Skip to main content
Download PDF

Relationship between gestational body mass index change and the risk of gestational diabetes mellitus: a community-based retrospective study of 41,845 pregnant women

BMC Pregnancy and Childbirth volume 22, Article number: 336 (2022) Cite this article

Abstract

Background

Gestational diabetes mellitus (GDM) is associated with adverse health consequences for women and their offspring. It is associated with maternal body mass index (BMI) and may be associated with gestational weight gain (GWG). But due to the heterogeneity of diagnosis and treatment and the potential effect of GDM treatment on GWG, the association between the two has not been thoroughly clarified. Compared to body weight, BMI has the advantage that it considers height during the whole course of pregnancy. Understanding BMI changes during pregnancy may provide new evidence for the prevention of GDM.

Methods

This study investigated the BMI change of pregnant women based on a retrospective study covering all communities in Tianjin, China. According to the results of GDM screening at 24–28 weeks of gestation, pregnancies were divided into the GDM group and the non-GDM group. We compared gestational BMI change and GWG in the two groups from early pregnancy to GDM screening. GWG was evaluated according to the IOM guidelines. Logistic regression was applied to determine the significance of variables with GDM.

Results

A total of 41,845 pregnant women were included in the final analysis (GDM group, n = 4257 vs. non-GDM group, n = 37,588). BMI gain has no significant differences between the GDM and non-GDM groups at any early pregnancy BMI categories (each of 2 kg/m2), as well as weight gain (P > 0.05). Early pregnancy BMI was a risk factor for GDM (OR 1.131, 95% CI 1.122–1.139). And BMI gain was associated with a decreased risk of GDM in unadjusted univariate analysis (OR 0.895, 95% CI 0.869–0.922). After adjusting on early pregnancy BMI and other confounding factors, the effect of BMI gain was no longer significant (AOR 1.029, 95% CI 0.999–1.061), as well as weight gain (AOR 1.006, 95% CI 0.995–1.018) and GWG categories (insufficient: AOR 1.016, 95% CI 0.911–1.133; excessive: AOR 1.044, 95% CI 0.957–1.138).

Conclusions

BMI in early pregnancy was a risk factor for GDM, while BMI gain before GDM screening was not associated with the risk of GDM. Therefore, the optimal BMI in early pregnancy is the key to preventing GDM.

Peer Review reports

Background

Gestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy defined as glucose intolerance first identified during pregnancy. It increases the risk of adverse pregnancy outcomes, such as preterm birth, cesarean delivery, macrosomia, postpartum type 2 diabetes mellitus, and metabolic diseases in offspring [1,2,3,4,5,6]. The prevalence of GDM is increasing rapidly worldwide along with the lifestyle changes, growing incidence of obesity, and older age of pregnant women [7, 8]. It currently affects 3–25% of pregnancies worldwide, constituting a significant global healthcare burden [9]. A meta-analysis review suggests that the total incidence of GDM in China is 14.8% [10]. It increased almost 3.5-fold from 1999 to 2012 according to the data of universal screening for GDM in Tianjin, China [11,12,13]. Genetic and environmental factors jointly promote its onset [14, 15]. Previous studies have helped to identify a multitude of potential risk factors for GDM. These include advancing maternal age, increasing pre-pregnancy body mass index (BMI), increasing parity, having a previous macrosomia baby, family history of diabetes, polycystic ovarian syndrome (PCOS), and habitual smoking [7, 13, 16, 17]. More attention should be paid to the prevention and control of GDM.

Lifestyle changes are essential in the management of GDM. The cornerstone of GDM treatment is medical nutrition therapy (MNT), together with weight management and physical exercise [18]. These measures have beneficial effects on glucose and insulin levels and can contribute to better pregnancy outcomes [19, 20]. Some studies have found that diet and exercise interventions during pregnancy could reduce risks of GDM [7, 21], and this effect may be relevant to the lifestyle improvements at the beginning of pregnancy that decrease the gestational weight gain (GWG) before the mid-second trimester [22,23,24]. However, today there are still many disputes, even regarding current indications.

During gestation, women experience a series of physical and metabolic modifications and adaptations, which aim to protect the fetus’s development and are closely related to both prepregnancy nutritional status and GWG [18]. The negative effects of both insufficient and excessive GWG on maternal-fetal outcomes have been taken into account by the IOM that developed universal guidelines for optimal GWG based on prepregnancy BMI categories [25, 26]. GWG below guidelines in the United States, Europe, and Asia was 21, 18, and 31%, and above was 51, 51, and 37% respectively [27]. The risks associated with excess GWG may be higher in women from Asia [27]. Regional BMI categories are acknowledged to be more applicable than WHO BMI categories when applying IOM GWG guidelines in the Asia population [27].

GWG is a modifiable risk factor for adverse pregnancy outcomes. Weight assessment in the first and second trimesters contributes to early identification, prevention, and intervention for adverse perinatal outcomes. GDM is related to maternal BMI and possibly to GWG, associations could not be assessed because of heterogeneity of diagnosis and treatment as well as the potential effect of GDM treatment on GWG [25, 28,29,30]. Many studies support that overweight and obesity before pregnancy and an excessive GWG are associated with a greater risk of developing GDM [31,32,33,34]. Recently, Chinese researchers report that women with excessive GWG had a significantly 32.8% increased risk of developing GDM compared with non-excessive GWG [35]. But some studies from the United States found that women with and without GDM had similar mean GWG before GDM screening [36, 37]. Furthermore, other studies from China have found an association of GWG above guidelines with a lower risk of GDM [38, 39]. Therefore, the association between GWG and the risk of GDM needs further confirmation.

