Gender differences in the association between maternal smoking during pregnancy and childhood growth trajectories: multilevel analysis

Abstract

Objectives:

This study examines the gender differences in the association between maternal smoking during pregnancy and later growth in childhood.

Design:

Ongoing prospective cohort study, which is called ‘the Project Koshu’, initiated in the foetal stage to the age of 9–10 years.

Setting:

Koshu City which was in Japanese rural area

Participants:

The study population comprised children born between 1 April 1991 and 31 March 1999 in Koshu City, Japan, and their mothers. Maternal smoking during early pregnancy was the exposure studied.

Main outcome measures:

Childhood body mass index (BMI) and BMI z-score trajectories of the children born to the smoking and non-smoking mothers by gender. Multilevel analysis that includes both individual and age as different-level variables was used for statistical analyses.

Results:

The participating mothers delivered 1619 babies during the study period. Birth weight and anthropometric data were collected from 1603 (at birth, 99.0%), 1358 (at age 3, 83.9%), 1248 (at age 5, 77.1%), 1270 (at age 7–8, 78.4%) and 1274 (at age 9–10, 78.7%) of these children. The mean birth weight of both the male and female children whose mothers had smoked during pregnancy was significantly low compared with those born to non-smoking mothers (P<0.01). However, the childhood BMI at each subsequent checkup age significantly increased only among the male children born to the smoking mothers. Moreover, this increase was continuously observed after 3 years of age. The results of BMI z-score analysis were also similar to these of BMI analysis.

Conclusions:

Smoking by pregnant women decreases the infant birth weight irrespective of gender but increases childhood weight gain especially by male children. The results might be valuable to explore the mechanism of fetal programming.

Introduction

Barker, who established the foetal origins of adult disease hypothesis,1 has stated that a cause for concern is that the rising rates of childhood obesity will fuel chronic disease epidemics, including those of coronary heart disease, increased blood pressure and adult-onset type 2 diabetes.2 The findings of some studies on foetal programming of chronic diseases, including obesity-related diseases, are consistent with the Barker hypothesis, which states that foetal adaptations to intrauterine undernourishment may have permanent and specific short- and long-term effects on the development of various organ systems, including the cardiovascular and metabolic systems.3, 4 Some studies from the United Kingdom, Finland and India have suggested that there might be a relationship between the specific path of growth, consisting of slow growth in foetal life and rapidly increasing body mass index (BMI) as an infant, and the development of type 2 diabetes or coronary heart disease.5, 6, 7, 8 Therefore, when considering the aetiologies of such diseases, it is necessary to examine the association between foetal or perinatal undernourishment and childhood growth.

Maternal smoking during pregnancy is a possible major cause of foetal undernourishment. Many studies have shown that maternal smoking during pregnancy affects placental and foetal circulation, which may lead to intrauterine growth retardation, low-birth-weight infants, and small-for-gestational age infants.9, 10, 11 We previously clarified this association by using a prospective cohort studied from early pregnancy.12 Moreover, several investigators have suggested that maternal smoking during pregnancy increases the child's risk of obesity during childhood and/or adulthood.13, 14, 15 We have also previously reported that maternal smoking during pregnancy may influence the onset of obesity and overweight in children aged 5 years, and found that this association persisted up to the age of 9–10 years in Japanese children.16, 17 Overweight children have a high risk of becoming overweight adults18, 19 and experience typical obesity-related morbidity.20 Therefore, the association between maternal smoking during pregnancy and childhood obesity or overweight might be consistent with the Barker hypothesis or might include a part of his hypothesis.

The term ‘life course epidemiology’ has recently become popular. The Barker hypothesis is probably the best-known example of a life course association. As it states that poor foetal nutrition, indicated by small birth size, leads to foetal adaptation that programmes the propensity to adult disease,3 it is necessary to conduct individual growth analysis that includes both individual and age as different-level variables. However, no study has conducted such a multilevel analysis to clarify the association between maternal smoking during pregnancy and childhood growth or development. Moreover, because there are gender differences in foetal growth, these differences may be a basis for gender differences in the sensitivity to foetal programming;21 however, such differences have not been studied previously. Therefore, in this study, we examined the gender differences in the association between maternal smoking during pregnancy and later growth in childhood by conducting multilevel analysis.

