Genetic ancestry influences body shape and obesity risk in Latin American populations

Obesity is not simply a matter of excess weight. It also involves changes in structure and proportion in body morphology that can vary between populations and within individuals as they develop and age. Anthropometric measurements and their derived indices are widely used to study obesity. However, they present limitations to capture variations of fat distribution in the human body within a given population, and among different populations. Particularly, currently a problem in epidemiology is that cut-off points and health risk classifications based on anthropometric measures such as BMI, WHR or WHtR may not be equally valid for all population groups, especially when there are differences in genetic ancestry. Using data from [Formula: see text] Latin American adults, we evaluated the accuracy of traditional indices across gradients of Native American, European, and African ancestry, and a comparison with three-dimensional (3D) body shape analysis, which offers a promising venue for capturing these complexities. We found that traditional indices systematically misclassified obesity-related risk in certain ancestry groups, with WHR and WHtR showing ancestry-specific biases. In contrast, 3D body shape promises to capture nuanced variations in fat distribution and reduced ancestry-related misclassification. By leveraging techniques based on advanced geometric morphometry and image and data processing, we can better characterize the interaction between genetic ancestry and body composition, ultimately improving the accuracy of obesity diagnosis and stratification in Latin American populations. These results highlight the need for ancestry-aware obesity diagnostics and demonstrate that integrating advanced 3D morphometric techniques can improve risk assessment and guide precision public health strategies in Latin America and beyond. We demonstrate that incorporating 3D body shape data alongside genetic ancestry data improves the accuracy of obesity risk stratification in Latin American populations. Our proposed methods could be adapted, expanded and applied to other populations.

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Publication Details

Journal
Scientific Reports
Published
2025-10-24
DOI
https://doi.org/10.1038/s41598-025-21071-w
Citations
1
Primary Topic
Nutrition, Genetics, and Disease
Type
article
Field-Weighted Citation Impact
1.82
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article

Genetic ancestry influences body shape and obesity risk in Latin American populations

Mariana Useglio, Samuel Canizales‐Quinteros, Winston Rojas, Amanda Rowlands et al.
1 citations
Scientific Reports
Nutrition, Genetics, and Disease
1.82
article

Genetic ancestry influences body shape and obesity risk in Latin American populations

Mariana Useglio, Samuel Canizales‐Quinteros, Winston Rojas, Amanda Rowlands, Giovanni Poletti, Rolando González‐José, Francisco Rothhammer, Bruno Pazos, Víctor Acuña-Alonzo, Carolina Paschetta, Pablo A. Nepomnaschy, G. Gasaneo, María Cátira Bortolini, Tamara Teodoroff, Magda Alexandra Trujillo‐Jiménez, Claudio Augusto Delrieux, Leonardo Morales, Anahí Ruderman, Pablo Navarro, Carla Gallo, Virgínia Ramallo, Andrés Ruiz‐Linares, L. Perez, Soledad de Azevedo, Shanesia Gasaneo, Pablo Toledo-Margalef, Cindy Freire-Gómez
article en
1 citations

Abstract

Obesity is not simply a matter of excess weight. It also involves changes in structure and proportion in body morphology that can vary between populations and within individuals as they develop and age. Anthropometric measurements and their derived indices are widely used to study obesity. However, they present limitations to capture variations of fat distribution in the human body within a given population, and among different populations. Particularly, currently a problem in epidemiology is that cut-off points and health risk classifications based on anthropometric measures such as BMI, WHR or WHtR may not be equally valid for all population groups, especially when there are differences in genetic ancestry. Using data from [Formula: see text] Latin American adults, we evaluated the accuracy of traditional indices across gradients of Native American, European, and African ancestry, and a comparison with three-dimensional (3D) body shape analysis, which offers a promising venue for capturing these complexities. We found that traditional indices systematically misclassified obesity-related risk in certain ancestry groups, with WHR and WHtR showing ancestry-specific biases. In contrast, 3D body shape promises to capture nuanced variations in fat distribution and reduced ancestry-related misclassification. By leveraging techniques based on advanced geometric morphometry and image and data processing, we can better characterize the interaction between genetic ancestry and body composition, ultimately improving the accuracy of obesity diagnosis and stratification in Latin American populations. These results highlight the need for ancestry-aware obesity diagnostics and demonstrate that integrating advanced 3D morphometric techniques can improve risk assessment and guide precision public health strategies in Latin America and beyond. We demonstrate that incorporating 3D body shape data alongside genetic ancestry data improves the accuracy of obesity risk stratification in Latin American populations. Our proposed methods could be adapted, expanded and applied to other populations.

Scientific ReportsVol. 15(1)
Universidade Federal do Rio Grande do Sul (BR), Simon Fraser University (CA), University of Tarapacá (CL), Universidad Peruana Cayetano Heredia (PE), National University of Patagonia San Juan Bosco (AR), Universidad de Antioquia (CO), Tecnológico Nacional de México (MX), Instituto de Física del Sur (AR), National Institute of Genomic Medicine (MX), Instituto Nacional de Rehabilitación Psicofísica (AR), Ministry of Education (KR), Asociación por los Derechos Civiles (AR), Vietnam National Children's Hospital (VN), Centro Científico Tecnológico Patagónico (AR), Instituto de Medicina Genómica (ES), Universidad Nacional del Sur (AR)
Openalex Percentile: Top 12%
Nutrition, Genetics, and Disease
1.82
DOIOpen Access PDFOpenAlex
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