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Abstract
Objective
This study investigated the relationship between maternal gestational weight gain (GWG) and the risk of adverse pregnancy outcomes in gestational diabetes mellitus (GDM)-negative pregnant women.
Methods
We did a retrospective cohort study between 1 July 2017, and 1 January 2020, at Women’s Hospital, Zhejiang University School of Medicine. Firstly, pregnant women were divided into subgroups according to the entire GWG (inadequate GWG, adequate GWG, and excessive GWG) and GDM status (positive and negative) during pregnancy. Secondly, the whole population of pregnant women with GDM was used as a reference to evaluate the relationship between GWG and adverse pregnancy outcomes in GDM-negative pregnant women. Lastly, subgroup analysis was conducted based on pre-pregnancy body mass index (pp-BMI).
Results
A total of 30,910 pregnant women were analysed. Included pregnancy women were divided into three groups based on GWG: 7569 (24.49%) pregnancy women had inadequate GWG, 13088 (42.34%) had adequate GWG, and 10,253 (33.17%) had excessive GWG. In addition to preterm birth and small for gestational age (SGA), the incidence of macrosomia and large for gestational age (LGA) continues to increase from inadequate GWG to excessive GWG groups. Pregnant women without GDM who have excessive GWG are at higher risk of macrosomia and LGA than pregnant women with GDM. Moreover, this risk increased with increasing pp-BMI. Pregnant women without GDM with inadequate GWG were at risk of preterm birth regardless of pp-BMI. Only those with inadequate GWG and pp-BMI < 18.5 kg/m2 had an increased risk of SGA.
Conclusions
In conclusion, inappropriate GWG is strongly associated with adverse pregnancy outcomes, even if they do not have GDM. Therefore, this population should receive attention and management before and during pregnancy.
What is already known on this subject? Several studies have focused on the GDM population and the risk of adverse pregnancy outcomes, but few have focused on GDM-negative populations. This is because GDM-negative women are perceived to be "safe," leading to less focus on themselves, which can lead to subsequent excessive weight gain during pregnancy. Whether this factor increases the risk of adverse pregnancy outcomes in this population remains unknown.
What do the results of this study add? Our study found an inverse relationship between GWG and GDM. Therefore, our study focuses on this group of GDM-negative pregnant women. Their excessive weight gain increases the risk of adverse pregnancy outcomes, even higher than GDM pregnant women.
What are the implications of these findings for clinical practice and/or further research? GWG is associated with adverse pregnancy outcomes. Therefore, pregnant women without GDM also need increased attention and management of their weight before and during pregnancy. Prenatal care providers can utilise tools such as diet, exercise counselling, weight tracking, and setting weight gain goals to reduce inappropriate weight gain and mitigate its adverse effects on pregnancy outcomes.
Impact Statement
1. Introduction
Gestational weight gain (GWG) is the change from pre-pregnancy to delivery. GWG is a factor that affects the risk of adverse pregnancy outcomes, especially if insufficient or excessive weight gain during pregnancy (Goldstein et al. Citation2017). Inappropriate GWG has been linked to adverse pregnancy outcomes for pregnant women (Champion et al. Citation2020). In addition, excessive GWG during pregnancy may affect the outcome of subsequent pregnancies if it is not reduced after delivery (McDowell et al. Citation2019). According to the Institute of Medicine, GWG ranges differ according to pre-pregnancy body mass index (pp-BMI), meaning that pp-BMI and GWG influence pregnancy outcomes (ACOG Citation2013). Overweight and obesity rates have increased dramatically due to insufficient exercise and inappropriate diets. For example, the obesity rate in China was considerably higher in 2017 than in 2007, suggesting that the number of overweight and obese women is increasing, with the prevalence of diabetes also rising (Li et al. Citation2021). Moreover, women who are overweight, obese, or have GDM are more likely to experience adverse maternal and neonatal outcomes (Cnattingius et al. Citation1998, Wendland et al. Citation2012).