Materials and methods

Study design

The study population comprised children born between 1 April 1991 and 31 March 1999 in Koshu City, Yamanashi Prefecture, Japan, and their mothers. These subjects are participants of Project Koshu (formerly Project Enzan), a dynamic, ongoing prospective cohort study of pregnant women and their children in rural Japan, which commenced in 1988. Details of this project have been described in previous articles.12, 16, 17 First, we conducted a questionnaire-based survey on the expectant mothers who visited the city office to register their pregnancy, to determine their lifestyles. We then administered a questionnaire on the lifestyle habits of the mothers and their children at each medical checkup for the children. During these checkups, we obtained data on the children's growth and physical characteristics, as well as anthropometric data from elementary school children, who are measured annually in April in each grade as per the Japanese school health law.

This study was approved by the Ethical Review Board of the University of Yamanashi, School of Medicine, and was conducted in accordance with the Guidelines Concerning Epidemiological Research (the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour and Welfare, Japan), with the cooperation of the Koshu City administration office.

Data collection

Data on the maternal smoking status during early pregnancy were obtained from the mothers by using a self-report questionnaire at pregnancy registration. In the study area, over 80% of the expectant mothers registered their pregnancy in the first trimester, and almost all expectant mothers registered by 18 gestational weeks. In Japan, it is mandatory for expectant mothers to register their pregnancy in order to access health care services during pregnancy. We used the following items to assess smoking status during early pregnancy: ‘smoking’, ‘have quit smoking’ or ‘have never smoked’. Moreover, we used these items dichotomously, that is, ‘smoking mother’ included those participants who only answered ‘smoking’ and ‘non-smoking mother’ included those who answered ‘have quit smoking’ or ‘have never smoked’.

The height and weight of these women at the first pregnant checkup, which was usually carried out just before pregnancy registration, were measured and recorded in the Maternal and Child Health Handbook by an obstetrician or a midwife. We used BMI as a parameter for evaluating maternal obesity, calculated according to the World Health Organization (WHO) standards (body weight (kg)/height (m2)). Data on the birth height and weight, birth order and gestational week of delivery were obtained from the Maternal and Child Health Handbook. Data on the height and body weight of the children were obtained from the physical measurements during their medical checkup at ages 3 and 5 years. These parameters were again measured during the medical checkups for grade 2 and grade 4 children at elementary schools (that is, at age 7–8 years and 9–10 years, respectively). Height was measured using a stadiometre (unit: 0.1 cm), and body weight was measured using conventional weighing scales (unit: 100 g).

Statistical analyses

We initially used the χ2 test to assess the association between the children's gender and maternal smoking status during early pregnancy. We compared the characteristics of birth order, birth weight, gestational week of delivery and maternal BMI at the first pregnant checkup between the smoking and non-smoking mothers by the children's gender. We then used the individual growth analysis method (SAS Proc Mixed, Cary, NC, USA) to compare the childhood BMI and BMI z-score trajectories of the children born to the smoking and non-smoking mothers by gender. Adopting the approach by Fitzmaurice, Laird and Ware, we used the following model to explore the differences of the slopes in each interval between the years of age because our previous findings showed nonlinearity in the slopes of BMI and BMI z-score.22

where i represents individual, t represents time, β1−4 represent estimates, and e is error term. In the final models, years were used as dummy variables of time. Sample clustering within individuals were addressed. In this analysis, we used the individual data of BMI, which was recorded at birth and one or more times after 3 years of age. Individual BMI z-score, which was based on the WHO standards was used to adjust the difference of BMI in each age of month within same age groups.23 All analyses were conducted using SAS version 9.1 (SAS Institute, Inc.).

Results

Participants

The participating mothers gave birth to 1619 babies during the study period, including 97 (6.0%) mothers who had smoked during early pregnancy. There was no significant difference in the number of smoking mothers by the children's gender. The follow-up rates were significantly lower among the female children than the male children at ages 7–8 and 9–10 (Table 1).

Table 1 The comparison of the rate of maternal smoking during pregnancy and the number of follow-up children at each age between male children and female children in the Koshu Project, Japan, 1991–2008

Participant characteristics

We next compared the characteristics of the mothers and their children by smoking status and gender (Table 2). Irrespective of gender, the children born to the smoking mothers had significantly lower birth weight than those born to the non-smoking mothers (P<0.001). No significant differences were found in terms of birth order, gestational week of delivery and maternal BMI at pregnancy registration.

