Journal of Epidemiology
Online ISSN : 1349-9092
Print ISSN : 0917-5040
ISSN-L : 0917-5040

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2版
Gestational Weight Gain Growth Charts Adapted to Japanese Pregnancies Using a Bayesian Approach in a Longitudinal Study: The Japan Environment and Children’s Study
Naho MorisakiAurélie PiedvacheSeiichi MorokumaKazushige NakaharaMasanobu OgawaKiyoko KatoMasafumi SanefujiEiji ShibataMayumi TsujiMasayuki ShimonoToshihiro KawamotoShouichi OhgaKoichi Kusuharathe Japan Environment and Children’s Study Group
著者情報
ジャーナル オープンアクセス 早期公開
電子付録

論文ID: JE20210049

この記事には本公開記事があります。
2版: 2022/02/11
1版: 2021/08/28
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Background: Tracking gestational weight gain (GWG) during pregnancy makes it possible to optimize pregnancy outcomes, and GWG growth curves are well suitable for this purpose. The GWG guidelines for Japanese were revised in 2021. However, currently, there are no GWG growth curves to guide women on how to gain weight to meet these guidelines.

Methods: Using data on 96,631 live births from the Japan Environment and Children’s Study (JECS), we created descriptive GWG percentile curves estimating the trajectory of GWG required to meet the GWG guidelines stratified by pre-pregnancy body mass index (BMI). For both analyses, Bayesian mixed models with restricted cubic splines adjusted for maternal characteristics were used.

Results: GWG curves substantially differed by pre-pregnancy BMI and were higher among multiparas and those with lower maternal age and with no previous disease. We estimated that underweight, normal weight, overweight, and obese women who gain 8.4 to 11.1 kg, 6.4 to 9.1 kg, 3.8 to 6.5 kg, and <1.9 kg at 30 weeks of gestation are on the trajectory to reach the new guidelines at 40 weeks of gestation.

Conclusions: We provide GWG percentiles curves for Japanese women, as well as GWG trajectory curves to meet the new GWG recommendations. These results may help pregnant women monitor weight during pregnancy.

著者関連情報
© 2021 Naho Morisaki et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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