Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort

Background The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among preterm infants and to determine feeding practices that could modify the identified risk factors. Methods A total of 338,413 mother-child pairs were enrolled in the Jiaxing Birth Cohort (1999 to 2013), and 2125 eligible singleton preterm born children were included for analyses. We obtained data on health examination, anthropometric measurement, lifestyle, and dietary habits of each participant at their visits to clinics. An interpretable machine learning-based analytic framework was used to identify early life predictors for childhood overweight/obesity, and Poisson regression was used to examine the associations between feeding practices and the identified leading predictor. Results Of the eligible 2125 preterm infants (863 [40.6%] girls), 274 (12.9%) developed overweight/obesity at age 4-7 years. We summarized early life variables into 25 features and identified two most important features as predictors for childhood overweight/obesity: trajectory of infant BMI (body mass index)Z-score change during the first year of corrected age and maternal BMI at enrollment. According to the impacts of different BMIZ-score trajectories on the outcome, we classified this feature into the favored and unfavored trajectories. Compared with early introduction of solid foods (