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Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using
Mohammed T Hudda1, Mary S Fewtrell2, Dalia Haroun3
1Population Health Research Institute, St George's, University of London, London SW17 0RE, UK.
Insights
A new model accurately predicts children's fat mass using simple measurements like height and weight. This tool aids in assessing body fatness for obesity prevention and management in children aged 4-15 years.
Area of Science:
- Pediatric Endocrinology
- Body Composition Analysis
- Public Health Research
Background:
- Accurate assessment of body fatness in children is crucial for identifying risks associated with obesity.
- Existing methods for assessing fat mass can be complex or less accurate, such as body mass index (BMI).
- There is a need for a validated prediction model using readily available data.
Purpose of the Study:
- To develop and validate a prediction model for fat mass in children aged 4-15 years.
- To utilize routine anthropometric and demographic data for fat mass prediction.
- To avoid the need for complex or specialized assessment methods.
Main Methods:
- An individual participant data meta-analysis was conducted using data from four cross-sectional studies.
- Multivariable linear regression was employed to develop the prediction model for fat-free mass, subsequently calculating fat mass.
- Internal validation and external validation using a separate dataset were performed to assess model performance and generalizability.
Main Results:
- The final prediction model, incorporating height, weight, age, sex, and ethnicity, demonstrated high predictive ability (optimism-adjusted R²: 94.8%) with excellent calibration.
- Internal validation confirmed good generalizability and minimal overfitting.
- External validation in a separate cohort showed promising generalizability (R²: 90.0%) with good calibration, predicting fat mass with a mean difference of -1.29 kg.
Conclusions:
- A robust prediction model for fat mass in children (aged 4-15 years) has been successfully developed and validated.
- The model relies on simple anthropometric and demographic factors, offering a more accessible assessment of body fatness.
- This tool has the potential to enhance the accuracy of body fat assessment in children, supporting obesity surveillance, prevention, and management strategies.
Objectives:
To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment.
Design:
Individual participant data meta-analysis.
Setting:
Four population based cross sectional studies and a fifth study for external validation, United Kingdom.
Participants:
A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass.
Main Outcome Measure:
Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model's predictive performance within the four development studies; external validation followed using the fifth dataset.
Results:
Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R2: 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R2: 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was -1.29 kg (95% confidence interval -1.62 to -0.96 kg).
Conclusion:
The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.
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