Association between being Overweight in Young Childhood and during School Age and Puberty
by
,
Genki Shinoda
1,2,†,
Yudai Nagaoka
3,†,
Fumihiko Ueno
1,2,
Naoyuki Kurokawa
4,
Ippei Takahashi
2,
Tomomi Onuma
1,
Aoi Noda
1,2,5,
Keiko Murakami
1,2,
Mami Ishikuro
1,2,
Taku Obara
1,2,5,*,
Hirohito Metoki
1,6,
Junichi Sugawara
1,2,5,7 and
Shinichi Kuriyama
1,2,8 1
Division of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-Machi, Aoba-Ku, Sendai 980-8573, Japan
2
Department of Molecular Epidemiology, Graduate School of Medicine, Tohoku University, 2-1 Seiryo-Machi, Aoba-Ku, Sendai 980-8573, Japan
3
School of Medicine, Tohoku University, 2-1 Seiryo-Machi, Aoba-Ku, Sendai 980-8573, Japan
4
Graduate School of Education, Miyagi University of Education, 149 Aramaki-Aza-Aoba, Aoba-Ku, Sendai 980-0845, Japan
5
Department of Pharmaceutical Sciences, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai 980-8574, Japan
6
Faculty of Medicine, Tohoku Medical and Pharmaceutical University, 1-15-1 Fukumuro, Miyagino-Ku, Sendai 983-8536, Japan
7
Suzuki Memorial Hospital, 3-5-5, Satonomori, Iwanumashi 989-2481, Japan
8
International Research Institute of Disaster Science, Tohoku University, 468-1 Aramakiaoba, Aoba-Ku, Sendai 980-8572, Japan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Children 2023, 10(5), 909; https://doi.org/10.3390/children10050909
Submission received: 14 March 2023
/
Revised: 12 May 2023
/
Accepted: 15 May 2023
/
Published: 22 May 2023
(This article belongs to the Special Issue Childhood and Adolescent Obesity and Weight Management: 3rd Edition)
Abstract
:To examine whether body type at birth, body weight, and obesity in early childhood are associated with overweight/obesity during school age and puberty. Data from maternal and child health handbooks, baby health checkup information, and school physical examination information of participants at birth and three-generation cohort studies were linked. Association between body type and body weight at different time intervals (at birth and at 1.5, 3.5, 6, 11, and 14 years of age) were comprehensively analyzed using a multivariate regression model adjusted for gender, maternal age at childbirth, maternal parity, and maternal body mass index, and drinking and smoking statuses at pregnancy confirmation. Children who are overweight in young childhood had a greater risk of being overweight. Particularly, overweight at one year of age during checkup was associated with overweight at 3.5 years (adjusted odds ratio (aOR), 13.42; 95% confidence interval (CI), 4.46–45.42), 6 years (aOR, 6.94; 95% CI, 1.64–33.46), and 11 years (aOR, 5.22; 95% CI, 1.25–24.79) of age. Therefore, being overweight in young childhood could increase the risk of being overweight and obese during school age and puberty. Early intervention in young childhood may be warranted to prevent obesity during school age and puberty.
1. Introduction
Obesity is an important public health concern because it contributes significantly to the major components of metabolic syndrome, carries a high risk of health complications, and is associated with social and economic consequences [1]. The World Health Organization has reported that the worldwide prevalence of overweight and obesity in childhood and puberty (aged 5–19 years old) has risen dramatically from 4% in 1975 to over 18% in 2016, and this increase has occurred in both genders [2]. In adulthood, obesity is a primary risk factor for cardiovascular disease [3], type 2 diabetes [4], cancer [5], psychiatric disorders [6], and other non-communicable diseases [7]. According to a study using data from the 2019 Global Burden of Disease Study, obesity-related disability-adjusted life years increased by 0.48% annually between 2000–2019 and is predicted to increase by 39.8% between 2020–2030 [8].
Obesity in childhood is carried into adulthood, a phenomenon known as “Tracking phenomena” [9]; more specifically, obesity in puberty puts an individual at greater risk of obesity in adulthood [10], and children who are obese in the first grade of elementary school are likely to be obese in the sixth grade [11]; approximately 80% of overweight/obese Japanese elementary school students are overweight/obese in junior high school [12]. Obese children are at an increased risk of morbidities, including diabetes, coronary heart disease, and a range of cancers [13]. These findings highlighted the need for interventions to prevent childhood obesity. Furthermore, maternal body mass index (BMI) [14], drinking alcohol [15], and smoking habits [16] have been reported to be risk factors for obesity in children. Collectively, tracking data during a child’s growth process from the prenatal period is required.
Based on the School Health Statistics (School Health Statistical Survey Report) and the Ministry of Education, Culture, Sports, Science, and Technology, the incidence of obesity among children in Japan has remained stable and has increased slightly over the past decade [17]. One possible explanation is that obesity in young childhood and early childhood persists during school age and beyond. However, in Japan, insufficient data have been accumulated to track individuals from birth to school age. Consequently, even if early interventions are implemented, patients are at risk of premature discontinuation.
Studies linking mother-child health and school health information to explore the correlation between child body type in young childhood and early childhood and obesity during puberty are lacking in Japan. Therefore, this study aimed to examine the association between overweight during young childhood, school age, and puberty.
