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Updated: Aug 2, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Development and validation of anthropometric-based fat-mass prediction equations using air displacement
Ameyalli M Rodríguez-Cano1, Omar Piña-Ramírez2, Carolina Rodríguez-Hernández1
1Nutrition and Bioprogramming Coordination, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, CP 11000, Ciudad de México, México.
Insights
New equations accurately predict infant fat mass using simple anthropometry. These methods offer an accessible way to assess body composition and identify risks associated with excessive adiposity in infants.
Area of Science:
- Pediatric Nutrition
- Body Composition Analysis
- Public Health
Background:
- Accurate fat-mass (FM) assessment from birth is vital for identifying risks of adverse metabolic outcomes linked to excessive adiposity.
- Current gold-standard methods for FM assessment can be inaccessible in routine clinical practice.
Purpose of the Study:
- To develop and validate prediction equations for infant fat mass (FM) using readily available anthropometric measurements.
- To compare anthropometric predictions against air-displacement plethysmography (ADP) for accuracy.
Main Methods:
- Collected anthropometric data (weight, length, BMI, circumferences, skinfolds) and FM by ADP from 133 infants at 1 month, 105 at 3 months, and 101 at 6 months in Mexico.
- Developed prediction models using LASSO regression for variable selection and Theil-Sen regressions with 12-fold cross-validation.
- Validated models using Bland-Altman plots and Deming regression to assess agreement between predicted and measured FM.
Main Results:
- Key predictors for FM included BMI, waist, thigh, and calf circumferences, and various skinfolds.
- Models achieved R-squared values of 0.54 (1M), 0.69 (3M), and 0.63 (6M).
- Predicted FM showed high correlations (r ≥ 0.73) and no significant differences compared to ADP-measured FM across all time points, with minimal bias.
Conclusions:
- Anthropometry-based prediction equations provide an inexpensive and accessible method for estimating infant body composition.
- The developed equations are validated and suitable for evaluating fat mass in Mexican infants, aiding in early identification of adiposity-related health risks.
Background/Objectives:
Fat-mass (FM) assessment since birth using valid methodologies is crucial since excessive adiposity represents a risk factor for adverse metabolic outcomes.
Aim:
To develop infant FM prediction equations using anthropometry and validate them against air-displacement plethysmography (ADP).
Subjects/Methods:
Clinical, anthropometric (weight, length, body-mass index -BMI-, circumferences, and skinfolds), and FM (ADP) data were collected from healthy-term infants at 1 (n = 133), 3 (n = 105), and 6 (n = 101) months enrolled in the OBESO perinatal cohort (Mexico City). FM prediction models were developed in 3 steps: 1) Variable Selection (LASSO regression), 2) Model behavior evaluation (12-fold cross-validation, using Theil-Sen regressions), and 3) Final model evaluation (Bland-Altman plots, Deming regression).
Results:
Relevant variables in the FM prediction models included BMI, circumferences (waist, thigh, and calf), and skinfolds (waist, triceps, subscapular, thigh, and calf). The R2 of each model was 1 M: 0.54, 3 M: 0.69, 6 M: 0.63. Predicted FM showed high correlation values (r ≥ 0.73, p < 0.001) with FM measured with ADP. There were no significant differences between predicted vs measured FM (1 M: 0.62 vs 0.6; 3 M: 1.2 vs 1.35; 6 M: 1.65 vs 1.76 kg; p > 0.05). Bias were: 1 M -0.021 (95%CI: -0.050 to 0.008), 3 M: 0.014 (95%CI: 0.090-0.195), 6 M: 0.108 (95%CI: 0.046-0.169).
Conclusion:
Anthropometry-based prediction equations are inexpensive and represent a more accessible method to estimate body composition. The proposed equations are useful for evaluating FM in Mexican infants.
