Weight-Loss Behaviors of Avoiding Between Meal and Midnight Snack in Teenagers Associated with Gestational Diabetes: The Japan Environment and ...
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Weight-Loss Behaviors of Avoiding Between Meal
and Midnight Snack in Teenagers Associated with
Gestational Diabetes: The Japan Environment and
Children’s Study
Marina Minami
Kochi University
Takafumi Watanabe
Kochi University
Masamitsu Eitoku ( meitoku@kochi-u.ac.jp )
Kochi University
Nagamasa Maeda
Kochi University
Mikiya Fujieda
Kochi University
Narufumi Suganuma
Kochi University
Research Article
Keywords: Gestational diabetes, snack, teenage years, Japan Environment and Children’s study
Posted Date: October 26th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-962177/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Page 1/16Abstract
Background: Dietary habits and weight control before pregnancy are important in preventing gestational
diabetes. This study aims to examine whether the weight-loss behavior of avoiding between meal and
midnight snacks in teenagers is associated with subsequent gestational diabetes mellitus (GDM).
Methods: A total of 89,227 (85.7% of the total) mother-infant pairs of live births were included in our
study of the Japan Environment and Children's Study (JECS). In the second or third trimesters,
participants were asked to report their weight-loss behaviors during their teenage years. Response items
included avoidance of meals and midnight snacking. The main outcome of our study was the prevalence
of GDM.
Results: Overall, 2,066 (2.3%) participants had GDM. Relative to those without GDM, women with GDM
were older, were smokers, had a higher prevalence of hypertension, previous cesarean delivery, mental
illness, and higher body mass index (BMI). Weight-loss behavior in their teenage years was associated
with a decreased risk of GDM (unadjusted crude odds ratio, 0.83; 95% confidence interval [CI]: 0.76–0.91),
model 1 (adjusted odds ratio [aOR], 0.86; 95% CI: 0.79–0.94), and model 2 (aOR, 0.80; 95% CI: 0.73–0.88).
Weight-loss behavior in teens was associated with a decreased risk of GDM in the normal weight [aOR,
0.79; 95% CI: 0.70–0.89) and overweight (aOR, 0.82; 95% CI: 0.69–0.98) groups.
Conclusions: The results suggest that weight-loss behaviors of avoiding in-between meals and midnight
snacking as teenagers are associated with a decreased risk of developing GDM.
Background
Gestational diabetes mellitus (GDM) is caused by an abnormality in glucose metabolism (1) resulting in
the inability to control blood glucose levels during pregnancy (2). Blood glucose levels should be
measured at any time during the first and second trimester of pregnancy, and if the blood glucose levels
are high, a glucose tolerance test should be performed to make the diagnosis of GDM (3). The treatment
of GDM is based on using dietary therapy to control blood sugar levels. To avoid a sudden increase in
blood glucose levels due to the intake of too many calories at one time, it is recommended that patients
diagnosed with GDM eat six small meals a day (three main meals and three in-between) (4).
It is difficult to change pre-pregnancy eating habits, and the establishment of healthy eating habits before
pregnancy is important for the prevention of GDM (5). Previous studies have shown that the eating habits
of teenagers influence their eating habits in adulthood. Eating habits are established through daily dietary
habits (6). Poor eating habits include skipping breakfast, fast eating, picky eating, midnight snacking, and
excessive snacking. Family eating habits influence the establishment of healthy eating habits in children
(7, 8). Therefore, to establish healthy eating habits, various efforts are being made targeting children at a
young age in Japan (9).
Page 2/16Between meal and midnight snacks are a way of compensating for a low-calorie intake which can cause
fatigue. However, these snacks can lead to obesity and overweight, depending on the way that they are
consumed (10). Consumption can be managed by setting a time, eating moderate amounts, and avoiding
high fat, high-calorie foods (11).
Excessive weight-loss behavior has been linked to psychological dysregulation. Gowey (12) has
suggested that psychological dysregulation is associated with a greater body mass index (BMI),
problematic eating patterns and behaviors, and body dissatisfaction, especially in adolescents and young
adults. To the best of our knowledge, no studies have investigated the association between teenage
weight-loss behavior and GDM.
Dietary habits (13) and weight control before pregnancy (14) are important in preventing GDM. Thus, this
study aims to examine whether an association exists between eating habits in teenagers and subsequent
GDM.
