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Customized versus population-based growth curves: prediction of low body fat percent at term corrected gestational

Tameeka L Law1, Lakshmi D Katikaneni, Sarah N Taylor

  • 1Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina, USA. tllaw@hotmail.com

The Journal of Maternal-Fetal & Neonatal Medicine : the Official Journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
|September 24, 2011
PubMed

Insights

Customized growth curves better identify preterm infants who are small-for-gestational-age (SGA) and have lower body fat percent (BF%). This approach offers improved differentiation compared to standard population-based curves.

Area of Science:

  • Neonatal research
  • Pediatric growth assessment
  • Body composition analysis

Background:

  • Accurate identification of small-for-gestational-age (SGA) preterm infants is crucial for clinical management.
  • Standard population-based growth curves may not adequately represent individual growth potential in preterm populations.
  • Body fat percent (BF%) is an important indicator of nutritional status and long-term health outcomes.

Purpose of the Study:

  • To compare the efficacy of customized versus population-based growth curves in identifying SGA and low body fat percent (BF%) in preterm infants.
  • To assess the predictive value of different growth curve methods for body composition at term corrected gestational age.

Main Methods:

  • A prospective cohort study involving 204 preterm infants.
  • Infants were classified as SGA or appropriate-for-gestational-age (AGA) using both population-based and customized growth curves.
  • Body fat percent (BF%) was measured using air-displacement plethysmography; statistical analyses included regression models.

Main Results:

  • Customized curves reclassified 30% of preterm infants, identifying more SGA infants.
  • SGA infants identified exclusively by customized curves had significantly lower BF% compared to AGA infants.
  • Customized growth curves were a significant predictor of BF% (p=0.02), unlike population-based curves (p=0.50).

Conclusions:

  • Customized growth potential assessment provides superior differentiation of SGA infants with low body fat percent in preterm populations.
  • This individualized approach enhances the identification of growth abnormalities compared to standard population-based growth curves.
Abstract

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