Association of Prenatal Serum Heavy Metals Exposure with Adverse Birth Outcomes: A Prospective Study

Association of Prenatal Serum Heavy Metals Exposure with Adverse Birth Outcomes: A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Prenatal Serum Heavy Metals Exposure with Adverse Birth Outcomes: A Prospective Study Juan Wang, Ye Zhou, Wanxin Wu, Jiamei Wang, Shuangshuang Bao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4750408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Exposure to metals during pregnancy has been found to be associated with adverse birth outcomes in the fetus. However, evidence for combined exposure is inconclusive. Therefore, it is important to explore the correlation between the combined effects of mixed metallic elements and adverse birth outcomes. Objectives The aim of this study was to investigate the association between maternal serum heavy metals concentrations in the second trimester of pregnancy and adverse neonatal outcomes, including PTB, birth defects, LBW, macrosomia, SGA and LGA. Methods Specifically, we examined the serum levels of various elements in pregnant women during mid-pregnancy, using the highly sensitive inductively coupled plasma mass spectrometer (ICP-MS). This study utilized advanced multiple exposure models, including Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS), to analyze the mixed exposure to elements. Results Both BKMR and WQS models showed that mixed metal exposure was positively associated with PTB, but negatively associated with birth defects and LGA. Tl and Fe were negatively associated with PTB, Se, Sb, and I were positively associated with PTB, and Se and Rb were negatively associated with birth defects. WQS regression analysis showed that metal mixed exposure was positively associated with preterm birth ( p = 0.043) and negatively associated with LGA ( p = 0.015). Conclusions The findings from this study contribute valuable insights into the potential health risks associated with mixed metals exposure during pregnancy. By elucidating the multifaceted impacts of metal mixtures on birth outcomes, this research offers a foundation for developing targeted interventions and preventive measures to safeguard maternal and child health. Metals Adverse birth outcomes Mixed exposure BKMR Figures Figure 1 Figure 2 Figure 3 1. Introduction With the acceleration of industrialization, a large number of metals and metalloid produced by continuous industrialization are released into the air, water, soil and crops,( 1 ) which makes human beings inevitably come into contact with them.( 2 ) Some heavy metals, such as iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), tin (Sn) and silicon are essential trace elements for the human body and their deficiency can lead to various diseases. Heavy metals have the potential to cause acute and chronic toxic effects on different organs of the human body including gastrointestinal and renal dysfunction, immune system dysfunction, nervous system disorders, and birth defects.( 3 ) The toxic effects of heavy metals are dose-dependent, and high dose exposure can lead to severe reactions in the body, causing more serious damage. Simultaneous exposure of multiple metals may have a cumulative effect.( 4 ) Metals and metalloids are rarely exposed alone, and co-exposure to multiple metals may be the norm,( 5 ) but there are limited studies exploring the interactive effects of co-exposure to multiple elements on fetal development.( 6 – 8 ) Fetuses are particularly vulnerable to heavy metal exposure compared to adults, as even low doses that do not harm the mother can have detrimental effects on the developing fetus. For instance, minimal levels of lead exposure during pregnancy can result in adverse birth outcomes and neurodevelopmental issues, such as cognitive decline and symptoms linked to attention deficit hyperactivity disorder (ADHD).( 9 , 10 ) The placental barrier is thought to protect the fetus from toxic substances by preventing the passage of harmful substances; however, it has been found that metallic elements, such as cadmium (Cd), mercury (Hg), lead (Pb), and selenium (Se), can cross the placental barrier and accumulate in embryonic tissue.( 11 ) Accumulation of these elements in embryonic tissues can lead to placental dysfunction and impair the support of embryonic growth.( 5 , 12 ) Epidemiologic studies have shown an association between prenatal exposure to certain metals and adverse birth outcomes.( 13 , 14 ) Exposure to elements such as arsenic (As), Pb, cadmium (Cd), and Mn have been associated with lower birth weights,( 15 – 18 ) shorter gestation periods,( 15 , 16 , 19 ) higher risk of preterm labor,( 20 ) smaller head circumferences,( 15 , 17 ) and shorter birth lengths.( 21 ) However, most of these studies have assessed the effects of single exposures to metallic elements, although some studies in recent years have analyzed the combined effects of mixed exposures to multiple metals, yet these studies have not produced consistent results.( 22 – 26 ) Most research in the field has concentrated on groups with high heavy metal exposure levels, rather than on those with typical exposure levels and no unusual exposures.( 27 , 28 ) The effects of metals and metalloids on pregnancy and birth outcomes have been extensively studied, and exposure to metal mixtures during pregnancy and their potential impact on birth outcomes has attracted increasing attention. In recent years, a number of epidemiologic studies have examined the association between heavy metals and a variety of adverse health outcomes in newborns, including low birth size, and a variety of congenital malformations, PTB, LBW, macrosomia, SGA, and LGA, are a major public health concern, as they are associated with an increased risk of maternal and neonatal morbidity and mortality.( 29 – 31 ) Although these findings may themselves be associated with morbidity in infancy and adulthood,( 32 ) they may result from a complex series of in utero events( 33 ) that may be associated with many other future complications, including behavioral changes in infancy,( 34 ) childhood obesity and various endocrine disorders.( 35 , 36 ) Therefore, it is critical to investigate any association between prenatal exposure to various heavy metals and measurable and sensitive birth outcomes. Therefore, in this study, we explored the association of prenatal exposure to multiple levels of metals with adverse birth outcomes from a prospectives cross section. The present study aims to explore: ( 1 ) whether prenatal exposure to metals would be association with adverse birth outcome; ( 2 ) Exposure to association of individual or multiple metals with adverse birth outcome. Thus, we measured 27 metals to investigate their effects of a single metal and the interactions of mixed exposures to multiple metals on adverse birth outcomes through utilized Bayesian kernel-machine regression (BKMR) modeling to explore the joint effects of mixed exposures to the elements. Exploring measures that can reduce exposure to risk factors and finding remedies to reduce the adverse health effects of environmental pollutants, such as heavy metals, on mothers and infants can have a positive impact on public health. 2. Materials and methods 2.1 Study population The study was conducted from October 2022 to July 2023 in in South China. The pregnant women included in the study were in good general health, without serious cardiovascular disease, liver and kidney disease, immune-related diseases, and no history of illicit drug use. Various relevant information regarding pregnant women and newborns, including the age of the pregnant woman, age at first menstruation, menstrual cycle, ethnicity, number of pregnancies, educational level, pre-pregnancy BMI, gestational age at birth for newborns, gender of newborns, presence of birth defects in newborns, birth weight and length of newborns are collected by trained nurses. Ethnic groups included Han and other ethnic groups, and the level of education was divided into three categories: junior college or below, bachelor's degree and master's degree or above. Information on the relevant conditions and health status of the pregnant woman's pregnancy was obtained from the electronic medical record. A total of 512 pregnant women in their second trimester (13 ~ 26 weeks) were recruited in this study. Serum samples were collected for the detection of metals and metalloids. In this study, the inclusion criteria for pregnant women included the following: (a) maternal age ≥ 18 years, (b) gestational age ≥ 12 weeks, and (c) live birth. After excluding women with multiple pregnancies, lack of information on pregnancy outcome and delivery, a total of 429 pregnant women were included in this study. The study was approved by the Institutional Review Board of Anhui Medical University. 2.2 Classification of adverse birth outcomes The study examined six adverse birth outcomes, including PTB, LBW, macrosomia, SGA, LGA, and various types of birth defects. These birth defects encompassed chromosomal abnormalities, congenital heart disease, polydactyly, and syndactyly. The gestational age of the fetus was based on the date of the last menstrual period of the mother. According to the clinical cut-off value classification, PTB was defined as gestational age before 37 weeks of gestation, LBW as birth weight less than 2500g and macrosomia as birth weight more than 4000g. SGA and LGA were defined as newborns with birth weight less than the 10th percentile and greater than the 90th percentile for gestational age, respectively. 2.3 Serum metals measurements The collected serum samples were stored at -80℃ in a refrigerator, and they were thawed from the refrigerator at -80℃to 4℃the night before testing. Prior to testing, the samples underwent centrifugation at 3500r for 10 minutes. Subsequently, 100 µL of each sample was diluted by weighing method with 1% nitric acid in a 50 mL volume centrifuge tube, resulting in a dilution factor of 30. The standard solution was prepared one week prior to testing. Before conducting daily tests, the standard solution underwent analysis to establish a standard curve and was thoroughly mixed by vortexing. Subsequently, the inductively coupled plasma mass spectrometer (ICP-MS) was tuned using a tuning solution prior to the analysis of both standard and test samples. Following the tuning process, the standard and test samples were analyzed. After completing the analysis of a batch of samples, a further analysis of the standard solution was carried out to verify instrument accuracy, followed by an analysis of a mixed quality control sample for quality assurance purposes. It was ensured that the recovery rate for detected elements fell within the acceptable range of 80–120%. In this study, we analyzed a total of twenty-seven elements, namely Beryllium (Be), Magnesium (Mg), Aluminum (Al), Calcium (Ca), Vanadium (V), Chromium (Cr), Mn, Fe, Cobalt (Co), Nickel (Ni), Cu, Zn, As, Se, Rubidium (Rb), Strontium (Sr), Molybdenum (Mo), Cd, Thallium (Tl), Antimony (Sb), Iodine (I), Cesium (Cs), Barium (Ba), Tungsten (W), Lead (Pb), and Bismuth (Bi), Sn. For samples with measurements below the detection limit, a common practice in analytical chemistry is to substitute these values with half of the detection limit. This approach helps to ensure that all data points are accounted for and included in the analysis, maintaining the integrity and completeness of the dataset. 