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Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes
Arthur Leroy1, Varsha Gupta2,3, Mya Thway Tint2
1Department of Computer Science, The University of Manchester, Manchester, UK.
Insights
A new method, MagmaClust, accurately predicts children's body mass index (BMI) trajectories and future obesity risk. This tool helps clinicians identify at-risk children for early intervention.
Area of Science:
- Pediatric Growth and Development
- Biostatistics and Computational Biology
- Public Health and Epidemiology
Background:
- Childhood body mass index (BMI) trajectories are crucial for assessing growth and predicting future obesity and disease risk.
- While retrospective analysis of BMI trajectories is common, prospective prediction models remain underdeveloped.
- Existing methods lack robustness in handling missing longitudinal data.
Purpose of the Study:
- To develop and evaluate a unified framework for modeling, clustering, and prospectively predicting continuous childhood BMI trajectories.
- To compare the proposed method's performance against established models like cubic B-spline and multilevel Jenss-Bayley.
- To assess the framework's sensitivity to missing data and its ability to predict future obesity risk.
Main Methods:
- Utilized a multi-task Gaussian process approach on longitudinal BMI measurements from birth to 10 years in a mother-offspring cohort.
- Developed MagmaClust, a unified, probabilistic, non-parametric framework for BMI trajectory analysis.
- Compared MagmaClust's predictive accuracy, robustness to missing data, and forecasting capabilities against alternative models.
Main Results:
- MagmaClust identified 5 distinct childhood BMI trajectory patterns.
- The method demonstrated superior accuracy in retrospective BMI trajectory analysis compared to B-spline and Jenss-Bayley models.
- MagmaClust showed enhanced robustness to missing data (up to 90%) and superior prospective forecasting of BMI trajectories up to 8 years.
- Predictions of overweight/obesity at age 10 using early BMI data showed high specificity (0.94) and accuracy (0.86).
Conclusions:
- MagmaClust offers a unified framework for modeling, clustering, and prospectively predicting childhood BMI trajectories and obesity risk.
- The tool enables clinicians to monitor child growth and identify high-risk individuals for timely interventions.
- The probabilistic, non-parametric approach provides a convenient and accurate method for clinical application in pediatric obesity prevention.
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.
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