Compared to body weight, BMI has the advantage that it considers height during the whole course of pregnancy. Prepregnancy BMI has been proved the main predictor of GDM. But the relationship between the change of BMI during pregnancy and the risk of GDM has not been elucidated. Previous studies have found that inter-pregnancy BMI change may be associated with the risk of obstetric complications [40, 41]. So we focus on the BMI before GDM screening and are committed to providing a new measurement to evaluate the relationship between energy balance during pregnancy and health outcomes. This just reflects the new insight and practical value of this study.

We aimed to investigate the relationship between inter-gestational BMI on the risk of GDM, and a better understanding of it is vital for developing evidence-based interventions and guidance.

Methods

Population and data collection

This study was based on a public women and children’s health care system in Tianjin, China. In Tianjin, more than 80,000 pregnant women attend antenatal care each year, and antenatal care coverage is maintained at over 95%. In this study, all the prenatal medical information was retrospectively collected from the Tianjin Women and Children Health Information System (TJWCHIS) database, and the data was anonymized. The study protocol was approved by the Human Subjects Committee of the Tianjin Women’s and Children’s Health Center. All methods were carried out in accordance with relevant guidelines and regulations. Since this was a retrospective analysis of data routinely collected from participants, the consent for participation was not applicable. The need for informed consent was waived by the Human Subjects Committee of the Tianjin Women’s and Children’s Health Center.

Basic characteristics of pregnant women were collected at the first antenatal visit. It included maternal age, ethnicity, education, gravidity, parity, history of diabetes, hypertension, PCOS, obstetrical history (e.g., history of macrosomia, infant death), family history of diabetes or hypertension, and lifestyle habits (e.g., habitual smoking). We included women aged 18–45 years with singletons pregnancy. All of them were followed up to measure their weight from early pregnancy to GDM screening. To avoid the influence of pre-pregnancy diseases on the results, we excluded women with diabetes or hypertension before pregnancy. In this study, all pregnant women were tested for blood glucose during the first antenatal examination. If the results met the criteria for diagnosis of diabetes in pregnancy (DIP), they were not included in the analysis. The criteria were: FG ≥7.0 mmol/L, and/or 2-h 75 g oral glucose tolerance test (OGTT) value ≥11.1 mmol/L, or random plasma glucose ≥11.1 mmol/L associated with signs and symptoms of diabetes [42] (Additional Fig. 1).

Screening and diagnosis of GDM

At present, the screening strategy and diagnostic criteria of GDM were inconsistent in various countries and regions [43]. In this study, a two-step strategy was used to screen for GDM at the 24th–28th week of gestation [13, 44]. The first step: perform a 50 g glucose challenge test (GCT) to measure the plasma glucose level at 1 h after the glucose load. If the plasma glucose level was ≥7.8 mmol/L, the pregnant woman will be required to perform a 75 g OGTT. The second step: The 75 g OGTT should be performed when the pregnant women were fasting for 10–12 h. At 8–9 am peripheral blood glucose levels of fasting, 1 and 2 h after taking glucose were measured. If at least one of them exceeded the thresholds of 5.1, 10.0, and 8.5 mmol/L at fasting, 1, and 2 h respectively, GDM was diagnosed [45]. Based on the results, pregnant women were classified as the GDM group and the non-GDM group.

Anthropometric measurement

Weight was measured at the first prenatal visit (mean for gestational weeks when the measurement was conducted) and the time of GDM screening (mean for gestational weeks when the measurement was conducted). While bodyweight at the first prenatal visit was used to calculate early pregnancy BMI, bodyweight at the time of GDM screening was used to calculate the BMI at the late second trimester. BMI was calculated by dividing weight by height squared. The BMI gain was calculated by the formula of BMI gain = BMI at the late second trimester - early pregnancy BMI.

The rate of weight gain at the second trimester was calculated as follows: (weight measured at the time of GDM screening − weight measured at the first prenatal visit – first-trimester weight gain) / (gestational age of weight measured at the time of GDM screening – 13 weeks). Weight gain throughout pregnancy followed a non-linear trajectory. The rate of weight gain was greater in the second than in the first half of pregnancy [46]. The average weight gain during the first trimester was assumed to be 0.5-2 kg [26]. Therefore, we divided pregnant women into three categories (insufficient, adequate, or excessive weight gain) based on their rate of weight gain at the second trimesters. According to the Institute of Medicine (IOM) guidelines, the recommended rate of weight gain in the second and third trimesters was 0.44–0.58, 0.35–0.50, 0.23–0.33, and 0.17–0.27 kg/week in the underweight, normal weight, overweight, and obese groups, respectively [26]. And BMI categories were commented the Chinese BMI criteria: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–23.9 kg/m2), overweight (BMI 24.0–28.0 kg/m2), and obese (BMI ≥ 28.0 kg/m2), respectively. Either self-reported prepregnancy or measured weight in the first trimester was usually used for calculating prepregnancy BMI and GWG [26]. But the accuracy of self-reported prepregnancy weight has been questioned, so we performed BMI categories based on the BMI calculated by measured weight at early pregnancy.

