Pre-pregnancy underweight and obesity are positively associated with small-for-gestational-age infants in a Chinese population.
Yuan Hua ChenLi LiWei ChenZhi Bing LiuLi MaXing Xing GaoJia Liu HeHua WangMei ZhaoYuan Yuan YangDe-Xiang XuPublished in: Scientific reports (2019)
The association between suboptimal pre-pregnancy body mass index (BMI) and small-for-gestational-age (SGA) infants is not well defined. We investigated the association between pre-pregnancy BMI and the risk of SGA infants in a Chinese population. We performed a cohort study among 12029 mothers with a pregnancy. This cohort consisted of pregnant women that were: normal-weight (62.02%), underweight (17.09%), overweight (17.77%) and obese (3.12%). Birth sizes were reduced in the underweight and obese groups compared with the normal-weight group. Linear regression analysis indicated that birth size was positively associated with BMI in both the underweight and normal-weight groups. Further analysis showed that 12.74% of neonates were SGA infants in the underweight group, higher than 7.43% of neonates reported in the normal-weight group (adjusted RR = 1.92; 95% CI: 1.61, 2.30). Unexpectedly, 17.60% of neonates were SGA infants in the obese group, much higher than the normal-weight group (adjusted RR = 2.17; 95% CI: 1.57, 3.00). Additionally, 18.40% of neonates were large-for-gestational-age (LGA) infants in the obese group, higher than 7.26% of neonates reported in the normal-weight group (adjusted RR = 3.00; 95% CI: 2.21, 4.06). These results suggest that pre-pregnancy underweight increases the risk of SGA infants, whereas obesity increases the risks of not only LGA infants, but also SGA infants.
Keyphrases
- gestational age
- body mass index
- preterm birth
- weight loss
- weight gain
- birth weight
- low birth weight
- physical activity
- pregnant women
- metabolic syndrome
- type diabetes
- bariatric surgery
- adipose tissue
- body weight
- insulin resistance
- mass spectrometry
- high fat diet induced
- high resolution
- data analysis
- neural network
- human health