Methods
Study design and setting
This study used a dataset (jecs-an-20180131) from a nationwide, prospective birth cohort study, the
Japan Environment and Children's Study (JECS). The detailed protocol and baseline information of
participants have been previously reported (15, 16). As a brief description, approximately 100,000
pregnant women living in Study Areas were recruited during early pregnancy at Co-operating health care
providers or at local government offices between January 2011 and March 2014. The JECS protocol was
reviewed and approved by the Ministry of the Environment’s Institutional Review Board on
Epidemiological Studies and the Ethics Committees of all participating institutions. Participants will be
followed up until the participating children reach 13 years of age. Eligibility was considered if a pregnant
woman was (1) residing in a Study Area at the time of recruitment and was expected to reside continually
in Japan; (2) expected to give birth between August 1, 2011, and mid-2014; and (3) capable of
understanding the Japanese language and completing the self-administered questionnaires. The JECS
collected demographic data and clinical and obstetric information through self-administered
questionnaires or medical record transcripts. The questionnaires were distributed during the first trimester
and second or third trimesters. Written informed consent was obtained from all study participants.
Study population
The dataset comprised 104,065 fetal records. In our analyses, we excluded participants with a history of
stillbirth or missing birth status (n = 3,921), multiple gestations (n = 1,889); multiple pregnancies (n =
5,465), under 20 years old (n = 1,132), who did not report weight-loss behavior during their teenage years
(n=1,643), with biologically implausible weight values measured before delivery (n = 4), and with a history
of type 1, type 2, or gestational diabetes mellitus (n = 784). Subsequently, 89,227 (85.7%) mother-infant
pairs of live births were included in our study (Figure 1).
Page 3/16Pregnant women’s weight-loss behavior during teens In the second or third trimester, participants were asked to report their dietary behaviors during their teenage years. Response items included avoiding between-meal and midnight snacks. Outcome measurements The main outcome of our study was GDM. GDM cases were identified using medical record transcripts, which were completed after delivery by physicians, Research Co-ordinators, midwives, nurses, or doctors. Other variables The JECS questionnaire and the records of Co-operating health care providers were used as possible adjustment factors. Maternal characteristics including maternal age, educational level, total energy intake (kcal/d), daily physical activity, smoking habit, alcohol consumption, marital status, and past medical history were obtained through the first and second waves of the questionnaires. Information on maternal age, height and weight, parity, and previous cesarean delivery were retrieved from medical records. Daily energy intake during pregnancy (kcal/d) was calculated based on the information collected through self-reported food frequency questionnaires (FFQ) (17) and used to form three groups (tertiles) with an approximately equal number of participants. Daily physical activity during pregnancy was obtained using the shortened Japanese version of the International Physical Activity Questionnaires, which considers all types of activities, including work- related and leisure-time activities and household chores (18, 19). We calculated metabolic equivalent minutes per day (MET-mins/day) and categorized it into three physical activity levels (tertiles). Maternal age was divided into two groups: 20–34 years and ≥35 years. Pre-pregnancy BMI was calculated as self-reported pre-pregnancy weight in kilograms divided by height in meters squared and stratified into underweight (