Statistical analysis

The analyses were performed using IBM SPSS Statistics (Version 21.0). The figures were drawn using GraphPad Prism (Version 8.0). Normal continuous variables were expressed by means (SD), which were compared between two or more groups using a t-test of independent samples or one-way analysis of variance (ANOVA). Categorical variables were described as numbers (percentage) and compared by the Chi-square test. Binary logistic regression analysis was used to demonstrate the effect of the factors on GDM. BMI gain and weight gain were adjusted for the gestational week of weight measurement. The potential risk factors of GDM included FG in the first trimester, blood pressure, age, multipara, PCOS, history of macrosomia, history of adverse fertility, family history of diabetes, and habitual smoking. The factors confirmed by univariate analysis will be adjusted as confounding factors in multiple analyses. A two-sided P-value of less than 0.05 was considered a statistically significant difference. Multiple imputations were performed for missing values.

Results

Characteristics of pregnant women

There were 41,845 pregnant women (GDM group, n = 4257 vs. non-GDM group, n = 37,588) eligible for inclusion in the final analysis, with a mean (SD) age of 27.62 (4.10) years at study enrollment in 2015 (Additional Fig. 1). Women in the GDM group were older, had higher stature, body weight, BMI, FG in the first trimester, blood pressure (SBP, DBP, and mean arterial pressure (MAP)), and a higher proportion of multipara, PCOS, history of macrosomia, history of adverse fertility, family history of diabetes, and habitual smoking compared with the non-GDM group (P <  0.05) (Table 1). But women in the GDM group had significantly less BMI gain and rate of weight gain at the second trimester than those in the non-GDM group (P <  0.001).

Table 1 Characteristics of the Study Population
Full size table

Early pregnancy BMI and BMI gain

The early pregnancy BMI of women in the GDM group was significantly higher than that in the non-GDM group (P <  0.001) (Table 1). And this difference remained until GDM screening (Fig. 1). Table 2 showed that BMI gain decreased gradually with increasing early pregnancy BMI categories (each of 2 kg/m2) in the GDM group and the non-GDM group (F = 46.623, P <  0.001; F = 236.640, P <  0.001) (Additional Fig. 2). There was no significant difference between the two groups at any BMI categories (each of 2 kg/m2) (each P > 0.05) (Table 2).

Fig. 1
figure 1

Comparison of BMI of pregnant women in the GDM group and the non-GDM group. * indicates P <  0.05, ** indicates P <  0.01; ns indicates P > 0.05. Data points were the means of maternal BMI, with the error bars corresponding to the standard deviation. The means of gestational age of weight measured were 10.3, 12.5, 16.4, 20.4, and 25.1 weeks, respectively. Abbreviation: GDM, gestational diabetes mellitus; BMI, body mass index

Full size image
Table 2 Comparison of BMI gain between the GDM and the non-GDM groups
Full size table

Inadequate or excessive weight gain and GDM

The IOM guidelines are an adaptation of the most widely used criteria for the evaluation of GWG. According to the IOM’s recommendations, we divided 41,845 pregnant women into three categories: inadequate (n = 3340, 9.8%), appropriate (n = 11,227, 33.0%), or excessive (n = 19,406, 57.1%) based on their rate of weight gain at the second trimester. In general, women who gained insufficient or excessive weight had a significantly higher prevalence of GDM than women who gained adequate weight (10.5, 10.5%; vs 8.9%, Chi-square test, P-value <  0.001) (Fig. 2). But the BMI subgroup analysis showed that such differences were only significant in normal-weight women (8.5, 7.5%; vs 8.4%, Chi-squared test P-value = 0.026). And normal-weight women who gain too much weight seemed to have the lowest prevalence of GDM (Additional Table 1).

Fig. 2
figure 2

Comparison of the prevalence of GDM among different weight gain categories. Weight gain was evaluated according to the IOM guidelines based on the Chinese BMI categories. It recommended the optimal rate of weight gain at the second trimester was 0.44–0.58, 0.35–0.50, 0.23–0.33, and 0.17–0.27 kg/week in the underweight, normal weight, overweight, and obese groups, respectively. *Abbreviation: GDM, gestational diabetes mellitus; BMI, body mass index

Full size image

It led us to further analyze the effect of early pregnancy BMI on the results. Figure 2 showed that the prevalence of GDM was 4.5, 7.9, 14.6, and 20.5% in the underweight, normal weight, overweight, and obese group, respectively (Chi-square value = 927.931, P-value <  0.001). Early pregnancy BMI of pregnant women in each Chinese BMI category were compared between the GDM group and the non-GDM group. In the normal weight, overweight and obese group, the mean early pregnancy BMI of women with GDM was still significantly higher than that of women without GDM (P <  0.001) (Additional Table 2). And it could significantly affect the prevalence of GDM.

