Evidence map›Paper›PMID 31273021›Full record

ArticleBMJ open2019

Metabolomics: population epidemiology and concordance in Australian children aged 11-12 years and their parents.

Susan Ellul, Melissa Wake, Susan A Clifford, Katherine Lange, Peter Würtz, Markus Juonala, Terence Dwyer, John B Carlin, David P Burgner, Richard Saffery

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 6% of its field
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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.

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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

10 authors at 6 institutions in 4 countries.

Susan EllulMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-1135-2015
Melissa WakeMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0001-7501-9257
Susan A CliffordMurdoch Children's Research Institute, Parkville, Victoria, Australia.
Katherine LangeMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-8175-6285
Peter WürtzResearch Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-5832-0221
Markus JuonalaMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0001-9498-364X
Terence DwyerMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-2091-7663
John B CarlinMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-2694-9463
David P BurgnerMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-8304-4302
Richard SafferyMurdoch Children's Research Institute, Parkville, Victoria, Australia.ORCID 0000-0002-9510-4181
The University of Melbourne · AUMurdoch Children's Research Institute · AUUniversity of Auckland · NZUniversity of Helsinki · FIUniversity of Tasmania · AUUniversity of Turku · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesNuclear magnetic resonance (NMR) metabolomics is high throughput and cost-effective, with the potential to improve the understanding of disease and risk. We examine the circulating metabolic profile by quantitative NMR metabolomics of a sample of Australian 11-12 year olds children and their parents, describe differences by age and sex, and explore the correlation of metabolites in parent-child dyads.

designThe population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children.

settingBlood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015-March 2016.

participants1180 children and 1325 parents provided a blood sample and had metabolomics data available. This included 1133 parent-child dyads (518 mother-daughter, 469 mother-son, 68 father-daughter and 78 father-son). OUTCOME MEASURES: 228 metabolic measures were obtained for each participant. We focused on 74 biomarkers including amino acid species, lipoprotein subclass measures, lipids, fatty acids, measures related to fatty acid saturation, and composite markers of inflammation and energy homeostasis.

resultsWe identified differences in the concentration of specific metabolites between childhood and adulthood and in metabolic profiles in children and adults by sex. In general, metabolite concentrations were higher in adults than children and sex differences were larger in adults than in children. Positive correlations were observed for the majority of metabolites including isoleucine (CC 0.33, 95% CI 0.27 to 0.38), total cholesterol (CC 0.30, 95% CI 0.24 to 0.35) and omega 6 fatty acids (CC 0.28, 95% CI 0.23 to 0.34) in parent-child comparisons.

conclusionsWe describe the serum metabolite profiles from mid-childhood and adulthood in a population-based sample, together with a parent-child concordance. Differences in profiles by age and sex were observed. These data will be informative for investigation of the childhood origins of adult non-communicable diseases and for comparative studies in other populations.

Indexed as

MetabolomeMetabolomicsParentsAdultAustraliaBiomarkersChildCholesterolCross-Sectional StudiesFatty Acids, Omega-6FemaleHumansIsoleucineLongitudinal StudiesMagnetic Resonance SpectroscopyMaleBiomarkersCholesterolFatty Acids, Omega-6Isoleucinechildrencorrelation studiescross-sectional studiesepidemiologic studiesinflammationinheritance patternslipidsmetabolomicsparentsreference values

Identifiers

PMID31273021
PMCPMC6624050
OpenAlexW2954255198

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.