GDM group differences concerning maternal characteristics were examined using the chi-squared test for categorical variables. We then constructed crude and adjusted logistic regression models to assess the associations of behavior with weight-loss behaviors of avoiding between meal and midnight snack in teens. In the adjusted model 1, we included the following maternal characteristics considered to be the determinants of group membership: maternal age, educational level, total energy intake, physical activity, smoking, alcohol consumption, marital status, parity, past medical history, history of hypertension, pregnancy hypertension mental illness, and previous cesarean delivery. In the adjusted model 2, we included model 1 plus BMI, with a 95% confidence interval (CI). We then performed subgroup analyses of adjusted logistic regression analysis by BMI category. A two-tailed p-value
Table 1
Characteristics of women with GDM (N = 89,227)
All GDM no-GDM
89,227 2,066 (2.3) 87,161 (97.7)
n (%)
Maternal age, years
20–34 67,433 (75.6) 1,221 (59.1) 66,212 (76.0)
≥35 21,778 (24.4) 845 (40.9) 20,933 (24.0)
Missing 16 (0.0) (0.0) 16 (0.0)
Educational level
High school or less 31,433 (35.2) 744 (36.0) 30,689 (35.2)
Vocational school/College 37,759 (42.3) 883 (42.7) 36,876 (42.3)
University or higher 19,531 (21.9) 425 (20.6) 19,106 (21.9)
Missing 504 (0.6) 14 (0.7) 490 (0.6)
Total energy intake, kcal/d
1st (lowest tertile) 29,671 (33.3) 667 (32.3) 29,004 (33.3)
2nd 29,596 (33.2) 670 (32.4) 28,926 (33.2)
3rd 29,502 (33.1) 716 (34.7) 28,786 (33.0)
Missing 458 (0.5) 13 (0.6) 445 (0.5)
Physical activity, MET-mins/d
1st (lowest tertile) 30,727 (34.4) 714 (34.6) 30,013 (34.4)
2nd 27,466 (30.8) 659 (31.9) 26,807 (30.8)
3rd 28,914 (32.4) 646 (31.3) 28,268 (32.4)
Missing 2,120 (2.4) 47 (2.3) 2,073 (2.4)
Smoking
Never smoked 51,560 (57.8) 1,132 (54.8) 50,428 (57.9)
Quit smoking 32,454 (36.4) 790 (38.2) 31,664 (36.3)
BMI, body mass index;
P-values are the results of Chi square test
Page 6/16All GDM no-GDM Currently smoking 4,128 (4.6) 118 (5.7) 4,010 (4.6) Missing 1,085 (1.2) 26 (1.3) 1,059 (1.2) Alcohol Never drank 30,264 (33.9) 757 (36.6) 29,507 (33.9) Quit drinking 49,132 (55.1) 1,088 (52.7) 48,044 (55.1) Currently drinking 8,992 (10.1) 195 (9.4) 8,797 (10.1) Missing 839 (0.9) 26 (1.3) 813 (0.9) Single mother 3,597 (4.0) 85 (4.1) 3,512 (4.0) Parity 0 36,715 (41.2) 867 (42.0) 35,848 (41.1) 1 32,779 (36.7) 749 (36.3) 32,030 (36.8) 2 or more 17,545 (19.7) 407 (19.7) 17,138 (19.7) Missing 2,188 (2.5) 43 (2.1) 2,145 (2.5) Past medical history Pre-pregnancy hypertension (yes) 2,059 (2.3) 91 (4.4) 1,968 (2.3) Pregnancy hypertension (yes) 844 (1.0) 46 (2.2) 798 (0.9) Mother's mental illness (yes) 7,086 (7.9) 186 (9.0) 6,900 (7.9) Previous cesarean delivery (yes) 6,978 (7.8) 262 (12.7) 6,716 (7.7) BMI categories, kg/m2
Association between avoiding between meal and midnight
snack and gestational diabetes
The results of the logistic regression analysis are presented in Table 2. Weight-loss behavior in teens was
associated with a decreased risk of GDM [unadjusted crude odds ratio 0.83 (95% CI 0.76–0.91), model 1
adjusted odds ratio (aOR) 0.86 (95% CI 0.79–0.94), and model 2 aOR 0.80 (95% CI 0.73–0.88)].
Table 2
Weight-loss behaviors of avoiding between meal and midnight snack in teens linked to GDM (N = 89,227)
Model 1 Model 2
crude OR 95% aOR 95% CI aOR 95% CI
CI
Avoiding between meal and midnight 0.83 (0.76-0.91) 0.86 (0.79- 0.80 (0.73-0.88)
snack 0.94)
Note: Bold font indicates significant result.
Model 1: Adjusted for maternal age, educational level, total energy intake, physical activity, smoking
during pregnancy, alcohol consumption, marital status, parity, pre-pregnancy hypertension, pregnancy
hypertension, mental illness, and previous cesarean delivery.
Model 2: Adjusted for model 1 plus BMI; CI, confidence interval; OR, odds ratio
Association between avoiding between meal and midnight
snack and gestational diabetes by BMI category
The results of the crude and adjusted logistic regression analyzing the association between weight-loss
behavior and GDM by BMI category are presented in Table 3. Weight-loss behavior in teens was
associated with a decreased risk of GDM in the normal weight [aOR 0.79 (95% CI 0.70–0.89)] and
overweight [aOR 0.82 (95% CI 0.69–0.98)] groups. No association was found in the underweight group
[aOR 0.87 (95% CI 0.65–1.18)].
