Email Alert | RSS

Journal of Peking University (Health Sciences) ›› 2024, Vol. 56 ›› Issue (3): 390-396. doi: 10.19723/j.issn.1671-167X.2024.03.003

Previous Articles     Next Articles

A prospective cohort study of association between early childhood body mass index trajectories and the risk of overweight

Zhihan YUE1,Na HAN2,Zheng BAO2,Jinlang LYU1,Tianyi ZHOU1,Yuelong JI1,Hui WANG1,Jue LIU3,Haijun WANG1,*()   

  1. 1. Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
    2. Tongzhou Maternal and Child Health Hospital of Beijing, Beijing 101101, China
    3. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
  • Received:2024-02-18 Online:2024-06-18 Published:2024-06-12
  • Contact: Haijun WANG E-mail:whjun@pku.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(81973053)

RICH HTML

   

PDF (PC)

Abstract:

Objective: To compare the association between body mass index (BMI) trajectories determined by different methods and the risk of overweight in early childhood in a prospective cohort study, and to identify children with higher risk of obesity during critical growth windows of early childhood. Methods: A total of 1 330 children from Peking University Birth Cohort in Tongzhou (PKUBC-T) were included in this study. The children were followed up at birth, 1, 3, 6, 9, 12, 18, and 24 months and 3 years of age to obtain their height/length and weight data, and calculate BMI Z-score. Latent class growth mixture modeling (GMM) and longitudinal data-based k-means clustering algorithm (KML) were used to determine the grouping of early childhood BMI trajectories from birth to 24 mouths. Linear regression was used to compare the association between early childhood BMI trajectories determined by different methods and BMI Z-score at 3 years of age. The predictive performance of early childhood BMI trajectories determined by different methods in predicting the risk of overweight (BMI Z-score > 1) at 3 years was compared using the average area under the curve (AUC) of 5-fold cross-validation in Logistic regression models. Results: In the study population included in this research, the three-category trajectories determined using GMM were classified as low, medium, and high, accounting for 39.7%, 54.1%, and 6.2% of the participants, respectively. The two-category trajectories determined using the KML method were classified as low and high, representing 50. 3% and 49. 7% of the participants, respectively. The three-category trajectories determined using the KML method were classified as low, medium, and high, accounting for 31.1%, 47.4%, and 21.5% of the participants, respectively. There were certain differences in the growth patterns reflected by the early childhood BMI trajectories determined using different methods. Linear regression analysis found that after adjusting for maternal ethnicity, educational level, delivery mode, parity, maternal age at delivery, gestational week at delivery, children' s gender, and breastfeeding at 1 month of age, the association between the high trajectory group in the three-category trajectories determined by the KML method (manifested by a slightly higher BMI at birth, followed by rapid growth during infancy and a stable-high BMI until 24 months) and BMI Z-scores at 3 years was the strongest. Logistic regression analysis revealed that the three-category trajectory grouping determined by the KML method had the best predictive performance for the risk of overweight at 3 years. The results were basically consistent after additional adjustment for the high bound score of the child' s diet balanced index, average daily physical activity time, and screen time. Conclusion: This study used different methods to identify early childhood BMI trajectories with varying characteristics, and found that the high trajectory group determined by the KML method was better able to identify children with a higher risk of overweight in early childhood. This provides scientific evidence for selecting appropriate methods to define early childhood BMI trajectories.

Key words: Early childhood, BMI trajectory, Overweight, Prospective cohort

CLC Number: 

  • R179

Figure 1

Inclusion and exclusion flow chart of participants in the study"

Figure 2

Grouping of early childhood BMI trajectories determined by different methods A, distribution of BMI Z-score at each time point among the participants; B, the three-category early childhood BMI trajectories identified by GMM method (presented by gender); C, the two-category early childhood BMI trajectories identified by KML method (presented by gender); D, the three-category early childhood BMI trajectories identified by KML method (presented by gender). BMI, body mass index; GMM, growth mixture modelling; KML, k-means for longitudinal data."