2. Materials and Methods
2.1. Study Design
Data were obtained from the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study), as previously described elsewhere [18,19,20]. Pregnant women and their children were recruited between 2013–2017 at obstetric clinics and hospitals in the Miyagi Prefecture, Japan. We worked with various municipal boards of education and schools to collect school checkup information for the participants in this cohort between 2018–2020. Inclusion criteria were children whose parents provided proxy consent for regular school health checkups (school checkups). The children were in grade three of a junior high school between 2018–2020, meaning they were born between 2 April 2003 and 1 April 2006. School checkup data were combined with infant health checkup (infant checkup) data collected from municipal mother-child health-related departments and maternal and child health handbooks collected from parents and guardians. Exclusion criteria were refusal to participate and missing data to define body type (gender, height, body weight, measurement dates, date of birth, and gestational age) at any time. In this study, 1.5 to 3.5 years of age was defined as “ young childhood”, 6 to 11 years old as “school age”, and 14 years old as “puberty”. The study protocol was approved by the Ethics Committee of the Tohoku Medical Megabank Organization and conformed to the provisions of the Declaration of Helsinki. Written informed consent was obtained from all the participants. We received guardian’s consent for children between 10–19 years of age with their own assent or consent and parental consent for children <10 years of age.
2.2. Definition of Body Type
Body type was defined by gender, height, body weight, measurement date, date of birth, and gestational age. We used data from the participants at six-time points (at birth, age 18–24 months, 36–47 months, 6 years, 11 years, and 14 years). Body type at birth was categorized by examining the body weight of the neonates relative to their gestational age based on Japanese data (growth standard charts for Japanese children, with mean and standard deviation values based on the 2000 national survey [2016]) [21] as follows: body weight <10th percentile, small for gestational age (SGA); body weight between the 10th and 90th percentiles, appropriate for gestational age; and body weight >90th percentile, large for gestational age (LGA) [22]. After birth, BMI z-scores were calculated using reference data for Japanese individuals [23], and body weight status was classified as follows: overweight, BMI z-score >1; normal weight, BMI z-score between −2 and 1; and thin, BMI z-score < −2.
2.3. Confounders
We collected data from the Maternal and Child Health Handbook on maternal age at childbirth, maternal parity, maternal height, body weight, maternal BMI, and drinking and smoking statuses when pregnancy was confirmed.
2.4. Statistical Analysis
The Wilcoxon rank-sum, Pearson’s chi-squared, and Fisher’s exact tests were used to examine the association between gender and other characteristics. The Fisher’s exact test and logistic regression analysis were used to examine the association between body type at any time point and subsequent time points. We calculated odds ratios (ORs) and 95% confidence intervals (95% CI) for being overweight at any time point and the risk of being overweight at subsequent time points. All analyses were conducted using R v.4.1.1, and statistical significance was set at p < 0.05.
3. Results
Figure 1 shows a flow diagram of the present study. The participants were 528 children whose guardians provided consent for the collection of regular school health checkup information. Among them, 7 participants withdrew their consent, 251 had missing data (height, body weight, and date of measurement) at all measurement points (at birth and at 1.5, 3.5, 6, 11, and 14 years of age), and 24 were classified as thin at any time point. In total, 246 participants were included in the analysis.
Table 1 shows the characteristics of the participants according to gender. Of the 246 individuals, 119 were boys and 127 were girls; there were no statistically significant differences between the boys and girls at birth in terms of gestational age (weeks), height, or body weight. In terms of body type at birth, four boys were SGA, 16 were LGA, four were SGA, and 20 were LGA. A slightly higher percentage of boys than girls were categorized as SGA (boys, 3.7% vs. girls, 3.4%). However, these differences were not statistically significant. Children for whom missing data were available at each measurement time point were excluded from the analysis for that time point.
Table 2 shows the characteristics of the mothers according to their children’s gender. The maternal age at birth for boys was slightly younger than the maternal age at birth for girls (24.8 ± 3.4 years for boys and 25.1 ± 3.7 years for girls). However, these differences were not statistically significant. Children for whom missing data were available at each measurement time point were excluded from the analysis for that time point.
No significant associations were observed when comparing the percentage of overweight individuals at subsequent measurement time points according to body type at birth. Being overweight at 18–23 months of age was significantly associated with being overweight at 36–47 months (crude odds ratio [cOR], 8.99; 95% CI, 4.13–20.29), 6 years (cOR, 3.86; 95% CI, 1.53–9.65), and 11 years (cOR, 2.89; 95% CI, 1.20–6.84) of age (Figure 2). This association remained after adjusting for potential confounders; at 36–47 months (aOR, 13.42; 95% CI, 4.46–45.42), 6 years (aOR, 6.94; 95% CI, 1.64–33.46), and 11 years (aOR, 5.22; 95% CI, 1.25–24.79) (Table 3). Being overweight at 36–47 months of age was significantly associated with being overweight at 6 years (cOR, 7.40; 95% CI, 2.99–19.38) and 11 years (cOR, 4.17; 95% CI, 1.77–10.01) of age (Figure 2), which remained the same after adjusting for potential confounders at 6 years (aOR, 9.55; 95% CI, 1.85–61.41) and 11 years (aOR, 3.22; 95% CI, 0.70–15.14) (Table 3).
4. Discussion
This study examined the association between body type at birth and overweight in young childhood, school age, and puberty and found no association between body type at birth and body weight at each measurement point. However, being overweight during young childhood was significantly correlated with being overweight at school and during puberty. In particular, being overweight during young childhood was strongly correlated with being overweight among school-aged children. Furthermore, this study suggests that the tracking phenomenon of obesity begins in young childhood (i.e., at 1.5 years of age), which supports the need for early intervention in obesity prevention.
