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Updated: Apr 13, 2026
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Prediction of Fat-Free Mass in Children
Hesham Saleh Al-Sallami1, Ailsa Goulding2, Andrea Grant3
1School of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand. hesham.al-sallami@otago.ac.nz.
Insights
A new maturation model accurately predicts fat-free mass (FFM) in children, improving pharmacokinetic understanding. The model performs well across genders, offering a valuable tool for pediatric drug development.
Area of Science:
- Pediatric pharmacology
- Body composition analysis
- Pharmacokinetic modeling
Background:
- Fat-free mass (FFM) is crucial for predicting drug clearance in adults.
- Existing FFM prediction models are limited in children.
- There's a need for mechanism-based FFM models in pediatric populations.
Purpose of the Study:
- To develop and evaluate a predictive model for fat-free mass (FFM) in children.
- To establish a model that accounts for maturation processes in pediatric FFM.
- To compare the new model against empirical and adult models.
Main Methods:
- Developed two models (M1: empirical, M2: maturation-based) using a large dataset (900+ children, ages 3-29).
- Compared M1 and M2 against a published adult model (M3).
- Assessed predictive performance using visual predictive checks, mean error (ME), and root mean squared error (RMSE) on a test dataset.
Main Results:
- The maturation model (M2) demonstrated strong predictive performance.
- M2 showed lower mean error (0.24 kg) and RMSE (1.58 kg) compared to the adult model (M3) in the index dataset.
- The adult model performed similarly to M2 for females, suggesting potential for simpler application in this subgroup.
Conclusions:
- A novel maturation model effectively predicts FFM in children, integrating with adult models.
- This model enhances understanding of pediatric body composition and its application in pharmacokinetics.
- The model's performance indicates its utility for improving drug dosing and therapeutic outcomes in children.
Background:
Fat-free mass (FFM) is an important covariate for predicting drug clearance. Models for predicting FFM have been developed in adults but there is currently a paucity of mechanism-based models developed to predict FFM in children.
Objective:
The aim of this study was to develop and evaluate a model to predict FFM in children.
Methods:
A large dataset (496 females and 515 males) was available for model building. Subjects had a relatively wide range of age (3-29 years) and body mass index values (12-44.9 kg/m(2)). Two types of models (M1 and M2) were developed to describe FFM in children. M1 was fully empirical and based on a linear model that contained all statistically significant covariates and their interactions. M2 was a simpler model that incorporated a maturation process. M1 was developed to provide the best possible description of the data (i.e. a positive control). In addition, a published adult model (M3) was applied directly as a reference description of the data. The predictive performances of the three models were assessed by visual predictive checks and by using mean error (ME) and root mean squared error (RMSE). A test dataset (90 females and 86 males) was available for external evaluation.
Results:
M1 consisted of nine terms with up to second-level interactions. M2 was a sigmoid hyperbolic model based on postnatal age with an asymptote at the adult prediction (M3). For the index dataset, the ME and 95 % CI for M1, M2 and M3 were 0.09 (0.03-0.16), 0.24 (0.14-0.33) and 0.29 (0.06-0.51) kg, respectively, and RMSEs were 1.12 (1.03-1.23), 1.58 (1.46-1.72) and 3.76 (3.54-3.97) kg.
Conclusions:
A maturation model that asymptoted to an established adult model was developed for prediction of FFM in children. This model was found to perform well in both male and female children; however, the adult model performed similarly to the maturation model for females. The ability to predict FFM in children from simple demographic measurements is expected to improve understanding of human body structure and function with direct application to pharmacokinetics.
