Type 2 diabetes (T2D) impairs the body’s ability to process and regulate sugar levels in the blood. Typically, the disease involves two factors: β-cells in the pancreas do not produce enough insulin to regulate sugar intake by cells, movement, and body cells do not respond appropriately to insulin, failing to absorb sugar.
Although diagnosis of diabetes is fairly straightforward, additional symptoms and syndromes associated with diabetes make treatment difficult. T2D is associated with major negative health outcomes, including CVD, diabetes-related microvascular complications, and mortality. There is also an increasing economy burden associated with increased T2D prevalence, which is expected to reach 12% globally among 20-79 year olds by 2045.
Despite multi-level approaches, including lifestyle interventions and pharmacological and non-pharmacological treatments, the prevalence of T2D has continued to increase. There’s a clear need for a better understanding of the complex causes and mechanisms behind T2D, including risk factors, to decrease the global burden of T2D.
Similarly, people’s propensity for and severity of diabetes are not well understood. Without an understanding of disease risk factors, disease prevention is a challenge to current medical capabilities.
Between 33 and 36 million Americans have type 2 diabetes. Historically, people tend to develop T2D after the age of 45. However, incidence of early-onset T2D has been increasing for over three decades.
With an increase in T2D cases, it’s important to improve diagnosis, treatment, and complication management. This article explores how next-generation sequencing technologies are tackling major challenges in diabetes management.
Protein biomarker research in particular has shed light on the mechanisms contributing to T2D. Research in the last decade has implicated cytokine IL-1β as a key contributor to T2D development. The protein may contribute to organ malfunction in diabetes.
Identifying disease mechanisms is an important step in developing better treatments for disease. Researchers have turned to the bodily processes that control the secretion of IL-1β. Initial research has suggested that blocking IL-1β could help prevent the development of T2D. There is also evidence that this could improve treatment options for existing cases of T2D.
Research has identified many possible mechanisms associated with T2D complications. One of the most common complications, diabetic neuropathy, impacts up to 60% of people with T2D. This condition involves nerve malfunctions, leading to numbness or pain in the extremities.
We know that DNA methylation and altered gene expression are related to neuropathy. However, it is unclear how DNA methylation contributes to nerve damage. In a Clinical Epigenetics study, researchers obtained nerve biopsy samples from 78 T2D patients with diabetic peripheral neuropathy (DPN). Those samples were then segmented into two groups based on HbA1C levels.
Transcriptomic and methylomic analysis uncovered 998 differentially expressed genes and 929 differentially methylated genes in samples with high HbA1C levels compared to samples with low HbA1C levels. These results indicate that DNA methylation has a direct role in regulating gene expression in DPN.
Researchers believe this shows that successful glycemic control is an important factor in preserving nerve function and promoting homeostasis in T2D patients with DPN. By tackling common complications, multiomics approaches are able to better understand causal relationships and therapeutic options for diabetes and associated conditions.
Recent studies have focused on identifying biomarkers of genetic predisposition for type 1 and type 2 diabetes. These insights serve two purposes: determining individuals’ risk of developing diabetes, and understanding the mechanisms of diabetes pathogenesis.
One challenge to applying genetic risk factors in clinical practice is that individual genetic variants typically have a limited influence on a person’s likelihood of developing diabetes. For this data to be useful, researchers are working toward using an overall picture of genetic variants to determine an individual’s predisposition. These polygenic scores are able to look beyond individual variants to determine an individual’s risk of developing diabetes, as well as allowing for differential diagnosis based on their genetic information.
Obesity is a major risk factor in the development of T2D, and one of the disease’s most common complications. Although increased fat levels are associated with insulin resistance, the nature of this connection at the molecular level is less clear.
Mouse models have identified several strong candidate genes that could be related to obesity in T2D. These genes may regulate glucose or lipid metabolism in humans. In another mouse study, inflammatory molecule LTB4 can cause insulin resistance. With a better understanding of obesity-related T2D at the molecular level, new treatments could better mitigate a person’s risk for disease development.
Genetic risk testing is a major tool toward prevention of polygenic diseases like diabetes. In a 2012 clinical trial, a genetic counseling intervention was applied to 72 participants. They received their genetic risk score for type 2 diabetes, general diabetes education, and the opportunity to participate in a diabetes prevention program in the form of lifestyle modifications.
Some drugs have been proven to reduce the risk of type 2 diabetes in at-risk populations. For example, the drug metformin has undergone several trials and follow-up outcome studies over the course of two decades. The three year risk of diabetes for patients taking metformin decreased by 31%.
Follow-up studies also analyzed genetic variants of participants, identifying several single nucleotide variants that impacted the success of treatment. The combination of clinical trials and genetic profiling is a key toward developing more effective preventive treatment.
Metabolomics, proteomics, and genomics technologies have helped to identify a number of biomarkers of T2D. Dr. Michael Snyder, chair of the Stanford University School of Medicine’s Genetics Department, caught his own diabetes very early using genome sequencing data.
Dr. Snyder and his team developed the Personal Omics Profile (iPOP). To generate this profile, this team conducts whole genome sequencing of an individual, then combines that data with transcriptomic, proteomic, metabolomic, and autoantibody profiles.
iPOP data can provide valuable insight regarding changes to expect during disease states. Dr. Snyder included himself as a subject in the development of the iPOP process. He discovered his pre-diabetic state during this development, despite his phenotypic profile and apparent state of health.
The diabetic state later appeared to be triggered by a viral infection. With the genomic and other information regarding his health risks, he is able to protect his health with diet and other lifestyle changes.
Genetic profiling has the potential to identify individuals with an elevated risk for developing T2D. This is particularly important for people who do not fit phenotypic profiles or who do not yet present with signs or symptoms of pre-diabetes.
Roughly 1.5 million Americans are diagnosed with T2D every year. If trends continue, the incidence of people under 20 years of age with T2D is expected to increase by 675% by 2060. Additional tools and treatment options are necessary to change this trend.
Genetic research has the potential to improve T2D prevention, diagnosis, and treatment. With the power of NGS technology, DNA sequencing will become a valuable aspect of medical science.