Obesity and overweight in childhood are a putative cause of obesity and overweight in adulthood [24,25]. In particular, for obesity during puberty, the greatest body weight gain occurs between 2–6 years of age, and most children who are obese at that age are obese during adolescence [26]. Additionally, obesity during adolescence is significantly associated with an increased risk of cardiovascular and metabolic diseases, such as type 2 diabetes, and several other diseases in adulthood [27]. Additionally, obesity is associated with the onset of cancer and is a potential cause of the onset of central nervous system tumors, including glioblastoma multiforme [28]. Therefore, our finding suggests that interventions for obesity, not only at school age but also during young childhood or earlier, are important for the prevention of obesity in puberty and adulthood. Our study did not observe any relationship between body weight, body type, gestational age at birth, obesity, and being overweight during puberty. A previous study reported modest associations between birth weight, overweight, and obesity at 15–20 years of age, whereas the influence of BMI at 2–4 and 5–7 years on overweight and obesity at 15–20 years was moderate to strong [29]. However, the association between body weight and body type at birth and the risk of being overweight or obese later in life remains inconclusive [30], and further studies are required to examine this association.
Studies using the so-called “life course data,” that is, including data from the child’s fetal stages up to puberty, have reported the latency of data linkage not only for overweight and obesity but also for various healthcare components [31,32,33,34]. In Japan, maternal, child, and school health information are not maximally utilized because of the different ministries and agencies that administer it. Considering the results of this study, linking the life course data is important for formulating measures to prevent obesity during puberty. As obesity in young childhood and childhood impairs body weight loss response to dietary treatments in adulthood, addressing obesity throughout early life may improve the effectiveness of body weight loss interventions in adulthood [35]. This finding highlights the importance of body weight maintenance interventions in infants. Furthermore, obesity is not just a problem for children and adults. With the increasing number of elderly people worldwide, obesity is an important risk factor affecting health in old age. In particular, dementia, including Alzheimer’s disease, is a factor that greatly reduces activities of daily living in old age. However, there is a fragmented understanding of the mechanisms connecting obesity and aging [36]. Therefore, the life course data, including not only childhood data but also adulthood and old age data, will help with proper weight management and obesity prevention.
The strength of this study is its use of existing real-world data, such as maternal and child health handbooks, baby health checkup information, and school physical examination information. However, this study did not include factors related to eating habits or physical activity. Factors of overweight and obesity at school age and puberty include eating habits [37] and physical activity [38], which are also included in obesity prevention programs [39,40]. Additionally, a relationship between nutritional status and being overweight in young childhood has been suggested [41,42], and it is important to consider lifestyle factors, such as diet and physical activity, when investigating childhood obesity. Moreover, the lifestyle of the mother during pregnancy has also been reported to be a risk factor for obesity in children [43,44]. It may be important to include in the analysis environmental factors surrounding the mother and child during the perinatal period, thereby utilizing information from the Maternal and Child Health Handbook, to prevent obesity in the fetal period and thereafter, in children. Young childhood and school age are important periods in which the basis of a child’s lifestyle is formed; therefore, various assessments and interventions based on the linkage of maternal and child health information and school-age information may be important. It is important to follow up with children with obesity and investigate their prognosis in the future. The determination of obesity in our study was based exclusively on height and body weight (i.e., physical indicators), with no examination of body fat. Additionally, the BMI z-scores were calculated with reference to the standard population. Therefore, especially for boys in puberty, it is possible that individuals who were muscular were assigned higher BMI values and categorized as obese. Therefore, the results of the present study should be interpreted with caution.
5. Conclusions
This study first linked public information, such as maternal and child health handbooks, baby health checkup information, and school physical examination information, to continuously track the changes in body weight and size from birth to puberty. The findings of our study suggest that among Japanese children, being overweight in young childhood may be associated with being overweight at school age and during puberty. Based on the above results, the prevention of being overweight at school age and puberty requires intervention against being overweight in young childhood, especially during the 1.5-year-old checkup period.
Author Contributions
G.S. and Y.N. were equally responsible for conducting the study, analyzing the data, interpreting the results, and drafting the manuscript. T.O. (Taku Obara) supervised the study. F.U. and I.T. contributed significantly to the data analysis. N.K., T.O. (Tomomi Onuma), A.N., K.M., M.I., H.M., J.S. and S.K. contributed to the interpretation of results and provided critical feedback. S.K. provided advice on essential intellectual content and helped draft the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the Japan Agency for Medical Research and Development (grant number JP21tm0124005) and the Japanese Ministry of Health, Labor, and Welfare Health Research on Children, Youth, and Families Program (grant number JP19DA1001).
Institutional Review Board Statement
This study was approved by the Tohoku Medical Megabank Organization’s Internal Review Board on 27 May 2013 (No. 2013-1-103-1). This study was conducted in accordance with the Declaration of Helsinki and other relevant guidelines and regulations.
Informed Consent Statement
Informed consent was obtained from all participants involved in the study.
Data Availability Statement
A biobank is being constructed based on the TMM BirThree Cohort Study. The full baseline data have been distributed to researchers who have been approved by the Sample and Data Access Committee of the Biobank since 2017.
Acknowledgments
We appreciate the participants, medical staff in hospitals and clinics, municipalities, schoolteachers, and other stakeholders who supported the TMM BirThree Cohort Study. A full list of the members of the Tohoku Medical Megabank Organization is available at https://www.megabank.tohoku.ac.jp/english/a220901/ (accessed on 14 May 2023).
