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A prospective study of early pregnancy metal concentrations and gestational diabetes mellitus based on a birth cohort in Northwest China

BMC Pregnancy and Childbirth volume 25, Article number: 387 (2025) Cite this article

Abstract

Background

Exposure to metals during early pregnancy may affect maternal glucose metabolism. We were aimed to assess the associations between early pregnancy whole blood concentrations of copper (Cu), zinc (Zn), calcium (Ca), iron (Fe), and magnesium (Mg) with GDM later in the second trimester among pregnant women in Northwest China.

Methods

This study included 5478 first-trimester pregnant women who participated in the birth cohort of the Northwest Women’s and Children’s Hospital between July 2018 and December 2023. Metal concentrations, basic demographic characteristics, lifestyle and behavior patterns were collected. An oral glucose tolerance test was performed in the second trimester. A generalized linear model was used to analyze the effects of metal concentrations on GDM. A two-piecewise regression model was adopted to examine the threshold effect and find out the turning point. Weighted Quantile Sum (WQS) regression was conducted using a dataset randomly split into training and validation sets at a 4:6 ratio to investigate the association between metal mixtures and GDM.

Results

Compared to the lowest tertile, the middle (RR = 0.82, 95%CI = 0.71, 0.95) and highest (RR = 0.84, 95%CI = 0.73, 0.97) tertiles of Ca concentrations could decrease the risk of GDM. However, the highest tertile of Cu concentration could increase the risk of GDM (RR = 1.18, 95%CI = 1.01, 1.39). Additionally, a non-linear relationship between Ca concentration with GDM and FPG was observed. The risk of GDM (RR = 0.08, 95%CI: 0.02, 0.31) and FPG (β=-0.56, 95%CI: -0.99, -0.12) decreased with 1 unit increase in ln-transformed Ca concentration below the turning point. However, the WQS index of maternal mixed metals was not correlated with the incidence of GDM (RR = 1.08, 95%CI = 0.98, 1.19).

Conclusions

Higher Cu concentration during early pregnancy may increase the risk of GDM in mothers. Increased Ca concentration may reduce the risk of GDM and lower the concentration of FPG below the turning point. Our findings could provide an early marker for potentially modifiable risk factors associated with maternal glucose dysregulation during pregnancy.

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Introduction

Gestational diabetes mellitus (GDM) is the most common metabolic disorder during pregnancy, affecting 14.0% of pregnant women globally [1]. Due to the revised diagnostic criteria and epidemiological factors such as increasing maternal age and rising obesity prevalence among women of reproductive age, GDM incidence is continuing to rise worldwide [2]. Hyperglycemia can interfere with the normal function of insulin, leading to increased insulin resistance, which in turn disrupts the glucose metabolism balance [3]. A continuous rise in blood glucose levels during pregnancy can significantly increase the risk of GDM [4]. The development of GDM not only leads to adverse pregnancy outcomes, but also has long-term effects on the health of both women and their children [5, 6]. Therefore, it is essential to identify modifiable risk factors that could impact blood glucose during pregnancy.

Metals are key substances for maintaining normal physiological functions of the human body, and several elements including copper (Cu), zinc (Zn), calcium (Ca), magnesium (Mg), and iron (Fe) are essential components, can affect glucose metabolism during pregnancy through insulin resistance, epigenetic modifications, inflammation, and oxidative stress [7,8,9]. In recent years, an increasing number of studies have explored the association between maternal metal exposure and GDM. However, current studies on the association between metals and GDM predominantly concentrate on heavy metals [10]. The research regarding the relationship between trace essential elements and GDM is still relatively limited, and the results are inconsistent. Hypomagnesemia during pregnancy, particularly a decrement in plasma Mg concentrations, has been found to be associated with an increased risk of GDM [11]. There is a positive association between urinary Zn and markers of early dysglycemia in mothers and offspring [12]. Ca has been negatively related to GDM in a dose-dependent manner [13]. Fe supplementation ≥ 60 mg/d and higher plasma ferritin concentration during pregnancy are independently associated with an increased risk of GDM [14]. Paradoxically, several studies have shown inconsistencies in the inverse or no relationship between maternal metal concentrations such as Ca, and Mg with GDM or blood glucose [15,16,17]. Since the potential health hazards of trace element metals may be rather concealed and difficult to detect in a timely manner [18], it is of great necessity to study their association with GDM. This will help us better understand the causes of GDM and provide more reference information for early prevention.

