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Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study

BACKGROUND: Defining the metabolic syndrome (MetS) in children remains challenging. Furthermore, a dichotomous MetS diagnosis can limit the power to study associations. We sought to characterize the serum metabolite signature of the MetS in early childhood using high-throughput metabolomic technolog...

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Autores principales: Azab, Sandi M., de Souza, Russell J., Lamri, Amel, Shanmuganathan, Meera, Kroezen, Zachary, Schulze, Karleen M., Desai, Dipika, Williams, Natalie C., Morrison, Katherine M., Atkinson, Stephanie A., Teo, Koon K., Britz-McKibbin, Philip, Anand, Sonia S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616718/
https://www.ncbi.nlm.nih.gov/pubmed/34823524
http://dx.doi.org/10.1186/s12916-021-02162-7
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author Azab, Sandi M.
de Souza, Russell J.
Lamri, Amel
Shanmuganathan, Meera
Kroezen, Zachary
Schulze, Karleen M.
Desai, Dipika
Williams, Natalie C.
Morrison, Katherine M.
Atkinson, Stephanie A.
Teo, Koon K.
Britz-McKibbin, Philip
Anand, Sonia S.
author_facet Azab, Sandi M.
de Souza, Russell J.
Lamri, Amel
Shanmuganathan, Meera
Kroezen, Zachary
Schulze, Karleen M.
Desai, Dipika
Williams, Natalie C.
Morrison, Katherine M.
Atkinson, Stephanie A.
Teo, Koon K.
Britz-McKibbin, Philip
Anand, Sonia S.
author_sort Azab, Sandi M.
collection PubMed
description BACKGROUND: Defining the metabolic syndrome (MetS) in children remains challenging. Furthermore, a dichotomous MetS diagnosis can limit the power to study associations. We sought to characterize the serum metabolite signature of the MetS in early childhood using high-throughput metabolomic technologies that allow comprehensive profiling of metabolic status from a biospecimen. METHODS: In the Family Atherosclerosis Monitoring In earLY life (FAMILY) prospective birth cohort study, we selected 228 cases of MetS and 228 matched controls among children age 5 years. In addition, a continuous MetS risk score was calculated for all 456 participants. Comprehensive metabolite profiling was performed on fasting serum samples using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariable regression models were applied to test metabolite associations with MetS adjusting for covariates of screen time, diet quality, physical activity, night sleep, socioeconomic status, age, and sex. RESULTS: Compared to controls, thirteen serum metabolites were identified in MetS cases when using multivariable regression models, and using the quantitative MetS score, an additional eight metabolites were identified. These included metabolites associated with gluconeogenesis (glucose (odds ratio (OR) 1.55 [95% CI 1.25–1.93]) and glutamine/glutamate ratio (OR 0.82 [95% CI 0.67–1.00])) and the alanine-glucose cycle (alanine (OR 1.41 [95% CI 1.16–1.73])), amino acids metabolism (tyrosine (OR 1.33 [95% CI 1.10–1.63]), threonine (OR 1.24 [95% CI 1.02–1.51]), monomethylarginine (OR 1.33 [95% CI 1.09–1.64]) and lysine (OR 1.23 [95% CI 1.01–1.50])), tryptophan metabolism (tryptophan (OR 0.78 [95% CI 0.64–0.95])), and fatty acids metabolism (carnitine (OR 1.24 [95% CI 1.02–1.51])). The quantitative MetS risk score was more powerful than the dichotomous outcome in consistently detecting this metabolite signature. CONCLUSIONS: A distinct metabolite signature of pediatric MetS is detectable in children as young as 5 years old and may improve risk assessment at early stages of development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02162-7.
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spelling pubmed-86167182021-11-26 Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study Azab, Sandi M. de Souza, Russell J. Lamri, Amel Shanmuganathan, Meera Kroezen, Zachary Schulze, Karleen M. Desai, Dipika Williams, Natalie C. Morrison, Katherine M. Atkinson, Stephanie A. Teo, Koon K. Britz-McKibbin, Philip Anand, Sonia S. BMC Med Research Article BACKGROUND: Defining the metabolic syndrome (MetS) in children remains challenging. Furthermore, a dichotomous MetS diagnosis can limit the power to study associations. We sought to characterize the serum metabolite signature of the MetS in early childhood using high-throughput metabolomic technologies that allow comprehensive profiling of metabolic status from a biospecimen. METHODS: In the Family Atherosclerosis Monitoring In earLY life (FAMILY) prospective birth cohort study, we selected 228 cases of MetS and 228 matched controls among children age 5 years. In addition, a continuous MetS risk score was calculated for all 456 participants. Comprehensive metabolite profiling was performed on fasting serum samples using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariable regression models were applied to test metabolite associations with MetS adjusting for covariates of screen time, diet quality, physical activity, night sleep, socioeconomic status, age, and sex. RESULTS: Compared to controls, thirteen serum metabolites were identified in MetS cases when using multivariable regression models, and using the quantitative MetS score, an additional eight metabolites were identified. These included metabolites associated with gluconeogenesis (glucose (odds ratio (OR) 1.55 [95% CI 1.25–1.93]) and glutamine/glutamate ratio (OR 0.82 [95% CI 0.67–1.00])) and the alanine-glucose cycle (alanine (OR 1.41 [95% CI 1.16–1.73])), amino acids metabolism (tyrosine (OR 1.33 [95% CI 1.10–1.63]), threonine (OR 1.24 [95% CI 1.02–1.51]), monomethylarginine (OR 1.33 [95% CI 1.09–1.64]) and lysine (OR 1.23 [95% CI 1.01–1.50])), tryptophan metabolism (tryptophan (OR 0.78 [95% CI 0.64–0.95])), and fatty acids metabolism (carnitine (OR 1.24 [95% CI 1.02–1.51])). The quantitative MetS risk score was more powerful than the dichotomous outcome in consistently detecting this metabolite signature. CONCLUSIONS: A distinct metabolite signature of pediatric MetS is detectable in children as young as 5 years old and may improve risk assessment at early stages of development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02162-7. BioMed Central 2021-11-26 /pmc/articles/PMC8616718/ /pubmed/34823524 http://dx.doi.org/10.1186/s12916-021-02162-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Azab, Sandi M.
de Souza, Russell J.
Lamri, Amel
Shanmuganathan, Meera
Kroezen, Zachary
Schulze, Karleen M.
Desai, Dipika
Williams, Natalie C.
Morrison, Katherine M.
Atkinson, Stephanie A.
Teo, Koon K.
Britz-McKibbin, Philip
Anand, Sonia S.
Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title_full Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title_fullStr Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title_full_unstemmed Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title_short Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
title_sort metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616718/
https://www.ncbi.nlm.nih.gov/pubmed/34823524
http://dx.doi.org/10.1186/s12916-021-02162-7
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