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Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications

Abstract

Background

The potential effects of pre-pregnancy body mass (BMI) and gestational weight gain (GWG) on pregnancy outcomes remain unclear. Thus, we investigated socio-demographic characteristics that affect pre-pregnancy BMIs and GWG and the effects of pre-pregnancy BMI and GWG on Chinese maternal and infant complications.

Methods

3172 women were enrolled in the Chinese Pregnant Women Cohort Study-Peking Union Medical College from July 25, 2017 to July 24, 2018, whose babies were delivered before December 31, 2018. Regression analysis was employed to evaluate the socio-demographic characteristics affecting pre-pregnancy BMI and GWG values and their effects on adverse maternal and infant complications.

Results

Multivariate logistic regression analysis revealed that age groups < 20 years (OR: 1.97), 25–30 years (OR: 1.66), 30–35 years (OR: 2.24), 35–40 years (OR: 3.90) and ≥ 40 years (OR: 3.33) as well as elementary school or education below (OR: 3.53), middle school (OR: 1.53), high school (OR: 1.40), and living in the north (OR: 1.37) were risk factors in maintaining a normal pre-pregnancy BMI. An age range of 30–35 years (OR: 0.76), living in the north (OR: 1.32) and race of ethnic minorities (OR: 1.51) were factors affecting GWG. Overweight (OR: 2.01) and inadequate GWG (OR: 1.60) were risk factors for gestational diabetes mellitus (GDM). Overweight (OR: 2.80) and obesity (OR: 5.42) were risk factors for gestational hypertension (GHp). Overweight (OR: 1.92), obesity (OR: 2.48) and excessive GWG (OR: 1.95) were risk factors for macrosomia. Overweight and excessive GWG were risk factors for a large gestational age (LGA) and inadequate GWG was a risk factor for low birth weights.

Conclusions

Overweight and obesity before pregnancy and an excessive GWG are associated with a greater risk of developing GDM, GHp, macrosomia and LGA. The control of body weight before and during the course of pregnancy is recommended to decrease adverse pregnancy outcomes, especially in pregnant women aged < 20 or > 25 years old educated below university and college levels, for ethnic minorities and those women who live in the north of China.

Trial registration

Registered at Clinical Trials (NCT03403543), September 29, 2017.

Peer Review reports

Background

In recent years, the pre-pregnancy BMI of women of childbearing ages has shown an upward trend in developed countries [1]. The Pregnancy Risk Assessment Monitoring System (PRAMS) revealed that obesity prior to conception was as high as 22%, an increase of 69.3% compared with 10 years ago in the United States [1]. In China, the 2002 national nutrition survey revealed that being overweight (a BMI ≥ 24 kg/m2) and obese (a BMI ≥ 28 kg/m2) for women aged 18–44 reached 21.8 and 6.1%, respectively [2], and that there was an increasing trend particularly in women of childbearing age [3].

The nutritional status of mothers-to-be is believed to be a good predictor of perinatal and adverse long-term outcomes for both the infant and the mother [4]. Being overweight or obese before becoming pregnancy are high risk factors for GDM, hypertensive syndrome and disorders of fetal growth [5, 6]. In contrast, underweight pregnant women are at an increased risk of preterm birth (PB) and for delivering small-for-gestational-age (SGA) newborns [7, 8]. In addition, women who present with inadequate weight gain may experience complications such as anemia [9], PB [10], low birth weight (LBW) [11] and SGA [12], whereas women with excessive weight gain are more likely to develop GDM [13], GHp [14], preeclampsia [9] and the need for caesarean sections [15]. Therefore, it is of particular relevance to study the effects of pre-pregnancy BMI and GWG on pregnancy and the newborn, and to develop a reasonable pregnancy weight control plan. Most of the current evidence on pre-pregnancy BMI and GWG values comes from Western or high income countries [16].

The Chinese Pregnant Women Cohort Study-Peking Union Medical College (CPWCS-PUMC) is a multicenter, prospective and ongoing cohort study, which was established to provide relevant scientific evidence to guide the healthcare of pregnant Chinese women. In the present study, pregnant women from the CPWCS-PUMC in their first trimester were selected as subjects. We aimed to find the socio-demographic characteristics that could affect pre-pregnancy BMI and GWG values and the effects that these values may have on maternal and infant complications.

