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

Anthropometric prediction models of body composition in 3 to 24month old infants: a multicenter international study

BACKGROUND

Accurate assessment of body composition during infancy is an important marker of early growth. This study aimed to develop anthropometric models to predict body composition in 3-24-month-old infants from diverse socioeconomic settings and ethnic groups.

METHODS

An observational, longitudinal, prospective, multi-country study of infants from 3 to 24 months with body composition assessed at three monthly intervals using deuterium dilution (DD) and anthropometry. Linear mixed modelling was utilized to generate sex-specific fat mass (FM) and fat-free mass (FFM) prediction equations, using length(m), weight-for-length (kg/m), triceps and subscapular skinfolds and South Asian ethnicity as variables. The study sample consisted of 1896 (942 measurements from 310 girls) training data sets, 941 (441 measurements from 154 girls) validation data sets of 3-24 months from Brazil, Pakistan, South Africa and Sri Lanka. The external validation group (test) comprised 349 measurements from 250 (185 from 124 girls) infants 3-6 months of age from South Africa, Australia and India.

RESULTS

Sex-specific equations for three age categories (3-9 months; 10-18 months; 19-24 months) were developed, validated on same population and externally validated. Root mean squared error (RMSE) was similar between training, validation and test data for assessment of FM and FFM in boys and in girls. RMSPE and mean absolute percentage error (MAPE) were higher in validation compared to test data for predicting FM, however, in the assessment of FFM, both measures were lower in validation data. RMSE for test data from South Africa (M/F-0.46/0.45 kg) showed good agreement with validation data for assessment of FFM compared to Australia (M/F-0.51/0.33 kg) and India(M/F-0.77/0.80 kg).

CONCLUSIONS

Anthropometry-based FFM prediction equations provide acceptable results. Assessments based on equations developed on similar populations are more applicable than those developed from a different population.

Associations of appetitive traits with growth velocities from infancy to childhood

Several studies have reported associations between appetitive traits and weight gain during infancy or childhood, but none have directly compared these associations across both age periods. Here, we tested the associations between appetitive traits and growth velocities from birth to childhood. Appetitive trait data were collected using the Children's Eating Behaviour Questionnaire (CEBQ) in 149 children from the Cambridge Baby Growth Study at age 9-17 years. These participants also provided anthropometric measurements during infancy (birth, 3, 12, 18, and 24 months) and childhood (5 to 11 years). Standardized growth velocities (in weight, length/height, BMI, and body fat percentage) for 0-3 months, 3-24 months, and 24 months to childhood were estimated using individual linear-spline models. Associations between each of the eight CEBQ traits and each growth velocity were tested in separate multilevel linear regression models, adjusted for sex, age at CEBQ completion, and the corresponding birth measurement (weight, length, BMI, or body fat percentage). The three food-approach traits (food responsiveness, enjoyment of food and emotional overeating) were positively associated with infancy and childhood growth velocities in weight, BMI, and body fat percentage. By contrast, only one of the food-avoidant traits, satiety responsiveness, was negatively associated with all growth velocities. Significant associations were mostly of similar magnitude across all age periods. These findings reveal a broadly consistent relationship between appetitive traits with gains in weight and adiposity throughout infancy and childhood. Future interventions and strategies to prevent obesity may benefit from measuring appetitive traits in infants and children and targeting these as part of their programs.

Skinfold-based-equations to assess longitudinal body composition in children from birth to age 5 years

BACKGROUND & AIMS

In order to identify children at risk for excess adiposity, it is important to determine body composition longitudinally throughout childhood. However, most frequently used techniques in research are expensive and time-consuming and, therefore, not feasible for use in general clinical practice. Skinfold measurements can be used as proxy for adiposity, but current anthropometry-based-equations have random and systematic errors, especially when used longitudinally in pre-pubertal children. We developed and validated skinfold-based-equations to estimate total fat mass (FM) longitudinally in children aged 0-5 years.

METHODS

This study was embedded in the Sophia Pluto study, a prospective birth cohort. In 998 healthy term-born children, we longitudinally measured anthropometrics, including skinfolds and determined FM using Air Displacement Plethysmography (ADP) by PEA POD and Dual energy X-ray Absorptiometry (DXA) from birth to age 5 years. Of each child one random measurement was used in the determination cohort, others for validation. Linear regression was used to determine the best fitting FM-prediction model based on anthropometric measurements using ADP and DXA as reference methods. For validation, we used calibration plots to determine predictive value and agreement between measured and predicted FM.

RESULTS

Three skinfold-based-equations were developed for adjoined age ranges (0-6 months, 6-24 months and 2-5 years), based on FM-trajectories. Validation of these prediction equations showed significant correlations between measured and predicted FM (R: 0.921, 0.779 and 0.893, respectively) and good agreement with small mean prediction errors of 1, 24 and -96 g, respectively.

CONCLUSIONS

We developed and validated reliable skinfold-based-equations which may be used longitudinally from birth to age 5 years in general practice and large epidemiological studies.

Prediction of fat-free mass in a multi-ethnic cohort of infants using bioelectrical impedance: Validation against the PEA POD

Background

Bioelectrical impedance analysis (BIA) is widely used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age.

Aim

This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months (n = 292 and 212, respectively).

Materials and methods

Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: (1) empirical prediction equations; (2) Cole modeling and mixture theory prediction. Sex-specific equations were developed among ∼70% of the cohort. Equations were validated in the remaining ∼30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages.

Results

Sex-specific equations based on weight and length explained 75-81% of FFM variance at 6 weeks but only 48-57% at 6 months. At both ages, the margin of error for these equations was 5-6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) < 5% when validated. Limits of agreement analyses showed that biases were low (< 100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11-12%).

Conclusion

The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.

Extensive Study of Breast Milk and Infant Growth: Protocol of the Cambridge Baby Growth and Breastfeeding Study (CBGS-BF)

Growth and nutrition during early life have been strongly linked to future health and metabolic risks. The Cambridge Baby Growth Study (CBGS), a longitudinal birth cohort of 2229 mother-infant pairs, was set up in 2001 to investigate early life determinant factors of infant growth and body composition in the UK setting. To carry out extensive profiling of breastmilk intakes and composition in relation to infancy growth, the Cambridge Baby Growth and Breastfeeding Study (CBGS-BF) was established upon the original CBGS. The strict inclusion criteria were applied, focusing on a normal birth weight vaginally delivered infant cohort born of healthy and non-obese mothers. Crucially, only infants who were exclusively breastfed for the first 6 weeks of life were retained in the analysed study sample. At each visit from birth, 2 weeks, 6 weeks, and then at 3, 6, 12, 24, and 36 months, longitudinal anthropometric measurements and blood spot collections were conducted. Infant body composition was assessed using air displacement plethysmography (ADP) at 6 weeks and 3 months of age. Breast milk was collected for macronutrients and human milk oligosaccharides (HMO) measurements. Breast milk intake volume was also estimated, as well as sterile breastmilk and infant stool collection for microbiome study.