Table 3
Weight-loss behaviors of avoiding between meal and midnight snack in teens by BMI category
associated with GDM (N = 89,227)
Underweight Normal weight Overweight
aOR 95% CI aOR 95% CI aOR 95% CI
Avoiding between meal and midnight snack 0.87 (0.65-1.18) 0.79 (0.70-0.89) 0.82 (0.69-0.98)
Note: Bold font indicates significant result.
Adjusted for maternal age, educational level, total energy intake, physical activity, smoking during
pregnancy, alcohol consumption, marital status, parity, pre-pregnancy hypertension, pregnancy
Page 8/16hypertension, mental illness, and previous cesarean delivery.
CI, confidence interval; OR, odds ratio
Discussion
The study is unique in that it analyzed the teenage weight-loss behaviors of avoiding between meal and
midnight snacking. About half of the respondents chose “avoiding eating between meals and having a
midnight snack” as a dietary behavior during their teenage years. It is very important to develop a diet
that includes three solid meals and not too many snacks (11, 21). It has been reported that eating too
many snacks prevents the body from getting minerals, fibers, and other nutrients that would otherwise be
available with a healthy meal (22, 23). It is said that forming habits from an early age are central to the
development of regular and healthy eating habits (24, 25). It is important to approach the parents as they
influence their teenagers' eating habits (7) (8). However, parents do not always have a clear
understanding of their children's diet and lifestyle (26). From the perspective of gestational maternal
management, we believe that it is very important to acquire correct knowledge about diet from teenagers
(27). In the future, we will be required to obtain information on snack intake and nutrition, incorporate it
into our daily lives, and make decisions for ourselves (28).
In this study, we focused on " avoiding between meal and midnight snacks " in teenagers. Although eating
habits are ingrained in us from an early age, teenagers inevitably make more independent choices about
their meals and snacks (29). Teenagers are more likely to spend their allowance freely and with
autonomy. They also tend to spend more of their allowance on snacks (30). Snacking not only provides
people with nutrients that they cannot obtain through food alone, but it also serves as a refreshing
change of pace from work or study and gives a sense of well-being. Sweet food satisfies people's desire
to eat, and for those who like sweet food, eating sweet food is an emotional experience. However, there
are various risks associated with eating too much sweet food. A previous study has shown that too much
added sugar can put an undue strain on the heart, regardless of whether a person is obese or not (31, 32).
Additionally, sugar is one of the main causes of weight gain. It has been reported that sugar is addictive,
and once a person consumes a high-calorie food (21), he or she craves more, which leads to extra calorie
intake and weight gain. It is very important to control one's sugar cravings from the teenage years.
GDM is caused by a genetic predisposition to type 2 diabetes (33) and insulin resistance during
pregnancy (34) (especially in the second or third trimesters of pregnancy). In healthy individuals,
maternal pancreatic beta cells become hypertrophic and hyperplastic in response to insulin resistance,
thereby enhancing insulin secretion. Insulin resistance is caused by the breakdown of insulin in the
placenta and a decrease in adiponectin levels (34). In addition, overeating and obesity may increase the
risk of GDM (35). The other risk factors that predispose to GDM include a family history of diabetes (36),
obesity (37), high body mass index (BMI) (38), older age (>35 years) (38), gestational hypertension, a
history of delivery of a large-for-gestational-age (LGA) baby, unexplained habitual preterm labor,
unexplained perinatal death, and delivery of a congenitally malformed baby. Gestational diabetes can
Page 9/16lead to complications in both infants and mothers. Maternal complications of diabetes include
gestational hypertension, abnormal amniotic fluid volume, shoulder dystocia, and retinopathy, while fetal
and neonatal complications include miscarriage, morphological abnormalities, giant babies, enlarged
heart, hypoglycemia, polycythemia, electrolyte abnormalities, jaundice, and fetal death. To the best of our
knowledge, this is the first study to show that eating behavior from the teenage years is associated with
gestational diabetes. To avoid developing GDM, it is important to have a diet that avoids between meal
and midnight snacks and does not lead to overweight and obesity.
The main strengths of our study include the large sample size, which is representative of pregnant
women in Japan, comprehensive information about maternal diet, and a wide range of potential
confounding factors which were adjusted for in the models. Our study had some potential limitations. For
example, owing to our exclusion criteria, our subject represented only full-term, live-born, singletons.