Influencing factors of GDM

We performed logistic regression analysis to confirm the role of inter-gestational BMI on the risk of GDM (Table 3). The result showed that early pregnancy BMI was a risk factor for GDM (OR 1.131, 95% CI 1.122–1.139). BMI gain was associated with a decreased risk of GDM in unadjusted univariate analysis (OR 0.895, 95% CI 0.869–0.922), as well as the rate of weight gain (OR 0.956, 95% CI 0.946–0.967). Both insufficient and excessive GWG contributed to a higher risk of GDM (OR 1.213, 95% CI 1.093–1.347; OR 1.211, 95% CI 1.115–1.316). It also showed that FG in the first trimester, MAP, age, multipara, PCOS, history of macrosomia, history of adverse fertility, family history of diabetes, and habitual smoking was all the risk factors of GDM (P <  0.05).

Table 3 Odds ratios (95% confidence intervals) of GDM by the effect of BMI gain and weight gain during pregnancy
Full size table

Then, a multiple regression equation was used to identify the actual influence of the factors with a stepwise selection of variables (Table 3). After adjusting for early pregnancy BMI, the effect of BMI gain was no longer significant (AOR 1.000, 95% CI 0.971–1.029). The results of the analysis on the rate of weight gain at the second trimester (AOR 0.996, 95% CI 0.985–1.007) and GWG categories (insufficient: AOR 1.007, 95% CI 0.905–1.121; excessive: AOR 1.000, 95% CI 0.918–1.088) were consistent with BMI gain. After additional adjustment for FG in the first trimester, MAP, and other confounding factors, the effect of BMI gain was still not significant (AOR 1.029, 95% CI 0.999–1.061), as well as the rate of weight gain (AOR 1.006, 95% CI 0.995–1.018) and GWG categories (insufficient: AOR 1.016, 95% CI 0.911–1.133; excessive: AOR 1.044, 95% CI 0.957–1.138).

Sensitivity analyses

When exploring the effect of BMI gain on the risk of GDM, we also analyzed weight gain (Additional Table 3). The conclusions were consistent with BMI gain. And when evaluating GWG according to IOM guidelines, the WHO BMI categories were also conducted. The conclusions were consistent with the Chinese BMI categories (Additional Table 4).

Discussion

To improve the health of mothers and their offspring, WHO has prioritized the achievement of ideal BMI before conception and prevention of excessive GWG. However, pre-gestational obesity represents a challenge of treatment, and nowadays there is new evidence as regards its management, especially the adequate GWG. Lifestyle interventions in pregnancy could help women attain recommended GWG. Optimal interventions and effects on outcomes are currently requiring research implementation [47]. Prior systematic reviews have not demonstrated that a healthy lifestyle and GWG reduced rates of GDM, even in high-risk populations [25]. It prompts us to rethink the implications of reducing GWG for the prevention of GDM.

This study investigated the correlation between maternal BMI and GDM by a large community-based cohort. The prevalence of GDM increases gradually with early pregnancy BMI (Fig. 2). And univariate logistic regression analysis also confirmed that BMI in early pregnancy was a risk factor for GDM (Table 3). Our results support the previous view that overweight and obesity significantly increase the risk of GDM [48, 49]. Women who develop GDM often have a subclinical metabolic dysfunction before pregnancy compared with women without GDM. Because of the significant decrease in insulin sensitivity in normal pregnancy, this predisposing initial insulin resistance is further exacerbated and, in combination with β-cell dysfunction, results in the development of GDM. However, today there are still many disputes, even regarding current indications.

This study found that BMI gain in the first months of pregnancy before GDM screening was not associated with GDM risk. Figure 1 showed that BMI increased in parallel between the GDM and non-GDM groups from the first to the second trimester. Independent sample t-test confirmed that there was no difference in BMI gain between the two groups. Furthermore, we also analyzed weight gain and got consistent results. This conclusion is consistent with some previous studies [36, 37], but it contradicts other studies [35, 38, 39]. This may need to be explained in terms of energy metabolism during pregnancy. GWG is the major determinant of the increase in energy demands during pregnancy. Although a total additional energy demand of about 76,000 kcal has been estimated during the whole pregnancy, there is an inter-individual variability of energy expenditure, linked not only to GWG but also to the pre-nutritional state, clear expression of the plasticity of the metabolic adaptations to the actual requirements [18]. But at present, the contribution of GWG to insulin resistance has not been clarified. The product of conception (placenta, fetus, amniotic fluid) accounts for about 35% of GWG [50]. Maternal body composition changes over the trimesters to support fetal growth. Maternal fat mass is the most variable component of GWG, which mostly contributes to the energy costs of pregnancy and positively correlates with GWG [51]. A previous study demonstrated that in obese women excessive GWG was associated with maternal fat, but not lean body mass accrual [52]. The results might explain why excess GWG is associated with long-term obesity and metabolic dysfunction. However, a variable change in the fat mass of the mother was mainly observed in the later stages of pregnancy [26, 50, 53]. In the first months of gestation, the changes in maternal body composition reflect the preparation of the maternal body for fetal development. Specifically, blood volume expands and the uterus and breast tissue of the maternal unit grows [18]. But mostly the accumulation of fat could be a reason for altered glucose tolerance. This may explain why excessive GWG in the first and second trimesters does not contribute to GDM by fat mass accrual. Therefore, the first half of BMI gain and weight gain was not associated with the risk of GDM.

