ArticleScientific reports2018
Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Ontology-guided clustering enables proteomic analysis of rare pediatric disorders.EMBO molecular medicine · 2025Article
- Changes in rat plasma proteomes during the first week after birth.Frontiers in veterinary science · 2025Article
- Article
- Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes.Scientific reports · 2023Article
- The development of blood protein profiles in extremely preterm infants follows a stereotypic evolution pattern.Communications medicine · 2023Article
- Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach.Nature communications · 2022Article
- Circulating proteins associated with allergy development in infants-an exploratory analysis.Clinical proteomics · 2021Article
- An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data.Nature communications · 2019Article
- Longitudinal effects of aging on plasma proteins levels in older adults - associations with kidney function and hemoglobin levels.PloS one · 2019Article
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12 authors at 5 institutions in 2 countries.
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Abstract
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 inter-individual 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.
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