Methods

Study design and participants

This study was based on CPWCS-PUMC population research from 2017 to 2018 in 24 hospitals (secondary grades and above, with maternal and child health care centers accounting for two-thirds and general hospitals one-third of institutions) in 15 provinces (municipalities and autonomous regions) (Supplementary Figure 1). CPWCS-PUMC utilizes self-designed surveys for pregnant women and physicians. The pregnant women surveys consisted of four phases, namely the first trimester, the second trimester, the third trimester and the postpartum 6-week survey. Every survey included basic information and the status of physical care, environmental, physical activity, dietary and nutrient supplement, sleep, psychological, health and economic status. The physician-side survey was completed by physicians and epidemiologists and included three surveys about information on prenatal examination, maternal delivery outcomes and infant outcomes.

The inclusion criteria of the CPWCS-PUMC cohort study were: (1) Chinese nationality; (2) pregnancy ≤12 gestational weeks; (3) maternity files had been established in hospital; (4) regular birth inspection; (5) online completion of the survey; (6) signing of informed consent. Exclusion criteria were: (1) pregnancy > 12 gestational weeks; (2) those who could not have regular birth inspection; (3) floating population who did not live in the local area for a long time; (4) those who have contraindications to pregnancy such as gynecological tumors. In the present study, only single pregnancy outcomes were investigated.

Our local ethics committee approved the study (HS-1345) and all recruited women provided signed written consent forms.

Recruitment

From July 25, 2017 to July 24, 2018, 7976 pregnant women in the first trimester who met the inclusion criteria took part in the CPWCS-PUMC cohort study. A total of 6916 pregnant women submitted valid first-trimester surveys. Singleton maternal and neonatal outcomes of 3767 pregnant women with babies delivered before December 31, 2018 were collected from the physician survey. 144 pregnant women without information on prenatal visits from the physician survey were excluded. Data about 3623 pregnant women with information on prenatal examinations as well as maternal and neonatal outcomes were evaluated. A total of 451 pregnant women without weight or height data measured during the first prenatal examination or delivery weights measured at the last prenatal examination, were not eligible for inclusion. In total, 3172 pregnant women were included in the data analysis of the present study (Fig. 1).

Fig. 1
figure1

Flow chart of this study

Data collection

We collected socio-demographic data from pregnant women surveys including race, age, education level, living region, census register type, occupation, family member, self-income and family-income. We also measured the heights and weights of the women at their first prenatal examination, including their weights recorded at the last prenatal examination (data obtained from the prenatal examination information of the physician survey). Maternal outcomes from the physician survey including gestational weeks, delivery mode, maternal complications (e.g., anemia, premature membrane rupture, gestational diabetes mellitus and hypertension) were collected by physicians at the 6-week postpartum follow-ups. Neonatal outcomes from the physician survey including low and normal birth weights, macrosomia and small, normal or large size for gestational age (GA) were collected during physicians’ home visits to the mother’s home at the sixth week postpartum.

Standard measurements

Physicians in the centers involved in the study collected anthropometric data. Mothers’ weights and heights were measured in light clothing but with no shoes on. Height was measured to the nearest 0.1 cm with a ruler and weight to the nearest 0.01 kg using calibrated electronic scales. Blood pressure was measured using a standard sphygmomanometer. The presiding physicians entered all relevant data into the hospital’s electronic medical records system.

BMI (kg/m2) values before pregnancy were calculated by measuring the height and weight of pregnant women at their first prenatal examination (pregnancy ≤12 gestational weeks). It is noteworthy that self-reported pre-pregnancy weights were highly correlated with those recorded at the initial prenatal visits [15]. BMI values before pregnancy were classified according to the WHO cut-off points for Asian adults [17, 18] (Supplementary Table 1). GWG refers to the difference between the weight measured at the last prenatal examination before delivery and the weight measured at the initial prenatal examination. GWG was classified following the 2009 Institute of Medicine (IOM) guidelines [19] (Supplementary Table 2).

GHp was defined as systolic blood pressure being ≥140 mmHg or diastolic pressure being ≥90 mmHg during the 3rd trimester, or if the mother-to-be had been prescribed medication to control hypertension [20]. GDM was diagnosed if one or more of the following criteria were met during pregnancy: fasting plasma glucose ≥5.1 mmol/L, 1 h plasma glucose levels ≥10.0 mmol/L, 2 h glucose levels ≥8.5 mmol/L after overnight fasting with a 75 g glucose load according to the WHO 2013 diagnostic criteria [21]. Prelabor rupture of membranes (PROM) was suspected based on symptoms and speculum examination and might have been supported by testing the vaginal fluid or by ultrasound [22]. Anemia in pregnancy was diagnosed as a hemoglobin (Hb) concentration < 110 g/L (11 g/dL) according to the WHO criteria [23].

