Causal models of human growth and their estimation using temporally-sparse data

Abstract

Existing models of human growth provide little insight into the mechanisms responsible for inter-individual and inter-population variation in children’s growth trajectories. Building on general theories linking growth to metabolic rates, we develop causal parametric models of height and weight growth incorporating a novel representation of human body allometry and a phase-partitioned representation of ontogeny. These models permit separation of metabolic causes of growth variation, potentially influenced by diet and disease, from allometric factors, potentially under strong genetic control. We estimate model parameters using a Bayesian multilevel statistical design applied to temporally-dense height and weight measurements of U.S. children, and temporally-sparse measurements of Indigenous Amazonian children. This facilitates a comparison of the metabolic and allometric contributions to observed cross-cultural variation in the growth trajectories of the two populations. These theoretical growth models constitute an initial step toward a better understanding of the causes of growth variation in our species, while potentially guiding the development of appropriate, and desired, healthcare interventions in societies confronting growth-related health challenges.