Table 1

Characteristics of children with overweight risk and without overweight risk at 3 years"

Items Overweight risk at 3 years Non-overweight risk at 3 years P
Participants in the study 172 (12.9) 1 158 (87.1)
BMI Z-score at 3 years 1.85±0.84 -0.42±0.84 <0.001
GMM (three-category trajectories) <0.001
  Class1 45 (8.5) 483 (91.5)
  Class2 108 (15.0) 612 (85.0)
  Class3 19 (23.2) 63 (76.8)
KML (two-category trajectories) <0.001
  Class1 16 (2.4) 653 (97.6)
  Class2 156 (23.6) 505 (76.4)
KML (three-category trajectories) <0.001
  Class1 8 (1.9) 405 (98.1)
  Class2 56 (8.9) 575 (91.1)
  Class3 108 (37.8) 178 (62.2)
Maternal ethnicity (missing=6) 0.534
  Han 160 (12.8) 1 089 (87.2)
  Minority 12 (16.0) 63 (84.0)
Maternal educational level (missing=6) 0.037
  Low (high school and below) 38 (13.5) 244 (86.5)
  Middle (junior college) 75 (15.8) 401 (84.2)
  High (university and above) 59 (10.4) 507 (89.6)
Parity 0.700
  Primipara 100 (12.6) 695 (87.4)
  Multipara 72 (13.5) 463 (86.5)
Caesarean section (missing=3) 0.396
  No 87 (12.2) 628 (87.8)
  Yes 85 (13.9) 527 (86.1)
Maternal age at delivery/years 30.20±3.78 30.41±3.97 0.512
Gestational age at birth/weeks 39.26±1.48 39.41±1.40 0.180
Children’s gender 0.806
  Boys 88 (13.2) 577 (86.8)
  Girls 84 (12.6) 581 (87.4)
Exclusive breastfeeding at the first month (missing=150) 0.035
  No 49 (16.8) 242 (83.2)
  Yes 105 (11.8) 784 (88.2)
Subset: participants with children’s questionnaires 73 (16.9) 360 (83.1)
  High bound score of DBI-C at 3 years (missing=20) 13.76±9.29 12.26±8.64 0.197
  Total physical activity at 3 years (hours per day) 2.13±1.64 2.76±2.43 0.037
  Screen time at 3 years (hours per day) 0.87±1.13 0.61±0.79 0.019

Table 2

Associations of different early childhood BMI trajectory groups with BMI Z-score at 3 years"

Items Crude model (n=1 330) Adjusted model 1 (n=1 172) Adjusted model 2 (subset: n=358)
GMM (three-category trajectories)
  Class3 vs. Class1 0.557 (0.296, 0.818) 0.787 (0.477, 1.097) 1.333 (0.735, 1.931)
  Class3 vs. Class2 0.313 (0.057, 0.570) 0.484 (0.188, 0.780) 1.052 (0.486, 1.617)
  Class3 vs. Class1+Class2 0.416 (0.164, 0.668) 0.567 (0.271, 0.862) 1.122 (0.558, 1.686)
KML (two-category trajectories)
  Class2 vs. Class1 1.204 (1.101, 1.307) 1.237 (1.124, 1.349) 1.447 (1.223, 1.671)
KML (three-category trajectories)
  Class3 vs. Class1 1.774 (1.633, 1.916) 1.828 (1.673, 1.983) 1.958 (1.656, 2.260)
  Class3 vs. Class2 1.004 (0.872, 1.135) 1.008 (0.867, 1.150) 1.116 (0.849, 1.383)
  Class3 vs. Class1+Class2 1.309 (1.178, 1.439) 1.324 (1.183, 1.465) 1.418 (1.156, 1.680)

Table 3

Prediction performance of different early childhood BMI trajectory groups on overweight risk at 3 years"

