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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...

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Autores principales: Fu, Yuanqing, Gou, Wanglong, Hu, Wensheng, Mao, Yingying, Tian, Yunyi, Liang, Xinxiu, Guan, Yuhong, Huang, Tao, Li, Kelei, Guo, Xiaofei, Liu, Huijuan, Li, Duo, Zheng, Ju-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350615/
https://www.ncbi.nlm.nih.gov/pubmed/32646442
http://dx.doi.org/10.1186/s12916-020-01642-6
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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
Zheng, Ju-Sheng
author_facet Fu, Yuanqing
Gou, Wanglong
Hu, Wensheng
Mao, Yingying
Tian, Yunyi
Liang, Xinxiu
Guan, Yuhong
Huang, Tao
Li, Kelei
Guo, Xiaofei
Liu, Huijuan
Li, Duo
Zheng, Ju-Sheng
author_sort Fu, Yuanqing
collection PubMed
description 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.
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spelling pubmed-73506152020-07-14 Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort Fu, Yuanqing Gou, Wanglong Hu, Wensheng Mao, Yingying Tian, Yunyi Liang, Xinxiu Guan, Yuhong Huang, Tao Li, Kelei Guo, Xiaofei Liu, Huijuan Li, Duo Zheng, Ju-Sheng BMC Med Research Article 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. BioMed Central 2020-07-10 /pmc/articles/PMC7350615/ /pubmed/32646442 http://dx.doi.org/10.1186/s12916-020-01642-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Fu, Yuanqing
Gou, Wanglong
Hu, Wensheng
Mao, Yingying
Tian, Yunyi
Liang, Xinxiu
Guan, Yuhong
Huang, Tao
Li, Kelei
Guo, Xiaofei
Liu, Huijuan
Li, Duo
Zheng, Ju-Sheng
Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title_full Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title_fullStr Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title_full_unstemmed Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title_short Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
title_sort integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350615/
https://www.ncbi.nlm.nih.gov/pubmed/32646442
http://dx.doi.org/10.1186/s12916-020-01642-6
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