Conflicts of Interest
The authors declare no conflict of interest.
References
- Drozdz, D.; Alvarez-Pitti, J.; Wójcik, M.; Borghi, C.; Gabbianelli, R.; Mazur, A.; Herceg-čavrak, V.; Lopez-Valcarcel, B.G.; Brzeziński, M.; Lurbe, E. Obesity and Cardiometabolic Risk Factors: From Childhood to Adulthood. Nutrients 2021, 13, 4176. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 28 March 2023).
- Powell-Wiley, T.M.; Poirier, P.; Burke, L.E.; Després, J.P.; Gordon-Larsen, P.; Lavie, C.J.; Lear, S.A.; Ndumele, C.E.; Neeland, I.J.; Sanders, P.; et al. Obesity and Cardiovascular Disease A Scientific Statement From the American Heart Association. Circulation 2021, 143, e984–e1010. [Google Scholar] [CrossRef] [PubMed]
- Al-Sulaiti, H.; Diboun, I.; Agha, M.V.; Mohamed, F.F.S.; Atkin, S.; Dömling, A.S.; Elrayess, M.A.; Mazloum, N.A. Metabolic Signature of Obesity-Associated Insulin Resistance and Type 2 Diabetes. J. Transl. Med. 2019, 17, 348. [Google Scholar] [CrossRef]
- Avgerinos, K.I.; Spyrou, N.; Mantzoros, C.S.; Dalamaga, M. Obesity and Cancer Risk: Emerging Biological Mechanisms and Perspectives. Metabolism 2019, 92, 121–135. [Google Scholar] [CrossRef] [PubMed]
- Lennerz, B.S.; Moss, A.; von Schnurbein, J.; Bickenbach, A.; Bollow, E.; Brandt, S.; Luetke-Brintrup, D.; Mühlig, Y.; Neef, M.; Ose, C.; et al. Do adolescents with extreme obesity differ according to previous treatment-seeking behavior? Youths with extreme obesity study (YES) cohort. Int. J. Obes. 2019, 43, 103–115. [Google Scholar] [CrossRef]
- Dhawan, D.; Sharma, S. Abdominal Obesity, Adipokines and Non-Communicable Diseases. J. Steroid. Biochem. Mol. Biol. 2020, 203, 105737. [Google Scholar] [CrossRef]
- Chong, B.; Jayabaskaran, J.; Kong, G.; Chan, Y.H.; Chin, Y.H.; Goh, R.; Kannan, S.; Ng, C.H.; Loong, S.; Kueh, M.T.W.; et al. Trends and predictions of malnutrition and obesity in 204 countries and territories: An analysis of the Global Burden of Disease Study 2019. eClinicalMedicine 2023, 57, 101850. [Google Scholar] [CrossRef]
- Tirthani, E.; Said, M.S.; Rehman, A. Genetics and Obesity; StatPearls: Treasure Island, FL, USA, 2023. [Google Scholar]
- Wang, L.Y.; Chyen, D.; Lee, S.; Lowry, R. The Association Between Body Mass Index in Adolescence and Obesity in Adulthood. J. Adolesc. Health 2008, 42, 512–518. [Google Scholar] [CrossRef]
- Komiya, H.; Kurokawa, N. Changes in the physique of obese children in first grade elementary school students: Longitudinal study of six years. Jpn. J. Phys. Educ. Health Sport. Sci. 2018, 63, 495–504. [Google Scholar] [CrossRef]
- Nakano, T.; Sei, M.; Ewis, A.A.; Munakata, H.; Onishi, C.; Nakahori, Y. Tracking Overweight and Obesity in Japanese Children; a Six Years Longitudinal Study. J. Med. Investig. 2010, 57, 114–123. [Google Scholar] [CrossRef]
- Llewellyn, A.; Simmonds, M.; Owen, C.G.; Woolacott, N. Childhood Obesity as a Predictor of Morbidity in Adulthood: A Systematic Review and Meta-Analysis. Obes. Rev. 2016, 17, 56–67. [Google Scholar] [CrossRef] [PubMed]
- Heslehurst, N.; Vieira, R.; Akhter, Z.; Bailey, H.; Slack, E.; Ngongalah, L.; Pemu, A.; Rankin, J. The Association between Maternal Body Mass Index and Child Obesity: A Systematic Review and Meta-Analysis. PLoS Med. 2019, 16, e1002817. [Google Scholar] [CrossRef] [PubMed]
- Correia-Costa, L.; Schaefer, F.; Afonso, A.C.; Correia, S.; Guimarães, J.T.; Guerra, A.; Barros, H.; Azevedo, A. Prenatal Alcohol Exposure Affects Renal Function in Overweight Schoolchildren: Birth Cohort Analysis. Pediatr. Nephrol. 2020, 35, 695–702. [Google Scholar] [CrossRef]
- Mizutani, T.; Suzuki, K.; Kondo, N.; Yamagata, Z. Association of Maternal Lifestyles Including Smoking during Pregnancy with Childhood Obesity. Obesity 2007, 15, 3133–3139. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Education, Culture, Sports, Science and Technology. Available online: https://www.mext.go.jp/b_menu/toukei/chousa05/hoken/kekka/k_detail/1411711_00004.htm (accessed on 8 February 2023).
