabolic properties.
These clusters include beta cell dysfunction, obesity, liver and lipid (fat) metabolism, etc., and also indicate that people with these clusters have increased or decreased insulin secretion, or increased or decreased insulin sensitivity. It was also characterized whether they showed
One of the corresponding authors Dr. Benjamin F. Voightsaid the professor at the Perelman School of Medicine at the University of Pennsylvania. MNT that “[i]The genetic variants that contributed to our cluster turned out to be non-overlapping [with] Because of their interaction, a patient’s disease risk will be influenced to varying degrees by these clusters. ”
The researchers then considered whether the eight clusters they determined could be used to predict cardiovascular disease outcomes in these participants.
They created polygenic scores for an additional 279,552 people for whom they had genomic data, including 30,288 people with type 2 diabetes, and investigated whether there was an association with cardiovascular disease outcomes and genetic variation clusters.
The most significant association found showed that people positive for the obesity cluster of genetic variants had a 15% increased risk of hospitalization for heart failure.
They also found that carrying the beta-cell proinsulin-positive cluster gene variant was associated with a 10% reduced risk of being hospitalized for heart failure. This cluster was also associated with a 10% lower risk of cardiovascular death and a 6% lower risk of major cardiovascular events or heart attack.
Dr. Voight said:
“The door this type of research could open is to begin to dissect how different genetic ‘subtypes’ of type 2 diabetes modulate the risk of diabetes complications, if at all. I think this is where you can do it. Moreover, identifying genetic subtypes may also provide better clues about the underlying genes and biology that have the most important influence on these complications. ”
Professor Prokopenko, who was not involved in the study, said the study represented a significant advance in the analysis of large datasets and increased the number of risk variants we are aware of by a quarter. Ta.
“For us as scientists, this is an important resource, allowing us to do a lot of follow-up studies, experiments, etc., and enable further drug discovery,” she said. MNT.
“The results of this study and the information generated will allow us to improve the lives of people with diabetes with new treatments, new methods of care, and new treatments. [treating] In the future, this will be achieved through the prediction of individual susceptibility and the ability to distinguish between potential subtypes of diabetes,” Professor Prokopenko said.