2.4 Statistical analysis Descriptive statistical analysis was employed to summarize the demographic characteristics of both mothers and newborns, encompassing variables such as maternal age, age at menarche, menstrual cycle, ethnic group, education level, number of pregnancies, husband's smoking and drinking habits during pregnancy, pre-pregnancy BMI, preterm birth occurrence, fetal gestational age and sex determination. Additionally included were birth weight and length measurements along with the presence or absence of any birth defects. Newborns were categorized into SGA and LGA groups based on their gestational age in relation to sex and birth weight. Furthermore, they were classified into LBW groupings (< 2500g) and macrosomia groupings (≥ 4000g) according to their actual birth weights. Continuous variables are presented as means accompanied by standard deviations (SD), while categorical variables are expressed as frequencies alongside proportions. The covariates considered in this study comprised maternal factors such as age at menarche, menstrual cycle regularity or irregularity status, ethnic background, educational attainment level, number of previous pregnancies experienced by the mother, whether the husband smoked or drank alcohol during pregnancy period, pre-pregnancy BMI values and delivery mode. Generalized linear regression models were utilized to examine the correlation between serum trace element levels and outcome variables; Pearson correlation was used specifically to analyze associations among serum trace element levels themselves. All statistical analyses were conducted using SPSS 26.0 software package combined with R 4.3.1 version; a significance level of p < 0.05 was adopted when assessing differences between various groups under investigation. We initially employed the generalized linear regression model to examine the association between serum trace elements and outcome variables. Subsequently, we identified the elements that exhibited a significant correlation with the outcome variables and proceeded with conducting combined effect analysis. Considering the potential interactions among multiple serum elements and their impact on the studied outcome variables, BKMR models were utilized to evaluate the joint effects of mixed exposure to various trace elements on these outcomes. This model employs non-parametric methods for mixture analysis and has been extensively applied in prenatal exposure studies. The BKMR model assessed associations between outcome variables and both continuous or dichotomous factors. In our study, we fitted the BKMR model using Markov chain Monte Carlo algorithm with 50,000 iterations, incorporating all metals as exposure factors within the model while calculating posterior inclusion probability (PIP) to determine each metal's contribution towards overall association strength. Additionally, we employed WQS modeling approach to estimate overall mixing effect in this study; subsequently utilizing weights derived from WQS modeling calculations allowed us to ascertain relative contributions of individual elements towards overall mixing exposure. 3. Results 3.1 Demographics characteristics and trace element levels A total of 429 pregnant women were included in this study; the baseline sample characteristics of pregnant women and infants are shown in Table 1, continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as frequency (%). In this study, the average age of the pregnant women was 29.58 years, 90.7% of them were Han nationality, 71.6% had college education or below, 28.4% of the pregnant women had husbands who smoked and 25.6% had husbands who drank alcohol during pregnancy. There were 218 males (50.8%) and 211 females (49.2%) among the newborns, with an average gestational age of 38.97 (SD=1.73) and an average birth weight of 3131.88g. Among the newborns, 23 (5.4%) were premature and 11 (2.6%) had birth defects. There were 74 (17.2%) SGA infants, 35 (8.2%) LGA infants, 29 (6.8%) low birth weight infants and 13 (3.0%) macrosomia infants. Table 2 displays the distribution of adverse birth outcome within the study population. Preterm neonates had a mean gestational age of 33.8 weeks (SD 3.08, p < 0.001) and a mean birth weight of 2184 grams (SD 676, p < 0.001). Among neonates with birth defects, the average number of deliveries per mother was 1.91 (SD 0.70, p = 0.02), and 6 out of 11 (54.5%) neonates had fathers who smoked during pregnancy ( p = 0.037). For SGA neonates, the mean maternal age was 28.6 years (SD 3.49, p = 0.01), the mean number of pregnancies was 1.80 (SD 1.01, p = 0.031), and the mean neonatal birth weight was 2643 grams (SD 330, p < 0.001). Among LGA neonates, the average maternal age was 31.0 years (SD 3.74, p = 0.023), the average number of pregnancies was 2.60 (SD 1.17, p = 0.005), and the average number of abortions was 0.74 (SD 0.89, p = 0.045). The mean maternal pre-pregnancy BMI was 22.5 (SD 2.75, p = 0.047), and 25 out of 35 (71.4%) mothers underwent non-vaginal deliveries (including cesarean section, forceps delivery, and fetal head traction) ( p < 0.001), with a mean birth weight of 3909 grams (SD 456, p < 0.001). For LBW infants, the mean maternal menstrual cycle length was 29.0 days (SD 1.46, p < 0.001), 17 out of 29 (58.6%) mothers did not have vaginal deliveries ( p = 0.015), the mean gestational age of newborns was 35.6 weeks (SD 3.95, p < 0.001), and the mean neonatal birth weight was 2065 grams (SD 498, p < 0.001). Among macrosomia, the mean maternal age was 32.0 years (SD 3.83, p = 0.038), the mean number of pregnancies was 2.85 (SD 0.90, p = 0.006), and 9 out of 13 (69.2%) mothers had non-vaginal deliveries ( p = 0.017). The mean neonatal birth weight was 4304 grams (SD 404, p < 0.001). Table S1 shows the distribution of the six elements in serum. 3.2 Associations between metals and adverse birth outcomes Linear regression models revealed associations between six metallic elements Fe, Se, Rb, Sn, I, and Tl and adverse birth outcomes in newborns (Table S2). Table S3 displays the a priori probability results from the BKMR model. Adverse birth outcomes were categorized as PTB, LBW, macrosomia, SGA, LGA, or birth defects. Figure 1A illustrates the overall impact of metal mixture exposure, suggesting that the incidence of adverse birth outcomes tended to increase with higher metal mixtures, although the differences were not statistically significant. The impact of individual metal element exposures on outcomes was evaluated while keeping the other metal elements constant at the 25th, 50th, and 75th percentiles (Figure 1B). To delve deeper into the univariate exposure-response relationships and potential interactions, univariate and bivariate interaction functions were calculated and depicted in Figure 1C. The figure showcases univariate concentration-response functions and their corresponding 95% confidence intervals (shaded areas), with the values of other elements set at the median. Furthermore, Figure1D presents the bivariate concentration-response functions for the six metal elements, aiming to explore potential interactions among them. Figure 2 illustrates the impact of mixed exposure to metal elements on adverse birth outcomes. When the six metal elements were held at specific percentiles, we observed an increase in preterm birth incidence with rising metal mixture levels, while the incidence of birth defects decreased compared to the 50th percentile group, with statistically significant variances Figure 2A, B). Notably, when other metals were fixed at the 25th, 50th, and 75th percentiles, the following associations were identified: Fe exhibited a negative correlation with PTB; high Fe concentrations were inversely related to birth defects, whereas low Fe concentrations showed a positive association with birth defects. Additionally, Se, Sb, and I were positively linked to PTB, and Se and Rb were negatively associated with birth defects (Figure 2C, D). These findings were statistically significant. The Supplementary material Figure S1 displays bivariate interactions between metallic elements and adverse birth outcomes. Notably, interactions were observed between Sn and Fe, I, and Se concerning the incidence of preterm birth. Additionally, interactions were noted between I and Se, as well as Tl and Rb. Moreover, interactions were identified between Fe and Rb, Fe and Se, and Rb and Se when assessing birth defects as outcomes. The regression results from the WQS analysis revealed a noteworthy positive correlation between mixed metal exposure and preterm birth. Specifically, the WQS index primarily influenced by Se and I was linked to heightened odds of preterm birth, while a significant negative association was observed between metal mixture exposure and LGA births. Conversely, the WQS index driven by Sb and Fe was associated with reduced odds of LGA. Furthermore, the results from the negative modeling indicated that Tl and Se dominated WQS indices were linked to a decrease in the odds of LGA (refer to Table 3 and Figure 3). 4. Discussion There is a growing body of evidence indicating that exposure to toxic metals can have negative impacts on fetal growth. However, the majority of studies have traditionally concentrated on the effects of individual metals. Given the potential for synergistic or antagonistic interactions among metals, the behavior of a metal when exposed in combination with others may differ from its behavior when exposed in isolation. As a result, an increasing number of studies have started to investigate the effects of metal mixtures on fetal growth.(26) In this study, we employed the Bayesian Kernel Machine Regression (BKMR) model and the Weighted Quantile Sum (WQS) model to evaluate the combined impacts of mixed metal exposure and investigate the relationship between maternal whole blood concentrations of metals during pregnancy and adverse birth outcomes. This pioneering study represents the first comprehensive analysis of the effects of mixed metal exposure during pregnancy on PTB, birth defects, LBW, macrosomia, SGA, and LGA infants. By utilizing advanced statistical models and considering the combined effects of multiple metals, we were able to provide a more nuanced understanding of how maternal metal exposure influences various birth outcomes. The findings of this study revealed that six metal elements, namely Fe, Se, Rb, Sn, I and Tl, were correlated with adverse birth outcomes in newborns. The incidence of adverse birth outcomes varied based on the concentrations and combinations of these metal elements, with statistically significant results observed. Both the BKMR model and the WQS model demonstrated a positive association between mixed metal exposure and PTB, underscoring the significant impact of mixed metal exposure on PTB outcomes. Moving forward, future investigations can delve deeper into elucidating the potential mechanisms linking metal exposure during pregnancy to preterm birth. The outcomes of this study underscore the importance of evaluating the health implications of mixed pollutant exposures using integrated exposure models and employing various statistical analysis methods to comprehensively assess the impact of individual and combined metal exposures on adverse birth outcomes. Some previous studies also pointed out the association between prenatal metal mixture exposure and birth outcomes. Tal Michael et al. found that birth weight was negatively correlated with Tl, which was consistent with our study. The WQS negative modeling results of this study showed that Tl and Se dominated WQS index were associated with the odds of LGA reduction.(32) Raul Cabrea-Rodriguez et al. quantified 44 elements in cord blood and found an inverse association between Sb and birth weight (Spearman's r = −0.106, p = 0.021),(24) This is consistent with the results of the WQS analysis of the present study, which showed a significant negative relationship between metal mixture exposure and LGA, and the forward modeling results showed that the Sb and Fe dominated WQS index was associated with the odds of LGA reduction. Sb compounds are found in common materials (such as textiles) and polyester, ceramics, glass and rubber as flame retardants, which pose a potential health threat. Some studies have found that the plasma and blood Se concentration of preterm pregnant women is lower than that of full-term pregnant women, and the relationship between Se level and birth weight is still controversial in the literature. Some studies suggest that the effect of PTB on birth weight may mask the association between Se level and birth weight.