- Kuriyama, S.; Metoki, H.; Kikuya, M.; Obara, T.; Ishikuro, M.; Yamanaka, C.; Nagai, M.; Matsubara, H.; Kobayashi, T.; Sugawara, J.; et al. Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): Rationale, Progress and Perspective. Int. J. Epidemiol. 2020, 49, 18–19m. [Google Scholar] [CrossRef] [PubMed]
- Ishikuro, M.; Obara, T.; Osanai, T.; Yamanaka, C.; Sato, Y.; Mizuno, S.; Miyashita, M.; Kikuya, M.; Sakurai, K.; Hozawa, A.; et al. Strategic Methods for Recruiting Grandparents: The Tohoku Medical Megabank Birth and Three-Generation Cohort Study. Tohoku J. Exp. Med. 2018, 246, 97–105. [Google Scholar] [CrossRef]
- Kuriyama, S.; Yaegashi, N.; Nagami, F.; Arai, T.; Kawaguchi, Y.; Osumi, N.; Sakaida, M.; Suzuki, Y.; Nakayama, K.; Hashizume, H.; et al. The Tohoku Medical Megabank Project: Design and Mission. J. Epidemiol. 2016, 26, 493–511. [Google Scholar] [CrossRef]
- Isojima, T.; Kato, N.; Ito, Y.; Kanzaki, S.; Murata, M. Growth Standard Charts for Japanese Children with Mean and Standard Deviation (SD) Values Based on the Year 2000 National Survey. Clin. Pediatr. Endocrinol. 2016, 25, 71–76. [Google Scholar] [CrossRef]
- Ogonowski, J.; Miazgowski, T. Intergenerational Transmission of Macrosomia in Women with Gestational Diabetes and Normal Glucose Tolerance. Eur. J. Obs. Gynecol. Reprod. Biol. 2015, 195, 113–116. [Google Scholar] [CrossRef]
- Kato, N.; Takimoto, H.; Sudo, N. The Cubic Functions for Spline Smoothed L, S and M Values for BMI Reference Data of Japanese Children. Clin. Pediatr. Endocrinol. 2011, 20, 47–49. [Google Scholar] [CrossRef]
- Ward, Z.J.; Long, M.W.; Resch, S.C.; Giles, C.M.; Cradock, A.L.; Gortmaker, S.L. Simulation of Growth Trajectories of Childhood Obesity into Adulthood. N. Engl. J. Med. 2017, 377, 2145–2153. [Google Scholar] [CrossRef] [PubMed]
- Singh, A.S.; Mulder, C.; Twisk, J.W.R.; Van Mechelen, W.; Chinapaw, M.J.M. Tracking of Childhood Overweight into Adulthood: A Systematic Review of the Literature. Obes. Rev. 2008, 9, 474–488. [Google Scholar] [CrossRef] [PubMed]
- Geserick, M.; Vogel, M.; Gausche, R.; Lipek, T.; Spielau, U.; Keller, E.; Pfäffle, R.; Kiess, W.; Körner, A. Acceleration of BMI in Early Childhood and Risk of Sustained Obesity. N. Engl. J. Med. 2018, 379, 1303–1312. [Google Scholar] [CrossRef] [PubMed]
- Weihrauch-Blüher, S.; Schwarz, P.; Klusmann, J.H. Childhood Obesity: Increased Risk for Cardiometabolic Disease and Cancer in Adulthood. Metabolism. 2019, 92, 147–152. [Google Scholar] [CrossRef]
- Sachdeva, P.; Ghosh, S.; Ghosh, S.; Han, S.; Banerjee, J.; Bhaskar, R.; Sinha, J.K. Childhood Obesity: A Potential Key Factor in the Development of Glioblastoma Multiforme. Life 2022, 12, 1673. [Google Scholar] [CrossRef]
- Evensen, E.; Emaus, N.; Kokkvoll, A.; Wilsgaard, T.; Furberg, A.S.; Skeie, G. The Relation between Birthweight, Childhood Body Mass Index, and Overweight and Obesity in Late Adolescence: A Longitudinal Cohort Study from Norway, the Tromsø Study, Fit Futures. BMJ Open 2017, 7, e015576. [Google Scholar] [CrossRef]
- Stettler, N.; Iotova, V. Early Growth Patterns and Long-Term Obesity Risk. Curr. Opin. Clin. Nutr. Metab. Care 2010, 13, 294–299. [Google Scholar] [CrossRef] [PubMed]
- Briollais, L.; Rustand, D.; Allard, C.; Wu, Y.; Xu, J.; Rajan, S.G.; Hivert, M.F.; Doyon, M.; Bouchard, L.; McGowan, P.O.; et al. DNA Methylation Mediates the Association between Breastfeeding and Early-Life Growth Trajectories. Clin. Epigenetics 2021, 13, 231. [Google Scholar] [CrossRef]
- Mollon, J.; David, A.S.; Zammit, S.; Lewis, G.; Reichenberg, A. Course of Cognitive Development from Infancy to Early Adulthood in the Psychosis Spectrum. JAMA Psychiatry 2018, 75, 270–279. [Google Scholar] [CrossRef]
- Russell, S.J.; Hope, S.; Croker, H.; Packer, J.; Viner, R.M. Is It Possible to Model the Impact of Calorie-Reduction Interventions on Childhood Obesity at a Population Level and across the Range of Deprivation: Evidence from the Avon Longitudinal Study of Parents and Children (ALSPAC). PLoS ONE 2022, 17, e0263043. [Google Scholar] [CrossRef]
- Bruce, A.; Mojarrad, N.G.; Santorelli, G. Association of Anthropometric Measures across the Life-Course with Refractive Error and Ocular Biometry at Age 15 Years. BMC Ophthalmol. 2020, 20, 269. [Google Scholar] [CrossRef] [PubMed]
- Morales, E.; Torres-Castillo, N.; Garaulet, M. Infancy and Childhood Obesity Grade Predicts Weight Loss in Adulthood: The Ontime Study. Nutrients 2021, 13, 2132. [Google Scholar] [CrossRef] [PubMed]
- Ghosh, S.; Sinha, J.; Raghunath, M. “Obesageing”: Linking Obesity & Ageing. Indian J. Med. Res. 2019, 149, 610–615. [Google Scholar] [CrossRef] [PubMed]
- Roblin, L. Childhood Obesity: Food, Nutrient, and Eating-Habit Trends and Influences. Appl. Physiol. Nutr. Metab. 2007, 32, 635–645. [Google Scholar] [CrossRef] [PubMed]
- Hills, A.P.; Andersen, L.B.; Byrne, N.M. Physical Activity and Obesity in Children. Br. J. Sport. Med. 2011, 45, 866–870. [Google Scholar] [CrossRef] [PubMed]
- De Giuseppe, R.; Di Napoli, I.; Porri, D.; Cena, H. Pediatric Obesity and Eating Disorders Symptoms: The Role of the Multidisciplinary Treatment. A Systematic Review. Front. Pediatr. 2019, 7, 123. [Google Scholar] [CrossRef] [PubMed]
- Dabas, A.; Seth, A. Prevention and Management of Childhood Obesity. Indian J. Pediatr. 2018, 85, 546–553. [Google Scholar] [CrossRef]
- Lifschitz, C. Early Life Factors Influencing the Risk of Obesity. Pediatr. Gastroenterol. Hepatol. Nutr. 2015, 18, 217–223. [Google Scholar] [CrossRef]
- Wang, J.; Wu, Y.; Xiong, G.; Chao, T.; Jin, Q.; Liu, R.; Hao, L.; Wei, S.; Yang, N.; Yang, X. Introduction of Complementary Feeding before 4 Months of Age Increases the Risk of Childhood Overweight or Obesity: A Meta-Analysis of Prospective Cohort Studies. Nutr. Res. 2016, 36, 759–770. [Google Scholar] [CrossRef]
- Rayfield, S.; Plugge, E. Systematic Review and Meta-Analysis of the Association between Maternal Smoking in Pregnancy and Childhood Overweight and Obesity. J. Epidemiol. Community Health 2017, 71, 162–173. [Google Scholar] [CrossRef]
- Marseglia, L.; Manti, S.; D’Angelo, G.; Cuppari, C.; Salpietro, V.; Filippelli, M.; Trovato, A.; Gitto, E.; Salpietro, C.; Arrigo, T. Obesity and Breastfeeding: The Strength of Association. Women Birth. 2015, 28, 81–86. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Flow chart of study participants. * Gender, height, body weight, measurement date, date of birth, and gestational age.
Figure 1.
Flow chart of study participants. * Gender, height, body weight, measurement date, date of birth, and gestational age.
Figure 2.
OR and 95% CI from logistic regression analysis of the association between LGA at birth or overweight at any time point and overweight at subsequent time points. cOR, crude odds ratio; CI, confidence interval; LGA, large-for-gestational age.
Figure 2.
OR and 95% CI from logistic regression analysis of the association between LGA at birth or overweight at any time point and overweight at subsequent time points. cOR, crude odds ratio; CI, confidence interval; LGA, large-for-gestational age.
Table 1.
Characteristics of children.
| Variables | n | Overall, n = 246 1 | Man, n = 119 1 | Woman, n = 127 1 | p-Value 2 |
|---|---|---|---|---|---|
| Length at birth (cm) | 227 | 49.54 (1.89) | 49.69 (1.90) | 49.39 (1.88) | 0.3 |
| Missing, n | 19 | 10 | 9 | ||
| Body weight at birth (g) | 227 | 3,111 (356) | 3,142 (336) | 3,081 (372) | 0.2 |
| Missing, n | 19 | 10 | 9 | ||
| Gestational age (weeks) | 227 | 39.56 (1.28) | 39.60 (1.06) | 39.51 (1.45) | 0.7 |
| Missing, n | 19 | 10 | 9 | ||
| Body types at birth | 227 | >0.9 | |||
| LGA, n (%) | 36 (15.9%) | 16 (14.7%) | 20 (16.9%) | ||
| AGA, n (%) | 183 (80.6%) | 89 (81.7%) | 94 (79.7%) | ||
| SGA, n (%) | 8 (3.5%) | 4 (3.7%) | 4 (3.4%) | ||
| Missing, n | 19 | 10 | 9 | ||
| Height at age 18–24 months (cm) | 191 | 81.08 (3.01) | 81.62 (2.81) | 80.56 (3.12) | 0.015 * |
| Missing, n | 55 | 26 | 29 | ||
| Body weight at age 18–24 months (kg) | 191 | 10.74 (1.20) | 11.04 (1.21) | 10.45 (1.11) | 0.002 * |
| Missing, n | 55 | 26 | 29 | ||
