Groundbreaking Digital Twins Model Newborn Metabolism for Precision Medicine

Photo byPhoto by Liv Bruce on Unsplash

Researchers at the University of Galway have developed advanced digital models, referred to as "digital babies," to better understand the unique metabolic processes of infants during their first 180 days of life. The research, conducted by the Digital Metabolic Twin Centre in collaboration with Heidelberg University, resulted in the creation of 360 sex-specific computational models that simulate newborn and infant metabolism. These models incorporate data from 10,000 real-life newborns, including information on sex, birth weight, and metabolite concentrations, allowing for personalized investigations of individual infant metabolism.

The digital babies represent a groundbreaking advancement in computational modelling, including 26 organs, six cell types, and over 80,000 metabolic reactions. The models are particularly valuable for precision medicine applications, offering insights into how an infant's unique metabolic features contribute to its growth and development. For instance, the models account for the higher energy demands of infants, necessary for regulating body temperature, especially since babies cannot shiver during their first six months.

The research aims to improve the diagnosis and treatment of medical conditions in newborns, mainly inherited metabolic diseases. The models have demonstrated the ability to predict known biomarkers for these diseases and accurately simulate metabolic responses to different treatment strategies. This capability underscores the potential of these digital twins in clinical settings, where they could revolutionize pediatric healthcare by enabling tailored disease management based on each infant's unique metabolic needs.

The project marks a significant step toward establishing digital metabolic twins for infants, providing a detailed and personalized understanding of infant metabolism that could enhance the effectiveness of newborn screening programs and improve infant health outcomes.