Gestational weight management and pregnancy outcomes among women of advanced maternal age
| Autor: | Yuelin Fu, Qing Han, Huale Zhang, Rongxin Chen, Juan Lin, Jianying Yan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Cancer Research medicine.medical_specialty Population advanced maternal age Overweight 03 medical and health sciences 0302 clinical medicine Immunology and Microbiology (miscellaneous) Fetal macrosomia Medicine Advanced maternal age education pre-pregnancy body mass index education.field_of_study Pregnancy pregnancy outcome business.industry Obstetrics Articles General Medicine medicine.disease Gestational diabetes 030104 developmental biology 030220 oncology & carcinogenesis gestational weight gain medicine.symptom business Body mass index Weight gain |
| Zdroj: | Experimental and Therapeutic Medicine |
| ISSN: | 1792-1015 1792-0981 |
| DOI: | 10.3892/etm.2019.7752 |
| Popis: | In this study, we assessed the effects of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) on the pregnancy outcomes of women of advanced age using a back-propagation (BP) artificial neural network. We conducted a retrospective analysis on postpartum and hospital delivery data from 1,015 women of advanced maternal age (AMA) hospitalized at the Fujian Provincial Maternity and Children's Hospital from January to June, 2017. Pre-pregnancy overweight was found to increase the incidence of gestational diabetes mellitus (GDM), hypertensive disorders complicating pregnancy (HDCP) and fetal macrosomia. In addition, poor weight gain during pregnancy increased the chances of pre-term births (PTBs). Furthermore, excessive weight gain during pregnancy increased the incidence of macrosomia in women of AMA. On the whole, the findings of this study suggest that controlling the pre-pregnancy BMI and the GWG may reduce the incidence of adverse pregnancy outcomes in women of AMA. The BP neural network is suitable for the study of weight changes in this population. |
| Databáze: | OpenAIRE |
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