Dataset Information

Anthropometry-based prediction of body composition in early infancy compared to air-displacement plethysmography.

ABSTRACT:

Background

Anthropometry-based equations are commonly used to estimate infant body composition. However, existing equations were designed for newborns or adolescents. We aimed to (a) derive new prediction equations in infancy against air-displacement plethysmography (ADP-PEA Pod) as the criterion, (b) validate the newly developed equations in an independent infant cohort and (c) compare them with published equations (Slaughter-1988, Aris-2013, Catalano-1995).

Methods

Cambridge Baby Growth Study (CBGS), UK, had anthropometry data at 6 weeks (N = 55) and 3 months (N = 64), including skinfold thicknesses (SFT) at four sites (triceps, subscapular, quadriceps and flank) and ADP-derived total body fat mass (FM) and fat-free mass (FFM). Prediction equations for FM and FFM were developed in CBGS using linear regression models and were validated in Sophia Pluto cohort, the Netherlands, (N = 571 and N = 447 aged 3 and 6 months, respectively) using Bland-Altman analyses to assess bias and 95% limits of agreement (LOA).

Results

CBGS equations consisted of sex, age, weight, length and SFT from three sites and explained 65% of the variance in FM and 79% in FFM. In Sophia Pluto, these equations showed smaller mean bias than the three published equations in estimating FM: mean bias (LOA) 0.008 (-0.489, 0.505) kg at 3 months and 0.084 (-0.545, 0.713) kg at 6 months. Mean bias in estimating FFM was 0.099 (-0.394, 0.592) kg at 3 months and -0.021 (-0.663, 0.621) kg at 6 months.

Conclusions

CBGS prediction equations for infant FM and FFM showed better validity in an independent cohort at ages 3 and 6 months than existing equations.

SUBMITTER: Olga L

PROVIDER: S-EPMC7614814 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

Anthropometry-based prediction of body composition in early infancy compared to air-displacement plethysmography.

Olga Laurentya L van Beijsterveldt Inge A L P IALP Hughes Ieuan A IA Dunger David B DB Ong Ken K KK Hokken-Koelega Anita C S ACS De Lucia Rolfe Emanuella E

Pediatric obesity 20210610 11

<h4>Background</h4>Anthropometry-based equations are commonly used to estimate infant body composition. However, existing equations were designed for newborns or adolescents. We aimed to (a) derive new prediction equations in infancy against air-displacement plethysmography (ADP-PEA Pod) as the criterion, (b) validate the newly developed equations in an independent infant cohort and (c) compare them with published equations (Slaughter-1988, Aris-2013, Catalano-1995).<h4>Methods</h4>Cambridge Bab ...[more]

PMID: 34114363

Similar Datasets

Project description:BackgroundWith 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.MethodsWe 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.ResultsWe 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.ConclusionsThese 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.

Dataset Information

Anthropometry-based prediction of body composition in early infancy compared to air-displacement plethysmography.

Background

Methods

Results

Conclusions

Publications

Anthropometry-based prediction of body composition in early infancy compared to air-displacement plethysmography.

Similar Datasets

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