Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

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Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes
Title:
Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes
Journal Title:
International Journal of Obesity
Publication Date:
15 November 2024
Citation:
Leroy, A., Gupta, V., Tint, M. T., Ooi, D. S. Q., Yap, F., Lek, N., Godfrey, K. M., Chong, Y. S., Lee, Y. S., Eriksson, J. G., Álvarez, M. A., Michael, N., & Wang, D. (2024). Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes. International Journal of Obesity. https://doi.org/10.1038/s41366-024-01679-0
Abstract:
Abstract Background Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated. Materials and methods Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity. Results MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust’s predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction. Conclusion MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the National Research Foundation (NRF), Singapore Ministry of Health’s National Medical Research Council (NMRC) and the Agency for Science, Technology and Research (A*STAR) - Open Fund-Large Collaborative Grant (OF-LCG)
Grant Reference no. : MOH-000504

This research / project is supported by the National Research Foundation (NRF) - RIE2025 Human Potential Programme - Human Health and Potential (HHP) Domain
Grant Reference no. :

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - A*STAR Early Childhood Grant
Grant Reference no. : H24P2M0005, H24P2M0006

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - A*STAR Pitchfest Career Development Fund
Grant Reference no. : 232D800032
Description:
ISSN:
0307-0565
1476-5497