What You Should Know:
– Helix, a leader in population genomics and precision health, today announced the launch of a new clinico-genomic dataset comprising over 15,000 patients treated with glucagon-like peptide-1 (GLP-1) agonists.
– These medications, which include semaglutide, tirzepatide, and liraglutide, are increasingly used to treat type 2 diabetes, obesity, and other metabolic conditions.
GLP-1 Agonist Virtual Registry
This research-ready dataset, known as a Virtual Registry, was built as part of the Helix Research Network (HRN). It combines comprehensive genomic information with longitudinal electronic health record (EHR) data, providing researchers with a powerful tool to investigate the effectiveness and safety of GLP-1 agonists in diverse populations.
The GLP-1 agonist dataset is one of many therapeutic area-specific Virtual Registries within the Helix Research Network. Other registries focus on cardiometabolic diseases, autoimmune conditions, neurodegeneration, and more.
Key features of the GLP-1 Agonist Virtual Registry include:
- Large Cohort: Includes data from over 15,000 patients treated with GLP-1 agonists.
- Comprehensive Genomic Data: Features whole exome sequencing data, providing insights into genetic variations that may influence treatment response.
- Longitudinal EHR Data: Includes demographics, clinical diagnoses (type 2 diabetes, cardiovascular disease, kidney disease, etc.), lab results, procedures, and medical and mortality claims data.
- Regularly Refreshed: The dataset is continuously updated with new information, ensuring researchers have access to the latest insights.
“The success of GLP-1 agonists like semaglutide and tirzepatide have created incredible advances in the treatment of cardiometabolic disease and obesity, and the demand for and uptake of these therapies are not slowing down anytime soon,” said James Lu, M.D., Ph.D., CEO of Helix. “Our registry has more than tripled in less than a year, and will continue to meet and even outpace usage trends, allowing insights from deep real-world data coupled with broad genomic data to play a key role in monitoring safety profiles, determining cost-effectiveness, and improving treatment efficacy in select populations.”