Prediction of fat-free mass in a multi-ethnic cohort of infants using bioelectrical impedance: Validation against the PEA POD.

air displacement plethysmography (ADP) bias bioelectrical impedance analysis (BIA) bioelectrical impedance spectroscopy (BIS) body composition fat-free mass (FFM) validation

Journal

Frontiers in nutrition
ISSN: 2296-861X
Titre abrégé: Front Nutr
Pays: Switzerland
ID NLM: 101642264

Informations de publication

Date de publication:
2022
Historique:
received: 28 06 2022
accepted: 12 09 2022
entrez: 31 10 2022
pubmed: 1 11 2022
medline: 1 11 2022
Statut: epublish

Résumé

Bioelectrical impedance analysis (BIA) is widely used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age. This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months ( Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: (1) empirical prediction equations; (2) Cole modeling and mixture theory prediction. Sex-specific equations were developed among ∼70% of the cohort. Equations were validated in the remaining ∼30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages. Sex-specific equations based on weight and length explained 75-81% of FFM variance at 6 weeks but only 48-57% at 6 months. At both ages, the margin of error for these equations was 5-6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) < 5% when validated. Limits of agreement analyses showed that biases were low (< 100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11-12%). The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.

Sections du résumé

Background UNASSIGNED
Bioelectrical impedance analysis (BIA) is widely used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age.
Aim UNASSIGNED
This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months (
Materials and methods UNASSIGNED
Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: (1) empirical prediction equations; (2) Cole modeling and mixture theory prediction. Sex-specific equations were developed among ∼70% of the cohort. Equations were validated in the remaining ∼30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages.
Results UNASSIGNED
Sex-specific equations based on weight and length explained 75-81% of FFM variance at 6 weeks but only 48-57% at 6 months. At both ages, the margin of error for these equations was 5-6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) < 5% when validated. Limits of agreement analyses showed that biases were low (< 100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11-12%).
Conclusion UNASSIGNED
The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.

Identifiants

pubmed: 36313113
doi: 10.3389/fnut.2022.980790
pmc: PMC9606768
doi:

Types de publication

Journal Article

Langues

eng

Pagination

980790

Informations de copyright

Copyright © 2022 Lyons-Reid, Ward, Derraik, Tint, Monnard, Ramos Nieves, Albert, Kenealy, Godfrey, Chan and Cutfield.

Déclaration de conflit d'intérêts

CM and JR are employees of Société des Produits Nestlé S.A. LW provides consultancy services to ImpediMed Ltd., (a manufacturer of devices for bioelectrical impedance analysis). ImpediMed Ltd. was not involved in the inception and conduct of this research, or in the writing of this manuscript. KG had received reimbursement for speaking at conferences sponsored by companies selling nutritional products, and KG, S-YC, and WC are part of an academic consortium that has received research funding from Abbott Nutrition, Nestec, BenevolentAI Bio Ltd., and Danone. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Jaz Lyons-Reid (J)

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

Leigh C Ward (LC)

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

José G B Derraik (JGB)

Liggins Institute, The University of Auckland, Auckland, New Zealand.
Department of Paediatrics: Child and Youth Health, School of Medicine, Faculty of Medical and Health Sciences, 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 (MT)

Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore, Singapore.
Human Potential Translational Research Program, 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.

Jose M Ramos Nieves (JM)

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

Benjamin B Albert (BB)

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

Timothy Kenealy (T)

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

Keith M Godfrey (KM)

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

Shiao-Yng Chan (SY)

Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore, Singapore.
Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Wayne S Cutfield (WS)

Liggins Institute, The University of Auckland, Auckland, New Zealand.
A Better Start-National Science Challenge, The University of Auckland, Auckland, New Zealand.

Classifications MeSH