Therefore, our aim was to explore the association between the concentrations of five common metals (Cu, Zn, Ca, Mg, and Fe) and their combined effects during early pregnancy with GDM, based on a birth cohort study in Northwest China. Additionally, we specifically assessed the effects of metal exposure on distribution of maternal blood glucose concentrations during the second trimester at three time points.

Methods

Study design and population

Data were collected from a prospective birth cohort study [19] conducted in Xi’an, Shaanxi province, from July 2018 to December 2023. Pregnant women were recruited at their initial prenatal visit to the obstetrics outpatient clinic of Northwest Women’s and Children’s Hospital. The study protocol was approved by the Human Research Ethics Committee of Beijing Obstetrics and Gynecology Hospital (No. 2018-KY-003-02) and Northwest Women’s and Children’s Hospital (No. 2018018), and written informed consent was obtained from all participants.

The inclusion criteria for the cohort were as follows: (1) gestational age between 6 and 13+ 6 weeks; (2) maternal age ≥ 18 years old; (3) lived locally for more than one year; (4) prenatal care and childbirth were plan to performed at Northwest Women’s and Children’s Hospital. The exclusion criteria was subjects request withdrawal from the study at any stage.

In this study, subjects who were spontaneous and induced abortion (n = 832), without metal detection (n = 19,116), without OGTT results (n = 1,083), twin or multiple pregnancies (n = 72), with diabetes before pregnancy (n = 9), with other endocrine and chronic diseases before pregnancy (n = 354) were excluded. Finally, this study included 5,478 pregnant women (Fig. 1). Subjects included in this analysis had no significant differences with excluded women in baseline characteristics (Table S1).

Fig. 1
figure 1

Study flowchart

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Data collection

After being recommended by doctors from the outpatient clinic, maternal general demographic characteristics and lifestyle behaviors during early pregnancy were collected through the birth cohort enterprise data center (EDC) cloud platform online system. Maternal metal concentrations (Cu, Zn, Ca, Mg, and Fe) and OGTT results during early pregnancy and the second trimester were recorded through the hospital outpatient laboratory system. To protect privacy, all identifying information of the subjects was anonymized.

Study variables

Between 8 and 16 weeks of gestation, 2mL whole blood samples from pregnant women were collected on an empty stomach in the morning between 8:00 am and 10:00 am. Vacuum blood collection tubes with heparin sodium as an anticoagulant were used. The samples were then transported to the laboratory center within an hour. The whole blood metal concentrations were detected using atomic absorption spectroscopy method (AAS, Beijing, China). Add 40 µL of whole blood samples or quality control materials to the reagent. After thoroughly mixing and letting the mixture stand at room temperature (10–30 °C) for half an hour, aspirate the processed samples to start the detection. In each batch, we also processed and analyzed a blank sample and a standard reference material to carefully monitor and control any potential contamination. The absorbance difference of each element analysis sensitivity of Cu, Zn, Ca, Mg, and Fe should be greater than 0.013 Abs, 0.029 Abs, 0.055 Abs, 0.055 Abs, and 0.021 Abs, respectively. The distribution and limit of detection (LOD) of five maternal metals during early pregnancy was shown in Table S2. For all the study samples, the measurements of the five elements were above the LOD. The accuracy of each element is within 10.0% and coefficient of variation ≤ 5%.

At 24–28 weeks of gestation, pregnant women underwent a 75 g oral glucose tolerance test (OGTT). After an overnight fast, they were asked to consume a standardized glucose solution made by dissolving 75 g of glucose in water. Blood samples were then collected at three time points: fasting (0 h), 1 h, and 2 h after glucose ingestion. During the test, the hexokinase method was used to measure the blood glucose concentrations in each sample. According to the International Association for Diabetes in Pregnancy Study Group’s (IADPSG) criteria, GDM was diagnosed if any of the following criteria were met: fasting plasma glucose (FPG) ≥ 5.1 mmol/L, 1-hour plasma glucose (PG) ≥ 10.0 mmol/L, or 2-hour PG ≥ 8.5 mmol/L.

Covariates

Confounding variables related to GDM from previous studies [20, 21] were collected, which included characteristics and lifestyles of pregnant women. The confounding variables included maternal age, ethnicity, education, occupation, body mass index (BMI) before pregnancy based on Chinese BMI criteria, yearly average household income (poverty: < 50,000 Chinese yuan (CNY), medium: 50,000 ~ 200,000 CNY, rich: > 200,000 CNY), first pregnancy, history of adverse pregnancy, complications in previous pregnancy, smoke or passive smoke, folic acid and multidimensional nutrient supplements. “History of adverse pregnancy” refers to mothers who experienced abortion, stillbirth, or fetal birth defect during a previous pregnancy. “Complications in previous pregnancy” refers to women who developed GDM, gestational hypertension or thyroid disorder during a previous pregnancy. “Smoke or passive smoke” refers to smoking at least one cigarette or passive inhalation of smoke for more than 15 min at least 1 day per week during pregnancy.