(37) Studies have linked Fe deficiency to adverse birth outcomes, including low birth weight and PTB.(15) Our findings showed an inverse association between Fe and adverse birth outcomes, and an Fe dominated WQS index was associated with odds of reduced LGA. Systematic reviews have shown that Fe deficiency in the first and second trimesters is associated with increased maternal morbidity and increased risk of adverse pregnancy outcomes, defined as low birth weight, PTB, or intrauterine growth restriction.(38) Some studies have found a small positive association between the I to creatinine ratio and birth weight. Consistent with the results of our mixed exposure BKMR analysis, there was no evidence of an association with other birth outcomes, including PTB, stillbirth, and congenital anomalies.(39) Research indicates that pregnant women are prone to developing iron deficiency anemia due to the shared need for iron by both the mother and the developing fetus. The expansion of red blood cell and the growth of the fetus and placenta elevate the maternal iron requirement throughout pregnancy.(40) Anemia in pregnant women has been linked to adverse outcomes in fetal, neonatal, and early childhood stages, such as increased risks of perinatal and neonatal mortality, low birth weight, premature birth, and altered gestational age.(41) Some studies have established a connection between Rb elements and potassium channels, underscoring their involvement in physiological regulation.(11) Disrupted potassium channels can impede proper embryonic blood vessel formation, with certain investigations suggesting a possible protective role of these elements during pregnancy.(42) Notably, Monangi et al. have proposed that heightened levels of selenium in maternal blood correlate with prolonged gestation, potentially contributing to enhanced birth weight.(43) While the precise mechanisms through which selenium operates during pregnancy remain unclear, it is postulated that selenium may inhibit factors involved in triggering labor in the fetal membranes and uterine muscle. Moreover, there is belief that selenium can form chemical bonds, which may mitigate the effects of harmful metals and support fetal growth.(44) Conversely, it has been postulated that exposure to thallium (Tl) could heighten oxidative stress in the placenta and fetus, potentially leading to intrauterine growth restriction. Studies have indicated that prenatal Tl exposure is linked to changes in maternal and fetal thyroid function, which could have implications for developmental disabilities in children, whether directly or indirectly.(32) The results of our current study are based on a prospective study and the causal relationship between metals and outcome variables cannot be determined. Although we adjusted for the effects of demographic characteristics and pregnancy information as much as possible, it is still possible that potential confounders, such as maternal psychological problems, could have been affected. Further studies are necessary to expand the sample size and area of the subjects, and conduct prospective design to explore the effect of mixed metal exposure during pregnancy on birth outcomes. Although there are certain limitations, this study also has certain strengths, including measuring mixed metals and using BKMR model and WQS model to evaluate the effect of complex mixed exposure to multiple metals during pregnancy on neonatal birth outcomes, with less bias than a single model. The complementary BKMR and WQS models allow quantifying and visualizing the effects of mixed exposure and each element while accounting for the remaining elements, thus making the results more rigorous. The combination of classical single exposure and multiple exposure models, in which the conclusions of the mixed exposure model are consistent with the results of the single exposure model, can complement each other, thereby helping us to fully understand these associations and making the results more credible. 5. Conclusion The findings from this study contribute valuable insights into the potential health risks associated with mixed metals exposure during pregnancy. By elucidating the multifaceted impacts of metal mixtures on birth outcomes, this research offers a foundation for developing targeted interventions and preventive measures to safeguard maternal and child health. Declarations Acknowledgments The authors thank all the participants in the study. Special acknowledgment is given to the educators at Longhua District Maternal and Child Health Hospital in Shenzhen for their role in conducting the observational study. Furthermore, recognition is expressed to the instructors at Anhui Medical University for their careful guidance and supervision during the study. Research Funding This study was supported by the National Natural Science Foundation of China (82103857), Research Fund of Anhui Institute of translational medicine (2022zhyx-B15), Anhui Provincial Natural Science Foundation (2208085QH231), the Natural Science Foundation in Higher Education of Anhui (KJ2020A0152 and 2022AH050708), and Grants for Scientific Research of BSKY (XJ2020012). Data availability The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request. Author Contributions Binbin Huang and Maozhen Han designed this study. Juan Wang, Wanxin Wu and Jiamei Wang wrote this manuscript. Juan Wang, Shuangshuang Bao and Ye Zhou performed the experiments and analyzed the data. Binbin Huang, Maozhen Han and Huan Qiu revised and edited this manuscript. All authors contributed to the article and approved the submitted version. Ethics declarations Competing Interests The authors declare no competing interests. Ethical approval and consent to participate The study protocol was reviewed and approved by the Biomedical Ethics Committee (No. 20210327) of Anhui Medical University on March 1, 2021, and approved. Relevant guidelines and regulations are performed on all methods. The designated institutional and licensing committees approved all experimental protocols. Informed consent was obtained from all subjects. Consent for publication Not applicable. Competing Interests The authors report no conflicts of interest. References Luo L, Wang B, Jiang J, Fitzgerald M, Huang Q, Yu Z, et al. Heavy Metal Contaminations in Herbal Medicines: Determination, Comprehensive Risk Assessments, and Solutions. Frontiers in pharmacology. 2020;11:595335. Guo S, Zhang Y, Xiao J, Zhang Q, Ling J, Chang B, et al. Assessment of heavy metal content, distribution, and sources in Nansi Lake sediments, China. Environmental science and pollution research international. 2021;28(24):30929-42. Balali-Mood M, Naseri K, Tahergorabi Z, Khazdair MR, Sadeghi M. Toxic Mechanisms of Five Heavy Metals: Mercury, Lead, Chromium, Cadmium, and Arsenic. Frontiers in pharmacology. 2021;12:643972. Gazwi HSS, Yassien EE, Hassan HM. Mitigation of lead neurotoxicity by the ethanolic extract of Laurus leaf in rats. Ecotoxicology and environmental safety. 2020;192:110297. Lu Y, Zhang Y, Guan Q, Xu L, Zhao S, Duan J, et al. Exposure to multiple trace elements and miscarriage during early pregnancy: A mixtures approach. Environment international. 2022;162:107161. Freire C, Amaya E, Gil F, Fernández MF, Murcia M, Llop S, et al. Prenatal co-exposure to neurotoxic metals and neurodevelopment in preschool children: The Environment and Childhood (INMA) Project. The Science of the total environment. 2018;621:340-51. Sanders AP, Claus Henn B, Wright RO. Perinatal and Childhood Exposure to Cadmium, Manganese, and Metal Mixtures and Effects on Cognition and Behavior: A Review of Recent Literature. Current environmental health reports. 2015;2(3):284-94. Valeri L, Mazumdar MM, Bobb JF, Claus Henn B, Rodrigues E, Sharif OIA, et al. The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20-40 Months of Age: Evidence from Rural Bangladesh. Environmental health perspectives. 2017;125(6):067015. Hong SB, Im MH, Kim JW, Park EJ, Shin MS, Kim BN, et al. Environmental lead exposure and attention deficit/hyperactivity disorder symptom domains in a community sample of South Korean school-age children. Environmental health perspectives. 2015;123(3):271-6. Iwai-Shimada M, Kameo S, Nakai K, Yaginuma-Sakurai K, Tatsuta N, Kurokawa N, et al. Exposure profile of mercury, lead, cadmium, arsenic, antimony, copper, selenium and zinc in maternal blood, cord blood and placenta: the Tohoku Study of Child Development in Japan. Environmental Health and Preventive Medicine. 2019;24(1):35. Chen Z, Myers R, Wei T, Bind E, Kassim P, Wang G, et al. Placental transfer and concentrations of cadmium, mercury, lead, and selenium in mothers, newborns, and young children. Journal of exposure science & environmental epidemiology. 2014;24(5):537-44. Jahan Toma N, Anwar S, Kabir T, Hosen MJ. Lead and lead-arsenic combined exposure induces mortality and developmental impairments in zebrafish embryos: a study using wild-caught zebrafish from Bangladesh. Drug and chemical toxicology. 2022;45(6):2833-42. Gustin K, Barman M, Stråvik M, Levi M, Englund-Ögge L, Murray F, et al. Low-level maternal exposure to cadmium, lead, and mercury and birth outcomes in a Swedish prospective birth-cohort. Environmental pollution (Barking, Essex : 1987). 2020;265(Pt B):114986. Rahman ML, Oken E, Hivert MF, Rifas-Shiman S, Lin PD, Colicino E, et al. Early pregnancy exposure to metal mixture and birth outcomes - A prospective study in Project Viva. Environment international. 2021;156:106714. Claus Henn B, Ettinger AS, Hopkins MR, Jim R, Amarasiriwardena C, Christiani DC, et al. Prenatal Arsenic Exposure and Birth Outcomes among a Population Residing near a Mining-Related Superfund Site. Environmental health perspectives. 2016;124(8):1308-15. Rahman ML, Valeri L, Kile ML, Mazumdar M, Mostofa G, Qamruzzaman Q, et al. Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible? Environment international. 2017;108:32-40. Tsai MS, Liao KW, Chang CH, Chien LC, Mao IF, Tsai YA, et al. The critical fetal stage for maternal manganese exposure. Environmental research. 2015;137:215-21. Yamamoto M, Sakurai K, Eguchi A, Yamazaki S, Nakayama SF, Isobe T, et al. Association between blood manganese level during pregnancy and birth size: The Japan environment and children's study (JECS). Environmental research. 2019;172:117-26. Kile ML, Cardenas A, Rodrigues E, Mazumdar M, Dobson C, Golam M, et al. Estimating Effects of Arsenic Exposure During Pregnancy on Perinatal Outcomes in a Bangladeshi Cohort. Epidemiology (Cambridge, Mass). 2016;27(2):173-81. Rahman ML, Kile ML, Rodrigues EG, Valeri L, Raj A, Mazumdar M, et al. Prenatal arsenic exposure, child marriage, and pregnancy weight gain: Associations with preterm birth in Bangladesh. Environment international. 2018;112:23-32. Taylor CM, Golding J, Emond AM. Moderate Prenatal Cadmium Exposure and Adverse Birth Outcomes: a Role for Sex-Specific Differences? Paediatric and perinatal epidemiology. 2016;30(6):603-11. Cassidy-Bushrow AE, Wu KH, Sitarik AR, Park SK, Bielak LF, Austin C, et al. In utero metal exposures measured in deciduous teeth and birth outcomes in a racially-diverse urban cohort. Environmental research. 2019;171:444-51. Kim SS, Meeker JD, Aung MT, Yu Y, Mukherjee B, Cantonwine DE, et al. Urinary trace metals in association with fetal ultrasound measures during pregnancy. Environmental epidemiology (Philadelphia, Pa). 2020;4(2). Cabrera-Rodríguez R, Luzardo OP, González-Antuña A, Boada LD, Almeida-González M, Camacho M, et al. Occurrence of 44 elements in human cord blood and their association with growth indicators in newborns. Environment international. 2018;116:43-51. Ashrap P, Watkins DJ, Mukherjee B, Boss J, Richards MJ, Rosario Z, et al. Maternal blood metal and metalloid concentrations in association with birth outcomes in Northern Puerto Rico. Environment international. 2020;138:105606. Howe CG, Claus Henn B, Eckel SP, Farzan SF, Grubbs BH, Chavez TA, et al. Prenatal Metal Mixtures and Birth Weight for Gestational Age in a Predominately Lower-Income Hispanic Pregnancy Cohort in Los Angeles. Environmental health perspectives. 