Figure 2 showed that the rate of GWG greater than or less than the IOM guideline recommendations, compared with that within recommended levels, was associated with a higher risk of GDM. It meets the expectation of the IOM guidelines for reducing adverse pregnancy outcomes. But the BMI subgroup analysis showed that such differences were not significant in underweight, overweight, or obese women, respectively. This result suggested that GWG is not associated with the risk of GDM, and was consistent with our previous conclusion. Among women with normal weight in early pregnancy, those who gained too much weight gain had a lower risk of GDM than those who gained optimal weight. Similar observations have been reported in previous studies from Chinese populations [38, 39]. And Table 1 showed that BMI gain and GWG were both lower in the GDM group than in the non-GDM group. We further analyzed the causes of these results in detail. Next, we divided pregnant women based on BMI categories and found that the early pregnancy BMI of women with GDM was still significantly higher than the women without GDM in the normal weight, overweight and obese group, respectively (Additional Table 2). In addition, both BMI and weight gain decreased with initial BMI (Table 2, Additional Table 3). It could significantly affect the prevalence of GDM in different GWG evaluation categories. And this may explain why excess GWG has been associated with a lower risk of GDM in some previous studies [38, 39]. This view was also confirmed in multivariate logistic regression analysis. Neither insufficient nor excessive GWG affected the risk of GDM after adjusting for BMI in the first trimester. In addition, this result also suggested that we must be alert to the influence of initial BMI on the conclusion in future research on GWG. Since changes in BMI and body weight during pregnancy are extremely sensitive to initial weight, it is not enough to adjust the prepregnancy BMI category alone.

The IOM guidelines were based on findings from observational studies focused on associations of GWG with preterm birth, small, and large size for gestational age at birth, cesarean delivery, postpartum weight retention, and childhood obesity [26]. This study provided a valuable complement to the guidelines. And more research is needed to confirm the effect of optimal weight gain recommended by the guidelines on the prevention of GDM.

Limitation

To obtain accurate body weight, we took the measured weight during the first antenatal examination to calculate the initial BMI. That is because the difference between prepregnancy weight and early pregnancy weight is not significant and is generally assumed to be less than 2 kg. Moreover, the two are often commended to calculate pre-pregnancy BMI and GWG. Therefore, self-reported pre-pregnancy weight was not performed in this study. Although we require all pregnant women to start antenatal examination as soon as possible after confirming pregnancy, the average gestational age of the initial antenatal visit was 10 weeks. Because of this inevitable shortcoming, we can’t accurately evaluate the weight or BMI at the first trimester before 10 weeks.

Conclusions

BMI in early pregnancy was a risk factor for GDM, while BMI gain before GDM screening was not associated with the risk of GDM. Therefore, the optimal BMI in early pregnancy is the key to preventing GDM.

Availability of data and materials

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

Abbreviations

GDM:

Gestational diabetes mellitus

BMI:

Body mass index

PCOS:

Polycystic ovary syndrome

MNT:

Medical nutrition therapy

GWG:

Gestational weight gain

IOM:

Institute of Medicine

TJWCHIS:

Tianjin Women and Children Health Information System

FG:

Fasting glucose

GCT:

Glucose challenge test

OGTT:

Oral glucose tolerance test

DIP:

Diabetes in pregnancy

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

MAP:

Mean arterial pressure

References

  1. Wang X, Zhang X, Zhou M, Juan J, Wang X. Association of Gestational Diabetes Mellitus with adverse pregnancy outcomes and its interaction with maternal age in Chinese urban women. J Diab Res. 2021;2021:5516937. https://doi.org/10.1155/2021/5516937.

    Article  Google Scholar 

  2. Lowe WL Jr, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, et al. Hyperglycemia and adverse pregnancy outcome follow-up study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism. Diabetes Care. 2019;42(3):372–80. https://doi.org/10.2337/dc18-1646.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tobias DK, Stuart JJ, Li S, Chavarro J, Rimm EB, Rich-Edwards J, et al. Association of history of gestational diabetes with Long-term cardiovascular disease risk in a large prospective cohort of US women. JAMA Intern Med. 2017;177(12):1735–42. https://doi.org/10.1001/jamainternmed.2017.2790.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Page KA, Luo S, Wang X, Chow T, Alves J, Buchanan TA, et al. Children exposed to maternal obesity or gestational diabetes mellitus during early fetal development have hypothalamic alterations that predict future weight gain. Diabetes Care. 2019;42(8):1473–80. https://doi.org/10.2337/dc18-2581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lu J, Gu Y, Wang L, Li W, Zhang S, Liu H, et al. Glucose metabolism among obese and non-obese children of mothers with gestational diabetes. BMJ Open Diabetes Res Care. 2020;8(1). https://doi.org/10.1136/bmjdrc-2019-000822.

  6. Li W, Leng J, Liu H, Zhang S, Wang L, Hu G, et al. Nomograms for incident risk of post-partum type 2 diabetes in Chinese women with prior gestational diabetes mellitus. Clin Endocrinol. 2019;90(3):417–24. https://doi.org/10.1111/cen.13863.

    Article  CAS  Google Scholar 

  7. Shepherd E, Gomersall JC, Tieu J, Han S, Crowther CA, Middleton P. Combined diet and exercise interventions for preventing gestational diabetes mellitus. Cochrane Database Syst Rev. 2017;11:CD010443. https://doi.org/10.1002/14651858.CD010443.pub3.