Five-fold CV AUC Crude model (n=1 330) Adjusted model 1 (n=1 172) Adjusted model 2 (subset: n=358)
GMM (three-category trajectories) 0.590±0.033 0.573±0.082 0.638±0.066
KML (two-category trajectories) 0.735±0.022 0.771±0.031 0.777±0.066
KML (three-category trajectories) 0.783±0.015 0.799±0.057 0.810±0.045
1 Barker DJ . The fetal and infant origins of adult disease[J]. BMJ, 1990, 301 (6761): 1111.
doi: 10.1136/bmj.301.6761.1111
2 Geserick M , Vogel M , Gausche R , et al. Acceleration of BMI in early childhood and risk of sustained obesity[J]. N Engl J Med, 2018, 379 (14): 1303- 1312.
doi: 10.1056/NEJMoa1803527
3 Yuan Y , Chu C , Zheng WL , et al. Body mass index trajectories in early life is predictive of cardiometabolic risk[J]. J Pediatr, 2020, 219, 31-37, e36.
doi: 10.1016/j.jpeds.2019.12.060
4 Aris IM , Bernard JY , Chen LW , et al. Infant body mass index peak and early childhood cardio-metabolic risk markers in a multi-ethnic Asian birth cohort[J]. Int J Epidemiol, 2017, 46 (2): 513- 525.
5 den Dekker HT , Jaddoe VWV , Reiss IK , et al. Fetal and infant growth patterns and risk of lower lung function and asthma. The generation R study[J]. Am J Respir Crit Care Med, 2018, 197 (2): 183- 192.
doi: 10.1164/rccm.201703-0631OC
6 Mattsson M , Maher GM , Boland F , et al. Group-based trajectory modelling for BMI trajectories in childhood: A systematic review[J]. Obes Rev, 2019, 20 (7): 998- 1015.
doi: 10.1111/obr.12842
7 Cao T , Zhao J , Hong X , et al. Cord blood metabolome and BMI trajectory from birth to adolescence: A prospective birth cohort study on early life biomarkers of persistent obesity[J]. Metabolites, 2021, 11 (11): 739.
doi: 10.3390/metabo11110739
8 Meir AY , Huang W , Cao T , et al. Umbilical cord DNA methylation is associated with body mass index trajectories from birth to adolescence[J]. EBioMedicine, 2023, 91, 104550.
doi: 10.1016/j.ebiom.2023.104550
9 Jin C , Lin L , Han N , et al. Effects of dynamic change in fetuin-A levels from the first to the second trimester on insulin resistance and gestational diabetes mellitus: A nested case-control study[J]. BMJ Open Diabetes Res Care, 2020, 8 (1): e000802.
doi: 10.1136/bmjdrc-2019-000802
10 World Health Organization. WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development [R]. Geneva: World Health Organization, 2006.
11 de Onis M , Lobstein T . Defining obesity risk status in the general childhood population: Which cut-offs should we use[J]. Int J Pediatr Obes, 2010, 5 (6): 458- 460.
doi: 10.3109/17477161003615583
12 World Health Organization, the United Nations Children' s Fund (UNICEF). Indicators for assessing infant and young child feeding practices: Definitions and measurement methods [R]. Geneva: World Health Organization and the United Nations Children' s Fund (UNICEF), 2021.
13 房玥晖, 何宇纳, 李春丽. 基于中国学龄前儿童平衡膳食指数的2010—2012年中国学龄前儿童膳食质量评价[J]. 中华预防医学杂志, 2020, 54 (6): 662- 667.
14 Wen LM , van der Ploeg HP , Kite J , et al. A validation study of assessing physical activity and sedentary behavior in children aged 3 to 5 years[J]. Pediatr Exerc Sci, 2010, 22 (3): 408- 420.
doi: 10.1123/pes.22.3.408
15 Proust-Lima C , Philipps V , Liquet B . Estimation of extended mixed models using latent classes and latent processes: The R package lcmm[J]. J Stat Softw, 2017, 78 (2): 1- 56.
16 Genolini C , Alacoque X , Sentenac M , et al. kml and kml3d: R packages to cluster longitudinal data[J]. J Stat Softw, 2015, 65 (4): 1- 34.
17 Stekhoven DJ , Bühlmann P . MissForest: Non-parametric missing value imputation for mixed-type data[J]. Bioinformatics, 2012, 28 (1): 112- 118.
doi: 10.1093/bioinformatics/btr597
18 Hu Z , Tylavsky FA , Han JC , et al. Maternal metabolic factors during pregnancy predict early childhood growth trajectories and obesity risk: The CANDLE study[J]. Int J Obes (Lond), 2019, 43 (10): 1914- 1922.