Statistical analysis

Categorical variables of baseline characteristics were expressed as frequency and percentages, and compared between groups using the χ2 test. Continuous variables were expressed as mean and standard deviation (SD) or median and interquartile range (IQR) and compared between groups using the t-test or Mann-Whitney U test after Kolmogorov-Smirnov test. Confounders were evaluated by using prior knowledge with the assistance of directed acyclic graphs (DAG) [22, 23]. The subsequent covariates were incorporated in the ultimate model: maternal age, education, BMI before pregnancy, first pregnancy, and smoking or passive smoking (Fig. 2). We used the multiple imputation (MI) method to handle the missing values in the covariates of pre-pregnancy BMI (0.11%) and multidimensional nutrients supplement (0.15%). We built a multiple regression model using variables like maternal age and education, and performed 10 imputations to ensure the reliability of the analysis results. The concentrations of metals were ln-transformed before regression analysis. Spearman correlation coefficients among all ln-transformed metal pairs were calculated. Generalized linear models (GLM) with a Poisson link function were performed to explore the association between maternal metal concentrations as both continuous and three - category variables, and GDM in three different models. Model 1 not adjusted. As the effect of a certain metal maybe influenced by others [24, 25], Model 2 adjusted for other four metals. Model 3 adjusted for 5 baseline covariates above and other four metals. Relative risks (RR) and their 95% confidence intervals (95% CI) were calculated to quantify the associations. Additionally, a smoothed plot was used to explore the relationship between maternal metal concentrations and GDM, and a two-piecewise regression model was applied to examine the threshold effect and select the turning point of Ca concentration with GDM, and FPG. Weighted quantile sum (WQS) regression was performed to explore the association between maternal metal mixtures and GDM after adjustment. The dataset was randomly divided into a training set and a validation set at a ratio of 4:6, and the repeated holdout method (repeated 10 times) was used to reduce the bias of random splitting. On the training set, the weights of each metal in the WQS model were estimated through 10,000 bootstraps. The validation set was used to evaluate the predictive performance of the model, and the relative importance of each metal in the metal mixture was interpreted based on its weight coefficient. For a more thorough and accurate exploration of the associations between metal concentrations and blood glucoses across different glucose distribution concentrations, quantile regression was employed to assess associations of metal concentrations with the 10th, 25th, 50th, 75th, and 90th quantiles of blood glucose concentrations at the three time points. Statistical analyses were performed using R software (version 4.2.3) and the packages “glmnet”, “gWQS”, and “quantreg“ [26,27,28]. Statistical significance was indicated by P < 0.05.

Fig. 2
figure 2

Directed acyclic graphs in identification selection of covariates

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Results

Characteristics of study population

Table 1 presents the maternal baseline characteristics. Compared to women without GDM, mothers diagnosed with GDM were more likely to be older, with senior high school or below educational level, overweight or obese, not first pregnancy, and have a history of adverse pregnancy and complications in previous pregnancy.

Table 1 General characteristics of study subjects [n (%) or Mean ± SD]
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Maternal metal status during early pregnancy

The metal concentrations during early pregnancy were tested averagely at 12.44 ± 1.87 (Mean ± SD) weeks of gestation. The distributions for the maternal blood Cu, Zn, Ca, Mg and Fe are presented in Table 2, women with GDM only have slightly higher concentrations of Cu, Zn, Fe, and slightly lower concentration of Ca during early pregnancy compared to mothers without GDM (P < 0.05). However, the distribution of maternal blood Mg was similar between the two groups (P > 0.05).