Gestational diabetes mellitus (GDM) is diagnosed when glucose intolerance is present or first detected during pregnancy, and its incidence increases rapidly (Szmuilowicz et al. Citation2019). GDM increases the risk of adverse pregnancy outcomes such as preterm birth and macrosomia, large for gestational age (LGA) and small for gestational age (SGA) (McIntyre et al. Citation2019). Although women with GDM generally return to normal after delivery, they are more likely to develop it in subsequent pregnancies. Furthermore, this group is at a higher risk of developing cardiovascular disease and type 2 diabetes in the future, which has long-term effects on both mother and offspring (Daly et al. Citation2018).
Appropriate GWG is essential for the health of the mother and newborn. However, pregnant women without GDM are more likely to experience excessive GWG. This is because this population is considered “safe” and receives much less attention. Few studies have also focused on the risk of GWG and adverse pregnancy outcomes in this population. Additionally, pp-BMI is an essential risk factor for adverse pregnancy outcomes. Therefore, this study explores the association between GWG and adverse pregnancy outcomes in pregnant women without GDM.
2. Methods
2.1. Participants and study design
This retrospective cohort study was conducted between 1 July 2017, and 1 January2020, at Women’s Hospital, Zhejiang University School of Medicine. Initially, pregnant women who completed the oral glucose tolerance test (OGTT) were included in this study. Maternal demographic characteristics, such as age, pre-pregnancy weight, height, gravidity, parity, and medical data, including gestational age, weight at delivery, birth weight, pregnancy and neonatal outcomes, and laboratory indexes, were extracted from medical databases. Pregnant women were identified for inclusion in this study using the following criteria: (1) singleton pregnancy, (2) termination of pregnancy in our hospital, and (3) complete medical records. The exclusion criteria were as follows: (1) pre-gestational diabetes, (2) multiple pregnancies, (3) incomplete medical history, (4) miscarriage or stillbirth, and (5) chronic hypertension.
2.2. Definition
Pp-BMI: pp-BMI is calculated as pre-pregnancy weight (kg) divided by height squared (m2). It is classified according to the World Health Organization’s BMI standards, which categorise it as underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), and overweight/obese (≥ 25 kg/m2).
GWG: GWG refers to the change in weight from before pregnancy to delivery. It is divided into three groups: inadequate, adequate, and excessive, based on the recommendation. For women with different pp-BMI, the recommended sufficient GWG is 12.5–18.0 kg for underweight women, 11.5–16.0 kg for normal-weight women, 7.0–11.5 kg for overweight women, and 5.0–9.0 kg for obese women (ACOG Citation2013).
GDM diagnosis was determined by OGTT during 24–28 weeks of pregnancy when any of the following glucose values are reached or exceeded: fasting ≥ 92 mg/dL, 1-hour ≥ 180 mg/dL, and 2-hour ≥ 153 mg/dL (Yang Citation2012).
Preterm birth was defined as delivery before 37 weeks gestation.Macrosomia was characterised by a newborn birth weight ≥ 4000 g, regardless of gestational age.
LGA and SGA were defined based on neonatal birth weight for gestational age and percentile in 23 cities in China. Newborn birth weight above the 90th percentile for gestational age was classified as LGA, while the lowest 10th percentile was classified as SGA (Zhu et al. Citation2015).
2.3. Statistical analysis
We utilised IBM SPSS (version 23.0, Armonk, NY, USA) to analyse the data with a significance level of P < 0.05. Continuous variables were presented as mean ± standard deviation (SD). We used one-way ANOVA with Dunn’s test for multiple groups to compare the differences. Categorical data were expressed as counts and percentages, and differences between groups were assessed using the Chi-square (χ2) test. A logistic regression model was employed to analyse the association between GWG and adverse pregnancy outcomes, controlling for confounding variables. The logistic regression model was based on GDM status and pp-BMI. In addition, a pp-BMI based subgroup analysis was performed to analyse the risk of GWG and adverse pregnancy outcomes.