| BMI at age 18–24 months (kg/m2) | 191 | 16.31 (1.20) | 16.55 (1.29) | 16.07 (1.07) | 0.009 * |
| Missing, n | 55 | 26 | 29 | ||
| Body types at age 18–24 months | 191 | 0.6 | |||
| Normal weight, n (%) | 155 (81.2%) | 77 (82.8%) | 78 (79.6%) | ||
| Overweight, n (%) | 36 (18.8%) | 16 (17.2%) | 20 (20.4%) | ||
| Missing, n | 55 | 26 | 29 | ||
| Height at age 36–47 months (cm) | 211 | 96.8 (3.9) | 96.9 (3.7) | 96.7 (4.0) | >0.9 |
| Missing, n | 35 | 16 | 19 | ||
| Body weight at age 36–47 months (kg) | 211 | 14.93 (1.76) | 14.91 (1.56) | 14.94 (1.94) | >0.9 |
| Missing, n | 35 | 16 | 19 | ||
| BMI at age 36–47 months (kg/m2) | 211 | 15.90 (1.17) | 15.85 (0.99) | 15.94 (1.32) | 0.7 |
| Missing, n | 35 | 16 | 19 | ||
| Body types at age 36–47 months | 211 | >0.9 | |||
| Normal weight, n (%) | 160 (75.8%) | 78 (75.7%) | 82 (75.9%) | ||
| Overweight, n (%) | 51 (24.2%) | 25 (24.3%) | 26 (24.1%) | ||
| Missing, n | 35 | 16 | 19 | ||
| Height at age 6 years (cm) | 179 | 116.7 (5.2) | 117.0 (5.1) | 116.3 (5.3) | 0.6 |
| Missing, n | 67 | 28 | 39 | ||
| Body weight at age 6 years (kg) | 179 | 22.2 (4.2) | 22.1 (4.0) | 22.3 (4.4) | 0.6 |
| Missing, n | 67 | 28 | 39 | ||
| BMI at age 6 years (kg/m2) | 179 | 16.20 (2.05) | 16.01 (1.86) | 16.38 (2.22) | 0.4 |
| Missing, n | 67 | 28 | 39 | ||
| Body types at age 6 years | 179 | 0.089 | |||
| Normal weight | 149 (83.2%) | 80 (87.9%) | 69 (78.4%) | ||
| Overweight | 30 (16.8%) | 11 (12.1%) | 19 (21.6%) | ||
| Missing, n | 67 | 28 | 39 | ||
| Height at age 11 years (cm) | 174 | 146 (7) | 145 (8) | 148 (7) | 0.005 * |
| Missing, n | 72 | 30 | 42 | ||
| Body weight at age 11 years (kg) | 174 | 41 (9) | 40 (10) | 42 (8) | 0.026 * |
| Missing, n | 72 | 30 | 42 | ||
| BMI at age 11 years (kg/m2) | 174 | 18.9 (3.2) | 18.7 (3.3) | 19.1 (3.1) | 0.2 |
| Missing, n | 72 | 30 | 42 | ||
| Body types at age 11 years | 174 | 0.4 | |||
| Normal weight | 139 (79.9%) | 69 (77.5%) | 70 (82.4%) | ||
| Overweight | 35 (20.1%) | 20 (22.5%) | 15 (17.6%) | ||
| Missing, n | 72 | 30 | 42 | ||
| Height at age 14 years (cm) | 176 | 161 (7) | 166 (6) | 157 (6) | <0.001 * |
| Missing, n | 70 | 32 | 38 | ||
| Body weight at age 14 years (kg) | 176 | 54 (10) | 56 (12) | 52 (8) | 0.037 * |
| Missing, n | 70 | 32 | 38 | ||
| BMI at age 14 years (kg/m2) | 176 | 20.9 (3.5) | 20.3 (3.7) | 21.4 (3.2) | 0.001 * |
| Missing, n | 70 | 32 | 38 | ||
| Body types at age 14 years | 176 | 0.15 | |||
| Normal weight, n (%) | 151 (85.8%) | 78 (89.7%) | 73 (82.0%) | ||
| Overweight, n (%) | 25 (14.2%) | 9 (10.3%) | 16 (18.0%) | ||
| Missing, n | 70 | 32 | 38 |
1 Mean (SD). 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. * Denotes significance, p-value < 0.05. SD, standard deviation; BMI, body mass index; LGA, large for gestational age; AGA, appropriate for gestational age; SGA, small for gestational age.
Table 2.
Characteristics of mothers.
| Variables | n | Overall, n = 246 1 | Man, n = 119 1 | Woman, n = 127 1 | p-Value 2 |
|---|---|---|---|---|---|
| Maternal age at the time of childbirth (years) | 227 | 25.0 (3.5) | 24.8 (3.4) | 25.1 (3.7) | 0.7 |
| Missing, n | 19 | 9 | 10 | ||
| Maternal parity | 229 | 0.5 | |||
| Multipara, n (%) | 45 (19.7%) | 20 (18.0%) | 25 (21.2%) | ||
| Primipara, n (%) | 184 (80.3%) | 91 (82.0%) | 93 (78.8%) | ||
| Missing, n | 17 | 8 | 9 | ||
| Maternal BMI when pregnancy was detected (kg/m2) | 178 | 20.70 (2.94) | 20.57 (2.97) | 20.83 (2.93) | 0.5 |
| Missing, n | 68 | 35 | 33 | ||
| Alcohol consumption when pregnancy was detected | 226 | 0.3 | |||
| Yes, n (%) | 17 (7.5%) | 9 (8.3%) | 8 (6.8%) | ||
| No, n (%) | 168 (74.3%) | 75 (69.4%) | 93 (78.8%) | ||
| Unknown, n (%) | 41 (18.1%) | 24 (22.2%) | 17 (14.4%) | ||
| Missing, n | 20 | 11 | 9 | ||
| Smoking when pregnancy was detected | 227 | 0.2 | |||
| Yes, n (%) | 18 (7.9%) | 11 (10.1%) | 7 (5.9%) | ||
| No, n (%) | 172 (75.8%) | 77 (70.6%) | 95 (80.5%) | ||
| Unknown | 37 (16.3%) | 21 (19.3%) | 16 (13.6%) | ||
| Missing, n | 19 | 10 | 9 |
1 Mean (SD). 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. SD, standard deviation; BMI, body mass index.
Table 3.
