Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data [儿童和青少年脂肪量预测模型的建立和验证：个体参与者数据的meta分析]

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BMJ 2019; 366 doi: https://doi.org/10.1136/bmj.l4293 (Published 24 July 2019)
Cite this as: BMJ 2019;366:l4293

Authors
Mohammed T Hudda, Mary S Fewtrell, Dalia Haroun, Sooky Lum, Jane E Williams, Jonathan C K Wells, Richard D Riley, Christopher G Owen, Derek G Cook, Alicja R Rudnicka, Peter H Whincup, Claire M Nightingale

Abstract
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.