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ArtikelAssociations between birth weight and later body composition: evidence from the 4-component model  
Oleh: Chomtho, Sirinuch ; Wells, Jonathan CK ; Williams, Jane E ; Lucas, Alan ; Fewtrell, Mary S.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The American Journal of Clinical Nutrition vol. 88 no. 04 (Oct. 2008), page 1040.
Ketersediaan
  • Perpustakaan FK
    • Nomor Panggil: A07.K.2008.04
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
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Isi artikelBackground: Higher birth weight is associated with higher body mass index, traditionally interpreted as greater fatness or obesity, in later life. However, its relation with individual body-composition components and fat distribution remains unclear. Objective: We investigated associations between birth weight and later fat mass (FM), fat-free mass (FFM), and fat distribution. Design: Body composition was assessed by the criterion 4-component model in 391 healthy children [mean (±SD) age, 11.7 ± 4.2 y; 188 boys]. FM and FFM were adjusted for height (FMI = FM/height2; FFMI = FFM/height2) and were expressed as SD scores (SDS). Findings were compared between the 4-component and simpler methods. Results: Birth weight was positively associated with height in both sexes and was significantly positively associated with FFMI in boys, equivalent to a 0.18 SDS (95% CI: 0.04, 0.32) increase in FFMI per 1 SDS increase in birth weight. These associations were independent of puberty, physical activity, social class, ethnicity, and parental body mass index. Birth weight was not significantly related to percentage fat, FMI, or trunk FMI in either sex. Equivalent analyses using simpler methods showed a trend for a positive relation between birth weight and FMI in boys that became nonsignificant after adjusting for confounders. Conclusions: FFMI in later life in males is influenced by birth weight, a proxy for prenatal growth, but evidence for fetal programming of later FM or central adiposity is weak. Different body-composition techniques and data interpretation can influence results and should be considered when comparing studies.
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