doi: 10.1038/s41366-019-0326-z
19 Montazeri P , Vrijheid M , Martinez D , et al. Maternal metabolic health parameters during pregnancy in relation to early childhood BMI trajectories[J]. Obesity (Silver Spring), 2018, 26 (3): 588- 596.
doi: 10.1002/oby.22095
20 Michael N , Gupta V , Fogel A , et al. Longitudinal characterization of determinants associated with obesogenic growth patterns in early childhood[J]. Int J Epidemiol, 2023, 52 (2): 426- 439.
doi: 10.1093/ije/dyac177
21 李春刚, 严双琴, 高国朋, 等. 母亲孕期饮食模式与儿童早期BMI变化轨迹关联的队列研究[J]. 中华流行病学杂志, 2023, 44 (11): 1769- 1775.
22 Xiong C , Chen K , Xu LL , et al. Associations of prenatal exposure to bisphenols with BMI growth trajectories in offspring within the first two years: Evidence from a birth cohort study in China[J]. World J Pediatr, 2023, 2023-11-29 (2024-02-01)
23 Nguena Nguefack HL , Pagé MG , Katz J , et al. Trajectory modelling techniques useful to epidemiological research: A comparative narrative review of approaches[J]. Clin Epidemiol, 2020, 12, 1205- 1222.
24 Den Teuling NGP , Pauws SC , van den Heuvel ER . A comparison of methods for clustering longitudinal data with slowly changing trends[J]. Commun Stat Simul Comput, 2023, 52 (3): 621- 648.
25 Huang W , Meir AY , Olapeju B , et al. Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: Methodologies and findings in the Boston birth cohort[J]. Precis Nutr, 2023, 2 (2): e00037.
[1] Jing CHEN,Rui SHAN,Wucai XIAO,Xiaorui ZHANG,Zheng LIU. Association between self-control and co-occurrence of depressive symptoms and overweight or obesity during adolescence and early adulthood: A ten-year prospective cohort study based on national surveys [J]. Journal of Peking University (Health Sciences), 2024, 56(3): 397-402.
[2] Yifan WU,Yingxiang YU,Lan XIE,Zhida ZHANG,Cuiqing CHANG. Characteristics of resting energy expenditure and evaluation of prediction formulas in young men with different body mass indexes [J]. Journal of Peking University (Health Sciences), 2024, 56(2): 247-252.
[3] Jia-jia DANG,Shan CAI,Pan-liang ZHONG,Ya-qi WANG,Yun-fei LIU,Di SHI,Zi-yue CHEN,Yi-hang ZHANG,Pei-jin HU,Jing LI,Jun MA,Yi SONG. Association of outdoor artificial light at night exposure with overweight and obesity among children and adolescents aged 9 to 18 years in China [J]. Journal of Peking University (Health Sciences), 2023, 55(3): 421-428.
[4] LIU Jie,GUO Chao. A prospective cohort study of the influence of positive/negative effectivity on the mortality risk of the Chinese elderly [J]. Journal of Peking University (Health Sciences), 2022, 54(2): 255-260.
[5] DONG Yan-hui, SONG Yi, DONG Bin, ZOU Zhi-yong, WANG Zheng-he, YANG Zhao-geng, WANG Xi-jie, LI Yan-hui, MA Jun. Association between the blood pressure status and nutritional status among Chinese students aged 7-18 years in 2014： based on the national blood pressure reference for Chinese children and adolescents [J]. Journal of Peking University(Health Sciences), 2018, 50(3): 422-428.
[6] CHENG Lan, LI Qin, SONG Yi, MA Jun, WANG Hai-Jun. Association of physical activities, sedentary behaviors with overweight/obesity in 9-11 year-old Chinese primary school students [J]. Journal of Peking University(Health Sciences), 2016, 48(3): 436-441.
[7] XIE Gao-Qiang-1△, YU Hui-2, CHEN Jing-Zhou-2, ZHAO Lian-Cheng-3△, REN Fu-Xiu-4, SHI Ping-4, WU Yang-Feng-1, 5 , 6 . Relationship of genetic variants and cardiovascular risk factors with interleukin-6 and interleukin-10 secreted by monocytes [J]. Journal of Peking University(Health Sciences), 2014, 46(4): 589-595.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Peking University (Health Sciences), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd