Prospective Prediction of Body Mass Index Trajectories using Multi-task Gaussian Processes
arxiv(2024)
Abstract
Clinicians often investigate the body mass index (BMI) trajectories of
children to assess their growth with respect to their peers, as well as to
anticipate future growth and disease risk. While retrospective modelling of BMI
trajectories has been an active area of research, prospective prediction of
continuous BMI trajectories from historical growth data has not been well
investigated. Using weight and height measurements from birth to age 10 years
from a longitudinal mother-offspring cohort, we leveraged a multi-task Gaussian
processes model, called MagmaClust, to derive probabilistic predictions for BMI
trajectories over various forecasting periods. Experiments were conducted to
evaluate the accuracy, sensitivity to missing values, and number of clusters.
The results were compared with cubic B-spline regression and a parametric
Jenss-Bayley mixed effects model. A downstream tool computing individual
overweight probabilities was also proposed and evaluated. In all experiments,
MagmaClust outperformed conventional models in prediction accuracy while
correctly calibrating uncertainty regardless of the missing data amount (up to
90% missing) or the forecasting period (from 2 to 8 years in the future).
Moreover, the overweight probabilities computed from MagmaClust's uncertainty
quantification exhibited high specificity (0.94 to 0.96) and accuracy
(0.86 to 0.94) in predicting the 10-year overweight status even from age 2
years. MagmaClust provides a probabilistic non-parametric framework to
prospectively predict BMI trajectories, which is robust to missing values and
outperforms conventional BMI trajectory modelling approaches. It also clusters
individuals to identify typical BMI patterns (early peak, adiposity rebounds)
during childhood. Overall, we demonstrated its potential to anticipate BMI
evolution throughout childhood, allowing clinicians to implement prevention
strategies.
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