Abstract

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 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.

Conclusions

The 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.

Details

Title
Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
Author
Fu, Yuanqing; Gou, Wanglong; Hu, Wensheng; Mao, Yingying; Tian, Yunyi; Liang, Xinxiu; Guan, Yuhong; Huang, Tao; Li, Kelei; Guo, Xiaofei; Liu, Huijuan; Li, Duo; Ju-Sheng, Zheng  VIAFID ORCID Logo 
Pages
1-10
Section
Research article
Publication year
2020
Publication date
2020
Publisher
BioMed Central
e-ISSN
17417015
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2424732891
Copyright
© 2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.