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An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

ABSTRACT: With the greatest burden of infant undernutrition and morbidity in low and middle income countries (LMICs), there is a need for suitable approaches to monitor infants in a simple, low-cost and effective manner. Anthropometry continues to play a major role in characterising growth and nutritional status.We developed a range of models to aid in identifying neonates at risk of malnutrition. We first adopted a logistic regression approach to screen for a composite neonatal morbidity, low and high body fat (BF%) infants. We then developed linear regression models for the estimation of neonatal fat mass as an assessment of body composition and nutritional status.We fitted logistic regression models combining up to four anthropometric variables to predict composite morbidity and low and high BF% neonates. The greatest area under receiver-operator characteristic curves (AUC with 95% confidence intervals (CI)) for identifying composite morbidity was 0.740 (0.63, 0.85), resulting from the combination of birthweight, length, chest and mid-thigh circumferences. The AUCs (95% CI) for identifying low and high BF% were 0.827 (0.78, 0.88) and 0.834 (0.79, 0.88), respectively. For identifying composite morbidity, BF% as measured via air displacement plethysmography showed strong predictive ability (AUC 0.786 (0.70, 0.88)), while birthweight percentiles had a lower AUC (0.695 (0.57, 0.82)). Birthweight percentiles could also identify low and high BF% neonates with AUCs of 0.792 (0.74, 0.85) and 0.834 (0.79, 0.88). We applied a sex-specific approach to anthropometric estimation of neonatal fat mass, demonstrating the influence of the testing sample size on the final model performance.These models display potential for further development and evaluation in LMICs to detect infants in need of further nutritional management, especially where traditional methods of risk management such as birthweight for gestational age percentiles may be variable or non-existent, or unable to detect appropriately grown, low fat newborns.

SUBMITTER: Huvanandana J

PROVIDER: S-EPMC5877876 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

Huvanandana Jacqueline J Carberry Angela E AE Turner Robin M RM Bek Emily J EJ Raynes-Greenow Camille H CH McEwan Alistair L AL Jeffery Heather E HE

PloS one 20180330 3

<h4>Background</h4>With the greatest burden of infant undernutrition and morbidity in low and middle income countries (LMICs), there is a need for suitable approaches to monitor infants in a simple, low-cost and effective manner. Anthropometry continues to play a major role in characterising growth and nutritional status.<h4>Methods</h4>We developed a range of models to aid in identifying neonates at risk of malnutrition. We first adopted a logistic regression approach to screen for a composite ...[more]

PMID: 29601596

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Project description:Background:The relevance of body composition (BC) to performance in sport has long been appreciated with special concern on the total and regional proportion of fat and muscle. Dual-energy X-ray absorptiometry (DXA) is able to accurately measure BC, but it may not be easily available in practice; anthropometry has long been used as a simple and inexpensive field method to objectively assess BC. The aim of this study was twofold: first, to develop and validate a sport-specific anthropometric predictive equation for total body fat mass (FM) and lean mass components in female handball players to be used in the sport setting; second, to cross-validate in female team handball players several independently developed, predictive equations for BC in female athletes. Methods:A total of 85 female team handball players (30 wings, 31 backs, 14 pivots, 10 goalkeepers) of different competitive levels underwent anthropometry and a whole-body DXA scan. Multiple linear regression analysis was used to develop predictive equations in a derivation sample (n = 60) of randomly selected players using demographic and anthropometric variables. The developed equations were used to predict DXA outcomes in an independent validation sample (n = 25). Results:Statistically significant (P < 0.001) models were developed for total body FM (adjusted R 2 = 0.943, standard error of the estimate, SEE = 1,379 g), percentage FM (adjusted R 2 = 0.877, SEE = 2.00%), fat-free soft tissue mass (FFSTM) (adjusted R 2 = 0.834, SEE = 2,412 g), fat-free mass (FFSTM + bone mineral content; adjusted R 2 = 0.829, SEE = 2,579 g). All models were robust to collinearity. Each developed equation was successfully validated in the remaining 25 players using correlation analysis, mean signed difference, t-test, and Bland-Altman plot. The whole dataset of team handball players (n = 85) was used to cross-validate several predictive equations independently developed by others in female athletes. Equations significantly (P < 0.001 for all; t-test) over- or underestimated the corresponding DXA measurements. Discussion:It is concluded that in team female handball players the anthropometric equations presented herein are able to estimate body fat and FFSTM with accuracy. Several BC predictive anthropometric equations developed in different female athletic populations revealed inaccurate when tested in team handball players. These results should be of use for coaches, physical trainers, and nutritionists when evaluating the physical status of female team handball players.

Dataset Information

An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

Publications

An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

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