3. Results
3.1. Baseline characteristics and pregnancy outcomes in pregnant women with different GWG
A total of 30,910 pregnant women were included in our study (). summarises the characteristics of maternal and pregnancy outcomes. Pregnant women included in this study were divided into three groups based on GWG: 7569 (24.49%) with inadequate GWG, 13,088 (42.34%) table with adequate GWG, and 10,253 (33.17%) with excessive GWG. Maternal age was higher in the inadequate GWG group than in the other two groups. The pp-BMI, gestational age, and birth weight were significantly higher in the excessive GWG group than in the inadequate GWG and adequate GWG groups. HbA1c and fasting glucose were higher in the excessive GWG group, but 1 and 2 hours of glucose were lower than in the other two groups. The prevalence of macrosomia, preterm birth, LGA, and SGA were 5.52%, 5.42%, 8.89%, and 5.65%, respectively. The incidence of macrosomia and LGA was highest in the excessive GWG group and lowest in the inadequate GWG group (P < 0.001). In contrast, preterm birth and SGA incidence were lowest in the excessive GWG group and highest in the inadequate GWG group (P < 0.001).
Figure 1. Flowchart for study population inclusion. OGTT: oral glucose tolerance test; GWG: gestational weight gain.
Inadequate GWG group: Weight gain during pregnancy is below the recommended range.
Adequate GWG group: Weight gain during pregnancy is in the recommended range.
Excessive GWG group: Weight gain during pregnancy is above the recommended range.
Table 1. Baseline characteristics and pregnancy outcomes in pregnant women with different GWG.
3.2. The association between GWG and adverse pregnancy in women with and without GDM
As shown in , GDM pregnant women have a higher rate of adverse pregnancy outcomes than non-GDM pregnant women. In GDM-negative pregnant women, the rate of macrosomia and LGA gradually increased from the GWG inadequate group to the GWG excessive group. In contrast, the incidence of SGA and preterm delivery gradually decreased. Pregnant women without GDM with excessive GWG had higher rates of macrosomia and LGA incidence and lower rates of preterm birth and SGA compared to pregnant women with GDM. In contrast, pregnant women with inadequate GWG without GDM had lower rates of macrosomia and LGA and higher rates of preterm birth and SGA.
Figure 2. The association between GWG and adverse pregnancy in women with and without GDM. A categorical variable is expressed as n (%). GWG: gestational weight gain; GDM: gestational diabetes mellitus; SGA: small for gestational age; LGA: large for gestational age. **P < 0.01, P values were calculated using Chi-square test, compared with GDM positive women.
3.3. The relative risk of adverse pregnancy outcomes in GDM-negative pregnant women according to different GWG
shows the risk of adverse pregnancy outcomes in GDM-negative pregnant women according to different GWG, using the entire group of pregnant women with GDM as a reference. After adjusting for confounders, GDM-negative women with excessive GWG had an elevated risk of macrosomia (a-OR 2.19; 95% CI, 1.83–2.63) and LGA (a-OR 2.19; 95% CI, 1.89–2.54) compared with GDM-positive pregnant women. In addition, the risk of preterm birth (a-OR 2.08; 95% CI, 1.73–2.51) was significantly higher in GDM-negative with inadequate GWG. However, GDM-negative women with adequate and excessive GWG had a lower SGA risk than GDM-positive women (a-OR 0.69; 95% CI, 0.57–0.83; a-OR 0.50; 95% CI, 0.41–0.61).
Table 2. Relative risks of adverse pregnancy outcomes in no GDM women according to different GWG.