Table 2 The comparison of characteristics about the children and their mother between smoking mothers and non-smoking mothers in the Koshu Project, Japan, 1991–2008

BMI trajectories of the male and female children

The BMI of the male children at birth did not differ significantly by maternal smoking status. However, the BMI of the female children born to the smoking mothers was significantly lower than that of the female children born to the non-smoking mothers (Tables 3 and 4). In the period between birth and 3 years of age, the mean childhood BMI changed significantly irrespective of gender. For the male gender, the BMI trajectory at each subsequent checkup age was significantly higher among those children born to the smoking mothers (Figure 1). For the female gender, on the other hand, a significant difference in the trajectory inclination was observed in the period between 3 and 5 years of age (Figure 2); however, after age 5 years, no significant difference was seen.

Table 3 The comparison of body mass index (BMI) and BMI z-score in the children between smoking mothers and non-smoking mothers in the Koshu Project, Japan, 1991–2008
Table 4 Solution for fixed effects about body mass index (BMI) in each age of children, smoking status of their mother and interaction between each age and smoking status of their mother in the Koshu Project, Japan, 1991–2008
Figure 1
figure1

Body mass index (BMI) trajectories of the male children, calculated by individual growth analysis between the smoking and non-smoking mothers.

Figure 2
figure2

Body mass index (BMI) trajectories of the female children, calculated by individual growth analysis between the smoking and non-smoking mothers.

BMI z-score trajectories of the male and female children

Even though the BMI z-score was applied to adjust the effect of the differences of month age in each years of age, the trajectories of childhood growth in both male and female children were almost similar to the results of the BMI trajectories. (Table 5, Figures 3 and 4) In the period between birth and 3 years of age, the mean childhood BMI z-score changed significantly irrespective of gender. For the male gender, there was very strong evidence that the BMI z-score trajectory at each subsequent checkup age was higher among those children born to the smoking mothers (Figure 3). For the female gender, on the other hand, there was only strong evidence that a difference in the slope of trajectory was observed in the period between 3 and 5 years of age. In addition, there was evidence that there was a difference in the slope of trajectory between 5 and 7–8 years, however, after age 7–8 years, no significant difference was seen (Figure 4).

Table 5 Solution for fixed effects about body mass index (BMI) z-score in each age of children, smoking status of their mother and interaction between each age and smoking status of their mother in the Koshu Project, Japan, 1991–2008
Figure 3
figure3

Body mass index (BMI) z-score trajectories of the male children, calculated by individual growth analysis between the smoking and non-smoking mothers.

Figure 4
figure4

Body mass index (BMI) z-score trajectories of the female children, calculated by individual growth analysis between the smoking and non-smoking mothers.

Discussion

In this study, we clarified that maternal smoking during pregnancy decreased the infant birth weight irrespective of gender, and found possible gender differences in the association between maternal smoking during pregnancy and childhood growth.

Although several investigators have suggested that maternal smoking during pregnancy increases the child's risk of obesity during childhood and/or adulthood,13, 14, 15, 16, 17 most of them did not use repeated measurements with multilevel data analyses and could not describe the individual growth trajectories. Therefore, we are the first to examine the association between maternal smoking during pregnancy and childhood growth, and clarify the gender differences in this association by using multilevel data analyses.

In this study, we did not find significant differences between the genders with regard to the association between maternal smoking during pregnancy and infant birth weight. This association has been clarified by many studies.9, 10, 11, 12 Some studies have suggested that the physiological effects of maternal smoking during pregnancy on foetal growth are a culmination of the vasoconstrictive effects of nicotine on the uterine and, potentially, umbilical arteries and the effects on oxygenation by carboxyhaemoglobin.24 Our results suggest that there might not be gender differences in these mechanisms with regard to maternal smoking during pregnancy and foetal growth. However, because we found that the BMI at birth was affected by maternal smoking during pregnancy in only the female children, gender differences between maternal smoking and birth height may exist.

