Abstract
Objective
To investigate the associations of body mass index (BMI) change and large for gestational age (LGA) among prepregnancy normal-weight women with and without gestational diabetes mellitus (GDM).
Methods
The retrospective study including 9515 normal-weight pregnant women (1331 women with GDM and 8184 without GDM) was conducted in Fujian Maternity and Child Health Hospital in 2020. The BMI change was calculated as gestational weight gain in kilograms by maternal height in meters. The binary logistic regression, stratified analyses, restricted cubic spline models and additive interaction analysis were adopted to reveal the relationship between BMI change and LGA.
Results
Pregnant women with GDM had a lower level of BMI change but a higher incidence of LGA compared with those without GDM. After adjustment for covariates variables, we found that the risk of LGA was associated with the highest quartile of BMI change (OR = 1.89, 95%CI:1.27–2.8 for GDM and OR = 1.48,95%CI:1.27–1.75 for non-GDM). There were significant linear relationships of BMI change and LGA with the inflection point of 5.096 and 5.401 kg/m2 in GDM and non-GDM groups. Significant additive interaction was observed between parity and BMI change level concerning LGA. A significant difference in BMI change and gestational weight gain (GWG) for LGA prediction was detected.
Conclusion
Higher BMI changes were significantly associated with a higher risk of LGA in pregnant women with or without GDM in a linear dose-response relationship, with the threshold around 5.096 and 5.401 kg/m2, respectively. These suggested that BMI changes may be a useful predictor for the incidence of LGA in singleton pregnant women.
Introduction
Birth weight directly reflects the fetal health and intrauterine nutritional status affecting the child’s future health [Citation1]. Large for gestational age (LGA) is described as a neonate weight at or above the 90th percentage according to gestational age and sex [Citation2]. Infants born with LGA increase risks of neonatal Apgar score at 5 min, shoulder dystocia [Citation3], neonatal hypoglycemia [Citation4], neonatal intensive care unit (NICU) admission [Citation5], longer hospital stays [Citation6] and adverse metabolic and cardiovascular outcomes [Citation7] in adult life. Concerning maternal outcomes, delivery LGA infants may increase the risks of cesarean delivery, postpartum hemorrhage, and perineal lacerations [Citation5].
The prevalence of LGA in some countries is rising over the past decades including Chinese, resulting in an increase in the number of research on the risk factors and prevention of LGA [Citation8–15]. It is well known that both gestational diabetes mellitus(GDM) and/or excessive maternal nutrition during pregnancy are associated with LGA fetuses [Citation16–19]. Maternal nutrition is commonly assessed using gestational weight gain. However, it may not reflect the body size changes during pregnancy in tall and short women if they have the same pregnancy-related weight gain. Body mass index (BMI) change, calculated as the gestational weight gain(kg)/maternal height2(m2), is put forward as a useful parameter for individual obstetrics maternal gestational weight gain management field [Citation20–22]. Many medical fields have reported the relationship of annual BMI change and chronic disease or cardiovascular disease [Citation23–25]. However, there are no clear BMI change recommendations for weight management and no study have focused on the associations between BMI change and LGA. Therefore, this article aimed to evaluate the dose-response relationship between BMI change and the incidence of LGA among low-risk pregnancies with normal pre-pregnancy BMI.
Methods
Study population
This retrospective study was conducted at Fujian Maternity and Child Health Hospital. This study was approved by the Research Ethics Committee of Fujian Maternity and Child Health Hospital (approval number:2020-2049) and complied with the Declaration of Helsinki. This study included pregnant women with a normal pre-pregnancy BMI who received perinatal care during the whole pregnancy at the obstetric clinic in 2020. Pregnant women who delivered singleton birth at term (37–42 weeks’ gestation)were eligible for the study. Preexisting diabetes, hypertension, chronic heart disease, kidney disease, and autoimmune disease, women aged less than 18 years or older than 45 years, missing information about pre-pregnancy BMI, maternal weight at delivery, and gestational age constituted the exclusion criteria. Detailed clinical data on maternal characteristics such as age, height, pre-pregnancy weight, educational level, parity, gravity and delivery outcomes were collected from the hospital’s electronic health record.