3.4. Subgroup analysis based on pp-BMI
As shown in , to further explore the relationship between GWG and adverse pregnancy outcomes, we examined the combined effect of pp-BMI and GWG on the risk of adverse pregnancy outcomes. Compared to GDM-positive pregnant women, GDM-negative women with excessive GWG showed a higher risk of macrosomia and LGA, increasing with pp-BMI. However, in the inadequate GWG group, pregnant women were at risk of preterm birth regardless of pp-BMI. In contrast, only pregnant women with inadequate GWG + pp-BMI < 18.5 kg/m2 were at elevated risk of SGA.
Table 3. Subgroup analysis based on pp-BMI.
4. Discussion
Our study revealed that GWG was associated with adverse pregnancy outcomes, including macrosomia, preterm birth, LGA, and SGA. The excessive GWG group had the highest incidence of macrosomia and LGA, while the inadequate GWG group had the highest incidence of preterm delivery and SGA. GDM-negative women with excessive GWG had a higher risk of macrosomia and LGA than those with GDM-positive, and the risk increased with pp-BMI. In the inadequate GWG group, the risk of preterm birth increased irrespective of pp-BMI, while only those with pp-BMI < 18.5 kg/m2 had an increased risk of SGA.
GDM is among the most common complications related to pregnancy. The prevalence of GDM has increased to 14.7–20.9% in China, making it a significant public health concern (Zhu et al. Citation2013, Zhu et al. Citation2016). Failure to effectively manage GDM during pregnancy can adverse effect on both the mother and offspring (McIntyre et al. Citation2019). Our study similarly found a higher incidence of adverse pregnancy outcomes in GDM-positive women. It is assumed that appropriate treatment and management given on time is beneficial to both mother and foetus regardless of whether the mother is pre-diabetes or GDM (Bapayeva et al. Citation2022). Excessive GWG during pregnancy is a risk factor for GDM, especially excessive weight gain before GDM screening (Hedderson et al. Citation2010). Emerging evidence suggests excessive GWG in early pregnancy may be linked to GDM. For example, women with excessive GWG in the first trimester are at a higher risk of developing GDM, regardless of pp-BMI (Lan et al. Citation2020). In addition, another study found that the rate of weight gain at the time of diagnosis of GDM was associated with an increased risk of GDM (Liu et al. Citation2014). Excessive weight gain in early pregnancy may increase insulin resistance, leading to GDM when insulin secretion is insufficient to compensate for insulin resistance. Hence, avoiding excessive weight gain during early pregnancy before GDM screening may help reduce the risk of GDM. However, our study showed an inverse relationship between GWG and GDM risk. This is because GWG in our study refers to weight gain throughout pregnancy. Pregnant women diagnosed with GDM receive more dietary and exercise advice, which can affect subsequent weight gain. Furthermore, foetal health has always been a concern for pregnant women, especially when diagnosed with GDM. Pregnant women are counselled on increasing their awareness of the maternal and foetal risks associated with GDM (Quaresima et al. Citation2021). Pregnant women will target changes in diet and nutrient intake, tighter control of blood glucose, and ultimately affect weight gain (Paul et al. Citation2016).
In China, there has been a rise in the number of overweight pregnant women, with approximately 25–45.9% having a GWG above the recommended range (Chen et al. Citation2018, Li et al. Citation2018). Our study found that 24.49% and 33.17% of pregnant women experienced inadequate and excessive GWG, respectively. We believe a lack of knowledge causes this situation, limited social and economic resources, and incorrect perceptions, such as the belief that consuming more food benefits the foetus. Our study found an inverse relationship between GWG and GDM. The risk of adverse pregnancy outcomes in GDM-negative women with excessive GWG was even higher than in GDM-pregnant women. Compared to women with GDM-positive, women with GDM-negative and excessive GWG had a higher risk of macrosomia and LGA, while women with GDM-negative and inadequate GWG had a lower risk of macrosomia and LGA. Our results demonstrate that GWG is significantly associated with LGA and macrosomia. Macrosomia poses considerable risks for mothers and infants, including a higher risk of caesarean section, vaginal laceration, postpartum haemorrhage, and a more extended stay in the neonatal intensive care unit (Champion,Harper 2020).