The childhood BMI and BMI z-score trajectories of the male children born to mothers who smoked during pregnancy significantly increased compared with that of the male children born to the non-smoking women; for the female children, however, the results of this analysis were different, especially after 5 years of age . These results suggest that there are some gender differences in childhood physical development, such as vascular development, which are affected by maternal smoking during pregnancy. De Zegher et al.21 have suggested that some critical time windows of development may be slightly different in male and female children, and this phenomenon may be a basis for the gender differences in the sensitivity to foetal programming, because male foetuses seem to grow not only more but also earlier than female foetuses.

Many studies have found gender differences in foetal programming of some chronic diseases in animal models.25, 26, 27 For example, consistent with our results, a study of pregnant mice revealed a gender-specific effect on foetal programming of blood pressure.25 With regard to vascular structure, Gariepy et al. have suggested possible protection of the female gender from early structural arterial alteration due to smoking, on the basis of the finding that smoking-related increase in intima–media thickness exists only in men, and not in women.28 Moreover, Gardiner29 has suggested that there might be early environmental influences on vascular programming. Although the participants of the Gariepy et al. study were adults, we believe that our results are consistent with their findings. Therefore, maternal smoking during pregnancy might affect childhood vascular development, and this effect might represent the phenotype of childhood obesity or overweight. Moreover, there might be gender differences in these mechanisms of foetal programming.

Finally, with regard to sex hormones, Smith et al.30 have suggested that prenatal nicotine increases testosterone levels in rat foetuses, and Blouin et al.31 suggested that androgens have an important role in the regulation of body fat distribution. Therefore, it is important to conduct further studies on the gender differences in foetal programming to clarify the mechanism of obesity-related diseases, such as coronary heart disease or type 2 diabetes. We consider our results to be a basis for such future studies.

Our study has certain strengths. Many birth cohort studies have been conducted worldwide; however, most of these studies did not investigate the effects of maternal lifestyle habits, such as smoking, during early pregnancy. Moreover, almost all the pregnant women in Japan register their pregnancy at a city office to obtain health care services during pregnancy. Consequently, we were able obtain data from almost every pregnant woman in the study area during the study period, because we had collected the data at pregnancy registration (that is, during the children's foetal stage of life). We consider this to be one of the main advantages of our study.

Further, because the children who participated were followed up until 9–10 years of age, the follow-up period was about 10 years. Although it is usually considerably difficult to follow-up study participants over such an extended duration, the follow-up rate of the participants was relatively high (78.7% at 9–10 years of age). This high rate is attributable to the fact that most of the city's population had not migrated elsewhere and we were able to obtain the children's height and body weight data from physical measurements taken during medical checkups for elementary-school children, conducted in all elementary schools in Koshu city.

This study, however, has some limitations. First, we designed a questionnaire to obtain data on maternal smoking during early pregnancy, but did not examine its validity. Nevertheless, a previous study demonstrated that pregnant women report their own smoking habits very accurately.32 On the basis of this report, our results can be considered valid. Next, we were unable to obtain data on the complications arising in these women, weight gain during pregnancy and foetal abnormalities because the study was not conducted in a clinical setting. With regard to the analyses, we were unable to adjust for the participants’ socio-economic status, which is a potential confounding factor. However, we consider that the effect of this limitation was relatively small. Because this study was conducted in a whole municipality and there was no significant gender difference in the rate of maternal smoking during pregnancy, we believe that there was little gender difference in these factors. In addition, there was no information about maternal smoking after delivery. The effect of this limitation might be also very small because it was suggested that almost all women who did not smoke during pregnancy did not start smoking after their delivery.33

Moreover, for the elementary-school children, we could obtain data only on their medical checkups performed in April annually. Therefore, the children's age at the medical checkups was inconsistent: for example, the age range at the medical checkup for the grade 4 students was 9–10 years. However, because we thought there was no gender difference in the distribution of birth month, the effect of this limitation was also relatively small. To investigate if the impact of this measurement error was crucial, we repeated our analysis by a random effects modelling approach using month as the unit of time. The result of this sensitivity analysis was very similar to our original result and did not change the conclusion of this study. In addition, the results of BMI z-score trajectories between male and female children were almost similar to these of the BMI trajectories.

Finally, in this study, the followup rates of the female children were significantly lower than those of the male children at 7–8 and 9–10 years of age. However, we consider that this difference might be caused by the female children of the non-smoking mothers (Table 3). Therefore, the impact of this limitation on the results might also be relatively small.