2020;128(11):117001. Gilbert-Diamond D, Emond JA, Baker ER, Korrick SA, Karagas MR. Relation between in Utero Arsenic Exposure and Birth Outcomes in a Cohort of Mothers and Their Newborns from New Hampshire. Environmental health perspectives. 2016;124(8):1299-307. Fang X, Qu J, Huan S, Sun X, Li J, Liu Q, et al. Associations of urine metals and metal mixtures during pregnancy with cord serum vitamin D Levels: A prospective cohort study with repeated measurements of maternal urinary metal concentrations. Environment international. 2021;155:106660. Kabiri D, Romero R, Gudicha DW, Hernandez-Andrade E, Pacora P, Benshalom-Tirosh N, et al. Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population-based standards. Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2020;55(2):177-88. Iliodromiti S, Mackay DF, Smith GC, Pell JP, Sattar N, Lawlor DA, et al. Customised and Noncustomised Birth Weight Centiles and Prediction of Stillbirth and Infant Mortality and Morbidity: A Cohort Study of 979,912 Term Singleton Pregnancies in Scotland. PLoS medicine. 2017;14(1):e1002228. Karakis I, Landau D, Yitshak-Sade M, Hershkovitz R, Rotenberg M, Sarov B, et al. Exposure to metals and congenital anomalies: a biomonitoring study of pregnant Bedouin-Arab women. The Science of the total environment. 2015;517:106-12. Michael T, Kohn E, Daniel S, Hazan A, Berkovitch M, Brik A, et al. Prenatal exposure to heavy metal mixtures and anthropometric birth outcomes: a cross-sectional study. Environmental health : a global access science source. 2022;21(1):139. Punshon T, Li Z, Jackson BP, Parks WT, Romano M, Conway D, et al. Placental metal concentrations in relation to placental growth, efficiency and birth weight. Environment international. 2019;126:533-42. Cowell W, Colicino E, Levin-Schwartz Y, Enlow MB, Amarasiriwardena C, Andra SS, et al. Prenatal metal mixtures and sex-specific infant negative affectivity. Environmental epidemiology (Philadelphia, Pa). 2021;5(2):e147. Gardner RM, Kippler M, Tofail F, Bottai M, Hamadani J, Grandér M, et al. Environmental exposure to metals and children's growth to age 5 years: a prospective cohort study. American journal of epidemiology. 2013;177(12):1356-67. Sun X, Liu W, Zhang B, Shen X, Hu C, Chen X, et al. Maternal Heavy Metal Exposure, Thyroid Hormones, and Birth Outcomes: A Prospective Cohort Study. The Journal of clinical endocrinology and metabolism. 2019;104(11):5043-52. Mariath AB, Bergamaschi DP, Rondó PH, Tanaka AC, Hinnig Pde F, Abbade JF, et al. The possible role of selenium status in adverse pregnancy outcomes. The British journal of nutrition. 2011;105(10):1418-28. Dewey KG, Oaks BM. U-shaped curve for risk associated with maternal hemoglobin, iron status, or iron supplementation. The American journal of clinical nutrition. 2017;106(Suppl 6):1694s-702s. Snart CJP, Threapleton DE, Keeble C, Taylor E, Waiblinger D, Reid S, et al. Maternal iodine status, intrauterine growth, birth outcomes and congenital anomalies in a UK birth cohort. BMC medicine. 2020;18(1):132. James AH. Iron Deficiency Anemia in Pregnancy. Obstetrics and gynecology. 2021;138(4):663-74. Jessani S, Saleem S, Hoffman MK, Goudar SS, Derman RJ, Moore JL, et al. Association of haemoglobin levels in the first trimester and at 26-30 weeks with fetal and neonatal outcomes: a secondary analysis of the Global Network for Women's and Children's Health's ASPIRIN Trial. BJOG : an international journal of obstetrics and gynaecology. 2021;128(9):1487-96. Zhang Y, Chen T, Zhang Y, Hu Q, Wang X, Chang H, et al. Contribution of trace element exposure to gestational diabetes mellitus through disturbing the gut microbiome. Environment international. 2021;153:106520. Monangi N, Xu H, Khanam R, Khan W, Deb S, Pervin J, et al. Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis. BMJ global health. 2021;6(9). Sun H, Chen W, Wang D, Jin Y, Chen X, Xu Y. The effects of prenatal exposure to low-level cadmium, lead and selenium on birth outcomes. Chemosphere. 2014;108:33-9. Tables Table 1. Maternal and fetal baseline information Maternal and fetal characteristics Overall(n=429) mean (SD) or n (%) Maternal age, years 29.58 (4.17) Age of menarche, years 13.69 (1.63) Menstrual cycle, days 30.55 (6.03) Ethnic Han 389 (90.7%) else 40 (9.3%) Education College or below 307 (71.6%) Undergraduate college 118 (27.5%) Master's degree or above 4 (0.9%) Number of pregnancies, times 2.04 (1.19) Number of deliveries, times 1.34 (0.79) Number of miscarriages, times 0.45 (0.75) Husband Smoking No 307 (71.6%) Yes 122 (28.4%) Pre-pregnancy BMI, kg/m² 21.54 (3.04) Delivery mode Natural birth 275 (64.1) Else 154 (35.9) Preterm birth Non-preterm 406 (94.6%) Preterm 23 (5.4%) Gestational age, weeks 38.97 (1.73) Neonatal sex Male 218 (50.8%) Female 211 (49.2%) Birth weight, g 3131.88 (499.21) Birth defects Without birth defects 418 (97.4%) With birth defects 11 (2.6%) SGA 74 (17.2%) LGA 35 (8.2%) LBW 29 (6.8%) Macrosomia 13 (3.0%) Abbreviations: SD, standard deviation; BMI, body mass index; n (%), frequency (percentage); SGA, small for gestational age; LGA, large for gestational age; LBW, low birth weight Table 2 is available in the Supplementary Files section. Table 3. WQS Model Used to estimate the Association between the WQS Index and adverse birth outcomes Outcomes β（ 95%CI ） p-value Preterm birth Positive 1.366(0.041, 2.692) 0.043 Negative -0.598(-1.419, 0.223) 0.153 LGA Positive -2.001(-3.623, -0.379) 0.015 Negative -2.224(-3.783, -0.666) 0.005 Additional Declarations No competing interests reported. Supplementary Files Table2.docx SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4750408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330166271,"identity":"627ee29b-4e2b-494f-8491-80dc55ab32de","order_by":0,"name":"Juan Wang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wang","suffix":""},{"id":330166272,"identity":"075da7f7-5b65-4593-b309-2eaa6f646c29","order_by":1,"name":"Ye Zhou","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhou","suffix":""},{"id":330166273,"identity":"b9b721f4-e0fc-472b-95b6-bfb1086d7dd0","order_by":2,"name":"Wanxin Wu","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanxin","middleName":"","lastName":"Wu","suffix":""},{"id":330166274,"identity":"109abdb9-7a7b-4e27-887c-8149012ed529","order_by":3,"name":"Jiamei Wang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Wang","suffix":""},{"id":330166275,"identity":"ab56ac8d-d8e6-4ed7-8e39-0292e38dd03d","order_by":4,"name":"Shuangshuang Bao","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Bao","suffix":""},{"id":330166276,"identity":"4c97fe00-d428-4bea-b54f-c03bc459d49b","order_by":5,"name":"Huan Qiu","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Qiu","suffix":""},{"id":330166277,"identity":"3fc4272b-6dd6-48ef-ba60-bc4c2f544d7a","order_by":6,"name":"Maozhen Han","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maozhen","middleName":"","lastName":"Han","suffix":""},{"id":330166278,"identity":"cfae5fac-0f2f-426a-9115-c6ec98a81b93","order_by":7,"name":"Binbin Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACAxDB2HBAjo29sfHBB1K0GPPxHG42nEGKlsR5Eult0hzEaDGXSH728OuOO4ltkg8bpBkY7OR0GwhosZyRZm4se+aZcZt0YoNxAUOysdkBQg67kWAmLdl2WBakJXkGw4HEbYS1pH8DaWFskzzYcJiHOC05ZpIf2w4rtkkwNjYTp+XMmzJpRqBf2HgSmxlnGBDjl+Pp2yR/7rgjJ99+/PmPDxV2cgS1gAAzD8IEIpSDAOMPIhWOglEwCkbBCAUArP9JSCDq7SEAAAAASUVORK5CYII=","orcid":"","institution":"Anhui Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-07-16 14:21:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4750408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4750408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62653325,"identity":"8bc2de5b-f1d2-4808-a726-c161b157b2a4","added_by":"auto","created_at":"2024-08-17 01:08:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324996,"visible":true,"origin":"","legend":"\u003cp\u003eBKMR Analysis of Mixed Exposure and Adverse Birth Outcomes. (A) Overall benefits and 95% confidence intervals for the six metals. (B) associations of individual elements with adverse birth outcomes. (C) Univariate concentration response functions with 95% confidence bands (shaded areas) for each element, with other pollutants fixed at the median. (D) Bivariate interaction, showing the bivariate exposure-response function for exposure 1 when exposure 2 was fixed at the 25th, 50th, or 75th percentile and the other elements were fixed at the 50th percentile.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/d82ec85621284f7a480137d4.png"},{"id":62653326,"identity":"712ba5c0-f2ce-4478-8135-81a8f70bd27d","added_by":"auto","created_at":"2024-08-17 01:08:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177649,"visible":true,"origin":"","legend":"\u003cp\u003eBKMR Analysis of Mixed Exposure and Adverse Birth Outcomes. (A, B) Overall effects and 95% confidence intervals for the six metals. (C, D) associations of single elements with adverse birth outcomes. Changes in single elements for preterm birth (C) and birth defects (D) are shown with 95% confidence intervals when other elements are fixed at the 25th, 50th, or 75th percentile.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/c92217e8d70863fb7c3a5c4b.png"},{"id":62652457,"identity":"fb1e179c-5e29-4137-967d-377ac3d7edc3","added_by":"auto","created_at":"2024-08-17 01:00:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119461,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between A, B metal mixture levels and PTB based on weighted quantile sum (WQS) regression analysis (A, positive weight, B, negative weight), and association between C,D metal mixture levels and LGA based on weighted quantile sum (WQS) regression analysis (C, positive weight, D, negative weight).\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/2b1a26e3dd167256b86fa823.png"},{"id":71229081,"identity":"4294580c-1a96-4255-a678-b2eb1b15f33e","added_by":"auto","created_at":"2024-12-12 10:32:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/1b98bcc4-2c23-443e-913d-575036d40dc0.pdf"},{"id":62652455,"identity":"b82e0b33-c814-4c12-b19c-1f460ed6cd70","added_by":"auto","created_at":"2024-08-17 01:00:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30284,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/6f1f9f6b9bb590551c6bcf51.docx"},{"id":62652459,"identity":"7950978a-6014-4249-9196-d560f3a8a174","added_by":"auto","created_at":"2024-08-17 01:00:50","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":703972,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4750408/v1/1b33b8a881ee00405c348c90.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Prenatal Serum Heavy Metals Exposure with Adverse Birth Outcomes: A Prospective Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the acceleration of industrialization, a large number of metals and metalloid produced by continuous industrialization are released into the air, water, soil and crops,(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) which makes human beings inevitably come into contact with them.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Some heavy metals, such as iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), tin (Sn) and silicon are essential trace elements for the human body and their deficiency can lead to various diseases. Heavy metals have the potential to cause acute and chronic toxic effects on different organs of the human body including gastrointestinal and renal dysfunction, immune system dysfunction, nervous system disorders, and birth defects.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The toxic effects of heavy metals are dose-dependent, and high dose exposure can lead to severe reactions in the body, causing more serious damage. Simultaneous exposure of multiple metals may have a cumulative effect.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Metals and metalloids are rarely exposed alone, and co-exposure to multiple metals may be the norm,(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) but there are limited studies exploring the interactive effects of co-exposure to multiple elements on fetal development.