We have already discussed the risk of adverse pregnancy outcomes with excessive GWG, but the risk of inadequate GWG should not be ignored. Although macrosomia or LGA during pregnancy is rare in women with inadequate GWG, it is linked to an increased risk of preterm birth and SGA. Pregnant women with GWG lower than the recommendation had a higher risk of SGA and preterm delivery and a lower risk of LGA and macrosomia than those with GWG within the recommended range (Goldstein et al. Citation2017). Similar results have been found in other studies, with a higher risk of having an SGA when GWG is below the recommendations (Li et al. Citation2015, Ricci et al. Citation2010). Furthermore, an analysis of 100,000 birth and infant death database records revealed that infants born to women with inadequate GWG had a higher risk of dying within a year of birth than those born to women with appropriate GWG (Ricci et al. Citation2010). This suggests inadequate GWG is associated with perinatal mortality. Preterm birth is a common adverse perinatal outcome that predisposes to brain damage and death (Back et al. Citation2014). However, another study analysed data from pregnant women with normal pp-BMI and found insufficient GWG did not increase their risk of SGA or preterm birth (Eraslan et al. Citation2019). This may explain the different findings because the population included in the above study were low-risk pregnant women with normal pp-BMI. Upon comparing our results to previous studies, some differences were found. Specifically, our investigation discovered that excessive GWG did not result in preterm birth but reduced the risk of preterm birth. In contrast, inadequate GWG was associated with a higher risk of preterm birth. Additionally, we noted that underweight women with inadequate GWG were likelier to have SGA, consistent with a retrospective cohort study that reported a similar association (Hung et al. Citation2016). However, another study concluded that inadequate GWG did not increase the risk of SGA (Gou et al. Citation2019). This may be because the study only included pregnant women with GDM, thus contributing to discrepancy in findings.
In addition to GWG, being overweight and obese are significant risk factors for adverse pregnancy outcomes. It has been shown that advanced age and increased pp-BMI are risk factors for pregnancy complications, with pp-BMI being the most important predictor of pregnancy complications in pregnant women with diabetes (Bapayeva et al. Citation2022). We found a significantly higher risk of macrosomia and LGA in pregnant women without GDM who had excessive GWG, and this risk increased with a higher pp-BMI. Our findings are consistent with another study, which demonstrated an increase in the incidence of macrosomia with increasing BMI (Su et al. Citation2019). However, in the group with inadequate GWG, women were at risk of preterm birth regardless of pp-BMI, and only underweight women had a higher risk of SGA infants. A Japanese study supported our results, indicating that inadequate GWG is a significant risk factor for SGA, particularly among non-obese women (Ikenoue et al. Citation2020). These complex mechanisms may be related to a pro-inflammatory state, altered placental function, oxidative stress, and insulin insensitivity (Catalano et al. Citation2017, Chen et al. Citation2020). Therefore, we believe that pp-BMI is strongly associated with adverse pregnancy outcomes and should be considered when assessing the risk of GWG and adverse pregnancy outcomes.
Our study used GDM-positive pregnant women as a control group and assessed the risk of adverse pregnancy outcomes in GDM-negative pregnant women, according to GWG. We also conducted a pp-BMI-based subgroup analysis. However, we must acknowledge certain limitations. Firstly, GWG is calculated as the change in weight between pre-pregnancy and delivery. Pre-pregnancy weight is generally self-reported, which may introduce recall bias. However, we believe the data are acceptable since data were collected early in pregnancy when there was no significant difference in women’s weight. A previous study has shown that maternal height and weight obtained through recall are accurate (Tomeo et al. Citation1999). Secondly, many women with complex conditions choose our hospital for pregnancy monitoring or termination. However, healthy pregnant women are likelier to deliver in other hospitals, this may imply a potential selection bias. Thirdly, because this is a retrospective study, we could not assess the women’s dietary and physical activity information, factors strongly associated with body weight. Future research should consider the confounding effects of maternal dietary intake and physical activity in women with GDM, as these factors can also impact GWG and pregnancy outcomes.
