Body fat in Singaporean infants: development of body fat prediction equations in Asian newborns View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-09

AUTHORS

I M Aris, S E Soh, M T Tint, S Liang, A Chinnadurai, S M Saw, K Kwek, K M Godfrey, P D Gluckman, Y S Chong, F K P Yap, Y S Lee

ABSTRACT

BACKGROUND/OBJECTIVES: Prediction equations are commonly used to estimate body fat from anthropometric measurements, but are population specific. We aimed to establish and validate a body composition prediction formula for Asian newborns, and compared the performance of this formula with that of a published equation. SUBJECTS/METHODS: Among 262 neonates (174 from day 0, 88 from days 1-3 post delivery) from a prospective cohort study, body composition was measured using air-displacement plethysmography (PEA POD), with standard anthropometric measurements, including triceps and subscapular skinfolds. Using fat mass measurement by PEA POD as a reference, stepwise linear regression was utilized to develop a prediction equation in a randomly selected subgroup of 62 infants measured on days 1-3, which was then validated in another subgroup of 200 infants measured on days 0-3. RESULTS: Regression analyses revealed subscapular skinfolds, weight, gender and gestational age were significant predictors of neonatal fat mass, explaining 81.1% of the variance, but not triceps skinfold or ethnicity. By Bland-Altman analyses, our prediction equation revealed a non-significant bias with limits of agreement (LOA) similar to those of a published equation for infants measured on days 1-3 (95% LOA: (-0.25, 0.26) kg vs (-0.23, 0.21) kg) and on day 0 (95% LOA: (-0.19, 0.17) kg vs (-0.17, 0.18) kg). The published equation, however, exhibited a systematic bias in our sample. CONCLUSIONS: Our equation requires only one skinfold site measurement, which can significantly reduce time and effort. It does not require the input of ethnicity and, thus, aid its application to other Asian neonatal populations. More... »

PAGES

922

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ejcn.2013.69

DOI

http://dx.doi.org/10.1038/ejcn.2013.69

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046491849

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23549200


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