RYGB Surgery: Gene Risk Score Predicts Results
- A new analysis reveals that a novel, machine learning-informed gene risk score can predict weight-loss outcomes following Roux-en-Y gastric bypass (RYGB) surgery.
- Andres Acosta, MD, PhD, co-founder of Phenomix Sciences, reported that patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) experienced significantly...
- The research, presented at Digestive Disease Week (DDW) 2025, included 707 patients with a history of bariatric procedures from the Mayo Clinic Biobank.
Decipher the future of weight loss. A cutting-edge gene risk score, facilitated by machine learning, accurately predicts outcomes for patients undergoing RYGB surgery, revolutionizing the potential for weight loss after gastric bypass. This innovative MyPhenome test pinpoints individuals most likely to benefit from bariatric procedures,offering insights into those at risk of long-term weight regain. News Directory 3 highlights how genotype analysis could pave the way for personalized interventions, improving weight-loss journeys. Discover how this groundbreaking research is shifting the approach to proactive weight management. Explore what is coming next for this study.
Gene Score Predicts Weight Loss After gastric bypass Surgery
Updated June 09, 2025
A new analysis reveals that a novel, machine learning-informed gene risk score can predict weight-loss outcomes following Roux-en-Y gastric bypass (RYGB) surgery. The study suggests the MyPhenome test could help doctors pinpoint patients most likely to benefit from bariatric procedures and those at higher risk of long-term weight regain.
Andres Acosta, MD, PhD, co-founder of Phenomix Sciences, reported that patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) experienced significantly poorer outcomes.This group maintained only 4.9% total body weight loss (TBWL) over 15 years, compared to up to 24.8% in other genetic groups.
The research, presented at Digestive Disease Week (DDW) 2025, included 707 patients with a history of bariatric procedures from the Mayo Clinic Biobank. Researchers excluded patients with duodenal switch, revisional procedures, or those who used antiobesity medications or became pregnant during follow-up.
The team collected anthropometric data for 442 patients for up to 15 years post-RYGB. They assessed for monogenic variants in the LMP, defining participants as carriers (LMP+) or noncarriers (LMP-), and defined the gene risk score (CTSGRS+ or CTSGRS-), resulting in four groups. Multiple regression analysis was used to analyze TBWL percentage between the groups at different time points, adjusting for baseline weight, age, and gender.
The LMP+/CTSGRS+ group showed significantly higher weight recurrence at the 10-year follow-up. At 15 years, the mean TBWL% for LMP+/CTSGRS+ was -4.9, compared to -20.3 for LMP+/CTSGRS-, -18.0 for LMP-/CTSGRS+,and -24.8 for LMP-/CTSGRS-.
According to the authors, genotyping patients could improve individualized weight-loss interventions and possibly explain factors associated with weight recurrence after RYGB.
“Patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) had significantly worse outcomes, maintaining only 4.9% total body weight loss [TBWL] over 15 years compared to up to 24.8% in other genetic groups,” Acosta said.
Acosta noted ongoing research to include more diverse populations by age, sex, and race, to understand whether certain demographic or physiological characteristics affect test performance, especially in bariatric surgery.
The team is also investigating the benefits of phenotyping for obesity comorbidities and exploring whether early interventions in high-risk patients can prevent long-term weight regain. Acosta added that they recently launched a prospective,placebo-controlled clinical trial using the MyPhenome test to predict response to semaglutide.
Onur Kutlu, MD, of the University of Miami, commented that integrating polygenic risk scores offers an innovative method for identifying patients at elevated risk for weight regain following RYGB.He added that this approach has the potential to shift the paradigm from reactive to proactive management of weight recurrence.
“By integrating polygenic risk scores into predictive models, the authors offer an innovative method for identifying patients at elevated risk for weight regain following RYGB,” Kutlu said.
What’s next
Acosta envisions a future where understanding a patient’s biological drivers of obesity leads to better decisions about procedures, follow-up, and long-term support, moving away from a one-size-fits-all model to care rooted in individual biology.