Gender‐dependent progression of systemic metabolic states in early childhood

Abstract

Abstract Little is known about the human intra‐individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age‐ and gender‐related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state‐based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state‐based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Keywords

• hidden Markov models
• lipid metabolism
• metabolomics
• multivariate longitudinal data
• pediatrics