Development and Validation of a Prediction Model for Infant Fat Mass

Abstract

Objectives: To develop and validate a prediction model for fat mass in infants ≤12 kg using easily accessible measurements such as weight and length.

Study design: We used data from a pooled cohort of 359 infants age 1-24 months and weighing 3-12 kg from 3 studies across Southern California and New York City. The training data set (75% of the cohort) included 269 infants and the testing data set (25% of the cohort) included 90 infants age 1-24 months. Quantitative magnetic resonance was used as the standard measure for fat mass. We used multivariable linear regression analysis, with backwards selection of predictor variables and fractional polynomials for nonlinear relationships to predict infant fat mass (from which lean mass can be estimated by subtracting resulting estimates from total mass) in the training data set. We used 5-fold cross-validation to examine overfitting and generalizability of the model's predictive performance. Finally, we tested the adjusted model on the testing data set.

Results: The final model included weight, length, sex, and age, and had high predictive ability for fat mass with good calibration of observed and predicted values in the training data set (optimism-adjusted R²: 92.1%). Performance on the test dataset showed promising generalizability (adjusted R²: 85.4%). The mean difference between observed and predicted values in the testing dataset was 0.015 kg (-0.043 to -0.072 kg; 0.7% of the mean).

Conclusions: Our model accurately predicted infant fat mass and could be used to improve the accuracy of assessments of infant body composition for effective early identification, surveillance, prevention, and management of obesity and future chronic disease risk.

Keywords: body composition; infant; prediction; validation.