The definition of macrosomia employed was a birth weight > 4000 g. A low birth weight was defined as < 2500 g, SGA as a birth weight < than the 10th percentile and LGA as a birth weight > than the 90th percentile for GA.

Statistical analyses

Data were collated and analyzed using Microsoft Office Excel 2007 and SPSS Statistics for Windows (Version 25.0, IBM Corp, NY, US). The classification index describes the number and percentage of various types, and the chi-squared test or the exact probability method (if the chi-squared test was not appropriate) was employed for comparisons between groups. A cumulative logistic regression model was employed to correct the effect of confounding factors in order to analyze the socio-demographic characteristics affecting the BMI values before pregnancy and GWG. Multivariate logistic regression models (including cumulative logistic regression and multinomial logistic regression) were employed to correct for confounding factors permitting the analysis of independent risk factors for adverse outcomes for mothers maternal and neonates. A P-value < 0.05 was considered to be a significant finding.

Results

Socio-demographic characteristics affecting BMI values before pregnancy and GWG

Pre-pregnancy BMIs were classified into 4 types namely: underweight, normal, overweight and obese women (see Table 1). There were significant differences in race (P <  0.001), age (P = 0.020), educational levels (P <  0.001), regions (P <  0.001), occupations (P = 0.030), self-income (P = 0.010) and family-income (P <  0.001) among the 4 pre-pregnancy BMI groups (Table 1). However, after correction by multivariate logistic regression analysis, it was found that in terms of age, compared with the 20–25 years age group, pregnant women of the age groups < 20 years old (OR: 1.97, P = 0.008), 25–30 years old (OR: 1.66, P <  0.001), 30–35 years old (OR: 2.24, P <  0.001), 35–40 years old (OR: 3.90, P <  0.001) and ≥ 40 years old (OR: 3.33, P <  0.001) were at risk to keep normal weight prior to pregnancy. From the view of education, compared with pregnant women with college or university degree, elementary school education and below (OR: 3.53, P = 0.006), middle school (OR: 1.53, P <  0.001) and high school (OR: 1.40, P = 0.001) were risk factors to keep normal pre-pregnancy BMI. From a regional perspective, pregnant women living in the south were more likely to control pre-pregnancy BMIs within the normal range than pregnant women in the north (OR: 1.37, P <  0.001; see Table 2).

Table 1 Comparison of socio-demographic characteristics in the four pre-pregnancy BMI groups
Table 2 Multivariate analysis of socio-demographic factors affecting pre-pregnancy BMI values

GWG was classified into 3 types (inadequate, adequate and excessive) according to IOM recommended criteria in Table 3. There were significant differences in age (P = 0.004), region (P = 0.002), census register type (P = 0.041) and family-income (P = 0.028) among the 3 GWG groups (Table 3). However, after correction by multivariate logistic regression analysis, it was found that compared with the age group range 20–25 years, 30–35 years old (OR: 0.76, P = 0.022) was a protective factor in gaining adequate weight during pregnancy. On the other hand, pregnant women living in the north (OR: 1.32, P <  0.001) and pregnant women of ethnic minorities (OR: 1.51, P = 0.041) were risk factors for gaining adequate weight during pregnancy (Table 4).

Table 3 Comparison of socio-demographic characteristics in the 3 GWG groups according to IOM recommendations
Table 4 Multivariate analysis of socio-demographic factors affecting GWG

Effect of pre-pregnancy BMI values on maternal and infant complications

In maternal outcomes, there were significant differences in the delivery mode, GDM and GHp (all P <  0.001) among the 4 pre-pregnancy BMI groups. For neonatal outcomes, there were significant differences in birth weights (and GA (both P <  0.001) among the 4 pre-pregnancy BMI groups (Table 5). After adjusting for the effects of confounding factors using a multivariate logistic regression model, we found that odd ratios in overweight pregnant women were 2.01 times and 2.80 times higher to suffer GDM (P <  0.001) and GHp (P <  0.001), and 1.92 times and 1.73 times higher to deliver macrosomia (P <  0.001) and LGA (P <  0.001) compared to normal weight pregnant women. Similarly, odd ratios in obese pregnant women were 5.42 times higher to suffer GHp (P <  0.001) and 2.48 times higher to deliver macrosomia (P = 0.019) compared to normal weight pregnant women (Table 7).

