Can Fetal Limb Soft Tissue Measurements in the Third Trimester Predict Neonatal Adiposity?
Authors:
- 14 July 2016
- journal article
- research article
- Published by Wiley in Journal of Ultrasound in Medicine
- Vol. 35 (9) , 1915-1924
Abstract
Objectives: Neonatal adiposity is associated with chronic metabolic sequelae such as diabetes and obesity. Identifying fetuses at risk for excess neonatal body fat may lead to research aimed at limiting nutritional excess in the prenatal period. We sought to determine whether fetal arm and leg soft tissue measurements at 28 weeks' gestation were predictive of neonatal percent body fatMethods: In this prospective observational cohort study of singleton term pregnancies, we performed sonography at 28 and 36 weeks' gestation, including soft tissue measurements of the fetal arm and thigh (fractional limb volume and cross‐sectional area). We estimated the neonatal body composition (percent body fat) using anthropometric measurements and air displacement plethysmography. We estimated Spearman correlations between sonographic findings and percent body fat and performed modeling to predict neonatal percent body fat using maternal characteristics and sonographic findings.Results: Our analysis of 44 women yielded a mean maternal age of 30 years, body mass index of 26 kg/m2, and birth weight of 3382 g. Mean neonatal percent body fat was 8.1% by skin folds at birth and 12.2% by air displacement plethysmography 2 weeks after birth. Fractional thigh volume measurements at 28 weeks yielded the most accurate model for predicting neonatal percent body fat (R2 = 0.697; P = .001), outperforming models that used abdominal circumference (R2= 0.516) and estimated fetal weight (R2= 0.489).Conclusions: Soft tissue measurements of the fetal thigh at 28 weeks correlated better with neonatal percent body fat than currently used sonographic measurements. After validation in a larger cohort, our models may be useful for prenatal intervention strategies aimed at the prevention of excess fetal fat accretion and, potentially, optimization of long‐term metabolic health.Keywords
Funding Information
- National Institutes of Health (R01DK078645)
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