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Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents
OBJECTIVES: Nuclear 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 t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624050/ https://www.ncbi.nlm.nih.gov/pubmed/31273021 http://dx.doi.org/10.1136/bmjopen-2017-020900 |
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author | Ellul, Susan Wake, Melissa Clifford, Susan A Lange, Katherine Würtz, Peter Juonala, Markus Dwyer, Terence Carlin, John B Burgner, David P Saffery, Richard |
author_facet | Ellul, Susan Wake, Melissa Clifford, Susan A Lange, Katherine Würtz, Peter Juonala, Markus Dwyer, Terence Carlin, John B Burgner, David P Saffery, Richard |
author_sort | Ellul, Susan |
collection | PubMed |
description | OBJECTIVES: Nuclear 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. DESIGN: The population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children. SETTING: Blood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015–March 2016. PARTICIPANTS: 1180 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. RESULTS: We 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. CONCLUSIONS: We 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. |
format | Online Article Text |
id | pubmed-6624050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-66240502019-07-28 Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents Ellul, Susan Wake, Melissa Clifford, Susan A Lange, Katherine Würtz, Peter Juonala, Markus Dwyer, Terence Carlin, John B Burgner, David P Saffery, Richard BMJ Open Childcheckpoint Series OBJECTIVES: Nuclear 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. DESIGN: The population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children. SETTING: Blood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015–March 2016. PARTICIPANTS: 1180 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. RESULTS: We 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. CONCLUSIONS: We 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. BMJ Publishing Group 2019-07-04 /pmc/articles/PMC6624050/ /pubmed/31273021 http://dx.doi.org/10.1136/bmjopen-2017-020900 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Childcheckpoint Series Ellul, Susan Wake, Melissa Clifford, Susan A Lange, Katherine Würtz, Peter Juonala, Markus Dwyer, Terence Carlin, John B Burgner, David P Saffery, Richard Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title | Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title_full | Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title_fullStr | Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title_full_unstemmed | Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title_short | Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents |
title_sort | metabolomics: population epidemiology and concordance in australian children aged 11–12 years and their parents |
topic | Childcheckpoint Series |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624050/ https://www.ncbi.nlm.nih.gov/pubmed/31273021 http://dx.doi.org/10.1136/bmjopen-2017-020900 |
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