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

Abstract 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 t...

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Autores principales:	Sandi M. Azab, Russell J. de Souza, Amel Lamri, Meera Shanmuganathan, Zachary Kroezen, Karleen M. Schulze, Dipika Desai, Natalie C. Williams, Katherine M. Morrison, Stephanie A. Atkinson, Koon K. Teo, Philip Britz-McKibbin, Sonia S. Anand
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Metabolic syndrome Cardiometabolic risk factors Early childhood Metabolomics Continuous risk score Tyrosine and alanine Medicine R
Acceso en línea:	https://doaj.org/article/8cf6b0dcf3f141f289bb4946e79559f3
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spelling	oai:doaj.org-article:8cf6b0dcf3f141f289bb4946e79559f32021-11-28T12:15:25ZMetabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study10.1186/s12916-021-02162-71741-7015https://doaj.org/article/8cf6b0dcf3f141f289bb4946e79559f32021-11-01T00:00:00Zhttps://doi.org/10.1186/s12916-021-02162-7https://doaj.org/toc/1741-7015Abstract 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.Sandi M. AzabRussell J. de SouzaAmel LamriMeera ShanmuganathanZachary KroezenKarleen M. SchulzeDipika DesaiNatalie C. WilliamsKatherine M. MorrisonStephanie A. AtkinsonKoon K. TeoPhilip Britz-McKibbinSonia S. AnandBMCarticleMetabolic syndromeCardiometabolic risk factorsEarly childhoodMetabolomicsContinuous risk scoreTyrosine and alanineMedicineRENBMC Medicine, Vol 19, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Metabolic syndrome Cardiometabolic risk factors Early childhood Metabolomics Continuous risk score Tyrosine and alanine Medicine R
spellingShingle	Metabolic syndrome Cardiometabolic risk factors Early childhood Metabolomics Continuous risk score Tyrosine and alanine Medicine R Sandi M. Azab Russell J. de Souza Amel Lamri Meera Shanmuganathan Zachary Kroezen Karleen M. Schulze Dipika Desai Natalie C. Williams Katherine M. Morrison Stephanie A. Atkinson Koon K. Teo Philip Britz-McKibbin Sonia S. Anand Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
description	Abstract 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.
format	article
author	Sandi M. Azab Russell J. de Souza Amel Lamri Meera Shanmuganathan Zachary Kroezen Karleen M. Schulze Dipika Desai Natalie C. Williams Katherine M. Morrison Stephanie A. Atkinson Koon K. Teo Philip Britz-McKibbin Sonia S. Anand
author_facet	Sandi M. Azab Russell J. de Souza Amel Lamri Meera Shanmuganathan Zachary Kroezen Karleen M. Schulze Dipika Desai Natalie C. Williams Katherine M. Morrison Stephanie A. Atkinson Koon K. Teo Philip Britz-McKibbin Sonia S. Anand
author_sort	Sandi M. Azab
title	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_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_sort	metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study
publisher	BMC
publishDate	2021
url	https://doaj.org/article/8cf6b0dcf3f141f289bb4946e79559f3
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Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study

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