In conclusion, smoking by pregnant mothers decreases infant birth weight irrespective of gender and increases childhood weight gain especially by male children. These results might be valuable to explore the mechanism of foetal programming, and are therefore important from the clinical viewpoint and with regard to public health. For example, it is very important to consider the gender differences in the aetiologies of obesity or obesity-related diseases to develop and conduct prevention and intervention programmes.

References

  1. 1

    Barker DJ, Osmond C . Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet 1986; 1: 1077–1081.

    CAS  Article  Google Scholar 

  2. 2

    Barker DJ . Obesity and early life. Obes Rev 2007; 8 (Suppl 1): 45–49.

    Article  Google Scholar 

  3. 3

    Barker DJ . Mothers, Babies, and Disease in Later Life. BMJ Publishing: London, UK, 1994.

    Google Scholar 

  4. 4

    Barker DJ . In utero programming of chronic disease. Clin Sci (Lond) 1998; 95: 115–128.

    CAS  Article  Google Scholar 

  5. 5

    Phillips DI . Insulin resistance as a programmed response to fetal undernutrition. Diabetologia 1996; 39: 1119–1122.

    CAS  Article  Google Scholar 

  6. 6

    Eriksson JG, Forsén T, Tuomilehto J, Osmond C, Barker DJ . Early adiposity rebound in childhood and risk of Type 2 diabetes in adult life. Diabetologia 2003; 46: 190–194.

    CAS  Article  Google Scholar 

  7. 7

    Bhargava SK, Sachdev HS, Fall CH, Osmond C, Lakshmy R, Barker DJ et al. Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. N Engl J Med 2004; 350: 865–875.

    CAS  Article  Google Scholar 

  8. 8

    Barker DJ, Osmond C, Forsén TJ, Kajantie E, Eriksson JG . Trajectories of growth among children who have coronary events as adults. N Engl J Med 2005; 353: 1802–1809.

    CAS  Article  Google Scholar 

  9. 9

    Kramer MS . Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ 1987; 65: 663–737.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Windham GC, Hopkins B, Fenster L, Swan SH . Prenatal active or passive tobacco smoke exposure and the risk of preterm delivery or low birth weight. Epidemiology 2000; 11: 427–433.

    CAS  Article  Google Scholar 

  11. 11

    England LJ, Kendrick JS, Wilson HG, Merritt RK, Gargiullo PM, Zahniser SC . Effects of smoking reduction during pregnancy on the birth weight of term infants. Am J Epidemiol 2001; 154: 694–701.

    CAS  Article  Google Scholar 

  12. 12

    Suzuki K, Tanaka T, Kondo N, Minai J, Sato M, Yamagata Z . Is maternal smoking during early pregnancy a risk factor for all low birth weight infants? J Epidemiol 2008; 18: 89–96.

    Article  Google Scholar 

  13. 13

    Montgomery SM, Ekbom A . Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. BMJ 2002; 324: 26–27.

    Article  Google Scholar 

  14. 14

    Toschke AM, Montgomery SM, Pfeiffer U, von Kries R . Early intrauterine exposure to tobacco-inhaled products and obesity. Am J Epidemiol 2003; 158: 1068–1074.

    CAS  Article  Google Scholar 

  15. 15

    Oken E, Levitan EB, Gillman MW . Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. Int J Obes (Lond) 2008; 32: 201–210.

    CAS  Article  Google Scholar 

  16. 16

    Mizutani T, Suzuki K, Kondo N, Yamagata Z . Association of maternal lifestyles including smoking during pregnancy with childhood obesity. Obesity (Silver Spring) 2007; 15: 3133–3139.

    Article  Google Scholar 

  17. 17

    Suzuki K, Ando D, Sato M, Tanaka T, Kondo N, Yamagata Z . The association between maternal smoking during pregnancy and childhood obesity persists to the age of 9–10 years. J Epidemiol 2009; 19: 136–142.

    Article  Google Scholar 

  18. 18

    Abraham S, Collins G, Nordsieck M . Relationship of childhood weight status to morbidity in adults. HSMHA Health Rep 1971; 86: 273–284.

    CAS  Article  Google Scholar 

  19. 19

    Charney E, Goodman HC, McBride M, Lyon B, Pratt R . Childhood antecedents of adults obesity. Do chubby infants become obese adults? N Engl J Med 1976; 295: 6–9.