    Article  PubMed  Google Scholar 

  8. Ferrara A. Increasing prevalence of gestational diabetes mellitus: a public health perspective. Diabetes Care. 2007;30(Suppl 2):S141–6. https://doi.org/10.2337/dc07-s206.

    Article  PubMed  Google Scholar 

  9. Melchior H, Kurch-Bek D, Mund M. The prevalence of gestational diabetes. Dtsch Arztebl Int. 2017;114(24):412–8. https://doi.org/10.3238/arztebl.2017.0412.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gao C, Sun X, Lu L, Liu F, Yuan J. Prevalence of gestational diabetes mellitus in mainland China: a systematic review and meta-analysis. J Diabetes Investig. 2019;10(1):154–62. https://doi.org/10.1111/jdi.12854.

    Article  CAS  PubMed  Google Scholar 

  11. Yang X, Hsu-Hage B, Zhang H, Yu L, Dong L, Li J, et al. Gestational diabetes mellitus in women of single gravidity in Tianjin City, China. Diabetes care. 2002;25(5):847–51. https://doi.org/10.2337/diacare.25.5.847.

    Article  PubMed  Google Scholar 

  12. Yang H, Wei Y, Gao X, Xu X, Fan L, He J, et al. Risk factors for gestational diabetes mellitus in Chinese women: a prospective study of 16,286 pregnant women in China. Diabetic Med. 2009;26(11):1099–104. https://doi.org/10.1111/j.1464-5491.2009.02845.x.

    Article  CAS  PubMed  Google Scholar 

  13. Leng J, Shao P, Zhang C, Tian H, Zhang F, Zhang S, et al. Prevalence of gestational diabetes mellitus and its risk factors in Chinese pregnant women: a prospective population-based study in Tianjin, China. PLoS One. 2015;10(3):e0121029. https://doi.org/10.1371/journal.pone.0121029.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Popova PV, Klyushina AA, Vasilyeva LB, Tkachuk AS, Vasukova EA, Anopova AD, et al. Association of Common Genetic Risk Variants with Gestational Diabetes Mellitus and Their Role in GDM prediction. Front Endocrinol. 2021;12:628582. https://doi.org/10.3389/fendo.2021.628582.

    Article  Google Scholar 

  15. Popova PV, Pustozerov EA, Tkachuk AS, Grineva EN. Improving nutrition for the prevention of gestational diabetes: current status and perspectives. World J Diabetes. 2021;12(9):1494–506. https://doi.org/10.4239/wjd.v12.i9.1494.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Popova P, Tkachuk A, Dronova A, Gerasimov A, Kravchuk E, Bolshakova M, et al. Fasting glycemia at the first prenatal visit and pregnancy outcomes in Russian women. Minerva Endocrinol. 2016;41(4):477–85.

    PubMed  Google Scholar 

  17. Wu L, Han L, Zhan Y, Cui L, Chen W, Ma L, et al. Prevalence of gestational diabetes mellitus and associated risk factors in pregnant Chinese women: a cross-sectional study in Huangdao, Qingdao, China. Asia Pac J Clin Nutr. 2018;27(2):383–8. https://doi.org/10.6133/apjcn.032017.03.

    Article  PubMed  Google Scholar 

  18. Parrettini S, Caroli A, Torlone E. Nutrition and metabolic adaptations in physiological and complicated pregnancy: focus on obesity and gestational diabetes. Front Endocrinol. 2020;11:611929. https://doi.org/10.3389/fendo.2020.611929.

    Article  Google Scholar 

  19. Gou BH, Guan HM, Bi YX, Ding BJ. Gestational diabetes: weight gain during pregnancy and its relationship to pregnancy outcomes. Chin Med J. 2019;132(2):154–60. https://doi.org/10.1097/CM9.0000000000000036.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Almeida VAH, Costa RAD, Paganoti CF, Mikami FC, Sousa A, Peres SV, et al. Diet quality indices and physical activity levels associated with adequacy of gestational weight gain in pregnant women with gestational diabetes mellitus. Nutrients. 2021;13(6). https://doi.org/10.3390/nu13061842.

  21. Silva-Zolezzi I, Samuel TM, Spieldenner J. Maternal nutrition: opportunities in the prevention of gestational diabetes. Nutr Rev. 2017;75(suppl 1):32–50. https://doi.org/10.1093/nutrit/nuw033.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang C, Wei Y, Zhang X, Zhang Y, Xu Q, Sun Y, et al. A randomized clinical trial of exercise during pregnancy to prevent gestational diabetes mellitus and improve pregnancy outcome in overweight and obese pregnant women. Am J Obstet Gynecol. 2017;216(4):340–51. https://doi.org/10.1016/j.ajog.2017.01.037.

    Article  PubMed  Google Scholar 

  23. Assaf-Balut C, Garcia de la Torre N, Duran A, Fuentes M, Bordiu E, Del Valle L, et al. A Mediterranean diet with additional extra virgin olive oil and pistachios reduces the incidence of gestational diabetes mellitus (GDM): a randomized controlled trial: the St. Carlos GDM prevention study. PLoS One. 2017;12(10):e0185873. https://doi.org/10.1371/journal.pone.0185873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ming WK, Ding W, Zhang CJP, Zhong L, Long Y, Li Z, et al. The effect of exercise during pregnancy on gestational diabetes mellitus in normal-weight women: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2018;18(1):440. https://doi.org/10.1186/s12884-018-2068-7.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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. https://doi.org/10.1001/jama.2017.3635.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Rasmussen KM, Yaktine AL, editors. Weight gain during pregnancy: reexamining the guidelines. Washington (DC): Institute of Medicine (US) and National Research Council (US) Committee; 2009.