Definition
At the first obstetric visit in our hospital, all pregnant women were asked to provide their pre-pregnancy weight and height and BMI was calculated according to the World Health Organization standard formula [body weight(kg)/height2(m2)] [Citation26]. Gestational weight gain was calculated as maternal weight at delivery minus the prepregnancy. The BMI change was expressed as gestational weight gain in kilograms by maternal height in meters square. GDM was diagnosed by a standardized 75 g oral glucose tolerance test (OGTT) at 24–28 weeks of pregnancy according to the International Association of the Diabetes and Pregnancy Study Group (IADPSG): fasting glucose 5.1 mmol/L, 1-h glucose 10.0 mmol/L, or 2-h glucose 8.5 mmol/L [Citation27]. The main pregnancy outcome in the current study was LGA, which was defined as a birth weight exceeding the 90th percentile based on the newborn sex and gestational age [Citation28]. Other covariates were also assessed: maternal age, educational level, gestational age, gravity, parity, and infant sex. Educational level was categorized into college and above, high school or equivalent, and less than high school. Gravity was classified as 1, 2, and ≥3 times. Parity was grouped into primiparous and multiparous. Infant sex was categorized into boy and girl.
Statistical analysis
The participant’s characteristics were reported as mean ± standard deviation for continuous variables and numbers with percentages for categorical variables. Baseline characteristic analysis stratified by GDM/non-GDM groups was compared using a two-sample t-test for continuous variables with a normal distribution, the Wilcoxon Mann-Whitney for continuous variables with a skewed distribution, and the chi-squares test for categorical variables. Binary logistic regression was used to assess the crude and adjusted odds ratios(ORs) and 95% confidence intervals(CIs) of the associations of BMI changes with LGA (per unit, per SD, and quartiles). We modified two Models. Models were adjusted for maternal age (continuous variables), education level (College and above, High school or equivalent and Less than high school), gravity (1,2 and ≥3), parity (primiparity or multiparity), gestational age at delivery (continuous variables), and infant sex (boy or girl). Model 2 was further adjusted for pre-pregnancy BMI (continuous variables). The dose-response relationship between BMI change and ORs of LGA among pregnant women with and without GDM using a restricted cubic spline model and the tests for nonlinearity were performed using the likelihood ratio test.
We performed the stratified analyses according to maternal age (<35, ≥35years), education level (College and above, high school or equivalent and less than high school), gravity (1, 2, and ≥3), parity(primiparity, multiparity) and infant sex (girl, boy) in pregnant women with and without GDM by testing the multiplicative interaction terms.
To examine the biological plausibility of interaction between BMI change (value higher than the cutoff as the reference level) and GDM status (yes or no, no as the reference level), we evaluated the three indices and their 95% CIs: the relative excess risk because of the interaction (RERI), the attributable proportion because of the interaction (AP) and interaction index(synergy index, SI) [Citation29]. The 95% CI of RERI and AP includes “0” and the 95% CI of SI includes “1” indicate the absence of additive interaction. The multiplicative interaction was reported by ORs (95%CI).
The receiver operating characteristic (ROC) analysis was used to compare the accuracy of gestational weight gain and BMI change in predicting LGA.
All analyses were conducted using R software (version 4.4.2). The level of statistical significance was defined as two-sided p < 0.05.
Results
Basic characteristics of the study population
shows the flowchart of study population’s screening. In total, 10024 pregnant women with singleton live births at term were included. Women with history of diabetes or hypertension (n = 28), preexisting chronic heart disease and kidney disease (n = 3), aged less than 18 or older than 45 years (n = 18), and missing information about pre-pregnancy BMI (n = 56) and maternal weight at delivery (n = 404) were excluded. Finally, there were 9515 eligible women. The baseline characteristics of 9515 participants stratified by GDM/non-GDM are shown in . The average BMI change among GDM was 5.10 ± 1.70 kg/m2 and 5.40 ± 1.70 kg/m2 among non-GDM. Overall, more than 48% of the women were in college and above. According to the Chinese gestational weight gain guidelines, more than half of the pregnant women gained weight outside of the recommendations and only 46.4% of women gained as recommended. Compared to those without GDM, those with GDM were older, shorter in maternal height, had less total gestational weight gain, and were more likely to deliver LGA infants. We found no significant differences in education level, pre-pregnancy weight, parity, and birth weight.
Table 1. Baseline characteristics of participants stratified by gestational diabetes mellitus.
Association between BMI change and risk of LGA
shows the ORs and 95% CIs for LGA by BMI change [per unit, per SD (Standard Deviation), and quartiles] among individuals with and without GDM. 282(21.2%) and 1379 (16.8%) pregnant women had delivered LGA infants among GDM and non-GDM patients.
Table 2. Association of maternal body mass index(BMI) change with large for gestational age(LGA) among individuals with and without gestational diabetes mellitus (GDM).
