Development and validation of anthropometric-based fat-mass prediction equations using air displacement plethysmography in Mexican infants

• Published in: European journal of clinical nutrition Vol. 77; no. 7; pp. 748 - 756
• Main Authors: Rodríguez-Cano, Ameyalli M., Piña-Ramírez, Omar, Rodríguez-Hernández, Carolina, Mier-Cabrera, Jennifer, Villalobos-Alcazar, Gicela, Estrada-Gutierrez, Guadalupe, Cardona-Pérez, Arturo, Coronado-Zarco, Alejandra, Perichart-Perera, Otilia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2023; Nature Publishing Group
• Subjects: 692/308/174; 692/700/1720; 692/700/2814; 692/700/478; Adipose tissue; Anthropometry; Babies; Body composition; Body fat; Body measurements; Clinical Nutrition; Epidemiology; Evaluation; Health risks; Infants; Internal Medicine; Medicine; Medicine & Public Health; Metabolic Diseases; Plethysmography; Prediction models; Public Health; Regression models; Risk factors; Thigh; Mexico
• ISSN: 0954-3007; 1476-5640; 1476-5640
• DOI: 10.1038/s41430-023-01285-9

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 R 2 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.