Another potential limitation might be that our analyses relied solely on dietary information collected at a
single time-point during pregnancy and the dietary intake could have changed at different pregnancy
stages. However, a previous study reported no significant changes in dietary intake among pregnant
Japanese women (39). The energy from FFQ may not reflect actual energy intake and may result in
under- or over-reporting (40, 41). However, our analyses examined energy intake on ordinal scales, and the
FFQ is a validated tool for grouping pregnant women according to high- or low-level energy intake at the
population level (41). Additionally, the questionnaires used to assess diet behaviors during teenage years
were not validated. However, while there are several other questions related to weight gain as a teenager,
the questions used in our study are the more common actions taken to lose weight. Moreover, our
analysis was adjusted for many confounders, although there may be others. There was a long time
between questionnaire and response owing to the exclusion of teenagers, which may have introduced
recall bias. Furthermore, we were unable to adjust for a family history of gestational diabetes.
Conclusion
The results suggest that weight-loss behaviors of avoiding between meal and midnight snacking as
teenagers are associated with a decreased risk of developing GDM. It is important to establish
appropriate snack eating habits at an early age and to acquire the correct knowledge on snacking in the
dietary management of pregnancy.
Abbreviations
aOR, adjusted odds ratio
BMI, Body Mass Index
CI, Confidence Interval
FFQ, Food Frequency Questionnaires
Page 10/16GDM, Gestational diabetes mellitus
JECS, Japan Environment and Children's Study
MET-mins/day, Metabolic Equivalent Minutes per Day
Declarations
Ethics approval and Consent to Participate: The JECS protocol was approved by the Institutional Review
Board on Epidemiological Studies of the Ministry of the Environment and by the ethics committees of all
the participating institutions, i.e., the National Institute for Environmental Studies, the National Center for
Child Health and Develop‑ ment, Hokkaido University, Sapporo Medical University, Asahikawa Medi‑ cal
University, Japanese Red Cross Hokkaido College of Nursing, Tohoku University, Fukushima Medical
University, Chiba University, Yokohama City University, University of Yamanashi, Shinshu University,
University of Toyama, Nagoya City University, Kyoto University, Doshisha University, Osaka University,
Osaka Medical Center and Research Institute for Maternal and Child Health, Hyogo College of Medicine,
Tottori University, Kochi University, University of Occupational and Environmental University, Kyushu
University, Kumamoto University, University of Miyazaki, and University of the Ryukyus. The JECS was
conducted in accordance with the Declaration of Helsinki and other nationally valid regulations. Written
informed consent was obtained from all participat‑ ing mothers.
Funding: This study was funded by the Ministry of the Environment, Japan. The findings and conclusions
of this article are solely the responsibility of the authors and do not represent the official views of the
above government.
Competing interests: The authors declare that they have no competing interests.
Consent for publication: Not applicable
Availability of data and materials: Data are unsuitable for public deposition because of ethical
considerations and restrictions as per legal framework of Japan. It is prohibited by the Act on the
Protection of Personal Information (Act No. 57 of 30 May 2003, amended on 9 September 2015) to
publicly deposit data containing personal information. Ethical Guidelines for Medical and Health
Research Involving Human Subjects, enforced by the Japan Ministry of Education, Culture, Sports,
Science and Technology and the Ministry of Health, Labour and Welfare, also restricts the open sharing
of epidemiologic data. All inquiries about access to data should be addressed Dr. Shoji F. Nakayama,
JECS Programme Office, National Institute for Environmental Studies, at jecs-en@nies.go.jp.
Author contributions: Conceptualization, Methodology: MM; Formal analysis and interpretation: MM, TW,
ME, NM, MF, and NS; Writing Original draft: MM and NS; Critical revision of the manuscript: MM, TW, ME,
NM, MF, NS, and JECS group. All authors have read and approved the final manuscript.
