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Gender-dependent progression of systemic metabolic states in early childhood.

Nikkilä J, Sysi-Aho M, Ermolov A, Seppänen-Laakso T, Simell O, Kaski S, Oresic M - Mol. Syst. Biol. (2008)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Science, Adaptive Informatics Research Centre and Helsinki Institute for Information Technology, Helsinki University of Technology, Espoo, Finland.

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.

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Related in: MedlinePlus

Lipid changes and HMM states in early childhood. (A) Lipid changes between the HMM states. Each block shows the significance, based on the bootstrap procedure, of the change for the marked lipid during the time period marked at the bottom (for instance, bottom left corner shows the change in metabolite TG(54:6) from state 1 to 2). (B) HMM state distribution for different age groups. The images have been computed from 4000 bootstrap samples. For each sample, an HMM was computed and the state progression of each individual was evaluated. The colors show the proportion of children and samples for which the child was in this specific state at the given time. (C) Metabolites separating boys and girls as a function of HMM state.
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f2: Lipid changes and HMM states in early childhood. (A) Lipid changes between the HMM states. Each block shows the significance, based on the bootstrap procedure, of the change for the marked lipid during the time period marked at the bottom (for instance, bottom left corner shows the change in metabolite TG(54:6) from state 1 to 2). (B) HMM state distribution for different age groups. The images have been computed from 4000 bootstrap samples. For each sample, an HMM was computed and the state progression of each individual was evaluated. The colors show the proportion of children and samples for which the child was in this specific state at the given time. (C) Metabolites separating boys and girls as a function of HMM state.

Mentions: The changes in metabolic profiles of boys and girls during the period of follow-up are shown in Figure 2A. Notably, most of the changes in phospholipid profiles (e.g., lysophosphatidylcholines such as GPCho(18:0/0:0) or sphingomyelins such as SM(d18:1/14:0)) and short- and medium-chain triacylglycerols occurred in the transition between the first and second HMM states, corresponding to approximately 1 year of age. Changes between the second and third states were dominated by longer chain triacylglycerols.


Gender-dependent progression of systemic metabolic states in early childhood.

Nikkilä J, Sysi-Aho M, Ermolov A, Seppänen-Laakso T, Simell O, Kaski S, Oresic M - Mol. Syst. Biol. (2008)

Lipid changes and HMM states in early childhood. (A) Lipid changes between the HMM states. Each block shows the significance, based on the bootstrap procedure, of the change for the marked lipid during the time period marked at the bottom (for instance, bottom left corner shows the change in metabolite TG(54:6) from state 1 to 2). (B) HMM state distribution for different age groups. The images have been computed from 4000 bootstrap samples. For each sample, an HMM was computed and the state progression of each individual was evaluated. The colors show the proportion of children and samples for which the child was in this specific state at the given time. (C) Metabolites separating boys and girls as a function of HMM state.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483410&req=5

f2: Lipid changes and HMM states in early childhood. (A) Lipid changes between the HMM states. Each block shows the significance, based on the bootstrap procedure, of the change for the marked lipid during the time period marked at the bottom (for instance, bottom left corner shows the change in metabolite TG(54:6) from state 1 to 2). (B) HMM state distribution for different age groups. The images have been computed from 4000 bootstrap samples. For each sample, an HMM was computed and the state progression of each individual was evaluated. The colors show the proportion of children and samples for which the child was in this specific state at the given time. (C) Metabolites separating boys and girls as a function of HMM state.
Mentions: The changes in metabolic profiles of boys and girls during the period of follow-up are shown in Figure 2A. Notably, most of the changes in phospholipid profiles (e.g., lysophosphatidylcholines such as GPCho(18:0/0:0) or sphingomyelins such as SM(d18:1/14:0)) and short- and medium-chain triacylglycerols occurred in the transition between the first and second HMM states, corresponding to approximately 1 year of age. Changes between the second and third states were dominated by longer chain triacylglycerols.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Science, Adaptive Informatics Research Centre and Helsinki Institute for Information Technology, Helsinki University of Technology, Espoo, Finland.

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.

Show MeSH
Related in: MedlinePlus