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

BMC Medicine

Katherine M. Morrison¹  Philip Britz-McKibbin²  Meera Shanmuganathan²  Zachary Kroezen²  Russell J. de Souza³  Dipika Desai⁴  Natalie C. Williams⁵  Sandi M. Azab⁵  Sonia S. Anand⁶  Koon K. Teo⁷  Amel Lamri⁷  Stephanie A. Atkinson⁸  Karleen M. Schulze⁹ 

[1] Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;Department of Pediatrics, McMaster University, Hamilton, ON, Canada;Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada;Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada;Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;Department of Medicine, McMaster University, Hamilton, ON, Canada;Department of Medicine, McMaster University, Hamilton, ON, Canada;Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada;Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;Department of Medicine, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;Department of Pediatrics, McMaster University, Hamilton, ON, Canada;Population Health Research Institute, Hamilton, ON, Canada;

关键词: Metabolic syndrome;    Cardiometabolic risk factors;    Early childhood;    Metabolomics;    Continuous risk score;    Tyrosine and alanine;    Gluconeogenesis;    Amino acids metabolism;    Fatty acids metabolism;   

DOI  :  10.1186/s12916-021-02162-7

来源: Springer

【 摘 要 】

BackgroundDefining 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.MethodsIn 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.ResultsCompared 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.ConclusionsA 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.

【 授权许可】

CC BY