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Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children...

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Autores principales: Miller, Alexander, Huvanandana, Jacqueline, Jones, Peter, Jeffery, Heather, Carberry, Angela, Slater, Christine, McEwan, Alistair
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001761/
https://www.ncbi.nlm.nih.gov/pubmed/33809363
http://dx.doi.org/10.3390/s21062028
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author Miller, Alexander
Huvanandana, Jacqueline
Jones, Peter
Jeffery, Heather
Carberry, Angela
Slater, Christine
McEwan, Alistair
author_facet Miller, Alexander
Huvanandana, Jacqueline
Jones, Peter
Jeffery, Heather
Carberry, Angela
Slater, Christine
McEwan, Alistair
author_sort Miller, Alexander
collection PubMed
description Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.
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spelling pubmed-80017612021-03-28 Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months Miller, Alexander Huvanandana, Jacqueline Jones, Peter Jeffery, Heather Carberry, Angela Slater, Christine McEwan, Alistair Sensors (Basel) Article Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings. MDPI 2021-03-12 /pmc/articles/PMC8001761/ /pubmed/33809363 http://dx.doi.org/10.3390/s21062028 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Miller, Alexander
Huvanandana, Jacqueline
Jones, Peter
Jeffery, Heather
Carberry, Angela
Slater, Christine
McEwan, Alistair
Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title_full Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title_fullStr Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title_full_unstemmed Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title_short Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
title_sort model development for fat mass assessment using near-infrared reflectance in south african infants and young children aged 3–24 months
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001761/
https://www.ncbi.nlm.nih.gov/pubmed/33809363
http://dx.doi.org/10.3390/s21062028
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