Table 2 Maternal metals status during early pregnancy [Median (IQR)]
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Maternal metal concentrations and GDM

As shown by the spearman correlation coefficients in Fig. S1, there was no high autocorrelation (|r|<0.7) observed between the metals. In comparison to the lowest tertile of Ca concentration, the middle (RR = 0.82, 95%CI = 0.71, 0.95) and highest (RR = 0.84, 95%CI = 0.73, 0.97) tertiles were associated with a decreased risk of GDM after adjustment. The highest tertile of Cu concentration (RR = 1.18, 95%CI = 1.01, 1.39) was associated with an increased risk of GDM compared to the lowest tertile. Additionally, every 1-unit increase in ln-transformed Ca concentration was associated with a decreased risk of GDM (RR = 0.36, 95%CI = 0.19, 0.69), while every 1-unit increase in ln-transformed Cu concentration was associated with an increased risk of GDM (RR = 1.23, 95% CI = 1.02, 1.49) (Table 3). However, the WQS index of maternal mixed metals was not correlated with the incidence of GDM (RR = 1.08, 95%CI = 0.98, 1.19, P = 0.108). As shown in Fig. S2, Cu showed the highest contribution (52.3%), followed by Fe (29.8%), and Zn (8.3%).

Table 3 Association between maternal metal concentrations and GDM
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Maternal ca, Cu and GDM

The non-linear and linear relationship between Ca (Fig. 3), Cu (Fig. S3) concentrations with GDM and FPG was observed, respectively. The turning point value of Ca concentration with GDM (Ca = 1.58 mmol/L; ln Ca = 0.46) and FPG (Ca = 1.51 mmol/L; ln Ca = 0.41) was found. The risk of GDM decreased (RR = 0.08, 95%CI: 0.02, 0.31) with 1 unit increase in ln-transformed Ca below 0.46, but it was not related to GDM with Ca over 0.46. The FPG decreased 0.56 mmol/L (β=-0.56, 95%CI: -0.99, -0.12) with 1 unit increase in ln-transformed Ca below 0.41, but it was not related to FPG with Ca over 0.41. The likelihood ratio test (P < 0.05) demonstrated a non-linear relationship between Ca concentration with GDM and FPG (Table 4).

Fig. 3
figure 3

Associations of Ca concentration with GDM (a) and FPG (b) by smoothing spline. Adjusted for maternal age, education, BMI before pregnancy, first pregnancy, history of and smoking or passive smoking, and other four metals

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Table 4 Threshold effects of ca concentration on GDM and FPG
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Associations between ca, Cu and blood glucoses

Table S3 revealed that ln-transformed Ca and Cu had different effects on blood glucose concentrations at different parts of the distribution for the three time points. The one-unit increase in Cu concentrations had positive effects on the 75th (Coeff = 0.27, 95% CI = 0.05, 0.50), and 90th (Coeff = 0.46, 95% CI = 0.15, 0.76) quantiles of the 1-hour PG distribution and the 25th (Coeff = 0.17, 95% CI = 0.03, 0.30), 50th (Coeff = 0.14, 95% CI = 0.01, 0.28), 75th (Coeff = 0.23, 95% CI = 0.07, 0.40), and 90th (Coeff = 0.29, 95% CI = 0.03, 0.56) quantiles of the 2-hour PG distribution. The one-unit increase in Ca concentrations had negative effects on the 50th (Coeff=-0.78, 95% CI=-1.41, -0.15), 75th (Coeff=-0.98, 95% CI=-1.77, -0.18), and 90th (Coeff=-1.52, 95% CI=-2.50, -0.56) quantiles of the 1-hour PG distribution and 90th (Coeff=-1.05, 95% CI=-1.97, -0.14) quantiles of the 2-hour PG distribution. The graphical illustration of quantile regression results is shown in Fig. 4.

Fig. 4
figure 4

Graphical illustration of quantile regression results of the ln-transformed Cu, Ca and FPG (a), 1-h PG (b) and 2-h PG (c). Adjusted for maternal age, education, BMI before pregnancy, first pregnancy, history of and smoking or passive smoking, and other four metals. Footnotes: The black dotted line represents the coefficients of each quantile and the grey area shows the 95% confidence interval. The red solid and dashed lines parallel to the x-axis represent the ordinary least squares model estimates and their 95% confidence intervals, respectively. The grey solid lines are the 0 lines

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Discussion

In this study, we explored the association between maternal metal concentrations and GDM in northwest China. Compared to the lowest tertile, the middle and highest tertile of Ca concentration were associated with a decreased risk of GDM, while the highest tertile of Cu concentration was associated with an increased risk of GDM. We also found the non-linear associations between Ca concentration with GDM and the U-shaped relationship between Ca concentration with FPG. Increased Ca concentration may reduce the risk of GDM and lower the concentration of FPG below the turning point. In addition, the Cu and Ca showed varying effects on three blood glucose concentrations at different parts of the distribution.

