ORCID Identifiers
Document Type
Article
Source of Publication
The BMJ
Publication Date
1-1-2019
Abstract
© Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to. 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 R 2: 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 (R 2: 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.
DOI Link
ISSN
Publisher
BMJ Publishing Group
Volume
366
First Page
l4293
Disciplines
Life Sciences
Keywords
deuterium, adolescent, age, anthropometry, Article, body height, body weight, child, data accuracy, demography, dilution, ethnicity, external validity, fat mass, gender, human, internal validity, measurement accuracy, outcome assessment, prediction, priority journal, risk factor, sample size, United Kingdom, validation process, adipose tissue, meta analysis, obesity, pathology, predictive value, preschool child, procedures, theoretical model, Adipose Tissue, Adolescent, Anthropometry, Child, Child, Preschool, Humans, Models, Theoretical, Obesity, Predictive Value of Tests
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Hudda, Mohammed T.; Fewtrell, Mary S.; Haroun, Dalia; Lum, Sooky; Williams, Jane E.; Wells, Jonathan C.K.; Riley, Richard D.; Owen, Christopher G.; Cook, Derek G.; Rudnicka, Alicja R.; Whincup, Peter H.; and Nightingale, Claire M., "Development and validation of a prediction model for fat mass in children and adolescents: Meta-analysis using individual participant data" (2019). All Works. 1248.
https://zuscholars.zu.ac.ae/works/1248
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series