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFetuses are particularly vulnerable to heavy metal exposure compared to adults, as even low doses that do not harm the mother can have detrimental effects on the developing fetus. For instance, minimal levels of lead exposure during pregnancy can result in adverse birth outcomes and neurodevelopmental issues, such as cognitive decline and symptoms linked to attention deficit hyperactivity disorder (ADHD).(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The placental barrier is thought to protect the fetus from toxic substances by preventing the passage of harmful substances; however, it has been found that metallic elements, such as cadmium (Cd), mercury (Hg), lead (Pb), and selenium (Se), can cross the placental barrier and accumulate in embryonic tissue.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Accumulation of these elements in embryonic tissues can lead to placental dysfunction and impair the support of embryonic growth.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Epidemiologic studies have shown an association between prenatal exposure to certain metals and adverse birth outcomes.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Exposure to elements such as arsenic (As), Pb, cadmium (Cd), and Mn have been associated with lower birth weights,(\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) shorter gestation periods,(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) higher risk of preterm labor,(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) smaller head circumferences,(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and shorter birth lengths.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) However, most of these studies have assessed the effects of single exposures to metallic elements, although some studies in recent years have analyzed the combined effects of mixed exposures to multiple metals, yet these studies have not produced consistent results.(\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Most research in the field has concentrated on groups with high heavy metal exposure levels, rather than on those with typical exposure levels and no unusual exposures.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe effects of metals and metalloids on pregnancy and birth outcomes have been extensively studied, and exposure to metal mixtures during pregnancy and their potential impact on birth outcomes has attracted increasing attention. In recent years, a number of epidemiologic studies have examined the association between heavy metals and a variety of adverse health outcomes in newborns, including low birth size, and a variety of congenital malformations, PTB, LBW, macrosomia, SGA, and LGA, are a major public health concern, as they are associated with an increased risk of maternal and neonatal morbidity and mortality.(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Although these findings may themselves be associated with morbidity in infancy and adulthood,(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) they may result from a complex series of in utero events(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) that may be associated with many other future complications, including behavioral changes in infancy,(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) childhood obesity and various endocrine disorders.(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) Therefore, it is critical to investigate any association between prenatal exposure to various heavy metals and measurable and sensitive birth outcomes.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we explored the association of prenatal exposure to multiple levels of metals with adverse birth outcomes from a prospectives cross section. The present study aims to explore: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) whether prenatal exposure to metals would be association with adverse birth outcome; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Exposure to association of individual or multiple metals with adverse birth outcome. Thus, we measured 27 metals to investigate their effects of a single metal and the interactions of mixed exposures to multiple metals on adverse birth outcomes through utilized Bayesian kernel-machine regression (BKMR) modeling to explore the joint effects of mixed exposures to the elements. Exploring measures that can reduce exposure to risk factors and finding remedies to reduce the adverse health effects of environmental pollutants, such as heavy metals, on mothers and infants can have a positive impact on public health.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe study was conducted from October 2022 to July 2023 in in South China. The pregnant women included in the study were in good general health, without serious cardiovascular disease, liver and kidney disease, immune-related diseases, and no history of illicit drug use. Various relevant information regarding pregnant women and newborns, including the age of the pregnant woman, age at first menstruation, menstrual cycle, ethnicity, number of pregnancies, educational level, pre-pregnancy BMI, gestational age at birth for newborns, gender of newborns, presence of birth defects in newborns, birth weight and length of newborns are collected by trained nurses. Ethnic groups included Han and other ethnic groups, and the level of education was divided into three categories: junior college or below, bachelor's degree and master's degree or above. Information on the relevant conditions and health status of the pregnant woman's pregnancy was obtained from the electronic medical record.\u003c/p\u003e \u003cp\u003eA total of 512 pregnant women in their second trimester (13\u0026thinsp;~\u0026thinsp;26 weeks) were recruited in this study. Serum samples were collected for the detection of metals and metalloids. In this study, the inclusion criteria for pregnant women included the following: (a) maternal age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (b) gestational age\u0026thinsp;\u0026ge;\u0026thinsp;12 weeks, and (c) live birth. After excluding women with multiple pregnancies, lack of information on pregnancy outcome and delivery, a total of 429 pregnant women were included in this study. The study was approved by the Institutional Review Board of Anhui Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Classification of adverse birth outcomes\u003c/h2\u003e \u003cp\u003eThe study examined six adverse birth outcomes, including PTB, LBW, macrosomia, SGA, LGA, and various types of birth defects. These birth defects encompassed chromosomal abnormalities, congenital heart disease, polydactyly, and syndactyly. The gestational age of the fetus was based on the date of the last menstrual period of the mother. According to the clinical cut-off value classification, PTB was defined as gestational age before 37 weeks of gestation, LBW as birth weight less than 2500g and macrosomia as birth weight more than 4000g. SGA and LGA were defined as newborns with birth weight less than the 10th percentile and greater than the 90th percentile for gestational age, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Serum metals measurements\u003c/h2\u003e \u003cp\u003eThe collected serum samples were stored at -80℃ in a refrigerator, and they were thawed from the refrigerator at -80℃to 4℃the night before testing. Prior to testing, the samples underwent centrifugation at 3500r for 10 minutes. Subsequently, 100 \u0026micro;L of each sample was diluted by weighing method with 1% nitric acid in a 50 mL volume centrifuge tube, resulting in a dilution factor of 30. The standard solution was prepared one week prior to testing. Before conducting daily tests, the standard solution underwent analysis to establish a standard curve and was thoroughly mixed by vortexing. Subsequently, the inductively coupled plasma mass spectrometer (ICP-MS) was tuned using a tuning solution prior to the analysis of both standard and test samples. Following the tuning process, the standard and test samples were analyzed. After completing the analysis of a batch of samples, a further analysis of the standard solution was carried out to verify instrument accuracy, followed by an analysis of a mixed quality control sample for quality assurance purposes. It was ensured that the recovery rate for detected elements fell within the acceptable range of 80\u0026ndash;120%. In this study, we analyzed a total of twenty-seven elements, namely Beryllium (Be), Magnesium (Mg), Aluminum (Al), Calcium (Ca), Vanadium (V), Chromium (Cr), Mn, Fe, Cobalt (Co), Nickel (Ni), Cu, Zn, As, Se, Rubidium (Rb), Strontium (Sr), Molybdenum (Mo), Cd, Thallium (Tl), Antimony (Sb), Iodine (I), Cesium (Cs), Barium (Ba), Tungsten (W), Lead (Pb), and Bismuth (Bi), Sn. For samples with measurements below the detection limit, a common practice in analytical chemistry is to substitute these values with half of the detection limit. This approach helps to ensure that all data points are accounted for and included in the analysis, maintaining the integrity and completeness of the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistical analysis was employed to summarize the demographic characteristics of both mothers and newborns, encompassing variables such as maternal age, age at menarche, menstrual cycle, ethnic group, education level, number of pregnancies, husband's smoking and drinking habits during pregnancy, pre-pregnancy BMI, preterm birth occurrence, fetal gestational age and sex determination. Additionally included were birth weight and length measurements along with the presence or absence of any birth defects. Newborns were categorized into SGA and LGA groups based on their gestational age in relation to sex and birth weight. Furthermore, they were classified into LBW groupings (\u0026lt;\u0026thinsp;2500g) and macrosomia groupings (\u0026ge;\u0026thinsp;4000g) according to their actual birth weights. Continuous variables are presented as means accompanied by standard deviations (SD), while categorical variables are expressed as frequencies alongside proportions. The covariates considered in this study comprised maternal factors such as age at menarche, menstrual cycle regularity or irregularity status, ethnic background, educational attainment level, number of previous pregnancies experienced by the mother, whether the husband smoked or drank alcohol during pregnancy period, pre-pregnancy BMI values and delivery mode. Generalized linear regression models were utilized to examine the correlation between serum trace element levels and outcome variables; Pearson correlation was used specifically to analyze associations among serum trace element levels themselves. All statistical analyses were conducted using SPSS 26.0 software package combined with R 4.3.1 version; a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted when assessing differences between various groups under investigation.\u003c/p\u003e \u003cp\u003eWe initially employed the generalized linear regression model to examine the association between serum trace elements and outcome variables. Subsequently, we identified the elements that exhibited a significant correlation with the outcome variables and proceeded with conducting combined effect analysis. Considering the potential interactions among multiple serum elements and their impact on the studied outcome variables, BKMR models were utilized to evaluate the joint effects of mixed exposure to various trace elements on these outcomes. This model employs non-parametric methods for mixture analysis and has been extensively applied in prenatal exposure studies. The BKMR model assessed associations between outcome variables and both continuous or dichotomous factors. In our study, we fitted the BKMR model using Markov chain Monte Carlo algorithm with 50,000 iterations, incorporating all metals as exposure factors within the model while calculating posterior inclusion probability (PIP) to determine each metal's contribution towards overall association strength. Additionally, we employed WQS modeling approach to estimate overall mixing effect in this study; subsequently utilizing weights derived from WQS modeling calculations allowed us to ascertain relative contributions of individual elements towards overall mixing exposure.