Limited epidemiological studies have investigated the relationship between Ca and GDM. Our study identified the non-linear association and threshold effects between maternal whole blood Ca concentration and GDM in a Chinese population. Previous studies have reported that increasing quartiles of Ca intake, particularly from Ca-rich low-fat dairy products and whole grains, were associated with a lower risk of GDM in American patients [29]. A nested case-control study in Wuhan, China, found that increased plasma concentrations of Ca (OR = 0.72, 95%CI = 0.56, 0.92) were associated with a significant decrease in the risk of GDM [25]. Serum calcium was also strongly associated with fasting insulin, post-challenge glucose, and GDM in the population of northern England [30]. These findings were consistent with our study, which suggests that increasing Ca concentration lower than 1.58 mmol/L could reduce the occurrence of GDM. Moreover, the protective effect of Ca was also observed in 1-h PG and 2-h PG at the medium and upper tail. Ca is one of the most important essential nutrients for human body and has many vital physiological functions, such as involvement in neurotransmitter release, regulation of enzyme activity and hormones secretion. Maternal Ca requirements were higher during pregnancy compared to general population [31]. Emerging data suggests that islets calcium dynamically responds to pregnancy-induced hormonal and signaling molecular changes. The flow of Ca into β-cells is critical in regulating glucose-stimulated insulin secretion and β-cell survival [32]. Calcium channel blockers have shown efficacy in treating or preventing GDM [33]. This also reflects that Ca dynamics could be a potential contributing factor to maternal blood glucose. Therefore, we cautiously speculate that increasing maternal whole blood Ca concentration within a certain range during early pregnancy in northwest China is conducive to reducing the occurrence of GDM.

Our study also revealed that the highest tertile of Cu concentration was associated with an increased risk of GDM. A meta-analysis revealed a linear relationship between circulating Cu concentration during pregnancy and the risk of GDM [34], which was consistent with our results. Another systematic review involving 141,175 subjects indicated that Cu (OR = 1.29, 95%CI = 1.02, 1.63) exposure exhibited a potential increase in GDM risk to some extent [35]. Cu concentrations were also found positively associated with FPG, 1-hour PG, and 2-hour PG during pregnancy in the Chinese population [36]. A multicenter singleton cohort study conducted in American found higher gestational glucose concentrations during second trimester associated with higher plasma Cu concentration and potential synergism between Cu and Zn on glucose concentrations [37]. In addition, the association of higher Cu concentrations of first-trimester with glucose was greater at the upper tails of glucose level distribution [38]. Excess Cu can lead to oxidative stress, which is a factor in the onset and development of type 2 diabetes mellitus [39]. Cu is an essential trace element in the human body and plays a significant role in many metalloenzymes, women need to absorb a large amount of Cu during pregnancy to meet the demands of cytochrome c oxidase and superoxide dismutase of enzymes [40]. However, excess Cu may promote the production of reactive oxygen species (ROS) by regulating the electron transfer and being highly reactive in redox reactions, which further accelerates the development of insulin resistance [41]. Moreover, Cu could induce human islets amyloid polypeptide to form structurally distinct stable toxic oligomers, prevent the fibrillation process, and may cause β-cell death [42]. Given the widespread use of microelement supplements in pregnancy women, randomized trials are necessary to investigate the optimal Cu dose for maternal and child health.

In addition to the main findings, we did not find the association between whole blood Fe, Zn and Mg concentrations during early pregnancy with GDM. Iron metabolism plays a central role in determining metabolic rates, gluconeogenesis, fuel selection, and insulin action, and it is involved in adipocyte, beta cell, and liver tissues [43]. A birth cohort study in Ma’ Anshan, China, observed a U-shape association between maternal Fe concentration during early pregnancy and risk of GDM [44]. Besides, a meta-analysis also did not find any meaningful associations between increased dietary iron intake, non-heme iron or supplemental iron and the risk of GDM [45]. These inconsistent results may be attributed due to the heterogeneity of studies, differences in ethnics, geographic location, limited sample size, and confounding variables [46]. A retrospective cohort study from South China reported serum Zn concentration was associated with elevated FPG at the upper tails [17]. However, another prospective cohort study conducted in the United States did not find correlations between plasma Zn concentrations and blood glucose concentrations [38], and a systematic review showed that Zn supplementation was effective in reducing maternal FPG and insulin concentrations in GDM patients [47]. The inconsistent results may be due to differences in baseline Zn exposure concentrations and physiological requirements among different populations. The potential role of Mg remains controversial in GDM among different populations or at different times during pregnancy [48,49,50]. Mg may affect blood glucose during pregnancy by enhancing the activity of glucose transporters or participating in insulin receptor signaling [51]. Overall, the influences and mechanisms of metal concentrations on GDM need to be further explored.

