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A Machine-learning Assisted Genetic Risk Score Identifies Improved Weight Loss After Endoscopic Sleeve Gastroplasty.

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A Machine-learning Assisted Genetic Risk Score Identifies Improved Weight Loss After Endoscopic Sleeve Gastroplasty.

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Related Experiment Video

Sleeve Gastrectomy in Mice using Surgical Clips

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Published on: November 14, 2020

A Machine-Learning Assisted Genetic Risk Score Identifies Improved Weight Loss After Endoscopic Sleeve Gastroplasty.

Thomas Fredrick¹, Daniel Maselli², Eric Vargas¹

¹Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, USA.

Obesity Surgery

|May 6, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A machine-learning genetic risk score (GRS) for calories to satiation predicts weight loss after endoscopic sleeve gastroplasty (ESG). Individuals with a low CTS-GRS achieved significantly greater total body weight loss post-ESG.

Keywords:

Bariatric Surgery Endoscopy Genetic Models Obesity

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Area of Science:

Genetics and Genomics
Obesity Medicine
Medical Technology

Background:

Obesity is a widespread epidemic with serious health implications.
Endoscopic sleeve gastroplasty (ESG) promotes substantial weight loss, but outcomes vary.
Previous research developed a machine-learning (ML) genetic risk score (GRS) for calorie intake and satiety, predicting response to anti-obesity drugs.

Purpose of the Study:

To assess novel GRSs for emotional hunger and calories to satiation (CTS).
To determine the predictive capability of these GRSs for weight loss following ESG.
To evaluate the performance of high or low CTS GRS in predicting ESG outcomes.

Main Methods:

Forty participants undergoing ESG completed genetic testing using the MyPhenome test.
ML-assisted GRSs for high/low CTS and emotional hunger were utilized.
Total body weight loss (TBWL) at 12 and 24 months was the primary endpoint, analyzed with LOCF, ANOVA, and Tukey's HSD.

Main Results:

The low CTS GRS group exhibited greater TBWL compared to other groups across all time points (3-24 months).
At 12 months, the low CTS GRS group showed significant TBWL (21.4%) compared to emotional hunger (13.7%) and high CTS GRS (14.9%) groups.
This significant weight loss difference persisted through 24 months.

Conclusions:

ML-assisted GRSs are linked to enhanced weight loss after ESG.
Identifying patients with a higher likelihood of superior weight loss response can optimize ESG candidate selection.
GRS may serve as a valuable tool for personalizing obesity treatment strategies.