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Anthropometric models to estimate fat mass at 3 days, 15 and 54 weeks
Mahalakshmi Gopalakrishnamoorthy1, Kathryn Whyte2, Michelle Horowitz2
1Division of Pediatric Endocrinology, Columbia University Irving Medical Center, New York, New York, USA.
Insights
New anthropometric equations accurately estimate infant fat mass (FM) using simple measurements. These equations offer a practical solution for routine clinical assessment of body composition in infants.
Area of Science:
- Pediatric Nutrition
- Human Physiology
- Biostatistics
Background:
- Current infant body composition methods are not practical for routine clinical use.
- Accurate body composition assessment is crucial for infant health monitoring.
- Developing accessible measurement tools is a key research area.
Purpose of the Study:
- To develop and validate anthropometric equations (AEs) for estimating fat mass (FM) in infants.
- To compare the predictive accuracy of AEs against criterion methods like PEA POD® and Infant-QMR.
- To establish practical tools for routine clinical assessment of infant body composition.
Main Methods:
- Recruited 191 multi-ethnic, full-term infants measured at 3 days, 15 weeks, and 54 weeks.
- Collected anthropometric data including weight, length, head circumference, and skinfolds (triceps, thigh, subscapular, iliac).
- Utilized PEA POD® and Infant-QMR as criterion methods for fat mass (FM) measurement; employed stepwise linear regression to develop predictive models.
Main Results:
- Weight, length, head circumference, and specific skinfolds (triceps, thigh, subscapular) significantly predicted FM across infancy.
- Sex showed an interaction effect at 3 days and 15 weeks for both criterion methods.
- Models demonstrated acceptable predictive accuracy with R² values ranging from 0.77 to 0.92 and low root mean square errors.
Conclusions:
- Developed anthropometric equations effectively predict infant fat mass using readily available measurements.
- These AEs provide a practical and accurate alternative for routine clinical body composition assessment in infants.
- The findings support the use of these equations for improved infant health monitoring and management.
Background:
Currently available infant body composition measurement methods are impractical for routine clinical use. The study developed anthropometric equations (AEs) to estimate fat mass (FM, kg) during the first year using air displacement plethysmography (PEA POD® Infant Body Composition System) and Infant quantitative magnetic resonance (Infant-QMR) as criterion methods.
Methods:
Multi-ethnic full-term infants (n = 191) were measured at 3 days, 15 and 54 weeks. Sex, race/ethnicity, gestational age, age (days), weight-kg (W), length-cm (L), head circumferences-cm (HC), skinfold thicknesses mm [triceps (TRI), thigh (THI), subscapular (SCP), and iliac (IL)], and FM by PEA POD® and Infant-QMR were collected. Stepwise linear regression determined the model that best predicted FM.
Results:
Weight, length, head circumference, and skinfolds of triceps, thigh, and subscapular, but not iliac, significantly predicted FM throughout infancy in both the Infant-QMR and PEA POD models. Sex had an interaction effect at 3 days and 15 weeks for both the models. The coefficient of determination [R2 ] and root mean square error were 0.87 (66 g) at 3 days, 0.92 (153 g) at 15 weeks, and 0.82 (278 g) at 54 weeks for the Infant-QMR models; 0.77 (80 g) at 3 days and 0.82 (195 g) at 15 weeks for the PEA POD models respectively.
Conclusions:
Both PEA POD and Infant-QMR derived models predict FM using skinfolds, weight, head circumference, and length with acceptable R2 and residual patterns.