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographics characteristics and trace element levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 429 pregnant women were included in this study; the baseline sample characteristics of pregnant women and infants are shown in Table 1, continuous variables are expressed as mean \u0026plusmn; standard deviation, and categorical variables are expressed as frequency (%). In this study, the average age of the pregnant women was 29.58 years, 90.7% of them were Han nationality, 71.6% had college education or below, 28.4% of the pregnant women had husbands who smoked and 25.6% had husbands who drank alcohol during pregnancy. There were 218 males (50.8%) and 211 females (49.2%) among the newborns, with an average gestational age of 38.97 (SD=1.73) and an average birth weight of 3131.88g. Among the newborns, 23 (5.4%) were premature and 11 (2.6%) had birth defects. There were 74 (17.2%) SGA infants, 35 (8.2%) LGA infants, 29 (6.8%) low birth weight infants and 13 (3.0%) macrosomia infants.\u003c/p\u003e\n\u003cp\u003eTable 2 displays the distribution of adverse birth outcome within the study population. Preterm neonates had a mean gestational age of 33.8 weeks (SD 3.08,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001) and a mean birth weight of 2184 grams (SD 676, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among neonates with birth defects, the average number of deliveries per mother was 1.91 (SD 0.70, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.02), and 6 out of 11 (54.5%) neonates had fathers who smoked during pregnancy (\u003cem\u003ep\u003c/em\u003e = 0.037). For SGA neonates, the mean maternal age was 28.6 years (SD 3.49, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.01), the mean number of pregnancies was 1.80 (SD 1.01, \u003cem\u003ep\u003c/em\u003e = 0.031), and the mean neonatal birth weight was 2643 grams (SD 330, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among LGA neonates, the average maternal age was 31.0 years (SD 3.74,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.023), the average number of pregnancies was 2.60 (SD 1.17, \u003cem\u003ep\u003c/em\u003e = 0.005), and the average number of abortions was 0.74 (SD 0.89,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.045). The mean maternal pre-pregnancy BMI was 22.5 (SD 2.75, \u003cem\u003ep\u003c/em\u003e = 0.047), and 25 out of 35 (71.4%) mothers underwent non-vaginal deliveries (including cesarean section, forceps delivery, and fetal head traction) (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), with a mean birth weight of 3909 grams (SD 456, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). For LBW infants, the mean maternal menstrual cycle length was 29.0 days (SD 1.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), 17 out of 29 (58.6%) mothers did not have vaginal deliveries (\u003cem\u003ep\u003c/em\u003e = 0.015), the mean gestational age of newborns was 35.6 weeks (SD 3.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and the mean neonatal birth weight was 2065 grams (SD 498, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among macrosomia, the mean maternal age was 32.0 years (SD 3.83,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.038), the mean number of pregnancies was 2.85 (SD 0.90, \u003cem\u003ep\u003c/em\u003e = 0.006), and 9 out of 13 (69.2%) mothers had non-vaginal deliveries (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.017). The mean neonatal birth weight was 4304 grams (SD 404, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Table S1 shows the distribution of the six elements in serum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Associations between metals and adverse birth outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear regression models revealed associations between six metallic elements Fe, Se, Rb, Sn, I, and Tl and adverse birth outcomes in newborns (Table S2). Table S3 displays the a priori probability results from the BKMR model. Adverse birth outcomes were categorized as PTB, LBW, macrosomia, SGA, LGA, or birth defects. Figure 1A illustrates the overall impact of metal mixture exposure, suggesting that the incidence of adverse birth outcomes tended to increase with higher metal mixtures, although the differences were not statistically significant. The impact of individual metal element exposures on outcomes was evaluated while keeping the other metal elements constant at the 25th, 50th, and 75th percentiles (Figure 1B). To delve deeper into the univariate exposure-response relationships and potential interactions, univariate and bivariate interaction functions were calculated and depicted in Figure 1C. The figure showcases univariate concentration-response functions and their corresponding 95% confidence intervals (shaded areas), with the values of other elements set at the median. Furthermore, Figure1D presents the bivariate concentration-response functions for the six metal elements, aiming to explore potential interactions among them.\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the impact of mixed exposure to metal elements on adverse birth outcomes. When the six metal elements were held at specific percentiles, we observed an increase in preterm birth incidence with rising metal mixture levels, while the incidence of birth defects decreased compared to the 50th percentile group, with statistically significant variances Figure 2A, B). Notably, when other metals were fixed at the 25th, 50th, and 75th percentiles, the following associations were identified: Fe exhibited a negative correlation with PTB; high Fe concentrations were inversely related to birth defects, whereas low Fe concentrations showed a positive association with birth defects. Additionally, Se, Sb, and I were positively linked to PTB, and Se and Rb were negatively associated with birth defects (Figure 2C, D). These findings were statistically significant. The Supplementary material Figure S1 displays bivariate interactions between metallic elements and adverse birth outcomes. Notably, interactions were observed between Sn and Fe, I, and Se concerning the incidence of preterm birth. Additionally, interactions were noted between I and Se, as well as Tl and Rb. Moreover, interactions were identified between Fe and Rb, Fe and Se, and Rb and Se when assessing birth defects as outcomes.\u003c/p\u003e\n\u003cp\u003eThe regression results from the WQS analysis revealed a noteworthy positive correlation between mixed metal exposure and preterm birth. Specifically, the WQS index primarily influenced by Se and I was linked to heightened odds of preterm birth, while a significant negative association was observed between metal mixture exposure and LGA births. Conversely, the WQS index driven by Sb and Fe was associated with reduced odds of LGA. Furthermore, the results from the negative modeling indicated that Tl and Se dominated WQS indices were linked to a decrease in the odds of LGA (refer to Table 3 and Figure 3).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThere is a growing body of evidence indicating that exposure to toxic metals can have negative impacts on fetal growth. However, the majority of studies have traditionally concentrated on the effects of individual metals. Given the potential for synergistic or antagonistic interactions among metals, the behavior of a metal when exposed in combination with others may differ from its behavior when exposed in isolation. As a result, an increasing number of studies have started to investigate the effects of metal mixtures on fetal growth.(26) In this study, we employed the Bayesian Kernel Machine Regression (BKMR) model and the Weighted Quantile Sum (WQS) model to evaluate the combined impacts of mixed metal exposure and investigate the relationship between maternal whole blood concentrations of metals during pregnancy and adverse birth outcomes. This pioneering study represents the first comprehensive analysis of the effects of mixed metal exposure during pregnancy on PTB, birth defects, LBW, macrosomia, SGA, and LGA infants. By utilizing advanced statistical models and considering the combined effects of multiple metals, we were able to provide a more nuanced understanding of how maternal metal exposure influences various birth outcomes.\u003c/p\u003e\n\u003cp\u003eThe findings of this study revealed that six metal elements, namely Fe, Se, Rb, Sn, I and Tl, were correlated with adverse birth outcomes in newborns. The incidence of adverse birth outcomes varied based on the concentrations and combinations of these metal elements, with statistically significant results observed. Both the BKMR model and the WQS model demonstrated a positive association between mixed metal exposure and PTB, underscoring the significant impact of mixed metal exposure on PTB outcomes. Moving forward, future investigations can delve deeper into elucidating the potential mechanisms linking metal exposure during pregnancy to preterm birth. The outcomes of this study underscore the importance of evaluating the health implications of mixed pollutant exposures using integrated exposure models and employing various statistical analysis methods to comprehensively assess the impact of individual and combined metal exposures on adverse birth outcomes.\u003c/p\u003e\n\u003cp\u003eSome previous studies also pointed out the association between prenatal metal mixture exposure and birth outcomes. Tal Michael et al. found that birth weight was negatively correlated with Tl, which was consistent with our study. The WQS negative modeling results of this study showed that Tl and Se dominated WQS index were associated with the odds of LGA reduction.(32) Raul Cabrea-Rodriguez et al. quantified 44 elements in cord blood and found an inverse association between Sb and birth weight (Spearman\u0026apos;s r = \u0026minus;0.106, \u003cem\u003ep\u003c/em\u003e = 0.021),(24) This is consistent with the results of the WQS analysis of the present study, which showed a significant negative relationship between metal mixture exposure and LGA, and the forward modeling results showed that the Sb and Fe dominated WQS index was associated with the odds of LGA reduction. Sb compounds are found in common materials (such as textiles) and polyester, ceramics, glass and rubber as flame retardants, which pose a potential health threat. Some studies have found that the plasma and blood Se concentration of preterm pregnant women is lower than that of full-term pregnant women, and the relationship between Se level and birth weight is still controversial in the literature. Some studies suggest that the effect of PTB on birth weight may mask the association between Se level and birth weight.(37) Studies have linked Fe deficiency to adverse birth outcomes, including low birth weight and PTB.(15) Our findings showed an inverse association between Fe and adverse birth outcomes, and an Fe dominated WQS index was associated with odds of reduced LGA. Systematic reviews have shown that Fe deficiency in the first and second trimesters is associated with increased maternal morbidity and increased risk of adverse pregnancy outcomes, defined as low birth weight, PTB, or intrauterine growth restriction.(38) Some studies have found a small positive association between the I to creatinine ratio and birth weight. Consistent with the results of our mixed exposure BKMR analysis, there was no evidence of an association with other birth outcomes, including PTB, stillbirth, and congenital anomalies.