The University of Galway researchers have developed digital babies to gain a better understanding of the health of infants during their critical first 180 days of life. They have generated 360 advanced computer models that replicate the distinct metabolic processes of each baby. These digital babies are the initial sex-specific computational whole-body models representing newborns and infants.The human body has 26 organs, six different types of cells, and over 80,000 metabolic reactions. By using real-life data from 10,000 newborns, such as their gender, birth weight, and metabolite levels, scientists were able to develop and validate models that can be personalized. This allows researchers to study an individual baby’s metabolism for precision medicine purposes. The study was carried out by a team of scientists from the University of Galway’s Digital Metabolic Twin Centre and Heidelberg University, led by Professor Ines Thiele, the principal investigator at APC Microbiome Ireland. Their goal is to progress precision medicine.The use of computational modeling is essential in understanding infant metabolism and improving the diagnosis and treatment of medical conditions during the early days of a baby’s life. Lead author Elaine Zaunseder, Heidelberg University, emphasizes that babies have unique metabolic features and need more energy for regulating body temperature due to their high surface-area-to-mass ratio. Computational modeling of babies is considered significant as it enhances our understanding of infant metabolism and provides opportunities to improve medical care for conditions such as inherited metabolic diseases.Infants undergo rapid growth and development in their first six months of life, requiring metabolic processes to regulate body temperature. Identifying and translating these processes into mathematical concepts was crucial for the research. By capturing metabolism in an organ-specific manner, the computational model can simulate the unique energy demands of different organs in infants compared to adults. Utilizing real breast milk data from newborns allows for the simulation of associated metabolism in the models.olism in a baby’s body, including different organs. By analyzing their nutrition, we were able to simulate the growth of digital babies over six months and found that they develop at a similar rate to real infants.”
The project’s lead, Professor Ines Thiele, emphasized the importance of newborn screening programs in detecting metabolic diseases early, which can improve infant survival and health outcomes. However, the variability in how these diseases appear in babies highlights the need for personalized approaches to disease management.
“Our models enable researchers to study metabolism throughout a baby’s body, including its various organs. By analyzing their nutrition, we were able to simulate the growth of digital babies over six months, and we found that they develop at a similar rate to real-life infants.” Professor Ines Thiele, who led the study, emphasized the significance of newborn screening programs in detecting metabolic diseases early, which can improve infant survival and health outcomes. However, the variability in how these diseases appear in babies highlights the need for personalized approaches to disease management.The study focused on both healthy infants and those with inherited metabolic diseases, which are typically examined during newborn screening. By simulating the metabolism of diseased infants, the models were able to predict specific biomarkers associated with these conditions. Additionally, the models successfully forecasted metabolic reactions to different treatment approaches, indicating their potential for use in clinical environments.”
Elaine Zaunseder stated: “This research lays the groundwork for creating digital metabolic twins for infants, offering a comprehensive understanding of their metabolic functions. These digital twins have the potential to transform pediatric healthcare.The study, published in Cell Metabolism, suggests that personalized metabolic whole-body models for newborns and infants can help predict growth and biomarkers of inherited metabolic diseases. This approach may improve healthcare by enabling tailored disease management for each infant’s unique metabolic needs.
