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A causal model of human growth and its estimation using temporally sparse data
John A Bunce1,2, Catalina I Fernández2,3, Caissa Revilla-Minaya1,2
1Division of Anthropology, American Museum of Natural History, New York, NY, USA.
Royal Society Open Science
|August 8, 2025
Summary
This study introduces a new causal model for human growth, separating metabolic and genetic factors to understand variation in children's height and weight. The model aids in comparing populations and simulating healthcare interventions for growth challenges.
Area of Science:
- Human biology
- Developmental biology
- Biostatistics
Background:
- Current human growth models inadequately explain variations in children's growth patterns across individuals and populations.
- Understanding the mechanisms behind growth disparities is crucial for addressing health challenges like malnutrition and stunting.
Purpose of the Study:
- To develop a causal parametric model of human height and weight growth.
- To differentiate between metabolic and allometric influences on growth trajectories.
- To compare growth variations in different populations and simulate intervention effects.
Main Methods:
- Developed a causal parametric model incorporating body allometry and ontogeny.
- Utilized Bayesian multilevel statistical design for parameter estimation.
- Applied the model to diverse datasets: dense U.S. children's data and sparse Indigenous Amazonian children's data.
Main Results:
- Successfully separated metabolic (e.g., nutrition, disease) and allometric (genetic) factors influencing growth.
- Quantified the contributions of metabolism and allometry to cross-cultural growth variations.
- Demonstrated the model's utility in simulating the impact of healthcare interventions on child growth.
Conclusions:
- The developed theoretical model offers a novel framework for investigating the drivers of human growth variation.
- This approach can inform the design of targeted healthcare interventions for growth-related issues, such as stunting and malnutrition.
- The model facilitates a deeper understanding of how environmental and genetic factors interact to shape growth trajectories.
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