(39)\u003c/p\u003e\n\u003cp\u003eResearch indicates that pregnant women are prone to developing iron deficiency anemia due to the shared need for iron by both the mother and the developing fetus. The expansion of red blood cell and the growth of the fetus and placenta elevate the maternal iron requirement throughout pregnancy.(40) Anemia in pregnant women has been linked to adverse outcomes in fetal, neonatal, and early childhood stages, such as increased risks of perinatal and neonatal mortality, low birth weight, premature birth, and altered gestational age.(41) Some studies have established a connection between Rb elements and potassium channels, underscoring their involvement in physiological regulation.(11) Disrupted potassium channels can impede proper embryonic blood vessel formation, with certain investigations suggesting a possible protective role of these elements during pregnancy.(42) Notably, Monangi et al. have proposed that heightened levels of selenium in maternal blood correlate with prolonged gestation, potentially contributing to enhanced birth weight.(43) While the precise mechanisms through which selenium operates during pregnancy remain unclear, it is postulated that selenium may inhibit factors involved in triggering labor in the fetal membranes and uterine muscle. Moreover, there is belief that selenium can form chemical bonds, which may mitigate the effects of harmful metals and support fetal growth.(44) Conversely, it has been postulated that exposure to thallium (Tl) could heighten oxidative stress in the placenta and fetus, potentially leading to intrauterine growth restriction. Studies have indicated that prenatal Tl exposure is linked to changes in maternal and fetal thyroid function, which could have implications for developmental disabilities in children, whether directly or indirectly.(32)\u003c/p\u003e\n\u003cp\u003eThe results of our current study are based on a prospective study and the causal relationship between metals and outcome variables cannot be determined. Although we adjusted for the effects of demographic characteristics and pregnancy information as much as possible, it is still possible that potential confounders, such as maternal psychological problems, could have been affected. Further studies are necessary to expand the sample size and area of the subjects, and conduct prospective design to explore the effect of mixed metal exposure during pregnancy on birth outcomes. Although there are certain limitations, this study also has certain strengths, including measuring mixed metals and using BKMR model and WQS model to evaluate the effect of complex mixed exposure to multiple metals during pregnancy on neonatal birth outcomes, with less bias than a single model. The complementary BKMR and WQS models allow quantifying and visualizing the effects of mixed exposure and each element while accounting for the remaining elements, thus making the results more rigorous. The combination of classical single exposure and multiple exposure models, in which the conclusions of the mixed exposure model are consistent with the results of the single exposure model, can complement each other, thereby helping us to fully understand these associations and making the results more credible.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe findings from this study contribute valuable insights into the potential health risks associated with mixed metals exposure during pregnancy. By elucidating the multifaceted impacts of metal mixtures on birth outcomes, this research offers a foundation for developing targeted interventions and preventive measures to safeguard maternal and child health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants in the study. Special acknowledgment is given to the educators at Longhua District Maternal and Child Health Hospital in Shenzhen for their role in conducting the observational study. Furthermore, recognition is expressed to the instructors at Anhui Medical University for their careful guidance and supervision during the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82103857), Research Fund of Anhui Institute of translational medicine (2022zhyx-B15), Anhui Provincial Natural Science Foundation (2208085QH231), the Natural Science Foundation in Higher Education of Anhui (KJ2020A0152 and 2022AH050708),\u0026nbsp;and\u0026nbsp;Grants for Scientific Research of BSKY (XJ2020012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBinbin Huang\u0026nbsp;and Maozhen Han designed this study. Juan Wang, Wanxin Wu and Jiamei Wang wrote this manuscript. Juan Wang, Shuangshuang Bao and Ye Zhou performed the experiments and analyzed the data. Binbin Huang, Maozhen Han and Huan Qiu revised and edited this manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Biomedical Ethics Committee (No. 20210327) of Anhui Medical University on March 1, 2021, and approved. Relevant guidelines and regulations are performed on all methods. The designated institutional and licensing committees approved all experimental protocols. Informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLuo L, Wang B, Jiang J, Fitzgerald M, Huang Q, Yu Z, et al. Heavy Metal Contaminations in Herbal Medicines: Determination, Comprehensive Risk Assessments, and Solutions. Frontiers in pharmacology. 2020;11:595335.\u003c/li\u003e\n\u003cli\u003eGuo S, Zhang Y, Xiao J, Zhang Q, Ling J, Chang B, et al. Assessment of heavy metal content, distribution, and sources in Nansi Lake sediments, China. Environmental science and pollution research international. 2021;28(24):30929-42.\u003c/li\u003e\n\u003cli\u003eBalali-Mood M, Naseri K, Tahergorabi Z, Khazdair MR, Sadeghi M. Toxic Mechanisms of Five Heavy Metals: Mercury, Lead, Chromium, Cadmium, and Arsenic. Frontiers in pharmacology. 2021;12:643972.\u003c/li\u003e\n\u003cli\u003eGazwi HSS, Yassien EE, Hassan HM. Mitigation of lead neurotoxicity by the ethanolic extract of Laurus leaf in rats. Ecotoxicology and environmental safety. 2020;192:110297.\u003c/li\u003e\n\u003cli\u003eLu Y, Zhang Y, Guan Q, Xu L, Zhao S, Duan J, et al. Exposure to multiple trace elements and miscarriage during early pregnancy: A mixtures approach. Environment international. 2022;162:107161.\u003c/li\u003e\n\u003cli\u003eFreire C, Amaya E, Gil F, Fern\u0026aacute;ndez MF, Murcia M, Llop S, et al. Prenatal co-exposure to neurotoxic metals and neurodevelopment in preschool children: The Environment and Childhood (INMA) Project. The Science of the total environment. 2018;621:340-51.\u003c/li\u003e\n\u003cli\u003eSanders AP, Claus Henn B, Wright RO. Perinatal and Childhood Exposure to Cadmium, Manganese, and Metal Mixtures and Effects on Cognition and Behavior: A Review of Recent Literature. Current environmental health reports. 2015;2(3):284-94.\u003c/li\u003e\n\u003cli\u003eValeri L, Mazumdar MM, Bobb JF, Claus Henn B, Rodrigues E, Sharif OIA, et al. The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20-40 Months of Age: Evidence from Rural Bangladesh. Environmental health perspectives. 2017;125(6):067015.\u003c/li\u003e\n\u003cli\u003eHong SB, Im MH, Kim JW, Park EJ, Shin MS, Kim BN, et al. Environmental lead exposure and attention deficit/hyperactivity disorder symptom domains in a community sample of South Korean school-age children. Environmental health perspectives. 2015;123(3):271-6.\u003c/li\u003e\n\u003cli\u003eIwai-Shimada M, Kameo S, Nakai K, Yaginuma-Sakurai K, Tatsuta N, Kurokawa N, et al. Exposure profile of mercury, lead, cadmium, arsenic, antimony, copper, selenium and zinc in maternal blood, cord blood and placenta: the Tohoku Study of Child Development in Japan. Environmental Health and Preventive Medicine. 2019;24(1):35.\u003c/li\u003e\n\u003cli\u003eChen Z, Myers R, Wei T, Bind E, Kassim P, Wang G, et al. Placental transfer and concentrations of cadmium, mercury, lead, and selenium in mothers, newborns, and young children. Journal of exposure science \u0026amp; environmental epidemiology. 2014;24(5):537-44.\u003c/li\u003e\n\u003cli\u003eJahan Toma N, Anwar S, Kabir T, Hosen MJ. Lead and lead-arsenic combined exposure induces mortality and developmental impairments in zebrafish embryos: a study using wild-caught zebrafish from Bangladesh. Drug and chemical toxicology. 2022;45(6):2833-42.\u003c/li\u003e\n\u003cli\u003eGustin K, Barman M, Str\u0026aring;vik M, Levi M, Englund-\u0026Ouml;gge L, Murray F, et al. Low-level maternal exposure to cadmium, lead, and mercury and birth outcomes in a Swedish prospective birth-cohort. Environmental pollution (Barking, Essex : 1987). 2020;265(Pt B):114986.\u003c/li\u003e\n\u003cli\u003eRahman ML, Oken E, Hivert MF, Rifas-Shiman S, Lin PD, Colicino E, et al. Early pregnancy exposure to metal mixture and birth outcomes - A prospective study in Project Viva. Environment international. 2021;156:106714.\u003c/li\u003e\n\u003cli\u003eClaus Henn B, Ettinger AS, Hopkins MR, Jim R, Amarasiriwardena C, Christiani DC, et al. Prenatal Arsenic Exposure and Birth Outcomes among a Population Residing near a Mining-Related Superfund Site. Environmental health perspectives. 2016;124(8):1308-15.\u003c/li\u003e\n\u003cli\u003eRahman ML, Valeri L, Kile ML, Mazumdar M, Mostofa G, Qamruzzaman Q, et al. Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible? Environment international. 2017;108:32-40.\u003c/li\u003e\n\u003cli\u003eTsai MS, Liao KW, Chang CH, Chien LC, Mao IF, Tsai YA, et al. The critical fetal stage for maternal manganese exposure. Environmental research. 2015;137:215-21.\u003c/li\u003e\n\u003cli\u003eYamamoto M, Sakurai K, Eguchi A, Yamazaki S, Nakayama SF, Isobe T, et al. Association between blood manganese level during pregnancy and birth size: The Japan environment and children\u0026apos;s study (JECS). Environmental research. 2019;172:117-26.\u003c/li\u003e\n\u003cli\u003eKile ML, Cardenas A, Rodrigues E, Mazumdar M, Dobson C, Golam M, et al. Estimating Effects of Arsenic Exposure During Pregnancy on Perinatal Outcomes in a Bangladeshi Cohort. Epidemiology (Cambridge, Mass). 2016;27(2):173-81.\u003c/li\u003e\n\u003cli\u003eRahman ML, Kile ML, Rodrigues EG, Valeri L, Raj A, Mazumdar M, et al. Prenatal arsenic exposure, child marriage, and pregnancy weight gain: Associations with preterm birth in Bangladesh. Environment international. 2018;112:23-32.\u003c/li\u003e\n\u003cli\u003eTaylor CM, Golding J, Emond AM. Moderate Prenatal Cadmium Exposure and Adverse Birth Outcomes: a Role for Sex-Specific Differences? Paediatric and perinatal epidemiology. 2016;30(6):603-11.\u003c/li\u003e\n\u003cli\u003eCassidy-Bushrow AE, Wu KH, Sitarik AR, Park SK, Bielak LF, Austin C, et al. In utero metal exposures measured in deciduous teeth and birth outcomes in a racially-diverse urban cohort. Environmental research. 2019;171:444-51.\u003c/li\u003e\n\u003cli\u003eKim SS, Meeker JD, Aung MT, Yu Y, Mukherjee B, Cantonwine DE, et al. Urinary trace metals in association with fetal ultrasound measures during pregnancy. Environmental epidemiology (Philadelphia, Pa). 2020;4(2).\u003c/li\u003e\n\u003cli\u003eCabrera-Rodr\u0026iacute;guez R, Luzardo OP, Gonz\u0026aacute;lez-Antu\u0026ntilde;a A, Boada LD, Almeida-Gonz\u0026aacute;lez M, Camacho M, et al. Occurrence of 44 elements in human cord blood and their association with growth indicators in newborns. Environment international. 2018;116:43-51.\u003c/li\u003e\n\u003cli\u003eAshrap P, Watkins DJ, Mukherjee B, Boss J, Richards MJ, Rosario Z, et al. Maternal blood metal and metalloid concentrations in association with birth outcomes in Northern Puerto Rico. Environment international. 2020;138:105606.\u003c/li\u003e\n\u003cli\u003eHowe CG, Claus Henn B, Eckel SP, Farzan SF, Grubbs BH, Chavez TA, et al. Prenatal Metal Mixtures and Birth Weight for Gestational Age in a Predominately Lower-Income Hispanic Pregnancy Cohort in Los Angeles. Environmental health perspectives. 2020;128(11):117001.\u003c/li\u003e\n\u003cli\u003eGilbert-Diamond D, Emond JA, Baker ER, Korrick SA, Karagas MR. Relation between in Utero Arsenic Exposure and Birth Outcomes in a Cohort of Mothers and Their Newborns from New Hampshire. Environmental health perspectives. 