Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Abstract:
      Abstract
      Extensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism. However, there is currently a lack of models representing infant metabolism taking into consideration its special requirements in energy balance, nutrition, and growth. Here, we present a resource of organ-resolved, sex-specific, anatomically accurate models of newborn and infant metabolism, referred to as infant-whole-body models (infant-WBMs), spanning the first 180 days of life. These infant-WBMs were parameterised to represent the distinct metabolic characteristics of newborns and infants accurately. In particular, we adjusted the changes in organ weights, the energy requirements of brain development, heart function, and thermoregulation, as well as dietary requirements and energy requirements for physical activity. Subsequently, we validated the accuracy of the infant-WBMs by showing that the predicted neonatal and infant growth was consistent with the recommended growth by the World Health Organisation. We assessed the infant-WBMs’ reliability and capabilities for personalisation by simulating 10,000 newborn models, personalised with blood concentration measurements from newborn screening and birth weight. Moreover, we demonstrate that the models can accurately predict changes over time in known blood biomarkers in inherited metabolic diseases. By this, the infant-WBM resource can provide valuable insights into infant metabolism on an organ-resolved level and enable a holistic view of the metabolic processes occurring in infants, considering the unique energy and dietary requirements as well as growth patterns specific to this population. As such, the infant-WBM resource holds promise for personalised medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism for personalised systematic simulations and treatment planning.

Citation: biorxiv;2023.10.20.563364v1,[Preprint]

Date Published: 23rd Oct 2023

Registered Mode: by DOI

Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

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Citation
Zaunseder, E., Mütze, U., Okun, J. G., Hoffmann, G. F., Kölker, S., Heuveline, V., & Thiele, I. (2023). Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. In []. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2023.10.20.563364
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Created: 16th Feb 2024 at 12:49

Last updated: 5th Mar 2024 at 21:25

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