    Google Scholar 

  27. Goldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018;16(1):153. https://doi.org/10.1186/s12916-018-1128-1.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Yew TW, Chi C, Chan SY, van Dam RM, Whitton C, Lim CS, et al. A randomized controlled trial to evaluate the effects of a smartphone application-based lifestyle coaching program on gestational weight gain, glycemic control, and maternal and neonatal outcomes in women with gestational diabetes mellitus: the SMART-GDM study. Diabetes Care. 2021;44(2):456–63. https://doi.org/10.2337/dc20-1216.

    Article  PubMed  Google Scholar 

  29. Black MH, Sacks DA, Xiang AH, Lawrence JM. The relative contribution of prepregnancy overweight and obesity, gestational weight gain, and IADPSG-defined gestational diabetes mellitus to fetal overgrowth. Diabetes Care. 2013;36(1):56–62. https://doi.org/10.2337/dc12-0741.

    Article  PubMed  Google Scholar 

  30. 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. https://doi.org/10.1371/journal.pone.0157081.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sun Y, Shen Z, Zhan Y, Wang Y, Ma S, Zhang S, et al. Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications. BMC Pregnancy Childbirth. 2020;20(1):390. https://doi.org/10.1186/s12884-020-03071-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hedderson MM, Gunderson EP, Ferrara A. Gestational weight gain and risk of gestational diabetes mellitus. Obstet Gynecol. 2010;115(3):597–604. https://doi.org/10.1097/AOG.0b013e3181cfce4f.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Brunner S, Stecher L, Ziebarth S, Nehring I, Rifas-Shiman SL, Sommer C, et al. Excessive gestational weight gain prior to glucose screening and the risk of gestational diabetes: a meta-analysis. Diabetologia. 2015;58(10):2229–37. https://doi.org/10.1007/s00125-015-3686-5.

    Article  PubMed  Google Scholar 

  34. Bouvier D, Forest JC, Dion-Buteau E, Bernard N, Bujold E, Pereira B, et al. Association of Maternal Weight and Gestational Weight Gain with maternal and neonate outcomes: a prospective cohort study. J Clin Med. 2019;8(12). https://doi.org/10.3390/jcm8122074.

  35. Peng Y, Han N, Su T, Zhou S, Bao H, Ji Y, et al. Gestational weight gain and the risk of gestational diabetes mellitus: a latent class trajectory analysis using birth cohort data. Diabetes Res Clin Pract. 2021;182:109130. https://doi.org/10.1016/j.diabres.2021.109130.

    Article  CAS  PubMed  Google Scholar 

  36. Chakkalakal RJ, Hackstadt AJ, Trochez R, Gregory R, Elasy TA. Gestational diabetes and maternal weight management during and after pregnancy. J Women's Health. 2019;28(5):646–53. https://doi.org/10.1089/jwh.2018.7020.

    Article  Google Scholar 

  37. Durst JK, Sutton AL, Cliver SP, Tita AT, Biggio JR. Impact of gestational weight gain on perinatal outcomes in obese women. Am J Perinatol. 2016;33(9):849–55. https://doi.org/10.1055/s-0036-1579650.

    Article  PubMed  Google Scholar 

  38. Hung TH, Hsieh TT. Pregestational body mass index, gestational weight gain, and risks for adverse pregnancy outcomes among Taiwanese women: a retrospective cohort study. Taiwan J Obstet Gynecol. 2016;55(4):575–81. https://doi.org/10.1016/j.tjog.2016.06.016.

    Article  PubMed  Google Scholar 

  39. 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. https://doi.org/10.1371/journal.pone.0082310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ochsenbein-Kolble N, Roos M, Gasser T, Zimmermann R. Cross-sectional study of weight gain and increase in BMI throughout pregnancy. Eur J Obstet Gynecol Reprod Biol. 2007;130(2):180–6. https://doi.org/10.1016/j.ejogrb.2006.03.024.

    Article  PubMed  Google Scholar 

  41. McClurg DP, Gissler M, Gatt M, Wallace J, Bhattacharya S. Does interpregnancy BMI change affect the risk of complications in the second pregnancy? Analysis of pooled data from Aberdeen, Finland and Malta. Int J Obes. 2022;46(1):178–85. https://doi.org/10.1038/s41366-021-00971-7.

    Article  Google Scholar 

  42. Hod M, Kapur A, Sacks DA, Hadar E, Agarwal M, Di Renzo GC, et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on gestational diabetes mellitus: a pragmatic guide for diagnosis, management, and care. Int J Gynaecol Obstet. 2015;131(Suppl 3):S173–211. https://doi.org/10.1016/S0020-7292(15)30033-3.

    Article  PubMed  Google Scholar 

  43. Popova P, Castorino K, Grineva EN, Kerr D. Gestational diabetes mellitus diagnosis and treatment goals: measurement and measures. Minerva Endocrinol. 2016;41(4):421–32.