2016;124(8):1299-307.\u003c/li\u003e\n\u003cli\u003eFang X, Qu J, Huan S, Sun X, Li J, Liu Q, et al. Associations of urine metals and metal mixtures during pregnancy with cord serum vitamin D Levels: A prospective cohort study with repeated measurements of maternal urinary metal concentrations. Environment international. 2021;155:106660.\u003c/li\u003e\n\u003cli\u003eKabiri D, Romero R, Gudicha DW, Hernandez-Andrade E, Pacora P, Benshalom-Tirosh N, et al. Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population-based standards. Ultrasound in obstetrics \u0026amp; gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2020;55(2):177-88.\u003c/li\u003e\n\u003cli\u003eIliodromiti S, Mackay DF, Smith GC, Pell JP, Sattar N, Lawlor DA, et al. Customised and Noncustomised Birth Weight Centiles and Prediction of Stillbirth and Infant Mortality and Morbidity: A Cohort Study of 979,912 Term Singleton Pregnancies in Scotland. PLoS medicine. 2017;14(1):e1002228.\u003c/li\u003e\n\u003cli\u003eKarakis I, Landau D, Yitshak-Sade M, Hershkovitz R, Rotenberg M, Sarov B, et al. Exposure to metals and congenital anomalies: a biomonitoring study of pregnant Bedouin-Arab women. The Science of the total environment. 2015;517:106-12.\u003c/li\u003e\n\u003cli\u003eMichael T, Kohn E, Daniel S, Hazan A, Berkovitch M, Brik A, et al. Prenatal exposure to heavy metal mixtures and anthropometric birth outcomes: a cross-sectional study. Environmental health : a global access science source. 2022;21(1):139.\u003c/li\u003e\n\u003cli\u003ePunshon T, Li Z, Jackson BP, Parks WT, Romano M, Conway D, et al. Placental metal concentrations in relation to placental growth, efficiency and birth weight. Environment international. 2019;126:533-42.\u003c/li\u003e\n\u003cli\u003eCowell W, Colicino E, Levin-Schwartz Y, Enlow MB, Amarasiriwardena C, Andra SS, et al. Prenatal metal mixtures and sex-specific infant negative affectivity. Environmental epidemiology (Philadelphia, Pa). 2021;5(2):e147.\u003c/li\u003e\n\u003cli\u003eGardner RM, Kippler M, Tofail F, Bottai M, Hamadani J, Grand\u0026eacute;r M, et al. Environmental exposure to metals and children\u0026apos;s growth to age 5 years: a prospective cohort study. American journal of epidemiology. 2013;177(12):1356-67.\u003c/li\u003e\n\u003cli\u003eSun X, Liu W, Zhang B, Shen X, Hu C, Chen X, et al. Maternal Heavy Metal Exposure, Thyroid Hormones, and Birth Outcomes: A Prospective Cohort Study. The Journal of clinical endocrinology and metabolism. 2019;104(11):5043-52.\u003c/li\u003e\n\u003cli\u003eMariath AB, Bergamaschi DP, Rond\u0026oacute; PH, Tanaka AC, Hinnig Pde F, Abbade JF, et al. The possible role of selenium status in adverse pregnancy outcomes. The British journal of nutrition. 2011;105(10):1418-28.\u003c/li\u003e\n\u003cli\u003eDewey KG, Oaks BM. U-shaped curve for risk associated with maternal hemoglobin, iron status, or iron supplementation. The American journal of clinical nutrition. 2017;106(Suppl 6):1694s-702s.\u003c/li\u003e\n\u003cli\u003eSnart CJP, Threapleton DE, Keeble C, Taylor E, Waiblinger D, Reid S, et al. Maternal iodine status, intrauterine growth, birth outcomes and congenital anomalies in a UK birth cohort. BMC medicine. 2020;18(1):132.\u003c/li\u003e\n\u003cli\u003eJames AH. Iron Deficiency Anemia in Pregnancy. Obstetrics and gynecology. 2021;138(4):663-74.\u003c/li\u003e\n\u003cli\u003eJessani S, Saleem S, Hoffman MK, Goudar SS, Derman RJ, Moore JL, et al. Association of haemoglobin levels in the first trimester and at 26-30 weeks with fetal and neonatal outcomes: a secondary analysis of the Global Network for Women\u0026apos;s and Children\u0026apos;s Health\u0026apos;s ASPIRIN Trial. BJOG : an international journal of obstetrics and gynaecology. 2021;128(9):1487-96.\u003c/li\u003e\n\u003cli\u003eZhang Y, Chen T, Zhang Y, Hu Q, Wang X, Chang H, et al. Contribution of trace element exposure to gestational diabetes mellitus through disturbing the gut microbiome. Environment international. 2021;153:106520.\u003c/li\u003e\n\u003cli\u003eMonangi N, Xu H, Khanam R, Khan W, Deb S, Pervin J, et al. Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis. BMJ global health. 2021;6(9).\u003c/li\u003e\n\u003cli\u003eSun H, Chen W, Wang D, Jin Y, Chen X, Xu Y. The effects of prenatal exposure to low-level cadmium, lead and selenium on birth outcomes. Chemosphere. 2014;108:33-9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Maternal and fetal baseline information\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal and fetal characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eOverall(n=429)\u003c/strong\u003e mean (SD) or n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eMaternal\u0026nbsp;age, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e29.58 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eAge of menarche, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e13.69 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eMenstrual cycle, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e30.55 (6.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eEthnic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e389 (90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e40 (9.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; College or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e307 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Undergraduate college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e118 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Master\u0026apos;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp;4 (0.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eNumber of pregnancies, times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e2.04 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eNumber of deliveries, times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e1.34 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eNumber of miscarriages, times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e0.45 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eHusband Smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e307 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e122 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy BMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e21.54 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eDelivery mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Natural birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e275 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e154 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003ePreterm birth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-preterm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e406 (94.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Preterm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e23 (5.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eGestational age, weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e38.97 (1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eNeonatal sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e218 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e211 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eBirth weight, g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e3131.88 (499.21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\" valign=\"top\"\u003e\n \u003cp\u003eBirth defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Without birth defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e418 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; With birth defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e11 (2.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e74 (17.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e35 (8.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eLBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e29 (6.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.921635434412266%\"\u003e\n \u003cp\u003eMacrosomia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.078364565587734%\"\u003e\n \u003cp\u003e13 (3.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; BMI, body mass index; n (%), frequency (percentage); SGA, small for gestational age; LGA, large for gestational age; LBW, low birth weight\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 is available in the Supplementary Files section.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eWQS Model Used to estimate the Association between the WQS Index and adverse birth outcomes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003ePreterm birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e1.366(0.041, 2.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e-0.598(-1.419, 0.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003eLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003e\u0026nbsp;Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e\u0026nbsp;-2.001(-3.623, -0.379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.384615384615383%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e\u0026nbsp;-2.224(-3.783, -0.666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metals, Adverse birth outcomes, Mixed exposure, BKMR","lastPublishedDoi":"10.21203/rs.3.rs-4750408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4750408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExposure to metals during pregnancy has been found to be associated with adverse birth outcomes in the fetus. However, evidence for combined exposure is inconclusive. Therefore, it is important to explore the correlation between the combined effects of mixed metallic elements and adverse birth outcomes.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe aim of this study was to investigate the association between maternal serum heavy metals concentrations in the second trimester of pregnancy and adverse neonatal outcomes, including PTB, birth defects, LBW, macrosomia, SGA and LGA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSpecifically, we examined the serum levels of various elements in pregnant women during mid-pregnancy, using the highly sensitive inductively coupled plasma mass spectrometer (ICP-MS). This study utilized advanced multiple exposure models, including Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS), to analyze the mixed exposure to elements.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth BKMR and WQS models showed that mixed metal exposure was positively associated with PTB, but negatively associated with birth defects and LGA. Tl and Fe were negatively associated with PTB, Se, Sb, and I were positively associated with PTB, and Se and Rb were negatively associated with birth defects. WQS regression analysis showed that metal mixed exposure was positively associated with preterm birth (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) and negatively associated with LGA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings from this study contribute valuable insights into the potential health risks associated with mixed metals exposure during pregnancy. By elucidating the multifaceted impacts of metal mixtures on birth outcomes, this research offers a foundation for developing targeted interventions and preventive measures to safeguard maternal and child health.\u003c/p\u003e","manuscriptTitle":"Association of Prenatal Serum Heavy Metals Exposure with Adverse Birth Outcomes: A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:00:45","doi":"10.21203/rs.3.rs-4750408/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b71d0a0b-6b7f-439b-ac06-3eb26f47f886","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-12T10:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 01:00:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4750408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4750408","identity":"rs-4750408","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}