    CAS  Article  Google Scholar 

  20. 20

    Dietz WH . Childhood weight affects adult morbidity and mortality. J Nutr 1998; 128: 411S–414S.

    CAS  Article  Google Scholar 

  21. 21

    de Zegher F, Devlieger H, Eeckels R . Fetal growth: boys before girls. Horm Res 1999; 51: 258–259.

    CAS  PubMed  Google Scholar 

  22. 22

    Fitzmaurice GM, Laird NM, Ware JH . Applied Longitudinal Analysis. Wiley-Interscience: New Jersey, US, 2004.

    Google Scholar 

  23. 23

    World Health Organization (WHO). Multicentre Growth Reference Study Group. 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. World Health Organization 2006.

  24. 24

    Lambers DS, Clark KE . The maternal and fetal physiologic effects of nicotine. Semin Perinatol 1996; 20: 115–126.

    CAS  Article  Google Scholar 

  25. 25

    Sardinha FL, Telles MM, Albuquerque KT, Oyama LM, Guimarães PA, Santos OF et al. Gender difference in the effect of intrauterine malnutrition on the central anorexigenic action of insulin in adult rats. Nutrition 2006; 22: 1152–1161.

    CAS  Article  Google Scholar 

  26. 26

    Lu F, Bytautiene E, Tamayo E, Gamble P, Anderson GD, Hankins GD et al. Gender-specific effect of overexpression of sFlt-1 in pregnant mice on fetal programming of blood pressure in the offspring later in life. Am J Obstet Gynecol 2007; 197: 418.e1–418.e5.

    Article  Google Scholar 

  27. 27

    Grigore D, Ojeda NB, Alexander BT . Sex differences in the fetal programming of hypertension. Gend Med 2008; 5 (Suppl A): S121–S132.

    Article  Google Scholar 

  28. 28

    Gariepy J, Denarie N, Chironi G, Salomon J, Levenson J, Simon A . Gender difference in the influence of smoking on arterial wall thickness. Atherosclerosis 2000; 153: 139–145.

    CAS  Article  Google Scholar 

  29. 29

    Gardiner HM . Early environmental influences on vascular development. Early Hum Dev 2007; 83: 819–823.

    Article  Google Scholar 

  30. 30

    Smith LM, Cloak CC, Poland RE, Torday J, Ross MG . Prenatal nicotine increases testosterone levels in the fetus and female offspring. Nicotine Tob Res 2003; 5: 369–374.

    CAS  Article  Google Scholar 

  31. 31

    Blouin K, Boivin A, Tchernof A . Androgens and body fat distribution. J Steroid Biochem Mol Biol 2008; 108: 272–280.

    CAS  Article  Google Scholar 

  32. 32

    Klebanoff MA, Levine RJ, Morris CD, Hauth JC, Sibai BM, Ben Curet L et al. Accuracy of self-reported cigarette smoking among pregnant women in the 1990s. Paediatr Perinat Epidemiol 2001; 15: 140–143.

    CAS  Article  Google Scholar 

  33. 33

    Lelong N, Kaminski M, Saurel-Cubizolles MJ, Bouvier-Colle MH . Postpartum return to smoking among usual smokers who quit during pregnancy. Eur J Public Health 2001; 11: 334–339.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank all the study participants for allowing the use of their personal data. We also thank the staff of the Administrative Office of Koshu City for their cooperation. This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI 20590639) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.

Author contributions

K Suzuki was primarily responsible for the data collection, analysis, interpretation and preparation of this report. N Kondo participated in the analysis and interpretation of data. M Sato, T Tanaka and D Ando were involved in the data collection and interpretation of data. Z Yamagata was the lead investigator of Project Koshu, and advised on issues of study design, participated in the analysis and interpretation of data and helped in the preparation of this report.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Z Yamagata.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Suzuki, K., Kondo, N., Sato, M. et al. Gender differences in the association between maternal smoking during pregnancy and childhood growth trajectories: multilevel analysis. Int J Obes 35, 53–59 (2011). https://doi.org/10.1038/ijo.2010.198

Download citation

Keywords

  • BMI
  • childhood growth
  • smoking
  • pregnancy
  • multilevel analysis
  • gender

Further reading

Search