Evidence map›Paper›PMID 34823524›Full record

ArticleBMC medicine2021

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

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 and 3 more

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In one paragraph

Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sandi M AzabDepartment of Medicine, McMaster University, Hamilton, ON, Canada.
Russell J de SouzaDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Amel LamriDepartment of Medicine, McMaster University, Hamilton, ON, Canada.
Meera ShanmuganathanDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada.
Zachary KroezenDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada.
Karleen M SchulzePopulation Health Research Institute, Hamilton, ON, Canada.
Dipika DesaiDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Natalie C WilliamsDepartment of Medicine, McMaster University, Hamilton, ON, Canada.
Katherine M MorrisonCentre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada.
Stephanie A AtkinsonDepartment of Pediatrics, McMaster University, Hamilton, ON, Canada.
Koon K TeoDepartment of Medicine, McMaster University, Hamilton, ON, Canada.
Philip Britz-McKibbinDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada.
Sonia S AnandDepartment of Medicine, McMaster University, Hamilton, ON, Canada. anands@mcmaster.ca.ORCID 0000-0003-3692-7441

Funding

CIHR 123309
6 · The paper itself

Abstract

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.

Indexed as

Metabolic SyndromeBirth CohortCase-Control StudiesChildChild, PreschoolCohort StudiesHumansProspective StudiesAmino acids metabolismCardiometabolic risk factorsContinuous risk scoreEarly childhoodFatty acids metabolismGluconeogenesisMetabolic syndromeMetabolomicsTyrosine and alanine

Identifiers

PMID34823524
PMCPMC8616718

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.