Page 11/16Acknowledgments: The authors are grateful to all the participants in the study. We thank all staff
members of the JECS. This study was funded by the Ministry of the Environment, Japan. The findings
and conclusions of this article are solely the responsibility of the authors and do not represent the official
views of the above government. Members of the JECS Group as of 2021: Michihiro Kamijima (principal
investigator, Nagoya City University, Nagoya, Japan), Shin Yamazaki (National Institute for Environmental
Studies, Tsukuba, Japan), Yukihiro Ohya (National Center for Child Health and Development, Tokyo,
Japan), Reiko Kishi (Hokkaido University, Sapporo, Japan), Nobuo Yaegashi (Tohoku University, Sendai,
Japan), Koichi Hashimoto (Fukushima Medical University, Fukushima, Japan), Chisato Mori (Chiba
University, Chiba, Japan), Shuichi Ito (Yokohama City University, Yokohama, Japan), Zentaro Yamagata
(University of Yamanashi, Chuo, Japan), Hidekuni Inadera (University of Toyama, Toyama, Japan), Takeo
Nakayama (Kyoto University, Kyoto, Japan), Hiroyasu Iso (Osaka University, Suita, Japan), Masayuki
Shima (Hyogo College of Medicine, Nishinomiya, Japan), Youichi Kurozawa (Tottori University, Yonago,
Japan), Narufumi Suganuma (Kochi University, Nankoku, Japan), Koichi Kusuhara (University of
Occupational and Environmental Health, Kitakyushu, Japan), and Takahiko Katoh (Kumamoto University,
Kumamoto, Japan). We also acknowledge all members of the Environmental Medicine Department of
Kochi University for their support.
References
1. Kuzuya T. Early diagnosis, early treatment and the new diagnostic criteria of diabetes mellitus. Br J
Nutr. 2000;84:S177-S81. doi:10.1079/09658219738864.
2. Bedell S, Hutson J, de Vrijer B, Eastabrook G. Effects of Maternal Obesity and Gestational Diabetes
Mellitus on the Placenta: Current Knowledge and Targets for Therapeutic Interventions. Curr Vasc
Pharmacol. 2021;19:176–92. doi:10.2174/1570161118666200616144512.
3. Tsakiridis I, Giouleka S, Mamopoulos A, Kourtis A, Athanasiadis A, Filopoulou D, et al. Diagnosis and
Management of Gestational Diabetes Mellitus: An Overview of National and International Guidelines.
Obstet Gynecol Surv. 2021;76:367–81. doi:10.1097/OGX.0000000000000899.
4. Tsirou E, Grammatikopoulou MG, Theodoridis X, Gkiouras K, Petalidou A, Taousani E, et al.
Guidelines for Medical Nutrition Therapy in Gestational Diabetes Mellitus: Systematic Review and
Critical Appraisal. J Acad Nutr Diet. 2019;119:1320–39. doi:10.1016/j.jand.2019.04.002.
5. Dong JY, Ikehara S, Kimura T, Cui M, Kawanishi Y, Kimura T, et al. Skipping breakfast before and
during early pregnancy and incidence of gestational diabetes mellitus: the Japan Environment and
Children's Study. Am J Clin Nutr. 2020;111:829–34. doi:10.1093/ajcn/nqaa014.
6. Scaglioni S, De Cosmi V, Ciappolino V, Parazzini F, Brambilla P, Agostoni C. Factors Influencing
Children’s Eating Behaviours. Nutrients. 2018;10:706. doi:10.3390/nu10060706.
7. Mahmood L, Flores-Barrantes P, Moreno LA, Manios Y, Gonzalez-Gil EM. The Influence of Parental
Dietary Behaviors and Practices on Children's Eating Habits. Nutrients. 2021;13:1138.
doi:10.3390/nu13041138.
Page 12/168. Malachowska A, Jezewska-Zychowicz M. Does Examining the Childhood Food Experiences Help to
Better Understand Food Choices in Adulthood? Nutrients. 2021;13:983. doi:10.3390/nu13030983.
9. Miyoshi M, Tsuboyama-Kasaoka N, Nishi N. School-based "Shokuiku" program in Japan: application
to nutrition education in Asian countries. Asia Pac J Clin Nutr. 2012;21:159–62.
10. Sun M, Hu X, Li F, Deng J, Shi J, Lin Q. Eating Habits and Their Association with Weight Status in
Chinese School-Age Children: A Cross-Sectional Study. Int J Environ Res Public Health.
2020;17:3571. doi:10.3390/ijerph17103571.
11. Costa CS, Del-Ponte B, Assuncao MCF, Santos IS. Consumption of ultra-processed foods and body
fat during childhood and adolescence: a systematic review. Public Health Nutr. 2018;21:148–59.
doi:10.1017/S1368980017001331.
12. Gowey MA, Reiter-Purtill J, Becnel J, Peugh J, Mitchell JE, Zeller MH, et al. Weight-related correlates
of psychological dysregulation in adolescent and young adult (AYA) females with severe obesity.
Appetite. 2016;99:211–8. doi:10.1016/j.appet.2016.01.020.