Table 5 Comparison of maternal and neonatal outcomes in the four pre-pregnancy BMI groups

Effect of GWG on maternal and infant complications

For maternal outcomes, there were significant differences in gestational weeks, delivery mode and GDM (all P <  0.001) and GHp (P = 0.004) among the 3 GWG groups. For neonatal outcomes, there were significant differences in birth weights and GA (both P <  0.001) in the 3 GWG groups (Table 6). After adjusting for the effects of confounding factors using a multivariate logistic regression model, we found that women who gained weight in the inadequate group had a 1.60 times higher odd ratio to suffer GDM (P <  0.001) and a 1.66 times higher odd ratios to give birth to weight babies (P = 0.022) compared to adequate weight gain women. Pregnant women who exhibited excessive weight gain had a 1.95 times higher odd ratio to deliver macrosomia (P <  0.001), and a 1.89 times higher odd ratios of delivering LGA (P <  0.001) compared to adequate weight gain pregnant women (Table 7).

Table 6 Comparison of maternal and neonatal outcomes in the three GWG groups according to IOM recommendations
Table 7 Multivariate analysis of the effect of pre-pregnancy BMI and GWG on maternal and infant complications (All subjects adjusted for age, race, education level, family income)

Discussion

Through this survey, we found that, age, education level and region of China were factors that affected pre-pregnancy BMI, and that age, region and race were factors that affected GWG. For maternal and neonatal complications, being overweight and obese before pregnancy and unwarranted GWG were associated with an increased risk of GDM, GHp, macrosomia and LGA, and inadequate GWG bear greater risks for GDM and a low infant birth weight.

In our survey, it was established that pregnant women aged < 20 years and > 25 years old did not control pre-pregnancy BMIs within the normal range compared with the 20–25 year old age group. Studies have shown that too early and too late delivery increased the risk of adverse pregnancy outcomes [24,25,26,27] and also the risk of malformations [28, 29]. Therefore, women of these age groups should be recommended to control their weight within the normal range before pregnancy.

From the perspective of regional distribution, our result revealed that pregnant women living in the south were more likely to maintain normal pre-pregnancy BMIs and adequate GWG than pregnant women in the north, suggesting that the geographical location had an impact on these variables. Possible reasons may be that dietary culture varies between southern and northern regions, perhaps due to different climates and agricultural practices. A normal diet in the south typically involves a high intake of rice as staple food. In contrast, people in the north cultivate mainly wheat as their staple food [30]. Rice is a low-energy food containing about twice the quantity of water and about 50% of the energy compared with the same quantity of bread made from steamed wheat [31]. In addition, a colder climate in the north is associated with reduced physical activity and an energy-rich diet, factors likely to account for the measured increase in body weights [32].

For maternal complications, our study confirmed that being overweight before pregnancy was a risk factor for GDM, a result consistent with other recent findings [33,34,35]. GDM can seriously threaten the health of mothers and offspring [36]. Although the pathogenesis remains unclear, related studies have shown that insulin resistance is mainly caused by a series of physiological and pathological changes during pregnancy [37]. Adipose tissue is resistant to insulin action, resulting in lower levels of insulin receptors in fat [38, 39] and the number of insulin receptors in the body gradually decreases with increasing BMI. Therefore, regardless of pregnancy, individuals with BMIs have a greater risk of being diabetic than those with low BMIs. At the same time, due to physiological changes in the pattern of glucose metabolism during pregnancy, glucose tolerance is reduced [40], which further amplifies the risk of developing diabetes for pregnant women with high BMIs.

Being overweight and obese before pregnancy was proven to increase the risk of GHp in the present study. The possible mechanism is that has been weight increase leads to the accumulation of estrogen in the body due to the accumulation of fat. By mediating aldosterone secretion, sodium retention is caused by the renin angiotensin system or by directly increasing the recollection of the renal tubules, resulting in hypertension [41]. Furthermore, excessive fat accumulation can cause abnormal blood lipid metabolism, which is also related to gestational diabetes and hypertension [42]. Studies have shown that weight loss and control of obese pregnant women during pregnancy can reduce the risk of GHp (OR = 0.31; 95% CI: 0.11 ~ 0.84) [43].

The energy sources that mothers provide for fetal development include energy reserves before pregnancy and food acquisition during pregnancy [44]. The neonatal complications revealed in our study strongly indicated that being overweight and obese, and excessive GWG were important risk factors for macrosomia and LGA, while inadequate GWG was a risk factor for low birth weight, indicating that there is a clear correlation between maternal obesity and infant size at birth. The findings were consistent with other research results [45,46,47]. Being overweight and obese before pregnancy, and unacceptable weight gain during pregnancy may lead to increased concentrations of glucose, amino acids and free fatty acids in pregnant women [48], thereby increasing the risk of abnormal infant weight at birth. Therefore, pre-pregnancy BMI and GWG have similar roles in infant size. Research led by Tiffany et al. [49] showed that regardless of the pre-pregnancy body mass index, controlling weight gain during pregnancy is of great significance for reducing the risk of SGA and LGA. Therefore, it is of great importance to pay attention to pre-pregnancy BMIs and GWGs to ensure normal birth weights of newborns.

