Abstract
BackgroundThe 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.MethodsA 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.ResultsOf 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 BMI Z-score trajectories on the outcome, we classified this feature into the favored and unfavored trajectories. Compared with early introduction of solid foods (≤ 3 months of corrected age), introducing solid foods after 6 months of corrected age was significantly associated with 11% lower risk (risk ratio, 0.89; 95% CI, 0.82 to 0.97) of being in the unfavored trajectory.ConclusionsThe trajectory of BMI Z-score change within the first year of life is the most important predictor for childhood overweight/obesity among preterm infants. Introducing solid foods after 6 months of corrected age is a recommended feeding practice for mitigating the risk of being in the unfavored trajectory.
Highlights
The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices
Mothers of the children who progressed to childhood overweight/obesity had a younger age of menarche, higher body mass index (BMI) at enrollment, and distinct pattern of BMI changes compared with their counterparts (Table 1)
Maternal and early life risk factors of childhood overweight/obesity identified by machine learning Figure 2a showed the Receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.74 in the validation set, which reflected the accuracy of the prediction model with all inputted features in the model
Summary
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. Preterm infants are at a higher risk of developing childhood obesity compared with term infants [3]. Risk factors of childhood obesity among this specific population of infants are still unclear [4,5,6,7]. Machine learning can help reveal relationships from the data without the need to define them a priori and derive predictive models without a need for strong assumptions about the underlying mechanisms [8, 9]. Understanding why a predictive model made a specific prediction or explaining the specific features that lead to the prediction is even more clinically meaningful as some factors may be modifiable
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