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Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort
BACKGROUND: Overweight or obesity (OWO) in school-age childhood tends to persist into adulthood. This study aims to address a critical need for early identification of children at high risk of developing OWO by defining and analyzing longitudinal trajectories of body mass index percentile (BMIPCT) d...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513013/ https://www.ncbi.nlm.nih.gov/pubmed/37745028 http://dx.doi.org/10.1097/PN9.0000000000000037 |
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author | Huang, Wanyu Meir, Anat Yaskolka Olapeju, Bolanle Wang, Guoying Hong, Xiumei Venkataramani, Maya Cheng, Tina L. Igusa, Tak Liang, Liming Wang, Xiaobin |
author_facet | Huang, Wanyu Meir, Anat Yaskolka Olapeju, Bolanle Wang, Guoying Hong, Xiumei Venkataramani, Maya Cheng, Tina L. Igusa, Tak Liang, Liming Wang, Xiaobin |
author_sort | Huang, Wanyu |
collection | PubMed |
description | BACKGROUND: Overweight or obesity (OWO) in school-age childhood tends to persist into adulthood. This study aims to address a critical need for early identification of children at high risk of developing OWO by defining and analyzing longitudinal trajectories of body mass index percentile (BMIPCT) during early developmental windows. METHODS: We included 3029 children from the Boston Birth Cohort (BBC) with repeated BMI measurements from birth to age 18 years. We applied locally weighted scatterplot smoothing with a time-limit scheme and predefined rules for imputation of missing data. We then used time-series K-means cluster analysis and latent class growth analysis to define longitudinal trajectories of BMIPCT from infancy up to age 18 years. Then, we investigated early life determinants of the BMI trajectories. Finally, we compared whether using early BMIPCT trajectories performs better than BMIPCT at a given age for predicting future risk of OWO. RESULTS: After imputation, the percentage of missing data ratio decreased from 36.0% to 10.1%. We identified four BMIPCT longitudinal trajectories: early onset OWO; late onset OWO; normal stable; and low stable. Maternal OWO, smoking, and preterm birth were identified as important determinants of the two OWO trajectories. Our predictive models showed that BMIPCT trajectories in early childhood (birth to age 1 or 2 years) were more predictive of childhood OWO (age 5–10 years) than a single BMIPCT at age 1 or 2 years. CONCLUSIONS: Using longitudinal BMIPCT data from birth to age 18 years, this study identified distinct BMIPCT trajectories, examined early life determinants of these trajectories, and demonstrated their advantages in predicting childhood risk of OWO over BMIPCT at a single time point. |
format | Online Article Text |
id | pubmed-10513013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-105130132023-09-22 Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort Huang, Wanyu Meir, Anat Yaskolka Olapeju, Bolanle Wang, Guoying Hong, Xiumei Venkataramani, Maya Cheng, Tina L. Igusa, Tak Liang, Liming Wang, Xiaobin Precis Nutr Original Study BACKGROUND: Overweight or obesity (OWO) in school-age childhood tends to persist into adulthood. This study aims to address a critical need for early identification of children at high risk of developing OWO by defining and analyzing longitudinal trajectories of body mass index percentile (BMIPCT) during early developmental windows. METHODS: We included 3029 children from the Boston Birth Cohort (BBC) with repeated BMI measurements from birth to age 18 years. We applied locally weighted scatterplot smoothing with a time-limit scheme and predefined rules for imputation of missing data. We then used time-series K-means cluster analysis and latent class growth analysis to define longitudinal trajectories of BMIPCT from infancy up to age 18 years. Then, we investigated early life determinants of the BMI trajectories. Finally, we compared whether using early BMIPCT trajectories performs better than BMIPCT at a given age for predicting future risk of OWO. RESULTS: After imputation, the percentage of missing data ratio decreased from 36.0% to 10.1%. We identified four BMIPCT longitudinal trajectories: early onset OWO; late onset OWO; normal stable; and low stable. Maternal OWO, smoking, and preterm birth were identified as important determinants of the two OWO trajectories. Our predictive models showed that BMIPCT trajectories in early childhood (birth to age 1 or 2 years) were more predictive of childhood OWO (age 5–10 years) than a single BMIPCT at age 1 or 2 years. CONCLUSIONS: Using longitudinal BMIPCT data from birth to age 18 years, this study identified distinct BMIPCT trajectories, examined early life determinants of these trajectories, and demonstrated their advantages in predicting childhood risk of OWO over BMIPCT at a single time point. Lippincott Williams & Wilkins 2023-04-21 /pmc/articles/PMC10513013/ /pubmed/37745028 http://dx.doi.org/10.1097/PN9.0000000000000037 Text en Copyright © 2023 The Author(s), Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Original Study Huang, Wanyu Meir, Anat Yaskolka Olapeju, Bolanle Wang, Guoying Hong, Xiumei Venkataramani, Maya Cheng, Tina L. Igusa, Tak Liang, Liming Wang, Xiaobin Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title | Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title_full | Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title_fullStr | Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title_full_unstemmed | Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title_short | Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort |
title_sort | defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the boston birth cohort |
topic | Original Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513013/ https://www.ncbi.nlm.nih.gov/pubmed/37745028 http://dx.doi.org/10.1097/PN9.0000000000000037 |
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