One strength of the present investigation was that data were collected from a large population-based cohort and that exposure and outcome measures were prospectively assessed. GWG was determined from authentic prenatal examination records instead of relying on memory recall at the end of pregnancy. However, several limitations should be taken into consideration. First, the sample size may still not be large enough for stratification, such as age, which may lack power for a robust assessment. Second, the pre-pregnancy weight and height were actually the weight and height measured during the initial prenatal examination and may therefore be biased. Moreover, measurement implementation and protocols for maternal anthropometry were standardized at the various study institutions, which may have biased the classification of the pre-pregnancy BMI. Third, there were no details about potential confounding factors such as clinical complications or lifestyle changes during pregnancy. Finally, there is no way to control completely pregnant women’s recall bias with regard to socio-demographic data.

Conclusions

Overweight and obesity before pregnancy and excessive GWG were linked to an increased risk of GDM, GHp, macrosomia and LGA. In clinical practice, physicians can guide pregnant women to manage and control weight gain during pregnancy in order to reduce the risk of adverse pregnancy outcomes. Women of childbearing age can be advised on the importance of maintaining an optimal BMI when planning to become pregnant. Pregnant women aged < 20 or > 25 years old, with an education level below university and college, the race of ethnic minorities and living in the north should be given particular guidance on perinatal health-related knowledge and necessary interventions during the perinatal care process.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

CPWCS:

Chinese Pregnant Women Cohort Study

CPWCS-PUMC:

Chinese Pregnant Women Cohort Study-Peking Union Medical College

GA:

Gestational age

GDM:

Gestational diabetes mellitus

GHp:

Gestational hypertension

GWG:

Gestational weight gain

LBW:

Low birth weight

PB:

Preterm birth

PROM:

Premature rupture of membranes

SGA:

Small-for-gestational-age

USD:

US dollar

References

  1. 1.

    Kim SY, Dietz PM, England L, Morrow B, Callaghan WM. Trends in pre-pregnancy obesity in nine states, 1993-2003. Obesity (Silver Spring). 2007;15(4):986–93.

    Google Scholar 

  2. 2.

    Ma GS, Li YP, Wu YF, Zhai FY, Cui ZH, Hu XQ, et al. The prevalence of body overweight and obesity and its changes among Chinese people during 1992 to 2002. Zhonghua Yu Fang Yi Xue Za Zhi. 2005;39(5):311–5.

    PubMed  Google Scholar 

  3. 3.

    Lai JQ, Yin SA. The impact of experience in bearing child on the body mass index and obesity in women. Zhonghua Yu Fang Yi Xue Za Zhi. 2009;43(2):108–12.

    PubMed  Google Scholar 

  4. 4.

    WHO. State of inequality: reproductive, maternal, newborn and child health 2015. Available from: www.who.int/gho/health_equity/report_2015/en.

  5. 5.

    Wei Y-M, Yang H-X, Zhu W-W, Liu X-Y, Meng W-Y, Wang Y-Q, et al. Risk of adverse pregnancy outcomes stratified for pre-pregnancy body mass index. J Matern Fetal Neonatal Med. 2016;29(13):2205–9.

    PubMed  Google Scholar 

  6. 6.

    Faucett AM, Metz TD, DeWitt PE, Gibbs RS. Effect of obesity on neonatal outcomes in pregnancies with preterm premature rupture of membranes. Am J Obstet Gynecol. 2016;214(2):287 e1–5.

    Google Scholar 

  7. 7.

    Sebire NJ, Jolly M, Harris J, Regan L, Robinson S. Is maternal underweight really a risk factor for adverse pregnancy outcome? A population-based study in London. BJOG. 2001;108(1):61–6.

    CAS  PubMed  Google Scholar 

  8. 8.

    Ronnenberg AG, Wang X, Xing H, Chen C, Chen D, Guang W, et al. Low preconception body mass index is associated with birth outcome in a prospective cohort of Chinese women. J Nutr. 2003;133(11):3449–55.

    CAS  PubMed  Google Scholar 

  9. 9.

    Vivatkusol Y, Thavaramara T, Phaloprakarn C. Inappropriate gestational weight gain among teenage pregnancies: prevalence and pregnancy outcomes. Int J Women's Health. 2017;9:347–52.

    Google Scholar 

  10. 10.

    Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al. Association of Gestational Weight Gain with Maternal and Infant Outcomes: a systematic review and meta-analysis. JAMA. 2017;317(21):2207–25.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Han Z, Lutsiv O, Mulla S, Rosen A, Beyene J, McDonald SD, et al. Low gestational weight gain and the risk of preterm birth and low birthweight: a systematic review and meta-analyses. Acta Obstet Gynecol Scand. 2011;90(9):935–54.

    PubMed  Google Scholar 

  12. 12.

    Xu Z, Wen Z, Zhou Y, Li D, Luo Z. Inadequate weight gain in obese women and the risk of small for gestational age (SGA): a systematic review and meta-analysis. J Matern Fetal Neonatal Med. 2017;30(3):357–67.

    PubMed  Google Scholar 

  13. 13.

    Morisset AS, Tchernof A, Dube MC, Veillette J, Weisnagel SJ, Robitaille J. Weight gain measures in women with gestational diabetes mellitus. J Women's Health (Larchmt). 2011;20(3):375–80.

    Google Scholar 

  14. 14.

    Macdonald-Wallis C, Tilling K, Fraser A, Nelson SM, Lawlor DA. Gestational weight gain as a risk factor for hypertensive disorders of pregnancy. Am J Obstet Gynecol. 2013;209(4):327 e1–17.

    Google Scholar 

  15. 15.

    Mamun AA, Callaway LK, O'Callaghan MJ, Williams GM, Najman JM, Alati R, et al. Associations of maternal pre-pregnancy obesity and excess pregnancy weight gains with adverse pregnancy outcomes and length of hospital stay. BMC Pregnancy Childbirth. 2011;11:62.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Viswanathan M, Siega-Riz AM, Moos MK, Deierlein A, Mumford S, Knaack J, et al. Outcomes of maternal weight gain. Evid Rep Technol Assess (Full Rep). 2008;(168):1–223.

  17. 17.

    WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–63.

  18. 18.

    Chen YH, Fu L, Hao JH, Wang H, Zhang C, Tao FB, et al. Influent factors of gestational vitamin D deficiency and its relation to an increased risk of preterm delivery in Chinese population. Sci Rep. 2018;8(1):3608.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Institute of M, National Research Council Committee to Reexamine IOMPWG. The National Academies Collection. Reports funded by National Institutes of Health. In: Rasmussen KM, Yaktine AL, editors. Weight Gain During Pregnancy: Reexamining the Guidelines. Washington (DC): National Academies Press (US) National Academy of Sciences; 2009.

    Google Scholar 

  20. 20.

    Brown MA, Lindheimer MD, de Swiet M, Van Assche A, Moutquin JM. The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the International Society for the Study of Hypertension in Pregnancy (ISSHP). Hypertens Pregnancy. 2001;20(1):Ix–xiv.

    CAS  PubMed  Google Scholar 

  21. 21.

    Organization WH. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy. Geneva: World Health Organization 2013; Available from:http://www.who.int/diabetes/publications/Definition%20and%20diagnosis%20of% 20diabetes_new.pdf.

  22. 22.

    Committee on Practice Bulletins-Obstetrics. ACOG Practice Bulletin No. 188: Prelabor rupture of membranes. Obstet Gynecol. 2018;131(1):e1–e14.

  23. 23.

    Organization WH. A Guide for Progamme Managers. Geneva: World Health Organization; Iron Deficiency Anemia. Assessment, Prevention and Control; 2001.

    Google Scholar 

  24. 24.

    McLennan MT, Harris JK, Kariuki B, Meyer S. Family history as a risk factor for pelvic organ prolapse. Int Urogynecol J Pelvic Floor Dysfunct. 2008;19(8):1063–9.

    PubMed  Google Scholar 

  25. 25.

    Tegerstedt G, Miedel A, Maehle-Schmidt M, Nyren O, Hammarstrom M. Obstetric risk factors for symptomatic prolapse: a population-based approach. Am J Obstet Gynecol. 2006;194(1):75–81.

    PubMed  Google Scholar 

  26. 26.

    Swift S, Woodman P, O'Boyle A, Kahn M, Valley M, Bland D, et al. Pelvic organ support study (POSST): the distribution, clinical definition, and epidemiologic condition of pelvic organ support defects. Am J Obstet Gynecol. 2005;192(3):795–806.

    PubMed  Google Scholar 

  27. 27.

    DeLancey JO. Anatomic aspects of vaginal eversion after hysterectomy. Am J Obstet Gynecol. 1992;166(6 Pt 1):1717–24 discussion 24-8.

    CAS  PubMed  Google Scholar 

  28. 28.

