Helix has unveiled new clinical research at the American Society of Human Genetics (ASHG) 2024 Annual Meeting, featuring a novel precision effectiveness model that predicts the 12-month weight loss response of semaglutide in diverse populations.
Researchers at Helix found that an integrated polygenic risk score with co-morbid factors such as the presence of type 2 diabetes and hypertension could predict the 12 month weight loss response to semaglutide treatment in individuals. Among 2.4mg semaglutide users, those in the fifth/top quintile of the newly developed score (after correcting for sex and genetic similarity) were approximately twice as likely to achieve 10% weight loss than those in the first/bottom quintile.
"This model has the potential to deliver on the promise of transforming precision medicine for patients being treated with semaglutide," said Dr Matthew Levy, senior research scientist at Helix. "By providing dosage-specific weight loss expectations and predicting which patients are most likely to respond, we can help ensure that each patient receives the most appropriate treatment from the start."
Helix's precision effectiveness model for semaglutide could be used for patients and providers to:
Identify individuals most likely to respond to treatment
Improve shared decision making around the best individual treatment for obesity
Provide estimates of expected weight loss over time, or the likelihood of response
Guide dosage considerations to help balance weight loss goals with possible side effects
This research was made possible by data collected from the Helix Research Network (HRN), the largest and fastest-growing precision clinical research network designed to advance science and health insights. The growing network comprises leading health systems across North America, including the Medical University of South Carolina, HealthPartners, Renown Health, and more. With its rich data, life science researchers can conduct innovative studies in a wide range of therapeutic areas including cardiometabolic diseases, neurodegenerative conditions, autoimmune disorders, and more to drive drug discovery and development.
"The Helix Research Network marks a significant leap forward in precision health, allowing us to extract meaningful insights from a comprehensive clinicogenomics dataset from across North America," said Dr William Lee, chief science officer at Helix. "Through our collaboration with our partners, we are generating insights that will shape the future of medicine – guiding more effective, personalised treatments across a range of diseases. We're thrilled to support this breakthrough research that can bring transformative changes for patients worldwide and help redefine healthcare."
Additional research presented at ASHG 2024 displaying the breadth and depth of how HRN data can be applied includes:
Research that underscores the importance of pharmacogenomic (PGx) testing or referencing patients' genotype before prescribing medications. In a retrospective study of clopidogrel, a drug that prevents blood clots, it was found that 25% of individuals prescribed this medication had a mismatch between their recommended dosage (based on their genotype) and their prescribed dose. Twelve percent of these mismatched individuals were poor metabolizers, meaning they should not be prescribed clopidogrel at all. In addition, 25% of these poor metabolisers experienced thrombosis (a blood clot) after the initiation of clopidogrel. These adverse outcomes could have been prevented by first undergoing PGx testing to assess metabolism.
A study that includes a method to help determine predictors of future risk for cardiovascular disease. Lipoprotein a (Lp(a)) is a useful predictor of cardiovascular disease. However, it can be difficult to obtain consistent and accurate measurements. Helix developed a method for estimating the number of KIV-2 repeats within an exome profile, which can then be used in combination with a genetic risk score to predict Lp(a). This variability is particularly impactful in non-European ancestries, which is significant as past risk score methods have not been effective for diverse populations.
Other abstracts and platform talks presented at ASHG highlight the magnitude of the autoimmune population in the Helix Research Network, identify individuals at low risk of disease using genetics, utilise a methodology to group rare coding variants to determine the impact to protein structure and more.
Comments