Genetic and physiological insights into satiation variability predict responses to obesity treatment

Satiation, the process that regulates meal size and termination, varies widely among adults with obesity. To better understand and leverage this variability, we assessed calories to satiation (CTS) through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. Although factors like baseline characteristics, body composition, and hormone levels partially explain CTS variability, they leave substantial variability unaccounted for. To address this gap, we developed a machine-learning-assisted genetic risk score (CTS<sub>GRS</sub>) to predict high CTS. In a randomized clinical trial, participants with high CTS or CTS<sub>GRS</sub> achieved greater weight loss with phentermine-topiramate over 52 weeks, whereas those with low CTS or CTS<sub>GRS</sub> responded better to liraglutide at 16 weeks in a separate trial. These findings highlight the potential of combining satiation measurements with genetic modeling to predict treatment outcomes and inform personalized strategies for obesity management.

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Publication Details

Journal
Cell Metabolism
Published
2025-06-06
DOI
https://doi.org/10.1016/j.cmet.2025.05.008
Citations
11
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
19.99

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article

Genetic and physiological insights into satiation variability predict responses to obesity treatment

Diego Añazco, Michael Ryks, Wissam Ghusn, Barham K. Abu Dayyeh et al.
11 citations
Cell Metabolism
Genetic Associations and Epidemiology
19.99
article

Genetic and physiological insights into satiation variability predict responses to obesity treatment

Diego Añazco, Michael Ryks, Wissam Ghusn, Barham K. Abu Dayyeh, Andres Martin Acosta, Alejandro Campos, Timothy O’Connor, William S. Harmsen, Alison McRae, Maria Daniela Hurtado, Sima Fansa, Lizeth Cifuentes, Elle Kolkin, Sunil Madhusudhan, Michael Camilleri, Donald D. Hensrud, Serban Ciotlos
article en
11 citations

Abstract

Satiation, the process that regulates meal size and termination, varies widely among adults with obesity. To better understand and leverage this variability, we assessed calories to satiation (CTS) through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. Although factors like baseline characteristics, body composition, and hormone levels partially explain CTS variability, they leave substantial variability unaccounted for. To address this gap, we developed a machine-learning-assisted genetic risk score (CTS<sub>GRS</sub>) to predict high CTS. In a randomized clinical trial, participants with high CTS or CTS<sub>GRS</sub> achieved greater weight loss with phentermine-topiramate over 52 weeks, whereas those with low CTS or CTS<sub>GRS</sub> responded better to liraglutide at 16 weeks in a separate trial. These findings highlight the potential of combining satiation measurements with genetic modeling to predict treatment outcomes and inform personalized strategies for obesity management.

Cell MetabolismVol. 37(8)
Mayo Clinic (US), Mayo Clinic in Arizona (US), PharmatrophiX (United States) (US), Mayo Clinic in Florida (US)
Mayo Clinic, Vivus, Novo Nordisk, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases
Zero hunger
Openalex Percentile: Top 0%
Genetic Associations and Epidemiology
19.99
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