    Boyd PA, Loane M, Garne E, Khoshnood B, Dolk H. Sex chromosome trisomies in Europe: prevalence, prenatal detection and outcome of pregnancy. Eur J Hum Genet. 2011;19(2):231–4.

    PubMed  Google Scholar 

  29. 29.

    Qi QW, Jiang YL, Zhou XY, Liu JT, Yin J, Bian XM. Genetic counseling, prenatal screening and diagnosis of Down syndrome in the second trimester in women of advanced maternal age: a prospective study. Chin Med J. 2013;126(11):2007–10.

    PubMed  Google Scholar 

  30. 30.

    Yu C, Shi Z, Lv J, Du H, Qi L, Guo Y, et al. Major dietary patterns in relation to general and central obesity among Chinese adults. Nutrients. 2015;7(7):5834–49.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Shi Z, Yuan B, Hu G, Dai Y, Zuo H, Holmboe-Ottesen G. Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu nutrition study. Br J Nutr. 2011;105(7):1047–54.

    CAS  PubMed  Google Scholar 

  32. 32.

    Xiao L, Ding G, Vinturache A, Xu J, Ding Y, Guo J, et al. Associations of maternal pre-pregnancy body mass index and gestational weight gain with birth outcomes in Shanghai, China. Sci Rep. 2017;7:41073.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Vinturache A, Moledina N, McDonald S, Slater D, Tough S. Pre-pregnancy body mass index (BMI) and delivery outcomes in a Canadian population. BMC Pregnancy Childbirth. 2014;14:422.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Enomoto K, Aoki S, Toma R, Fujiwara K, Sakamaki K, Hirahara F. Pregnancy outcomes based on pre-pregnancy body mass index in Japanese women. PLoS One. 2016;11(6):e0157081.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PLoS One. 2013;8(12):e82310.

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Coustan DR. Gestational diabetes mellitus. Clin Chem. 2013;59(9):1310–21.

    CAS  PubMed  Google Scholar 

  37. 37.

    Akbay E, Tiras MB, Yetkin I, Toruner F, Ersoy R, Uysal S, et al. Insulin secretion and insulin sensitivity in normal pregnancy and gestational diabetes mellitus. Gynecol Endocrinol. 2003;17(2):137–42.

    CAS  PubMed  Google Scholar 

  38. 38.

    Agha M, Agha RA, Sandall J. Interventions to reduce and prevent obesity in pre-conceptual and pregnant women: a systematic review and meta-analysis. PLoS One. 2014;9(5):e95132.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Vrachnis N, Belitsos P, Sifakis S, Dafopoulos K, Siristatidis C, Pappa KI, et al. Role of adipokines and other inflammatory mediators in gestational diabetes mellitus and previous gestational diabetes mellitus. Int J Endocrinol. 2012;2012:549748.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jin Z, Ma S, Dong L, Li X, Zhu J, Zhang H, et al. Chin J Public Health. 2009;25(4):415–6.

    Google Scholar 

  41. 41.

    Li B, Kong Y. Research progress on the relationship between BMI and pregnancy outcome in pregnant women. Chin J Family Plann. 2017;25(10):715–7.

    Google Scholar 

  42. 42.

    Jarvie E, Ramsay JE. Obstetric management of obesity in pregnancy. Semin Fetal Neonatal Med. 2010;15(2):83–8.

    PubMed  Google Scholar 

  43. 43.

    Bogaerts A, Ameye L, Martens E, Devlieger R. Weight loss in obese pregnant women and risk for adverse perinatal outcomes. Obstet Gynecol. 2015;125(3):566–75.

    PubMed  Google Scholar 

  44. 44.

    Cedergren M. Effects of gestational weight gain and body mass index on obstetric outcome in Sweden. Int J Gynaecol Obstet. 2006;93(3):269–74.

    CAS  PubMed  Google Scholar 

  45. 45.

    Yogev Y, Langer O, Xenakis EM, Rosenn B. The association between glucose challenge test, obesity and pregnancy outcome in 6390 non-diabetic women. J Matern Fetal Neonatal Med. 2005;17(1):29–34.

    PubMed  Google Scholar 

  46. 46.

    ZHAO J, HUANG W, QIN B, Ruiqing Z. Associations of pre-pregnancy BMI, pregnancy weight gain and newborn weight. China Med Her. 2018;15(30):84–6.

  47. 47.

    Zhao R, Xu L, Wu M, Li R, Zhang Z, Cao X. Relationship between pre-pregnancy bodymass index,gestational weight gain and birth weight. Acta Univ Med Anhui. 2017;52(05):709–14.

  48. 48.