13. Mijatovic-Vukas J, Capling L, Cheng S, Stamatakis E, Louie J, Cheung NW, et al. Associations of Diet
and Physical Activity with Risk for Gestational Diabetes Mellitus: A Systematic Review and Meta-
Analysis. Nutrients. 2018;10:698. doi:10.3390/nu10060698.
14. Najafi F, Hasani J, Izadi N, Hashemi-Nazari SS, Namvar Z, Shamsi H, et al. Risk of gestational
diabetes mellitus by pre-pregnancy body mass index: A systematic review and meta-analysis.
Diabetes Metab Syndr. 2021;15:102181. doi:10.1016/j.dsx.2021.06.018.
15. Michikawa T, Nitta H, Nakayama SF, Yamazaki S, Isobe T, Tamura K, et al. Baseline Profile of
Participants in the Japan Environment and Children's Study (JECS). J Epidemiol. 2018;28:99–104.
doi:10.2188/jea.JE20170018.
16. Kawamoto T, Nitta H, Murata K, Toda E, Tsukamoto N, Hasegawa M, et al. Rationale and study
design of the Japan environment and children's study (JECS). BMC Public Health. 2014;14:25.
doi:10.1186/1471-2458-14-25.
17. Yokoyama Y, Takachi R, Ishihara J, Ishii Y, Sasazuki S, Sawada N, et al. Validity of Short and Long
Self-Administered Food Frequency Questionnaires in Ranking Dietary Intake in Middle-Aged and
Elderly Japanese in the Japan Public Health Center-Based Prospective Study for the Next Generation
(JPHC-NEXT) Protocol Area. J Epidemiol. 2016;26:420–32. doi:10.2188/jea.JE20150064.
18. Murase N, Katsumura T, Ueda C, et al. International standardization of physical activity level:
reliability and validity study of the Japanese version of the International Physical Activity
Questionnaire (IPAQ) (Kosei no Shihyo). J.Health Welfare Stat. 2002;49:1–9.
19. Craig CL, Marshall AL, Sjostrom M, Wu W, Zhang X, Huo W, et al. International physical activity
questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–95.
doi:10.1249/01.MSS.0000078924.61453.FB.
20. Minakami H, Maeda T, Fujii T, Fujii T, Hamada H, Iitsuka Y, et al. Guidelines for obstetrical practice in
Japan: Japan Society of Obstetrics and Gynecology (JSOG) and Japan Association of Obstetricians
Page 13/16and Gynecologists (JAOG) 2014 edition. J Obstet Gynaecol Res. 2014;40:1469–99.
doi:10.1111/jog.12419.
21. Tey SL, Brown RC, Gray AR, Chisholm AW, Delahunty CM. Long-term consumption of high energy-
dense snack foods on sensory-specific satiety and intake. Am J Clin Nutr. 2012;95:1038–47.
doi:10.3945/ajcn.111.030882.
22. Syauqy A, Hsu CY, Rau HH, Chao JC. Association of Dietary Patterns with Components of Metabolic
Syndrome and Inflammation among Middle-Aged and Older Adults with Metabolic Syndrome in
Taiwan. Nutrients. 2018;10:143. doi:10.3390/nu10020143.
23. McCartney DM, Younger KM, Walsh J, O'Neill M, Sheridan C, Kearney JM. Socio-economic
differences in food group and nutrient intakes among young women in Ireland. Br J Nutr.
2013;110:2084–97. doi:10.1017/S0007114513001463.
24. Alcantara CM, Silva ANS, Pinheiro P, Queiroz MV. Digital technologies for promotion of healthy eating
habits in teenagers. Rev Bras Enferm. 2019;72:513–20. doi:10.1590/0034-7167-2018-0352.
25. Corazza I, Pennucci F, De Rosis S. Promoting healthy eating habits among youth according to their
preferences: Indications from a discrete choice experiment in Tuscany. Health Policy. 2021.
doi:10.1016/j.healthpol.2021.03.014.
26. Bak-Sosnowska M, Skrzypulec-Plinta V. Eating habits and physical activity of adolescents in
Katowice--the teenagers' declarations vs. their parents' beliefs. J Clin Nurs. 2012;21:2461–8.
doi:10.1111/j.1365-2702.2012.04099.x.