After adjusting for maternal age, education level, gravity, parity, gestational age at delivery, infant sex, and pre-pregnancy body mass index (Model 2), a 1-unit and a 1-SD increase in BMI increased the risk of LGA by 1.10 and 1.18 times for GDM, and 1.09 and 1.15 times for non-GDM, respectively. In pregnant women with GDM, the ORs for incidence of LGA were 1.63(95%CI:1.10 ∼ 2.41) for quartile3 and 1.89 (95%CI:1.27 ∼ 2.8) for quartile4. Non-GDM women with the highest quartile of BMI change compared with the lowest had a 48% higher LGA risk (OR:1.48, 95%CI:1.25 ∼ 1.75, Ptrend < 0.001) after the covariates variables adjustment. Additionally, compared to quartile1, quartile 3 also had a higher risk of LGA among GDM women, but a lower risk than quartile 4 in the adjusted model 2.
In the restricted cubic spline regression models, the dose–response relationship between BMI change and the incidence of LGA was shown in . In the GDM group, significant linear relationships were found between BMI change and the incidence of LGA (Pnonlinear = 0.107, ). Similarly, in the non-GDM group, significant linear relationships were also observed (Pnonlinear = 0.096, ). Furthermore, the inflection point of 5.096 and 5.401 by threshold analysis was detected between BMI change and LGA in GDM and non-GDM groups. When BMI change exceeds the inflection point, increased BMI change was significantly associated with increased risk of LGA [(aORGDM = 1.43, 95%CI:1.091,1.87)and (aORnon-GDM = 1.14,95%CI:1.01,1.28)]. But there was no significant association between BMI change and LGA when BMI change was less than the inflection point (Supplementary Table 1).
Figure 1. Flowchart of subject selection.
Figure 2. Dose-response relationship between maternal body mass index (BMI) change and large for gestational age (LGA) among pregnant women with or without gestational diabetes mellitus (GDM). The solid line and shadow part represents the unadjusted probability and adjusted 95% confidence intervals. Adjusted for maternal age, education level, gravidity, parity, gestational age at delivery, infant sex, oral glucose tolerance test level and pre-pregnancy body mass index. (A) GDM; (B) Non-GDM.
Stratification analysis on the association of BMI changes with LGA
We further analyzed the association of BMI changes with LGA stratified by maternal age (<35, ≥35years), education level (College and above, high school or equivalent and less than high school), gravity (1, 2, and ≥3), parity (primiparity, multiparity) and infant sex (girl, boy) in pregnant women with and without GDM. The stratification analysis demonstrated that no significant interaction was detected between the stratifying variables and the BMI change quartile among GDM (). Among pregnant women without GDM, consistent results were found in most subgroups, while positive associations of LGA and BMI change quartile seemed to be stronger in primiparous.
Table 3. Stratification analysis on the association between maternal body mass index (BMI) change and large for gestational age (LGA).
Additive interaction of covariates and BMI change for LGA
The RERI, AP, SI, and their 95%CI were calculated to measure the additive interaction of covariates and BMI change quartiles with regard to LGA. Showed in Supplementary Table 2, no significant additive interaction between covariates and BMI change quartiles was observed (95% CI of RERI and AP include “0” and the 95% CI of SI include “1”), except parity.
Comparison the LGA prediction of gestational weight gain and BMI change
For nonGDM population, a significant relationship between gestational weight gain and LGA was established(area under curve [AUC] = 0.52, p = 0.028, 95% confidence interval [CI] = 0.50-0.54). The ROC curve analysis for testing the significance of BMI change in prediction of LGA showed an AUC of 0.59 (p < 0.001, 95%CI = 0.57–0.60). The Delong test for the two AUC showed significantly different (Z = 16.59, p < 0.001)(Supplementary Figure 1). Similarly, for GDM population, the ROC curve analysis for testing the significance of gestational weight gain (GWG) and BMI change in prediction of LGA revealed an AUC of 0.54 (p = 0.032, 95% CI = 0.50–0.58) and 0.55 (p = 0.007, 95% CI = 0.51–0.59), respectively. The Z value of Delong test for the two AUC was 2.19 with p = 0.028 (Supplementary Figure 2).
Discussion
In this retrospective study, we discovered that there was a linear association between the maternal BMI change and the incidence of LGA among pregnant women with or without GDM. Compared with the lowest quartiles of BMI change, the highest quartile increased 89% risk of LGA among pregnant women with GDM and 48% among women without GDM. Our findings were consistent with previous studies that found a positive association between BMI change and the incidence of LGA. A retrospective research study in pregnant women with GDM [Citation22] revealed a significant linear relationship between BMI change and the incidence of LGA among GDM. Higher BMI change may be explained as higher gestational weight gain and the association between higher maternal weight gain during pregnancy and fetal overgrowth is remarkably stable. Various studies evaluated gestational weight gain and fetal overgrowth [Citation30–33]. While these studies focused on total gestational weight gain targeted to the gestational weight gain guidelines to compare the adverse pregnancy outcomes apart from maternal height. As it has been a long time demonstrated that maternal height and prepregnancy BMI are all independently correlated with gestational weight gain. That is,for women with the same BMI but large differences in height, the appropriate weight gain as recommended by the guidelines would cause a large deviation in the change in body size for both. Thence, the use of gestational weight gain alone may introduce a bias, as it may not reflect the body size and the BMI change could be a better parameter for reflecting the weight gain during pregnancy. For example, for a prepregnancy normal-weight woman with a height of 150 cm, the best gestational weight gain is less than 11.3 kg, and 16.3 kg for a woman with a height of 180 cm. Therefore, 11.3 kg and 16.3 kg could be used for the upper limit of gestational weight gain, compared with the 8–14kg recommended by the Chinese guidelines [Citation34].