5. Conclusion
In conclusion, inappropriate GWG is associated with a risk of adverse pregnancy outcomes in pregnant women without GDM, and even this risk is more significant than in those with GDM. Therefore, this population also requires more attention and management before and during pregnancy. Prenatal care providers can utilise tools such as diet, exercise counselling, weight tracking, and weight gain goals to reduce the occurrence of inappropriate GWG and mitigate its adverse effects on pregnancy outcomes.
Ethical approval
The Medical Ethics Committee of the Women’s Hospital of Zhejiang University School of Medicine approved the study (IRB-20210330-R). However, the informed consent of study participants was exempted because this was a retrospective observational study without intervention.
Acknowledgments
We thank all participants of this study.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
Additional information
Funding
References
- ACOG, 2013. ACOG Committee opinion no. 548: weight gain during pregnancy. Obstet Gynecol, 121 (1), 210–212.
- Back, S.A., et al., 2014. Brain injury in premature neonates: A primary cerebral dysmaturation disorder? Annals of Neurology, 75 (4), 469–486.
- Bapayeva, G., et al., 2022. Pregnancy outcomes in women with diabetes mellitus-the impact of diabetes type and treatment. Przeglad Menopauzalny, 21 (1), 37–46.
- Bapayeva, G., et al., 2022. The influence of advanced age and obesity on pregnancy course and outcome in patients with diabetes mellitus. Przeglad Menopauzalny = Menopause Review, 21 (3), 170–179.
- Catalano, P.M., et al., 2017. Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ, 356, j1.
- Champion, M.L., et al., 2020. Gestational Weight Gain: Update on Outcomes and Interventions. Current Diabetes Reports, 20 (3), 11.
- Chen, C., et al., 2018. Estimated global overweight and obesity burden in pregnant women based on panel data model. PLoS One, 13 (8), e202183.
- Chen, C.N., et al., 2020. Maternal Prepregnancy Body Mass Index, Gestational Weight Gain, and Risk of Adverse Perinatal Outcomes in Taiwan: A Population-Based Birth Cohort Study. International Journal of Environmental Research and Public Health, 17 (4), 1221.
- Cnattingius, S., et al., 1998. Prepregnancy weight and the risk of adverse pregnancy outcomes. The New England Journal of Medicine, 338 (3), 147–152.
- Daly, B., et al., 2018. Increased risk of ischemic heart disease, hypertension, and type 2 diabetes in women with previous gestational diabetes mellitus, a target group in general practice for preventive interventions: A population-based cohort study. PLoS Medicine, 15 (1), e1002488.
- Eraslan, S.M., et al., 2019. Effect of gestational weight gain on perinatal outcomes in low risk pregnancies with normal prepregnancy body mass index. Biomed Research International., 2019, 3768601.
- Goldstein, R.F., et al., 2017. Association of gestational weight gain with maternal and infant outcomes: a systematic review and meta-analysis. JAMA, 317 (21), 2207–2225.
- Gou, B.H., et al., 2019. Gestational diabetes: weight gain during pregnancy and its relationship to pregnancy outcomes. Chinese Medical Journal, 132 (2), 154–160.
- Hedderson, M.M., et al., 2010. Gestational weight gain and risk of gestational diabetes mellitus. Obstetrics and Gynecology, 115 (3), 597–604.
- Hung, T.H., et al., 2016. Pregestational body mass index, gestational weight gain, and risks for adverse pregnancy outcomes among Taiwanese women: a retrospective cohort study. Taiwanese Journal of Obstetrics & Gynecology, 55 (4), 575–581.
