Researchers at the University of Galway have developed digital babies to study infant health during their first 180 days. They created 360 computer models mimicking each baby’s metabolic processes. These models, representing newborn and infant metabolism, include 26 organs, six cell types, and over 80,000 metabolic reactions.
By harnessing data from 10,000 real newborns, including factors such as sex, birth weight, and metabolite levels, the team has not only validated but also personalized these models. This breakthrough allows scientists to study individual infant metabolism, paving the way for precision medicine applications.
A team of scientists from the University of Galway’s Digital Metabolic Twin Centre and Heidelberg University, led by Professor Ines Thiele, aimed to improve precision medicine through computational modeling. Their work focuses on understanding infant metabolism to enhance early diagnosis and treatment of medical conditions like inherited metabolic diseases.
Elaine Zaunseder, the lead author from Heidelberg University, has been instrumental in this research. She explained that babies have unique metabolic needs, such as the ability to regulate body temperature without shivering in their first six months.
The team has ingeniously translated these metabolic processes into mathematical concepts, giving birth to these computational models. Using real newborn data, especially from breast milk, they have successfully simulated the metabolism of digital babies over six months, demonstrating that they grow in a manner similar to real infants.
Professor Ines Thiele, who led the study, emphasized the importance of newborn screening programs for detecting metabolic diseases early and improving infant health outcomes. She also highlighted the need for personalized approaches to managing these diseases due to their variable presentation in babies.
Thiele explained that their models enable researchers to study the metabolism of both healthy infants and those with inherited metabolic diseases, including those screened in newborn screening. By simulating metabolism in diseased infants, the models accurately predict biomarkers and responses to different treatments, demonstrating their potential in clinical practice.
Elaine Zaunseder said, “This work is a first step towards establishing digital metabolic twins for infants, providing a detailed view of their metabolic processes. Such digital twins have the potential to revolutionize pediatric healthcare by enabling tailored disease management for each infant’s unique metabolic needs.”
Journal reference:
- Elaine Zaunseder, Ulrike Mütze, et al., Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metabolism. DOI: 10.1016/j.cmet.2024.05.006.
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