In our study, based on the retrospective sample, pregnant women with GDM had a lower BMI change level to prevent LGA. Specifically, the threshold level was slightly lower in pregnant women with GDM than in subjects without GDM, indicating the combined effects on LGA. The different patterns in the relationship between BMI change and LGA among pregnant women with and without GDM illustrate it is important to take into account GDM status in the weight gain management during pregnancy in obstetric clinical and the studies.
In the subgroup analysis, there was no significant association between each class and the incidence of LGA in pregnant women with GDM, while a positive relationship was detected between parity and the incidence of LGA in pregnant women without GDM. Additionally, the additive interactions also revealed the interactive effect of parity and BMI change was greater than the sum of the two individual effects. These results suggest that primiparous may suffer more from getting more BMI change. The possible reason was that the primiparious for their first pregnancy have poor knowledge of the consequence of excessive gestational weight gain, perceiving that higher gestational weight gain is beneficial for the baby health and finally resulting in LGA infants, especially among individuals without GDM. Additionally, we further quantified the additive interaction between covariates including GDM status and BMI change quartiles to reflect the biological plausibility of interaction. The result also revealed the interactive effect of risk of parity and BMI change quartiles was greater than the sum of the two individual effects. These results suggested that primiparity are more likely to be an at-risk population for delivering LGA infant.
Normalizing fetal growth is important in pregnant women’s weight management and it directly or indirectly reflects most another perinatal outcome. Thus, the weight gain that normalizes fetal growth may represent the appropriate weight gain, to some extent. However, the length of gestation and maternal height should be taken into consideration in the optimal gestational weight gain. In the current study, we targeted our study population to prepregnancy normal-weight women who delivered at term and integrate total weight gain and maternal height as BMI change to classify change and association between the BMI change quartiles and the incidence of LGA. Each category represented the different levels of weight gain and pregnant women with the highest BMI change quartiles had the highest risk. Using our method, the upper limitation of weight gain may be individualized for pregnant women and also for obstetric healthcare providers to direct management decisions. Identification of those pregnant women with high BMI change may inform the appropriate allocation of healthcare individualization and may benefit those with low BMI change by reducing the number of ultrasounds to monitor fetal growth or glycemic monitoring for GDM women which generally reduces the corresponding medical costs. While this would allow the limited medical healthcare resource better use for women at risk of fetal overgrowth directly. Therefore, our proposal may be helpful forward in the personalization of Chinese or Institute of Medicine (IOM) recommendations. That is, the gestational weight gain standards including Chinese or IOM recommendations could be revised to improve the approach used in daily clinical practice. Furthermore, our study findings may serve as the basis for future study about the weight gain management on reducing the incidence of fetal overgrowth.
There are some limitations in the current study. Firstly, the study data were all from single-center, and the generalizability of results to the outside population is unknown. Secondly, although we have adjusted some covariates, there still exists some unknown and residual confounding that could not be entirely excluded. One of the primary strengths of the current study is the use of BMI change rather than the total gestational weight gain to define the weight gain during pregnancy. Also, we grouped women as GDM and non-GDM may help to direct management decisions. Moreover, a stratified analysis of the association of BMI change with LGA was performed across maternal age, education level, gravity, parity, and infant sex to explore the impact of potential confounders on the association and to improve statistical power.
In conclusion, a nonlinear association was observed in pre-pregnancy normal-weight women, with a threshold of BMI change of 5.096 kg/m2 and 5.401 kg/m2 in GDM and non-GDM. This method can be easy to use in the clinical without special equipment and allow healthcare providers to individualize maternal weight gain recommendations.
Authors’ contributions
Jianying Yan and Juan Lin designed the study and revised the manuscript. Lihua Lin collected data, analysed data and drafted the manuscript. All authors approved the final version of the manuscript.
Supplementary Material
Download PDF (482.6 KB)Acknowledgments
The authors thank the perinatal care providers at the Department of Obstetrics for their hard work on management of patients.
Disclosure statement
The authors report there are no competing interests to declare.
Data availability statement
The datasets used during the current study are available from the corresponding author on reasonable request.
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References
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