- Ikenoue, S., et al., 2020. Impaired fetal growth in mothers with inadequate gestational weight gain: a retrospective study in Japanese uncomplicated pregnancy. The Journal of Maternal-Fetal & Neonatal Medicine, 33 (13), 2227–2231.
- Lan, X., et al., 2020. Excessive gestational weight gain in the first trimester is associated with risk of gestational diabetes mellitus: a prospective study from Southwest China. Public Health Nutrition, 23 (3), 394–401.
- Li, C., et al., 2015. Joint and independent associations of gestational weight gain and pre-pregnancy body mass index with outcomes of pregnancy in chinese women: a retrospective cohort study. PLoS One, 10 (8), e136850.
- Li, D.T., et al., 2018. Relations between pregestational body mass index, gestational weight gain and birth weight of neonates among women in the Southwest areas of China: A prospective cohort study. Zhonghua Liu Xing Bing Xue Za Zhi, 39 (10), 1319–1323.
- Li, Y., et al., 2021. Changes in the prevalence of obesity and hypertension and demographic risk factor profiles in China over 10 years: two national cross-sectional surveys. The Lancet Regional Health. Western Pacific, 15, 100227.
- Liu, Z., et al., 2014. Gestational weight gain and risk of gestational diabetes mellitus among Chinese women. Chin Med J (Engl), 127 (7), 1255–1260.
- McDowell, M., et al., 2019. Excessive gestational weight gain. J Midwifery Womens Health, 64 (1), 46–54.
- McIntyre, H.D., et al., 2019. Gestational diabetes mellitus. Nature Reviews. Disease Primers, 5 (1), 47.
- Paul, C., et al., 2016. Inositol’s and other nutraceuticals’ synergistic actions counteract insulin resistance in polycystic ovarian syndrome and metabolic syndrome: state-of-the-art and future perspectives. Gynecological Endocrinology, 32 (6), 431–438.
- Quaresima, P., et al., 2021. Awareness of gestational diabetes mellitus foetal-maternal risks: an Italian cohort study on pregnant women. BMC Pregnancy and Childbirth, 21 (1), 692.
- Ricci, E., et al., 2010. Pre-pregnancy body mass index, maternal weight gain during pregnancy and risk of small-for-gestational age birth: results from a case-control study in Italy. The Journal of Maternal-Fetal & Neonatal Medicine, 23 (6), 501–505.
- Su, W.J., et al., 2019. Effects of prepregnancy body mass index, weight gain, and gestational diabetes mellitus on pregnancy outcomes: a population-based study in xiamen, china, 2011-2018. Annals of Nutrition & Metabolism, 75 (1), 31–38.
- Szmuilowicz, E.D., et al., 2019. Gestational diabetes mellitus. Endocrinology and Metabolism Clinics of North America, 48 (3), 479–493.
- Tomeo, C.A., et al., 1999. Reproducibility and validity of maternal recall of pregnancy-related events. Epidemiology, 10 (6), 774–776.
- Wendland, E.M., et al., 2012. Gestational diabetes and pregnancy outcomes-a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria. BMC Pregnancy and Childbirth, 12 (1), 23.
- Yang, H.X., 2012. Diagnostic criteria for gestational diabetes mellitus (WS 331-2011). Chinese Medical Journal, 125 (7), 1212–1213.
- Zhu, L., et al., 2015. Chinese neonatal birth weight curve for different gestational age. Zhonghua Er Ke Za Zhi, 53 (2), 97–103.
- Zhu, W.W., et al., 2013. Evaluation of the value of fasting plasma glucose in the first prenatal visit to diagnose gestational diabetes mellitus in china. Diabetes Care, 36 (3), 586–590.
- Zhu, Y., et al., 2016. Prevalence of Gestational Diabetes and Risk of Progression to Type 2 Diabetes: a Global Perspective. Current Diabetes Reports, 16 (1), 7.