NIDDK big data programs help to enhance, share new research in diabetes
New digital tools and technologies are being used to parse through big data and allow researchers to study diabetes in unprecedented ways, according to a speaker at the ENDO annual meeting.
Griffin P. Rodgers, MD, MACP, director of the National Institute of Diabetes and Digestive and Kidney Diseases, said big data — large, complex datasets — is being used in several programs at the NIDDK to further diabetes research in ways that were not possible in the past.
“The NIDDK is integrating big data tools and analysis to help optimize and accelerate research in diabetes and other mission areas,” Rodgers told Healio. “These technologies allow us to harness vast amounts of clinical, laboratory and diagnostic data in a way that’s accessible to researchers and scientists around the country and the world to enable them to better understand and develop treatments for diabetes and other conditions.”
Using big data to enhance diabetes research
During his presentation, Rodgers discussed four NIDDK programs that are using big data to analyze genes, beta cells and more. In The Environmental Determinants of Diabetes in the Young study, or TEDDY, researchers are examining how genes interact with environmental factors to determine environmental triggers for type 1 diabetes. More than 6,000 children who are genetically at a high risk for developing type 1 diabetes are being followed from birth to age 15 years in TEDDY. The study has resulted in the collection of more than 3.2 million samples, and sophisticated technology is being used to analyze thousands of genome, proteome and metabolome samples.
“This approach will allow us to identify even fairly subtle environmental and biological factors that distinguish children who go on to develop type 1 diabetes from those who do not, so that one day we may be able to prevent many cases of this disease,” Rodgers said.
Another program is set up to test new preventive efforts. The Type 1 Diabetes TrialNet is an international consortium for clinical trials to delay or prevent type 1 diabetes progression. Through TrialNet, more than 200,000 relatives of people with type 1 diabetes have been screened for eligibility in trials. TrialNet continues to screen more than 15,000 people per year.
The NIDDK has used big data to advance research in its Human Islet Research Network. In the Human Pancreas Analysis Program, researchers are identifying, collecting and characterizing primary pancreatic tissues, beta cells, antibodies, and rare forms of islet dysfunction in type 1 diabetes. Imaging-based analysis of the samples allows researchers to see how cells interact with each other during type 1 diabetes progression and could lead to new therapies for prevention or treatment.
NIDDK is also working to make big data more accessible. In the Accelerating Medicine Partnership Type 2 Diabetes Program, a type 2 diabetes knowledge portal was created with DNA sequences and functional genomic, epigenomic and clinical data from type 2 diabetes studies with cardiac and renal complications from hundreds of thousands of people across the world.
“It was developed to turn data on genetic variations contributing to diabetes into a deeper insight into potential therapeutic targets and disease precursors,” Rodgers said.
Big data in the future
To keep its work in big data going strong into the future, the NIDDK has several training programs to support future researchers. The organization supports awards and grant for research in bioinformatics. It also runs its Medical Student Research Program in Diabetes, a summer program that has allowed more than 900 students from more than 120 medical schools to provide pathways to keep students in bioinformatics.
Rodgers believes big data will continue to transform diabetes research in the future. The NIH recently launched the Precision Medicine Initiative to identify personalized, maximumly effective treatments for individuals based on gene, environment and lifestyle. In addition, the NIH All of Us Program is gathering data from at least one million volunteers to reflect the diversity of the U.S., according to Rodgers.
“In the future, these approaches might even make the term ‘type 2 diabetes’ obsolete by allowing us to identify new subgroups or forms of the disease that progress and/or respond to various therapeutic approaches in distinct and predictable ways,” Rodgers said. “This knowledge could greatly improve our ability to treat and prevent diabetes and its complications by using the best available interventions and treatment goals for each individual patient.”