Ontology type: schema:ScholarlyArticle Open Access: True
2013-09
AUTHORSI 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
ABSTRACTBACKGROUND/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... »
PAGES922
http://scigraph.springernature.com/pub.10.1038/ejcn.2013.69
DOIhttp://dx.doi.org/10.1038/ejcn.2013.69
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1046491849
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/23549200
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{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"8804070"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/ejcn.2013.69"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1046491849"
]
}
],
"sameAs": [
"https://doi.org/10.1038/ejcn.2013.69",
"https://app.dimensions.ai/details/publication/pub.1046491849"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T19:49",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000469.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/ejcn201369"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/ejcn.2013.69'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ejcn.2013.69'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ejcn.2013.69'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ejcn.2013.69'
This table displays all metadata directly associated to this object as RDF triples.
337 TRIPLES
21 PREDICATES
79 URIs
37 LITERALS
25 BLANK NODES