27. Hamulka J, Wadolowska L, Hoffmann M, Kowalkowska J, Gutkowska K. Effect of an Education
Program on Nutrition Knowledge, Attitudes toward Nutrition, Diet Quality, Lifestyle, and Body
Composition in Polish Teenagers. The ABC of Healthy Eating Project: Design, Protocol, and
Methodology. Nutrients. 2018;10:1439. doi:10.3390/nu10101439.
28. Van den Bulck J, Eggermont S. Media use as a reason for meal skipping and fast eating in
secondary school children. J Hum Nutr Diet. 2006;19:91–100. doi:10.1111/j.1365-
277X.2006.00683.x.
29. Barbara R, Ferreira-Pego C. Changes in Eating Habits among Displaced and Non-Displaced University
Students. Int J Environ Res Public Health. 2020;17:5369. doi:10.3390/ijerph17155369.
30. Aljawad A, Morgan MZ, Rees JS, Fairchild R. The availability of novelty sweets within high school
localities. Br Dent J. 2016;220:575–9. doi:10.1038/sj.bdj.2016.412.
31. Nestel PJ, Beilin LJ, Clifton PM, Watts GF, Mori TA. Practical Guidance for Food Consumption to
Prevent Cardiovascular Disease. Heart Lung Circ. 2021;30:163–79. doi:10.1016/j.hlc.2020.08.022.
32. Semnani-Azad Z, Khan TA, Blanco Mejia S, de Souza RJ, Leiter LA, Kendall CW, et al. Association of
Major Food Sources of Fructose-Containing Sugars With Incident Metabolic Syndrome: A Systematic
Review and Meta-analysis. JAMA Netw Open. 2020;3:e209993.
doi:10.1001/jamanetworkopen.2020.9993.
33. Powe CE, Kwak SH. Genetic Studies of Gestational Diabetes and Glucose Metabolism in Pregnancy.
Curr Diab Rep. 2020;20:69. doi:10.1007/s11892-020-01355-3.
Page 14/1634. Kampmann U, Knorr S, Fuglsang J, Ovesen P. Determinants of Maternal Insulin Resistance during
Pregnancy: An Updated Overview. J Diabetes Res. 2019;2019:5320156. doi:10.1155/2019/5320156.
35. Parnell AS, Correa A, Reece EA. Pre-pregnancy Obesity as a Modifier of Gestational Diabetes and
Birth Defects Associations: A Systematic Review. Matern Child Health J. 2017;21:1105–20.
doi:10.1007/s10995-016-2209-4.
36. Moosazadeh M, Asemi Z, Lankarani KB, Tabrizi R, Maharlouei N, Naghibzadeh-Tahami A, et al.
Family history of diabetes and the risk of gestational diabetes mellitus in Iran: A systematic review
and meta-analysis. Diabetes Metab Syndr. 2017;11:S99-s104. doi:10.1016/j.dsx.2016.12.016.
37. Indarti J, Susilo SA, Hyawicaksono P, Berguna JS, Tyagitha GA, Ikhsan M. Maternal and Perinatal
Outcome of Maternal Obesity at RSCM in 2014-2019. Obstet Gynecol Int. 2021;2021:6039565.
doi:10.1155/2021/6039565.
38. Makgoba M, Savvidou MD, Steer PJ. An analysis of the interrelationship between maternal age, body
mass index and racial origin in the development of gestational diabetes mellitus. BJOG.
2012;119:276–82. doi:10.1111/j.1471-0528.2011.03156.x.
39. Kubota K, Itoh H, Tasaka M, Naito H, Fukuoka Y, Muramatsu Kato K, et al. Changes of maternal
dietary intake, bodyweight and fetal growth throughout pregnancy in pregnant Japanese women. J
Obstet Gynaecol Res. 2013;39:1383–90. doi:10.1111/jog.12070.
40. McGowan CA, McAuliffe FM. Maternal nutrient intakes and levels of energy underreporting during
early pregnancy. Eur J Clin Nutr. 2012;66:906–13. doi:10.1038/ejcn.2012.15.
41. Brantsaeter AL, Haugen M, Alexander J, Meltzer HM. Validity of a new food frequency questionnaire
for pregnant women in the Norwegian Mother and Child Cohort Study (MoBa). Matern Child Nutr.
2008;4:28–43. doi:10.1111/j.1740-8709.2007.00103.x.
Figures
Page 15/16Figure 1
Flowchart for selection of participants from JECS JECS = Japan Environment and Children’s Study
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