Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated
levels of variation in the serum proteome is important when using serum proteins as markers for
childhood diseases. The aim of this study was to establish a reference model for the evolution of a
healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were
performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages
of 3–36 months. A flexible Gaussian process-based probabilistic modelling framework was developed
to evaluate the effects of different variables, including age, living environment and individual variation,
on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age
was the most dominant factor influencing approximately half of the studied proteins, and the most
prominent age-associated changes were observed already during the first year of life. High interindividual
variability was also observed for multiple proteins. These data provide important details on
the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are
conserved in the first years of life. Additionally, our novel modelling approach provides a statistical
framework to detect associations between covariates and non-linear time series data.