Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort

Wanyu Huang; Anat Yaskolka Meir; Bolanle Olapeju; Guoying Wang; Xiumei Hong; Maya Venkataramani; Tina L Cheng; Tak Igusa; Liming Liang; Xiaobin Wang

doi:10.1097/PN9.0000000000000037

Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort

Precis Nutr. 2023 Apr 21;2(2):e00037. doi: 10.1097/PN9.0000000000000037. eCollection 2023 Jun.

Authors

Wanyu Huang¹, Anat Yaskolka Meir², Bolanle Olapeju³, Guoying Wang⁴, Xiumei Hong⁴, Maya Venkataramani^{5

6}, Tina L Cheng⁷, Tak Igusa¹, Liming Liang^{2

8}, Xiaobin Wang^{4

5}

Affiliations

¹ Department of Civil and Systems Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
² Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
³ School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
⁴ Center on Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
⁵ Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁶ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁷ Cincinnati Children's Hospital Medical Center and Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
⁸ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Abstract

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.

Keywords: Body mass index; Child health; Children BMI trajectory; Data imputation; Longitudinal birth cohort; Multinomial logistic regression; Overweight or obesity.

Abstract

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