Prediction of fat-free mass in young children using bioelectrical impedance spectroscopy.


Journal

European journal of clinical nutrition
ISSN: 1476-5640
Titre abrégé: Eur J Clin Nutr
Pays: England
ID NLM: 8804070

Informations de publication

Date de publication:
31 Jul 2023
Historique:
received: 11 12 2022
accepted: 19 07 2023
revised: 17 07 2023
medline: 1 8 2023
pubmed: 1 8 2023
entrez: 31 7 2023
Statut: aheadofprint

Résumé

Bioimpedance devices are practical for measuring body composition in preschool children, but their application is limited by the lack of validated equations. To develop and validate fat-free mass (FFM) bioimpedance prediction equations among New Zealand 3.5-year olds, with dual-energy X-ray absorptiometry (DXA) as the reference method. Bioelectrical impedance spectroscopy (SFB7, ImpediMed) and DXA (iDXA, GE Lunar) measurements were conducted on 65 children. An equation incorporating weight, sex, ethnicity, and impedance was developed and validated. Performance was compared with published equations and mixture theory prediction. The equation developed in ~70% (n = 45) of the population (FFM [kg] = 1.39 + 0.30 weight [kg] + 0.39 length We developed and validated a bioimpedance equation that can accurately predict FFM. Further external validation of the equation is required.

Sections du résumé

BACKGROUND BACKGROUND
Bioimpedance devices are practical for measuring body composition in preschool children, but their application is limited by the lack of validated equations.
OBJECTIVES OBJECTIVE
To develop and validate fat-free mass (FFM) bioimpedance prediction equations among New Zealand 3.5-year olds, with dual-energy X-ray absorptiometry (DXA) as the reference method.
METHODS METHODS
Bioelectrical impedance spectroscopy (SFB7, ImpediMed) and DXA (iDXA, GE Lunar) measurements were conducted on 65 children. An equation incorporating weight, sex, ethnicity, and impedance was developed and validated. Performance was compared with published equations and mixture theory prediction.
RESULTS RESULTS
The equation developed in ~70% (n = 45) of the population (FFM [kg] = 1.39 + 0.30 weight [kg] + 0.39 length
CONCLUSIONS CONCLUSIONS
We developed and validated a bioimpedance equation that can accurately predict FFM. Further external validation of the equation is required.

Identifiants

pubmed: 37524804
doi: 10.1038/s41430-023-01317-4
pii: 10.1038/s41430-023-01317-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : DH | National Institute for Health Research (NIHR)
ID : NF-SI-0515-10042
Organisme : DH | National Institute for Health Research (NIHR)
ID : IS-BRC-1215-20004
Organisme : British Heart Foundation (BHF)
ID : RG/15/17/3174
Organisme : EC | Erasmus+
ID : 598488-EPP-1-2018-1-DE-EPPKA2-CBHE-JP
Organisme : MOH | National Medical Research Council (NMRC)
ID : NMRC/CSA-INV/0010/2016
Organisme : MOH | National Medical Research Council (NMRC)
ID : MOH-CSAINV19nov-0002

Informations de copyright

© 2023. The Author(s).

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Auteurs

Jaz Lyons-Reid (J)

Liggins Institute, University of Auckland, Auckland, New Zealand.

Leigh C Ward (LC)

School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.

José G B Derraik (JGB)

Liggins Institute, University of Auckland, Auckland, New Zealand.
Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand.
Environmental-Occupational Health Sciences and Non-communicable Diseases Research Group, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand.
Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.

Mya Thway-Tint (M)

Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Cathriona R Monnard (CR)

Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé S.A., Lausanne, Switzerland.

J Manuel Ramos Nieves (JM)

Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé S.A., Lausanne, Switzerland.

Benjamin B Albert (BB)

Liggins Institute, University of Auckland, Auckland, New Zealand.

Timothy Kenealy (T)

Liggins Institute, University of Auckland, Auckland, New Zealand.
Department of Medicine and Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand.

Keith M Godfrey (KM)

MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.

Shiao-Yng Chan (SY)

Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Department of Obstetrics & Gynaecology, National University of Singapore, Singapore, Singapore.

Wayne S Cutfield (WS)

Liggins Institute, University of Auckland, Auckland, New Zealand. w.cutfield@auckland.ac.nz.
A Better Start - National Science Challenge, University of Auckland, Auckland, New Zealand. w.cutfield@auckland.ac.nz.

Classifications MeSH