  44. Yang HX. Diagnostic criteria for gestational diabetes mellitus (WS 331-2011). Chin Med J. 2012;125(7):1212–3.

    PubMed  Google Scholar 

  45. International Association of D, Pregnancy Study Groups Consensus P, Metzger BE, Gabbe SG, Persson B, Buchanan TA, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82. https://doi.org/10.2337/dc09-1848.

    Article  CAS  Google Scholar 

  46. Santos S, Eekhout I, Voerman E, Gaillard R, Barros H, Charles MA, et al. Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania. BMC Med. 2018;16(1):201. https://doi.org/10.1186/s12916-018-1189-1.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Champion ML, Harper LM. Gestational weight gain: update on outcomes and interventions. Curr Diab Rep. 2020;20(3):11. https://doi.org/10.1007/s11892-020-1296-1.

    Article  PubMed  Google Scholar 

  48. Poblete JA, Olmos P. Obesity and gestational diabetes in pregnant care and clinical practice. Curr Vasc Pharmacol. 2021;19(2):154–64. https://doi.org/10.2174/1570161118666200628142353.

    Article  CAS  PubMed  Google Scholar 

  49. Zehravi M, Maqbool M, Ara I. Correlation between obesity, gestational diabetes mellitus, and pregnancy outcomes: an overview. Int J Adolesc Med Health. 2021;33(6):339–45. https://doi.org/10.1515/ijamh-2021-0058.

    Article  PubMed  Google Scholar 

  50. Pitkin RM. Nutritional support in obstetrics and gynecology. Clin Obstet Gynecol. 1976;19(3):489–513. https://doi.org/10.1097/00003081-197609000-00002.

    Article  CAS  PubMed  Google Scholar 

  51. Lederman SA, Paxton A, Heymsfield SB, Wang J, Thornton J, Pierson RN Jr. Body fat and water changes during pregnancy in women with different body weight and weight gain. Obstet Gynecol. 1997;90(4 Pt 1):483–8. https://doi.org/10.1016/s0029-7844(97)00355-4.

    Article  CAS  PubMed  Google Scholar 

  52. Berggren EK, Groh-Wargo S, Presley L, Hauguel-de Mouzon S, Catalano PM. Maternal fat, but not lean, mass is increased among overweight/obese women with excess gestational weight gain. Am J Obstetr Gynecol. 2016;214(6):745 e741–5. https://doi.org/10.1016/j.ajog.2015.12.026.

    Article  Google Scholar 

  53. Most J, Marlatt KL, Altazan AD, Redman LM. Advances in assessing body composition during pregnancy. Eur J Clin Nutr. 2018;72(5):645–56. https://doi.org/10.1038/s41430-018-0152-8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors sincerely thank the obstetricians and nurses in the community hospitals who were involved in the collection of prenatal medical information.

Funding

This work was supported by the National Key Research and Development Program of China (grant number: 2018YFC1313900, 2018YFC1313903); the National Natural Science Foundation of China (grant number: 81703247), the Natural Science Foundation of Tianjin, China (grant number: 19JCYBJC28000), the Maternal and Child Nutrition and Health Research Project of China CDC (grant number: 2019FYH012), and the Tianjin Key Medical Discipline (Specialty) Construction.

Author information

Authors and Affiliations

  1. Tianjin Women’s and Children’s Health Center, No. 96 Guizhou Road, Heping District, Tianjin, 300070, China

    Shuang Zhang, Huikun Liu, Nan Li, Wei Dong, Weiqin Li, Leishen Wang, Yu Zhang & Junhong Leng

  2. Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, 300070, China

    Yingzi Yang

Authors
  1. Shuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huikun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weiqin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leishen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yingzi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Junhong Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SZ conceived the study design, analysis, and interpretation of data, and wrote the initial draft. HKL, NL, WD, and YZ contributed to the acquisition of data. WQL, LSW, and YZY performed data analysis visualization and graphing. JHL supervised the study. All authors have reviewed the data and contributed to manuscript writing. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Junhong Leng.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Human Subjects Committee of the Tianjin Women’s and Children’s Health Center. All methods were carried out in accordance with relevant guidelines and regulations. Since this was a retrospective analysis of data routinely collected from participants, the consent for participation was not applicable. The need for informed consent was waived by the Human Subjects Committee of the Tianjin Women’s and Children’s Health Center.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Additional Fig. 1.

Study flow chart. Additional Fig. 2. Correlation between BMI gain and baseline BMI in the GDM group and the non-GDM group. Additional Table 1. Compare the prevalence of GDM in different weight gain categories. Additional Table 2. Compare baseline BMI in the GDM and the non-GDM groups. Additional Table 3. Compare weight gain in the GDM and the non-GDM groups. Additional Table 4. Odds ratios (95% confidence intervals) of GDM by the effect of weight gain during pregnancy.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Liu, H., Li, N. et al. Relationship between gestational body mass index change and the risk of gestational diabetes mellitus: a community-based retrospective study of 41,845 pregnant women. BMC Pregnancy Childbirth 22, 336 (2022). https://doi.org/10.1186/s12884-022-04672-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12884-022-04672-5

Keywords

  • Gestational diabetes mellitus
  • Body mass index
  • Community-based study

BMC Pregnancy and Childbirth

ISSN: 1471-2393

Contact us