OR and 95% CI from logistic regression analysis of the association between body types at any time point and that of subsequent time points.
Table 3.
OR and 95% CI from logistic regression analysis of the association between body types at any time point and that of subsequent time points.
| Outcome | At Age 18–23 Months | At Age 36–47 Months | At Age 6 Years | At Age 11 Years | At Age 14 Years | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Exposure | n (%) | aOR * (95% CI) | n (%) | OR * (95% CI) | n (%) | OR * (95% CI) | n (%) | OR * (95% CI) | n (%) | OR *(95% CI) |
| At birth | 120 | 124 | 92 | 86 | 93 | |||||
| AGA | 97 | 1.00 | 101 | 1.00 | 78 | 1.00 | 72 | 1.00 | 77 | 1.00 |
| (80.8) | (Ref.) | (81.5) | (Ref.) | (84.8) | (Ref.) | (83.7) | (Ref.) | (82.8) | (Ref.) | |
| LGA | 19 | 1.26 | 18 | 2.59 | 11 | 0.00 | 11 | 0.45 | 12 | 5.38 |
| (15.8) | (0.32–4.22) | (14.5) | (0.80–7.98) | (12.0) | (N.A. −3.06 × 1047) | (12.8) | (0.02–3.35) | (82.8) | (0.17–115.71) | |
| SGA | 4 | 2.40 × 10−8 | 5 | 1.29 | 3 | 1.05 × 10−7 | 3 | 5.43 | 4 | 103.64 |
| (3.3) | (N.A. −1.13 × 10,148) | (4.0) | (0.06–9.76) | (3.3) | (N.A. −2.41 × 10,184) | (3.5) | (0.20–87.69) | (4.3) | (1.89–10,610.63) | |
| At age 18–23 months | 119 | 95 | 88 | 91 | ||||||
| Normal weight | 93 | 1.00 | 73 | 1.00 | 66 | 1.00 | 73 | 1.00 | ||
| (78.2) | (Ref.) | (76.8) | (Ref.) | (75.0) | (Ref.) | (80.2) | (Ref.) | |||
| Overweight | 26 | 13.42 | 22 | 6.94 | 22 | 5.22 | 18 | 1.61 | ||
| (21.8) | (4.46–45.42) | (23.2) | (1.64–33.46) | (25.0) | (1.25–24.79) | (19.8) | (0.14–14.96) | |||
| At age 36–47 months | 89 | 85 | 91 | |||||||
| Normal weight | 69 | 1.00 | 64 | 1.00 | 71 | 1.00 | ||||
| (77.5) | (Ref.) | (75.3) | (Ref.) | (78.0) | (Ref.) | |||||
| Overweight | 20 | 9.55 | 21 | 3.22 | 20 | 0.29 | ||||
| (22.5) | (1.85–61.41) | (24.7) | (0.70–15.14) | (22.0) | (0.01–4.47) | |||||
| At age 6 years | 91 | 80 | ||||||||
| Normal weight | 80 | 1.00 | 74 | 1.00 | ||||||
| (87.9) | (Ref.) | (92.5) | (Ref.) | |||||||
| Overweight | 11 | 1021.24 | 6 | 13.15 | ||||||
| At age 11 years | (12.1) | (48.96–81,487.89) | 79 | (0.97–261.80) | ||||||
| Normal weight | 69 | 1.00 | ||||||||
| (87.3) | (Ref.) | |||||||||
| Overweight | 10 | 60.46 | ||||||||
| (12.7) | (2.92–5001.37) | |||||||||
* Adjusted for gender, maternal age at childbirth, maternal parity, maternal BMI when determining pregnancy, maternal drinking status, pregnancy confirmation, and smoking status at pregnancy confirmation. aOR, adjusted odds ratio; CI, confidence interval; Ref., reference value; N.A., not available; LGA, large for gestational age; AGA, appropriate for gestational age; SGA, small for gestational age.
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MDPI and ACS Style
Shinoda, G.; Nagaoka, Y.; Ueno, F.; Kurokawa, N.; Takahashi, I.; Onuma, T.; Noda, A.; Murakami, K.; Ishikuro, M.; Obara, T.; et al. Association between being Overweight in Young Childhood and during School Age and Puberty. Children 2023, 10, 909. https://doi.org/10.3390/children10050909
AMA Style
Shinoda G, Nagaoka Y, Ueno F, Kurokawa N, Takahashi I, Onuma T, Noda A, Murakami K, Ishikuro M, Obara T, et al. Association between being Overweight in Young Childhood and during School Age and Puberty. Children. 2023; 10(5):909. https://doi.org/10.3390/children10050909
Chicago/Turabian StyleShinoda, Genki, Yudai Nagaoka, Fumihiko Ueno, Naoyuki Kurokawa, Ippei Takahashi, Tomomi Onuma, Aoi Noda, Keiko Murakami, Mami Ishikuro, Taku Obara, and et al. 2023. "Association between being Overweight in Young Childhood and during School Age and Puberty" Children 10, no. 5: 909. https://doi.org/10.3390/children10050909
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