    Hull HR, Thornton JC, Ji Y, Paley C, Rosenn B, Mathews P, et al. Higher infant body fat with excessive gestational weight gain in overweight women. Am J Obstet Gynecol. 2011;205(3):211.e1–7.

    Google Scholar 

  49. 49.

    Simas TA, Waring ME, Liao X, Garrison A, Sullivan GM, Howard AE, et al. Prepregnancy weight, gestational weight gain, and risk of growth affected neonates. J Women's Health (Larchmt). 2012;21(4):410–7.

    Google Scholar 

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Acknowledgements

Thanks for the cooperation of 24 hospitals of China, the name as follows: Xinjiang Uygur Autonomous Region Urumqi Maternal and Child Health Hospital; Guiyang Maternal and Child Health Hospital, Guizhou Province; Northwest Women and Children’s Hospital of Shan’xi Province; Changsha Maternal and Child Health Hospital, Hunan Province; Maternal and Child Health Hospital, He’nan Province; Jiaxian Maternal and Child Health Hospital of He’nan Province; Changzhou Second People’s Hospital of Jiangsu Province; Maternal and Child Health Hospital of Jin’an District, Luan City, Anhui Province; People’s Hospital of Dong’e County, Shandong Province; Dongguan Maternal and Child Health Hospital, Guangdong Province; Maternal and Child Health Hospital of Linhe District, Bayannaoer City, Inner Mongolia; Affiliated Hospital of Guizhou Medical University; Yangzhou Maternal and Child Health Hospital, Jiangsu Province; Affiliated hospital of Ji’ning Medical College; Zaozhuang Maternal and Child Health Hospital, Shandong Province; Sino-Japanese Friendship Hospital Affiliated to Jilin University; Affiliated Hospital of Jiujiang Medical College; First Affiliated Hospital of Nanchang University; Chengdu Women and Children’s Central Hospital, Sichuan Province; Chongqing Three Gorges Central Hospital; Shan’xi People’s Hospital; Xingyang Maternal and Child Health Hospital, He’nan Province; Changchun Obstetrics and Gynecology Hospital of Jilin Province; Tongzhou District Maternal and Child Health Hospital of Beijing. Finally, we thank the Chinese Medical Association for their assistance.

Funding

Chinese Academy of Medical Sciences Innovation Fund supported the work for Medical Sciences (CIFMS) (Grant no: 2016-I2 and M-1-008). The funder had no role in the design of the study or the collection, analysis, and interpretation of data, or in writing the manuscript.

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Authors

Contributions

YS: conception and design, analysis and interpretation, data collection, manuscript writing, editing and revision. ZZS: analysis and interpretation, data collection, statistical analysis, manuscript writing. YLZ: analysis and interpretation, data collection, statistical analysis. YWW: data collection, manuscript editing and revision. SM: data collection, manuscript editing and revision. SHZ: data collection, manuscript editing and revision. JTL: data collection, manuscript editing and revision. SSW: data collection, manuscript editing and revision. YHF: data collection, manuscript editing and revision. YLC: data collection, statistical analysis. SYC: data collection, manuscript editing and revision. YJS: data collection, manuscript editing and revision. LKM: conception and design, analysis and interpretation, data collection, manuscript editing and revision. YJ: conception and design, analysis and interpretation, data collection, manuscript editing and revision. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Liangkun Ma or Yu Jiang.

Ethics declarations

Ethics approval and consent to participate

The study was registered with Clinical Trials (NCT03403543) and approved by the Ethics Review Committee in the Department of Scientific Research, Peking Union Medical College Hospital, Beijing, China (HS-1345). Written informed consent was obtained from all participants prior to enrollment. The raw data from CPWCS-PUMC were stored in a computer with password security. Any reasonable request for data had to be addressed to the director (Yu Jiang) of the Department of Epidemiology and Health Statistics, School of Public Health of Peking Union Medical College.

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Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

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Supplementary information

Additional file 1: Supplementary Table 1.

Pre-pregnancy BMI categorization according to the WHO cut-points for Asian adults. Supplementary Table 2. Gestational weight gain (GWG) categorization according to the 2009 Institute of Medicine (IOM) recommendations.

Additional file 2: Supplementary Figure 1.

CPWCS-PUMC project site distribution map.

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Sun, Y., Shen, Z., Zhan, Y. et al. Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications. BMC Pregnancy Childbirth 20, 390 (2020). https://doi.org/10.1186/s12884-020-03071-y

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Keywords

  • Chinese pregnant women
  • Gestational weight gain
  • Cohort study
  • Pre-pregnancy BMI
  • Maternal outcomes
  • Neonatal outcomes