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  • 标题:Maternal Smoking During Pregnancy and Childhood Growth Trajectory: A Random Effects Regression Analysis
  • 本地全文:下载
  • 作者:Kohta Suzuki ; Naoki Kondo ; Miri Sato
  • 期刊名称:Journal of Epidemiology
  • 印刷版ISSN:0917-5040
  • 电子版ISSN:1349-9092
  • 出版年度:2012
  • 卷号:22
  • 期号:2
  • 页码:175-178
  • DOI:10.2188/jea.JE20110033
  • 出版社:Japan Epidemiological Association
  • 摘要:Background: Although maternal smoking during pregnancy has been reported to have an effect on childhood overweight/obesity, the impact of maternal smoking on the trajectory of the body mass of their offspring is not very clear. Previously, we investigated this effect by using a fixed-effect model. However, this analysis was limited because it rounded and categorized the age of the children. Therefore, we used a random-effects hierarchical linear regression model in the present study. Methods: 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. The body mass index (BMI) z-score trajectory of children born to smoking and non-smoking mothers, by gender, was used as the outcome. We modeled BMI trajectory using a 2-level random intercept and slope regression. Results: The participating mothers delivered 1619 babies during the study period. For male children, there was very strong evidence that the effect of age in months on the increase in BMI z-score was enhanced by maternal smoking during pregnancy ( P < 0.0001). In contrast, for female children, there was only weak evidence for an interaction between age in months and maternal smoking during pregnancy ( P = 0.054), which suggests that the effect of maternal smoking during pregnancy on the early-life BMI trajectory of offspring differed by gender. Conclusions: These results may be valuable for exploring the mechanism of fetal programming and might therefore be clinically important.
  • 关键词:body mass index;childhood growth;gender;multi-level analysis;pregnancy;smoking
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