Strengths and limitations should be considered when interpreting our study results. Firstly, this prospective birth cohort study provided high-quality data in an underexplored population, allowing us to explore the impact of maternal metal concentrations during early pregnancy on GDM. Secondly, we also identified the potential U-shaped relationship and threshold effect between maternal Ca concentration and FPG in northwest China. Thirdly, the WQS regression allowed us to assess the effects of mixed metal exposure on GDM, while quantile regression provided insights into the global relationships between metal concentrations and blood glucose distribution at three time-points. However, our study had some limitations. Firstly, the research subjects were all from a single center, with inevitable selection bias. The generalizability of our findings to a broader population is severely restricted. Secondly, this study only explored the effects of a few metals on GDM and blood glucoses, and we might have overlooked the potential interactions and combined effects of other metals. In addition, we only measured metal concentrations at a single time point during early pregnancy. Metal exposure levels can fluctuate throughout pregnancy, and we failed to capture this dynamic change, and potentially missed important information about the temporal relationship between metal exposure and GDM development. Furthermore, we did not follow up with the pregnant women long enough to observe the long-term impacts of metal exposure on maternal and child health. As pregnancy women are generally exposed to various kinds of metals in their daily life, future multi-center polymetalomics studies on pregnant women are needed to promote the health of women and children.

Conclusion

Among a prospective cohort of pregnant women, we found that higher Cu concentration during early pregnancy may increase the risk of GDM. We also observed that increased Ca concentration may reduce the risk of GDM and lower the concentration of FPG below the turning point. These findings may have important implications for health counseling for women of childbearing age and further verification would be needed through high-quality multicenter studies with larger sample sizes in different populations.

Data availability

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

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Acknowledgements

We would like to express our gratitude to the participants of all mothers. We also thank staff members from Northwest Women’s and Children’s Hospital and Xi’an Jiaotong University for their collaborating with data collection in this study.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82103924), the Innovation Talents Promotion Program of Shaanxi Province (No. 2023KJXX-109), the Key Research and Development Program of Shaanxi Province (No. 2024SF-YBXM-238; No. 2022ZDLSF02-11; No. 2021ZDLSF02-14), the Strengthening Basic Disciplines Program of Xi’an City (No. 22YXYJ0095), and the Northwest Women’s and Children’s Hospital Incubation Scientific Research Project (No. 2024FH03).

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Authors and Affiliations

  1. Translational Medicine Center, Northwest Women’s and Children’s Hospital, Xi’an, China

    Doudou Zhao, Danmeng Liu & Pengfei Qu

  2. Department of Health Statistics, Fourth Military Medical University, Xi’an, China

    Doudou Zhao & Lei Shang

  3. Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China

    Jie Chen, Xiayang Li, Yishuai Huang & Yu Zhang

  4. School of Nursing, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China

    Fuyang Zhao

  5. Department of Gynecology, Northwest Women’s and Children’s Hospital, Xi’an, China

    Li Shan

  6. Department of Obstetrics, Northwest Women’s and Children’s Hospital, Xi’an, China

    Yang Mi

  7. Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China

    Pengfei Qu

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  1. Doudou Zhao
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  2. Jie Chen
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  5. Yu Zhang
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  8. Li Shan
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  9. Yang Mi
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  10. Lei Shang
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Contributions

D. Z., Y. M., L. S. and P.Q. conceived and designed the study; D. Z., Y. M., L. S. and P.Q. drafted and revised the manuscript; J. C., X. L., Y. H. and Y. Z. interpreted and analyzed the data; D. Z., F. Z., D. L. and L. S. collected and processed the data. The final version of this manuscript was read and approved by all authors.

Corresponding authors

Correspondence to Yang Mi, Lei Shang or Pengfei Qu.

Ethics declarations

Ethics approval and consent to participate

The procedures and protocols of the study was reviewed and approved by the Human Research Ethics Committee of Beijing Obstetrics and Gynecology Hospital (No. 2018-KY-003-02) and Northwest Women’s and Children’s Hospital (No. 2018018). The informed consent form was signed by all the individuals before the initiation of this study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Zhao, D., Chen, J., Li, X. et al. A prospective study of early pregnancy metal concentrations and gestational diabetes mellitus based on a birth cohort in Northwest China. BMC Pregnancy Childbirth 25, 387 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12884-025-07336-2

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Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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